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IEEE/IFIP International Conference on Embedded and Ubiquitous Computing, EUC

作者： Haining Meng Wei Li Wenjiang Ji Yi Zheng Xinyu Tong Xinhong Hei School of Computer Science and Engineering Xi'an University of Technology Xi'an China Shaanxi Key Lab Network Computer and Security Technology Xi'an China

ISBN: (纸本)9781665400374

Whether the railway track can run smoothly for a long time directly affects the safety of the railway. In view of the nonlinear, random, and sudden characteristics of the time series data of railway track, we propose a WT-GA-GRU model to forecast the track irregularity of high-speed railway. Firstly, the original time series is decomposed by wavelet transform (WT), and the decomposed multiple time series are forecasted by the gated recurrent unit (GRU) networks optimized by genetic algorithm (GA). Then the forecasted results are obtained by wavelet reconstruction. Experimental results show that, compared with support vector machine (SVM) and long short-term memory (LSTM) model, the combined WT-GA-GRU model proposed in this paper has higher forecasting accuracy.

关键词： Wavelet transforms Support vector machines Time series analysis Predictive models Feature extraction Ubiquitous computing Rail transportation

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Acoustic Characteristics of Mandarin Speech in Male Drug Users

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Journal of Voice 2023年

作者： Geng, Puyang Fan, Ningxue Ling, Rong Li, Zhijun Guo, Hong Lu, Qimeng Chen, Xingwen Department of Audio Video and Electronic Forensics Academy of Forensic Science Shanghai China Shanghai Forensic Service Platform Key Laboratory of Forensic Science Ministry of Justice Shanghai China Information Security and Social Management Innovation Lab Shanghai Open University Shanghai China Network Security Team Public Security Department of Guangxi Province Guangxi Nanning China

Aim: Drug use/addiction has a profound impact on the physical and mental health of individuals. Previous studies have indicated that drug users may experience speech perception disorders, including speech illusion and challenges in recognizing emotional speech. However, the influence of drugs on speech production, as another crucial aspect of speech communication, has not been thoroughly examined. Therefore, the current study aimed to investigate how drugs affect the acoustic characteristics of speech in Chinese male drug users. Method: Speech recordings were collected from a total of 160 male drug users (including 106 heroin users, 23 ketamine users, and 31 methamphetamine users) and 55 male healthy controls with no history of drug use. Acoustic analysis was conducted on the collected speech data from these groups, and classification analysis was performed using five supervised learning algorithms. Results: The results demonstrated that drug users exhibited smaller F0 standard deviation, reduced loudness, cepstral peak prominence, and formant relative energies, as well as higher H1-A3, longer unvoiced segments, and fewer voiced segments per second compared to the control group. The classification analyses yielded good performance in classifying drug users and non-drug users, with an accuracy above 86%. Moreover, the identification of the three groups of drug users achieved an accuracy of approximately 70%. Additionally, the study revealed different effects on speech production among the three types of drugs. Conclusion: The above findings indicate the presence of speech disorders, such as vocal hoarseness, in drug users, thus confirming the assumption that the acoustic characteristics of speech in drug users deviates from the norm. This study not only fills the knowledge gap regarding the effects of drugs on the speech production of Chinese male drug users but also provides a more comprehensive understanding of how drugs impact human behaviors. Furthermore, this re

关键词： Acoustic characteristics Drug users Heroin Ketamine Mandarin speech Methamphetamine

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The Adaptive Fault-tolerant Routing Based on an Improved Local security information Model of the Exchanged Hypercube

The Adaptive Fault-tolerant Routing Based on an Improved Loc...

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

作者： Yazhi Zhang Chuanwen Luo Guijuan Wang Li Zhang Mengjie Lv Jiguo Yu School of Computer Science and Technology Qilu University of Technology Jinan China School of Information Science and Technology Beijing Forestry University Beijing China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Nanjing University of Posts and Telecommunications Nanjing China Big Data Research Institute Qilu University of Technology (Shandong Academy of Sciences) Jinan China

With the increasing amount of computation in high-performance computing, the scale of interconnection networks is becoming larger and larger. It is inevitable that processors or links in the network become faulty. Therefore, it is very important for a processor to understand the security status of itself and neighbors, and then adaptively and reliably communicate. However, the existing security information models consider faulty processors or faulty links separately, and do not consider the situation where both processors and links fail simultaneously. The Exchanged Hypercube is an excellent interconnection network structure which achieves a balance between network functionality and hardware overhead while preserving many excellent properties of the Hypercube. In this paper, we propose an adaptive fault-tolerant routing algorithm of the Exchanged Hypercube based on an improved local security information model, which considers the faulty processors and faulty links simultaneously. This algorithm always can select suitable processors for routing based on the local security information and unsafe coefficients of processors, which greatly reduces the impact of the faulty of networks. The adaptive fault-tolerant routing algorithm of the Exchanged Hypercube network is not only suitable for regular networks but also for heterogeneous networks since the Exchanged Hypercube is a semi regular network. Sufficient experimental results show that the algorithm proposed in this paper has a lower packet loss rate, a higher success rate, and a shorter average path length.

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Natural Language Processing-Based Requirements Modeling: A Case Study on Problem Frames

Natural Language Processing-Based Requirements Modeling: A C...

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Asia-Pacific Conference on Software Engineering

作者： Jiahao Wei Xuan Chen Hongbin Xiao Shangzhi Tang Xiaolan Xie Zhi Li School of Computer Science and Engineering Guangxi Normal University Guilin China College of Information Science and Engineering Guilin University of Technology Guilin China Key Lab of Education Blockchain & Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Laboratory of Multi-source Information Mining and Security Guangxi Normal University Guilin China

Natural Language Processing (NLP) aims to study various theories and methods that enable effective communication between humans and computers in natural language. One specific technique, known as keyphrase Extraction (KPE), has achieved significant success in recent years through pre-trained Language Models (LM), particularly BERT and ELMo. Currently, researchers have presented NLP4RE at Requirements Engineering (RE) conferences, contemplating how to leverage the cutting-edge advancements in NLP to achieve the integration of closely related research domains. In the practice of requirements engineering, it is not always assumed that the initial requirements description is complete, which can lead to requirements missing or changes. To address this issue, this paper proposes an unsupervised keyword extraction modeling method. Specifically, using the problem frames model as a case study, this method is integrated into the team's development of an iOS-based Problem Frames (PF) modeling tool in the form of an assisting dropdown list. It is linked with external knowledge bases to predict new keywords. We compare five unsupervised keyword extraction techniques with different principles and evaluate them using samples from the requirements engineering domain. In addition, an eye movement experiment is conducted to further assess the proposed method.

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PPA: Preference Profiling Attack Against Federated Learning

arXiv

引用

arXiv 2022年

作者： Zhou, Chunyi Gao, Yansong Fu, Anmin Chen, Kai Dai, Zhiyang Zhang, Zhi Xue, Minhui Zhang, Yuqing School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Science China Data61 CSIRO Sydney Australia National Computer Network Intrusion Protection Center University of Chinese Academy of Science China

Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require direct access to local datasets and thus aims to mitigate data privacy concerns. However, data privacy leakage in FL still exists due to inference attacks, including membership inference, property inference, and data inversion. Copyright © 2022, The Authors. All rights reserved.

关键词： Data privacy

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Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation

arXiv

引用

arXiv 2022年

作者： Zhang, Zekang Gao, Guangyu Fang, Zhiyuan Jiao, Jianbo Wei, Yunchao School of Computer Science Beijing Institute of Technology China School of Computer Science University of Birmingham United Kingdom WEI Lab Institute of Information Science Beijing Jiaotong University China Beijing Key Laboratory of Advanced Information Science and Network China

Incremental or continual learning has been extensively studied for image classification tasks to alleviate catastrophic forgetting, a phenomenon that earlier learned knowledge is forgotten when learning new concepts. For class incremental semantic segmentation, such a phenomenon often becomes much worse due to the background shift, i.e., some concepts learned at previous stages are assigned to the background class at the current training stage, therefore, significantly reducing the performance of these old concepts. To address this issue, we propose a simple yet effective method in this paper, named Mining unseen Classes via Regional Objectness for Segmentation (MicroSeg). Our MicroSeg is based on the assumption that background regions with strong objectness possibly belong to those concepts in the historical or future stages. Therefore, to avoid forgetting old knowledge at the current training stage, our MicroSeg first splits the given image into hundreds of segment proposals with a proposal generator. Those segment proposals with strong objectness from the background are then clustered and assigned newly-defined labels during the optimization. In this way, the distribution characterizes of old concepts in the feature space could be better perceived, relieving the catastrophic forgetting caused by the background shift accordingly. Extensive experiments on Pascal VOC and ADE20K datasets show competitive results with state-of-the-art, well validating the effectiveness of the proposed MicroSeg. Code is available at https://***/zkzhang98/MicroSeg. © 2022, CC BY.

关键词： Semantics

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Dynamic Power Distribution Controlling for Directional Chargers

Dynamic Power Distribution Controlling for Directional Charg...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yuzhuo Ma Dié Wu Jing Gao Wen Sun Jilin Yang Tang Liu College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Virtual Reality Key Lab Sichuan Normal University Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Cyberspace Security Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, deploying static chargers to construct timely and robust Wireless Rechargeable Sensor networks (WRSNs) has become an important research issue for solving the limited energy problem of wireless sensor networks. However, the established fixed power distribution lacks flexibility in response to dynamic charging requests from sensors and may render some sensors to be continuously impacted by destructive wave interference. This results in a gap between energy supply and practical demand, making the charging process less efficient. In this paper, we focus on the real-time sensor charging requests and formulate a dynamic power disTributIon controlling for Directional chargErs (TIDE) problem to maximize the overall charging utility. To solve the problem, we first build a charging model for directional chargers while considering wave interference and extract the candidate charging orientations from the continuous search space. Then we propose the neighbor set division method to narrow the scope of calculation. Finally, we design a dynamic power distribution controlling algorithm to update the neighbor sets timely and select optimal orientations for chargers. Our experimental results demonstrate the effectiveness and efficiency of the proposed scheme, it outperforms the comparison algorithms by 142.62% on average.

关键词： Wireless communication Wireless sensor networks Heuristic algorithms Computational modeling Power distribution Interference Search problems

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A Secure key-Aggregate keyword Retrieval Scheme Over Encrypted Data in Cloud Computing

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IEEE Access 2024年 1-1页

作者： Lou, Chunwei Cao, Mingsheng Luo, Yaohua Xu, Hua Gao, Yuan Wang, Weiwei Wang, Dong University of Electronic Science and Technology of China Chengdu 611731 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu 611731 China School of Information Science and Technology Chengdu University of Technology Chengdu 610059 China School of Physics and Electronic Engineering Yancheng Teachers University Yancheng 224007 China China Academy of Railway Sciences Beijing 100081 China School of Computer Science and Engineering and the Center for Cyber Security University of Electronic Science and Technology of China Chengdu 611731 China

key-aggregate keyword retrieval primitive enables a cloud server on behalf of delegated users to securely perform a keyword search over the data encrypted with various public keys. However, existing key-aggregate searchable encryption schemes are insecure against keyword guessing attacks, which result in the privacy leakage of keyword ciphertext and trapdoor. In this paper, we for the first time formulate a secure and efficient key-aggregate searchable encryption for cloud-assisted IoT applications, which not only enables a data owner to securely share the selected documents to multiple users, but also supports data users in delegating the capability of keyword search to a cloud server for searching the desired documents without leaking any privacy of keyword ciphertext and trapdoor. Then, the security of the proposed scheme is formally defined and proven to be secure against the indistinguishable selective-file chosen keyword attacks. The flexibility and practicability of the formulated scheme is also demonstrated by theoretical evaluations and experimental simulations. Author

关键词： Aggregates

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Adaptive Traffic Forecasting on Daily Basis: A Spatio-Temporal Context Learning Approach

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IEEE Transactions on Knowledge and Data Engineering 2025年

作者： Li, Xiaoyu Zhang, Yitian Long, Guodong Hu, Yupeng Lu, Wenpeng Chen, Meng Zhang, Chengqi Gong, Yongshun Shandong University School of Software Jinan250100 China McGill University Electrical and Computer Engineering MontrealQCH3A 0G4 Canada University of Technology Sydney School of Computer Science SydneyNSW2007 Australia Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Jinan250316 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250100 China

Traffic forecasting plays a crucial role in establishing an Intelligent Transportation System (ITS) by providing essential insights. Existing traffic forecasting relies on the assumption that there is a hidden invariant spatial-temporal pattern in the large-scale dataset. However, the traffic patterns are easily influenced by many unpredictable external factors, such as policy interventions and climate changes. Due to the dynamic nature of these exogenous factors, the traffic network's spatial-temporal patterns are also changed, thus impacting the performance of traffic forecasting models. Thus, there is an urgent need to rethink the traffic forecasting model in a fast-adaptive manner. To solve this challenge, this paper proposes an Adaptive Spatio Temporal Context Learning framework named ASTCL, which achieves desired forecasting accuracy using daily basis traffic data collected from dozens of sensors. ASTCL constructs adaptive spatio-temporal contexts for target locations in the traffic network and generates dynamic sequence graphs based on semantic similarities. The adaptive contexts aggregate valuable information from available data, while the graphs reveal dynamic trends in traffic properties. Further, ASTCL introduces a joint convolution and attention mechanism to model intricate spatio-temporal relationships from multiple perspectives. Extensive experiments conducted on four real-world datasets demonstrate that ASTCL achieves remarkable fast adaptability and outperforms other state-of-the-art methods by a significant margin. © 1989-2012 IEEE.

关键词： Global warming

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Direct Participant Recruitment Strategy in Sparse Mobile Crowdsensing

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Jisuanji Xuebao/Chinese Journal of computers 2022年第7期45卷 1539-1556页

作者： Tu, Chun-Yu Yu, Zhi-Yong Han, Lei Zhu, Wei-Ping Huang, Fang-Wan Guo, Wen-Zhong Wang, Le-Ye College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Department of Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350108 China School of Computer Science Northwestern Polytechnical University Xi'an710072 China Key Lab of High Confidence Software Technologies Peking University Beijing100871 China School of Computer Science Peking University Beijing100871 China

Sparse Mobile Crowdsensing (Sparse MCS) selects a small part of sub-areas for data collection and infers the data of other sub-areas from the collected data. Compared with Mobile Crowdsensing (MCS) that does not use data inference methods, Sparse MCS saves sensing costs while ensuring the quality of global data. However, the existing research works on Sparse MCS only focus on selecting a small part of sub-areas with higher value. It does not consider whether the recruited participants can collect the data of the required sub-areas, and also ignores the value of other data collected by the participants. In order to solve the limitations of traditional methods in sub-areas selection, this paper starts from the perspective of participants and concentrates on the contribution of the data collected by each participant to the entire collection task. All the data contributions collected by each participant will become the basis for decision-making for the participant's choice. And correspondingly, a new idea to deal with the problem of participant selection under Sparse MCS is proposed. In view of the fact that each person's daily movement trajectory is basically stable, and the data collected by different people on their respective trajectories have different values, this paper uses this regularity and difference to study how to directly recruit participants who can collect high-value data. Furthermore, the participant selection problem considered in this paper is not limited to the data collection in the next cycle, but directly recruits some participants to continue the data collection task in the next multiple cycles. The participant selection problem that spans multiple cycles can be modeled as a dynamic decision-making problem. Since heuristic strategies may fall into a local optimal solution, this paper uses reinforcement learning to solve the participant selection problem: We use the participant selection system as an agent of reinforcement learning, and design the

关键词： Compressed sensing

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