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14th International Conference on Cloud Computing, data science and Engineering, Confluence 2024

作者： Arora, Anshika Chakraborty, Pinaki Bhatia, M.P.S. Yadav, Sourabh Singla, Neetu Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India School of Computer Science Engineering and Technology Bennett University Greater Noida India The NorthCap University Department of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350344837

Smartphones are ubiquitous platforms and the most prevalent digital media devices use by university students for various activities. The lockdowns due to COVID-19 and online education are making students use smartphones frequently and for longer durations leading them to feel nomophobic. The objective of this study is to identify the prevalence of nomophobia among university students during lockdowns in India using unsupervised machine learning. This study is conducted on 111 undergraduate students (mean age: 19.93 years, S.D.: 0.69) in an Indian university volunteered. Their perception about their smartphone using a questionnaire and their smartphone usage pattern using a purpose-built android application has been studied. It has been found that 12.6% of the students were suffering from severe nomophobia and another 78.4% of them were suffering from moderate nomophobia. The average daily smartphone usage and the average daily nighttime smartphone usage of the students were 8.17 hours (S.D.: 2.73) and 5.38 hours (S.D.: 2.03), respectively. The average daily phone unlock frequency was 132.64 (S.D.: 64.01). Nomophobic behavior was found be associated with daily smartphone usage and daily nighttime smartphone usage of the students using Pearson's correlation. An analysis of smartphone dependence and usage pattern using unsupervised machine learning revealed that 18.9% of the students were at a high risk of smartphone addiction. © 2024 IEEE.

关键词： COVID-19

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Generalized Extended Welch Bound for Multi-Cell Non-Orthogonal Sequence Sets

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IEEE Communications Letters 2025年第6期29卷 1176-1180页

作者： Shen, Bingsheng Liu, Kaiqiang Gu, Zhi Yang, Yang Zhou, Zhengchun Southwest Jiaotong University School of Information Science and Technology Chengdu China Southwest Jiaotong University School of Mathematics Chengdu China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Sequence sets with minimum total squared correlation (TSC) have important application in communications. In a multi-cell system, due to the difference in channel strength between the home cell and the neighboring cells, the TSC can be expressed as the sum of squares of weighted correlations among sequences. In this letter, we obtain a closed-form expression for the lower bound of weighted TSC under multi-cell systems which can be regarded as a generalization of the extended Welch bound, namely generalized extended welch bound (GEWB). Moreover, we find a class of sequence sets whose parameters can achieve the theoretical lower bound of the weighted TSC, indicating that the derived GEWB is tight. © 1997-2012 IEEE.

关键词： Plant cell culture

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Synthetic Privacy-Preserving Trajectories with Semantic-aware Dummies for Location-Based Services

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IEEE Transactions on Services Computing 2025年第3期18卷 1811-1824页

作者： Huang, Haojun Sun, Hao Wu, Weimin Wang, Chen Liu, Wuwu Miao, Wang Min, Geyong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China University of Exeter Department of Mathematics and Computer Science ExeterEX4 4QF United Kingdom

Trajectory synthesis with a series of fake locations has been deemed as a promising obfuscation technology to preserve the individual privacy of users in Location-Based Services (LBSs). However, a number of previous approaches fail to take into consideration the geographic distance and motion direction of the real locations to synthesize trajectories. As a result, most of them always cannot represent the statistical characteristics of real trajectories in a privacy-preserving manner, and thus suffer from various attacks through data analysis. To tackle this issue, this paper presents SPSD, a novel privacy-preserving trajectory synthesis approach with a k-anonymous guarantee, through extracting the semantic, geographic and directional similarity of locations from the real trajectories to create plausible trajectories. SPSD first classifies all historical trajectory data into a series of sets for location identity, by introducing the visiting time and visiting duration, which can clearly represent the semantic information of locations. Then, 4k locations and 2k of 4k ones have been selected from each set to act as the initial disguises of each corresponding real location, with quantitative semantic and geographic similarities, respectively. In order to find enough fake locations for each real location in less time, the candidate locations have been narrowed down to k in direction recovery through step-by-step screening, with the k-anonymous property. Experiment results built on the real-world trajectory datasets indicate that SPSD has outperformed the previous approaches in terms of semantic similarity, directional accuracy and security resistance to synthesize privacy-preserving trajectories at the tolerable time cost. © 2008-2012 IEEE.

关键词： Differential privacy

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Provably Secure Conditional-Privacy Access Control Protocol for Intelligent Customers-Centric Communication in VANET

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1747-1756页

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Shamshad, Salman Ayub, Muhammad Faizan Bashir, Ali Kashif Omar, Marwan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan The University of Lahore Department of Software Engineering Lahore54590 Pakistan Manchester Metropolitan University Department of Computing and Mathematics ManchesterM15 6BH United Kingdom Woxsen University Woxsen School of Business Hyderabad502345 India Lebanese American University Department of Computer Science and Mathematics Beirut1102 2801 Lebanon Illinois Institute of Technology Information Technology and Management ChicagoIL60555 United States

Globally, the development of Intelligent Cyber-Physical Transportation Systems (ICTS) aims to tackle several challenges, including reducing traffic accidents and fuel usage, alleviating congestion, shortening travel time, and enhancing overall transportation safety. These systems leverage advanced customer-centric communication and networked control methods, such as inter-vehicle, vehicle-to-roadside (V2R), and vehicle-to-vehicle (V2V) communication through the use of vehicular ad hoc networks (VANETs) to cover all aspects of transportation-based information. In existing systems, once devices are registered with a Trusted Authority (TA), subsequent authentication still relies on the TA's assistance. However, these devices typically remain stationary, and frequent interaction with the TA becomes impractical and costly in highly mobile VANET environments. To address this challenge, we propose a secure access control protocol with conditional privacy for VANETs. Unlike other protocols, our protocol does not require the TA's involvement during authentication between vehicles and RSUs. Additionally, our protocol leverages pseudonym mechanisms to provide conditional privacy, enabling legitimate vehicles to remain anonymous while malicious ones can be tracked. Our proposed scheme is supported by both formal and informal security analyses and has been shown to be secure against several known attacks in VANETs. Furthermore, compared to relevant studies, our protocol achieves 30.2505% of efficiency in terms of computation cost and 11.09276% of efficiency in terms of communication cost, respectively. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Generation Z’s adoption of IoT: protection motivation theory as the underlying model and gender as a moderator

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Journal of Systems and Information Technology 2023年第2期25卷 133-159页

作者： Mahmud, Arif Yusoff, Mohd Najwadi Husin, Mohd Heikal School of Computer Sciences Universiti Sains Malaysia George Town Malaysia Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh

Purpose: The factors that motivate Generation Z individuals to use the Internet of Things for security purposes have yet to be explored. Therefore, the purpose of this paper is to close a research gap by verifying the protection motivation theory using gender as a moderator. Design/methodology/approach: The authors used a purposive sampling approach to collect data from Dhaka city, in which 370 valid responses were selected. Additionally, the quantitative and cross-sectional survey used a seven-point Likert scale. Afterward, the evaluation approach included three phases: a measurement model, a structural model and multi-group analysis. Findings: Vulnerability, self-efficacy and response-efficacy were discovered to be critical predictors with a variance of 60.4%. Moreover, there was a significant disparity between males and females in two relationships, response efficacy and intention as well as response cost and intention. Practical implications: This research expands our understanding of Generation Z consumers' behavioral intentions to take measures against household threats, allowing preventative programs to be improved. Further, in the case of applying coping strategies, a practical difference between males and females has been found that must be bridged through awareness campaigns. Originality/value: This study has made a unique contribution to the information system literature. First, the role of protection motivation theory factors in addressing security concerns in homes has been assessed. Second, the coping evaluation process has a greater impact on users' intentions than the threat appraisal process. However, males and females use slightly different approaches to defending themselves against the threat. © 2023, Emerald Publishing Limited.

关键词： Internet of things

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Boosting 3D Object Detection with Semantic-Aware Multi-Branch Framework

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第6期35卷 5697-5710页

作者： Jing, Hao Wang, Anhong Zhao, Lijun Yang, Yakun Bu, Donghan Zhang, Jing Zhang, Yifan Hou, Junhui The School of Electronic Information Engineering Taiyuan University of Science and Technology No. 66 Waliu Road Taiyuan030024 China The Department of Computer Science City University of Hong Kong Kowloon Tong Hong Kong

In autonomous driving, LiDAR sensors are vital for acquiring 3D point clouds, providing reliable geometric information. However, traditional sampling methods of preprocessing often ignore semantic features, leading to detail loss and ground point interference in 3D object detection. To address this, we propose a multi-branch two-stage 3D object detection framework using a Semantic-aware Multi-branch Sampling (SMS) module and multi-view consistency constraints. The SMS module includes random sampling, Density Equalization Sampling (DES) for enhancing distant objects, and Ground Abandonment Sampling (GAS) to focus on non-ground points. The sampled multi-view points are processed through a Consistent KeyPoint Selection (CKPS) module to generate consistent keypoint masks for efficient proposal sampling. The first-stage detector uses multi-branch parallel learning with multi-view consistency loss for feature aggregation, while the second-stage detector fuses multi-view data through a Multi-View Fusion Pooling (MVFP) module to precisely predict 3D objects. The experimental results on the KITTI dataset and Waymo Open dataset show that our method achieves excellent detection performance improvement for a variety of backbones, especially for low-performance backbones with simple network structures. The code will be publicly available at https://***/HaoJing-SX/SMS. © 1991-2012 IEEE.

关键词： Object detection

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Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2575-2588页

作者： Diao, Yufeng Zhang, Yichi She, Changyang Zhao, Philip Guodong Li, Emma Liying University of Glasgow School of Computing Science Glasgow United Kingdom University of Manchester Department of Computer Science Manchester United Kingdom University of Glasgow School of Engineering Glasgow United Kingdom University of Sydney School of Electrical and Information Engineering Sydney Australia

Existing communication systems aim to reconstruct the information at the receiver side, and are known as reconstruction-oriented communications. This approach often falls short in meeting the real-time, task-specific demands of modern AI-driven applications such as autonomous driving and semantic segmentation. As a new design principle, task-oriented communications have been developed. However, it typically requires joint optimization of encoder, decoder, and modified inference neural networks, resulting in extensive cross-system redesigns and compatibility issues. This paper proposes a novel communication framework that aligns reconstruction-oriented and task-oriented communications for edge intelligence. The idea is to extend the Information Bottleneck (IB) theory to optimize data transmission by minimizing task-relevant loss function, while maintaining the structure of the original data by an information reshaper. Such an approach integrates taskoriented communications with reconstruction-oriented communications, where a variational approach is designed to handle the intractability of mutual information in high-dimensional neural network features. We also introduce a joint source-channel coding (JSCC) modulation scheme compatible with classical modulation techniques, enabling the deployment of AI technologies within existing digital infrastructures. The proposed framework is particularly effective in edge-based autonomous driving scenarios. Our evaluation in the Car Learning to Act (CARLA) simulator demonstrates that the proposed framework significantly reduces bits per service by 99.19% compared to existing methods, such as JPEG, JPEG2000, and BPG, without compromising the effectiveness of task execution. © 1983-2012 IEEE.

关键词： Semantic Segmentation

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Comparative Study of Machine Learning Models and Distributed Runoff Models for Predicting Flood Water Level

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IEIE Transactions on Smart Processing and Computing 2023年第3期12卷 215-222页

作者： Kubo, Tasuku Okazaki, Takeo Graduate school of Engineering and Science University of the Ryukyus Senbaru Nishihara Japan Department of Computer Science and Intelligent Systems University of the Ryukyus Senbaru Nishihara Japan

Conventional flood forecasting methods can be roughly classified as physics-based models and data-oriented models, which both require parameter optimization. In parameter optimization, a search is generally done to minimize the magnitude of overall errors. However, under- and overestimation errors are not equivalent in flood forecasting since underestimation of the water level leads to delays in decision making. We propose a risk-aware forecasting method that uses a weighted loss function. We applied the proposed method to both physics-based models and machine learning models and compared the prediction results to clarify the difference in the prediction results according to the base model used. The results show that the model optimized by the weighted loss function reduced the underestimation error while maintaining the overall error. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Errors

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A Deep Graph Convolutional Network based Web API Classification Approach for Mashup Creation 25

A Deep Graph Convolutional Network based Web API Classificat...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Lianyong Yan, Boyuan Zheng, Jiaqi Xu, Xiaolong Dou, Wanchun Zhou, Xiaokang Zhang, Jie School of Computer Science Qufu Normal University China China Nanjing University Department of Computer Science and Technology China School of Computer and Software Nanjing University of Information Science and Technology China Shiga University Faculty of Data Science Japan Riken Center for Advanced Intelligence Project Japan

ISBN: (纸本)9798350330014

Software developers create mashups of their interest by searching, selecting, and combining web Application programming Interfaces (APIs) with diverse functionalities. However, achieving the selection of APIs relies on a crucial prerequisite: accurate classification of APIs. However, the traditional web API classification methods are often based on a single piece of API function's information such as manual annotations and description documents which decreases the accuracy of API classification results significantly, especially for the web API sharing communities with sparse data (e.g., ***). To tackle this issue, we propose BDGCN which improves some existing web API classification approaches by adding isomorphic graph of strong correlation among different web APIs on the basis of API description documents. Concretely, at the first, we integrate the information of multiple dimensions of web APIs as embedded inputs in which we then build an API-API strongly correlated isomorphic network to strengthen the association among API individuals, and to initialize the embedding vector from the description of each API by tuning the pre-trained Bidi-rectional Encoder Representation from Transformers (BERT) model. Besides, in order to smooth the impact of high-order features between neighboring nodes, a deep graph convolutional network is introduced in this paper that is consistent with the problem. In fact, we set up a categorization layer and utilize cross-entropy function to achieve accurate classification of APIs. Experimental results demonstrate the superiority of our method compared to the existing methods. © 2023 IEEE.

关键词： Classification (of information)

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A Novel Split Learning-Based Consumer Electronics Network Traffic Anomaly Detection Framework for Smart City Environment

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 4197-4204页

作者： Kim, Dong-Jo Amma, N. G. Bhuvaneswari Sarveshwaran, Velliangiri Sunchon National University Department of Image Design Sunchon57922 Korea Republic of Vellore Institute of Technology School of Computer Science and Engineering Chennai600 127 India Srm Institute of Science and Technology Department of Computational Intelligence Chennai603 203 India National Chung Cheng University Department of Computer Science and Information Engineering Chiayi621301 Taiwan

Due to technology advancement, connectivity and intelligence have revolutionized the consumer electronics (CE) world and IoT systems pose a new cybersecurity threat because smart devices are constantly connected to sensors, which are connected directly to enormous cloud servers. To address the cybersecurity vulnerabilities associated with IoT in smart cities, we provide a Software Defined Network (SDN) orchestrated anomaly detection system based on Split Learning (SL). The traffic generated by the CE is pre-processed and the relevant traffic features are selected. Then the model is split into at least two sub-models using SL to detach model training from the necessity for direct access to raw data. Client-side sub-models are trained on local training data on client devices, whereas server-side sub-models are trained on servers. Experiments show that the proposed SL-based CE network anomaly detection framework decreases computation needs on the client side by just computing a sub-model rather than the complete computationally costly model. The proposed solution has the potential to detect compromised CE network traffic in smart city environments. As evidenced by our results, the proposed framework performed significantly better than the existing state-of-the-art anomaly detection system in terms of accuracy. © 1975-2011 IEEE.

关键词： Smart city

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