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6th International Conference on Networking, Intelligent systems and Security, NISS 2023

作者： Wahbi, Azeddine Ouchatti, Abderrahmane Elghayyaty, Mohamed El Habti El Idrissi, Anas Roukhe, Ahmed Bensassi, Bahloul Hlou, Laamari Lab. of Elec. and Industrial Engineering Information Processing Computer Sciences and Logistic Faculty of Sciences Ain Chock Casablanca Hassan Ii University Maarif Casablanca Morocco Laboratory of Electronic Systems Information Processing and Energetics Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Atomic Mechanical Photonics and Energy Faculty of Science University Moulay Ismail Meknes Morocco

ISBN: (纸本)9798400700194

ANC systems have emerged as the most widely adopted technologies in voice communications. This paper purpose a comparison between the adaptive approaches in time and frequency domain, which is used to model, design and implement these systems. All designs are developed by creating new embedded blocks using Simulink environment. Furthermore, the performance of the proposed embedded designs based adaptive algorithm is analyzed and evaluated on the basis of Signal to Noise Ratio (SNR) and computational complexity improvement, where these designs are applied on ANC to compensate linear distortion in the noise path. Moreover, the produced results showed that using embedded blocks based adaptive algorithm in frequency domain gives the best result than the rest of the other embedded designs. © 2023 ACM.

关键词： Adaptive algorithms

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Discriminative fine-grained network for vehicle re-identification using two-stage re-ranking

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Science China(information Sciences) 2020年第11期63卷 147-158页

作者： Qi WANG Weidong MIN Daojing HE Song ZOU Tiemei HUANG Yu ZHANG Ruikang LIU School of Information Engineering Nanchang University School of Software Nanchang University Jiangxi Key Laboratory of Smart City Nanchang University School of Computer Science and Software Engineering East China Normal University

Research on the application of vehicle re-identification to video surveillance has attracted increasingly growing attention. Existing methods are associated with the difficulties of distinguishing different instances of the same car model owing to the incapability of recognizing subtle differences among these instances and the possibility that a subtle difference may lead to incorrect results of ranking. In this paper, a discriminative fine-grained network for vehicle re-identification based on a two-stage re-ranking framework is proposed to address these issues. This discriminative fine-grained network(DFN) is composed of fine-grained and Siamese networks. The proposed hybrid network can extract discriminative features of the vehicle instances with subtle differences. The Siamese network is rather suitable for general object re-identification using two streams of the network, while the fine-grained network is capable of detecting subtle *** proposed two-stage re-ranking method allows obtaining a more reliable ranking list by using the Jaccard metric and merging the first and second re-ranking lists, where the latter list is formed using the sample mean feature. Experimental results on the VeR i-776 and VehicleID datasets show that the proposed method achieves the superior performance compared to the state-of-the-art methods used in vehicle re-identification.

关键词： vehicle re-identification DFN two-stage re-ranking fine-grained Jaccard metric

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GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

GCFormer: Granger Causality based Attention Mechanism for Mu...

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IEEE International Conference on Data Mining (ICDM)

作者： Shiwang Xing Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences China

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

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Research and development of an information system for speech recognition based on neural network algorithms metrics

Research and development of an information system for speech...

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Smart information systems and Technologies (SIST), IEEE International Conference on

作者： Maksat Kanashev Assel Mukasheva Didar Yedilkhan Sabina Rakhmetulaeva Sergiy Gnatyuk Mirambayeva Nazym Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan Department of Computer Engineering Astana IT University Astana Kazakhstan Department of Information Systems International University of Information Technologies Almaty Kazakhstan Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields.

关键词： Performance evaluation Adaptation models Vocabulary Accuracy Data visualization Speech recognition Libraries

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ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, Word Segmentation and Entity Dictionary 2nd

ALFLAT: Chinese NER Using ALBERT, Flat-Lattice Transformer, ...

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2nd EAI International Conference on Applied Cryptography in computer and Communications, AC3 2022

作者： Lv, Haifeng Ding, Yong School of Data Science and Software Engineering Wuzhou University Wuzhou China Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Wuzhou China

ISBN: (纸本)9783031170805

Recently, the character-word lattice structure has been proved to be effective for Chinese named entity recognition (NER) by incorporating the word information. However, one hand, since the lattice structure is dynamic and complex, although some existing lattice-based models are effectively utilize the parallel computation of GPUs, they do not fully utilize word segmentation boundary tags that as features are helpful for NER task. On the other hand, the character-word vector needs to be trained, and the user-defined entity dictionary cannot be effectively used. In this paper, we propose ALFLAT: based on a flat-lattice Transformer to incorporate ALBERT pre-trained model, word segmentation information and user-defined entity dictionary for Chinese NER. ALFLAT converts the lattice structure into a flat structure consisting of spans, integrate word segmentation embedding with the output of flat-lattice Transformer model, then modifies the emission scores according to the user-defined entity dictionary, finally utilize Viterbi decoding of the CRF layer to obtain the correct entity results. Each span corresponds to a character or latent word and its position in the original lattice. With the power of ALBERT pre-trained model, Transformer and position encoding, ALFLAT can fully leverage the lattice, word segmentation and user-defined entity dictionary information. Experiments on MSRA dataset show ALFLAT outperforms other lexicon-based models in performance and efficiency. © 2022, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Computational linguistics

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Learning label shift correction for test-agnostic long-tailed recognition 24

Learning label shift correction for test-agnostic long-taile...

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Proceedings of the 41st International Conference on Machine Learning

作者： Tong Wei Zhen Mao Zi-Hao Zhou Yuanyu Wan Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Software Technology Zhejiang University Ningbo China

Long-tail learning primarily focuses on mitigating the label distribution shift between longtailed training data and uniformly distributed test data. However, in real-world applications, we often encounter a more intricate challenge where the test label distribution is agnostic. To address this problem, we first theoretically establish the substantial potential for reducing the generalization error if we can precisely estimate the test label distribution. Motivated by the theoretical insight, we introduce a simple yet effective solution called label shift correction (LSC). LSC estimates the test label distribution within the proposed framework of generalized black box shift estimation, and adjusts the predictions from a pre-trained model to align with the test distribution. Theoretical analyses confirm that accurate estimation of test label distribution can effectively reduce the generalization error. Extensive experimental results demonstrate that our method significantly outperforms previous state-of-the-art approaches, especially when confronted with non-uniform test label distribution. Notably, the proposed method is general and complements existing longtail learning approaches, consistently improving their performance. The source code is available at https://***/Stomach-ache/label-shift-correction.

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Circular Decomposition and Cross-Modal Recombination for Multimodal Sentiment Analysis

Circular Decomposition and Cross-Modal Recombination for Mul...

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

作者： Haijian Liang Weicheng Xie Xilin He Siyang Song Linlin Shen School of Computer Science & Software Engineering Computer Vision Institue Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University School of Computing and Mathematical Sciences University of Leicester National Engineering Laboratory for Big Data System Computing Technology Shenzhen University

Multimodal Sentiment Analysis is a burgeoning research area, leveraging various modalities to predict the sentiment score. Nevertheless, previous studies have disregarded the impact of noise interference on specific modal sentiments during video recording, thereby compromising the accuracy of sentiment prediction. In this paper, we propose the Guided Circular Decomposition and Cross-Modal Recombination (GCD-CMR) model, which aims to eliminate contaminated sentiment features in a fine-grained way. To achieve this, we utilize tailored global information specific to each modality to guide the circular decomposing process in the GCD module, to produce a set of sentiment prototypes. Subsequently, in the CMR module, we align cross-modal sentiment prototypes and remove the contaminated prototypes for recombination. Experimental results on two publicly available datasets demonstrate that our model surpasses state-of-the-art models, confirming the effectiveness of our proposed method. We release the code at: https://***/nianhua20/GCD-CMR.

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

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IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization software defined networking

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Improving students’programming quality with the continuous inspection process:a social coding perspective

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Frontiers of computer Science 2020年第5期14卷 43-60页

作者： Yao Lu Xinjun Mao Tao Wang Gang Yin Zude Li College of Computer National University of Defense TechnologyChangsha410073China Key Laboratory of Software Engineering for Complex Systems Changsha410073China School of Information Science and Engineering Central South UniversityChangsha410012China

College students majoring in computer science and software engineering need to master skills for high-quality ***,rich research has shown that both the teaching and learning of high-quality programming are challenging and deficient in most college education ***,the continuous inspection paradigm has been widely used by developers on social coding sites(e.g.,GitHub)as an important method to ensure the internal quality of massive code *** paper presents a case where continuous inspection is introduced into the classroom setting to improve students’programming *** the study,we first designed a specific continuous inspection process for students’collaborative projects and built an execution environment for the *** then conducted a controlled experiment with 48 students from the same course during two school years to evaluate how the process affects their programming *** results show that continuous inspection can help students in identifying their bad coding habits,mastering a set of good coding rules and significantly reducing the density of code quality issues introduced in the ***,we describe the lessons learned during the study and propose ideas to replicate and improve the process and its execution platform.

关键词： continuous inspection programming quality SonarQube

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Enhancing Drug-Drug Interaction Prediction by Knowledge Graph Embedding

Enhancing Drug-Drug Interaction Prediction by Knowledge Grap...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： CHAO LIU XIZHAO WANG QIANG LIU Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

Drug-drug interactions (DDIs) refer to the synergistic or antagonistic effects between different drugs. Synergistic effects can enhance therapeutic efficacy, while antagonistic effects may reduce efficacy or even trigger adverse reactions, worsening the patient's condition. Most existing DDI prediction studies overlook the features of chemical bonds between atoms within drug molecules and the interaction types between drug pairs, which to some extent limits the accuracy and reliability of the predictions. In view of this, we have designed a novel framework called KGE-DDI, which leverages knowledge graph embedding (KGE) technology to enhance the prediction of DDIs. Specifically, for individual drug molecules, KGE-DDI first integrates the features of atoms and chemical bond. Then, by introducing attention mechanisms and graph neural network technology, it learns representations of the drug molecules. For pairs of drug molecules, KGE-DDI models their interactions utilizing atom-level Pearson correlation matrices. Finally, it predicts the interactions between drug pairs by employing our designed scoring function. We conducted comprehensive experiments under both transductive and inductive settings, and the results demonstrate the effectiveness and superiority of KGE-DDI in three scenarios: (existing drug, existing drug), (new drug, new drug), and (new drug, existing drug).

关键词： Drugs Measurement Correlation Knowledge graphs Atoms Predictive models Graph neural networks Reliability Chemicals Cybernetics

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