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2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

作者： Cai, Weicheng Cai, Zexin Liu, Wenbo Wang, Xiaoqi Li, Ming School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Data Science Research Center Duke Kunshan University Kunshan China Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States Jiangsu Jinling Science and Technology Group Limited China

ISBN: (纸本)9781538646588

A novel interpretable end-to-end learning scheme for language identification is proposed. It is in line with the classical GMM i-vector methods both theoretically and practically. In the end-to-end pipeline, a general encoding layer is employed on top of the frontend CNN, so that it can encode the variable-length input sequence into an utterance level vector automatically. After comparing with the state-of-the-art GMM i-vector methods, we give insights into CNN, and reveal its role and effect in the whole pipeline. We further introduce a general encoding layer, illustrating the reason why they might be appropriate for language identification. We elaborate on several typical encoding layers, including a temporal average pooling layer, a recurrent encoding layer and a novel learnable dictionary encoding layer. We conducted experiment on NIST LRE07 closed-set task, and the results show that our proposed end-to-end systems achieve state-of-the-art performance. © 2018 IEEE.

关键词： Encoding Feature extraction Pipelines Dictionaries Task analysis Neural networks Speech recognition"

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Energy-based Proportional Fairness in Cooperative Edge Computing

arXiv

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arXiv 2022年

作者： Vu, Thai T. Chu, Nam H. Phan, Khoa T. Hoang, Dinh Thai Nguyen, Diep N. Dutkiewicz, Eryk School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia

By executing offloaded tasks from mobile users, edge computing augments mobile devices with computing/communications resources from edge nodes (ENs), thus enabling new services/applications (e.g., real-time gaming, virtual/augmented reality). However, despite being more resourceful than mobile devices, allocating ENs’ computing/communications resources to a given favorable set of users (e.g., closer to edge nodes) may block other devices from their services. This is often the case for most existing task offloading and resource allocation approaches that only aim to maximize the network social welfare or minimize the total energy consumption but do not consider the computing/battery status of each mobile device. This work develops an energy-based proportionally fair task offloading and resource allocation framework for a multi-layer cooperative edge computing network to serve all user equipments (UEs) while considering both their service requirements and individual energy/battery levels. The resulting optimization involves both binary (offloading decisions) and continuous (resource allocation) variables. To tackle the NP-hard mixed integer optimization problem, we leverage the fact that the relaxed problem is convex and propose a distributed algorithm, namely the dynamic branch-and-bound Benders decomposition (DBBD). DBBD decomposes the original problem into a master problem (MP) for the offloading decisions and multiple subproblems (SPs) for resource allocation. To quickly eliminate inefficient offloading solutions, the MP is integrated with powerful Benders cuts exploiting the ENs’ resource constraints. We then develop a dynamic branch-and-bound algorithm (DBB) to efficiently solve the MP considering the load balance among ENs. The SPs can either be solved for their closed-form solutions or be solved in parallel at ENs, thus reducing the complexity. The numerical results show that the DBBD returns the optimal solution in maximizing the proportional fairness among U

关键词： Energy efficiency

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Penrose Demosaicking

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IEEE Transactions on Image Processing 2015年第5期24卷 1672-1684页

作者： Bai, Chenyan Li, Jia Lin, Zhouchen Yu, Jian Chen, Yen-Wei Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China School of Electronic Engineering and Computer Science Peking University Beijing100871 China College of Information Science and Engineering Ritsumeikan University Kusatsu525-8577 Japan

The Penrose pixel layout, an aperiodic pixel layout in rhombus Penrose tiling, has been shown to substantially outperform the existing square pixel layout in super-resolution. However, it was tested only on grayscale images. To study its performance on color images, we have to reconstruct regular color images from Penrose raw images, i.e., images with only one color component at each Penrose pixel, resulting in the problem of demosaicking from Penrose pixels. Penrose demosaicking is more difficult than regular demosaicking, because none of the color components of the reconstructed regular color images are available. Therefore, most of the traditional demosaicking methods do not apply. We develop a sparse representation-based method for Penrose demosaicking. Extensive experiments show that Penrose pixel layout outperforms regular pixel layouts in terms of both perceptual evaluation and S-CIELAB. The Penrose pixel layout is unique among all irregular layouts because it is uniformly three-colorable and it has only two pixel shapes, thick and thin rhombi, making its manufacturing relatively easy. © 1992-2012 IEEE.

关键词： Pixels

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Enhancing Business Intelligence Through A Semi-Distributed Blockchain Framework for Predictive Analytics On Sales Growth 2

Enhancing Business Intelligence Through A Semi-Distributed B...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Mullaikodi, C. Jagannathan, Sharath Kumar Jayanthi, N. Singh, Rakhi Tomar, Yesha Jaison, B. J. N. N Institute of Engineering Department of Ece Tamilnadu Tiruvallur India Data Science Institute Saint Peter's University NJ United States Chennai India Sgt University Faculty of Commerce and Management Haryana Gurugrm India School of Management Doon University Uttarakhand Dehradun India Rmk Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350371055

The cutting-edge technologies in the ever-evolving sector of data analytics has been accelerated by the requirement for efficient and secure techniques of acquiring business intelligence. This approach provides a solution to the existing issues. In order to address the issues of data integrity, security, and transparency that are associated with the existing methodologies, there is a need for a paradigm shift that will lead to a solution that is more resilient. The traditional centralised data analytics solutions have their limitations when it comes to guaranteeing the accuracy and dependability of forecasts, and this is the most important aspect of the situation. A semi-distributed blockchain paradigm is proposed as a solution to the problems that are associated with centralised systems. This paradigm spreads the computing burden while maintaining a certain degree of decentralisation in order to enhance security. The approach that is currently being considered is the construction and deployment of a blockchain network that is semi-distributed and contains data analytics techniques. Smart contracts simplify the processes of data transmission and validation among participants, hence ensuring transparency and confidence in the predictive analytics model. These findings reveal an improvement in the system efficiency, data quality, and accuracy of forecasting. © 2024 IEEE.

关键词： Business Intelligence Predictive Analytics Sales Growth Semi-Distributed Blockchain Smart Contracts

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Token2vec: A Joint Self-Supervised Pre-Training Framework Using Unpaired Speech and Text

Token2vec: A Joint Self-Supervised Pre-Training Framework Us...

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

作者： Xianghu Yue Junyi Ao Xiaoxue Gao Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Self-supervised pre-training has been successful in both text and speech processing. Speech and text offer different but complementary information. The question is whether we are able to perform a speech-text joint pre-training on unpaired speech and text. In this paper, we take the idea of self-supervised pre-training one step further and propose token2vec, a novel joint pre-training framework for unpaired speech and text based on discrete representations of speech. Specifically, we introduce two modality-specific tokenizers for speech and text. Based on these tokenizers, we convert speech/text sequences into discrete speech/text token sequences consisting of similar language units, thus mitigating the domain mismatch problem and length mismatch problem, which are caused by the distinct characteristics between speech and text. Finally, we feed the discrete speech and text tokens into a modality-agnostic Transformer encoder and pre-train with token-level masking language modeling (tMLM). Experiments show that token2vec is significantly superior to various speech-only pre-training baselines, with up to 17.7% relative WER reduction. Token2vec model is also validated on a non-ASR task, i.e., spoken intent classification, and shows good transferability.

关键词： Analytical models Signal processing Benchmark testing Transformers Acoustics Feeds Task analysis

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New Insights on Relieving Task-Recency Bias for Online Class Incremental Learning

arXiv

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arXiv 2023年

作者： Liang, Guoqiang Chen, Zhaojie Chen, Zhaoqiang Ji, Shiyu Zhang, Yanning Northwestern Polytechnical University 710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology China Key Laboratory of Big Data Storage and Management China School of Computer Science China School of Software Engineering China

—To imitate the ability of keeping learning of human, continual learning which can learn from a never-ending data stream has attracted more interests recently. In all settings, the online class incremental learning (OCIL), where incoming samples from data stream can be used only once, is more challenging and can be encountered more frequently in real world. Actually, all continual learning models face a stability-plasticity dilemma, where the stability means the ability to preserve old knowledge while the plasticity denotes the ability to incorporate new knowledge. Although replay-based methods have shown exceptional promise, most of them concentrate on the strategy for updating and retrieving memory to keep stability at the expense of plasticity. To strike a preferable trade-off between stability and plasticity, we propose an Adaptive Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous samples and non-target logits in model learning. Through a deep analysis of the task-recency bias caused by class imbalance, we propose a revised focal loss to mainly keep stability. By utilizing a new weight function, the revised focal loss will pay more attention to current ambiguous samples, which are the potentially valuable samples to make model progress quickly. To promote plasticity, we introduce a virtual knowledge distillation. By designing a virtual teacher, it assigns more attention to non-target classes, which can surmount overconfidence and encourage model to focus on inter-class information. Extensive experiments on three popular datasets for OCIL have shown the effectiveness of AFS. The code will be available at https://***/czjghost/AFS. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

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Fractional-order Modified Windkessel Model of the Human Arterial Vascular System

Fractional-order Modified Windkessel Model of the Human Arte...

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2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023

作者： Bahloul, Mohamed A. Aboelkassem, Yasser Belkhatir, Zehor Laleg-Kirati, Taous-Meriem Alfaisal University College of Engineering Electrical Engineering Department Riyadh11533 Saudi Arabia University of Michigan-Flint College of Innovation and Technology MI48502 United States University of Michigan Michigan Institute for Data Science Ann ArborMI48109 United States University of Southampton Digital Health & Biomedical Engineering Group School of Electronics and Computer Science Southampton United Kingdom Computer Electrical and Mathematical Sciences and Engineering Makkah Saudi Arabia National Institute for Research in Digital Science and Technology Paris-Saclay France

ISBN: (纸本)9798350300260

This paper explores fractional-order derivatives to model the apparent arterial compliance dynamics in human vascular aging subjects. The proposed model employs fractional-order capacitor (FOC) elements that combine complex and frequency-dependence characteristics of proximal and distal arterial compliances. The FOC modeling approach accounted for both resistive and capacitive properties. The mathematical derivation has been combined into a global arterial lumped parameter representation forming a novel fractional-order modified arterial Windkessel representation. The model is then validated using aortic pressure and flow rate data acquired from human subjects at different ages. The results show that the FOC model is an accurate and flexible approach that can estimate arterial compliance dynamics and central blood pressure distribution while maintaining a low model complexity. © 2023 IEEE.

关键词： aortic input impedance Arterial Wind-kessel Fractional calculus fractional-order capacitor vascular compliance

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ISSSR 2022 Keynote Speech II: Human-Machine Cooperation in Junior Science Education: Development, Opportunities, and Challenges

ISSSR 2022 Keynote Speech II: Human-Machine Cooperation in J...

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International Symposium on System and Software Reliability (ISSSR)

作者： Liang Luo Assistant Director of National & Local Joint Engineering Lab of Next Generation Internet Data Processing Technology School of Computer Science and Engineering (School of Cyberspace Security) University of Electronic Science and Technology of China

ISBN: (数字)9781665453974

ISBN: (纸本)9781665453981

Human-Machine-Environment (HME) Tri-Co (Coexisting, Cooperative, Cognitive) Education is an indispensable prerequisite and the main content of Artificial Intelligence empowering junior science education. Moreover, it is also a critical requirement for improving the quality of education and promoting the high-end development of education information. Building a unique learning environment with scene perception and an intelligent cognitive process is a tremendous challenge for diverse needs in junior scientific learning scenarios. Therefore, it is necessary to study human-machine cooperation scenarios and design advanced software and hardware. This talk will introduce some of our recent work in affective analysis, multimodal affection data processing methods, and reliability and security of affective computing systems, etc. We will also discuss some directions that are worthy of further studies.

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A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening

A Multi-Modal Behavior Quantitative Analysis Model for Autis...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Lei, Jiayi Zhang, E. She, Yingying Wang, Xin Liao, Yuhan Hu, Bin Wu, Hang Yang, Minqiang Tian, Jiajia Wang, Yong School of Informatics Xiamen University China University of Kansas Medical Center Department of Occupational Therapy Education United States School of Film National Institute for Data Science in Health and Medicine Xiamen University China School of Computer Science and Technology Beijing Institute of Technology China Together Education Institute China School of Information Science and Engineering Lanzhou University China Fujian Medical University Union Hospital Pediatric Department China

ISBN: (纸本)9798350337020

Human-computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance. © 2023 IEEE.

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