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Raw Ultrasound-Based Phonetic Segments Classification Via Mask Modeling

Raw Ultrasound-Based Phonetic Segments Classification Via Ma...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Kang You Bo Liu Kele Xu Yunsheng Xiong Qisheng Xu Ming Feng Tamás Gábor Csapó Boqing Zhu Tongji University Shanghai China National University of Defense Technology Changsha China National Key Lab of Parallel and Distributed Processing Changsha China Budapest University of Technology and Economics Budapest Hungary

Ultrasound tongue imaging is widely used in clinical linguistics and phonetics. Recently, deep neural networks, especially convolutional neural networks, have been widely used in the interpretation and analysis of ultrasound tongue images (UTI). Despite achieving satisfactory performance, deep models rely on a large amount of manually labeled data, which is often difficult to obtain in practical settings. To address this issue, this paper focuses on how to utilize a large amount of unlabeled UTI data to improve the performance of UTI classification task. Specifically, we explore self-supervised learning with masking modeling strategy. By predicting the masked part, our pre-trained model enables the neural network to infer contextual information. Then, we fine-tune the pre-trained model with a small amount of labeled data. Compared with the previous competing algorithms, our method can improve the classification accuracy by an average of 13.33% in four different scenarios.

关键词： Ultrasonic imaging Tongue neural networks Signal processing algorithms Imaging Self-supervised learning Phonetics

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Memory-Centric Fusion-based CNN Accelerator with 3D-NoC and 3D-DRAM 19

Memory-Centric Fusion-based CNN Accelerator with 3D-NoC and ...

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19th International SoC Design conference (ISOCC) - SoC Technology Towards a New Era of Innovation

作者： Lu, Wei Ge, Pei-Yu Huang, Po-Tsang Chen, Hung-Ming Hwang, Wei Natl Yang Ming Chiao Tung Univ Inst Elect Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Int Coll Semicond Technol Hsinchu Taiwan

ISBN: (纸本)9781665459716

Deep neural networks require enormous computation capacity, great amounts of data accesses from memory and data movement among processing elements (PEs). In fact, data access energy costs on the data access exceed that of computation. Therefore, managing data reuse efficiently and reducing data movement have become the critical design issues. In this paper, an energy-efficient fusion-based DNN is proposed based on a 3D scalable network-on-chip (NoC) with vertical 3D stacked memory to reduce large memory footprints. The effective reuse of data through this approach reduces the data access and energy consumption over current 2D-DRAM designs. A high-efficiency data flow with pipeline, which can process more data in parallel, is also proposed for increasing the resource utilization. Additionally, dynamic voltage/frequency scaling (DVFS) for 3D memory interface and a prefetch technique are utilized into distributed vault controllers to improve system efficiency. Overall, the proposed fusion-based 3D NoC with 3D-DRAM decreases the energy-delay product (EDP) up to 148x compared to conventional NoC with 2D baseline DDR3.

关键词： Fused-Layer 3D network-on-Chip 3D Memory Deep neural network

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Sleep Stage Recognition from EEG Using a distributed Multi-Channel Decision-Making System 15

Sleep Stage Recognition from EEG Using a Distributed Multi-C...

引用

15th International conference on Signal processing and Communication Systems (ICSPCS)

作者： Cheng, Yi-Hsuan Lech, Margaret Wilkinson, Richardt RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (纸本)9781665436991

This study investigates the problem of automatic sleep stage recognition using EEG signals collected simultaneously from multiple sensors (electrodes). Given that the multi-channel diagnosis does not always lead to an anonymous agreement between diagnostic channels (assessors), the question was how to arbitrate between different assessors to determine a final diagnosis. A multi-channel classification system consisting of interconnected neural networks was tested and compared with the baseline single-channel approaches and a multi-channel method using arbitrary decision-making rules. The system has a distributed problem-solving structure. It included a set of parallel assessor networks performing independent diagnoses based on data collected from different sensors. These individual assessments were assembled and passed to the final decision-making network working as an arbitrator and determining the final diagnostic outcome. The proposed method was validated using publicly available EEG data from the Sleep Disorders Center of the Ospedale Maggiore of Parma, Italy (***). The presented automatic classification and decision-making approach led to a 98.26% average accuracy, outperforming the baseline single-channel classifiers by 20%-33% and the multi-channel systems with traditional arbitrary decision-making methods by 4%-6%.

关键词： Machine learning distributed decision-making EEG sleep classification transfer learning

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Subgroup Generalization and Fairness of Graph neural networks 35

Subgroup Generalization and Fairness of Graph Neural Network...

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35th conference on neural Information processing Systems (NeurIPS)

作者： Ma, Jiaqi Deng, Junwei Mei, Qiaozhu Univ Michigan Sch Informat Ann Arbor MI 48109 USA Univ Michigan Dept EECS Ann Arbor MI 48109 USA

ISBN: (纸本)9781713845393

Despite enormous successful applications of graph neural networks (GNNs), theoretical understanding of their generalization ability, especially for node-level tasks where data are not independent and identically-distributed (IID), has been sparse. The theoretical investigation of the generalization performance is beneficial for understanding fundamental issues (such as fairness) of GNN models and designing better learning methods. In this paper, we present a novel PAC-Bayesian analysis for GNNs under a non-IID semi-supervised learning setup. Moreover, we analyze the generalization performances on different subgroups of unlabeled nodes, which allows us to further study an accuracy-(dis)parity-style (un)fairness of GNNs from a theoretical perspective. Under reasonable assumptions, we demonstrate that the distance between a test subgroup and the training set can be a key factor affecting the GNN performance on that subgroup, which calls special attention to the training node selection for fair learning. Experiments across multiple GNN models and datasets support our theoretical results(4).

关键词： Graph neural networks

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Reliable Resource Matching in 6G Computing Power network for Digital Twin Service

Reliable Resource Matching in 6G Computing Power Network for...

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International conference on Communication Technology (ICCT)

作者： Shilong Zhao Junye Zhang Honglin Fang Can Tan Peng Yu Wenjing Li Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350363760

ISBN: (纸本)9798350363777

Due to the complex and dynamic service demands involved in digital twin services, traditional resource matching methods often fail to meet the requirements. To address this challenge, we leverage Graph neural networks (GNN) for task graph matching to enhance the deployment of computing resources. The crux of our approach is an adaptive service decomposition process that breaks down complex services into smaller, more manageable subtasks. These subtasks are then strategically deployed across various nodes within the computing power network (CPN), utilizing distributed resources to maximize efficiency. Moreover, we employ dynamic task scheduling based on Service Level Agreement (SLA) constraints to prioritize tasks, thereby enhancing the quality of computing resource matching. The deployment strategy is grounded in GNN-facilitated low-complexity deep graph matching techniques, aimed at effectively integrating network functionalities with service demands. Our proposed service matching method based on GNN considering dynamic task scheduling (DTSM) has been validated through simulation experiments, with results indicating significant improvements in load balance and overall service processing delay.

关键词： Processor scheduling Heuristic algorithms Dynamic scheduling Load management Graph neural networks Robustness Digital twins Delays Service level agreements Testing

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DeepD_DrugC: Deep and distributed workflow to predict drug- candidates 4

DeepD_DrugC: Deep and distributed workflow to predict drug- ...

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4th International conference on Pattern Analysis and Intelligent Systems, PAIS 2022

作者： Sid, Karima Zertal, Soumia Mezioud, Chaker University of Oum El Bouaghi Department of Mathematics & Computer Science Algeria Algeria

ISBN: (数字)9781665461610

ISBN: (纸本)9781665461610

The applications of computational tools at various stages of drug discovery is one of the most active axes of research. Virtual Screening (VS) is a very common application, which aims to screen and analyze large chemical libraries using algorithms and models to extract drug-candidates that can bind to therapeutic targets. Machine learning (ML) techniques are widely applied as a tool to analyze the chemical libraries in ligand-based virtual screening (LBVS). Deep learning (DL) is a novel mode of machine learning that provides several new architectures primarily based on classical Artificial neural network algorithms, but with many hidden layers to learn features with multiple levels of abstraction. Recently, chemical libraries are identified as Big Data, due to their huge size, the variety of data, and the speed at which they are created, streamed and aggregated. In this context, we need advanced tools to handle and treat this type of data. Apache Spark is the most widely used engine for big data processing, with many improvements that make it more suitable for virtual screening analysis. In this work, we propose a novel workflow named DeepD_ DrugC based on Spark and Deep neural network model implemented with Deeplearning4j (DL4J) to improve the prediction results in LBVS. To evaluate the workflow, we suggest a process to create training datasets using the PubChem Bioassay database for cancer disease. The evaluation results show a good precision more than 93%, with acceptable scaling behavior. © 2022 IEEE.

关键词： Big data

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Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approach to network Security

Intelligent DoS and DDoS Detection: A Hybrid GRU-NTM Approac...

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Smart Electronics and Communication (ICOSEC), International conference on

作者： Caroline Panggabean Chandrasekar Venkatachalam Priyanka Shah Sincy John P Renuka Devi Shanmugavalli Venkatachalam Departement of CSE (AI) JAIN (Deemed – to – be University) Bangalore Karnataka Departement of CSE (AIML) JAIN (Deemed – to – be University) Bangalore Karnataka Department of CSE KSR College of Engineering Namakkal Tamil Nadu

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Detecting Denial of Service (DoS) and distributed Denial of Service (DDoS) attacks remains a critical challenge in cybersecurity. This research introduces a hybrid deep learning model combining Gated Recurrent Units (GRUs) and a neural Turing Machine (NTM) for enhanced intrusion detection. Trained on UNSW-NB15 and BoT-IoT datasets, the model employs GRU layers for sequential data processing and an NTM for long-term pattern recognition. The model achieves 99% accuracy in distinguishing between normal, DoS, and DDoS traffic. This approach offers promising advancements in realtime threat detection, potentially improving network security across various domains.

关键词： Accuracy Turing machines network intrusion detection network security Denial-of-service attack Threat assessment Real-time systems Pattern recognition Resource management Computer crime

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Probabilistic deep neural network based signal processing for Brillouin gain and phase spectrums of vector BOTDA system 21

Probabilistic deep neural network based signal processing fo...

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conference on Optical Interconnects XXI

作者： Lalam, Nageswara Venketeswaran, Abhishek Lu, Ping Buric, Michael P. Natl Energy Technol Lab 626 Cochrans Mill Rd Pittsburgh PA 15236 USA Leidos 626 Cochrans Mill Rd Pittsburgh PA 15236 USA Natl Energy Technol Lab 3610 Collins Ferry Rd Morgantown WV 26505 USA

ISBN: (数字)9781510642201

ISBN: (纸本)9781510642201;9781510642195

We demonstrate a novel probabilistic Brillouin frequency shift (BFS) estimation framework for both Brillouin gain and phase spectrums of vector Brillouin optical time-domain analysis (BOTDA). The BFS profile is retrieved along the fiber distance by processing the measured gain and phase spectrums using a probabilistic deep neural network (PDNN). The PDNN enables the prediction of the BFS along with its confidence intervals. We compare the predictions obtained from the proposed PDNN with the conventional curve fitting and evaluate the BFS uncertainty and data processing time for both methods. The Brillouin phase spectrum generally provides a better measurement accuracy with reduced measurement time in comparison to the Brillouin gain spectrum based measurement, for an equal signal-to-noise ratio and linewidth. The proposed method is demonstrated using a 25 km sensing fiber with 1 m spatial resolution. The PDNN based signal processing of the vector BOTDA system provides a pathway to enhance the BOTDA system performance.

关键词： distributed fiber sensing deep neural network Brillouin gain spectrum Brillouin phase spectrum

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Efficient and Systematic Partitioning of Large and Deep neural networks for Parallelization 27th

Efficient and Systematic Partitioning of Large and Deep Neur...

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27th International European conference on Parallel and distributed Computing (Euro-Par)

作者： Wang, Haoran Li, Chong Tachon, Thibaut Wang, Hongxing Yang, Sheng Limet, Sebastien Robert, Sophie Huawei Technol Paris France Univ Orleans Orleans France

ISBN: (纸本)9783030856656;9783030856649

Deep neural networks (DNNs) are playing an increasingly important role in our daily life. Since the size of DNNs is continuously growing up, it is highly important to train them effectively by distributing computation on multiple connected devices. The efficiency of training depends on the quality of chosen parallelization strategy. Being able to find a good parallelization strategy for a DNN in a reasonable amount of time is not trivial. Previous research demonstrated the possibility to systematically generate good parallelization strategies. However, systematic partitioning still suffers from either a heavy preprocessing or poor quality of parallelization. In this paper, we take a purely symbolic analysis approach by leveraging the features of DNNs like dense tensor balanced computation. We propose the Flex-Edge Recursive Graph and the Double Recursive Algorithm, successfully limiting our parallelization strategy generation to a linear complexity with a good quality of parallelization strategy. The experiments show that our solution significantly reduces the parallelization strategy generation time from hours to seconds while maintaining the parallelization quality.

关键词： distributed algorithm distributed machine learning neural network partitioning

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Continuous Speech Separation with Ad Hoc Microphone Arrays 29

Continuous Speech Separation with Ad Hoc Microphone Arrays

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29th European Signal processing conference (EUSIPCO)

作者： Wang, Dongmei Yoshioka, Takuya Chen, Zhuo Wang, Xiaofei Zhou, Tianyan Meng, Zhong Microsoft Redmond WA 98052 USA

ISBN: (纸本)9789082797060

Speech separation has been shown effective for multi-talker speech recognition. Under the ad hoc microphone array setup where the array consists of spatially distributed asynchronous microphones, additional challenges must be overcome as the geometry and number of microphones are unknown beforehand. Prior studies show, with a spatial-temporal-interleaving structure, neural networks can efficiently utilize the multi-channel signals of the ad hoc array. In this paper, we further extend this approach to continuous speech separation. Several techniques are introduced to enable speech separation for real continuous recordings. First, we apply a transformer-based network for spatio-temporal modeling of the ad hoc array signals. In addition, two methods are proposed to mitigate a speech duplication problem during single talker segments, which seems more severe in the ad hoc array scenarios. One method is device distortion simulation for reducing the acoustic mismatch between simulated training data and real recordings. The other is speaker counting to detect the single speaker segments and merge the output signal channels. Experimental results for AdHoc-LibiCSS, a new dataset consisting of continuous recordings of concatenated LibriSpeech utterances obtained by multiple different devices, show the proposed separation method can significantly improve the ASR accuracy for overlapped speech with little performance degradation for single talker segments.

关键词： ad hoc microphone array speech separation spatially distributed microphones speaker counting

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