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18th International Symposium on Problems of Redundancy in Information and Control Systems, REDUNDANCY 2023

作者： Hou, Hanxu Song, Linqi Dongguan University of Technology Dongguan Key Lab of Artificial Information Network School of Electrical Engineering & Intelligentization China City University of Hong Kong Department of Computer Science Hong Kong

ISBN: (纸本)9798350381528

Generalized Expanded-Blaum-Roth (GEBR) codes [You et al. 2020] are designed for large-scale distributed storage systems that have larger recoverability for single-symbol failures, multi-column failures and multi-row failures, compared with locally recoverable codes (LRC). GEBR codes encode an α × k information array into a pτ ×(k + r) array such that lines of slope i with 0 ≤ i ≤ r -1 have even parity and each column contains pτ-α local parity symbols, where p is an odd prime and k +r ≤ pτ. Necessary and sufficient conditions for GEBR codes to be (n, k) recoverable (i.e., any k out of n = k+r columns can retrieve all information symbols) are given in [Hou et al. 2023] for α = (p -1)τ. However, the (n, k) recoverable condition of GEBR codes is unknown when α © 2023 IEEE.

关键词： Multiprocessing systems

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Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract) 39

Anomaly Detection in Quasi-Periodic Time Series based on Aut...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Fan Zhou, Xingshe Cao, Jinli Wang, Zhu Wang, Tianben Wang, Hua Zhang, Yanchun Nanjing Research Institute of Electronics Technology Nanjing China School of Computer Science Northwestern Polytechnical University Xi'an China Department of Computer Science and Computer Engineering La Trobe University Melbourne Australia Northwest A&f University Yangling China College of Engineering and Science Victoria University Melbourne Australia Guangzhou University Guangzhou China New Network Dept. Pengcheng Lab Shenzhen China

ISBN: (纸本)9798350322279

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. © 2023 IEEE.

关键词： Anomaly detection

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Investigating the Impact of Virtual Avatars and Owner Gender on Virtual Partner Selection in Avatar-Based Interactions: A Study Design

Investigating the Impact of Virtual Avatars and Owner Gender...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Anh Nguyen Seoyoung Kang Woontack Woo Kangsoo Kim Department of Computer Science University of Calgary UVR Lab KAIST Department of Electrical and Software Engineering University of Calgary

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

As real-life social interactions transition to virtual environments using Virtual/Augmented Reality (VR/AR) technologies, understanding how gender representation in virtual avatars affects choices and behaviors becomes increasingly relevant. In this paper, we investigate the combined impact of avatars' gender representation and their owner's gender on virtual partner selection in physical, intellectual, social, and romantic scenarios. We introduce our preliminary research plan, outlining both the study design and system development. The research will contribute to our understanding of social dynamics and gender effects in avatar-mediated interactions.

关键词： Three-dimensional displays Avatars Design methodology Conferences Virtual environments User interfaces

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Improving Few-Shot Learning for Talking Face System with TTS Data Augmentation 48

Improving Few-Shot Learning for Talking Face System with TTS...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Qi Ma, Ziyang Liu, Tao Tan, Xu Lu, Qu Yu, Kai Chen, Xie Shanghai Jiao Tong University X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence Ai Institute China Microsoft Research Asia China Shanghai Media Tech China

ISBN: (纸本)9781728163277

Audio-driven talking face has attracted broad interest from academia and industry recently. However, data acquisition and labeling in audio-driven talking face are labor-intensive and costly. The lack of data resource results in poor synthesis effect. To alleviate this issue, we propose to use TTS (Text-To-Speech) for data augmentation to improve few-shot ability of the talking face system. The misalignment problem brought by the TTS audio is solved with the introduction of soft-DTW, which is first adopted in the talking face task. Moreover, features extracted by HuBERT are explored to utilize underlying information of audio, and found to be superior over other features. The proposed method achieves 17%, 14%, 38% dominance on MSE score, DTW score and user study preference repectively over the baseline model, which shows the effectiveness of improving few-shot learning for talking face system with TTS augmentation. © 2023 IEEE.

关键词： few-shot HuBERT low-resource soft-DTW

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Reliability-Aware Network Slicing based on Multi-Objective Optimization 13

Reliability-Aware Network Slicing based on Multi-Objective O...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xia, Qiqi Wang, Jin Huang, Chengqiang Yao, Xin Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China Huawei Technologies Co. Ltd. Reliability Technology Lab Shenzhen China Lingnan University Department of Computing and Decision Sciences Hong Kong

ISBN: (纸本)9798350308365

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric. © 2024 IEEE.

关键词： Optimization algorithms

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Dependency Practices for Vulnerability Mitigation

arXiv

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arXiv 2023年

作者： Jafari, Abbas Javan Costa, Diego Elias Abdellatif, Ahmad Shihab, Emad Lab The Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

Relying on dependency packages accelerates software development, but it also increases the exposure to security vulnerabilities that may be present in dependencies. While developers have full control over which dependency packages (and which version) they use, they have no control over the dependencies of their dependencies. Such transitive dependencies, which often amount to a greater number than direct dependencies, can become infected with vulnerabilities and put software projects at risk. To mitigate this risk, Practitioners need to select dependencies that respond quickly to vulnerabilities to prevent the propagation of vulnerable code to their project. To identify such dependencies, we analyze more than 450 vulnerabilities in the npm ecosystem to understand why dependent packages remain vulnerable. We identify over 200,000 npm packages that are infected through their dependencies and use 9 features to build a prediction model that identifies packages that quickly adopt the vulnerability fix and prevent further propagation of vulnerabilities. We also study the relationship between these features and the response speed of vulnerable packages. We complement our work with a practitioner survey to understand the applicability of our findings. Developers can incorporate our findings into their dependency management practices to mitigate the impact of vulnerabilities from their dependency supply chain. Copyright © 2023, The Authors. All rights reserved.

关键词： Ecosystems

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Unsupervised Style Transfer of Modern Hebrew using Generative Language Modeling and Zero-Shot Prompting

Unsupervised Style Transfer of Modern Hebrew using Generativ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Kaganovich, Pavel Munz-Manor, Ophir Tsur, Elishai Ezra Reichman University Department of Computer Science Herzelia Israel The Open University of Israel Department of History Philosophy and Judaic Studies Raanana Israel The Open University of Israel Neuro-Biomorphic Engineering Lab Raanana Israel

ISBN: (纸本)9798350324457

Style transfer is one of the most intriguing hallmarks of natural language processing. It involves the semantic preserving conversion of artistic 'style'. Style transfer of the Hebrew language is an exceptionally challenging task due to the language's intricate morphology, inflectional structure, and orthography, which have undergone significant transformations throughout its history. In this work, we present the first generative language model for unsupervised textual style transfer for modern Hebrew, which rewrites sentences in a target style in the absence of parallel style corpora. We create a pseudo-parallel corpus through back translation, fine-tunes a pre-trained Hebrew language model, and leverages zero-shot learning. Our results demonstrate the first significant results in Hebrew style transfer in terms of transfer accuracy, semantic similarity, and fluency. © 2023 IEEE.

关键词： Computational Literary Studies Hebrew Language Language Model Machine Learning Modern Hebrew Literature Natural Language Processing Style Transfer

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ICEL: Learning with Inconsistent Explanations

ICEL: Learning with Inconsistent Explanations

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Biao Liu Xiaoyu Wu Bo Yuan Department of Computer Science & Engineering Southern University of Science and Technology RAMS Lab Huawei

Generating the heatmaps is one of the explanation methods to show what regions the model use to predict in vision tasks. GradCAM is a popular approach to provide such heatmaps. However, GradCAM is post-hoc, and its heatmaps are not always meeting human annotations. Inspired by CGC (Contrastive GradCAM consistency), we propose ICEL (InConsistent Explanation Learning) method which introduces inconsistent explanation loss measured by cosine similarity on heatmaps. We show that our method can preserve classification accuracy, while the heatmaps generated by explanation methods are more consistent with human annotations, and the computational complexity is reduced from O(n 2 ) to O(n), compared with CGC.

关键词： Heating systems Learning systems Annotations Computational modeling Signal processing Predictive models Loss measurement

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An Image Mosaicing-Based Method for Bird Identification on Edge Computing Devices 1

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Teterja, Dmitrij Garcia-Rodriguez, Jose Azorin-Lopez, Jorge Sebastian-Gonzalez, Esther van der Walt, Rita Elise Booysen, M.J. Department of Computer Science and Technology University of Alicante Alicante Spain Department of Ecology University of Alicante Alicante Spain Department of Electrical and Electronic Engineering/MIH Media Lab Stellenbosch University Stellenbosch South Africa Department of Electrical and Electronic Engineering Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

Bird identification is an important task in wildlife monitoring and conservation. However, traditional methods for bird identification often require significant computational resources, making them impractical for use on edge computing devices. In this paper, we propose an image mosaicing-based method for bird identification on edge computing devices. Our method combines image mosaicing and deep learning techniques to accurately identify bird species from a series of individual images. It achieves excellent accuracy of 99.51% for ResNet50 and 97.37% for MobileNetV2, depending on the specific use case. These results demonstrate the effectiveness and potential of our approach for real-time bird recognition and identification on edge computing devices, making it suitable for a wide range of applications including monitoring and conservation of bird populations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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TP-NET: Training Privacy-Preserving Deep Neural Networks under Side-Channel Power Attacks 8

TP-NET: Training Privacy-Preserving Deep Neural Networks und...

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8th IEEE International Symposium on Smart Electronic Systems, iSES 2022

作者： Hu, Hui Gegax-Randazzo, Jessa Carper, Clay Borowczak, Mike University of Wyoming Cybersecurity Education and Research Lab Department of Electrical Engineering & Computer Science LaramieWY82071 United States

ISBN: (纸本)9798350399226

Privacy in deep learning is receiving tremendous attention with its wide applications in industry and academics. Recent studies have shown the internal structure of a deep neural network is easily inferred via side-channel power attacks in the training process. To address this pressing privacy issue, we propose TP-NET, a novel solution for training privacy-preserving deep neural networks under side-channel power attacks. The key contribution of TP-NET is the introduction of randomness into the internal structure of a deep neural network and the training process. Specifically, the workflow of TP-NET includes three steps: First, Independent Sub-network Construction, which generates multiple independent sub-networks via randomly se-lecting nodes in each hidden layer. Second, Sub-network Random Training, which randomly trains multiple sub-networks such that power traces keep random in the temporal domain. Third, Prediction, which outputs the predictions made by the most accu-rate sub-network to achieve high classification performance. The performance of TP-NET is evaluated under side-channel power attacks. The experimental results on two benchmark datasets demonstrate that TP-NET decreases the inference accuracy on the number of hidden nodes by at least 38.07% while maintaining competitive classification accuracy compared with traditional deep neural networks. Finally, a theoretical analysis shows that the power consumption of TP-NET depends on the number of sub-networks, the structure of each sub-network, and atomic operations in the training process. © 2022 IEEE.

关键词： Deep neural networks

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