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Semantic Transition Detection for Self-supervised Video Scene Segmentation 1

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Chen, Lu Tan, Jiawei Yang, Pingan Wang, Hongxing Ministry of Education Chongqing China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (数字)9783031533112

ISBN: (纸本)9783031533105

Video scene segmentation is a crucial task in temporally parsing long-form videos into basic story units. Most advanced self-supervised methods of video scene segmentation focus heavily on learning video shot features in the pre-training stage. However, these methods ignore to encode shot relations, which are essential to video scene segmentation, resulting in over-segmentation of video scenes. A straightforward solution to the above problem is to use sufficient scene boundaries to model the shot relations. In this paper, we introduce a high-quality pseudo-scene boundary generation method, Semantic Transition Detection (STD), by discovering semantic inconsistencies in temporal video chunks. Taking scene boundary prediction as the pretext task, we propose a self-supervised method for video scene segmentation, by which we can fine-tune a STD pre-trained model with limited scene boundary ground truths. In addition, considering the impact of shot duration on the segmentation results, we integrate the shot duration information into the fine-tuning stage. Experiments on widely used benchmark datasets demonstrate that our approach effectively mitigates over-segmentation and achieves remarkable results in comparison with the state of the arts. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Semantics

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Personality Enhanced Emotion Generation Modeling for Dialogue Systems

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COGNITIVE COMPUTATION 2024年第1期16卷 293-304页

作者： Liu, Zhiqiang Jia, Wenchao Zhou, Yutong Xu, Biqi Liu, Zhiqiang Wu, Zhuoyi College of Data Science and Application Inner Mongolia University of Technology Hohhot 010080 Inner Mongolia China Inner Mongolia Autonomous Region Engineering & Technology Research Centre of Big Data Based Software Service Hohhot 010080 Inner Mongolia China

Emotion plays a crucial role in human communication, as it adds depth and richness to conversations. In recent years, there has been growing interest in developing conversation systems with the ability to generate emotions. However, to create more engaging and realistic interactions, it is essential to consider the influence of personality on emotion generation. This paper proposes a novel approach that combines personality modeling with emotion generation for conversation systems. By incorporating personality traits into the emotion generation process, we aim to create more personalized and contextually appropriate emotional responses. Drawing from bigFive model and emotion computation techniques, our model takes into account individual differences in personality to generate emotions that align with each user's unique characteristics. Experiments show that combining emotion modeling with personality in a dialogue system helps improve the performance of emotion generation models. Additionally, it is also verified that our approach outperforms other baselines on several metrics.

关键词： Dialogue system Emotion generation Personality Anthropomorphism

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DSFM: Enhancing Functional Code Clone Detection with Deep Subtree Interactions 24

DSFM: Enhancing Functional Code Clone Detection with Deep Su...

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44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Xu, Zhiwei Qiang, Shaohua Song, Dinghong Zhou, Min Wan, Hai Zhao, Xibin Luo, Ping Zhang, Hongyu Kliss BNRist School of Software Tsinghua University Beijing China School of Big Data and Software Engineering Chongqing University Chongqing China

ISBN: (纸本)9798400702174

Functional code clone detection is important for software maintenance. In recent years, deep learning techniques are introduced to improve the performance of functional code clone detectors. By representing each code snippet as a vector containing its program semantics, syntactically dissimilar functional clones are detected. However, existing deep learning-based approaches attach too much importance to code feature learning, hoping to project all recognizable knowledge of a code snippet into a single vector. We argue that these deep learning-based approaches can be enhanced by considering the characteristics of syntactic code clone detection, where we need to compare the contents of the source code (e.g., intersection of tokens, similar flow graphs, and similar subtrees) to obtain code clones. In this paper, we propose a novel deep learning-based approach named DSFM, which incorporates comparisons between code snippets for detecting functional code clones. Specifically, we improve the typical deep clone detectors with deep subtree interactions that compare every two subtrees extracted abstract syntax trees (ASTs) of two code snippets, thereby introducing more fine-grained semantic similarity. By conducting extensive experiments on three widely-used datasets, GCJ, OJClone, and bigCloneBench, we demonstrate the great potential of deep subtree interactions in code clone detection task. The proposed DSFM outperforms the state-of-the-art approaches, including two traditional approaches, two unsupervised and four supervised deep learning-based baselines. © 2024 ACM.

关键词： Semantics

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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Belief χ2 Divergence-Based Dynamical Complexity Analysis for Biological Systems 4th

Belief χ2 Divergence-Based Dynamical Complexity Analysis fo...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Zhang, Lang Xiao, Fuyuan School of Big Data and Software Engineering Chongqing University Chongqing401331 China

ISBN: (纸本)9783031201011

Physiological signals contain the information of physical state in healthy systems. Especially, the complexity of dynamical time series data is a valid indicator to measure the pathological states. However, how to quantify the complexity of physical signals is still an open issue. In this paper, a novel complexity analysis algorithm based on divergence, called DCA, is proposed to figure out the complexity of biological system time series. Specifically, DCA algorithm splits biological systems time series data into different slices with boundaries. In addition, the feature of each time series data is extracted by converted into the basic probability assignments (BPAs) based on the Dempster-Shafer (D-S) evidence theory. DCA algorithm considers that the average divergence of BPAs indicates the complexity in a piece of time series. Moreover, an application in cardiac inter-beat interval time series is carried out to demonstrate the effectiveness of the proposed algorithm, which performs well in a pathological states analysis issue. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Biological systems

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Unraveling the intricacies of EEG seizure detection: A comprehensive exploration of machine learning model performance, interpretability, and clinical insights

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Multimedia Tools and Applications 2024年第41期83卷 89217-89247页

作者： Mridha, Krishna Priyok, Masrur Ahsan Shukla, Madhu Department of Computer Engineering Marwadi University Rajkot India Department of Software Engineering Tianjin University Tianjin China Department of Computer Engineering Artificial Intelligence and Big Data Analytics Marwadi University Gujarat Rajkot India

In neurology, it is critical to promptly and precisely identify epileptic episodes using EEG data. Interpretability and thorough model evaluation are still crucial to guarantee reliability, even though machine learning provides sophisticated tools for automated EEG analysis. We undertook a thorough investigation of EEG seizure classification in this work, with equal emphasis on statistical validation, interpretability, and model performance. Following thorough preprocessing, we used statistical significance tests in conjunction with Chernoff and Bhattacharyya Bounds to select features. Top performers were found to be the Random Forest which had accuracies of 96%. A paired t-test and bootstrapped 95% confidence intervals were used to statistically validate the performance robustness, demonstrating the large variation in model performances. Going beyond simple performance measures, we set out to decipher the nuances of model choices by utilizing methods devoted to model interpretability and shedding light on the importance of distinct aspects. This helped neurologists grasp the primary EEG indicators predictive of seizures and opened the door for future clinical insights. It also provided transparency in diagnostic predictions. Our efforts highlight the mutually reinforcing importance of predictability and model interpretability in the context of medical machine-learning applications, hence intensifying the demand for the creation and use of trustworthy and transparent neurology diagnostic instruments. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurology

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing

Group Signature with Time-Bound Keys for Secure E-health Rec...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Junze Hu, Chunqiang Ruan, Conghao Cai, Bin Xiang, Tao Chongqing University School of Big Data & Software Engineering China Chongqing University College of Computer Science China

ISBN: (纸本)9798350386226

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient. © 2024 IEEE.

关键词： Electronic health record

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COMET ■: “cone of experience” enhanced large multimodal model for mathematical problem generation

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Science China(Information Sciences) 2024年第12期67卷 97-98页

作者： Sannyuya LIU Jintian FENG Zongkai YANG Yawei LUO Qian WAN Xiaoxuan SHEN Jianwen SUN National Engineering Research Center of Educational Big Data Central China Normal University Faculty of Artificial Intelligence in Education Central China Normal University School of Software Technology Zhejiang University

The impact of generative artificial intelligence on education is unprecedented [1]. Researchers have been exploring possibilities of combining the large multimodal model(LMM)with the teaching process. Specifically, Luo and Yang [2]have explored large model collaborative domain models to support smart education, fostering personalized and adaptive educational experiences. However, existing studies still lack in-depth research on generating educational resources,especially in mathematical problem generation.

关键词： Personnel training

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An adaptive graph embedding method for feature extraction of hyperspectral images based on approximate NMR model

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Optoelectronics Letters 2023年第7期19卷 443-448页

作者： QIU Hong WANG Renfang JIN Heng WANG Feng College of Big Data and Software Engineering Zhejiang Wanli UniversityNingbo 315200China College of Information Shanghai Ocean UniversityShanghai 200120China

This paper introduces an approximate nuclear norm based matrix regression projection(ANMRP) model,an adaptive graph embedding method,for feature extraction of hyperspectral *** ANMRP utilizes an approximate NMR model to construct an adaptive neighborhood map between *** globally optimal weight matrix is obtained by optimizing the approximate NMR model using fast alternating direction method of multipliers(ADMM).The optimal projection matrix is then determined by maximizing the ratio of the local scatter matrix to the total scatter matrix,allowing for the extraction of discriminative *** results demonstrate the effectiveness of ANMRP compared to related methods.

关键词： approximate embedding matrix

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Evaluation and enhancement of smart community system based on big data 3

Evaluation and enhancement of smart community system based o...

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3rd International Conference on Electronic Information engineering, big data, and Computer Technology, EIBDCT 2024

作者： Wu, Huiwen Huang, Xiaoping Natural Language Processing Software Engineering Institute of Guangzhou Guangzhou China Big Data Nanfang College Guangzhou China

ISBN: (纸本)9781510680449

With the rapid development of big data technology, the application of smart community system is more and more extensive, but it also faces the problem of evaluation and enhancement. This paper aims to explore the evaluation and enhancement of smart community system based on big data. First, it introduces the basic concepts and characteristics of smart community system, then expounds the index system and methods of smart community system evaluation based on big data, then analyzes the key technologies and strategies of smart community system enhancement, and finally puts forward the future research direction and prospect. Improve the efficiency of community management and service. Through big data analysis, the services and management of the community can be intelligently optimized, so as to improve the efficiency of community management and service. Improve the quality of life of community residents: The smart community system can provide a more convenient and comfortable living environment for community residents through various intelligent means. For example, through the smart home system, the remote control of home equipment can be achieved to improve the convenience of life. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： big data

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