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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Lee, Dongmin Makur, Anuran Purdue University Department of Computer Science West LafayetteIN United States School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

ISBN: (纸本)9798331541033

Contraction coefficients for Kullback-Leibler (KL) divergence are channel-dependent constants that are used to sharpen data processing inequalities for KL divergence into strong data processing inequalities. In this work, we present a method to analytically compute the KL contraction coefficients of product q-ary symmetric channels. Although such contraction coefficients have many applications in information theory as well as probability and statistics more broadly, deriving exact values is considered to be challenging for complex channels such as product channels. While recent discoveries have reduced the search space of the optimization problems defining contraction coefficients significantly, the problem is still far from trivial. We find that the symmetry inherent in channels such as the q-ary symmetric channel allows for further simplification, making an exact closed-form expression for contraction coefficients for KL divergence possible to derive. We begin by obtaining the KL contraction coefficients of product binary symmetric channels, and then generalize our results to larger alphabets. Our methods are not specific to product q-ary symmetric channels and can be used for wider classes of channels that satisfy certain symmetry properties. © 2024 IEEE.

关键词： Data handling

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Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6723-6738页

作者： Qin, Lutong Zhang, Lei Li, Chengrun Song, Chaoda Cheng, Dongzhou Wang, Shuoyuan Wu, Hao Song, Aiguo Nanjing Normal University School of Electrical and Automation Engineering Nanjing210023 China Shanghai200240 China Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States University of Macau Department of Computer and Information Science Taipa999078 China Yunnan University School of Information Science and Engineering Kunming650500 China Southeast University Department of Instrument Science and Engineering Nanjing210096 China

Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. © 2020 IEEE.

关键词： Budget control

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computer Vision-Based Hydroponic Lettuce Contour and Area Recognition System

Computer Vision-Based Hydroponic Lettuce Contour and Area Re...

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2023 International Conference on electrical engineering and Informatics, ICEEI 2023

作者： Pranowo, Samuel Raymond Sukra, Zener Lie Asy'ari, Muhammad Zacky Paryasto, Marisa Binus Aso School of Engineering Bina Nusantara University Automotive and Robotics Program Computer Engineering Department Jakarta Indonesia Telkom University Computer Engineering Program Electrical Engineering Department Bandung Indonesia

ISBN: (纸本)9798350381290

Agriculture is a vital industry for the people of Indonesia, but there are several obstacles, including limited space in urban areas and inefficient conventional sorting and harvesting methods. Using computer vision to choose hydroponic lettuce that is ready to be harvested and a robotic arm to bring the selected lettuce yields, the produced system aims to solve this problem. The system's computer vision algorithms include color space conversion, intensity transformation, and an algorithm for following borders. This project's research methodology combines a quantitative approach with an experimental approach, which consists of conducting several trials on the built system. Several trials demonstrated that the computer vision software was able to accomplish the specified objectives. The average success rate of the developed computer vision system is 93%, while the average success rate of the robot arm is 85%. © 2023 IEEE.

关键词： Lettuce

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Multi-Modal Sleep Stage Classification With Two-Stream Encoder-Decoder

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IEEE Transactions on Neural Systems and Rehabilitation engineering 2024年 32卷 2096-2105页

作者： Zhang, Zhao Lin, Bor-Shyh Peng, Chih-Wei Lin, Bor-Shing Wuyi University College of Mechanical and Electrical Engineering Fujian Wuyishan354300 China National Taipei University College of Electrical Engineering and Computer Science Department of Computer Science and Information Engineering New Taipei City237303 Taiwan Institute of Imaging and Biomedical Photonics National Yang Ming Chiao Tung University Tainan71150 Taiwan Taipei Medical University School of Biomedical Engineering College of Biomedical Engineering School of Gerontology and Long-Term Care College of Nursing Taipei11031 Taiwan National Taipei University Department of Computer Science and Information Engineering New Taipei City237303 Taiwan

Sleep staging serves as a fundamental assessment for sleep quality measurement and sleep disorder diagnosis. Although current deep learning approaches have successfully integrated multimodal sleep signals, enhancing the accuracy of automatic sleep staging, certain challenges remain, as follows: 1) optimizing the utilization of multi-modal information complementarity, 2) effectively extracting both long- and short-range temporal features of sleep information, and 3) addressing the class imbalance problem in sleep data. To address these challenges, this paper proposes a two-stream encode-decoder network, named TSEDSleepNet, which is inspired by the depth sensitive attention and automatic multi-modal fusion (DSA2F) framework. In TSEDSleepNet, a two-stream encoder is used to extract the multiscale features of electrooculogram (EOG) and electroencephalogram (EEG) signals. And a self-attention mechanism is utilized to fuse the multiscale features, generating multi-modal saliency features. Subsequently, the coarser-scale construction module (CSCM) is adopted to extract and construct multi-resolution features from the multiscale features and the salient features. Thereafter, a Transformer module is applied to capture both long- and short-range temporal features from the multi-resolution features. Finally, the long- and short-range temporal features are restored with low-layer details and mapped to the predicted classification results. Additionally, the Lovász loss function is applied to alleviate the class imbalance problem in sleep datasets. Our proposed method was tested on the Sleep-EDF-39 and Sleep-EDF-153 datasets, and it achieved classification accuracies of 88.9% and 85.2% and Macro-F1 scores of 84.8% and 79.7%, respectively, thus outperforming conventional traditional baseline models. These results highlight the efficacy of the proposed method in fusing multi-modal information. This method has potential for application as an adjunct tool for diagnosing sleep disorde

关键词： Sleep research

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A Natural Approach for Synthetic Short-Form Text Analysis 30

A Natural Approach for Synthetic Short-Form Text Analysis

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Shao, Ruiting Schwarz, Ryan Clifton, Christopher Delp, Edward J. School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Department of Computer Science Purdue University West LafayetteIN United States

ISBN: (纸本)9782493814104

Detecting synthetically generated text in the wild has become increasingly difficult with advances in Natural Language Generation techniques and the proliferation of freely available Large Language Models (LLMs). Social media and news sites can be flooded with synthetically generated misinformation via tweets and posts while authentic users can inadvertently spread this text via shares and retweets. Most modern natural language processing techniques designed to detect synthetically generated text focus primarily on long-form content, such as news articles, or incorporate stylometric characteristics and metadata during their analysis. Unfortunately, for short form text like tweets, this information is often unavailable, usually detached from its original source, displayed out of context, and is often too short or informal to yield significant information from stylometry. This paper proposes a method of detecting synthetically generated tweets via a Transformer architecture and incorporating unique style-based features. Additionally, we have created a new dataset consisting of human-generated and Large Language Model generated tweets for 4 topics and another dataset consisting of tweets paraphrased by 3 different paraphrase models. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Large datasets

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Optimizing land mine detection across diverse mining environments: A hyperspectral data approach with regression models

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International Journal of Intelligent Networks 2024年第1期5卷 351-363页

作者： Anand, R. Andrew, J. Makki, Ihab Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka 576104 India Department of Nouvelle Aquitaine France

The detection of landmines, namely anti-tank mines, explosive devices, and unexploded ordnance, is a formidable obstacle for the global community. The visible consequences of unobserved explosives in communities affected by war are characterized by significant devastation and human suffering. In order to effectively tackle this matter, it is imperative to use proactive strategies that focus on the identification and mitigation of these perilous substances prior to their potential infliction of harm. Nevertheless, the majority of current solutions exhibit significant deficiencies, such as exorbitant expenses, inefficiencies, and apprehensions over accuracy. These drawbacks are further compounded by the inherent trade-offs that exist between these elements, where improvements in one area often come at the expense of another. Contrarily, recent breakthroughs in the areas of deep learning, unmanned aerial vehicles, and sensor technologies are being recognized as potentially transformative elements in the domain of landmine identification and removal. This paper presents a thorough examination of recent scholarly investigations that integrate computerized technology in the field of landmine detection. To the extent of our current understanding, there has been no prior investigation that has thoroughly examined this particular domain. The main aim of this study is to investigate the incorporation of machine learning based regression methods in the field of landmine detection. The study specifically emphasizes the identification and resolution of existing issues that hinder the development of efficient automated solutions, hence enhancing performance optimization. The Sum of Sine Curve Fit Regression Model is proposed and proved a powerful and adaptable tool for extracting relevant information from this hyperspectral images. © 2024 The Authors

关键词： Unmanned aerial vehicles (UAV)

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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Illuminating the Interconnected Complexity of Cyberbullying Intentions within the Holistic Social Media Ecosystem 15

Illuminating the Interconnected Complexity of Cyberbullying ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shraya, Bejgum Dopathi, Hari Charan Reddy Pattanaik, Balachandra Anitha, G. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Wallaga University Department of Electrical and Computer Engineering Africa Nekemte Ethiopia

ISBN: (纸本)9798350370249

The research being presented here looks into the problems related to cyberbullying using data from Twitter and Wikipedia. To try and understand why people behave in this way online, the research included several challenging techniques, including a Support Vector Machine, Random Forest, Gaussian Naive Bayes, and a Voting Classifier that combines Logistic Regression, Random Forest, and Gaussian Naive Bayes. The accuracy of the Voting Classifier was 92.86% after Twitter data analysis. This demonstrates how well it detects and comprehends abusive tendencies. Not to mention, the Wikipedia dataset showed strong performance from the Voting Classifier, which had a success rate of 97.12%. Compared to other methods of research, it performed better. The research shows how machine learning algorithms can recognize and determine the identity of individuals attempting to cause harm to others via the internet. © 2024 IEEE.

关键词： Cyber Harassment Gaussian Naive Bayes Machine Learning Random Forest Support Vector Machine Voting Classifier

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Novel Topology and Design for Wideband Bandpass Filter with Frequency-and Attenuation-Reconfigurable In-Band Notch

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IEEE Transactions on Microwave Theory and Techniques 2023年第4期71卷 1663-1676页

作者： Liu, Bin Yang, Tao Li, Kun Chi, Pei-Ling University of Electronic Science and Technology of China School of Electronic Science and Engineering Chengdu610054 China National Chiao Tung University Department of Electrical and Computer Engineering Hsinchu300 Taiwan

A novel topology and the design method are proposed for a wideband bandpass filter (BPF) with frequency-and attenuation-reconfigurable in-band notch by engineering the complex frequencies of transmission zeros. The proposed topology consists of a fifth-order wideband BPF and two coupled notch resonators. With the two coupled notch resonators, a pair of complex in-band transmission zeros are formed. By controlling the frequency difference between the two notch resonators, a frequency-and attenuation-reconfigurable notch can be obtained in the passband of the wideband BPF. A matrix analysis method is presented to characterize the frequency-and attenuation-tunable in-band notch precisely. The relationship between complex transmission zeros and in-band attenuation is also investigated. To demonstrate the proposed techniques, a prototype using microstrip and evanescent mode resonators is designed and fabricated. The proposed wideband BPF exhibits a reconfigurable in-band notch with frequency tuning from 1.494 to 2.004 GHz and attenuation tuning from 2 to 79 dB. The simulated and measured results are in good agreement with each other, validating the proposed design and method. © 1963-2012 IEEE.

关键词： Bandpass filters

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Optimizing Service Placement in Edge-to-Cloud AR/VR Systems Using a Multi-Objective Genetic Algorithm 14

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems ...

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Herabad, Mohammadsadeq G. Taheri, Javid Ahmed, Bestoun S. Curescu, Calin Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Ericsson AB Stockholm Sweden

ISBN: (纸本)9789897587016

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software). © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Augmented reality

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