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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Xingyi Zhao, Zhelin Xiang, Ju Hu, Jialu Shang, Xuequn Northwestern Polytechnical University School of Computer Science Xi'an710072 China Northwestern Polytechnical University School of Software Xi'an710072 China Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410000 China

ISBN: (纸本)9781665468190

The prognosis of breast cancer is challenging, which is an urgent problem to be solved. The prognostic biomarkers for breast cancer can help us predict the clinical outcomes of patients, and network-based methods are widely introduced to find prognostic biomarkers. According to the difference of input biological data, existing network-based biomarker prediction methods are mainly classified into two types: integrating single-source network or multi-source networks. However, the interactome of single-source network remains incomplete, and biological networks are noisy, which will hamper the network-based identification accuracy of biomarkers. In this study, we introduce a multi-source fusion method, DualMarker, which integrates multiple biological information sources and constructs a dual-layer heterogeneous network by fast network embedding. Next, we introduce a network enhancement method to denoise the constructed dual-layer heterogeneous network, and we implement network propagation algorithm on the constructed dual-layer heterogeneous network to rank the features. After comparing with competitive methods, we find that DualMarker substantially outperforms these methods. In addition, we verify that the biomarkers identified by DualMarker are closely related to the prognosis of breast cancer patients. © 2022 IEEE.

关键词： Heterogeneous networks

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A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

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Noise-Aware Speech Separation with Contrastive Learning

Noise-Aware Speech Separation with Contrastive Learning

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

作者： Zizheng Zhang Chen Chen Hsin-Hung Chen Xiang Liu Yuchen Hu Eng Siong Chng School of Software and Microelectronics Peking University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Electrical and Computer Engineering Georgia Institute of Technology USA

Recently, speech separation (SS) task has achieved remarkable progress driven by deep learning technique. However, it is still challenging to separate target speech from noisy mixture, as the neural model is vulnerable to assign background noise to each speaker. In this paper, we propose a noise-aware SS (NASS) method, which aims to improve the speech quality for separated signals under noisy conditions. Specifically, NASS views background noise as an additional output and predicts it along with other speakers in a mask-based manner. To effectively denoise, we introduce patch-wise contrastive learning (PCL) between noise and speaker representations from the decoder input and encoder output. PCL loss aims to minimize the mutual information between predicted noise and other speakers at multiple-patch level to suppress the noise information in separated signals. Experimental results show that NASS achieves 1 to 2dB SI-SNRi or SDRi over DPRNN and Sepformer on WHAM! and LibriMix noisy datasets, with less than 0.1M parameter increase.

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SDCM: An Efficient Camellia Oleifera Fruits Detection Algorithm in Dense Occlusion Scenes 6

SDCM: An Efficient Camellia Oleifera Fruits Detection Algori...

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6th International Conference on Computing and Big Data, ICCBD 2023

作者： Zhang, Youyang Chen, Qiuxia Cui, Wenhua Yang, Jinfeng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China Institute of Applied Artificial Intelligence The Guang-dong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen China

ISBN: (纸本)9798350317008

To realize intelligent management in the Camellia oleifera fruit industry and foster the efficient growth of its supply chain, the accurate identification and detection of Camellia oleifera fruits are essential. However, in natural environments, these fruits exhibit challenges such as clustering, mutual occlusion, and obstruction by leaves. This paper proposes a Camellia oleifera fruits detection algorithm based on an improved YOLOv7 deep convolutional neural network, which is referred to as the SDCM network model, and add the function of counting the number of fruits. We introduce an enhancement to the backbone layer of the YOLOv7 model by incorporating a module called SDCM (Shortcut and Dilation Convolution based Module). This module enriches multiscale edge information, enabling the network to extract features more effectively, thus improving detection accuracy. The experimental results show that the detection accuracy of the SDCM network model is significantly improved compared with the original YOLOv7 model, and the detection performance of the model in this paper is superior compared with other classical detection models. By enhancing the detection accuracy of the model and adding the function of fruit counting, the model in this paper can provide more reliable and accurate detection performance and the function of predicting fruit yield for the development of the Camellia oleifera fruit industry, improve the production efficiency, and reduce the artificial resources. © 2023 IEEE.

关键词： Supply chains

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HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Quality Assessment Metric

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Q...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Yuanming Yao, Jiaxuan Deng, Xiangyu Yang, Yuhong Liao, Ruiqi Tu, Weiping Lin, Cedar National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. Guangdong China

ISBN: (纸本)9798350368741

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception;(2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data;and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality. © 2025 IEEE.

关键词： auditory perception spatial audio Speech and audio quality assessment subjective experiment timbre quality

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Congress on Evolutionary Computation

作者： Wenqiang Ma Yaqing Hau Hua Yu Xiangrong Tong Zexuan Zhu Qiang Zhang School of Computer Science and Technology Dalian University of Technology Dalian China School of Computer and Control Engineering Yantai University Yantai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Deep neural networks are powerful and popular learning models; however, recent studies have shown that deep neural network-based policies are susceptible to deception by adversarial attacks. A minimalistic attack is a specialized form of adversarial attack that aims to accomplish successful attacks at the lowest possible cost. Recently, transfer optimization algorithms have been applied to deceive previously trained policies by acquiring knowledge from previously solved tasks. Experiments indicate that the transfer optimization algorithms perform well compared to traditional optimization algorithms. However, current transfer algorithms for addressing minimalistic attacks not only select a single source task for knowledge transfer but also tend to overly rely on identified appropriate source tasks. To address this issue, this paper introduces a similar locality based transfer evolutionary optimization algorithm. It can adaptively select multiple source tasks and extract valuable knowledge from these source tasks. Moreover, by leveraging the concept of similar locality, the algorithm alleviates its excessive dependence on familiar tasks, thereby providing fresh knowledge for the optimization of the target task. On this basis, the algorithm can mine more valuable knowledge from the large source task space to achieve a successful attack in a shorter period. The algorithm is tested on three Atari games-BeamRider, Qbert, and Seaquest-demonstrating its ability and potential to outperform other transfer optimization algorithms currently available in solving this problem.

关键词： Sociology Fitting Optimization methods Games Reinforcement learning Security Task analysis

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RepoChat: An LLM-Powered Chatbot for GitHub Repository Question-Answering

RepoChat: An LLM-Powered Chatbot for GitHub Repository Quest...

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International Mining software Repositories, MSR

作者： Samuel Abedu Laurine Menneron SayedHassan Khatoonabadi Emad Shihab Department of Computer Science and Software Engineering Concordia University Montreal Canada CESI Graduate School of Engineering Saint-Nazaire France

ISBN: (数字)9798331501839

ISBN: (纸本)9798331501846

software repositories contain a wealth of data about the software development process, such as source code, documentation, issue tracking, and commit histories. However, accessing and extracting meaningful insights from these data is timeconsuming and requires technical expertise, posing challenges for software practitioners, especially non-technical stakeholders like project managers. Existing solutions, such as software engineering chatbots leveraging LLMs, have demonstrated significant limitations in retrieving relevant data to answer user questions. In this paper, we introduce RepoChat, a web-based tool designed to answer repository-related questions by synergizing LLMs with knowledge graphs. RepoChat operates in two steps: (1) the Data Ingestion step, where it collects and constructs a knowledge graph from repository metadata, such as commits, issues, files and users; and (2) the Interaction step, where it takes the users natural language question, translates it into graph queries using an LLM, executes these queries against the knowledge graph, and generates a user-friendly response to the question using the query results as context. We evaluate RepoChat by conducting a user study in which participants asked a series of repository-related questions representing common developer intents. RepoChat achieved an accuracy of $90 \%$, correctly answering 36 out of 40 questions, demonstrating its effectiveness in accurately retrieving relevant information to answer user’s questions. RepoChat is available at https://***/, and its source code is accessible on Zenodo [1].

关键词： Translation Source coding Knowledge graphs Metadata Chatbots software Data mining Stakeholders software development management software engineering

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A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications 37

A Theory of Transfer-Based Black-Box Attacks: Explanation an...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Chen, Yanbo Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9781713899921

Transfer-based attacks [1] are a practical method of black-box adversarial attacks in which the attacker aims to craft adversarial examples from a source model that is transferable to the target model. Many empirical works [2-6] have tried to explain the transferability of adversarial examples from different angles. However, these works only provide ad hoc explanations without quantitative analyses. The theory behind transfer-based attacks remains a mystery. This paper studies transfer-based attacks under a unified theoretical framework. We propose an explanatory model, called the manifold attack model, that formalizes popular beliefs and explains the existing empirical results. Our model explains why adversarial examples are transferable even when the source model is inaccurate as observed in Papernot et al. [7]. Moreover, our model implies that the existence of transferable adversarial examples depends on the "curvature" of the data manifold, which further explains why the success rates of transfer-based attacks are hard to improve. We also discuss our model's expressive power and applicability. © 2023 Neural information processing systems foundation. All rights reserved.

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A Smart Wearable Ring Device for Sensing Hand Tremor of Parkinson’s Patients

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computer Modeling in engineering & sciences 2021年第3期126卷 1217-1238页

作者： Haixia Yang Yixian Shen Wei Zhuang Chunming Gao Dong Dai Weigong Zhang School of Instrument Science and Engineering Southeast UniversityNanjing210096China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China School of Engineering and Technology University of WashingtonTacoma98402USA School of Cyber Science and Engineering Southeast UniversityNanjing210096China

Parkinson’s disease(PD)is a very common neurodegenerative disease that occurs mostly in the *** are many main clinical manifestations of PD,such as tremor,bradykinesia,muscle rigidity,*** on the current research on PD,the accurate and convenient detection of early symptoms is the key to detect *** the development of microelectronic and sensor technology,it is much easier to measure the barely noticeable tremor in just one hand for the early detection of Parkinson’s *** this paper,we present a smart wearable device for detecting hand tremor,in which MPU6050(MIDI Processing Unit)consisting of a 3-axis gyroscope and a 3-axis accelerometer is used to collect acceleration and angular velocity of *** analyzing the time of specific finger movements,we successfully recognized the tremor signals with high ***,with Bluetooth 4.0(Bluetooth Low Energy,BLE)and networking terminal ability,tremor data can be transferred to a monitoring device in real time with extremely lowenergy *** experimental results have shown that the proposed device(smart ring)is convenient for long-term tremor detection which is vital for early detection and treatment for Parkinson’s disease.

关键词： Parkinson’s disease tremor detection IMU sensor MPU6050 BLE

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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