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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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Performance Disparities between Accents in Automatic Speech Recognition 37

Performance Disparities between Accents in Automatic Speech ...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： DiChristofano, Alex Shuster, Henry Chandra, Shefali Patwari, Neal Division of Computational & Data Sciences Washington University St. Louis United States Department of Computer Science & Engineering Washington University St. Louis United States Department of Women Gender and Sexuality Studies Washington University St. Louis United States Department of History Washington University St. Louis United States Department of Electrical & Systems Engineering Washington University St. Louis United States

ISBN: (纸本)9781577358800

In this work, we expand the discussion of bias in Automatic Speech Recognition (ASR) through a large-scale audit. Using a large and global data set of speech, we perform an audit of some of the most popular English ASR services. We show that, even when controlling for multiple linguistic covariates, ASR service performance has a statistically significant relationship to the political alignment of the speaker's birth country with respect to the United States' geopolitical power. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Speech recognition

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Kuldeep Singh Kaswan Kiran Malik Jagjit Singh Dhatterwal Mahaveer Singh Naruka D Govardhan School of Computing Science and Engineering Galgotias University Greater Noida UP India Department of Computer Science and Engineering MRIEM Rohtak India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Department of Aeronautical Engineering Institute of Aeronautical Engineering Hyderabad

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital.

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Optoelectronic device based failure management using content based multispectral image retrieval and deep learning model

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Optical and Quantum Electronics 2024年第2期56卷 239-239页

作者： Bhukya, Raghuram Arunsundar, B. Tatini, Narendra Babu Sadala, Triveni Mohan Elshafie, Hashim Qamar, Shamimul Deaprtment of Computer Science and Engineering Kakatiya Institute of Technologey and Science Telangana Warangal India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chengalpattu Kattankulathur India Department of Internet of Things Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Department: CS & AI SR UNIVERSITY Telungana Warangal India Department of Computer Engineering College of Computer Science King Khalid University Main Campus Al Farah Abha 61421 Saudi Arabia Computer Science and Engineering College of Sciences and Arts King Khalid University Dhahran Al Janoub Campus Abha 64261 Saudi Arabia

The need for optoelectronic devices is growing right now, but the production of these devices is having a difficult time keeping up with the advancement of the machinery, instruments, and manufacturing techniques they support. Pictures from big, unlabeled image collections are commonly retrieved using a technique called content-based image retrieval (CBIR). The availability of photographs is also growing as internet growth and transmission networks increase. This study suggests brand-new image retrieval methods for multispectral images used in Optoelectronic device monitoring that are based on image segmentation and classification methods with deep learningtechniques for failure management. Optoelectronic device Monitoring field-based multispectral images were used as the input, which was then processed for noise removal, resizing, and *** c-means clustering-based image segmentation was used to divide up this processed image into its component parts. Following that, a hybrid multilayer transfer learning perception was used to classify the clustered segmented picture. The proposed technique has an accuracy of 95%, precision of 85%, recall of 75%, F-1 score of 63%, error rate of 51%, MAP of 55%;existing MRCN accuracy of 85%, precision of 75%, recall of 63%, F-1 score of 55%, error rate of 45%, MAP of 51%, OC-LBP accuracy of 89%, the precision of 79%, recall of 71%, F-1 score of 59%, error rate of 49%, MAP of 53%. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Classification Deep learning Failure management Multispectral image retrieval Optoelectronic device Monitoring Segmentation

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The Role of Edge Computing in Enhancing Cloud Performance and Reducing Latency

The Role of Edge Computing in Enhancing Cloud Performance an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Puneet Sharma J Shyamala Devi K Manikandan R Ani Minisha R Harini S. Karthik School of CS & AI SR University Warangal Telangana India Rajalakshmi Institute of Technology Chennai School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Artificial Intelligence and Data Science R.M. K. Engineering College Department of Computer Science and Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing Random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing.

关键词： Cloud computing Machine learning algorithms Job shop scheduling Smart cities Processor scheduling Security Reliability Low latency communication Random forests Edge computing

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Multi-Agent Deep Reinforcement Learning for Computation Offloading in Multi-IRS Assisted Mobile Edge Computing Networks

Multi-Agent Deep Reinforcement Learning for Computation Offl...

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IEEE Conference on Wireless Communications and Networking

作者： Lingxiao Chen Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

Mobile edge computing (MEC) as a potential technology can offload tasks from user devices (UDs) to network edges to alleviate network congestion and reduce task execution delay. However, computation offloading faces two challenges: 1) Poor wireless channel quality causes high transmission delay; 2) Computing tasks may be obtained by eavesdroppers (Eves) during task offloading. Therefore, we consider deploying intel-ligent reflecting surface (IRS) in MEC networks to increase data transmission rate and ensure data transmission security. This paper investigates the issue of joint computation offloading and resource allocation in a multi-IRS assisted MEC network. Our goal is to minimize task execution delay. To address this problem, we propose a multi-agent deep deterministic policy gradient algorithm to determine the optimal offloading strategy for each UD. Simulation results show that the proposed algorithm can significantly reduce task execution delay and ensure data transmission security.

关键词： Wireless communication Multi-access edge computing Simulation Delays data communication Security Resource management

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A Comprehensive Evaluation of Driver Drowsiness Identification System using Camera based Improved Deep Learning Methodology 9

A Comprehensive Evaluation of Driver Drowsiness Identificati...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Malathi, P. Sivakami Sundari, M. Karkuzhali, S. Umamaheswari, B. Thenmozhi, A. Aiyyasamy, Velu Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics Tamil Nadu Chennai India OMR Chennai Agni College of Technology Department of Mathematics Tamil Nadu Chennai India R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of M.E.- Applied Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

Particularly on highways, drowsy driving causes a large number of collisions while driving. With the goal to identify driver sleepiness while enhancing roadway security, it is now vital to grasp the situation and take early remedial steps. Using a proposed Image based Learning Strategy for Drowsiness Identification (ILSDI) and cross-validation with the conventional model called Convolutional Neural Network (CNN), the suggested framework offers an approach to assess the degree of fatigue among drivers according to modifications in a driver eyeballs motion. This will assist in deal with the problem concerning roadway protection. In addition, four types of expressions on the face were identified and categorized - open, closed, blinking, and no gazing using ILSDI and CNN models, indicating levels of tiredness. Finding, following, and analyzing the driver's face and eyes in real-time to calculate a sleepiness index is the goal of this technology, which operates in different lighting circumstances. Avoiding these kinds of accidents is possible with the help of a driver sleepiness monitoring structure, which uses a digital camera and accompanying software to measure the rate of blinking as well as the dimension of the driver's eyes. The driver sleepiness recognition system can identify when the driver is getting sleepy and sound an alarm if necessary. It is based on an offline implementation of a deep learning algorithm that uses ILSDI and CNN. © 2023 IEEE.

关键词： Convolutional neural networks

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MTTM: Metamorphic Testing for Textual Content Moderation Software

arXiv

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arXiv 2023年

作者： Wang, Wenxuan Huang, Jen-Tse Wu, Weibin Zhang, Jianping Huang, Yizhan Li, Shuqing He, Pinjia Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University Zhuhai China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

The exponential growth of social media platforms such as Twitter and Facebook has revolutionized textual communication and textual content publication in human society. However, they have been increasingly exploited to propagate toxic content, such as hate speech, malicious advertisement, and pornography, which can lead to highly negative impacts (e.g., harmful effects on teen mental health). Researchers and practitioners have been enthusiastically developing and extensively deploying textual content moderation software to address this problem. However, we find that malicious users can evade moderation by changing only a few words in the toxic content. Moreover, modern content moderation software's performance against malicious inputs remains underexplored. To this end, we propose MTTM, a Metamorphic Testing framework for Textual content Moderation software. Specifically, we conduct a pilot study on 2, 000 text messages collected from real users and summarize eleven metamorphic relations across three perturbation levels: character, word, and sentence. MTTM employs these metamorphic relations on toxic textual contents to generate test cases, which are still toxic yet likely to evade moderation. In our evaluation, we employ MTTM to test three commercial textual content moderation software and two state-of-the-art moderation algorithms against three kinds of toxic content. The results show that MTTM achieves up to 83.9%, 51%, and 82.5% error finding rates (EFR) when testing commercial moderation software provided by Google, Baidu, and Huawei, respectively, and it obtains up to 91.2% EFR when testing the state-of-the-art algorithms from the academy. In addition, we leverage the test cases generated by MTTM to retrain the model we explored, which largely improves model robustness (0% ∼ 5.9% EFR) while maintaining the accuracy on the original test set. A demo can be found in this link. Copyright © 2023, The Authors. All rights reserved.

关键词： Software testing

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Transductive – Long Short-Term Memory Network for the Fake News Detection

Transductive – Long Short-Term Memory Network for the Fake ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Rajiv Avacharmal R. Archana Reddy Piyush Kumar Pareek Pankaj Zanke School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Center for Data Science and Artificial intelligence New York Life Insurance Company Farmington Hills Michigan USA School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Sapiens International Corporation USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Recently, fake news and rumors are distributing majorly and rapidly in all over world. That circumstance causes the production and propagation of incorrect news articles. As well as mis data and fake news are maximized through user without correct verification. In this manuscript, proposed a Transductive - Long Short-Term Memory (T-LSTM) method for detecting fake news. The Buzzfeed and ISOT datasets are used in this manuscript for fake news detection and it is pre-processed by tokenization, removal of stop word and stemming. Then, the reduction of feature is performed by using Probability Principal Component Analysis (PPCA) and it is classified by using T-LSTM method. The proposed T-LSTM method attained accuracy 96.51 %, precision 94.64 %, recall 92.33 % and f1-score 93.57% on Buzzfeed dataset. The proposed T-LSTM method attained accuracy 98.43%, precision 97.76%, recall 97.02% and f1-score 97.35% on ISOT dataset. The proposed T-LSTM method performed well than other existing methods like LSTM -Levy Flight (LSTM-LF) and OptNet.

关键词： Circuits Production Feature extraction Tokenization Fake news Distributed computing Long short term memory

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A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective

arXiv

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arXiv 2024年

作者： Moubayed, Abdallah Injadat, MohammadNoor Alhindawi, Nouh Samara, Ghassan Abuasal, Sara Alazaidah, Raed School of Computing and Augmented Intelligence Arizona State University GilbertAZ United States Electrical and Computer Engineering Department Lebanese American University Byblos Lebanon Data Science and Artificial Intelligence Department Faculty of Information Technology Zarqa University Zarqa Jordan Electrical and Computer Engineering Department Western University LondonON Canada Faculty of Science and Information Technology Jadara University Irbid Jordan Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Software Engineering Department Faculty of Information Technology Zarqa University Zarqa Jordan

Analyzing and evaluating students’ progress in any learning environment is stressful and time consuming if done using traditional analysis methods. This is further exasperated by the increasing number of students due to the shift of focus toward integrating the Internet technologies in education and the focus of academic institutions on moving toward e-Learning, blended, or online learning models. As a result, the topic of student performance prediction has become a vibrant research area in recent years. To address this, machine learning and data mining techniques have emerged as a viable solution. To that end, this work proposes the use of deep learning techniques (CNN and RNN-LSTM) to predict the students’ performance at the midpoint stage of the online course delivery using three distinct datasets collected from three different regions of the world. Experimental results show that deep learning models have promising performance as they outperform other optimized traditional ML models in two of the three considered datasets while also having comparable performance for the third dataset. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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