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32nd ACM International Conference on Multimedia, MM 2024

作者： Jin, Zeyu Jia, Jia Wang, Qixin Li, Kehan Zhou, Shuoyi Zhou, Songtao Qin, Xiaoyu Wu, Zhiyong Department of Computer Science and Technology Tsinghua University Beijing China BNRist Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education Beijing China Shenzhen International Graduate School Tsinghua University Shenzhen China

ISBN: (纸本)9798400706868

Speech-language multi-modal learning presents a significant challenge due to the fine nuanced information inherent in speech styles. Therefore, a large-scale dataset providing elaborate comprehension of speech style is urgently needed to facilitate insightful interplay between speech audio and natural language. However, constructing such datasets presents a major trade-off between large-scale data collection and high-quality annotation. To tackle this challenge, we propose an automatic speech annotation system for expressiveness interpretation that annotates in-the-wild speech clips with expressive and vivid human language descriptions. Initially, speech audios are processed by a series of expert classifiers and captioning models to capture diverse speech characteristics, followed by a fine-tuned LlaMA for customized annotation generation. Unlike previous tag/templet-based annotation frameworks with limited information and diversity, our system provides in-depth understandings of speech style through tailored natural language descriptions, thereby enabling accurate and voluminous data generation for large model training. With this system, we create SpeechCraft, a fine-grained bilingual expressive speech dataset. It is distinguished by highly descriptive natural language style prompts, containing approximately 2,000 hours of audio data and encompassing over two million speech clips. Extensive experiments demonstrate that the proposed dataset significantly boosts speech-language task performance in stylist speech synthesis and speech style understanding. © 2024 Owner/Author.

关键词： Large datasets

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ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems 31

ML-based Modeling to Predict I/O Performance on Different St...

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31st Annual IEEE International Conference on High Performance computing, Data, and Analytics, HiPC 2024

作者： Xu, Yiheng Sivaraman, Pranav Devarajan, Hariharan Mohror, Kathryn Bhatele, Abhinav Palantir Technologies Inc. Denver United States Department of Computer Science University of Maryland College Park United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States

ISBN: (纸本)9798331509095

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application’s I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify performance bottlenecks when using burst buffers and parallel file systems, and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and file system selections. We use IOR, an I/O benchmark to gather performance data for training the machine learning model. Our model can predict the better performing storage system for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%. © 2024 IEEE.

关键词： Supercomputers

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An Incremental Multi-Level, Multi-Scale Approach to Assessment of Multifidelity HPC Systems 24

An Incremental Multi-Level, Multi-Scale Approach to Assessme...

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Proceedings of the SC '24 Workshops of the International Conference on High Performance computing, Network, Storage, and Analysis

作者： Shilpika Bethany Lusch Venkatram Vishwanath Michael E. Papka Argonne Leadership Computing Facility Argonne National Laboratory Argonne Leadership Computing Facility Argonne National Laboratory & Department of Computer Science University of Illinois Chicago

ISBN: (纸本)9798350355543

With the growing complexity in architecture and the size of large-scale computing systems, monitoring and analyzing system behavior and events has become daunting. Monitoring data amounting to terabytes per day are collected by sensors housed in these massive systems at multiple fidelity levels and varying temporal resolutions. In this work, we develop an incremental version of multiresolution dynamic mode decomposition (mrDMD), which converts high-dimensional data to spatial-temporal patterns at varied frequency ranges. Our incremental implementation of the mrDMD algorithm (I-mrDMD) promptly reveals valuable information in the massive environment log dataset, which is then visually aligned with the processed hardware and job log datasets through our generalizable rack visualization using D3 visualization integrated into the Jupyter Notebook interface. We demonstrate the efficacy of our approach with two use scenarios on a real-world dataset from a Cray XC40 supercomputer, Theta.

关键词： Dynamic mode decomposition

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VoxInstruct: Expressive Human Instruction-to-Speech Generation with Unified Multilingual Codec Language Modelling 24

VoxInstruct: Expressive Human Instruction-to-Speech Generati...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhou, Yixuan Qin, Xiaoyu Jin, Zeyu Zhou, Shuoyi Lei, Shun Zhou, Songtao Wu, Zhiyong Jia, Jia Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Computer Science and Technology Tsinghua University Beijing China BNRist Tsinghua University Key Laboratory of Pervasive Computing Ministry of Education Beijing China

ISBN: (纸本)9798400706868

Recent AIGC systems possess the capability to generate digital multimedia content based on human language instructions, such as text, image and video. However, when it comes to speech, existing methods related to human instruction-to-speech generation exhibit two limitations. Firstly, they require the division of inputs into content prompt (transcript) and description prompt (style and speaker), instead of directly supporting human instruction. This division is less natural in form and does not align with other AIGC models. Secondly, the practice of utilizing an independent description prompt to model speech style, without considering the transcript content, restricts the ability to control speech at a fine-grained level. To address these limitations, we propose VoxInstruct, a novel unified multilingual codec language modeling framework that extends traditional text-to-speech tasks into a general human instruction-to-speech task. Our approach enhances the expressiveness of human instruction-guided speech generation and aligns the speech generation paradigm with other modalities. To enable the model to automatically extract the content of synthesized speech from raw text instructions, we introduce speech semantic tokens as an intermediate representation for instruction-to-content guidance. We also incorporate multiple Classifier-Free Guidance (CFG) strategies into our codec language model, which strengthens the generated speech following human instructions. Furthermore, our model architecture and training strategies allow for the simultaneous support of combining speech prompt and descriptive human instruction for expressive speech synthesis, which is a first-of-its-kind attempt. Codes, models and demos are at: https://***/thuhcsi/VoxInstruct. © 2024 Owner/Author.

关键词： C (programming language)

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Noise-tolerant fixed-time leader-follower consensus controller design for multi-agent systems via fuzzy-neural-network

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Neural computing and Applications 2025年 1-23页

作者： Dai, Jianhua Tan, Ping Xiao, Lin Wang, Zidong He, Yongjun Zuo, Qiuyue Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing MOE-LCSM Hunan Normal University Hunan Changsha410081 China Department of Computer Science Brunel University London UxbridgeUB8 3PH United Kingdom

The leader-follower consensus control problem in multi-agent systems (MASs) is critical and has received significant attention. However, the simultaneous achievement of fixed-time stability and robustness is often challenging in MASs due to their inherent complexity and uncertainty. This paper developed a controller based on the proposed noise-tolerant fixed-time fuzzy neural network (NF-FNN) model to realize the leader-follower consensus of MASs. Specifically, the introduction of an integral error term makes the NF-FNN model have powerful noise tolerance, and a fuzzy gain parameter generated by the Takagi-Sugeno fuzzy logic system makes the NF-FNN model have fuzzy adaptiveness. In addition, a new partition-sign-by-power activation function is developed to ensure fixed-time stability of the NF-FNN model. Theoretical analysis and comparative simulations confirm the superb swift stability and excellent noise tolerance of controllers based on the NF-FNN model for achieving the leader-follower consensus of MASs, as compared with existing methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Fuzzy neural networks

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IAACS: Image aesthetic assessment through color composition and space formation

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Virtual Reality & Intelligent Hardware 2023年第1期5卷 42-56页

作者： Bailin YANG Changrui ZHU Frederick WBLI Tianxiang WEI Xiaohui LIANG Qingxu WANG School of Computer and Information Engineering Zhejiang Gongshang UniversityHangzhou 310018China Department of Computer Science University of DurhamDH13LEUnited Kingdom College of Intelligence and Computing Tianjin UniversityTianjin 300072China State Key Laboratory of Virtual Reality Technology and Systems Beihang UniversityBeijing 100191China

Background Determining how an image is visually appealing is a complicated and subjective task. This motivates the use of a machine-learning model to evaluate image aesthetics automatically by matching the aesthetics of the general public. Although deep learning methods have successfully learned good visual features from images,correctly assessing the aesthetic quality of an image remains a challenge for deep learning. Methods To address this, we propose a novel multiview convolutional neural network to assess image aesthetics assessment through color composition and space formation(IAACS). Specifically, from different views of an image––including its key color components and their contributions, the image space formation, and the image itself––our network extracts the corresponding features through our proposed feature extraction module(FET) and the Image Net weight-based classification model. Result By fusing the extracted features, our network produces an accurate prediction score distribution for image aesthetics. The experimental results show that we have achieved superior performance.

关键词： Image aesthetic assessment Color composition Space formation Deep learning

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Binary Grey Wolf Optimizer for Mapping Real Time Applications on MPSOCs Architecture 7

Binary Grey Wolf Optimizer for Mapping Real Time Application...

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7th International Conference on Informatics and Applied Mathematics, IAM 2024

作者： Boumaza, Farid Zouache, Djaafar Belazzoug, Mouhoub Hadji, Atmane Aroui, Abdelkader Computer Science Department University of Mohamed El Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Laboratory of Parallel Embedded architectures and Intensive Computing University of Oran1 Oran31000 Algeria LISI Laboratory Computer Science Department University Center A. Boussouf Mila Mila43000 Algeria Center for Space Techniques Palestine Avenue Arzew Oran31200 Algeria

With the growing complexity of real-time applications, Multi-Processor Systems-on-Chip (MPSoCs) have become a vital solution for meeting stringent performance, power, and scalability requirements. Efficient task mapping plays a crucial role in optimizing the performance of such systems, particularly for real-time applications that demand strict timing constraints. Traditional mapping techniques, including static and dynamic strategies, struggle with balancing execution time, energy efficiency, and communication overhead in heterogeneous MPSoC architectures. In this paper, we propose a novel approach for optimizing task mapping in MPSoCs, based on the Grey Wolf Optimizer (GWO), a bio-inspired metaheuristic renowned for its effectiveness in solving complex optimization problems. This methodology aims to minimize task execution times and communication delays by intelligently mapping real-time tasks onto heterogeneous processing elements (PEs), while also adhering to real-time constraints and reducing energy consumption. The results confirm that the improved GWO algorithm is a powerful tool for addressing the challenges of real-time task mapping in MPSoCs, providing a robust and scalable solution for future embedded systems. © 2024 Copyright for this paper by its authors.

关键词： System-on-chip

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Attention-Based Hybrid Deep Learning Model for Intrusion Detection in IIoT Networks 28th

Attention-Based Hybrid Deep Learning Model for Intrusion Det...

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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Ullah, Safi Boulila, Wadii Koubaa, Anis Ahmad, Jawad Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Computer Science Quaid-i-Azam University Islamabad44000 Pakistan RIADI Laboratory National School of Computer Science University of Manouba Manouba Tunisia School of Computing Edinburgh Napier University EdinburghEH10 5DT United Kingdom

The integration of Industrial Internet of Things (IIoT) technology into the industrial sector has produced numerous significant advantages. However, the notable concern remains the absence of robust security and privacy measures in these interconnected critical environments. To secure IIoT networks, several researchers and experts employ intrusion detection systems (IDS) for detecting cyberattacks. The current systems exhibit efficient performance when handling a few categories of attack classes, even in the presence of slight imbalances. However, these models face challenges when confronted with vast categories of attack classes and highly imbalanced data. To tackle these issues, this study introduces an attention-based hybrid deep learning (AB-HDL) model designed to monitor network traffic and predict cyberattacks within the network. The proposed model comprises an attention mechanism and a hybrid deep learning model that integrates convolutional neural networks (CNN) and an autoencoder (AE). The effectiveness of the proposed AB-HDL is assessed using publicly accessible datasets: Edge-IIoTset and X-IIoTID. To ascertain the efficacy of AB-HDL, a comparative analysis is conducted with various other machine learning (ML) and deep learning (DL) algorithms. The outcome analysis indicates that the proposed AB-HDL surpasses the performance of the other algorithms and exhibits optimal efficiency in detecting cyber attacks within IIoT networks. © 2024 The Authors.

关键词： Network intrusion

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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CoChain: High Concurrency Blockchain Sharding via Consensus on Consensus 42

CoChain: High Concurrency Blockchain Sharding via Consensus ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Li, Mingzhe Lin, You Zhang, Jin Wang, Wei Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Research Institute of Trustworthy Autonomous Systems Computer Science and Engineering Department China Hong Kong University of Science and Technology Computer Science and Engineering Department Hong Kong Institute of High Performance Computing and Agency for Science Technology and Research Singapore

ISBN: (纸本)9798350334142

Sharding is an effective technique to improve the scalability of blockchain. It splits nodes into multiple groups so that they can process transactions in parallel. To achieve higher parallelism and concurrency at large scales, it is desirable to maintain a large number of small shards. However, simply configuring small shards easily results in a higher fraction of malicious nodes inside shards, causing shard corruption and compromising system security. Existing sharding techniques hence demand large shards, at the expense of limited concurrency. To address this limitation, we propose CoChain: a blockchain sharding system that can securely configure small shards for enhanced concurrency. CoChain allows some shards to be corrupted. For security, each shard is monitored by multiple other shards. The latter reach a cross-shard Consensus on the Consensus results of their monitored shard. Once a corrupted shard is found, its subsequent consensus will be taken over by another shard, hence recovering the system. Via Consensus on Consensus, CoChain allows the existence of shards with more fraction of malicious nodes ( © 2023 IEEE.

关键词： Blockchain

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