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Communication, Security and Artificial Intelligence (ICCSAI), International Conference on

作者： Chhote Lal Prasad Gupta Dinesh Rajassekharan Dilip Kumar Sharma Mohanraj Elangovan Varatharaj Myilsamy Kamal Upreti Dept. of Computer Science and Engineering Bansal Institute of Engineering and Technology Lucknow India Faculty of Computer Science Peninsula College Peninsula Malaysia Department of Mathematics Jaypee University of Engineering and Technology Guna Madhya Pradesh India Department of Artificial Intelligence and Data Science K. S. Rangasamy College of Technology Tiruchengode Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Computer Science CHRIST(Deemed to be University) Delhi-NCR Ghaziabad India

Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data.

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Constructions of Binary MDS Array Codes with Optimal Cooperative Repair Bandwidth

Constructions of Binary MDS Array Codes with Optimal Coopera...

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IEEE International Symposium on Information Theory

作者： Lei Li Xinchun Yu Chenhao Ying Liang Chen Yuanyuan Dong Yuan Luo Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Institute of Data and Information Tsinghua University Shenzhen International Graduate School Shenzhen China Alibaba Group Hangzhou China

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Erasure codes are widely implemented in distributed storage systems to provide high fault tolerance with small storage overhead. Maximum distance separable codes are an common choice as they achieve the optimal tradeoff between fault tolerance and storage overhead. In this paper, we focus on the repair of multiple erasures of binary MDS array codes. Specifically, we present constructions of binary MDS array codes with optimal cooperative repair bandwidth by stacking multiple Blaum-Roth code instances whose “evaluation points” are judiciously designed. The constructed array codes with length $n$ and dimension $k$ can achieve the optimal cooperative repair bandwidth for $2\leq h\leq n-k$ and $k+1\leq d\leq n-h$ where $h$ and $d$ are the numbers of failed nodes and helper nodes, respectively. As the codes are constructed on a special polynomial ring over binary field, computation operations involved in nodes repair and file reconstruction for these codes are only XORs and cyclic shifts. Moreover, due to the inherent parallel structure of the codes, both the encoding and decoding procedures can be finished in parallel, speeding up the computing process.

关键词： Fault tolerance Codes Stacking Fault tolerant systems Bandwidth Maintenance engineering Polynomials

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Visual Programming for Artificial Intelligent and Robotic Application (VPAR) Framework

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2021年 343卷 64-80页

作者： Pongthanisorn, Goragod Viriyavit, Waranrach Charoenporn, Thatsanee Sornlertlamvanich, Virach Department of Computer Engineering Faculty of Engineering Thai-Nichi Institute of Technology Thailand School of ICT Sirindhorn International Institute of Technology Thammasat University Thailand Graduate School of Science and Engineering Chiba University Japan Faculty of Data Science Musashino University Japan Faculty of Engineering Thammasat University 99 Moo 18 Paholyothin Road Klong Nueng Klong Luang Pathumthani12120 Thailand

ISBN: (纸本)9781643682426

computer programming is popularized in 21st century education in terms of allowing intensive logical thinking for students. Artificial Intelligent and robotic field is considered to be the most attractive for programming today. However, for the first-time learners and novice programmers, they may encounter a difficulty in understanding the text-based style programming language with its special syntax, sematic, libraries, and the structure of the program itself. In this work, we proposed a visual programming environment for artificial intelligent and robotic application using Google Blockly. The development framework is a web application which is capable of using Google Blockly to create a program and translate the result of visual programming style to conventional text-based programming. This allows almost instant programming capability for learners of programming in such a complex system. © 2021 The authors and IOS Press.

关键词： Application programs

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User Power Measurement Based IRS Channel Estimation via Single-Layer Neural Network

User Power Measurement Based IRS Channel Estimation via Sing...

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IEEE Conference on Global Communications (GLOBECOM)

作者： He Sun Weidong Mei Lipeng Zhu Rui Zhang Department of Electrical and Computer Engineering National University of Singapore Singapore National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China China School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen Guangdong China

One main challenge for implementing intelligent reflecting surface (IRS) aided communications lies in the difficulty to obtain the channel knowledge for the base station (BS)-IRS-user cascaded links, which is needed to design high-performance IRS reflection in practice. Traditional methods for estimating IRS cascaded channels are usually based on the additional pilot signals received at the BS/users, which increase the system training overhead and also may not be compatible with the current communication protocols. To tackle this challenge, we propose in this paper a new single-layer neural network (NN)-enabled IRS channel estimation method based on only the knowledge of users' individual received signal power measurements corresponding to different IRS random training reflections, which are easily accessible in current wireless systems. To evaluate the effectiveness of the proposed channel estimation method, we design the IRS reflection for data transmission based on the estimated cascaded channels in an IRS-aided multiuser communication system. Numerical results show that the proposed IRS channel estimation and reflection design can significantly improve the minimum received signal-to-noise ratio (SNR) among all users, as compared to existing power measurement based designs.

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WiOpen: A Robust Wi-Fi-based Open-set Gesture Recognition Framework

arXiv

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arXiv 2024年

作者： Zhang, Xiang Huang, Jingyang Yan, Huan Zhao, Peng Zhuang, Guohang Liu, Zhi Liu, Bin CAS Key Laboratory of Electromagnetic Space Information University of Science and Technology of China Hefei230026 China School of Computer and Information Hefei University of Technology Hefei230601 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China Department of Computer and Network Engineering The University of Electro-Communications Tokyo1828585 Japan

Recent years have witnessed a growing interest in Wi-Fi-based gesture recognition. However, existing works have predominantly focused on closed-set paradigms, where all testing gestures are predefined during training. This poses a significant challenge in real-world applications, as unseen gestures might be misclassified as known classes during testing. To address this issue, we propose WiOpen, a robust Wi-Fi-based Open-Set Gesture Recognition (OSGR) framework. Implementing OSGR requires addressing challenges caused by the unique uncertainty in Wi-Fi sensing. This uncertainty, resulting from noise and domains, leads to widely scattered and irregular data distributions in collected Wi-Fi sensing data. Consequently, data ambiguity between classes and challenges in defining appropriate decision boundaries to identify unknowns arise. To tackle these challenges, WiOpen adopts a two-fold approach to eliminate uncertainty and define precise decision boundaries. Initially, it addresses uncertainty induced by noise during data preprocessing by utilizing the CSI ratio. Next, it designs the OSGR network based on an uncertainty quantification method. Throughout the learning process, this network effectively mitigates uncertainty stemming from domains. Ultimately, the network leverages relationships among samples’ neighbors to dynamically define open-set decision boundaries, successfully realizing OSGR. Comprehensive experiments on publicly accessible datasets confirm WiOpen’s effectiveness. Notably, WiOpen also demonstrates superiority in cross-domain tasks when compared to state-of-the-art approaches. © 2024, CC BY-SA.

关键词： Learning systems

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TREE-WASSERSTEIN DISTANCE FOR HIGH DIMENSIONAL data WITH A LATENT FEATURE HIERARCHY

arXiv

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arXiv 2024年

作者： Lin, Ya-Wei Eileen Coifman, Ronald R. Mishne, Gal Talmon, Ronen Viterbi Faculty of Electrical and Computer Engineering Technion United States Department of Mathematics Yale University United States Halicioǧlu Data Science Institute University of California San Diego United States

Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimensional data with two key aspects. First, our TWD is specifically designed for data with a latent feature hierarchy, i.e., the features lie in a hierarchical space, in contrast to the usual focus on embedding samples in hyperbolic space. Second, while the conventional use of TWD is to speed up the computation of the Wasserstein distance, we use its inherent tree as a means to learn the latent feature hierarchy. The key idea of our method is to embed the features into a multi-scale hyperbolic space using diffusion geometry and then present a new tree decoding method by establishing analogies between the hyperbolic embedding and trees. We show that our TWD computed based on data observations provably recovers the TWD defined with the latent feature hierarchy and that its computation is efficient and scalable. We showcase the usefulness of the proposed TWD in applications to word-document and single-cell RNA-sequencing datasets, demonstrating its advantages over existing TWDs and methods based on pre-trained models. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Investigating the Efficiency of Deep Learning Models in Bioinspired Object Detection

Investigating the Efficiency of Deep Learning Models in Bioi...

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Ajagbe, Sunday A. Oki, Olukayode A. Oladipupo, Matthew A. Nwanakwaugwum, Andrew University of Technology Ladoke Akintola Department of Computer Engineering Ogbomoso Nigeria Walter Sisulu University Department of Information Technology East London South Africa University of Salford Department of Data Science Greater Manchester SalfordM5 4WT United Kingdom

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

Object detection is the process of using a camera to track an object or a group of objects over time. It has numerous applications like human-computer interactions (HCI), security and surveillance, bioinspired approach, traffic control, and public areas such as airports, subway stations, and event centres. This application has prompted an extensive study in the field of computer vision for more than the last decade now. Visual recognition which includes picture categorization, localization, and detection, is at the heart of all of these applications and has gathered a lot of research attention. These visual identification algorithms have achieved extraordinary performance thanks to considerable advancements in neural networks, particularly deep learning (DL). Despite all successes recorded in object detection through the use of DL models, the experimental-based approach to investigate the performance of DL models for bioinspired object detection (BOD) still remain an open issue. Thus, this paper investigates the efficiency of DL models in BOD using six (6) performance metrics. Based on the literature, eight common DL models were selected for the experiment. Beetles Bee and Morder hornet were contained in the bioinspired datasets that were used as object detection images on MATLAB 2018a. The results show that CNN outperformed the other 7 DL models in the achieved training time, detection accuracy, sensitivity, specificity, and precision metrics. These results suggest CNN as the efficient model that can be considered taking into account the focus of the project at hand. The modification of the eight DL models' layers and architectures to study their efficiency under different scenarios was highlighted as the future scope of this study. © 2022 IEEE.

关键词： Object detection

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Joint Optimization of Sensing and Communications in Vehicular Networks: A Graph Neural Network-Based Approach

Joint Optimization of Sensing and Communications in Vehicula...

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IEEE International Conference on Communications (ICC)

作者： Xuefei Li Mingzhe Chen Zhilong Zhang Danpu Liu Yuchen Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

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Optimizing Purchase Intention Prediction in E-Commerce

Optimizing Purchase Intention Prediction in E-Commerce

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Women in Computing (InCoWoCo), International Conference for

作者： S. Jayanthi Rajeshwari D Naresh Goud M R. Geetha S. Benjamin Franklin Pannangi Rajyalakshmi Department of Artificial Intelligence &Data Science Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education (IFHE) Telangana India Department of CSE Sri Indu Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telangana India Department of CSE (AI&ML) Nagarjuna College of Engineering and Technology Bengaluru India Department of Social Work Faculty of Behavioural and Social Sciences SRIHER (DU) Chennai India Department of CSE TKR College of Engineering and Technology Hyderabad India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This study investigates the effectiveness of deep learning models—Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM—in predicting purchase intention on e-commerce platforms using session-level data. The dataset, sourced from the UCI Machine Learning Repository, comprises 12,330 sessions with features such as ‘PageValues,’ ‘BounceRates,’ ‘ExitRates,’ and user behavior attributes like ‘Month’ and ‘VisitorType.’ After preprocessing categorical variables using Label Encoding and standardizing numerical features, the data was split into an 80-20% training and testing set. Each of the five models was then trained and assessed using metrics such as accuracy, ROC-AUC, and other classification measures. Among the models, the CNN achieved the highest performance with an accuracy of 90.34% and a ROC-AUC score of 0.9156, followed by the LSTM model with an accuracy of 89.72% and a ROC-AUC score of 0.9102. Although the RNN model performed well, it showed relatively lower metrics with an accuracy of 88.40% and a ROC-AUC of 0.8934. ‘PageValues’ exhibited the strongest positive correlation with purchase intention (Pearson correlation: 0.4926, Spearman correlation: 0.6264), while ‘ExitRates’ and ‘BounceRates’ had significant negative correlations of -0.2071 and -0.1507, respectively. This study identifies CNN and LSTM as robust models for predicting purchase intentions in e-commerce, with ‘PageValues’ being the most influential predictor. The results provide valuable insights for optimizing e-commerce platforms to improve conversion rates. Future research will focus on external validation and expanding the dataset to ensure broader applicability across various e-commerce contexts.

关键词： Measurement Deep learning Accuracy Recurrent neural networks Correlation Predictive models data models Numerical models Electronic commerce Long short term memory

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

arXiv

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arXiv 2023年

作者： Li, Zhen Zhang, Ruqian Zou, Deqing Wang, Ning Li, Yating Xu, Shouhuai Chen, Chen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Colorado Colorado Springs United States Center for Research in Computer Vision University of Central Florida United States School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier;the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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