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2nd International Conference on Electrical, computer and Communication engineering, ECCE 2019

作者： Kabir, Muhammad Nomani Tayan, Omar Alginahi, Yasser Hasan, Md Munirul Rahman, Md Arafatur Faculty of Computer Systems Software Engineering University Malaysia Pahang Kuantan Pahang Malaysia Dept. of Computer Engineering NOOR Research Center College of Computer Science and Engineering Taibah University Madinah Saudi Arabia Dept. of Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (纸本)9781538691113

Web extension is a software that can be installed on a web browser. A web-extension link is displayed as an icon on the toolbar of the browser. Based on browsing activity, the extension works automatically or by clicking the extension icon depending on the functionalities made in the extension software. In this work, we developed a web extension on Google Chrome browser to verify online texts by simply clicking on an extension button. Upon clicking the button, the underlying algorithm in the extension software retrieves the texts from the current web-page being displayed. Verification and authentication of texts are performed by comparing the retrieved texts with text database. According to the comparison, texts are highlighted in colors. We consider authentication of Arabic Hadith texts for a case study. The authentic Hadith texts are highlighted by green color;authentic texts with partial diacritics, by yellow;and unauthentic texts, by red. This technique can also be used to authenticate laws, constitutions and Government documents in any language. © 2019 IEEE.

关键词： Authentication

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Intelligent Management Frameworks for Global Cooperation

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International Journal of Intelligent systems 2025年第1期2025卷

作者： Ana M. Gonzalez de Miguel Antonio Sarasa-Cabezuelo Computer Science Faculty Department of Software Engineering and Artificial Intelligence Complutense University of Madrid 9 Prof. J. Garcia Santesmases Ciudad Universitaria Madrid 28040 Spain ucm.es Computer Science Faculty Department of Information Systems and Computing Complutense University of Madrid 9 Prof. J. Garcia Santesmases Ciudad Universitaria Madrid 28040 Spain ucm.es

This paper presents the definition, use, and evaluation of intelligent management frameworks for global cooperation. The research work brings new concepts and ideas to design new management models and artificial intelligence solutions in sustainable environments. An intelligent management framework is a flexible and efficient vertical association of models, architectures, and processes. It is a mixed (architectural and methodological) association of services and procedures across IT departments of global organizations. The paper presents a general top-down approach to design these frameworks for global, cooperative models of intelligence. The approach includes five levels of abstraction and three refinement techniques. These elements are used to design an evaluation case study with global services and process-oriented cooperation for current sustainable targets in education. In our future work, we will implement these management solutions for government organizations currently involved with digital transformations.

关键词： artificial intelligence global cooperation intelligence sustainability management frameworks

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Evidence-Based Quality-Aware Agile software Development Process: Design and Evaluation

SSRN

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SSRN 2023年

作者： Karhapää, Pertti Behutiye, Woubshet Seppänen, Pertti Rodríguez, Pilar Oivo, Markku Franch, Xavier Martinez-Fernandez, Silverio López, Lidia Choraś, Michal Bagnato, Alessandra Aaramaa, Sanja Partanen, Jari University of Oulu Oulu90014 Finland Department of Service and Information System Engineering UPC-BarcelonaTech Barcelona08034 Spain Kaiserslautern67663 Germany ITTI Sp. z o.o Poznan61-612 Poland University of Science and Technology UTP Univerisy of Science and Technology Bydgoszcz5-796 Poland Softeam Paris75016 France Nokia Oy Oulu90620 Finland Bittium Wireless Ltd. Oulu90590 Finland University of Puget Sound United States Dep. Of Languages Computer Systems and Software Engineering Faculty of Computer Science Universidad Politécnica de Madrid Spain Dep. ESSI GESSI Research Group UPC-BarcelonaTech Barcelona08034 Spain Centro Nacional de Supercomputacíon Barcelona08034 Spain

Context Agile software development (ASD) aims to deliver high-quality software. However, software quality often gets less attention due to focus on *** an evidence-based quality-aware ASD process that organizations can use to create a more quality-aware development process and improve software *** methodUsing design science research, we collaborated with four organizations applying ASD in different contexts. Involving a total of 35 practitioners, we designed company-specific evidence-based quality-aware ASD processes, which were validated through simulated scenarios, implemented, and evaluated by the practitioners. ResultsA general ASD process model, derived from the company-specific models, was created, which shows the specific activities, frequencies, roles, and development phases where evidence can be used to improve software quality. The new process model contributed towards improved bottleneck identification, release readiness, and quality issues prioritization. We also learned that companies must be mindful of the rules that determine the use of evidence. Conclusion Integrating the use of quality related evidence into the ASD process can make it quality-aware, supporting software quality management, and enabling timely reaction through fast feedback loops. The evidence-based quality-aware ASD process is applicable in any Scrum-like ASD process. Applicability in other forms of ASD, however, requires further research. © 2023, The Authors. All rights reserved.

关键词： Quality control

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A Graph-based Methodology for Tracking Covid-19 in Time Series Datasets

A Graph-based Methodology for Tracking Covid-19 in Time Seri...

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International Conference on Electronics, Control, Optimization and computer Science (ICECOCS)

作者： Zakariyaa Ait El Mouden Abdeslam Jakimi Rachida Moulay Taj Moha Hajar Software Engineering & Information Systems Engineering Faculty of Sciences and Techniques Errachidia Moulay Ismail University Meknes Morocco Operational Research & Computer Science Faculty of Sciences and Techniques Errachidia Moulay Ismail University Meknes Morocco

ISBN: (数字)9781728169217

ISBN: (纸本)9781728169224

Since its first appearance in December 2019, Covid-19 has become a wide field of scientific research. Starting from biology to bioinformatic solutions, Artificial Intelligence has contributed in turn as a powerful tool for tracking and predicting the outbreak of Covid-19 using different types of datasets. Chest X-ray images are widely used in computer vision applications and Time series datasets are used for predicting the spread of the novel coronavirus. Graph analytics is a recent field of study that links the mathematical definition and operations of a graph to its application in computer science as a complex data structure, this combination has played a critical role in making graph-based applications present in different fields. One of the most powerful graph analytics is community detection which is an intelligent and unsupervised grouping of a set of graph structured data using the similarity between them. The aim of this work is to highlight the importance of graph-based algorithms in tracking Covid-19 using time series datasets, our work will also focus on Spectral Clustering (SC) as a community detection approach to extract clusters from the input datasets. Further applications are needed in order to validate the proposed theoretical approach.

关键词： COVID-19 Data models Time series analysis Clustering algorithms Laplace equations X-ray imaging Object oriented modeling

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Prediction of global spread of covid-19 pandemic: A review and research challenges

TechRxiv

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TechRxiv 2020年

作者： Shah, Saloni Mulahuwaish, Aos Ghafoor, Kayhan Zrar Maghdid, Halgurd S. Department of Computer Science and Information Systems Saginaw Valley State University MI United States Department of Software Engineering Salahaddin University Erbil Iraq School of Mathematics and Computer Science University of Wolverhampton Wolverhampton United Kingdom Department of Software Engineering Faculty of Engineering Koya University Kurdistan Iraq

Since the initial reports of the Coronavirus surfacing in Wuhan, China;the novel virus currently without a cure has spread like a wildfire across the globe. The virus spread exponentially across all inhabited continent;catching local governments by surprise in many cases and bringing the world economy to a standstill. As local authorities work on a response to deal with the virus, the scientific community has stepped in to help analyse and predict the pattern and conditions that would influence the spread of this unforgiving virus. Using existing statistical modelling tools to latest AI technology;the scientific community has used public and privately available data to help with predictions. A lot of this data research has enabled local authorities to plan their response – whether that is to deploy tightly available medical resources like ventilators or how and when to enforce policies to social distance including lockdowns. On one hand, this paper shows what accuracy of research brings to enable fighting this disease;while on the other hand it also shows what lack of response from local authorities can do in spreading this virus. This is our attempt in compiling different research methods and comparing their accuracy in predicting the spread of COVID-19. © 2020, CC BY.

关键词： Viruses

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An algorithm of conversion between relational data and graph schema 2nd

An algorithm of conversion between relational data and graph...

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2nd International conference on Europe Middle East and North Africa Information systems and Technologies to support Learning, EMENA-ISTL 2018

作者： Ait El Mouden, Zakariyaa Jakimi, Abdeslam Hajar, Moha Software Engineering and Information Systems Engineering Team Faculty of Sciences Technologies Errachidia Moulay Ismail University Meknes Morocco Operational Research and Computer Science Team Faculty of Sciences Technologies Errachidia Moulay Ismail University Meknes Morocco

ISBN: (纸本)9783030035761

This paper presents an approach of modeling a relational data by graphs using a specific open source format named Graph Exchange XML Format (GEXF), this approach can take us from a random data stored in a relational model database to a graph which models those data by nodes and edges to present the links between those data as a first step to the graph-oriented database. The advantage of this approach is that it can show as the whole stored data in a relational modal and with a simple look we can decide which data are more useful and which data can be ignored, using the visualization of the generated GEXF schema we can generate several graphs, where each SQL query in the input relational database generates a different graph. © Springer Nature Switzerland AG 2019.

关键词： Data visualization

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Proving Cloud SaaS Layer Security Vulnerabilities 2nd

Proving Cloud SaaS Layer Security Vulnerabilities

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2nd International Conference on Advanced Data and Information engineering, DaEng 2015

作者： Harun, Sulaiman Ameedeen, Mohamed Ariff Faculty of Computer Systems & Software Engineering University Malaysia Pahang KuantanPahang26300 Malaysia IBM Centre of Excellence University Malaysia Pahang KuantanPahang26300 Malaysia

ISBN: (纸本)9789811317972

Cloud computing is the new age of the ICT world and it is a powerful computing platform that enables individual and organizations to perform variety level of task with minimum costs. Cloud computing can provide a flexible IT architecture and environment, which are accessible through internet and can be access through a lightweight portable devices. In previous years, the number of people using cloud services had dramatically increased and lots of data had been stored inside the cloud computing environments. Apparently, the data that breaches into the cloud services had also increased every year. It is due to the hackers whom will always try to exploit the security vulnerabilities of the cloud’s architecture. Primarily, this paper describes the vulnerabilities in SaaS layer together with its attack possibilities. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Web services

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Investigation of the Impact of Height and Bandwidth on Path Loss Exponent of 5G FR1 Wireless Networks at 800, 3500, and 5900 MHz

Investigation of the Impact of Height and Bandwidth on Path ...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II) 20

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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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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