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24th International Conference on computer and Information Technology, ICCIT 2021

作者： Hossain, Md. Anower Hasan, Md. Imrul Islam, Md Rashedul Ahmed, Nadeem Data and BI Engineering Progoti Systems Limited Dhaka1213 Bangladesh University of Asia Pacific Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9781665494359

Management of data backup and restore in case of emergency is a crucial process in every organization. This paper discusses an effective database recovery technique called Point In Time Recovery (PITR) in postgreSQL database management system. Despite emerging big data technology, relational database management system (RDBMS) is still performing the key role for storing and processing of data in most of the organizations. Almost all kinds of financial organizations like banks and mobile financial service (MFS) organizations use RDBMS as their database tool for storing their users information and all kinds of transactional information related to that organization. Nowadays those type of organizations focus on customer acquisition strategy and thus data is growing rapidly. In spite of proper system management system crash is not very uncommon while processing large volumes of data. It results loss of data and a huge financial loss for the organization. To tackle such tragedy for the business a proper data recovery system is required for every organization. Generally organizations use backup using pg_dump command and restore using pg_restore but this traditional recovery system cannot restore the data which is created or altered after the backup taken. Also this process is time inefficient because this process reconstruct the database to the state of the last dump file. Thus our research paper implements a potent process of data recovery technique in postgreSQL that can recover all data which is created or altered after the backup taken. Again this process is time efficient because it works restoring using Write Ahead log (WAL) file from the base backup. © 2021 IEEE.

关键词： Losses

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Design and Implementation of Low Power Posit Arithmetic Unit for Efficient Hardware Accelerators

Design and Implementation of Low Power Posit Arithmetic Unit...

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Japan-Egypt International Conference on Electronics, Communications and computers (JEC-ECC)

作者： Mohammed Essam Ahmed Shalaby Mohamed Taher Computer and Systems Engineering Department Ain Shams University Cairo Egypt Computer Science Department Benha University Benha Egypt

ISBN: (纸本)9781665464642

There is an increasing interest in hardware accelerators, both in academia and industry. The industry invests in application-level accelerators, like Graphics Processing Units (GPUs) or Field programmable Gate Array (FPGA) accelerators connected to the PCIe bus. Hardware accelerators outperform general purpose Central Processing Units (CPUs) in terms of power consumption and performance. Hardware accelerators seek to optimize arithmetic operations, since it is the heart of the computation circuitry in different algorithms and applications. In this context, posit is proposed to replace IEEE Standard 754-2008 floating point and offers more efficient arithmetic units in terms of accuracy and Power-Performance-Area (PPA) matrix. In this paper, we introduce a low power Verilog HDL design and implementation of Posit Arithmetic Unit (PAU) for efficient hardware accelerators. Our regular proposed PAU is synthesized on Xilinx ZYNQ-7000. The results show34% area improvement and 14% power saving, while our compact PAU achieves 25% area reduction and 45% power saving.

关键词： Industries Power demand Logic gates IEEE Standards Resource management Hardware design languages Hardware acceleration

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Stealthy Attacks on Sampled-Data Control and Encryption-Based Countermeasures

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IEEE Transactions on Automatic Control 2025年

作者： Shang, Jun Li, Yuzhe Chen, Tongwen Tongji University Department of Control Science and Engineering Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai200092 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110004 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

This paper investigates stealthy attacks on sampled-data control systems, where a continuous process is sampled periodically, and the resultant discrete output and control signals are transmitted through dual channels in wireless communication networks. Acknowledging the vulnerability of these transmitted data to cyber threats, we analyze two types of stealthy attacks that compromise either one or both channels. We explicitly present the necessary and sufficient conditions for the existence of stealthy single-channel attacks. To mitigate these security risks, we implement encryption-based countermeasures, including linear time-invariant and periodic encryption methods. We detail sufficient conditions under which these encryption strategies effectively thwart single- and dual-channel stealthy attacks. The effectiveness of these approaches and the theoretical findings are demonstrated through numerical simulations. © 1963-2012 IEEE.

关键词： Cyber attacks

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Using DEMATEL for Contextual Learner Modeling in Personalized and Ubiquitous Learning

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computers, Materials & Continua 2021年第12期69卷 3981-4001页

作者： Saurabh Pal Pijush Kanti Dutta Pramanik Musleh Alsulami Anand Nayyar Mohammad Zarour Prasenjit Choudhury Department of Computer Science and Engineering National Institute of TechnologyDurgapurIndia Department of Information Systems Umm Al-Qura UniversityMakkahKSA Graduate School Duy Tan UniversityDa NangVietnam Prince Sultan University RiyadhSaudi Arabia

With the popularity of e-learning,personalization and ubiquity have become important aspects of online *** make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation *** contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning *** this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are ***(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among *** acquiring methods of these contexts are also *** the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation *** a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.

关键词： Personalized e-learning DEMATEL learner model ontology learner context personalized recommendation adaptive decisions

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Performance Evaluation and Proposed Enhancement of Latest Image Watermarking Schemes Against Specific Forgery Attacks

Performance Evaluation and Proposed Enhancement of Latest Im...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Adnan Nadeem AlHassan Muhammad Ashraf Amir Mehmood Hani Almoamari Saad Said Alqahtany Muhammad Sarim Qammer H. Abbasi Faculty of Computer and Information Systems Islamic University of Madinah Madinah Kingdom of Saudi Arabia Department of Physics Federal Urdu University of Arts Science and Technology Karachi Pakistan Department of Computer Science Al-Kawthar University Karachi Pakistan Department of Computer Science Federal Urdu University of Arts Science and Technology Karachi Pakistan James Watt School of Engineering (of Affiliation) University of Glasgow Glasgow United Kingdom

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

Detection. Watermarking techniques can be classified based on various criteria, such as visibility, fragility, application domain, and extraction methods. However, their performance degrades against certain forgery attacks due to specific algorithmic loopholes. To deal with this issue, an in-depth performance evaluation and gap analysis of the latest invisible image watermarking schemes is presented in this paper. Then we propose an enhancement in terms of defining and using secret keys for the process of watermarking to improve temper detection and its localization against forgery attacks. First comprehensive testing is conducted regarding the imperceptibility and temper detection capability of these watermarking schemes against copy-paste, object deletion, text addition, and collage attacks. This testing results in finding the security weaknesses against collage attacks. Finally, we implemented the proposed enhancement and illustrated improved results up-to 99% in terms of localizing forgery detection in certain scenarios of collage attacks enhancement.

关键词： Performance evaluation Location awareness Protocols Watermarking Forgery Internet Classification algorithms Testing

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Diagnosis of Parkinson’s Disease by Deep Learning Techniques Using Handwriting Dataset 1

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6th International Symposium on Advances in Signal Processing and Intelligent Recognition systems, SIRS 2020

作者： Al-Wahishi, Atiga Belal, Nahla Ghanem, Nagia College of Computing and Information Technology Computer Science Arab Academy for Science and Technology Alexandria Egypt College of Computing and Information Technology Computer Science Arab Academy for Science and Technology Smart Village Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Alexandria University Alexandria Egypt

ISBN: (数字)9789811604256

ISBN: (纸本)9789811604249

Diagnosis and evaluation of Parkinson’s disease (PD) by clinicians is normally dependent on several established clinical criteria. Measuring the severity level according to these criteria depends heavily on the doctor’s expertise, which is subjective and inefficient. Hence, the current work aims to provide a quantitative and comprehensive evaluation in order to improve the diagnosis and assess the severity of PD using deep learning approaches. In the current study, a machine learning based method is proposed to automatically diagnose the PD severity from handwritten exams, by extracting the information from static and dynamic tests. A hybrid model for prediction is suggested by fusing convolutional neural network (CNN) and long-short term memory (LSTM). The suggested hybrid model (CNN + LSTM) obtained accuracies of 85.5% and 99.3% using the ParkinsonHW and HandPD datasets, respectively. We conclude that the quantitative evaluation provided by our model may be considered a helpful tool in the clinical detection of Parkinson’s disease. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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An approach to determination of representative set of exemplars for basic-level category mapping in cognitive agents.

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Procedia computer science 2022年 207卷 4237-4247页

作者： Wojciech Lorkiewicz Mariusz Mulka Radosław Katarzyniak Department of Computer Science and Systems Engineering Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 50-370 Wrocław Poland

Cognitive agents should be equipped with computational methods enabling autonomous formation and management of their internal cognitive structures. Moreover, due to social nature of multiagent systems a dedicated subsystem is required to allow for establishing common structures allowing for successful interaction between agents. Current implementation tend to use shared and predefined system-wide ontologies, thus limiting the fexibility and autonomy of individual agents. In this research, we focus on an approach that can remove this dependency by allowing the agents to maintain individual internal ontologies and map them to internal ontologies exploited by other agents. In particular, we propose a general strategy in which agent engages in an interplay that enables determination of certain types of relation between concepts. Further, we focus on one of the initial steps of this strategy, namely, determination of the representative set of exemplars for basic-level categories, which is utilised during the interplay and serves as the baseline to identify the mapping.

关键词： basic-level categories concept mapping cognitive agent

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The New Frontier in Media: AI-Driven Content Creation for Ad-Supported TV using Generative Adversarial Network

The New Frontier in Media: AI-Driven Content Creation for Ad...

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International Conference of computer and Informatics engineering (IC2IE)

作者： Shashishekhar Ramagundam Niharika Karne Software Development & Engineering in Content Discovery & AI Comcast USA AL. Department of Computer Science Masters in Computer Science Troy University Montgomery Campus USA Sr. Principal Systems Engineer in C& RS Platform Services DT Amtrak USA Department of Electrical Engineering Masters in Electrical Engineering University of South Alabama USA

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

The use of Artificial intelligence (AI) technology in the content creation to produce creative aspects like editing, audience analysis, creating ideas, writing copy, etc. The major aim is to streamline and automate the process of content creation and convert it into a more efficient and effective way. There is a lack of transparency in the AI content production process and hence it does not exactly mimic human activities, which includes fantasizing and picking up new abilities. AI content creation requires more creativity while investigating with a specific goal in mind. Although, AI faces many challenges since it constantly expands its base knowledge. One of the biggest ethical problems is the probability that AI content is used to deceive or influence people. Hence, to overcome these difficulties a machine learning-based Artificial Intelligent content creation framework is generated for content creation in Ad-supported TV. Here, an effective model Generative Adversarial Network (GAN) introduced to deliver enterprise data, customize descriptions, and adjust content based on consumer behaviors. This developed model produces material that is closely related to the preferences of the user. AI content creation increases efficiency and productivity by saving time and money. It also leads to the development of better content. Finally, the experimental analysis is performed to find the effectiveness of the developed deep learning-based AI content creation framework via various metrics.

关键词： Productivity Measurement Deep learning Ethics TV Media Generative adversarial networks Artificial intelligence Informatics Faces

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Optimal solving of a scheduling problem using quantum annealing metaheuristics on the D-Wave quantum solver

TechRxiv

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TechRxiv 2023年

作者： Bożejko, Wojciech Klempous, Ryszard Pempera, Jaroslaw Rozenblit, Jerzy Smutnicki, Czeslaw Uchroński, Mariusz Wodecki, Mieczyslaw The Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Poland University of Arizona United States Department of Computer Engineering Wroclaw University of Science and Technology Poland Department of Control Systems and Mechatronics Wroclaw University of Science and Technology Poland Department of Telecommunications and Teleinformatics Wroclaw University of Science and Technology Poland

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. The main disadvantage of calculations on quantum computers is their non-determinism. For optimization problems, of course, it is possible to get surprisingly good results but without a guarantee of the actual optimality of the result, i.e., an assurance that there exists no better solution which could potentially be found by the quantum machine. In this paper, we propose a novel approach that provides such a guarantee of optimality. We generate a solution that is optimal in the strict mathematical sense, without probabilistic considerations. To accomplish this, we use the D-Wave quantum machine working as a sampler implementing quantum annealing – an approach considered a hardware metaheuristic – to obtain upper and lower bounds on the value of the objective function of the problem under consideration. Then, we use the Branch and Bound scheme controlled by quantum annealing. This allows us to obtain very quickly – in constant time – the boundaries of the considered subproblems. Our approach is a combination of calculations realized on a quantum machine controlled by procedures implemented on a classical computer, allowing us to generate optimal solutions to the NP-hard problems of task scheduling on a single machine with a total weighted tardiness criterion. The proposed quantum algorithm shows a significant advantage over the classical CPU approach both in terms of calculation time and the number of generated solution tree nodes. © 2023, CC BY.

关键词： Quantum computers

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Risk-Averse Learning with Non-Stationary Distributions

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Yi, Xinlei Zavlanos, Michael M. Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Mechanical Engineering and Material Science Duke University DurhamNC27708 United States

Considering non-stationary environments in online optimization enables decision-maker to effectively adapt to changes and improve its performance over time. In such cases, it is favorable to adopt a strategy that minimizes the negative impact of change to avoid potentially risky situations. In this paper, we investigate risk-averse online optimization where the distribution of the random cost changes over time. We minimize risk-averse objective function using the Conditional Value at Risk (CVaR) as risk measure. Due to the difficulty in obtaining the exact CVaR gradient, we employ a zeroth-order optimization approach that queries the cost function values multiple times at each iteration and estimates the CVaR gradient using the sampled values. To facilitate the regret analysis, we use a variation metric based on Wasserstein distance to capture time-varying distributions. Given that the distribution variation is sub-linear in the total number of episodes, we show that our designed learning algorithm achieves sub-linear dynamic regret with high probability for both convex and strongly convex functions. Moreover, theoretical results suggest that increasing the number of samples leads to a reduction in the dynamic regret bounds until the sampling number reaches a specific limit. Finally, we provide numerical experiments of dynamic pricing in a parking lot to illustrate the efficacy of the designed algorithm. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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