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1st ACM Workshop on Security Implications of Deepfakes and Cheapfakes, WDC 2022, co-located with ACM AsiaCCS 2022

作者： Park, Geon-Woo Park, Eun-Ju Woo, Simon S. Computer Science & Engineering Dept. Sunkyunkwan University Suwon Korea Republic of Applied Data Science Dept. Sunkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781450391788

With the rapid growth of deep learning methods, AI technologies for generating deepfake videos also have been significantly advanced. Nowadays, the manipulated videos such as deepfakes are so sophisticated that one cannot easily differentiate between real and fake, and one can create such videos with little effort. However, such technologies can be likely to be abused by people with malicious intents. To address this issue, approaches and efforts to detect deepfakes have been researched significantly. However, the performances of the detectors in general depends on the quantity and quality of the training data. In this paper, we introduce a new deepfake dataset, Zoom-DF, which can be injected during the remote meeting and video conferencing, to create a sequence of fake participant images. While most deepfake datasets focus on the face area, our dataset primarily targets for the remote meeting, and manipulates movements of the participants. We evaluate existing deepfake detectors on our new Zoom-DF dataset and present the performance results. © 2022 ACM.

关键词： Video conferencing

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Performance Measurement of Distributed Systems via Single-Host Parallel Requesting using (Single, Multi and Pool) Threads 3

Performance Measurement of Distributed Systems via Single-Ho...

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3rd Information Technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Jghef, Yousif Sufyan Zeebaree, Subhi R. M. Ageed, Zainab Salih Shukur, Hanan M. College of Engineering Knowledge University Computer Engineering Dept. Erbil Iraq Technical College of Engineering Duhok Polytechnic University Energy Eng. Dept. Duhok Iraq College of Science Nawroz University Computer Science Dept. Duhok Iraq

ISBN: (纸本)9798350334968

Distributed systems and cloud computing are essential in IT (ICT). Scholars have not contemplated merging distributed systems and cloud computing to measure execution time in milliseconds and capacity in gigabytes. Distributed Systems and Cloud Computing Execute Time and Capacity Methodology This title covered two networking types. Multi-scenario distributed networking begins. A TCP socket links a client-server application (Transmission Control Protocol). This example also networks between computers. In another example, Remote Method Invocation Technology (RMI) links subservers to the main server to handle all client inquiries. Second-generation networking involves distributed systems and cloud computing. Google's API downloaded files to my machine and sent them to the main server. Other examples include using numerous computers or a client server software on the same machine. Multi-client and server threads helped the system find correct results. Pool threads were used elsewhere. This thesis examines execution time and file capacity. The system showed how cloud computing affects distributed systems in two ways to apply many scenarios and draw conclusions (execution time and capacity). This project utilizes TCP sockets, RMI, and API. Threads and a hash table software structure were used to load data into the system between the client and the server. © 2022 IEEE.

关键词： Cloud computing

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Improving Image Classification Using Attention Mechanisms in Deep Learning Models 4

Improving Image Classification Using Attention Mechanisms in...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Sharma, Vinod Mishra, Atul Kumar Kumar, Anil Shakti, Nanda Sharma, Madhuri Srinju, M. Slp University Dept of Cse Gwalior India Madhav University Dept. of Commerce & Management Pindwara India Mangalayatan University Dept. of Eee Aligarh India Usha Martin University Dept. of Engineering and Applied Sciences Ranchi India Chri Campus University Dept. of Life Sciences Gwalior India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India

ISBN: (纸本)9798350364729

In this study, we explore the application of attention mechanisms to enhance deep learning models in the context of image classification. We assess several types of attention mechanisms across diverse image classification datasets. Our findings indicate substantial enhancements in model accuracy facilitated by these attention mechanisms. Additionally, we delve into implementation challenges such as computational overhead and the necessity for extensive datasets. Furthermore, we investigate the potential benefits of transfer learning and fine-tuning pretrained models integrated with attention mechanisms to optimize performance and streamline training efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Cloud Resource Allocation Efficiency with Harris Hawk Optimization Algorithm(HHOA)

Enhancing Cloud Resource Allocation Efficiency with Harris H...

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2024

作者： Manickavasagan, V. Petchiammal, Eswari Sudha, S Rahini Rajesh, C. Sree, S. Jindhiya Yamini, V.S. Sri Sai Ram Engineering College Dept. of Csbs Chennai India Velammal Institute of Technology Dept. of Information Technology Chennai India Panimalar Engineering College Dept. of Csbs Chennai India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Dept. of Artificial Intelligence and Data Science Chennai India Velammal Institute of Technology Department of Information Technology Chennai India

ISBN: (纸本)9798350348460

The demand for efficient cloud resource allocation has surged with the growing reliance on cloud computing for a wide array of applications. Optimizing resource allocation in cloud environments is crucial for cost-effectiveness, performance, and energy conservation. In this context, bio-inspired optimization algorithms have gained prominence for their ability to address complex allocation problems. This paper investigates the application of the Harris Hawk Optimization algorithm (HHOA) to enhance cloud resource allocation efficiency. HHOA, inspired by the hunting behavior of Harris's hawks, is an efficient and versatile metaheuristic algorithm known for its ability to find optimal solutions in complex, dynamic environments. By harnessing the HHOA algorithm, this study aims to improve the allocation of resources such as virtual machines and storage within cloud infrastructures as demanded by the tasks. In our proposed approach we present a comprehensive examination of HHOA's suitability for cloud resource allocation and compares its performance with conventional optimization algorithms like Stimulated Annealing (SA), Genetic Algorithm (GA) and Tabu Search (TS). Through a series of experiments, we found that Harris Hawk Optimization Algorithm (HHOA) is proven to be very effective in improving operational efficiency by enhanced reliability, lowered latency and lowered average response time. © 2024 IEEE.

关键词： Cloud computing GA HHOA Resource allocation Resource optimization Tabu search

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Improved Dynamic Coupled Graph Convolutional Recurrent Networks for Traffic Forecasting

Improved Dynamic Coupled Graph Convolutional Recurrent Netwo...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Han, Seunghoon Kim, Sungsoo Lim, Sungsu Chungnam National University Dept. of Computer Science & Engineering Daejeon Korea Republic of ETRI Smart Data Research Section Daejeon Korea Republic of

ISBN: (纸本)9798350324457

Traffic forecasting is a crucial application of the Intelligent Transportation System (ITS), with research focusing on various methods, from classical statistical approaches to graph-based methods integrated with RNN-based approaches to capture spatial and temporal correlations simultaneously. During the traffic data collection phase, the absence of vehicles on each road or sensor malfunctions can result in the collection of traffic time series data as zeros. However, storing such zero values makes accurate traffic prediction more challenging. To address this challenge, we present a novel model for improving traffic forecasting using graph convolutional recurrent neural networks. The proposed method is evaluated on two real-world public benchmark datasets and compared with six baseline models, showcasing its superior performance. © 2023 IEEE.

关键词： Graph Neural Networks Spatio-Temporal Time-Series Traffic Forecasting

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Multi-layer network modelling of genomic and transcriptomic data to investigate the response to checkpoint inhibitors in NSCLC

Multi-layer network modelling of genomic and transcriptomic ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mascolo, Davide Farina, Lorenzo Petti, Manuela Sapienza University of Rome Program in Data Science Rome Italy Sapienza University of Rome Dept. of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350337488

Recent innovations and developments in molecular biology and biotechnology have made it possible to acquire and store large omics datasets. In particular genomics, transcriptomics and the study of their relationship represent the key elements to understand how genotype influences phenotype. In this work we investigate the potential of multi-layer network modeling in integrating genomic and transcriptomic data of 152 advanced non-small cell lung cancer (NSCLC) patients treated with anti-PD-(L)1 therapy. For the transcriptomic layer, we performed differential expression and differential co-expression analyses in order to identify a subset of key genes in differentiating responder patients from non-responders. Adding to the genomic-transcriptomic model other three layers related to immune, myeloid and curated immunotherapy-based literature signatures we obtained a 5-layer Patient Similarity Network. The application of Similarity Network Fusion algorithm revealed a statistically significant stratification of patients (p-value ≤ 0.05) which allows the identification of two clusters characterized by patients responding and not responding to immunotherapy. © 2023 IEEE.

关键词： immunotherapy multi-layer network Network-based omics integration non-small cell lung cancer patient Stratification

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Performance Evaluation of data Imputation Methods for Graph Deep Learning-Based Traffic Prediction

Performance Evaluation of Data Imputation Methods for Graph ...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Kim, Jeongseon Kim, Sungsoo Lim, Sungsu Chungnam National University Dept. of Computer Science & Engineering Daejeon Korea Republic of Smart Data Research Section Etri Daejeon Korea Republic of

ISBN: (纸本)9798350324457

We present five approaches for interpolating missing data in real-world traffic datasets using basic, statistical, and generative model methods. The effectiveness of these approaches is evaluated using graph deep learning models, and the results show that imputing missing data improves the performance of traffic prediction models, especially when dealing with a higher proportion of missing data. The experiments are conducted on two real-world datasets, METR-LA and PEMS-BAY, to evaluate the proposed methods comprehensively. The results demonstrate that imputing missing data significantly enhances the performance of traffic prediction models, particularly when dealing with a higher proportion of missing data. © 2023 IEEE.

关键词： Deep Learning on Graphs Missing data Imputation Spatio-Temporal Time-Series

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Can We Estimate a Useful Range of Prices for Popular Models of Used Cars Using Past data? 11

Can We Estimate a Useful Range of Prices for Popular Models ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Bhuiyan, Md Shoaib Suzuka University of Medical Science Faculty of Medical Engineering Dept. Health Data Science Mie Suzuka Japan

ISBN: (纸本)9781665492324

There are some models of cars that remain popular with the consumers, even if pre-used. However, they sell at a wide variety of prices. We built a dataset of past prices for two popular Toyota SUVs, Harrier and RAV4 using the final selling price obtained from a large Japanese auction house, USS. We then tried to predict a useful range of prices for each of these two models of pre-used Toyota SUVs, based on the parameters of that particular car like how many kilometers has it already travelled, whether the powertrain is hybrid or runs on gasoline only, which year was it originally built in, 2019 or in 2020 or later, etc. We also tried to find out whether options like having Sunroof/moonroof had any effect on the final bid of auction prices. Feature Selection and Feature Transformation were done using the Classification Learner App of the MATLAB Statistics and Machine Learning Toolbox version R2020b. Our analysis shows that we can establish a useful range of prices. © 2022 IEEE.

关键词： Costs

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Enhancing Printed Circuit Board Defect Detection through Ensemble Learning 1

Enhancing Printed Circuit Board Defect Detection through Ens...

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1st IEEE International Workshop on Future Intelligent Technologies for Young Researchers, FITYR 2024

作者： Law, Ka Nam Canaan Yu, Mingshuo Zhang, Lianglei Zhang, Yiyi Xu, Peng Gao, Jerry Liu, Jun San José State University Department of Applied Data Science San José United States Virginia Tech Dept. of ISE Blacksburg United States San José State University Dept. of Computer Engineering San José United States

ISBN: (纸本)9798331539757

The quality control of printed circuit boards (PCBs) is paramount in advancing electronic device technology. While numerous machine learning methodologies have been utilized to augment defect detection efficiency and accuracy, previous studies have predominantly focused on optimizing individual models for specific defect types, often overlooking the potential synergies between different approaches. This paper introduces a comprehensive inspection framework leveraging an ensemble learning strategy to address this gap. Initially, we utilize four distinct PCB defect detection models utilizing state-of-The-Art methods: EfficientDet, MobileNet SSDv2, Faster RCNN, and YOLOv5. Each method is capable of identifying PCB defects independently. Subsequently, we integrate these models into an ensemble learning framework to enhance detection performance. A comparative analysis reveals that our ensemble learning framework significantly outperforms individual methods, achieving a 95% accuracy in detecting diverse PCB defects. These findings underscore the efficacy of our proposed ensemble learning framework in enhancing PCB quality control processes. © 2024 IEEE.

关键词： Contrastive Learning

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