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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Junhui Li Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Science and Engineering University of Bristol Bristol U.K

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate.

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A Pervasive Multi-physiological Signal-Based Emotion Classification with Shapelet Transformation and Decision Fusion 17th

A Pervasive Multi-physiological Signal-Based Emotion Classif...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Shichao Zheng, Xiangwei Zhang, Mingzhe Guo, Gengyuan Ji, Cun School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9783030926342

Emotion classification is a hot pot at present. Since physiological signals are objective and difficult to hide, physiological signals are commonly used in emotion classification methods. However, traditional emotional classification based on physiological signals faced the following challenges: low accuracy and low interpretability. For this, this paper proposes a pervasive multi-physiological signal-based emotion classification with shapelet transformation and decision fusion (PMSEC). In PMSEC, the shapelet transformation and feature extraction are carried out for ECG, GSR, and RA, respectively. Following by, six sub-classifiers are constructed for different physiological signals. Lastly, decision-level fusion is implemented to obtain final emotion results. Experimental results show that the proposed PMSEC is not only highly competitive but also has a broad application prospect compared with EEG-based classification method. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Electrocardiograms

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Demystifying What Code Summarization Models Learned

arXiv

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arXiv 2023年

作者： Wang, Yu Wang, Ke Nanjing University China Visa Research United States State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Jiangsu Nanjing210023 China Security Cryptography Visa Research Palo AltoCA United States

Study patterns that models have learned has long been a focus of pattern recognition research. Explaining what patterns are discovered from training data, and how patterns are generalized to unseen data are instrumental to understanding and advancing the pattern recognition methods. Unfortunately, the vast majority of the application domains deal with continuous data (i.e. statistical in nature) out of which extracted patterns can not be formally defined. For example, in image classification, there does not exist a principle definition for a label of cat or dog. Even in natural language, the meaning of a word can vary with the context it is surrounded by. Unlike the aforementioned data format, programs are a unique data structure with a well-defined syntax and semantics, which creates a golden opportunity to formalize what models have learned from source code. This paper presents the first formal definition of patterns discovered by code summarization models (i.e. models that predict the name of a method given its body), and gives a sound algorithm to infer a context-free grammar (CFG) that formally describes the learned patterns. We realize our approach in PATIC which produces CFGs for summarizing the patterns discovered by code summarization models. In particular, we pick two prominent instances, code2vec and code2seq, to evaluate PATIC. PATIC shows that the patterns extracted by each model are heavily restricted to local, and syntactic code structures with little to none semantic implication. Based on these findings, we present two example uses of the formal definition of patterns: a new method for evaluating the robustness and a new technique for improving the accuracy of code summarization models. Our work opens up this exciting, new direction of studying what models have learned from source code. © 2023, CC BY.

关键词： Deep neural networks

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Weighted Poisson-disk Resampling on Large-Scale Point Clouds 39

Weighted Poisson-disk Resampling on Large-Scale Point Clouds

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jiao, Xianhe Lv, Chenlei Zhao, Junli Yi, Ran Wen, Yu-Hui Pan, Zhenkuan Wu, Zhongke Liu, Yong-Jin College of Computer Science and Technology Qingdao University China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Computer Science & Technology Beijing Jiaotong University China School of Artificial Intelligence Beijing Normal University China Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

ISBN: (纸本)157735897X

For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often struggle with decreased efficiency and accuracy. To address such issues, we propose a weighted Poisson-disk (WPD) resampling method to improve the usability and efficiency for the processing. We first design an initial Poisson resampling with a voxel-based estimation strategy. It is able to estimate a more accurate radius of the Poisson-disk while maintaining high efficiency. Then, we design a weighted tangent smoothing step to further optimize the Voronoi diagram for each point. At the same time, sharp features are detected and kept in the optimized results with isotropic property. Finally, we achieve a resampling copy from the original point cloud with the specified point number, uniform density, and high-quality geometric consistency. Experiments show that our method significantly improves the performance of large-scale point cloud resampling for different applications, and provides a highly practical solution. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Geometry

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Dynamic Graph Learning with Long and Short-Term for Multivariate Time Series Anomaly Detection

Dynamic Graph Learning with Long and Short-Term for Multivar...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: technology and Applications

作者： Yuyin Tian Rong Gao Lingyu Yan Donghua Liu Zhiwei Ye School of Computer Science Hubei University of Technology Wuhan China State Key Laboratory for Novel Software Technology Nanjing University Nanjing University Nanjing China Information Center China Waterborne Transport Research Institue Beijing China

Existing multivariate time series anomaly detection methods utilize graph neural networks to model inter-sensor dependencies and achieve satisfactory results. However, these methods often employ a predefined or self-learning static adjacency matrix for graph information aggregation, disregarding the dynamic characteristics of inter-sensor dependencies in the real world, which change over time and are observable at different time scales as dynamic changing graph structure information. Furthermore, existing techniques struggle to capture long-term temporal contextual relationships. To address these issues, we propose a Dynamic Graph Learning with Long and Short-term time model (DGL-LS) for multivariate time series anomaly detection. The network integrates a dynamic graph structure learning method among sensors in multi-time mode and long- and short-term temporal context information to enhance the prediction of multivariate time series anomalies. We propose a strategy for learning dynamic graphs that aggregates sensor node representations at various time intervals, and then uses an attention-based gating unit to evolve the node dynamic characterization and construct a series of adjacency matrices that describe the dynamic changes in graph structure over time. Moreover, we formulate a long-short time convolution to capture the long-term and short-term temporal dependencies within the time series. We demonstrate the superiority of our approach over other state-of-the-art methods by conducting extensive experiments on three publicly available datasets.

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The application of PAD teaching method in computer network course 8

The application of PAD teaching method in computer network c...

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8th Annual International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2020

作者： Liu, Lining Zhang, Yongsheng Zhang, Penglong Sun, Wenqian School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology China

As a new teaching mode and method, PAD has been developed in recent years. This teaching mode has been applied in the teaching of universities, ordinary primary and secondary schools and vocational colleges. It has achieved good teaching results. In order to improve teaching effect, improve the students' autonomy and enthusiasm and cultivate the independent innovative talents needed by the society, more and more computer teachers have also begun to apply PAD to the computer classroom. "computer Network" is a basic and compulsory course for learning computers. "transmission media" in this course is selected as the specific content, and PAD is used for teaching design. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Teaching

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PersuasiveToM: A Benchmark for Evaluating Machine Theory of Mind in Persuasive Dialogues

arXiv

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arXiv 2025年

作者： Yu, Fangxu Jiang, Lai Huang, Shenyi Wu, Zhen Dai, Xinyu National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Department of Computer Science and Engineering Shanghai Jiao Tong University China University of California San DiegoCA United States

The ability to understand and predict the mental states of oneself and others, known as the Theory of Mind (ToM), is crucial for effective social interactions. Recent research has emerged to evaluate whether Large Language Models (LLMs) exhibit a form of ToM. Although recent studies have evaluated ToM in LLMs, existing benchmarks focus predominantly on physical perception with principles guided by the Sally-Anne test in synthetic stories and conversations, failing to capture the complex psychological activities of mental states in real-life social interactions. To mitigate this gap, we propose PERSUASIVETOM, a benchmark designed to evaluate the ToM abilities of LLMs in persuasive dialogues. Our framework introduces two categories of questions: (1) ToM Reasoning, assessing the capacity of LLMs to track evolving mental states (e.g., desire shifts in persuadees), and (2) ToM Application, evaluating whether LLMs can take advantage of inferred mental states to select effective persuasion strategies (e.g., emphasize rarity) and evaluate the effectiveness of persuasion strategies. Experiments across eight state-of-the-art LLMs reveal that while models excel on multiple questions, they struggle to answer questions that need tracking the dynamics and shifts of mental states and understanding the mental states in the whole dialogue comprehensively. Our aim with PERSUASIVETOM is to allow an effective evaluation of the ToM reasoning ability of LLMs with more focus on complex psychological activities. Our code is available at https://***/YuFangxu/PersuasiveToM. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Revisiting Attacks and Defenses in Connected and Autonomous Vehicles 1

引用

13th International Conference on Security, Privacy, and Anonymity in Computation, Communication, and Storage, SpaCCS 2020

作者： Fang, Ziyan Zhang, Weijun Li, Zongfei Tang, Huaao Han, Hao Xu, Fengyuan College of Computer Sciences and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing210093 China State Key Laboratory for Novel Technology Nanjing University Nanjing210046 China

ISBN: (数字)9783030688516

ISBN: (纸本)9783030688509

With the development of the automotive industry, the security of connected and autonomous vehicles (CAVs) has become a hot research field in recent years. However, previous studies mainly focus on the threats and defending mechanisms from the networking perspective, while newly emerging attacks are targeting the core component – AI of CAVs. Therefore, the defense methods against these attacks are urgently needed. In this paper, we revisit emerging attacks and their technical countermeasures for CAVs in a layered inventory, including in-vehicle systems, V2X, and self-driving. We believe that this survey provides insights on defending adversary attacks on CAVs and will shed light on the future research in this area. © 2021, Springer Nature Switzerland AG.

关键词： Autonomous vehicles

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EPINE: Enhanced Proximity Information Network Embedding

EPINE: Enhanced Proximity Information Network Embedding

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Ming Xu Luoyi Zhang Baoming Zhang Meng Cao Jinliang Yuan Chongjun Wang State Key Laboratory for Novel Software Technology Computer Science and Technology Nanjing University

In this work, we focus on improving high-order proximity of unsupervised homogeneous network embedding (NE). NE is proposed to represents every vertex of networks into a low-dimensional vector and meanwhile preserves network information as most as possible. Adjacency matrices retain most of the information in network data, and directly characterize the first-order proximity. In consideration of structure information in networks, we devote ourselves to mining valuable information in adjacency matrices at a deeper level. Under the objective, many NE methods proposed to calculate high-order proximity by the powers of adjacency matrices, which is not well- designed enough as they may contain redundancy information. To further explore high-order proximity, we propose to redefine high-order proximity in a more intuitive way. Specifically, we calculate the strength of high-order proximity by shortest paths between nodes and weights along with the paths, which is closely related to the edge weights. Consequently, for unweighted networks, we evaluate edge weights based on endpoint degrees. In comprehensive experiments on real-world network datasets, our method outperforms other competitive methods in downstream machine learning tasks such as network reconstruction and node classification.

关键词： Redundancy Machine learning Data mining Task analysis

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Unsupervised Fact-Checking via Recursively Verifying Presuppositions

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2189-2199页

作者： Xiucheng Lyu Runcong Zhao Jiazheng Li Bin Liang Min Yang Lin Gui Ruifeng Xu School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China Department of Informatics King’s College London London U.K. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong SAR China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Peng Cheng Laboratory Shenzhen China

Presuppositions are implicit assumptions that interlocutors take for granted. Verifying presuppositions is crucial for reasoning-based fact-checking that aims to reason about True or False, but existing pipelines have overlooked it. We first raise this critical issue with the community and propose a system that addresses the generation, verification, and reasoning of presuppositions through a recursive question-answering process. Importantly, our system automatically determines which knowledge entails which presuppositions, making it an unsupervised system for evidence retrieval, fact-checking, and justification production. We conduct a comprehensive experiment on the FEVER, Vitamin C, and FEVEROUS-S datasets. Our system outperforms competitive baselines, including LLM, and achieves state-of-the-art unsupervised performance. We highlight this critical issue to encourage further research on reasoning-based fact-checking.

关键词： Cognition Training Speech processing Pipelines Electronic mail Production Semantics Tree traversal Security Reliability

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