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In-Memory Neural Stochastic Differential Equations with Probabilistic Differential Pair Achieved by In-situ P-bit using CMOS Integrated Voltage-Controlled Magnetic Tunnel Junctions

In-Memory Neural Stochastic Differential Equations with Prob...

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2024 IEEE International Electron Devices Meeting, IEDM 2024

作者： Xiao, Zhihua Hou, Yaoru Tong, Zihan Jiang, Yicheng Zhang, Yiyang Wu, Xuezhao Lee, Albert Wu, Di Cai, Hao Shao, Qiming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong AI Chip Center for Emerging Smart Systems InnoHK Centers Hong Kong Science Park Hong Kong Department of Physics The Hong Kong University of Science and Technology Hong Kong InstonTech Suzhou China School of Integrated Circuit Southeast University Nanjing China

ISBN: (纸本)9798350365429

The probabilistic bit (P-bit) is the core of probabilistic computing. We propose a novel in-situ P-bit compatible with compute-in-memory (CIM) schemes using voltage-controlled magnetic tunnel junctions (MTJs) to eliminate the generation-sample-transfer-compute paradigm of current P-bits. The conventional approach of sampling and transferring random sequences between separate P-bits and computing units reintroduces the memory bottleneck seen in von Neumann architectures, thereby limiting the efficiency of probabilistic computing. By pairing a data-bit and a P-bit as a probabilistic differential pair in a crossbar array, we enable random sequences to be directly utilized for computing. This generation-compute scheme eradicates the sampling and transfer costs associated with previous probabilistic computing methods. Full reuse of devices in the differential cells allows for probabilistic CIM with a large number of P-bits and high parallelism, suitable for real-world probabilistic computing tasks. We demonstrated in-memory neural stochastic differential equations for the reverse diffusion process in generative models. The results shows that without the bottlenecks, in-situ P-bit throughput is 6×faster and 2.19×more efficient than ex-situ P-bits using the same technology. Compared to other devices and schemes, the proposed scheme is 3×faster than state-of-the-art CMOS designs and 1.36×more energy efficient. © 2024 IEEE.

关键词： CMOS integrated circuits

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Performance Analysis of 5G New Radio V2X Communication

Performance Analysis of 5G New Radio V2X Communication

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IEEE Annual Conference on Wireless and Microwave Technology (WAMICON)

作者： G. G. Md. Nawaz Ali Sameer Ahmed Sharief Mohammad Nazmus Sadat Md. Suruz Miah Department of Computer Science & Information Systems Bradley University IL 61625 USA Department of Electrical and Computer Engineering Bradley University IL 61625 USA

This paper aims in analyzing the performance of the New Radio (NR) technology introduced by the Third Generation Partnership Project (3GPP) for Vehicle-to-everything (V2X) communication. The NR system is believed to be a complementary service for the already existing Long Term Evolution (LTE) Cellular-vehicle-to-everything (C-V2X) for advanced services in vehicular communications. The 5th generation (5G) NR systems are believed to outperform LTE C-V2X in terms of providing high throughput, low latency, and high reliability in dense traffic situations. Our aim in this paper is to implement and analyze the 5G NR V2X open source module in network simulation-3 (ns-3) and then to demonstrate the NR V2X performance in terms of different key performance indicators by varying a number of situations, including transmit power, communication range, packet size and sub-carrier spacing in the broadcast channel.

关键词： Wireless communication Key performance indicator Microwave communication New Radio Throughput Performance analysis Reliability

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Effective Anomaly Detection for Microservice systems with Real-Time Feature Selection

Effective Anomaly Detection for Microservice Systems with Re...

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Asia-Pacific Conference on Software engineering

作者： Siqi Zhou Xian Zhan Linlin Li Yepang Liu Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Microservice architecture is getting increasingly popular in recent years for building web-based systems. Finding runtime anomalies in such systems is crucial for improving their reliability. For this purpose, existing AIOps research has proposed various machine learning-based algorithms. However, a common limitation of existing algorithms is that they are sensitive to the settings of thresholds for anomaly identification when dealing with the high-dimensional multivariate time series data collected by monitoring the running instances of microservices. As a result, the performance of anomaly detection can be easily influenced by threshold changes. To tackle this problem, we propose a new anomaly detection framework called COAD (Combinatorial Optimization enhanced Anomaly Detection), which can work with various anomaly detection algorithms and enhance their detection process by performing real-time feature selection via metaheuristic algorithms. We have evaluated our method on three different testbeds based on a representative microservice system open-sourced by Google. The results show that real-time feature selection can significantly reduce the underlying algorithms' sensitivity to threshold settings (142% reduction on average). At the same time, the best anomaly detection performance (evaluated by f1-score) is improved by 5.67% on average. These results demonstrate the effectiveness and potential usefulness of the approach.

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A Study on the Effectiveness of Interactive systems Using Electromyography

A Study on the Effectiveness of Interactive Systems Using El...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Hanawa, Shinnosuke Ho, Yihsin Kobayashi, Iciro Sato-Shimokawara, Eri Shibata, Hiroki Obo, Takenori Takushoku University Graduate School of Engineering 815-1 TatemachiHachioji Tokyo Japan Takushoku University Faculty of Engineering 815-1 TatemachiHachioji Tokyo Japan Tokyo Metropolitan University Department of Computer Science 6-6 AsahigaokaHino Tokyo Japan Tokyo Metropolitan University Department of Mechanical Systems Engineering 6-6 AsahigaokaHino Tokyo Japan

ISBN: (纸本)9798331531614

The paper presented an intuitive control system using electromyography (EMG) data that is obtained from the Myo gesture control armband. The aim of this study is to enable users to control multiple devices with a single EMG device in an intuitive way. The presented system shows the ability of EMG-based gestures to control Phillips Hue that allow user to control light bulb by simple hand movements. Moreover, the authors also developed new functionalities, which is according to users' preferences, to register gestures. These functions aim to improve the usability and enhance the naturalness of operations. Additionally, unique gestures, which less common in everyday life, is defined to reduce misrecognition when switching between different devices. Furthermore, to achieve reliable and accurate recognition of multiple gestures, the Support Vector Machine (SVM) models is considered to be a machine learning method for training processed EMG data. The experiment results demonstrate significant improvements in user experience and practical applicability in various interactive scenarios. © 2025 IEEE.

关键词： Support vector machines

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Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Novel Technique for Improving Pedestrian Detection Accuracy and Processing Efficiency 7

Softsign Gaussian Deep Belief Neural Network (SGDBNN): A Nov...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Bhavani, N.P.G. Sudha, R.V. Priya, K.Shanmuga Arun Raj, S.R. Yadav, Vinay Kumar Balaram, Allam Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Electronics And Communication Engineering Tamilnadu India Knowledge Institute of Technology Department of Computer Science And Business Systems Selliampalayam Tamil Nadu Kakapalayam India KSR Institute For Engineering And Technology Department of Information Technology TamilNadu Tiruchengode637215 India U.B.D.T College of Engineering Department of Electronics & Communication Engineering Karnataka Davanagere577004 India Glocal University Glocal School of Science And Technology Uttar Pradesh India Mlr Institute of Technology Department of Computer Science And Engineering Hyderabad500043 India

ISBN: (纸本)9798350371406

Pedestrian detection is a critical task in computer vision with the aim of accurately identifying pedestrians in images or video frames. In this work, we tackle the issue of enhancing pedestrian detection precision while reducing processing time. To achieve this, we propose a novel technique called Softsign Gaussian Deep Belief Neural Network (SGDBNN) for pedestrian detection. The proposed SGDBNN technique leverages the power of recurrent deep neural networks to handle the complexities of pedestrian detection. The input layer receives images as input, which are then processed in the hidden layer. Here, a Gaussian activation function is employed to extract relevant features, effectively detecting pedestrian objects in the images. The incorporation of deep learning and recurrent behavior in the neural networks results in reduced incorrect classifications, leading to a lower false positive rate and improved pedestrian detection accuracy. The softsign activation function at the hidden layer allows the proposed technique to efficiently compare extracted features with pre-stored templates, enabling effective pedestrian identification in the images. By effectively utilizing the recurrent behavior and advanced activation functions, the SGDBNN demonstrates superior performance compared to existing methods, such as Deep Convolutional Neural Networks and Extended Deep Model. Experimental evaluations illustrate the effectiveness of the proposed SGDBNN technique. It achieves a significant increase in Positive Detection Accuracy (PDA) of up to 20% and 16%, while simultaneously reducing the False Positive Rate (FPR) by up to 18% and 3% compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Additionally, the proposed SGDBNN reduces training time by up to 30% and 12% and decreases memory requirements by up to 47% and 30% when compared to existing Deep Convolutional Neural Networks and Extended Deep Model, respectively. Likewise, the SGDBNN tec

关键词： computer vision

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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LIDS: Lightweight Dynamic Scheduling Technique for 6G-enabled Massive LoRa based IoT systems

LIDS: Lightweight Dynamic Scheduling Technique for 6G-enable...

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2022 IEEE GLOBECOM Workshops, GC Wkshps 2022

作者： Kumbam, Akshitha Choudhury, Nikumani Nasralla, Moustafa M. Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Pilani Hyderabad India Prince Sultan University Dept. of Communications & Networks Engineering Riyadh Saudi Arabia

ISBN: (纸本)9781665459754

The sixth-generation (6G) wireless communication networks are envisioned to deliver improved Quality of Services (QoS) such as data-rate, latency, localization, etc., compared to 5G services. This demand will mainly arise due to the massive Internet of Things (IoT) connections among billions of embedded devices. It is worth noting that these IoT devices are constrained in nature in terms of memory, computation and most importantly power. Low-power wireless computing necessitates a link-layer technology that considers the aforementioned IoT device requirements such as low-power, low data-rate, long-range and energy efficient reliable communications. Long Range Radio (LoRa) technology has recently become widely popular to facilitate low-power, long-range communication, while other similar technologies struggle to achieve long-range transmissions. The LoRA standard defines several PHY and MAC sub-layer specifications for Low-Power Wide Area Networks (LPWAN) that can be used to build several IoT applications. With 6G communications, an IoT application is expected to consist of hundreds of devices connected at a time, and this will need a proper scheduling mechanism among these devices for optimal performance. The LoRa standard does not define any scheduling mechanism and leaves it to the research community to explore. In this paper, we propose a distributed scheduling mechanism under the supervision of a common LoRa gateway. The proposed mechanism focuses on a Lightweight DynamicScheduling (LIDS) scheme to minimize loss in data transmissions through collisions. The proposed mechanism optimizes the power consumption to achieve the desired level of QoS requirements such as throughput and latency. © 2022 IEEE.

关键词： Internet of things

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FUSE: a federated learning and U-shape split learning-based electricity theft detection framework

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science China(Information sciences) 2024年第4期67卷 339-340页

作者： Xuan LI Naiyu WANG Liehuang ZHU Shuai YUAN Zhitao GUAN School of Control and Computer Engineering North China Electric Power University School of Cyberspace Science and Technology Beijing Institute of Technology Department of Finance Operationsand Information Systems(FOIS)Brock University

In recent years, the data-driven electricity theft detection methods integrated with edge cloud computing [1, 2] have not only demonstrated superior detection accuracy but also improved efficiency, making them viable alternatives to indoor inspections. Energy service providers(ESPs) typically manage regions by dividing them into various transformer districts(TDs). The detection of electricity theft in a particular region is performed by the associated TD,

关键词： Sensitive data

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Noisy Node Classification by Bi-level Optimization Based Multi-Teacher Distillation 39

Noisy Node Classification by Bi-level Optimization Based Mul...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yujing Wu, Zongqian Lu, Zhengyu Nie, Ci Wen, Guoqiu Zhu, Yonghua Zhu, Xiaofeng School of Computer Science and Engineering Guangxi Normal University Guilin China Guangxi Key Lab of Multisource Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Information Engineering Anyang Institute of Technology Anyang China Information Systems Technology Design Pillar Singapore University of Technology and Design Singapore

ISBN: (纸本)157735897X

Previous graph neural networks (GNNs) usually assume that the graph data is with clean labels for representation learning, but it is not true in real applications. In this paper, we propose a new multi-teacher distillation method based on bi-level optimization (namely BO-NNC), to conduct noisy node classification on the graph data. Specifically, we first employ multiple self-supervised learning methods to train diverse teacher models, and then aggregate their predictions through a teacher weight matrix. Furthermore, we design a new bi-level optimization strategy to dynamically adjust the teacher weight matrix based on the training progress of the student model. Finally, we design a label improvement module to improve the label quality. Extensive experimental results on real datasets show that our method achieves the best results compared to state-of-the-art methods. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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Enhanced Capuchin Search Algorithm Using Cooperative Island Model with Application of Evolutionary Feedforward Neural Networks

Enhanced Capuchin Search Algorithm Using Cooperative Island ...

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Intelligent Computing, Communication, Networking and Services (ICCNS), International Conference on

作者： Thaer Thaher Mohammed Awad Alaa Sheta Mohammed Aldasht Department of Computer Systems Engineering Arab American University Jenin Palestine Computer Science Department Southern Connecticut State University CT 06515 USA Department of Computer Engineering Palestine Polytechnic University Hebron Palestine

This paper introduces an enhanced version of the Capuchin Search Algorithm (CapSA) called ECapSA. CapSA draws inspiration from the collective intelligence of Capuchin monkeys and has shown success in solving real-world problems. However, it may encounter challenges handling complex optimization tasks, such as premature convergence or being trapped in local optima. ECapSA employs a local escaping mechanism operating the abandonment limit concept to exploit potential solutions and introduce diversification trends. Additionally, the ECapSA algorithm is improved by integrating the principles of the cooperative island model, resulting in the iECapSA. This modification enables better management of population diversity and a more optimal balance between exploration and exploitation. The efficiency of iECapSA is validated through a series of experiments, including the IEEE-CEC2014 benchmark functions and training the feedforward neural network (FNN) on seven biomedical datasets. The performance of iECapSA is compared to other metaheuristic techniques, namely differential evolution (DE), sine cosine algorithm (SCA), and whale optimization algorithm (WOA). The results of the comparative study demonstrate that iECapSA is a strong contender and surpasses other training algorithms in most datasets, particularly in terms of its ability to avoid local optima and its improved convergence speed.

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