Lei Jiao

• Halenka, Vojtech; Amini, Mohammadreza; Andersen, Per-Arne; Granmo, Ole-Christoffer; Jiao, Lei & Kantarci, Burak (2026). Analyzing Backdoor Vulnerabilities in Interpretable Machine Learning for Wireless Jamming Detection Using Tsetlin Machines. IEEE Transactions on Network Science and Engineering (IEEE TNSE). 13, p. 11869–11886. doi: 10.1109/TNSE.2026.3727545. Full text in Research Archive Show summary

  Interpretable machine learning is increasingly sought in next-generation cellular networks, where trust, accountability, and robustness are essential and jamming remains a persistent threat. Federated learning enables collaborative jamming detection without sharing raw in-phase and quadrature data, while Tsetlin Machines provide a logic-based option for transparent, resource-constrained deployments. However, the security implications of interpretability in federated wireless environments remain poorly understood. We introduce a backdoor threat model for interpretable federated jamming detectors, showing how Tsetlin Machine clause structure can be exploited by a compromised client to induce targeted detection failures. In a federated framework with weighted Coalesced Tsetlin Machines and Krum aggregation, we analyze clause-based vulnerability to spectrogram-domain trigger perturbations and formulate a multi-objective problem balancing trigger effectiveness, model deviation, and aggregation selection. We further show that clause-informed trigger initialization lowers adversarial optimization effort, revealing a tension between transparency and robustness. Evaluation on 60,000 spectrograms from a 5G NR synchronization-signal testbed shows that optimized triggers can exceed 70% trial-level triggered false-negative rates in optimizer benchmarks, reaching approximately 90% in best-case configurations; supplemental multi-round federated evaluation shows that trial-level and global triggered false-negative rate (TFR) can diverge substantially (gaps from -31.4 to +3.0 percentage points across evaluated partitions), with no sustained global TFR≥70% after the last malicious selection round. These results highlight the need for defense-aware design of explainable models in next-generation networks.

• Kadhim, Ahmed Khalid; Lei, Jiao; Shafik, Rishad Ahmed; Granmo, Ole-Christoffer & Bhattarai, Bimal (2025). Scalable Multi-phase Word Embedding Using Conjunctive Propositional Clauses. In Lei, Jiao & Shafik, Rishad Ahmed (Ed.), Tsetlin Machine (ISTM), International Symposium on the. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331569228. doi: 10.1109/istm67926.2025.00024. Full text in Research Archive
• Bilal, Muhammad Talha; Tian, Doris Siyu; Choux, Martin Marie Hubert; Jiao, Lei & Tyapin, Ilya (2025). Optimizing Automated Battery Demanufacturing Through Simulation-Based Analysis and Genetic Algorithm. Robotics. 14(11). doi: 10.3390/robotics14110156. Full text in Research Archive Show summary

  The automation of recycling processes for electric vehicle lithium-ion battery packs is crucial for the advancement of green energy transportation. Testing disassembly strategies on real equipment is time consuming, expensive, and poses significant safety risks. This paper presents a novel simulation-based framework that leverages the integration of a high-fidelity virtual environment with a Robot Operating System (ROS) to visualize and accurately calculate the time required for complex robotic disassembly operations. The calculated operation times are then used as input for genetic algorithm optimization to improve process efficiency. The results demonstrate that automation significantly improves the total speed of the disassembly process compared to manual methods. By utilizing this novel simulation and optimization approach, a 25% improvement in performance was achieved for the pack-to-module disassembly stage. This method provides a safe and cost-effective approach for process design, contributing directly to the development of a circular economy and supporting the transition towards sustainable transportation.

• Tunheim, Svein Anders; Zheng, Yujin; Jiao, Lei; Shafik, Rishad; Yakovlev, Alex & Granmo, Ole-Christoffer (2025). An All-Digital 8.6-nJ/Frame 65-nm Tsetlin Machine Image Classification Accelerator. IEEE Transactions on Circuits and Systems I: Regular Papers. ISSN 1549-8328. 73(2), p. 1107–1120. doi: 10.1109/TCSI.2025.3586698. Full text in Research Archive Show summary

  We present an all-digital programmable machine learning accelerator chip for image classification, underpinning on the Tsetlin machine (TM) principles. The TM is an emerging machine learning algorithm founded on propositional logic, utilizing sub-pattern recognition expressions called clauses. The accelerator implements the coalesced TM version with convolution, and classifies booleanized images of 28×28 pixels with 10 categories. A configuration with 128 clauses is used in a highly parallel architecture. Fast clause evaluation is achieved by keeping all clause weights and Tsetlin automata (TA) action signals in registers. The chip is implemented in a 65 nm low-leakage CMOS technology, and occupies an active area of 2.7 mm2. At a clock frequency of 27.8 MHz, the accelerator achieves 60.3 k classifications per second, and consumes 8.6 nJ per classification. This demonstrates the energy-efficiency of the TM, which was the main motivation for developing this chip. The latency for classifying a single image is 25.4 μ s which includes system timing overhead. The accelerator achieves 97.42%, 84.54% and 82.55% test accuracies for the datasets MNIST, Fashion-MNIST and Kuzushiji-MNIST, respectively, matching the TM software models.

• Tarasyuk, Olga; Gorbenko, Anatoliy; Rahman, Tousif; Jiao, Lei; Granmo, Ole-Christoffer & Shafik, Rishad [Show all 7 contributors for this article] (2025). Learning dynamics, pattern recognition capability and interpretability of the Tsetlin Machine. Pattern Recognition. ISSN 0031-3203. 174. doi: 10.1016/j.patcog.2025.113028. Full text in Research Archive Show summary

  The inability to trace an AI’s reasoning process and understand why it makes each decision is known as the black box problem. This remains one of the major barriers to the trusted and widespread use of machine learning in many application domains. The paper explores pattern recognition performance and learning dynamics of the Tsetlin Machine – a new explainable logic-based machine-learning approach. Tsetlin Machine uses a collection of finite-state automata with a unique logic-based learning mechanism and provides a promising alternative to Artificial Neural Networks with several advantages, such as interpretability, low complexity, suitability for hardware implementation and high performance. This work investigates Tsetlin Machine’s mechanism for constructing conjunctive clauses from data and their interpretation for pattern recognition on several datasets. We demonstrate that during training the logical clauses learn persistent sub-patterns within the class. Each clause creates a class template by clustering a certain number of similar class samples, combining them through literal-wise logical conjunction (i.e., AND-ing). The number of class samples that each clause combines depends on Tsetlin Machine’s hyperparameters. The more class samples that are combined, the more general the clauses become. The paper aims at uncovering how Tsetlin Machine’s hyperparameters influence the balance between clause generalization and specialization and how this affects the accuracy of pattern recognition. It also studies the evolution of the machine’s internal state, its convergence and training completion.

• Asres, Mulugeta Weldezgina; Lei, Jiao & Omlin, Christian Walter Peter (2025). Low-Latency Video Anonymization for Crowd Anomaly Detection: Privacy Versus Performance. IEEE Transactions on Information Forensics and Security. ISSN 1556-6013. 21, p. 1–16. doi: 10.1109/TIFS.2025.3630347. Full text in Research Archive Show summary

  Recent advancements in artificial intelligence hold ample potential for monitoring applications using surveillance cameras. However, concerns about privacy and model bias have made it challenging to utilize them in public. Although de-identification approaches have been proposed in the literature, aiming to achieve a certain level of anonymization (AN), most of them employ deep learning models that are computationally demanding for real-time edge deployment. This study revisits conventional AN solutions for privacy protection and real-time video anomaly detection (VAD) applications. We propose a lightweight adaptive AN for VAD (LA3D) that employs dynamic adjustment to enhance full-body privacy protection. We have evaluated privacy protection and VAD utility retention efficacy using several publicly available datasets to examine the strengths and weaknesses of different AN methods and highlight the promising leverage of our approach. Our experiment demonstrates that the LA3D enables substantial improvement in privacy AN without severely degrading VAD efficacy, outperforming conventional and deep learning approaches.

• Jeeru, Sindhusha; Omslandseter, Rebekka Olsson; Andersen, Per-Arne; Morrison, Aiden James; Sokolova, Nadezda & Granmo, Ole-Christoffer [Show all 7 contributors for this article] (2025). MixCTME: A Mixture of Convolutional Tsetlin Machine Experts Using Diverse Spectrogram Visualizations for Jamming Signal Classification. IEEE Internet of Things Journal. ISSN 2327-4662. 12(22), p. 47667–47678. doi: 10.1109/jiot.2025.3606366. Full text in Research Archive Show summary

  global navigation satellite systems (GNSSs) are vulnerable to jamming, which can degrade or even disable their services by interfering with signal reception. Such interference may interrupt GNSS positioning, navigation, timing, and communication functions. Therefore, robust jamming detection strategies are essential to ensure continuous and reliable services. Many existing jamming detection approaches use closed box machine learning (ML) models, which lack transparency and accuracy. In this article, we propose a novel mixture of convolutional Tsetlin machine experts (MixCTME) approach, using diverse spectrogram visualizations to enhance the accuracy and interpretability of jamming signal classification. To facilitate this, we collected raw in-phase and quadrature (IQ) data and named it the RealRFI dataset, which corresponds to ten different jamming classes. The data were collected through multiple radio-frequency interference (RFI) monitoring stations deployed at SINTEF companies across Europe and Scandinavia. Next, diverse visualizations were generated using short time Fourier transform (STFT), dominant frequency, fast Fourier transform (FFT), power spectral density, and standard deviation. These visualizations were binarized using the Otsu thresholding technique, and the binarized spectrograms were fed to the MixCTME model. Our method utilizes a combination of distinct convolutional Tsetlin machine (CTM) experts and employs a novel nonlinear technique, referred to as confidence-based gating, for weighting the experts’ inputs to make the final decision. We compared our method against state-of-the-art approaches via the self-collected and labeled dataset. Through experiments, our model achieved an accuracy of 90.70% on the collected data and 99.46% on a benchmark dataset, outperforming the state-of-the-art approaches. Additionally, we highlight the trustworthiness, scalability, and interpretability of the proposed approach, presenting a promising solution for accurate and interpretable jamming classification.

• Zhengguang, Song; Zhijiang, Li; Liqin, Cao; Lei, Jiao & Zhang, Xuan (2025). GC3: Grouped convolutional color constancy. Pattern Recognition. ISSN 0031-3203. 158. doi: 10.1016/j.patcog.2024.110957. Full text in Research Archive Show summary

  Computational color constancy is an ill-posed problem since different combinations of objects and illumination may appear the same color in an image. Most methods produce a single illuminant estimate and cannot explain the potential ambiguity between possible illuminant solutions. In this work, a grouped convolutional color constancy (GC) method is proposed. It approaches the computational color constancy as a grouped regression problem, dealing with ambiguity by generating multiple possible illuminant solutions. The GC consists of two components: fast feature extraction (FFE) and grouped illuminant regression (GIR). FFE uses small convolution kernels and rapid down-sampling scheme for quick feature extraction, while GIR employs grouped convolution layers for feature grouping regression, which estimates the illuminant solution and its probability for each group with two separate branches. The final illumination estimation is obtained by averaging all illuminant solutions, weighted by the normalized probability for each group. The proposed method yields competitive results on the ColorChecker dataset and achieves the state-of-the-art accuracy on the NUS and Cube+ datasets. Moreover, when compared with the state-of-the-art methods, our proposed approach exhibits superior time efficiency, making it the most efficient method available.

• Belaid, Mohamed-Bachir; Sharma, Jivitesh; Lei, Jiao; Granmo, Ole-Christoffer; Andersen, Per-Arne & Yazidi, Anis (2025). Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning. In Walsh, Toby; Shah, Julie & Kolter, Zico (Ed.), Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence. AAAI Press. ISSN 9781577358978. p. 15524–15531. doi: 10.1609/aaai.v39i15.33704. Full text in Research Archive
• Sharif, Md Haidar; Lei, Jiao & Omlin, Christian Walter Peter (2025). Deep crowd anomaly detection: state-of-the-art, challenges, and future research directions. Artificial Intelligence Review. ISSN 0269-2821. 58. doi: 10.1007/s10462-024-11092-8. Full text in Research Archive Show summary

  Crowd anomaly detection is one of the most popular topics in computer vision in the context of smart cities. A plethora of deep learning methods have been proposed that generally outperform other machine learning solutions. Our review primarily discusses algorithms that were published in mainstream conferences and journals between 2020 and 2022. We present datasets that are typically used for benchmarking, produce a taxonomy of the developed algorithms, and discuss and compare their performances. Our main findings are that the heterogeneities of pre-trained convolutional models have a negligible impact on crowd video anomaly detection performance. We conclude our discussion with fruitful directions for future research.

• Jeeru, Sindhusha; Lei, Jiao; Andersen, Per-Arne & Granmo, Ole-Christoffer (2025). Interpretable Rule-based Architecture for GNSS Jamming Signal Classification. IEEE Sensors Journal. ISSN 1530-437X. 25(10), p. 17942–17959. doi: 10.1109/JSEN.2025.3558966. Full text in Research Archive Show summary

  Jamming is a fatal threat to a global navigation satellite system (GNSS), and an efficient anti-jamming system relies on successful classification and identification of jamming types to respond effectively. The existing solutions suffer either from poor accuracy or lack of interpretability, and they are prone to learning simple statistical correlations rather than more fundamental and general relationships. In this study, we propose a novel approach to classify GNSS jamming signals as intentional or unintentional. The approach introduces a new standard deviation-based denoising method, which makes it possible to use the logical rule-based architecture of the convolutional Tsetlin machine (CTM) for interpretable jamming signal analysis. CTM is a recently developed algorithm that solves complex classification problems using conjunctive propositional formulas through a team of Tsetlin automata (TA). Unlike traditional opaque models based on deep learning, our approach goes beyond classification and provides a human-level interpretation of features. This interpretation capability allows a deeper comprehension of the characteristics and underlying patterns of the jamming signals, significantly easing the decision-making process. Furthermore, the CTM approach is also evaluated with different Booleanization techniques. Through experiments, we show that the proposed approach with CTM achieves an F1-score of 98.7%, on the collected dataset, which is superior to the state-of-the-art.

• Kadhim, Ahmed Khalid; Granmo, Ole-Christoffer; Lei, Jiao & Shafik, Rishad Ahmed (2024). Exploring State Space and Reasoning by Elimination in Tsetlin Machines. In NN, NN (Eds.), 2024 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331504984. Full text in Research Archive Show summary

  The Tsetlin Machine (TM) has gained significant attention in Machine Learning (ML). By employing logical fundamentals, it facilitates pattern learning and representation, offering an alternative approach for developing comprehensible Artificial Intelligence (AI) with a specific focus on pattern classification in the form of conjunctive clauses. In the domain of Natural Language Processing (NLP), TM is utilised to construct word embedding and describe target words using clauses. To enhance the descriptive capacity of these clauses, we study the concept of Reasoning by Elimination (RbE) in clauses' formulation, which involves incorporating feature negations to provide a more comprehensive representation. In more detail, this paper employs the Tsetlin Machine Auto-Encoder (TM-AE) architecture to generate dense word vectors, aiming at capturing contextual information by extracting feature-dense vectors for a given vocabulary. Thereafter, the principle of RbE is explored to improve descriptivity and optimise the performance of the TM. Specifically, the specificity parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> and the voting margin parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> are leveraged to regulate feature distribution in the state space, resulting in a dense representation of information for each clause. In addition, we investigate the state spaces of TM-AE, especially for the forgotten/excluded features. Empirical investigations on artificially generated data, the IMDB dataset, and the 20 Newsgroups dataset showcase the robustness of the TM, with accuracy reaching 90.62% for the IMDB.

• Tunheim, Svein Anders; Lei, Jiao; Shafik, Rishad; Yakovlev, Alex & Granmo, Ole-Christoffer (2024). Tsetlin Machine-Based Image Classification FPGA Accelerator With On-Device Training. IEEE Transactions on Circuits and Systems I: Regular Papers. ISSN 1549-8328. 72(2), p. 830–843. doi: 10.1109/TCSI.2024.3519191. Full text in Research Archive
• Halenka, Vojtech; Kadhim, Ahmed Khalid; Paul, Clarke; Bhattarai, Bimal; Saha, Rupsa & Granmo, Ole-Christoffer [Show all 8 contributors for this article] (2024). Exploring Effects of Hyperdimensional Vectors for Tsetlin Machines. In NN, NN (Eds.), 2024 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798331504984. doi: 10.1109/ISTM62799.2024.10931546. Full text in Research Archive Show summary

  Tsetlin machines (TMs) have been successful in several application domains, operating with high efficiency on Boolean representations of the input data. However, Booleanizing complex data structures such as sequences, graphs, images, signal spectra, chemical compounds, and natural language is non-trivial. In this paper, we propose a hypervector (HV) based method for expressing arbitrarily large sets of concepts associated with any input data. Using a hyperdimensional space to build vectors drastically expands the capacity and flexibility of the TM. We demonstrate how images, chemical compounds, and natural language text are encoded according to the proposed method, and how the resulting HV -powered TM can achieve significantly higher accuracy and faster learning on well-known benchmarks. Our results thus open up a new research direction for TMs, namely how to expand and exploit the benefits of operating in hyperspace, including new booleanization strategies, optimization of TM inference and learning, as well as new TM applications.

• Bhattarai, Bimal; Granmo, Ole-Christoffer; Jiao, Lei; Yadav, Rohan Kumar & Sharma, Jivitesh (2024). Tsetlin Machine Embedding: Representing Words Using Logical Expressions, Findings of the Association for Computational Linguistics: EACL 2024. Association for Computational Linguistics (ACL). ISSN 9798891760936. p. 1512–1520. Full text in Research Archive
• Maheshwari, Sidharth; Rahman, Tousif; Shafik, Rishad; Yakovlev, Alex; Rafiev, Ashur & Lei, Jiao [Show all 7 contributors for this article] (2023). REDRESS: Generating Compressed Models for Edge Inference Using Tsetlin Machines. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 45(9), p. 11152–11168. doi: 10.1109/TPAMI.2023.3268415. Full text in Research Archive Show summary

  Inference at-the-edge using embedded machine learning models is associated with challenging trade-offs between resource metrics, such as energy and memory footprint, and the performance metrics, such as computation time and accuracy. In this work, we go beyond the conventional Neural Network based approaches to explore Tsetlin Machine (TM), an emerging machine learning algorithm, that uses learning automata to create propositional logic for classification. We use algorithm-hardware co-design to propose a novel methodology for training and inference of TM. The methodology, called REDRESS, comprises independent TM training and inference techniques to reduce the memory footprint of the resulting automata to target low and ultra-low power applications. The array of Tsetlin Automata (TA) holds learned information in the binary form as bits: {0,1} , called excludes and includes, respectively. REDRESS proposes a lossless TA compression method, called the include-encoding, that stores only the information associated with includes to achieve over 99% compression. This is enabled by a novel computationally minimal training procedure, called the Tsetlin Automata Re-profiling, to improve the accuracy and increase the sparsity of TA to reduce the number of includes, hence, the memory footprint. Finally, REDRESS includes an inherently bit-parallel inference algorithm that operates on the optimally trained TA in the compressed domain, that does not require decompression during runtime, to obtain high speedups when compared with the state-of-the-art Binary Neural Network (BNN) models. In this work, we demonstrate that using REDRESS approach, TM outperforms BNN models on all design metrics for five benchmark datasets viz. MNIST, CIFAR2, KWS6, Fashion-MNIST and Kuzushiji-MNIST. When implemented on an STM32F746G-DISCO microcontroller, REDRESS obtained speedups and energy savings ranging 5-5700× compared with different BNN models.

• Omslandseter, Rebekka Olsson; Lei, Jiao & Oommen, John (2023). Pioneering approaches for enhancing the speed of hierarchical LA by ordering the actions. Information Sciences. ISSN 0020-0255. 647, p. 1–17. doi: 10.1016/j.ins.2023.119487. Full text in Research Archive Show summary

  Fixed Structure Stocastic Automata (FSSA), Variable Structure Learning Automata (VSSA), and their discretized versions have been significantly improved by utilizing inexpensive estimates of the actions' reward probabilities. These represent the fastest LA to date. However, the concept of ordering the actions has never been used within the field, and the reason for this is that there is no way to order the actions a priori. The recently-introduced Hierarchical Discrete Pursuit Automaton (HDPA) has an interesting concept of placing two-action LA along the nodes of a tree, implying that the leaves signify an underlying ordering. In this paper, we show that if estimates are available (as in the case of estimator algorithms), these can be used to place the actions at the leaf level to further enhance the convergence capabilities of the overall ensemble of the two-action LAs. This paper contains the design of this HDPA, the proof of this assertion, and the simulation results on benchmark Environments. Based on the results, we believe that it is the fastest and most accurate LA to date. Our position is that it will be very hard to beat its performance, since it has been incorporated all the salient features of the entire field of LA.

• Sharif, Md Haidar; Lei, Jiao & Omlin, Christian Walter Peter (2023). Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks. Electronics (Basel). 12(7). doi: 10.3390/electronics12071517. Full text in Research Archive Show summary

  Abnormal event detection is one of the most challenging tasks in computer vision. Many existing deep anomaly detection models are based on reconstruction errors, where the training phase is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. It is assumed that the reconstruction error gap between frames of normal and abnormal scores is high for abnormal events during the testing phase. Yet, this assumption may not always hold due to superior capacity and generalization of deep neural networks. In this paper, we design a generalized framework (rpNet) for proposing a series of deep models by fusing several options of a reconstruction network (rNet) and a prediction network (pNet) to detect anomaly in videos efficiently. In the rNet, either a convolutional autoencoder (ConvAE) or a skip connected ConvAE (AEc) can be used, whereas in the pNet, either a traditional U-Net, a non-local block U-Net, or an attention block U-Net (aUnet) can be applied. The fusion of both rNet and pNet increases the error gap. Our deep models have distinct degree of feature extraction capabilities. One of our models (AEcaUnet) consists of an AEc with our proposed aUnet has capability to confirm better error gap and to extract high quality of features needed for video anomaly detection. Experimental results on UCSD-Ped1, UCSD-Ped2, CUHK-Avenue, ShanghaiTech-Campus, and UMN datasets with rigorous statistical analysis show the effectiveness of our models.

• Sharma, Jivitesh; Yadav, Rohan Kumar; Granmo, Ole-Christoffer & Lei, Jiao (2023). Drop Clause: Enhancing Performance, Robustness and Pattern Recognition Capabilities of the Tsetlin Machine. In Chen, Yiling & Neville, Jennifer (Ed.), Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence. AAAI Press. ISSN 9781577358800. p. 13547–13555. doi: 10.1609/aaai.v37i11.26588. Full text in Research Archive
• Oommen, John; Omslandseter, Rebekka Olsson & Lei, Jiao (2023). The object migration automata: its field, scope, applications, and future research challenges. Pattern Analysis and Applications. ISSN 1433-7541. 26, p. 917–928. doi: 10.1007/s10044-023-01163-x. Full text in Research Archive Show summary

  Partitioning, in and of itself, is an NP-hard problem. Prior to the Artificial Intelligence (AI)-based solutions, it was solved in the 1970s by optimization-based strategies. However, AI-based solutions appeared in the 1980s in a pioneering way, by using a Learning Automaton (LA)-motivated strategy known as the so-called Object Migrating Automaton (OMA). Although the OMA and its derivatives have been used in numerous applications since then, the basic kernel has remained the same. Because the number of possible partitions in a partitioning problem can be combinatorially exponential and the underlying tasks are NP-hard, the most advanced OMA algorithms could, until recently, only solve issues involving equally sized groups. Due to our recent innovations cited in the body of this paper, the enhanced OMA now also handles non-equally sized groups. Earlier, we had presented in Omslandseter (Pattern Anal Appl, 2023), a comprehensive survey of the state-of-the-art enhancements of the best-known OMA. We believe that these results will be the benchmark for a few decades and that it will be very hard to beat these results. This is a companion paper, intended to augment the contents of Omslandseter (Pattern Anal Appl, 2023). In this paper, we first discuss the OMA’s prior applications, its historical and current innovations, and the OMA-based algorithms’ relevance to societal needs. We also provide well-specified guidelines for future researchers so that they can use them for unresolved tasks, and also develop further advancements.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2023). An Interpretable Knowledge Representation Framework for Natural Language Processing with Cross-Domain Application, Advances in Information Retrieval. ECIR 2023. Springer. ISSN 9783031282447. p. 167–181. doi: 10.1007/978-3-031-28244-7_11. Full text in Research Archive Show summary

  Data representation plays a crucial role in natural language processing (NLP), forming the foundation for most NLP tasks. Indeed, NLP performance highly depends upon the effectiveness of the preprocessing pipeline that builds the data representation. Many representation learning frameworks, such as Word2Vec, encode input data based on local contextual information that interconnects words. Such approaches can be computationally intensive, and their encoding is hard to explain. We here propose an interpretable representation learning framework utilizing Tsetlin Machine (TM). The TM is an interpretable logic-based algorithm that has exhibited competitive performance in numerous NLP tasks. We employ the TM clauses to build a sparse propositional (boolean) representation of natural language text. Each clause is a class-specific propositional rule that links words semantically and contextually. Through visualization, we illustrate how the resulting data representation provides semantically more distinct features, better separating the underlying classes. As a result, the following classification task becomes less demanding, benefiting simple machine learning classifiers such as Support Vector Machine (SVM). We evaluate our approach using six NLP classification tasks and twelve domain adaptation tasks. Our main finding is that the accuracy of our proposed technique significantly outperforms the vanilla TM, approaching the competitive accuracy of deep neural network (DNN) baselines. Furthermore, we present a case study showing how the representations derived from our framework are interpretable. (We use an asynchronous and parallel version of Tsetlin Machine: available at https://github.com/cair/PyTsetlinMachineCUDA).

• Tunheim, Svein Anders; Lei, Jiao; Shafik, Rishad Ahmed; Yakovlev, Alexandre & Granmo, Ole-Christoffer (2023). Convolutional Tsetlin Machine-based Training and Inference Accelerator for 2-D Pattern Classification. Microprocessors and Microsystems: Embedded Hardware Design (MICPRO). ISSN 0141-9331. 103. doi: 10.1016/j.micpro.2023.104949. Full text in Research Archive Show summary

  The Tsetlin Machine (TM) is a machine learning algorithm based on an ensemble of Tsetlin Automata (TAs) that learns propositional logic expressions from Boolean input features. In this paper, the design and implementation of a Field Programmable Gate Array (FPGA) accelerator based on the Convolutional Tsetlin Machine (CTM) is presented. The accelerator performs classification of two pattern classes in 4 × 4 Boolean images with a 2 × 2 convolution window. Specifically, there are two separate TMs, one per class. Each TM comprises 40 propositional logic formulas, denoted as clauses, which are conjunctions of literals. Include/exclude actions from the TAs determine which literals are included in each clause. The accelerator supports full training, including random patch selection during convolution based on parallel reservoir sampling across all clauses. The design is implemented on a Xilinx Zynq XC7Z020 FPGA platform. With an operating clock speed of 40 MHz, the accelerator achieves a classification rate of 4.4 million images per second with an energy per classification of 0.6 J. The mean test accuracy is 99.9% when trained on the 2-dimensional Noisy XOR dataset with 40% noise in the training labels. To achieve this performance, which is on par with the original software implementation, Linear Feedback Shift Register (LFSR) random number generators of minimum 16 bits are required. The solution demonstrates the core principles of a CTM and can be scaled to operate on multi-class systems for larger images.

• Tunheim, Svein Anders; Lei, Jiao & Granmo, Ole-Christoffer (2023). Model Export for the Convolutional Coalesced Tsetlin Machine, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350344776. doi: 10.1109/ISTM58889.2023.10455048. Full text in Research Archive
• Granmo, Ole-Christoffer; Andersen, Per-Arne; Lei, Jiao; Zhang, Xuan; Blakely, Christian Dallas & Berge, Geir Thore [Show all 7 contributors for this article] (2023). Learning Minimalistic Tsetlin Machine Clauses with Markov Boundary-Guided Pruning, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350344776. doi: 10.1109/ISTM58889.2023.10454914. Full text in Research Archive
• Oommen, John; Omslandseter, Rebekka Olsson & Lei, Jiao (2023). Learning automata-based partitioning algorithms for stochastic grouping problems with non-equal partition sizes. Pattern Analysis and Applications. ISSN 1433-7541. 26, p. 751–772. doi: 10.1007/s10044-023-01131-5. Full text in Research Archive Show summary

  It is very fascinating that the principles of Artificial Intelligence, that were proposed many decades ago, have remained to be the foundations of many of the modern-day techniques, and a brief summary of these principles is given in the paper itself. While this truism is valid for problem solving and game playing, in the context of this paper, we emphasize that it is also pertinent for the so-called partitioning problems. In this paper, we consider the general partitioning problem that has been studied for decades. Unlike the heuristic and search strategies, our attention focuses on learning automata (LA) and reinforcement learning methods, and their powerful ability to solve problems in stochastic environments. These render the latter tools to be applicable to various complex tasks. As we shall explain, LA have been employed for partitioning, and in particular, the paradigm of Object Migration Automata (OMA) has offered state-of-the-art adaptive methods for solving grouping and partitioning problems. However, because the number of possible partitions is combinatorially exponential, and of the NP-hardness of the underlying tasks, the existing state-of-the-art OMA algorithms can only solve problems of equal-sized groups. To resolve this, in this paper, we propose two new OMA variants that can solve both equally and unequally sized partitioning problems. The key here is to provide additional information to the algorithms, and in doing so, to expand the solution space. The first variant, the Greatest Common Divisor OMA (GCD-OMA), relaxes the constraint of having equally sized groups by mapping the actions onto a larger state space, and thus permitting it to solve problems where the group sizes have a GCD that is greater than unity. Subsequently, we propose a second variant named the Partition Size Required OMA (PSR-OMA) to make the algorithm more versatile. The PSR-OMA can handle groups of arbitrary sizes when the group sizes are known a priori. We demonstrate the proposed algorithms’ strength in solving stochastic grouping problems through extensive simulations, even with high noise levels. The GCD-OMA and the PSR-OMA represent the current state-of-the-art when it concerns resolving the extremely complex problem of partitioning and can be used in any of the numerous applications in which the OMA itself has been utilized .

• Opalic, Sven Myrdahl; Palumbo, Fabrizio; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2023). COST-WINNERS: COST reduction WIth Neural NEtworks-based augmented Random Search for simultaneous thermal and electrical energy storage control. Journal of Energy Storage. ISSN 2352-152X. 72(B). doi: 10.1016/j.est.2023.108202. Full text in Research Archive Show summary

  The combination of local renewable energy production, dynamic loads, and multiple energy storage systems with different dynamics requires sophisticated control systems to maximize the energy cost efficiency of the combined energy system. Battery and thermal energy storage systems can be combined to increase the local use of on-site renewable energy, reduce peak power demand, and exploit time-of-use energy pricing. In this paper, we focus on how the augmented random search algorithm and artificial neural networks can be used together to solve an energy cost optimization problem involving the control of a battery energy storage system and a thermal energy storage system at the same time in a smart warehouse. As part of this work, a simulated training environment made using the data from the smart warehouse’s operations. In addition to the energy storage systems, the warehouse energy system has integrated a large roof mounted photovoltaic power plant and an industrial-scale cooling system. The developed solution is able to minimize the energy costs by modulating both energy systems, depending on the situation. Additionally, when it is tested against the state-of-the-art solutions, our developed solution at worst matches performance when the alternative algorithm is allowed to increase training time by a factor of nearly three. On average, our presented solution doubles the performance of the benchmark algorithm with much less computational resource expenditure.

• Abeyrathna, Kuruge Darshana; Abouzeid, Ahmed Abdulrahem Othman; Bhattarai, Bimal; Giri, Charul; Glimsdal, Sondre & Granmo, Ole-Christoffer [Show all 11 contributors for this article] (2023). Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size. In Elkind, Edith (Eds.), Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. AAAI Press. ISSN 9781956792034. p. 3395–3403. doi: 10.24963/ijcai.2023/378. Full text in Research Archive Show summary

  Tsetlin machine (TM) is a logic-based machine learning approach with the crucial advantages of being transparent and hardware-friendly. While TMs match or surpass deep learning accuracy for an increasing number of applications, large clause pools tend to produce clauses with many literals (long clauses). As such, they become less interpretable. Further, longer clauses increase the switching activity of the clause logic in hardware, consuming more power. This paper introduces a novel variant of TM learning – Clause Size Constrained TMs (CSC-TMs) – where one can set a soft constraint on the clause size. As soon as a clause includes more literals than the constraint allows, it starts expelling literals. Accordingly, oversized clauses only appear transiently. To evaluate CSC-TM, we conduct classifcation, clustering, and regression experiments on tabular data, natural language text, images, and board games. Our results show that CSC-TM maintains accuracy with up to 80 times fewer literals. Indeed, the accuracy increases with shorter clauses for TREC, IMDb, and BBC Sports. After the accuracy peaks, it drops gracefully as the clause size approaches a single literal. We fnally analyze CSC-TM power consumption and derive new convergence properties.

• Sharif, Md Haidar; Lei, Jiao & Omlin, Christian Walter Peter (2023). CNN-ViT Supported Weakly-Supervised Video Segment Level Anomaly Detection. Sensors. 23(18). doi: 10.3390/s23187734. Full text in Research Archive Show summary

  Video anomaly event detection (VAED) is one of the key technologies in computer vision for smart surveillance systems. With the advent of deep learning, contemporary advances in VAED have achieved substantial success. Recently, weakly supervised VAED (WVAED) has become a popular VAED technical route of research. WVAED methods do not depend on a supplementary self-supervised substitute task, yet they can assess anomaly scores straightway. However, the performance of WVAED methods depends on pretrained feature extractors. In this paper, we first address taking advantage of two pretrained feature extractors for CNN (e.g., C3D and I3D) and ViT (e.g., CLIP), for effectively extracting discerning representations. We then consider long-range and short-range temporal dependencies and put forward video snippets of interest by leveraging our proposed temporal self-attention network (TSAN). We design a multiple instance learning (MIL)-based generalized architecture named CNN-ViT-TSAN, by using CNN- and/or ViT-extracted features and TSAN to specify a series of models for the WVAED problem. Experimental results on publicly available popular crowd datasets demonstrated the effectiveness of our CNN-ViT-TSAN.

• Bhattarai, Bimal; Granmo, Ole-Christoffer; Lei, Jiao; Andersen, Per-Arne; Tunheim, Svein Anders & Shafik, Rishad Ahmed [Show all 7 contributors for this article] (2023). Contracting Tsetlin Machine with Absorbing Automata, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350344776. doi: 10.1109/ISTM58889.2023.10455040. Full text in Research Archive
• Liland, Kristian Hovde; Tomic, Oliver; Indahl, Ulf Geir; Futsæther, Cecilia Marie; Lei, Jiao & Granmo, Ole-Christoffer [Show all 7 contributors for this article] (2023). Tsetlin Machine in DNA sequence classification : Application to prokaryote gene prediction / A match made in silico, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350344776. doi: 10.1109/ISTM58889.2023.10454960. Full text in Research Archive
• Tarasyuk, Olga; Rahman, Tousif; Shafik, Rishad Ahmed; Yakovlev, Alexandre; Gorbenko, Anatoliy & Granmo, Ole-Christoffer [Show all 7 contributors for this article] (2023). Systematic Search for Optimal Hyper-parameters of the Tsetlin Machine on MNIST Dataset, 2023 International Symposium on the Tsetlin Machine (ISTM). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350344776. doi: 10.1109/ISTM58889.2023.10454969. Full text in Research Archive
• Tunheim, Svein Anders; Yadav, Rohan Kumar; Lei, Jiao; Shafik, Rishad & Granmo, Ole-Christoffer (2022). Cyclostationary Random Number Sequences for the Tsetlin Machine. In Fournier-Viger, Philippe; Fujita, Hamido; Ali, Moonis & Wang, Yinglin (Ed.), Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Kitakyushu, Japan, July 19–22, 2022, Proceedings. Springer. ISSN 9783031085291. p. 844–856. doi: 10.1007/978-3-031-08530-7_71. Full text in Research Archive Show summary

  The Tsetlin Machine (TM) constitutes an emerging machine learning algorithm that has shown competitive performance on several benchmarks. The underlying concept of the TM is propositional logic determined by a group of finite state machines that learns patterns. Thus, TM-based systems naturally lend themselves to low-power operation when implemented in hardware for micro-edge Internet-of-Things applications. An important aspect of the learning phase of TMs is stochasticity. For low-power integrated circuit implementations the random number generation must be carried out efficiently. In this paper, we explore the application of pre-generated cyclostationary random number sequences for TMs. Through experiments on two machine learning problems, i.e., Binary Iris and Noisy XOR, we demonstrate that the accuracy is on par with standard TM. We show that through exploratory simulations the required length of the sequences that meets the conflicting tradeoffs can be suitably identified. Furthermore, the TMs achieve robust performance against reduced resolution of the random numbers. Finally, we show that maximum-length sequences implemented by linear feedback shift registers are suitable for generating the required random numbers.

• Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei & Oommen, John (2022). User grouping and power allocation in NOMA systems: a novel semi-supervised reinforcement learning-based solution. Pattern Analysis and Applications. ISSN 1433-7541. 26. doi: 10.1007/s10044-022-01091-2. Full text in Research Archive Show summary

  In this paper, we present a pioneering solution to the problem of user grouping and power allocation in non-orthogonal multiple access (NOMA) systems. The problem is highly pertinent because NOMA is a well-recognized technique for future mobile radio systems. The salient and difficult issues associated with NOMA systems involve the task of grouping users together into the pre-specified time slots, which are augmented with the question of determining how much power should be allocated to the respective users. This problem is, in and of itself, NP-hard. Our solution is the first reported reinforcement learning (RL)-based solution, which attempts to resolve parts of this issue. In particular, we invoke the object migration automaton (OMA) and one of its variants to resolve the grouping in NOMA systems. Furthermore, unlike the solutions reported in the literature, we do not assume prior knowledge of the channels’ distributions, nor of their coefficients, to achieve the grouping/partitioning. Thereafter, we use the consequent groupings to heuristically infer the power allocation. The simulation results that we have obtained confirm that our learning scheme can follow the dynamics of the channel coefficients efficiently, and that the solution is able to resolve the issue dynamically.

• Opalic, Sven Myrdahl; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2022). Augmented Random Search with Artificial Neural Networks for energy cost optimization with battery control. Journal of Cleaner Production. ISSN 0959-6526. 380(2). doi: 10.1016/j.jclepro.2022.134676. Full text in Research Archive Show summary

  Intermittent renewable energy production and dynamic load must be balanced through appropriate control of integrated energy storage to account for the temporal discrepancy among power supply and demand. Intelligent control systems are required to anticipate and optimize the charging and discharging of energy storage. In recent years, reinforcement learning based techniques have been applied to a multitude of problems, including building integrated energy storage solutions. In this work, the focus is on the application of reinforcement learning based techniques to the specific energy optimization problem of controlling a battery energy storage system in a smart warehouse. This paper adopts data from a real operational battery energy storage system installed in a smart warehouse, integrated with photovoltaic, for food distribution on the west coast of Norway. In the smart warehouse, an intelligent energy management system controls the on-site battery energy storage using machine learning predictions of load and photovoltaic production, and an optimization algorithm is presented to generate a schedule for effective utilization of battery energy storage in coordination with a thermal storage system. This paper presents the combination of the augmented random search reinforcement algorithm with artificial neural networks as a basis to design an intelligent energy management system for controlling energy flows of battery energy storage systems to minimize the energy cost. The developed algorithm finds very promising solutions in the considered case-study of a smart house for energy cost minimization through a battery energy storage system, achieving an average of 99.2% accuracy across 10 seeded trials.

• Jenul, Anna Selina; Bhattarai, Bimal; Liland, Kristian Hovde; Lei, Jiao; Schrunner, Stefan & Futsæther, Cecilia Marie [Show all 8 contributors for this article] (2022). Component Based Pre-filtering of Noisy Data for Improved Tsetlin Machine Modelling. In Shafik, Rishad (Eds.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665471169. p. 57–64. doi: 10.1109/ISTM54910.2022.00019. Full text in Research Archive Show summary

  Tabular data with few observations and many features are common in the healthcare domain. With its rule-based approach to data modelling, the Tsetlin Machine has considerable potential to be a valuable tool in healthcare data analysis by providing interpretability to medical personnel. However, the performance of Tsetlin Machine models may be hampered by the presence of noise, which may often be the case with healthcare data due to individual differences across patients. This study shows that intelligent pre-filtering of healthcare measurement data using so-called component-based methods, such as Principal Component Analysis or Partial Least Squares Regression, can be beneficial for the performance of Tsetlin Machines. Modelling four healthcare data sets shows that the Tsetlin Machine achieved better predictive performance on pre-filtered data for data sets with high features-to-observations ratios.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). Explainable Tsetlin Machine Framework for Fake News Detection with Credibility Score Assessment. In Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Ed.), Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association. ISSN 9791095546726. p. 4894–4903. Full text in Research Archive Show summary

  The proliferation of fake news, i.e., news intentionally spread for misinformation, poses a threat to individuals and society. Despite various fact-checking websites such as PolitiFact, robust detection techniques are required to deal with the increase in fake news. Several deep learning models show promising results for fake news classification, however, their black-box nature makes it difficult to explain their classification decisions and quality-assure the models. We here address this problem by proposing a novel interpretable fake news detection framework based on the recently introduced Tsetlin Machine (TM). In brief, we utilize the conjunctive clauses of the TM to capture lexical and semantic properties of both true and fake news text. Further, we use clause ensembles to calculate the credibility of fake news. For evaluation, we conduct experiments on two publicly available datasets, PolitiFact and GossipCop, and demonstrate that the TM framework significantly outperforms previously published baselines by at least 5% in terms of accuracy, with the added benefit of an interpretable logic-based representation. In addition, our approach provides a higher F1-score than BERT and XLNet, however, we obtain slightly lower accuracy. We finally present a case study on our model’s explainability, demonstrating how it decomposes into meaningful words and their negations.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). ConvTextTM: An Explainable Convolutional Tsetlin Machine Framework for Text Classification. In Calzolari, Nicoletta; Béchet, Frédéric; Blache, Philippe; Choukri, Khalid; Cieri, Christopher; Declerck, Thierry; Goggi, Sara; Isahara, Hitoshi; Maegaard, Bente; Mariani, Joseph; Mazo, Hélène; Odijk, Jan & Piperidis, Stelios (Ed.), Proceedings of the Thirteenth Language Resources and Evaluation Conference. European Language Resources Association. ISSN 9791095546726. p. 3761–3770. Full text in Research Archive Show summary

  Recent advancements in natural language processing (NLP) have reshaped the industry, with powerful language models such as GPT-3 achieving superhuman performance on various tasks. However, the increasing complexity of such models turns them into “black boxes”, creating uncertainty about their internal operation and decision-making. Tsetlin Machine (TM) employs human-interpretable conjunctive clauses in propositional logic to solve complex pattern recognition problems and has demonstrated competitive performance in various NLP tasks. In this paper, we propose ConvTextTM, a novel convolutional TM architecture for text classification. While legacy TM solutions treat the whole text as a corpus-specific set-of-words (SOW), ConvTextTM breaks down the text into a sequence of text fragments. The convolution over the text fragments opens up for local position-aware analysis. Further, ConvTextTM eliminates the dependency on a corpus-specific vocabulary. Instead, it employs a generic SOW formed by the tokenization scheme of the Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2019a). The convolution binds together the tokens, allowing ConvTextTM to address the out-of-vocabulary problem as well as spelling errors. We investigate the local explainability of our proposed method using clause-based features. Extensive experiments are conducted on seven datasets, to demonstrate that the accuracy of ConvTextTM is either superior or comparable to state-of-the-art baselines.

• Tunheim, Svein Anders; Lei, Jiao; Shafik, Rishad Ahmed; Yakovlev, Alexandre & Granmo, Ole-Christoffer (2022). A Convolutional Tsetlin Machine-based Field Programmable Gate Array Accelerator for Image Classification. In Shafik, Rishad (Eds.), 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781665471169. p. 21–28. doi: 10.1109/ISTM54910.2022.00013. Full text in Research Archive Show summary

  This paper presents a Field Programmable Gate Array (FPGA) implementation of an image classification accelerator based on the Convolutional Tsetlin Machine (CTM). The work is a concept design, and the solution demonstrates recognition of two classes in 4 × 4 images with a 2 × 2 convolution window. More specifically, there are two sub-Tsetlin Machines (TMs), one per class. A single sub-TM employs 40 clauses, each controlled by 20 Tsetlin Automata. The accelerator features random patch selection, in parallel for all clauses, based on reservoir sampling. The design is implemented in a Xilinx Zync XC7Z020 FPGA. With an operating clock speed of 30 MHz, the accelerator is capable of inferring at the rate of 3.3 million images per second with an additional power consumption of 20 mW from idle mode. The average test accuracy is 96.7% when trained on data with 10% noise. A training session with 100 epochs and 8192 examples takes 1.5 seconds. Due to the limited hardware resources required, the CTM accelerator represents a promising concept for online learning in energy-frugal systems. The solution can be scaled to multi-class systems and larger images.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Lei, Jiao (2022). Word-level human interpretable scoring mechanism for novel text detection using Tsetlin Machines. Applied Intelligence - The International Journal of Research on Intelligent Systems for Real Life Complex Problems. ISSN 0924-669X. doi: 10.1007/s10489-022-03281-1. Full text in Research Archive Show summary

  Recent research in novelty detection focuses mainly on document-level classification, employing deep neural networks (DNN). However, the black-box nature of DNNs makes it difficult to extract an exact explanation of why a document is considered novel. In addition, dealing with novelty at the word level is crucial to provide a more fine-grained analysis than what is available at the document level. In this work, we propose a Tsetlin Machine (TM)-based architecture for scoring individual words according to their contribution to novelty. Our approach encodes a description of the novel documents using the linguistic patterns captured by TM clauses. We then adapt this description to measure how much a word contributes to making documents novel. Our experimental results demonstrate how our approach breaks down novelty into interpretable phrases, successfully measuring novelty.

• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2022). Enhancing the Speed of Hierarchical Learning Automata by Ordering the Actions - A Pioneering Approach. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13728, p. 775–788. doi: 10.1007/978-3-031-22695-3_54. Full text in Research Archive Show summary

  For the past six decades, the operation of Learning Automata (LA) has involved states and action probabilities. These have been central to “remembering” the quality of the actions chosen during the learning. The latest enhancements have also incorporated estimates of the actions’ reward probabilities. However, a phenomenon that has never been used to-date is that of considering how these actions themselves, can be ordered. Ordering the actions in traditional LA is rather meaningless unless one resorts to invoking the theory of Random Races [1]. However, we show that such an ordering makes sense if the automata operate hierarchically, within a tree, with the actions being placed at the leaves. In this paper, we shall show that when the LA are arranged “in a tree formation”, and when the learning is achieved within such a tree, the hierarchical LA has a superior performance if the actions located at the leaves of the tree are arranged suitably. While this concept can be incorporated in any hierarchical LA, we demonstrate its power for the most recent machine, i.e., the Hierarchical Discretized Pursuit Automaton (HDPA). These strategies can also be included in the Hierarchical Continuous Pursuit Automaton (HCPA), and to both of these which utilize traditional Maximum Likelihood (ML) or Bayesian estimates [2]. The experimental results presented here are very impressive, and so, if we consider the chronology of LA from FSSA through VSSA, the Estimator schemes, and the recent hierarchical LA, our modest claim is that the inclusion of the ADE represents the state-of-the-art which is not easily surpassed.

• Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis & Oommen, John (2022). The Hierarchical Discrete Pursuit Learning Automaton: A Novel Scheme with Fast Convergence and Epsilon-Optimality. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 35(6), p. 8278–8292. doi: 10.1109/TNNLS.2022.3226538. Full text in Research Archive Show summary

  Since the early 1960s, the paradigm of learning automata (LA) has experienced abundant interest. Arguably, it has also served as the foundation for the phenomenon and field of reinforcement learning (RL). Over the decades, new concepts and fundamental principles have been introduced to increase the LA’s speed and accuracy. These include using probability updating functions, discretizing the probability space, and using the “Pursuit” concept. Very recently, the concept of incorporating “structure” into the ordering of the LA’s actions has improved both the speed and accuracy of the corresponding hierarchical machines, when the number of actions is large. This has led to the $\epsilon$ -optimal hierarchical continuous pursuit LA (HCPA). This article pioneers the inclusion of all the above-mentioned phenomena into a new single LA, leading to the novel hierarchical discretized pursuit LA (HDPA). Indeed, although the previously proposed HCPA is powerful, its speed has an impediment when any action probability is close to unity, because the updates of the components of the probability vector are correspondingly smaller when any action probability becomes closer to unity. We propose here, the novel HDPA, where we infuse the phenomenon of discretization into the action probability vector’s updating functionality, and which is invoked recursively at every stage of the machine’s hierarchical structure. This discretized functionality does not possess the same impediment, because discretization prohibits it. We demonstrate the HDPA’s robustness and validity by formally proving the $\epsilon$ -optimality by utilizing the moderation property. We also invoke the submartingale characteristic at every level, to prove that the action probability of the optimal action converges to unity as time goes to infinity. Apart from the new machine being $\epsilon$ -optimal, the numerical results demonstrate that the number of iterations required for convergence is significantly reduced for the HDPA, when compared to the state-of-the-art HCPA scheme.

• Omslandseter, Rebekka Olsson; Jiao, Lei; Zhang, Xuan; Yazidi, Anis & Oommen, John (2022). The Hierarchical Discrete Learning Automaton Suitable for Environments with Many Actions and High Accuracy Requirements. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13151, p. 507–518. doi: 10.1007/978-3-030-97546-3_41. Full text in Research Archive Show summary

  Since its early beginning, the paradigm of Learning Automata (LA), has attracted much interest. Over the last decades, new concepts and various improvements have been introduced to increase the LA’s speed and accuracy, including employing probability updating functions, discretizing the probability space, and implementing the “Pursuit” concept. The concept of incorporating “structure” into the ordering of the LA’s actions is one of the latest advancements to the field, leading to the ϵ-optimal Hierarchical Continuous Pursuit LA (HCPA) that has superior performance to other LA variants when the number of actions is large. Although the previously proposed HCPA is powerful, its speed has a handicap when the required action probability of an action is approaching unity. The reason for this slow convergence is that the learning parameter operates in a multiplicative manner within the probability space, making the increment of the action probability smaller as its probability becomes close to unity. Therefore, we propose the novel Hierarchical Discrete Learning Automata (HDPA) in this paper, which does not possess the same impediment as the HCPA. The proposed machine infuse the principle of discretization into the action probability vector’s updating functionality, where this type of updating is invoked recursively at every depth within a hierarchical tree structure and we pursue the best estimated action in all iterations through utilization of the Estimator phenomenon. The proposed machine is ϵ-optimal, and our experimental results demonstrate that the number of iterations required before convergence is significantly reduced for the HDPA, when compared with the HCPA.

• Lei, Jiao; Zhang, Xuan; Granmo, Ole-Christoffer & Abeyrathna, Kuruge Darshana (2022). On the Convergence of Tsetlin Machines for the XOR Operator. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 45(5), p. 6072–6085. doi: 10.1109/TPAMI.2022.3203150. Full text in Research Archive Show summary

  The Tsetlin Machine (TM) is a novel machine learning algorithm with several distinct properties, including transparent inference and learning using hardware-near building blocks. Although numerous papers explore the TM empirically, many of its properties have not yet been analyzed mathematically. In this article, we analyze the convergence of the TM when input is non-linearly related to output by the XOR-operator. Our analysis reveals that the TM, with just two conjunctive clauses, can converge almost surely to reproducing XOR, learning from training data over an infinite time horizon. Furthermore, the analysis shows how the hyper-parameter T guides clause construction so that the clauses capture the distinct sub-patterns in the data. Our analysis of convergence for XOR thus lays the foundation for analyzing other more complex logical expressions. These analyses altogether, from a mathematical perspective, provide new insights on why TMs have obtained the state-of-the-art performance on several pattern recognition problems.

• Fei, Zixuan; Wang, Ying; Zhao, Junwei; Wang, Xue & Lei, Jiao (2022). Joint Computational and Wireless Resource Allocation in Multicell Collaborative Fog Computing Networks. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 21(11), p. 9155–9169. doi: 10.1109/TWC.2022.3173365. Full text in Research Archive Show summary

  In 6G and future networks, joint optimization of communication and computational resources lays the foundation for various delay-sensitive intelligent IoT services in the fog computing architecture. In this paper, we present a multi-device collaborative computing architecture in the cell association environment to accelerate the processing procedure of data generated by smart IoT devices. In this scenario, a two-tier task scheduling scheme and an uplink and downlink power allocation factor are jointly optimized to reduce the data processing delay and improve fairness among different users, which is in nature a hard problem due to a series of non-convex constraints. To make the problem tractable, the problem is transformed into a smooth non-convex problem with the introduction of auxiliary variables and then decoupled into two subproblems based on the data transmission and processing procedure. Thereafter, different methods such as Successive Convex Approximation (SCA) and Block Successive Upperbound Minimization (BSUM) are employed to reconstruct several upper-bound convex optimization subproblems. Besides, a fast 0–1 binary offloading scheme is proposed based on the original algorithm. Finally, the simulation results depict the effectiveness of the proposed algorithms in detail, and the scalability of the system is also examined.

• Oommen, John; Zhang, Xuan & Lei, Jiao (2022). A Comprehensive Survey of Estimator Learning Automata and Their Recent Convergence Results. Lecture Notes in Networks and Systems. ISSN 2367-3370. 289, p. 33–52. doi: 10.1007/978-3-030-87049-2_2. Full text in Research Archive Show summary

  The pre-cursor field to Reinforcement Learning is that of Learning Automata (LA). Within this field, Estimator Algorithms (EAs) can be said to be the state-of-the-art. Further, the subset of Pursuit Algorithms (PAs), discovered by Thathachar and Sastry [34, 39], were the pioneering schemes. This chapter contains a comprehensive survey of the various EAs, and the most recent convergence results for PAs. Unlike the prior LA, EAs are based on a fundamentally distinct phenomenon. They are also the most accurate LA, converging in the least time. EAs operate on two vectors, namely, the action probability vector which is updated using responses from the Environment, and quickly-computed estimates of the reward probabilities of the various actions. The proofs that they are ε-optimal is thus very complex. They have to incorporate two rather snon-orthogonal phenomena, which are the convergence of these estimates and the convergence of the probabilities of selecting the various actions. For almost three decades, the reported proofs of PAs possessed an infirmity (or flaw), which we refer to as the claim of the “monotonicity” property. This flaw was discovered by the authors of [37], who also provided an alternate proof for a specific PA where the scheme’s parameter decreased with time. This paper first records all the reported EAs. It then reports a comprehensive survey of the proofs from a different perspective. These proofs have not required that the sequence of action probabilities of selecting the optimal action satisfies the property of monotonicity. On the other hand, whenever any action probability is close enough to unity, we require that the process jumps to an absorbing barrier at the next time instant, i.e., in a single step. By requiring such a constraint, these proofs invoke the weaker property, i.e., the submartinagale property of pm(t), to demonstrate the ε-optimality. We have thus proven the ε-optimality for the Absorbing CPA [49, 50], the Discretized PA [51, 52], and for the family of Bayesian PA [53], where the estimates are obtained by a Bayesian (rather than a Maximum Likelihood (ML)) process.

• Goodwin, Morten; Halvorsen, Kim Aleksander Tallaksen; Jiao, Lei; Knausgård, Kristian Muri; Martin, Angela Helen & Moyano, Marta [Show all 10 contributors for this article] (2022). Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook. ICES Journal of Marine Science. ISSN 1054-3139. 79(2), p. 319–336. doi: 10.1093/icesjms/fsab255. Full text in Research Archive Show summary

  The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. These endeavours require collaboration across ecological and data science disciplines, which can be challenging to initiate. To promote the use of DL towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular DL approaches for ecological data analysis, focusing on supervised learning techniques with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of DL to marine ecology. We present case studies on plankton, fish, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field’s opportunities and challenges, including potential technological advances and issues with managing complex data sets.

• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2022). Robust Interpretable Text Classification against Spurious Correlations Using AND-rules with Negation. In Raed, Luc de (Eds.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. AAAI Press. ISSN 9781956792003. p. 4439–4446. doi: 10.24963/ijcai.2022/616. Full text in Research Archive Show summary

  The state-of-the-art natural language processing models have raised the bar for excellent performance on a variety of tasks in recent years. However, concerns are rising over their primitive sensitivity to distribution biases that reside in the training and testing data. This issue hugely impacts the performance of the models when exposed to out-of-distribution and counterfactual data. The root cause seems to be that many machine learning models are prone to learn the shortcuts, modelling simple correlations rather than more fundamental and general relationships. As a result, such text classifiers tend to perform poorly when a human makes minor modifications to the data, which raises questions regarding their robustness. In this paper, we employ a rule-based architecture called Tsetlin Machine (TM) that learns both simple and complex correlations by ANDing features and their negations. As such, it generates explainable AND-rules using negated and non-negated reasoning. Here, we explore how non-negated reasoning can be more prone to distribution biases than negated reasoning. We further leverage this finding by adapting the TM architecture to mainly perform negated reasoning using the specificity parameter s. As a result, the AND-rules becomes robust to spurious correlations and can also correctly predict counterfactual data. Our empirical investigation of the model’s robustness uses the specifcity s to control the degree of negated reasoning. Experiments on publicly available Counterfactually-Augmented Data demonstrate that the negated clauses are robust to spurious correlations and outperform Naive Bayes, SVM, and Bi-LSTM by up to 20%, and ELMo by almost 6% on counterfactual test data.

• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Interpretability in Word Sense Disambiguation using Tsetlin Machine, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 9789897584848. p. 402–409. doi: 10.5220/0010382104020409. Full text in Research Archive
• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Enhancing Interpretable Clauses Semantically using Pretrained Word Representation, Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP. Association for Computational Linguistics (ACL). ISSN 9781955917063. p. 265–274. doi: 10.18653/v1/2021.blackboxnlp-1.19. Full text in Research Archive
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Shafik, Rishad; Lei, Jiao; Wheeldon, Adrian & Yakovlev, Alex [Show all 8 contributors for this article] (2021). A multi-step finite-state automaton for arbitrarily deterministic Tsetlin Machine learning. Expert Systems. ISSN 0266-4720. 40(4). doi: 10.1111/exsy.12836. Full text in Research Archive
• Abeyrathna, Kuruge Darshana; Bhattarai, Bimal; Goodwin, Morten; Gorji, Saeed Rahimi; Granmo, Ole-Christoffer & Lei, Jiao [Show all 8 contributors for this article] (2021). Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling. Proceedings of Machine Learning Research (PMLR). Full text in Research Archive Show summary

  Using logical clauses to represent patterns, Tsetlin Machine (TM) have recently obtained competitive performance in terms of accuracy, memory footprint, energy, and learning speed on several benchmarks. Each TM clause votes for or against a particular class, with classification resolved using a majority vote. While the evaluation of clauses is fast, being based on binary operators, the voting makes it necessary to synchronize the clause evaluation, impeding parallelization. In this paper, we propose a novel scheme for desynchronizing the evaluation of clauses, eliminating the voting bottleneck. In brief, every clause runs in its own thread for massive native parallelism. For each training example, we keep track of the class votes obtained from the clauses in local voting tallies. The local voting tallies allow us to detach the processing of each clause from the rest of the clauses, supporting decentralized learning. This means that the TM most of the time will operate on outdated voting tallies. We evaluated the proposed parallelization across diverse learning tasks and it turns out that our decentralized TM learning algorithm copes well with working on outdated data, resulting in no significant loss in learning accuracy. Furthermore, we show that the approach provides up to 50 times faster learning. Finally, learning time is almost constant for reasonable clause amounts (employing from 20 to 7,000 clauses on a Tesla V100 GPU). For sufficiently large clause numbers, computation time increases approximately proportionally. Our parallel and asynchronous architecture thus allows processing of more massive datasets and operating with more clauses for higher accuracy.

• Yadav, Rohan Kumar; Lei, Jiao; Goodwin, Morten & Granmo, Ole-Christoffer (2021). Positionless aspect based sentiment analysis using attention mechanism. Knowledge-Based Systems. ISSN 0950-7051. 226. doi: 10.1016/j.knosys.2021.107136. Full text in Research Archive
• Nunavath, Vimala; Johansen, Sahand; Johannessen, Tommy Sandtorv; Jiao, Lei; Hansen, Bjørge Hermann & Berntsen, Sveinung [Show all 7 contributors for this article] (2021). Deep Learning for Classifying Physical Activities from Accelerometer Data. Sensors. 21(16). doi: 10.3390/s21165564. Full text in Research Archive Show summary

  Physical inactivity increases the risk of many adverse health conditions, including the world’s major non-communicable diseases, such as coronary heart disease, type 2 diabetes, and breast and colon cancers, shortening life expectancy. There are minimal medical care and personal trainers’ methods to monitor a patient’s actual physical activity types. To improve activity monitoring, we propose an artificial-intelligence-based approach to classify physical movement activity patterns. In more detail, we employ two deep learning (DL) methods, namely a deep feed-forward neural network (DNN) and a deep recurrent neural network (RNN) for this purpose. We evaluate the two models on two physical movement datasets collected from several volunteers who carried tri-axial accelerometer sensors. The first dataset is from the UCI machine learning repository, which contains 14 different activities-of-daily-life (ADL) and is collected from 16 volunteers who carried a single wrist-worn tri-axial accelerometer. The second dataset includes ten other ADLs and is gathered from eight volunteers who placed the sensors on their hips. Our experiment results show that the RNN model provides accurate performance compared to the state-of-the-art methods in classifying the fundamental movement patterns with an overall accuracy of 84.89% and an overall F1-score of 82.56%. The results indicate that our method provides the medical doctors and trainers a promising way to track and understand a patient’s physical activities precisely for better treatment.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Jiao, Lei (2021). Measuring the Novelty of Natural Language Text using the Conjunctive Clauses of a Tsetlin Machine Text Classifier, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 9789897584848. p. 410–417. doi: 10.5220/0010382204100417. Full text in Research Archive Show summary

  Most supervised text classification approaches assume a closed world, counting on all classes being present in the data at training time. This assumption can lead to unpredictable behaviour during operation, whenever novel, previously unseen, classes appear. Although deep learning-based methods have recently been used for novelty detection, they are challenging to interpret due to their black-box nature. This paper addresses interpretable open-world text classification, where the trained classifier must deal with novel classes during operation. To this end, we extend the recently introduced Tsetlin machine (TM) with a novelty scoring mechanism. The mechanism uses the conjunctive clauses of the TM to measure to what degree a text matches the classes covered by the training data. We demonstrate that the clauses provide a succinct interpretable description of known topics, and that our scoring mechanism makes it possible to discern novel topics from the known ones. Empirically, our TM-b ased approach outperforms seven other novelty detection schemes on three out of five datasets, and performs second and third best on the remaining, with the added benefit of an interpretable propositional logic-based representation.

• Yadav, Rohan Kumar; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). Human-Level Interpretable Learning for Aspect-Based Sentiment Analysis. Proceedings of the AAAI Conference on Artificial Intelligence. ISSN 2159-5399. p. 14203–14212. Full text in Research Archive Show summary

  This paper proposes human-interpretable learning of aspect-based sentiment analysis (ABSA), employing the recently introduced Tsetlin Machines (TMs). We attain interpretability by converting the intricate position-dependent textual semantics into binary form, mapping all the features into bag-of-words (BOWs). The binary form BOWs are encoded so that the information on the aspect and context words are nearly lossless for sentiment classification. We further adapt the BOWs as input to the TM, enabling learning of aspect-based sentiment patterns in propositional logic. To evaluate interpretability and accuracy, we conducted experiments on two widely used ABSA datasets of SemEval 2014: Restaurant 14 and Laptop 14. The experiments show how each relevant feature takes part in conjunctive clauses that contain the context information for the corresponding aspect word, demonstrating human-level interpretability. At the same time, the obtained accuracy is competitive with existing neural network models, reaching 78.02% on Restaurant 14 and 73.51% on Laptop 14.

• Bhattarai, Bimal; Granmo, Ole-Christoffer & Jiao, Lei (2021). A Tsetlin Machine Framework for Universal Outlier and Novelty Detection, Proceedings of the 13th International Conference on Agents and Artificial Intelligence (ICAART 2021). SciTePress. ISSN 9789897584848. doi: 10.1007/978-3-031-10161-8_14. Full text in Research Archive
• Ali, Zaiwar; Khaf, Sadia; Abbas, Ziaul Haq; Abbas, Ghulam; Lei, Jiao & Irshad, Amna [Show all 8 contributors for this article] (2021). A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing. Computers, Materials and Continua (CMC). ISSN 1546-2218. 66(2), p. 1461–1477. doi: 10.32604/cmc.2020.013743. Full text in Research Archive
• Knausgård, Kristian Muri; Wiklund, Arne; Sørdalen, Tonje Knutsen; Halvorsen, Kim Tallaksen; Kleiven, Alf Ring & Lei, Jiao [Show all 7 contributors for this article] (2021). Temperate fish detection and classification: a deep learning based approach. Applied Intelligence - The International Journal of Research on Intelligent Systems for Real Life Complex Problems. ISSN 0924-669X. 52. doi: 10.1007/s10489-020-02154-9. Full text in Research Archive Show summary

  A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27% using the pre-training model. The accuracies using the post-training model are also high; 83.68% and 87.74% with and without image augmentation, respectively. This strongly indicates that the solution is viable with a more extensive dataset.

• Abbas, Ziaul Haq; Ali, Zaiwar; Abbas, Ghulam; Lei, Jiao; Bilal, Muhammad & Suh, Doug-Young [Show all 7 contributors for this article] (2021). Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach. Sensors. 21(10), p. 1–18. doi: 10.3390/s21103523. Full text in Research Archive
• Zhang, Xuan; Lei, Jiao; Granmo, Ole-Christoffer & Goodwin, Morten (2021). On the convergence of Tsetlin machines for the IDENTITY- and NOT operators. IEEE Transactions on Pattern Analysis and Machine Intelligence. ISSN 0162-8828. 44(10), p. 6345–6359. doi: 10.1109/TPAMI.2021.3085591. Full text in Research Archive
• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2021). A Learning-Automata Based Solution for Non-equal Partitioning: Partitions with Common GCD Sizes. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 12799, p. 227–239. doi: 10.1007/978-3-030-79463-7_19. Full text in Research Archive Show summary

  The Object Migration Automata (OMA) has been used as a powerful tool to resolve real-life partitioning problems in random Environments. The virgin OMA has also been enhanced by incorporating the latest strategies in Learning Automata (LA), namely the Pursuit and Transitivity phenomena. However, the single major handicap that it possesses is the fact that the number of objects in each partition must be equal. Obviously, one does not always encounter problems with equally-sized groups (When the true underlying problem has non-equally-sized groups, the OMA reports the best equally-sized solution as the recommended partition.). This paper is the pioneering attempt to relax this constraint. It proposes a novel solution that tackles partitioning problems where the partition sizes can be both equal and/or unequal, but when the cardinalities of the true partitions have a Greatest Common Divisor (GCD). However, on attempting to resolve this less-constrained version, we encounter a few problems that deal with implementing the inter-partition migration of the objects. To mitigate these, we invoke a strategy that has been earlier used in the theory of automata, namely that of mapping the machine’s state space onto a larger space. This paper details how this strategy can be incorporated, and how such problems can be solved. In essence, it presents the design, implementation, and testing of a novel OMA-based method that can be implemented with the OMA itself, and also in all of its existing variants, including those incorporating the Pursuit and Transitivity phenomena. Numerical results demonstrate that the new approach can efficiently solve partitioning problems with partitions that have a common GCD.

• Chen, Yuanbin; Wang, Ying & Lei, Jiao (2021). Robust Transmission for Reconfigurable Intelligent Surface Aided Millimeter Wave Vehicular Communications with Statistical CSI. IEEE Transactions on Wireless Communications. ISSN 1536-1276. 21(2). doi: 10.1109/TWC.2021.3100492. Full text in Research Archive Show summary

  The integration of reconfigurable intelligent surface (RIS) into millimeter wave (mmWave) vehicular communications offers the possibility to unleash the potential of future proliferating vehicular applications. However, the high-mobility-induced rapidly varying channel state information (CSI) has been making it challenging to obtain the accurate instantaneous CSI (I-CSI) and to cope with the incurable high signaling overhead. The situation may become worse when the RIS with a large number of passive reflecting elements is deployed. To overcome this challenge, we investigate in this paper a robust transmission scheme for the time-varying RIS-aided mmWave vehicular communications, in which, specifically, a multi-antenna base station (BS) serves vehicle user equipments (VUEs) with the help of RIS at the mmWave frequency. The uplink average achievable rate is maximized relying only upon the imperfect knowledge of statistical CSI. Considering the time-varying characteristics, we first propose an effective transmission protocol by reasonably configuring the time-scale of CSI acquisition in order to significantly relax the frequency of channel information updates, which constitutes one of the most critical issues in RIS-aided vehicular communications. Then, the formulated resource allocation problem is discussed in the single- and multi-VUE case, respectively. To be specific, for the single-VUE case, a closed-form expression of the average rate is derived by extracting the statistical characteristics of mmWave channels, and an alternating optimization (AO)-based algorithm is proposed. For the multi-VUE case, we develop an efficient algorithm, called JAPMC, to circumvent the unavailability of the closed-form of the objective function and probabilistic constraint by constructing quadratic surrogates of that. Simulation results confirm the effectiveness and robustness of our proposed algorithms as compared to benchmark schemes.

• Omslandseter, Rebekka Olsson; Jiao, Lei & Oommen, John (2021). Object Migration Automata for Non-equal Partitioning Problems with Known Partition Sizes. IFIP Advances in Information and Communication Technology. ISSN 1868-4238. doi: 10.1007/978-3-030-79150-6_11. Full text in Research Archive Show summary

  Solving partitioning problems in random environments is a classic and challenging task, and has numerous applications. The existing Object Migration Automaton (OMA) and its proposed enhancements, which include the Pursuit and Transitivity phenomena, can solve problems with equi-sized partitions. Currently, these solutions also include one where the partition sizes possess a Greatest Common Divisor (GCD). In this paper, we propose an OMA-based solution that can solve problems with both equally and non-equally-sized groups, without restrictions on their sizes. More specifically, our proposed approach, referred to as the Partition Size Required OMA (PSR-OMA), can solve general partitioning problems, with the only additional requirement being that the unconstrained partitions’ sizes are known a priori. The scheme is a fundamental contribution in the field of partitioning algorithms, and the numerical results presented demonstrate that PSR-OMA can solve both equi-partitioning and non-equi-partitioning problems efficiently, and is the only known solution that resolves this problem.

• Omslandseter, Rebekka Olsson; Lei, Jiao; Liu, Yuanwei & Oommen, John (2020). User Grouping and Power Allocation in NOMA Systems: A Reinforcement Learning-Based Solution, Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices.. Springer Nature. ISSN 9783030557898. p. 299–311. doi: 10.1007/978-3-030-55789-8_27. Full text in Research Archive
• Yang, Zhong; Liu, Yuanwei; Chen, Yue & Lei, Jiao (2020). Learning Automata Based Q-Learning for Content Placement in Cooperative Caching. IEEE Transactions on Communications. ISSN 0090-6778. 68(6), p. 3667–3680. doi: 10.1109/TCOMM.2020.2982136. Full text in Research Archive
• Ullah, Arif; Haq, Abbas; Abbas, Ghulam; Muhammad, Fazal & Lei, Jiao (2020). Performance analysis of user-centric SBS deployment with load balancing in heterogeneous cellular networks: A Thomas cluster process approach. Computer Networks. ISSN 1389-1286. 170, p. 1–14. doi: 10.1016/j.comnet.2020.107120. Full text in Research Archive
• Yadav, Rohan Kumar; Bhattarai, Bimal; Lei, Jiao; Goodwin, Morten & Granmo, Ole-Christoffer (2020). Indoor Space Classification Using Cascaded LSTM. IEEE Conference on Industrial Electronics and Applications. p. 1110–1114. doi: 10.1109/ICIEA48937.2020.9248347. Full text in Research Archive Show summary

  Indoor space classification is an important part of localization that helps in precise location extraction, which has been extensively utilized in industrial and domestic domain. There are various approaches that employ Bluetooth Low Energy (BLE), Wi-Fi, magnetic field, object detection, and Ultra Wide Band (UWB) for indoor space classification purposes. Many of the existing approaches need extensive pre-installed infrastructure, making the cost higher to obtain reasonable accuracy. Therefore, improvements are still required to increase the accuracy with minimum requirements of infrastructure. In this paper, we propose an approach to classify the indoor space using geomagnetic field (GMF) and radio signal strength (RSS) as the identity. The indoor space is an open big test bed divided into different indiscernible subspace. We collect GMF and RSS at each subspace and classify it using cascaded Long Short Term Memory (LSTM). The experimental results show that the accuracy is significantly improved when GMF and RSS are combined to make distinct features. In addition, we compare the performance of the proposed model with the state-of-the-art machine learning methods.

• Ullah, Arif; Abbas, Ziaul Haq; Muhammad, Fazal; Abbas, Ghulam & Lei, Jiao (2020). Capacity driven small cell deployment in heterogeneous cellular networks: Outage probability and rate coverage analysis. Transactions on Emerging Telecommunications Technologies. ISSN 1124-318X. 31(6), p. 1–21. doi: 10.1002/ett.3876. Full text in Research Archive
• Chen, Yuanbin; Wang, Ying; Liu, Man; Zhang, Jiayi & Lei, Jiao (2020). Network Slicing Enabled Resource Management for Service-Oriented Ultra-Reliable and Low-Latency Vehicular Networks. IEEE Transactions on Vehicular Technology. ISSN 0018-9545. 69(7), p. 7847–7862. doi: 10.1109/TVT.2020.2991723. Full text in Research Archive
• Opalic, Sven Myrdahl; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed; Pardiñas, Ángel Á. & Hafner, Armin [Show all 7 contributors for this article] (2020). ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse. Journal of Cleaner Production. ISSN 0959-6526. 260, p. 1–10. doi: 10.1016/j.jclepro.2020.120887. Full text in Research Archive Show summary

  Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management system. The operating temperature and pressure measurements, as well as the operating frequency of compressors, are used in developing operational model of the cooling system, which outputs electrical consumption and refrigerant mass flow without the need for additional physical measurements. The presented model is superior to a generalized theoretical model, as it learns from data that includes individual compressor type characteristics. The results show that the presented approach is relatively precise with a Mean Average Percentage Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as 1.8%.

• Xiao, Xiang; Zeng, Fanzi; Hu, Zhenzhen & Lei, Jiao (2020). Dynamic Flow-Adaptive Spectrum Leasing with Channel Aggregation in Cognitive Radio Networks. Sensors. 20(13). doi: 10.3390/s20133800. Full text in Research Archive Show summary

  Cognitive radio networks (CRNs), which allow secondary users (SUs) to dynamically access a network without affecting the primary users (PUs), have been widely regarded as an effective approach to mitigate the shortage of spectrum resources and the inefficiency of spectrum utilization. However, the SUs suffer from frequent spectrum handoffs and transmission limitations. In this paper, considering the quality of service (QoS) requirements of PUs and SUs, we propose a novel dynamic flow-adaptive spectrum leasing with channel aggregation. Specifically, we design an adaptive leasing algorithm, which adaptively adjusts the portion of leased channels based on the number of ongoing and buffered PU flows. Furthermore, in the leased spectrum band, the SU flows with access priority employ dynamic spectrum access of channel aggregation, which enables one flow to occupy multiple channels for transmission in a dynamically changing environment. For performance evaluation, the continuous time Markov chain (CTMC) is developed to model our proposed strategy and conduct theoretical analyses. Numerical results demonstrate that the proposed strategy effectively improves the spectrum utilization and network capacity, while significantly reducing the forced termination probability and blocking probability of SU flows.

• Li, Zhijiang; Zheng, Yingping; Cao, Liqin; Lei, Jiao; Zhong, Yanfei & Zhang, Caiyi (2020). A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering. Color Research and Application. ISSN 0361-2317. 45(4), p. 656–670. doi: 10.1002/col.22491. Full text in Research Archive
• Opalic, Sven Myrdahl; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2020). A Deep Reinforcement Learning scheme for Battery Energy Management. In Solic, P (Eds.), 2020 5th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE Press. ISSN 9789532901054. p. 1–6. doi: 10.23919/SpliTech49282.2020.9243797. Full text in Research Archive
• Zhang, Xuan; Jiao, Lei; Oommen, John & Granmo, Ole-Christoffer (2019). A Conclusive Analysis of the Finite-Time Behavior of the Discretized Pursuit Learning Automaton. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 31(1). doi: 10.1109/TNNLS.2019.2900639. Full text in Research Archive Show summary

  This paper deals with the finite-time analysis (FTA) of learning automata (LA), which is a topic for which very little work has been reported in the literature. This is as opposed to the asymptotic steady-state analysis for which there are, probably, scores of papers. As clarified later, unarguably, the FTA of Markov chains, in general, and of LA, in particular, is far more complex than the asymptotic steady-state analysis. Such an FTA provides rigid bounds for the time required for the LA to attain to a given convergence accuracy. We concentrate on the FTA of the Discretized Pursuit Automaton (DPA), which is probably one of the fastest and most accurate reported LA. Although such an analysis was carried out many years ago, we record that the previous work is flawed. More specifically, in all brevity, the flaw lies in the wrongly ``derived'' monotonic behavior of the LA after a certain number of iterations. Rather, we claim that the property should be invoked is the submartingale property. This renders the proof to be much more involved and deep. In this paper, we rectify the flaw and reestablish the FTA based on such a submartingale phenomenon. More importantly, from the derived analysis, we are able to discover and clarify, for the first time, the underlying dilemma between the DPA's exploitation and exploration properties. We also nontrivially confirm the existence of the optimal learning rate, which yields a better comprehension of the DPA itself.

• Opalic, Sven Myrdahl; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2019). Modelling of Compressors in an Industrial CO 2-Based Operational Cooling System Using ANN for Energy Management Purposes, Engineering Applications of Neural Networks. EANN 2019.. Springer. ISSN 9783030202569. doi: 10.1007/978-3-030-20257-6_4. Full text in Research Archive Show summary

  Large scale cooling installations usually have high energy consumption and fluctuating power demands. There are several ways to control energy consumption and power requirements through intelligent energy and power management, such as utilizing excess heat, thermal energy storage and local renewable energy sources. Intelligent energy and power management in an operational setting is only possible if the time-varying performance of the individual components of the energy system is known. This paper presents an approach to model the compressors in an industrial, operational two-stage cooling system, with CO 2 as the working fluid, located in an advanced food distribution warehouse in Norway. An artificial neural network is adopted to model the compressors using the operational data. The model is trained with cooling medium evaporation and condensation temperature, suction gas temperature and compressor operating frequency, and outputs electrical power load and cooling load. The best results are found by using a single hidden layer with 45 hidden neurons and a hyperbolic tangent activation function trained with the Adam optimizer, with a resulting mean squared error as low as 0.08% for both the training and validation data sets. The trained model will be part of a system implemented in a real-world setting to determine the cooling load, compressor power load, and coefficient of performance. An intelligent energy management system will utilize the model for energy and power optimization of the cooling system by storing energy in a thermal energy storage, using predictions of energy demand and cooling system performance.

• Omslandseter, Rebekka Olsson; Lei, Jiao & Haglund, Magne Arild (2019). Field Measurements and Parameter Calibrations of Propagation Model for Digital Audio Broadcasting in Norway. IEEE Vehicular Technology Conference (VTC). ISSN 1090-3038. doi: 10.1109/VTCFall.2018.8690746. Full text in Research Archive
• Naushad, Alamgir; Abbas, Ghulam; Abbas, Ziaul Haq; Lei, Jiao & Muhammad, Fazal (2019). A Novel Faster Failure Detection Strategy for Link Connectivity using Hello Messaging in Mobile Ad Hoc Networks. International Journal of Ad Hoc and Ubiquitous Computing. ISSN 1743-8225. 32(2), p. 75–98. doi: 10.1504/IJAHUC.2019.102451. Full text in Research Archive
• Granmo, Ole-Christoffer; Glimsdal, Sondre; Lei, Jiao; Goodwin, Morten; Omlin, Christian & Berge, Geir Thore (2019). The Convolutional Tsetlin Machine. arXiv.org. ISSN 2331-8422. Full text in Research Archive
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Lei, Jiao & Goodwin, Morten (2019). The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems. In Oliveira, P Moura (Eds.), Progress in Artificial Intelligence. EPIA 2019. Springer. ISSN 9783030302443. p. 268–280. doi: 10.1007/978-3-030-30244-3_23. Full text in Research Archive
• Berge, Geir Thore; Granmo, Ole-Christoffer; Tveit, Tor Oddbjørn; Goodwin, Morten; Lei, Jiao & Matheussen, Bernt Viggo (2019). Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications. IEEE Access. 7. doi: 10.1109/ACCESS.2019.2935416. Full text in Research Archive
• Abeyrathna, Kuruge Darshana; Granmo, Ole-Christoffer; Zhang, Xuan; Lei, Jiao & Goodwin, Morten (2019). The regression Tsetlin machine: a novel approach to interpretable nonlinear regression. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. ISSN 1364-503X. doi: 10.1098/rsta.2019.0165. Full text in Research Archive
• Olsvik, Erlend; Trinh, Christian M.D.; Knausgård, Kristian Muri; Wiklund, Arne; Sørdalen, Tonje Knutsen & Kleiven, Alf Ring [Show all 8 contributors for this article] (2019). Biometric fish classification of temperate species using convolutional neural network with squeeze-and-excitation. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11606 LNAI, p. 89–101. doi: 10.1007/978-3-030-22999-3_9. Full text in Research Archive
• Yazidi, Anis; Zhang, Xuan; Lei, Jiao & Oommen, John (2019). The Hierarchical Continuous Pursuit Learning Automation: A Novel Scheme for Environments With Large Numbers of Actions. IEEE Transactions on Neural Networks and Learning Systems. ISSN 2162-237X. 31(2). doi: 10.1109/TNNLS.2019.2905162. Full text in Research Archive Show summary

  Although the field of learning automata (LA) has made significant progress in the past four decades, the LA-based methods to tackle problems involving environments with a large number of actions is, in reality, relatively unresolved. The extension of the traditional LA to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and so, most components of the vector will soon have values that are smaller than the machine accuracy permits, implying that they will never be chosen . This paper presents a solution that extends the continuous pursuit paradigm to such large -actioned problem domains. The beauty of the solution is that it is hierarchical, where all the actions offered by the environment reside as leaves of the hierarchy. Furthermore, at every level, we merely require a two -action LA that automatically resolves the problem of dealing with arbitrarily small action probabilities. In addition, since all the LA invoke the pursuit paradigm, the best action at every level trickles up toward the root. Thus, by invoking the property of the “max” operator, in which the maximum of numerous maxima is the overall maximum, the hierarchy of LA converges to the optimal action. This paper describes the scheme and formally proves its $\epsilon $ -optimal convergence. The results presented here can, rather trivially, be extended for the families of discretized and Bayesian pursuit LA too. This paper also reports extensive experimental results (including for environments having 128 and 256 actions) that demonstrate the power of the scheme and its computational advantages. As far as we know, there are no comparable pursuit-based results in the field of LA . In some cases, the hierarchical continuous pursuit automaton requires less than 18% of the number of iterations than the benchmark $L_{R-I}$ scheme, which is, by all metrics, phenomenal .

• Li, Zhijiang; Tan, Zhenshan; Cao, Liqin; Chen, Hu; Lei, Jiao & Zhong, Yanfei (2019). Directive local color transfer based on dynamic look-up table. Signal processing. Image communication. ISSN 0923-5965. 79, p. 1–12. doi: 10.1016/j.image.2019.06.010. Full text in Research Archive
• Zaiwar, Ali; Lei, Jiao; Baker, Thar; Abbas, Ghulam; Abbas, Ziaul Haq & Khaf, Sadia (2019). A Deep Learning Approach for Energy Efficient Computational Offloading in Mobile Edge Computing. IEEE Access. 7(1), p. 149623–1496633. doi: 10.1109/ACCESS.2019.2947053. Full text in Research Archive Show summary

  Mobile edge computing (MEC) has shown tremendous potential as a means for computationally intensive mobile applications by partially or entirely offloading computations to a nearby server to minimize the energy consumption of user equipment (UE). However, the task of selecting an optimal set of components to offload considering the amount of data transfer as well as the latency in communication is a complex problem. In this paper, we propose a novel energy-efficient deep learning based offloading scheme (EEDOS) to train a deep learning based smart decision-making algorithm that selects an optimal set of application components based on remaining energy of UEs, energy consumption by application components, network conditions, computational load, amount of data transfer, and delays in communication. We formulate the cost function involving all aforementioned factors, obtain the cost for all possible combinations of component offloading policies, select the optimal policies over an exhaustive dataset, and train a deep learning network as an alternative for the extensive computations involved. Simulation results show that our proposed model is promising in terms of accuracy and energy consumption of UEs.

• Li, Zhijiang; Zhu, Haonan; Cao, Liqin; Lei, Jiao; Zhong, Yanfei & Ma, Ailong (2019). Face Inpainting via Nested Generative Adversarial Networks. IEEE Access. 7, p. 155462–155471. doi: 10.1109/ACCESS.2019.2949614. Full text in Research Archive
• Albrigtsen, Svein Inge; Imenes, Alexander; Goodwin, Morten; Lei, Jiao & Nunavath, Vimala (2018). Neuroevolution of Actively Controlled Virtual Characters - An Experiment for an Eight-Legged Character, 19th International Conference on Engineering Applications of Neural Networks. Springer Publishing Company. ISSN 9783319982038. p. 94–105. doi: 10.1007/978-3-319-98204-5_8. Full text in Research Archive

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• Lei, Jiao & Shafik, Rishad Ahmed (2025). Tsetlin Machine (ISTM), International Symposium on the. IEEE (Institute of Electrical and Electronics Engineers). ISBN 9798331569228. doi: https:/ieeexplore.ieee.org/xpl/conhome/11300350/proceeding. Full text in Research Archive
• Andersen, Per-Arne & Jiao, Lei (2024). Exploring Efficient Job Shop Scheduling Using Deep Reinforcement Learning. Springer. ISBN 9783031779183. doi: 10.1007/978-3-031-77918-3_20. Full text in Research Archive
• Shafik, Rishad; Granmo, Ole-Christoffer; Lei, Jiao & Zadorozhny, Vladimir (2022). 2022 International Symposium on the Tsetlin Machine (ISTM 2022). IEEE (Institute of Electrical and Electronics Engineers). ISBN 9781665471169. Full text in Research Archive
• Lei, Jiao (2020). Channel Aggregation and Fragmentation for Traffic Flows. Springer. ISBN 9783030330804. 107 p. Full text in Research Archive Show summary

  This book introduces the impact of channel aggregation (CA) and channel fragmentation (CF) on traffic flows, through analytical models, computer simulations, and test-bed implementations. Its content includes the concept of CA and CF, the basic concept and calculation of Markov chains (MCs), the modeling process of the CA and CF enabled system via MCs, the process of simulations, and a test-bed study based on a software defined radio. This book can serve as a study guide for advanced-level students, who are interested in studying the impact of CA and CF techniques on traffic flows. This book would also interest communication engineers, who would like to learn MC modeling for performance evaluations, as it includes a step-by-step guidance for the modeling process via MCs, as well as its simulation approaches.

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• Lei, Jiao (2024). Classification of GNSS Jamming Signals Using Tsetlin Machine. Full text in Research Archive
• Asres, Mulugeta Weldezgina & Lei, Jiao (2023). AI for Policing-Aware Utility-Preservation on CCTV Anonymization. Full text in Research Archive Show summary

  CCTV surveillance cameras have become increasingly widespread in safety and security, generating vast volumes of data. However, privacy concerns have made it challenging to utilize these technologies. Several de-identification approaches have been proposed for CCTV footage to address this issue, aiming to achieve different levels of anonymization. Nevertheless, most existing solutions focus on generic anonymization, which may need to be more effective in meeting policing requirements, such as anonymous attributes, preserved attributes, and algorithm complexity. In this presentation, we discuss CCTV anonymization challenges and solutions that balance the protection of privacy attributes with maintaining the necessary footage utilities for crowd policing applications. We present approaches that utilize deep learning models to implement end-to-end security and anonymization, which can effectively de-identify CCTV images and video data. We emphasize a versatile design that caters explicitly to the varied requirements of policing applications. It provides the necessary footage while ensuring flexibility in attribute manipulation and preservation control mechanisms. The results we are presenting are based on publicly available surveillance datasets that have been approved by Norwegian authorities or presented in published articles.

• Liland, Kristian Hovde; Tomic, Oliver; Indahl, Ulf Geir; Futsæther, Cecilia Marie; Jiao, Lei & Granmo, Ole-Christoffer [Show all 7 contributors for this article] (2023). Tsetlin Machine in DNA sequence classification. Full text in Research Archive Show summary

  The Tsetlin machine (TM) is a logic-based machine learning model with the crucial advantages of transparency and hardware-friendliness. In TM, groups of Tsetlin Automata (TAs) produce Boolean expressions in the form of conjunctive clauses in AND-rules. In this work, we show that the DNA nucleotide sequence, coded using the four-letter alphabet A, C, G, T, is a perfect match to the Boolean logic-based TM. A coalesced integer-weighted TM is applied to the problem of prokaryote gene prediction demonstrating beyond state-of-the-art performance on reference genomes. This paves the way for highly efficient bioinformatic analysis in a large range of applications.

• Tunheim, Svein Anders; Lei, Jiao & Granmo, Ole-Christoffer (2023). Model Export for the Convolutional Coalesced Tsetlin Machine. Full text in Research Archive
• Jasinskaite, Emilija; Skjei, Øyvor Ystad; Lei, Jiao & Goodwin, Morten (2022). DP-ATT: Combining DeepPrivacy with AttGAN to preserve gender and age in de-identified CCTV footage. Full text in Research Archive
• Asres, Mulugeta Weldezgina & Lei, Jiao (2022). Anonymization for CCTV footage: Challenges and Possible Solutions. Full text in Research Archive Show summary

  An enormous amount of CCTV surveillance cameras produces huge volumes of data When gathered and analyzed, it can be a great contributing factor to a smart city. While we are eager to embrace new technology, we are not willing to intrude on our personal privacy. Privacy and safety security are the cornerstones of a thriving society and are often intricately linked to one another. Safety and security help reduce crime and build trust and cooperation, leading to economic prosperity. Privacy enables freedom of expression, individual autonomy, and protection from abuse. Several de-identification approaches have been proposed for CCTV footage to address this issue, aiming to achieve different levels of anonymization. In this presentation, we discuss the state of the art of CCTV AI-based anonymization approaches, outlining their potential leverages and challenges. We also present how different de-identification methods meet policing requirements for real-time safety monitoring.

• Lei, Jiao; Granmo, Ole-Christoffer; Blakely, Christian Dallas & Zadorozhny, Vladimir (2021). Denne chipen skjuler en norsk oppfinnelse som kan redde liv. [Internet]. NRK. Full text in Research Archive
• Opalic, Sven Myrdahl; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Kolhe, Mohan Lal (2020). A Deep Reinforcement Learning scheme for Battery Energy Management. Full text in Research Archive
• Kolhe, Mohan Lal; Goodwin, Morten; Lei, Jiao; Nielsen, Henrik Kofoed & Opalic, Sven Myrdahl (2023). Advanced Warehouse Energy Storage System Control Using Deep Supervised- and Reinforcement Learning. Universitetet i Agder. ISSN 9788284271521. Full text in Research Archive

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