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Research Square 2021年

作者： Lansky, Jan Mohammadi, Mokhtar Mohammed, Adil Hussein Taher Karim, Sarkhel H. Rashidi, Shima Rahmani, Amir Masoud Hosseinzadeh, Mehdi Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Information Technology Lebanese French University Kurdistan Region Erbil Iraq Department of Communication and computer engineering Cihan university-Erbil Kurdistan Region Erbil Iraq Computer Department College of Science University of Halabja Halabja Iraq Sulaimani Polytechnic University Technical College of Informatics Computer Networks Department Sulaymaniyah Iraq Department of Computer Science University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of computer science Khazar University Baku Azerbaijan Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Mental Health Research Center Psychosocial Health Research Institute Iran University of Medical Sciences Tehran Iran

Mobile Edge Computing (MEC) is an interesting technology aimed at providing various processing and storage resources at the edge of the Internet of things (IoT) networks. However, MECs contain limited resources, and they should be managed effectively to improve resource utilization. Workflow scheduling is a process that tries to map the workflow tasks to the most proper set of computing resources regarding some objectives. For this purpose, this paper presents DBOA, a discrete version of the Butterfly Optimization Algorithm (BOA) that applies the Levy flight to improve its convergence speed and prevent the local optima problem. Then, DBOA is applied for DVFS-based data-intensive workflow scheduling and data placement in MEC environments. This scheme also employs the HEFT algorithm's task prioritization method to find the task execution order in the scientific workflows. For evaluating the performance of the proposed scheduling scheme, extensive simulations are conducted on various well-known scientific workflows with different sizes. The obtained experimental results indicate that this method can outperform other algorithms regarding energy consumption, data access overheads, etc. © 2021, CC BY.

关键词： Optimization

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Two-stage polytomous attribute estimation for cognitive diagnostic models: overcoming computational challenges in large-scale assessments with many polytomous attributes

Humanities and Social Sciences Communications

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humanities and Social sciences Communications 2025年第1期12卷 1-14页

作者： Yuting Han Feng Ji Zhehan Jiang Cognitive Science and Allied Health School Beijing Language and Culture University Beijing China Institute of Life and Health Sciences Beijing Language and Culture University Beijing China Key Laboratory of Language and Cognitive Science (Ministry of Education) Beijing Language and Culture University Beijing China Department of Applied Psychology and Human Development University of Toronto Toronto Canada Institute of Medical Education Peking University Beijing China National Center for Health Professions Education Development Peking University Beijing China Peking University Health Science Center-Chaoxing Joint Laboratory for Digital and Smart Medical Education Beijing China

Cognitive diagnosis models (CDMs) have been advocated as a useful tool in calibrating large-scale assessments, yet the computational challenges are inevitably amplified when the modeling complexity (e.g., the number and the levels of attributes) increases. This study presents a critical scenario, a large-scale national medical certification exam, where CDM with many polytomous attributes (mpCDM) is of great utility, but poses great computational challenges to many popular open-source CDM software packages. We developed a novel two-stage estimation method and assessed its performance through a Monte Carlo simulation study under various conditions of attribute number, item number, item quality, and sample size. Results indicate that the proposed method maintains high accuracy in handling large-scale data while effectively overcoming computational capacity limitations, especially in scenarios with many polytomous attributes, large numbers of items, and substantial sample sizes. Furthermore, we applied the proposed method to a large-scale health examination dataset, demonstrating its effectiveness in practice. This study contributes to the field of psychometrics by offering a simple yet effective solution to the computational challenges inherent in implementing mpCDMs for large-scale assessments, providing a practical tool for diagnostic analyses in educational and professional certification contexts.

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Successfully Applying the Stabilized Lottery Ticket Hypothesis to the Transformer Architecture

arXiv

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arXiv 2020年

作者： Brix, Christopher Bahar, Parnia Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department RWTH Aachen University AachenD-52056 Germany

Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stabilized lottery ticket hypothesis states that networks can be pruned after none or few training iterations, using a mask computed based on the unpruned converged model. On the transformer architecture and the WMT 2014 English→German and English→French tasks, we show that stabilized lottery ticket pruning performs similar to magnitude pruning for sparsity levels of up to 85%, and propose a new combination of pruning techniques that outperforms all other techniques for even higher levels of sparsity. Furthermore, we confirm that the parameter’s initial sign and not its specific value is the primary factor for successful training, and show that magnitude pruning cannot be used to find winning lottery tickets. Copyright © 2020, The Authors. All rights reserved.

关键词： Network architecture

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Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

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Heliyon 2024年第21期10卷 e39735页

作者： Williams, Kathleen A. Numssen, Ole Guerra, Juan David Kopal, Jakub Bzdok, Danilo Hartwigsen, Gesa Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Methods and Development Group “Brain Networks” Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany The Neuro - Montreal Neurological Institute (MNI) McConnell Brain Imaging Centre Department of Biomedical Engineering Faculty of Medicine School of Computer Science McGill University Montreal Canada Mila - Quebec Artificial Intelligence Institute Montreal QC Canada Wilhelm Wundt Institute for Psychology Leipzig University Germany

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition. © 2024 The Authors

关键词： Cognition cTBS fMRI Functional connectivity Resting state TMS

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DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks

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Vehicular Communications 2024年 47卷

作者： Hosseinzadeh, Mehdi Ali, Saqib Ahmad, Husham Jawad Alanazi, Faisal Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Darwesh, Aso Rahmani, Amir Masoud Lee, Sang-Woong Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11942 Saudi Arabia Center of Research and Strategic Studies Lebanese French University Kurdistan Region Iraq Department of Information Technology University of Human Development Kurdistan Region Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

In flying ad hoc networks (FANETs), unmanned aerial vehicles (UAVs) communicate with each other without any fixed infrastructure. Because of frequent topological changes, instability of wireless communication, three-dimensional movement of UAVs, and limited resources, especially energy, FANETs deal with many challenges, especially the instability of UAV swarms. One solution to address these problems is clustering because it maintains network performance and increases scalability. In this paper, a dynamic clustering scheme based on fire hawk optimizer (DCFH) is proposed for FANETs. In DCFH, each cluster head calculates the period of hello messages in its cluster based on its velocity. Then, a fire hawk optimizer (FHO)-based dynamic clustering operation is carried out to determine the role of each UAV (cluster head (CH) or cluster member (CM)) in the network. To calculate the fitness value of each fire hawk, a fitness function is suggested based on four elements, namely the balance of energy consumption, the number of isolated clusters, the distribution of CHs, and the neighbor degree. To improve cluster stability, each CH manages the movement of its CMs and adjusts it based on its movement in the network. In the last phase, DCFH defines a greedy routing process to determine the next-hop node based on a score, which consists of distance between CHs, energy, and buffer capacity. Finally, DCFH is simulated using the network simulator version 2 (NS2), and its performance is compared with three methods, including the mobility-based weighted cluster routing scheme (MWCRSF), the dynamic clustering mechanism (DCM), and the Grey wolf optimization (GWO)-based clustering protocol. The simulation results show that DCFH well manages the number of clusters in the network. It improves the cluster construction time (about 55.51%), cluster lifetime (approximately 11.13%), energy consumption (about 15.16%), network lifetime (about 2.6%), throughput (approximately 3.9%), packet deliver

关键词： Artificial intelligence (AI) Clustering Flying ad hoc networks (FANETs) Optimization algorithms Unmanned aerial vehicles (UAVs)

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Tracing fields, methods and origins of Covid-19

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AIP Conference Proceedings 2024年第1期3009卷

作者： Khattab M. Ali Alheeti Muzhir Shaban Al-Ani Salwa Mohammed Nejrs Dimah Mezher Shaban Al-Ani Zainab Mezher Shaban Al-Ani College of Computer Sciences and Information Technology – Computer Networking Systems Dept. University of Anbar Ramadi-Iraq University of Human Development College of Science and Technology Department of Information Technology Sulaymaniyah-Iraq University of Misan College of Science Department of physics Misan-Iraq University of Philadelphia College of Pharmacy Department of Pharmacy Amman-Jordan Ramadi Teaching Hospita Ramadi – Iraq

Coronavirus (Covid-19) appeared at the end of 2019 in the Chinese city of Wuhan, and it should be mentioned that there are research centers in both America and China that have worked on these viruses for many years ago before the appearance of this virus. At the beginning of the year 2020, this virus has spread widely in most countries worldwide and has led to great human and material losses, and the World Health Organization considered it a pandemic. A review analysis is conducted to be focused on many factors affecting the published papers on Covid-19 that applied to science Direct library on 7/4/2020. We observed that most research studies have shown that this virus attacks the human respiratory system, which leads to shortness of breath and also leads to death in some cases. The obtained results showed that most of these research papers are focusing on statistics (41%), medicine (40%), management (17%), and healthcare (2%) respectively. Statistical analysis is helpful to understand the orientation of research papers published on Covid-19. Statistics and medicine respectively, occupy a large part of the research side of the Covid-19. It is a short time since the spread of the Corona virus, so research results cannot reach maturity.

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Implementation and Enabling Artificial Intelligence in Wireless Communication Networks

Implementation and Enabling Artificial Intelligence in Wirel...

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International Conference on Computing for Sustainable Global development (INDIACom)

作者： Rahul Suryawanshi Swati Rawat Charanjeet Singh Harsha Sammangi Shaik Vaseem Akram M. Kalyan Chakravarthi Balaji Institute of Management Human Resource Development (BIMHRD) Sri Balaji University Pune (SBUP) M.M. Institute of Computer Technology & Business Management (MCA) Maharishi Markandeshwar (Deemed to be University) Mullana-Ambala Haryana India Department of Electronics and Communication Deenbandhu Chhotu Ram University of Science and Technology Murthal Dakota State University Uttaranchal Institute of Technology Uttaranchal University School of Electronics Engineering VIT-AP University Amaravathi India

The long-term adaptation of a multi-input multi-output (MIMO) antenna enabling 5 th generation (5G) wireless networks is proposed in this study using artificial intelligence (AI). The monopole-antenna combined with the swappable design mechanism is described in this study. The long-term development-centered MIMO antenna is configured with 4 antennas and is proficient in offering 4 simultaneous datasets, quadrupling the conceptual optimum data throughput, and enabling the ground station to transfer 4 main signals via 4 distinct transmission antennas for specific user hardware. When utilized in conjunction with 5G wireless communication networks, the long-term development of MIMO is intended to employ multi-trial streaming to deliver improvements in signal efficiency, throughput, and spectrum effectiveness. Consequently, the research suggests a long-term development of MIMO 4 transmission antennae having 4 different signal transmitting capacities that operate at a 3.501 Gigahertz frequency. To determine the capability of passive transportation of the numerous antennas employing the varied collection of trends, the laid-out architecture is assessed utilizing a MIMO signal analyzer. The results were found to allow the AI antenna of a wireless communication network to decide which antenna should be implemented in different situations to increase throughput, signal efficiency, and data transmission rate.

关键词： Wireless communication 5G mobile communication Wireless networks Transmitting antennas Transportation Throughput Weaving

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Analytics in Higher Education: Scoping the Landscape of Research in the Area

Analytics in Higher Education: Scoping the Landscape of Rese...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Natashaa Kaul Amruta Deshpande Amit Mittal Vishal Jain Symbiosis Center for Management and Human Resource Development Pune India Symbiosis International University Pune India Indira School of Business Studies PGDM Pune India Chitkara Business School Chitkara University Punjab India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida U.P. India

ISBN: (纸本)9781665487351

Understanding the research landscape of any domain is of primary importance at all stages of a concept's evolution. Research in the domain of analytics has increased exponentially in the past decade, analytics is a tool being applied to increase effectiveness and efficiency within all domains. Similarly, there are numerous research studies that have applied analytics to the domain of higher education. Assessing research activity is important for planning future research and identifying the way forward to make meaningful contributions. The objective of this paper is to examine the current research in this domain and to identify emerging areas of research to make meaningful contributions. Bibliometric analysis was applied using Scopus. Documents on analytics and higher education keywords in business, accounting and management and published in journals were considered for the analysis. Vos Viewer was used for bibliometric analysis in the area, to identify emerging research themes and to understand the scope of applying analytics in higher education. The search query found 1635 documents when using the keywords of “analytics” and “higher education”. This was further narrowed down to research in Business Management and Accounting and using Journal articles and conference articles, finally yielding 103 articles. After a primary read to remove articles misplaced in the group the final number of articles include for the bibliometric analysis came down to 98. There has been an increase in publications since 2018 with interest increasing post pandemic in the area. The research categories are Student outcomes and perspective, academician outcomes and perspective, design of learning systems, etc. Based on this, it is recommended that research should focus on theoretical aspects of applying analytics to HEIs, ethical issues in data collection, etc..

关键词： Learning systems Pandemics Education Bibliometrics Data collection Market research Mathematical models

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OLSR+: A new routing method based on fuzzy logic in flying ad-hoc networks (FANETs)

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Vehicular Communications 2022年 36卷

作者： Rahmani, Amir Masoud Ali, Saqib Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Javaheri, Danial Lalbakhsh, Pooia Hassan Ahmed, Omed Hosseinzadeh, Mehdi Lee, Sang-Woong Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Computer Engineering Chosun University Gwangju 61452 South Korea Department of Data Science and Artificial Intelligence Faculty of Information Technology Monash University Clayton 3800 VIC Australia Department of Information Technology University of Human Development Sulaymaniyah Iraq Pattern Recognition and Machine Learning Lab Gachon University 1342 Seongnamdaero Sujeonggu Seongnam 13120 South Korea

Flying ad-hoc networks (FANETs) have many applications in military, industrial and agricultural areas. Due to specific features of FANETs, such as high-speed nodes, low density of nodes in the network, and rapid changes in the topology, most routing protocols designed for mobile ad hoc networks (MANETs) or vehicular ad hoc networks (VANETs) are not compatible with FANETs. In this paper, we propose a fuzzy logic-based routing approach called OLSR+ for FANETs. In this scheme, we seek to improve the optimized link state routing protocol (OLSR) so that it can efficiently be used in FANETs. OLSR+ includes four main phases: 1) Discovering neighboring nodes. In this phase, we propose a new and efficient technique for estimating the lifetime of the link between two unmanned ariel vehicles (UAVs) based on the link quality, distance, relative velocity, and movement direction. 2) Selecting multipoint relays (MPRs). In this phase, we present a fuzzy mechanism for selecting a set of MPR nodes. According to this mechanism, when a node has higher residual energy, higher link lifetime, and more neighborhood degree compared to others, it achieves more fitness to be selected as MPR. 3) Discovering the network topology. In this phase, we modify the format of the topology control (TC) message and add two fields, including route energy and route lifetime to this message. 4) Calculating the routing table. In OLSR+, we consider two parameters, including route energy and route lifetime, for establishing stable paths. Finally, we simulate OLSR+ using NS3 and compare its performance with two methods, namely greedy optimized link state routing (G-OLSR) and optimized link state routing (OLSR). The simulation results show that OLSR+ successfully reduces delay compared to G-OLSR and OLSR. In addition, it has higher packet delivery rate and throughput than others. Also, it improves energy consumption in the network. However, OLSR+ has more routing overhead than G-OLSR. © 2022 Elsevier Inc.

关键词： Artificial intelligence (AI) Flying ad hoc networks (FANETs) Fuzzy logic Routing Unmanned ariel vehicles (UAVs)

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Internet of Thing and smart city: State of the art and future trends 2nd

Internet of Thing and smart city: State of the art and futur...

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2nd International Conference on computer, Communication and Computational sciences, IC4S 2017

作者： Jaafar, Ako A. Sharif, Karzan H. Ghareb, Mazen I. Jawawi, Dayang N. A. Computer Science Department University Technology Malaysia Johor Bahru Malaysia Computer Science Department University of Human Development Sulaymaniyah Iraq Computer Science Department University of Huddersfield Huddersfield United Kingdom Software Engineering Department University Technology Malaysia Johor Bahru Malaysia

ISBN: (纸本)9789811303432

Fast growing of cities, urban places, and population presents major challenges in our daily lives. Finding proper solution from different perspectives of cities became a concern by both researchers and industries concern. Smart city (SC) is the answer to overcome this issue. Internet of thing (IoT) is a crucial part of SC which has a tremendous impact on all the cities’ sectors such as governance, health care, education, environment, and transportation. This paper provides a state of the art on Internet of thing (IoT)-based smart city (SC) from platform, architecture, application domain, and technology perspectives, also it concludes the relations between different technologies used in developing smart cities. Challenges and future trends of SC based on IoT will be discussed. As this research conducts, the SC solution based on IoT still confronts many challenges and problems which required further researches. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Internet of things

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