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Lecture Notes on Data engineering and Communications Technologies 2023年 178卷 v-viii页

作者： Faure, Emil Danchenko, Olena Bondarenko, Maksym Tryus, Yurii Bazilo, Constantine Zaspa, Grygoriy Department of Information Security and Computer Engineering Cherkasy State Technological University Cherkasy Ukraine Department of Computer science and Systems Analysis Cherkasy State Technological University Cherkasy Ukraine Department of Instrumentation Mechatronics and Computerized Technologies Cherkasy State Technological University Cherkasy Ukraine Department of Software of Automated Systems Cherkasy State Technological University Cherkasy Ukraine

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Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning 29

Reasoning like human: Hierarchical reinforcement learning fo...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wan, Guojia Pan, Shirui Gong, Chen Zhou, Chuan Haffari, Gholamreza School of Computer Science Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China Faculty of Information Technology Monash University Australia School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computing Hong Kong Polytechnic University Hong Kong Academy of Mathematics and Systems Science Chinese Academy of Sciences China

ISBN: (纸本)9780999241165

Knowledge Graphs typically suffer from incompleteness. A popular approach to knowledge graph completion is to infer missing knowledge by multi-hop reasoning over the information found along other paths connecting a pair of entities. However, multi-hop reasoning is still challenging because the reasoning process usually experiences multiple semantic issue that a relation or an entity has multiple meanings. In order to deal with the situation, we propose a novel Hierarchical Reinforcement Learning framework to learn chains of reasoning from a Knowledge Graph automatically. Our framework is inspired by the hierarchical structure through which a human being handles cognitionally ambiguous cases. The whole reasoning process is decomposed into a hierarchy of two-level Reinforcement Learning policies for encoding historical information and learning structured action space. As a consequence, it is more feasible and natural for dealing with the multiple semantic issue. Experimental results show that our proposed model achieves substantial improvements in ambiguous relation tasks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Knowledge graph

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Incorporating a Virtual Reality Environment in the Teaching of Analysis of software Requirements 49

Incorporating a Virtual Reality Environment in the Teaching ...

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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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Using Internet of Things Technologies to Ensure Cargo Transportation Safety

Using Internet of Things Technologies to Ensure Cargo Transp...

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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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NEVAE: a deep generative model for molecular graphs

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2020年第1期21卷 4556-4588页

作者： Bidisha Samanta Abir De Gourhari Jana Vicenç Gomez Pratim Kumar Chattaraj Niloy Ganguly Manuel Gomez-Rodriguez Department of Computer Science and Engineering IIT Kharagpur Kharagpur India Department of Computer Science and Engineering IIT Bombay Mumbai India Department of Chemistry IIT Kharagpur Kharagpur India DTIC Universitat Pompeu Fabra Barcelona Spain Max Planck Institute for Software Systems Kaiserslautern Germany

Deep generative models have been praised for their ability to learn smooth latent representations of images, text, and audio, which can then be used to generate new, plausible data. Motivated by these success stories, there has been a surge of interest in developing deep generative models for automated molecule design. However, these models face several difficulties due to the unique characteristics of molecular graphs--their underlying structure is not Euclidean or grid-like, they remain isomorphic under permutation of the nodes' labels, and they come with a different number of nodes and edges. In this paper, we first propose a novel variational autoencoder for molecular graphs, whose encoder and decoder are specially designed to account for the above properties by means of several technical innovations. Moreover, in contrast with the state of the art, our decoder is able to provide the spatial coordinates of the atoms of the molecules it generates. Then, we develop a gradient-based algorithm to optimize the decoder of our model so that it learns to generate molecules that maximize the value of certain property of interest and, given any arbitrary molecule, it is able to optimize the spatial configuration of its atoms for greater stability. Experiments reveal that our variational autoencoder can discover plausible, diverse and novel molecules more effectively than several state of the art models. Moreover, for several properties of interest, our optimized decoder is able to identify molecules with property values 121% higher than those identified by several state of the art methods based on Bayesian optimization and reinforcement learning.

关键词： drug design molecule discovery deep generative models variational autoencoders geometric deep learning

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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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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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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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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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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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