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20th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2021

作者： Kinsner, Witold Zhu, Haibin Baciu, George Luo, Guiming Rubio, Fernando Sui, Jie Huang, Runhe Hiraishi, Hironori Peng, Jun Chen, Liang Univ. of Manitoba FI2CICC Dept of ECE Winnipeg Canada Nipissing University FI2CICC ON Canada Hong Kong Polytechnic University FI2CICC Dept. of Computing Hong Kong Tsinghua University FI2CICC Software Institute Beijing China Complutense Univ. of Madrid FI2CICC Dept. of Computer Systems and Computation Spain Aberdeen University FI2CICC Aberdeen United Kingdom Hosei University FI2CICC Faculty of Computer and Information Sciences Japan Ashikaga University Faculty of Engineering Tochigi326-8558 Japan Chongqing Univ. of Science and Technology FI2CICC School of AI Chongqing China Univ. of Northern British Columbia Dept. of Computer Science BC Canada

ISBN: (纸本)9781665421195

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-Two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-And-mission-critical systems. © 2021 IEEE.

关键词： Cognitive systems

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Simultaneous spectral-spatial feature selection and extraction for hyperspectral images

arXiv

引用

arXiv 2019年

作者： Zhang, Lefei Zhang, Qian Du, Bo Huang, Xin Tang, Yuan Yan Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Beijing Samsung Telecom R&D Center Beijing100028 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Computer and Information Science University of Macau Macau999078 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectralspatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Correction: Agile meets quantum: a novel genetic algorithm model for predicting the success of quantum software development project

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Automated software engineering 2024年第1期32卷 1-11页

作者： Khan, Arif Ali Akbar, Muhammad Azeem Lahtinen, Valtteri Paavola, Marko Niazi, Mahmood Alatawi, Mohammed Naif Alotaibi, Shoayee Dlaim M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Quanscient Oy Tampere Finland VTT Technical Research Ctr of Finland Ltd Oulu Finland Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Interdisciplinary Research Centre for Intelligent Secure Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Information Technology Department Faculty of Computers and Information Technology University of Tabuk Tabuk Saudi Arabia Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Hail Hail Saudi Arabia

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Research and development of two-factor authentication methods using neural networks

Research and development of two-factor authentication method...

引用

Smart Information systems and Technologies (SIST), IEEE International Conference on

作者： Temirkhan Darbek Assel Mukasheva Alibek Bissembayev Sergiy Gnatyuk Dinargul Mukammejanova Department of Information Systems and Cybersecurity Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine Department of Artificial Intelligence and Big Data Kazakh National University named after Al-Farabi Almaty Kazakhstan

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

This article is devoted to the research and development of innovative two-factor authentication (2FA) methods using neural networks. 2FA plays a key role in ensuring the security of data and accounts in the modern digital world, and the use of neural networks opens up new prospects in strengthening protection and improving the user experience. The article presents an analysis of existing 2FA methods, identifies their limitations, and discusses the advantages of using neural networks in this area. The main focus is on the development of new authentication algorithms based on deep learning, providing effective anomaly detection and support for a dynamic adaptive approach. In the context of the article, the issues of privacy and data security are also considered, as well as the possibilities of applying the developed methods in various fields, including finance, medicine and the Internet of Things. The results of the study represent a significant contribution to the field of digital

关键词： Codes Face recognition Heuristic algorithms Data security Neural networks Authentication Chatbots

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Influence of Network Speed on Structured Query Language (SQL) Database Data Transmission Performance in an Internet of Things (IoT) Sensing Device on Single Board computers (SBC)

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IOP Conference Series: Materials Science and engineering 2019年第1期551卷

作者： A Nor Azhar S Nur Ayuni Hasna AH Roslina Z Fauziah M Rahmah Department of Computer Science Department of Computer Systems & Networking Department of Software Engineering Department of Graphic & Multimedia Technology Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak Gambang 26300 Kuantan Pahang Malaysia

One of the crucial elements in an Internet of Things (IoT) environment is a database. IoT performance will be at stake if the wrong database is adopted. In this research, three Structured Query Language (SQL) databases were tested against multiple network speeds in an IoT device. Single board computers (SBC) were used as a media of testing instead of an ordinary computer. A controlled wireless sensor network (WSN) was developed with several uniform constants and variable network speeds. A new data transmission rate equation also was proposed and tested. From the result, it was found that not all SQL databases can cater IoT devices. Some of them showed that performance decreased along with network speed. The correlation, between 0.90 and 0.96, proved the strong influence between the research subjects. In addition, file-type database is the best option available for SQL-wise IoT storage.

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Design, monitoring, and testing of microservices systems: The Practitioners’ perspective

arXiv

引用

arXiv 2021年

作者： Waseem, Muhammad Liang, Peng Shahin, Mojtaba Salle, Amleto Di Márquez, Gastón School of Computer Science Wuhan University Wuhan430072 China Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne3800 Australia Department of Information Engineering Computer Science and Mathematics University of L’Aquila L’AquilaI-67100 Italy Department of Electronics and Informatics Federico Santa María Technical University Concepción4030000 Chile

Context: Microservices Architecture (MSA) has received significant attention in the software industry. However, little empirical evidence exists on design, monitoring, and testing of microservices systems. Objective: This research aims to gain a deep understanding of how microservices systems are designed, monitored, and tested in the industry. Method: A mixed-methods study was conducted with 106 survey responses and 6 interviews from microservices practitioners. Results: The main findings are: (1) a combination of domain-driven design and business capability is the most used strategy to decompose an application into microservices, (2) over half of the participants used architecture evaluation and architecture implementation when designing microservices systems, (3) API gateway and Backend for frontend patterns are the most used MSA patterns, (4) resource usage and load balancing as monitoring metrics, log management and exception tracking as monitoring practices are widely used, (5) unit and end-to-end testing are the most used testing strategies, and (6) the complexity of microservices systems poses challenges for their design, monitoring, and testing, for which there are no dedicated solutions. Conclusions: Our findings reveal that more research is needed to (1) deal with microservices complexity at the design level, (2) handle security in microservices systems, and (3) address the monitoring and testing challenges through dedicated solutions. © 2021, CC BY.

关键词： Surveys

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Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

SSRN

引用

SSRN 2022年

作者： Onile, Abiodun Emmanuel Belikov, Juri Levron, Yoash Petlenkov, Eduard Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Electricity consumers are often faced with challenges relating the choice of optimal energy saving plan, increasing integration of transient’s renewable sources of energy promises tantalizing solution which also constitutes emerging stability challenges for the electricity Grid. Demand side management using Battery storage as grid balancing measures are crucial towards extending physical limits of existing electricity grid. On the one hand, battery storage is assuming a critical role in decarbonization efforts partly due to its tendencies to store excess electricity obtained via renewable energy sources thus enabling unlimited potentials for seasonal storage. On the other hand, while solving the identified problem, other problems relating consumer behavior towards adoption of energy/battery efficiency technologies, consumers comfort-feedback and control loop integration exist. In this study, we presented electricity energy storage (EES) technologies which are embedded into the grid and further enhanced with the use of Reinforcement learning control and recommendation system technologies for improving the grid management, the grid reliability and sustainability, mitigated operational issues, improved BESS utilization and attain self-consumption goals. The result of the study shows that operating the BESS using the multi-agent RL control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Explaining predictions of deep neural classifier via activation analysis

arXiv

引用

arXiv 2020年

作者： Stano, Martin Benesova, Wanda Marták, Lukáš Samuel Department of Informatics Informational Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Department of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Slovak University of Technology Bratislava842 16 Slovakia Institute of Computational Perception Johannes Kepler University Linz4040 Austria LIT Artificial Intelligence Lab Johannes Kepler University Linz4040 Austria

In many practical applications, deep neural networks have been typically deployed to operate as a black box predictors. Despite high amount of work on interpretability and high demand on reliability of these systems, they typically still have to include a human actor in the loop, to validate the decisions and handle unpredictable failures and unexpected corner cases. This is true in particular for failure-critical application domains, such as medical diagnosis. We present a novel approach to explain and support an interpretation of a decision-making process to a human expert operating a deep learning system based on Convolutional Neural Network (CNN). By modeling activation statistics on selected layers of a trained CNN via Gaussian Mixture Models (GMM), we develop a novel perceptual code in binary vector space that describes how input sample is processed by the CNN. By measuring distances between pairs of samples in this perceptual encoding space, for any new input sample, we can now retrieve a set of most perceptually similar and dissimilar samples from an existing atlas of labeled samples, to support and clarify the decision made by the CNN model. Possible uses of this approach include for example computer-Aided Diagnosis (CAD) systems working with medical imaging data, such as Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) scans. We demonstrate the viability of our method in the domain of medical imaging for patient condition diagnosis, as proposed decision explanation method via similar ground truth domain examples (e.g. from existing diagnosis archives) will be interpretable by the operating medical personnel. Our results indicate that our method is capable of detecting distinct prediction strategies which enables to identify most similar predictions from an Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Persuasive agents for sustainable business practice: A theoretical study

引用

AIP Conference Proceedings 2019年第1期2129卷

作者： Bokolo Anthony, Jnr Mazlina Abdul Majid Awanis Romli 1Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang 26300 Gambang Pahang Malaysia

Presently practitioners in businesses are faced with issues related to the implementation of sustainable practices. Accordingly, practitioners in businesses need to make decision based on their current practice to verify if present practice considers the environmental, economic, and social elements of sustainability. But, at the moment businesses utilizes manual specification based ranking and benchmarking approaches to ascertain that practitioners consider the natural environment and society as they make profit. Nevertheless, there is need for an autonomous method of measuring firms sustainable practice implementation. Therefore, this paper develops a persuasive agent’s model to measure; benchmark and rate businesses current practice based on review of prior sustainability studies. The persuasive agents’ model comprises of sustainable practice operations question, alternatives, and persuasive agents measurement procedures. Respectfully, findings from the study depict how the integrated persuasive agents autonomously measure, benchmark, rate and provide recommendations to practitioners. This study provides implications on how businesses can improve their business operation towards social, economic and environmental sustainable practice implementation.

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A scheme for finding and blocking black hole nodes in mobile ad hoc networks

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International Journal of Network Security 2019年第6期21卷 1021-1030页

作者： Zardari, Zulfiqar Ali He, Jingsha Zhu, Nafei Pathan, Muhammad Salman Memon, Muhammad Qasim Hussain, Muhammad Iftikhar He, Peng Chang, Chengyue Faculty of Information Technology Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing100124 China College of Computer and Information Science China Three Gorges University China Advanced Innovation Center for Future Education Beijing Normal University China Machinery Industry Information Center Beijing100823 China

Security of wireless nodes are a major concern in mobile ad hoc networks (MANETs). In this paper, we propose a scheme for detecting and blocking malicious nodes in MANETs. Our proposed scheme relies on specially deployed nodes called FBS nodes to continuously monitor the behaviour of the network nodes. When an FBS node detects a node that exhibits suspicious behaviour, it declares that node as a black hole node. Afterwards, all data and control messages from that node will be discarded. Experiment results show that the proposed scheme can reduce the number of packets dropped by malicious nodes with a low false positive rate. © 2019 Femto Technique Co., Ltd.

关键词： Mobile ad hoc networks

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