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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Igor Shostak Mariia Danova Olga Morozova Tamara Kliuieva Department of Software Engineering National Aerospace University "KhAI" Kharkiv Ukraine Department of Computer Systems National Aerospace University "KhAI" Kharkiv Ukraine

ISBN: (数字)9781728199573

ISBN: (纸本)9781728199580

The article is devoted to the problem of automatic monitoring of the driver's physical conditions while driving. The purpose of this article is to describe an approach to recognizing driver fatigue using a video camera mounted on the front panel of a vehicle, which allows recording facial expressions of a driver's face for further analysis by managers of logistic company and using of the received information by Internet of Things objects in the freight management system. The system will allow managers of the logistics company to gain access to the movements of each driver and to statistics of dangerous conditions recorded during the trip, which, in turn, will allow to control the routes, compliance with traffic rules, as well as the work and rest of the drivers of the logistics companies.

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Semi-Supervised Domain-Adversarial Training for Intrusion Detection against False Data Injection in the Smart Grid

Semi-Supervised Domain-Adversarial Training for Intrusion De...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yongxuan Zhang Jun Yan Department of Computer Science and Software Engineering (CSSE) Concordia University Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montréal Canada

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

The smart grid faces with increasingly sophisticated cyber-physical threats, against which machine learning (ML)-based intrusion detection systems have become a powerful and promising solution to smart grid security monitoring. However, many ML algorithms presume that training and testing data follow the same or similar data distributions, which may not hold in the dynamic time-varying systems like the smart grid. As operating points may change dramatically over time, the resulting data distribution shifts could lead to degraded detection performance and delayed incidence responses. To address this challenge, this paper proposes a semi-supervised framework based on domain-adversarial training to transfer the knowledge of known attack incidences to detect returning threats at different hours and load patterns. Using normal operation data of the ISO New England grids, the proposed framework leverages adversarial training to adapt learned models against new attacks launched at different times of the day. Effectiveness of the proposed detection framework is evaluated against the well-studied false data injection attacks synthesized on the IEEE 30-bus system, and the results demonstrated the superiority of the framework against persistent threats recurring in the highly dynamic smart grid.

关键词： Feature extraction Training Smart grids Data mining Intrusion detection Testing

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A Policy Recommendations Framework to Resolve Global software Development Issues 3

A Policy Recommendations Framework to Resolve Global Softwar...

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3rd International Conference on Innovative Computing, ICIC 2019

作者： Hassan, Mujtaba Hussain, Muzammil Ayubi, Salah-U-Din Irfan, Maham School of Systems and Technology University of Management and Technology Department of Software Engineering Lahore Pakistan School of Systems and Technology University of Management and Technology Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781728146829

Global software development (GSD) is basically a development which is done through low cost in given time frame by sitting in remote areas within cities, countries and around the globe. The global software development is facing major challenges such as, time zone differences, language barrier, cultural differences, geographical distance, communication gap, coordination problems, lack of project guidance, lack of experienced software testers, customer dissatisfaction, poor project management and so on. These challenges has major impact on software quality in GSD and decreases the acceptability among its users. In recent years, some strategies had been applied to reduce quality related issues that exist in GSD, however there is still a need to develop effective and efficient methods, techniques and best practices that can lead to enhance the quality of software development. The main objective of this article is to highlight the existing issues of GSD and to provide the policy recommendations to mitigate them. Therefore, a quantitative analysis have been performed that provides detailed statistical analysis on data by analyzing and applying nonparametric test (rejected the null hypothesis with less than 0.5 significance) and one sample T-test (with 2-tailed significance difference 0.00) through SPSS. Hence, this work contributes by making strong policy recommendations which is explained how GSD project should be handled by selecting best process, and how to achieve maximum quality of software product for customers to satisfy them, improve the acceptance among its users, bring their trust back to gain more business, and overall development in (software) industry. © 2019 IEEE.

关键词： Project management

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Image Steganography Based on Local Variance 2019

Image Steganography Based on Local Variance

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Proceedings of the 2019 2nd International Conference on Watermarking and Image Processing

作者： Ofer Hadar Adiel Sol Or Hamra Armin Shmilovici School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9781450372800

In this paper, a simple and robust watermarking algorithm is presented by using the first, second, third and the fourth Least Significant Bits (LSBs). We embed two bits in two places out of four LSBs according to the local variance value. Compared to the simple LSB algorithm where we use bits 7 and 8 to embed information, the proposed algorithm is more robust to white noise and JPEG compression. Experimental results show that the quality of the watermarked image is high in terms of Peak Signal-to-Noise (PSNR) and Structural Similarity Index (SSIM).

关键词： Steganography Image watermark hiding data Digital watermarking

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Image steganography based on local variance 2

Image steganography based on local variance

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2nd International Conference on Watermarking and Image Processing, ICWIP 2019

作者： Hadar, Ofer Sol, Adiel Hamra, Or Shmilovici, Armin School of Electrical and Computer Engineering Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel

ISBN: (纸本)9781450372800

关键词： Steganography

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Vertical Federated Learning: Challenges, Methodologies and Experiments

arXiv

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arXiv 2022年

作者： Wei, Kang Li, Jun Ma, Chuan Ding, Ming Wei, Sha Wu, Fan Chen, Guihai Ranbaduge, Thilina School of Electrical and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Ministry of Education China Data61 CSIRO SydneyNSW2015 Australia Beijing100191 China The Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Recently, federated learning (FL) has emerged as a promising distributed machine learning (ML) technology, owing to the advancing computational and sensing capacities of end-user devices, as well as the increasing concerns on users' privacy. As a special architecture in FL, vertical FL (VFL) is capable of constructing a hyper ML model by embracing sub-models from different types of clients. These sub-models are trained locally by vertically partitioned data with distinct attributes. Therefore, the design of VFL is fundamentally different from that of conventional FL, raising new and unique research issues. In this paper, we aim to discuss key challenges in VFL with effective solutions, and conduct experiments on real-life datasets to shed light on these issues. Specifically, we first propose a general framework on VFL, and highlight the key differences between VFL and conventional FL. Then, we discuss research challenges rooted in VFL systems from four aspects, i.e., security and privacy risks, expensive computation and communication costs, possible structural damage caused by model splitting, and system heterogeneity. Afterwards, we develop solutions to addressing the aforementioned challenges, and conduct extensive experiments to showcase the effectiveness of the proposed solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Federated learning

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Author name disambiguation in bibliographic databases: A survey

arXiv

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arXiv 2020年

作者： Shoaib, Muhammad Daud, Ali Amjad, Tehmina Department of Computer Science Comsats University Sahiwal Campus Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan

Entity resolution is a challenging and hot research area in the field of Information systems since last decade. Author Name Disambiguation (AND) in Bibliographic Databases (BD) like DBLP1, Citeseer2, and Scopus3 is a specialized field of entity resolution. Given many citations of underlying authors, the AND task is to find which citations belong to the same author. In this survey, we start with three basic AND problems, followed by need for solution and challenges. A generic, five-step framework is provided for handling AND issues. These steps are;(1) Preparation of dataset (2) Selection of publication attributes (3) Selection of similarity metrics (4) Selection of models and (5) Clustering Performance evaluation. Categorization and elaboration of similarity metrics and methods are also provided. Finally, future directions and recommendations are given for this dynamic area of research. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Differential privacy–based location privacy enhancing in edge computing

Differential privacy–based location privacy enhancing in ed...

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作者： Miao, Qiucheng Jing, Weipeng Song, Houbing College of Information and Computer Engineering Northeast Forestry University Harbin China Department of Electrical Computer Software and Systems Engineering Embry-Riddle Aeronautical University Daytona Beach United States

In the era of edge computing, real-time data preprocessing on the edge node has the potential to improve computational efficiency and data accuracy. However, a significant challenge is private data disclosure, particularly in the case of location-based services. To address this challenge, in this paper, by leveraging differential privacy, we propose a privacy-aware framework for mobile edge computing called MEPA to protect the location privacy in which the edge node is regarded as an anonymous central server. The proposed framework can provide computing services without deploying special infrastructure. To be specific, in order to solve the problem of constrained computing resources in the edge nodes, the algorithm of Quadtree Differential Privacy based on Hilbert curve division (QTDP-H) two-dimensional spatial data query transmission is proposed. First, a noise quadtree is established and the privacy budget is divided according to the tree level. Then, the constructed quadtree is represented by quanternary, so that the partition based on Hilbert curve can be established and the two-dimensional data in the area can be converted into one-dimensional, which can greatly improve the retrieval efficiency. The effectiveness of the proposed algorithm in terms of time complexity and retrieval accuracy has been verified by extensive experimental results. Compared with traditional methods of (D,Ε) − LP, the average runtime can be reduced by 15%-20%, and the average relative error is reduced by 20%. © 2018 John Wiley & Sons, Ltd.

关键词： Location based services

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Cloud storage architecture: Research challenges and opportunities

arXiv

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arXiv 2020年

作者： Ghani, Anwar Badshah, Afzal Jan, Saeedullah Alshdadi, Abdulrahman A. Daud, Ali Department of Computer Science & Software Engineering International Islamic University Islamabad44000 Pakistan Department of Computer Science & IT University of Malakand Chakdara18800 Pakistan Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia Department of Information Systems and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia

From home appliances to industrial enterprises, the Information and Communication Technology (ICT) industry is revolutionizing the world. We are witnessing the emergence of new technologies (e.g, Cloud computing, Fog computing, Internet of Things (IoT), Artificial Intelligence (AI) and Block-chain) which proves the growing use of ICT (e,g. business, education, health and home appliances), resulting in massive data generation. It is expected that more than 175 ZB data will be processed annually by 75 billion devices by 2025. The 5G technology (i.e. mobile communication technology) dramatically increases network speed, enabling users to upload ultra high definition videos in real-time, will generate a massive stream of big data. Furthermore, smart devices, having artificial intelligence, will act like a human being (e.g, a self-driving vehicle etc) on the network, will also generate big data. This sudden shift and massive data generation created serious challenges in storing and managing heterogeneous data at such a large scale. This article presents a state-of-the-art review of the issues and challenges involved in storing heterogeneous big data, their countermeasures (i.e, from security and management perspectives), and future opportunities of cloud storage. These challenges are reviewed in detail and new dynamics for researchers in the field of cloud storage are discovered. Copyright © 2020, The Authors. All rights reserved.

关键词： Cloud security

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Regressor driven incremental instance segmentation for contraband items identification

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engineering Applications of Artificial Intelligence 2025年 157卷

作者： Ammara Nasim Mahboob Anwar Muhammad Owais Samet Akcay Divya Velayudhan Muhammad Usman Akram Mohammed Ghazal Naoufel Werghi Taimur Hassan Department of Computer and Software Engineering National University of Sciences and Technology (NUST) Islamabad 44000 Pakistan Department of Electrical Computer and Biomedical Engineering Abu Dhabi University Abu Dhabi 59911 United Arab Emirates Khalifa University Center for Autonomous Robotic Systems (KUCARS) Khalifa University Abu Dhabi 127788 United Arab Emirates Research and Development Division Intel Corporation United Kingdom

Detecting suspicious objects contained within passenger baggage is one of the most difficult tasks, even for the security experts. To address this problem, many researchers have developed computer-aided screening methods employing deep learning models to detect the presence of contraband items using X-ray imagery. However, all of these models have limitations or bottlenecks when it comes to detecting prohibited objects that are heavily obscured, cluttered, overlapping, and well concealed within the baggage. To overcome these challenges, we present a novel instance segmentation framework that transforms the conventional semantic segmentation models to perform instance-aware segmentation, via incremental learning, to automatically detect suspicious baggage items. This framework leverages knowledge distillation, enabling the model to iteratively learn and retain multiple instances of threat items. By continuously adapting, the model improves its ability to distinguish between different instances, showcasing significant advancements in security and threat detection. To overcome the under-segmentation and over-segmentation problem of the incremental instance segmentation, we introduced the use of a lightweight regressor model that can accurately identify the overlapping instances of the suspicious objects which enables the optimal selection of the segmentation model to perform the required task. Moreover, the proposed framework has been rigorously tested on two publicly available datasets on which it achieved the mask mean average precision scores of 0.54 and 0.51, respectively, at the inference stage. Similarly, the proposed method outperformed the state-of-the-art methods by 5.88%, and 8.51% in terms of mask mean average precision scores across both datasets, respectively. In addition to this, the proposed framework provides an optimal trade-off between performance and efficiency as compared to its competitors.

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