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49th IEEE Frontiers in Education Conference, FIE 2019

作者： Ochoa, Omar Babbit, Adam Embry-Riddle Aeronautical University Department of Electrical Computer Software Systems Engineering Daytona BeachFL32114 United States

ISBN: (纸本)9781728117461

This Work in Progress paper in the Innovative Practice Category presents an approach to incorporate the use of a virtual reality environment in the teaching of analysis of software requirements through modeling. Common models include use case diagrams, state charts, data flow diagrams, etc., all serving their own role in defining different perspectives of a system. These different perspectives of the same system can be a powerful technique to facilitate the analysis of requirements about the system. Using a virtual reality environment allows for the linking of multiple perspectives from the separate analysis models to be viewed in three dimensions. This approach can be particularly beneficial to students in learning how to analyze models by providing a mechanism to examine the visual relationship between the perspectives. Using this approach allows the student to view, create, manipulate, and alter models within the native three-dimensional space, which can provide an indepth experience better suited for learning. The virtual reality environment also allows models to be nested to provide better abstractions and layered to provide a level of complexity impossible to replicate in the traditional two-dimensional space. This paper explores a prototype virtual reality environment to facilitate the use of software requirement models for undergraduate software engineering students. As part of this work, students will be exposed to the tool and tasked with using the virtual reality environment to analyze the models of systems. Assessment of the use of the approach will be conducted and results will be presented including feedback from the students, benefits found, insights and potential ways to evolve the approach for other areas such as design or testing. © 2019 IEEE.

关键词： Students

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Spatio-temporal split learning for privacy-preserving medical platforms: Case studies with COVID-19 CT, X-Ray, and cholesterol data

arXiv

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arXiv 2021年

作者： Miss, Yoo Jeong Ha Yoo, Minjae Lee, Gusang Jung, Soyi Choi, Sae Won Kim, Joongheon Yoo, Seehwan Department of Electrical and Computer Engineering Korea University Seoul Korea Republic of School of Software Hallym University Chuncheon Korea Republic of Office of Hospital Information Seoul National University Hospital Seoul Korea Republic of Department of Mobile Systems Engineering Dankook University Yongin Korea Republic of

Machine learning requires a large volume of sample data, especially when it is used in high-accuracy medical applications. However, patient records are one of the most sensitive private information that is not usually shared among institutes. This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations. Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns. The proposed split learning consists of a number of clients and a centralized server. Each client has only has one hidden layer, which acts as the privacy-preserving layer, and the centralized server comprises the other hidden layers and the output layer. Since the centralized server does not need to access the training data and trains the deep neural network with parameters received from the privacy-preserving layer, privacy of original data is guaranteed. We have coined the term, spatio-temporal split learning, as multiple clients are spatially distributed to cover diverse datasets from different participants, and we can temporally split the learning process, detaching the privacy preserving layer from the rest of the learning process to minimize privacy breaches. This paper shows how we can analyze the medical data whilst ensuring privacy using our proposed multi-site spatio-temporal split learning algorithm on Coronavirus Disease-19 (COVID-19) chest Computed Tomography (CT) scans, MUsculoskeletal RAdiographs (MURA) X-ray images, and cholesterol levels. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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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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Independent and Identically Distributed (IID) Data Assessment in Federated Learning

Independent and Identically Distributed (IID) Data Assessmen...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Mohamad Arafeh Ahmad Hammoud Hadi Otrok Azzam Mourad Chamseddine Talhi Zbigniew Dziong Department of Software Engineering Ecole de Technologie Superieure (ETS) Montreal QC Canada Department of Electrical Engineering Ecole de Technologie Superieure (ETS) Montreal Canada Department of EECS Center of Cyber-Physical Systems (C2PS) Khalifa University Abu Dhabi UAE Department of Computer science and Mathematics Lebanese American University Beirut Lebanon Division of Science New York University Abu Dhabi UAE

ISBN: (纸本)9781665435413

Federated learning extends the centralized machine learning architecture by enabling data privacy for its providers. The distributed structure of the emerged federated architecture imposes a problem of the data being not independent and identically distributed (non-IID), which drastically affects the performance of the learning process. While the majority of the recent works in the federated learning domain have accepted this limitation, only a few scholars addressed the non-IID problem straightforwardly. Nevertheless, these works lack the fundamental analysis of the data’ IIDness, and/or contradict the privacy feature of the federated learning paradigm. In this paper, we focus on evaluating the harmony of the participants by studying their data distribution and calculating their level of compatibility. The devised tool, in this work, is an assessment technique integrated within the federated learning framework to analyze the data distribution among the trainers. Our proposed method is proven by experimenting with several scenarios, and results show that our utility can fairly assess the selected participants before initiating the learning process.

关键词： Data privacy Federated learning Distributed databases Coherence Global communication Convergence

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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

引用

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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Using an Expert Panel to Validate the Malaysian SMEs-software Process Improvement Model (MSME-SPI) 4th

Using an Expert Panel to Validate the Malaysian SMEs-Softwar...

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4th Computational Methods in systems and software, CoMeSySo 2020

作者： Almomani, Malek A. Basri, Shuib Almomani, Omar Capretz, Luiz Fernando Balogun, Abdullateef Husni, Moath Gilal, Abdul Rehman Department of Software Engineering The World Islamic Sciences and Education University Amman11947 Jordan Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia Department of Computer Network and Information Systems The World Islamic Sciences and Education University Amman11947 Jordan Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science University of Ilorin PMB 1515 Ilorin Nigeria Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

ISBN: (纸本)9783030633219

This paper presents the components of a newly developed Malaysian SMEs - software Process Improvement model (MSME-SPI) that can assess SMEs software development industry in managing and improving their software processes capability. The MSME-SPI is developed in response to practitioner needs that were highlighted in an empirical study with the Malaysian SME software development industry. After the model development, there is a need for independent feedback to show that the model meets its objectives. Consequently, the validation phase is performed by involving a group of software process improvement experts in examining the MSME-SPI model components. Besides, the effectiveness of the MSME-SPI model is validated using an expert panel. Three criteria were used to evaluate the effectiveness of the model namely: usefulness, verifiability, and structure. The results show the model effective to be used by SMEs with minor modifications. The validation phase contributes towards a better understanding and use of the MSME-SPI model by the practitioners in the field. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： software design

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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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6G Internet-of-Things assisted smart homes and buildings: Enabling technologies, opportunities and challenges

Internet of Things

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Internet of Things 2025年 32卷

作者： Arif Ullah Fawad Aamir Nadeem Muhammad Arif Muhammad Mehran Bashir Wooyeol Choi School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom Gwangju Institute of Science and Technology Gwangju Republic of Korea Chonnam National University Gwangju Republic of Korea Institute of Networked and Embedded Systems University of Klagenfurt 9020 Klagenfurt Austria Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs 9020 Klagenfurt Austria Department of Electrical Engineering Muhammad Nawaz Sharif University of Engineering and Technology Multan Pakistan School of Computer Science and Engineering College of Software Chung-Ang University Seoul Republic of Korea

Smart homes (SHs) and smart buildings (SBs) play a crucial role in addressing global urbanization and are key elements of smart cities (SCs). They utilize integrated systems to process large volumes of data, enabling intelligent responses and improved quality of life. However, the growing number of connected devices and increasing data demands have exceeded the capabilities of existing network technologies, reducing service effectiveness. This article provides a comprehensive survey of emerging technologies, especially those underexplored in SH and SB contexts. It highlights the potential of sixth-generation (6G) wireless networks and the Internet of Things (IoT) to transform smart indoor environments through greater automation, intelligence, and adaptability. The authors first analyze the limitations of current IoT-based systems and then examine advanced 6G technologies, including integrated sensing and communication (ISAC), machine learning (ML), visible light communication (VLC), reconfigurable intelligent surfaces (RIS), and blockchain (BC). These technologies aim to resolve issues related to scalability, connectivity, and interoperability that challenge current IoT/5G models. Unlike previous studies focusing on narrow areas such as energy or security, this survey takes a holistic approach. It aligns 6G capabilities with SH/SB needs like human-device interaction, energy efficiency, and robust security, and introduces new applications such as AI-driven digital twins and edge intelligence. The article also identifies research gaps, including resource allocation in diverse networks and ethical AI deployment. It offers guidance for future research and urges collaboration, standardization, and human-centric design to enable next-generation intelligent living environments.

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Data efficient surrogate modeling for engineering design: Ensemble-free batch mode deep active learning for regression

arXiv

引用

arXiv 2022年

作者： Vardhan, Harsh Timalsina, Umesh Volgyesi, Peter Sztipanovits, Janos Department of Computer Science Vanderbilt University 1400 18th Ave. S. NashvilleTN37212-2846 United States Institute for Software Integrated Systems Vanderbilt University 1025 16th Ave. S. NashvilleTN37212-2328 United States Department of Electrical and Computer Engineering Vanderbilt University 400 24th Ave. S. NashvilleTN37212 United States

In a computer-aided engineering design optimization problem that involves notoriously complex and time-consuming simulator, the prevalent approach is to replace these simulations with a data-driven surrogate that approximates the simulator’s behavior at a much cheaper cost. The main challenge in creating an inexpensive data-driven surrogate is the generation of a sheer number of data using these computationally expensive numerical simulations. In such cases, Active Learning (AL) methods have been used that attempt to learn an input–output behavior while labeling the fewest samples possible. The current trend in AL for a regression problem is dominated by the Bayesian framework that needs training an ensemble of learning models that makes surrogate training computationally tedious if the underlying learning model is Deep Neural Networks (DNNs). However, DNNs have an excellent capability to learn highly nonlinear and complex relationships even for a very high dimensional problem. To leverage the excellent learning capability of deep networks along with avoiding the computational complexity of the Bayesian paradigm, in this work we propose a simple and scalable approach for active learning that works in a student-teacher manner to train a surrogate model. By using this proposed approach, we are able to achieve the same level of surrogate accuracy as the other baselines like DBAL and Monte Carlo sampling with up to 40% fewer samples. We empirically evaluated this method on multiple use cases including three different engineering design domains:finite element analysis, computational fluid dynamics, and propeller design. © 2022, CC BY.

关键词： Deep neural networks

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