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International Conference on Renewable Energy Research and Applications (ICRERA)

作者： Batyr Orazbayev Ainur Zhumadillayeva Kulman Orazbayeva Nazgul Kurbangalieva Department of Systems Analysis and Control L.NGumilyov Eurasian National University Astana Kazakhstan Department of Computer and Software Engineering L.NGumilyov Eurasian National University Astana Kazakhstan Department of Management Esil University Astana Kazakhstan L.NGumilyov Eurasian National University Astana Kazakhstan

ISBN: (数字)9798350375589

ISBN: (纸本)9798350375596

The key technological parameters of the sulfur production unit have been identified, and their influence on the sulfur production processes has been investigated. In this process, the released hydrogen can be used as hydrogen energy. Based on the results of the experimental studies, graphs have been constructed showing the effects of the unit's parameters that significantly impact the processes of converting hydrogen sulfide into produced sulfur. By approximating the obtained graphical dependencies, polynomial equations have been derived that describe the established relationships of the key technological parameters of the sulfur production unit on the sulfur production process. The derived equations describing the relationships between the main parameters of the installation and the degree of hydrogen sulfide conversion into sulfur form the basis of mathematical models that enable computer simulation of various operating modes of the installation and the selection of its optimal operating modes. The originality of the obtained results lies in the development of graphical dependencies and mathematical models that describe the influence of input parameters of the studied object on the sulfur production processes.

关键词： Temperature dependence Renewable energy sources Computational modeling Hydrogen Production Mathematical models Raw materials Polynomials Sulfur Inductors

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A 3d convolutional neural network for bacterial image classification 3rd

A 3d convolutional neural network for bacterial image classi...

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3rd International Conference on Big-Data and Cloud Computing, ICBDCC 2019

作者： Mhathesh, T.S.R. Andrew, J. Martin Sagayam, K. Henesey, Lawrence Department of Bioinformatics Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore641114 India Department of Systems and Software Engineering School of Engineering Blekinge Institute of Technology Karlskrona Sweden

ISBN: (纸本)9789811552847

Identification and analysis of biological microscopy images need high focus and years of experience to master the art. The rise of deep neural networks enables analyst to achieve the desired results with reduced time and cost. Light sheet fluorescence microscopies are one of the types of 3D microcopy images. Processing microscopy images is tedious process as it consists of low-level features. It is necessary to use proper image processing techniques to extract the low-level features of the biological microscopy images. Deep neural networks (DNN) are efficient in extracting the features of images and able to classify with high accuracy. Convolutional neural networks (CNN) are one of the types of neural networks that can provide promising results with less error rates. The ability of CNN to extract the low-level features of images makes it popular for image classification. In this paper, a CNN-based 3D bacterial image classification is proposed. 3D images contain more in-depth features than 2D images. The proposed CNN model is trained on 3D light sheet fluorescence microscopy images of larval zebrafish. The proposed CNN model classifies the bacterial and non-bacterial images effectively. Intense experimental analyses are carried out to find the optimal complexity and to get better classification accuracy. The proposed model provides better results than human comprehension and other traditional machine learning approaches like random forest, support vector classifier, etc. The details of network architecture, regularization, and hyperparameter optimization techniques are also presented. © Springer Nature Singapore Pte Ltd 2021.

关键词： Image classification

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Mining Student Information System Records to Predict Students’ Academic Performance 4th

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4th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2019

作者： Saa, Amjad Abu Al-Emran, Mostafa Shaalan, Khaled Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Gambang Malaysia

ISBN: (纸本)9783030141172

Educational Data Mining (EDM) is an emerging field that is concerned with mining and exploring the useful patterns in educational data. The main objective of this study is to predict the students’ academic performance based on a new dataset extracted from a student information system. The dataset was extracted from a private university in the United Arab of Emirates (UAE). The dataset includes 34 attributes and 56,000 records related to students’ information. The empirical results indicated that the Random Forest (RF) algorithm was the most appropriate data mining technique used to predict the students’ academic performance. It is also revealed that the most important attributes that have a direct effect on the students’ academic performance are belonged to four main categories, namely students’ demographics, student previous performance information, course and instructor information, and student general information. The evidence from this study would assist the higher educational institutions by allowing the instructors and students to identify the weaknesses and factors affecting the students’ performance, and act as an early warning system for predicting the students’ failures and low academic performance. © 2020, Springer Nature Switzerland AG.

关键词： Students

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Routing in LEO Satellite Networks: How Many Link-State Updates Do We Need?

Routing in LEO Satellite Networks: How Many Link-State Updat...

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Satellite Computing (Satellite), IEEE International Conference on

作者： Qian Shan Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing China

Low earth orbit (LEO) satellite networks equipped with inter-satellite links (ISLs) will be an indispensable component of the future Internet. Routing protocol installed on LEO satellite networks plays a significant role on achieving efficient data delivery. LEO satellite networks exhibit two typical topology features, i.e., deterministic neighbor relationship and intermittent ISLs, which are different from terrestrial Internet. Hence the classic link-state routing protocols (e.g., OSPF) designed for terrestrial Internet are naturally unable to accommodate these topology features. In this paper, we present the design of localized fine-grained (LoFi) link-state routing protocol for LEO satellite networks. The key idea of LoFi is to reduce the dissemination range of coarse-grained link connectivity and incorporate fine-grained link-load awareness. Specifically, LoFi has two key parameters, i.e., state dissemination range and load variation threshold, which determine how far to disseminate link state update and how precise to detect the link load change, respectively. The two parameters allow the LEO satellite networks to flexibly achieve a desired tradeoff between good packet delivery performance and small control overhead. We evaluate the performance of LoFi via extensive packet-level experiments on OMNeT++ under Iridium constellation. Results show that LoFi improves the packet delivery performance (up to 94.4 %) and reduces the control overhead (up to 85.7 %) compared to state-of-the-art link-state protocol ELB.

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Corrigendum to: Facial Surface Anthropometric Features and Measurements With an Emphasis on Rhinoplasty

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Aesthetic surgery journal 2022年第2期42卷 230-231页

作者： M Mazhar Celikoyar Michael F Pérez M Ilhan Akbas Oguzhan Topsakal Department of Otolaryngology Istanbul Florence Nightingale Hospital Istanbul Turkey. Computer Science Department Florida Polytechnic University Lakeland FL USA. Electrical Computer Software and Systems Engineering Department Embry-Riddle Aeronautical University Daytona Beach FL USA.

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Optimizing Link-Identified Forwarding Framework in LEO Satellite Networks

Optimizing Link-Identified Forwarding Framework in LEO Satel...

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International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt)

作者： Hefan Zhang Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems State Key Laboratory of Software Development Environment Institute of Artificial Intelligence Beihang University Beijing China

Low earth orbit (LEO) satellite networks have the potential to provide low-latency communication with global coverage. To unleash this potential, it is crucial to achieve efficient data delivery. In this paper, we analyze the topology characteristics of LEO satellite networks, and propose a source-route-style forwarding framework. Specifically, we leverage the deterministic neighbor relationship and identify all the unidi-rectional inter-satellite links (ISLs). Moreover, our framework utilizes the in-packet bloom filter (BF) to store the source-route-style forwarding information. This way, the source satellite could encode multiple ISL identifiers into the BF, which actually specifies the forwarding path. The intermediate satellites only need to check whether the outgoing ISLs are encoded and forward packets accordingly. Due to false positives caused by BF, the more ISLs are encoded at a time, the more redundant forwardings emerge. To reduce forwarding overhead, we take into account segment encoding, allowing the source and intermediate satellites to encode part of ISLs towards the destination. Overall, segment encoding seeks the right balance between forwarding overhead and encoding delay. We characterize a wide range of segment encoding policy in a unified framework, and derive the expected forwarding overhead in a closed-form. The segment encoding design is formulated as a binary non-linear programming, which is NP-hard. To overcome the challenge, we leverage its decomposable structure, and propose an efficient algorithm to solve it optimally. Finally, we validate our analytical results via packet-level experiments. Results also show that our proposed segment encoding policy significantly reduces the queuing delay compared to source encoding.

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Authentic Learning on DevOps Security with Labware: Git Hooks To Facilitate Automated Security Static Analysis

Authentic Learning on DevOps Security with Labware: Git Hook...

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IEEE Annual International computer software and Applications Conference (COMPSAC)

作者： Md Mostafizur Rahman MD Abdul Barek Mst Shapna Akter Abm Kamrul Islam Riad Md Abdur Rahman Hossain Shahriar Akond Rahman Fan Wu Department of Information Technology University of West Florida Florida USA Department of Intelligent Systems and Robotics University of West Florida Florida USA Center for Cyber Security University of West Florida Florida USA Dept. of Computer Science and Software Engineering Auburn University USA Tuskegee University Alabama USA

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

This paper presents an innovative approach to DevOps security education, addressing the dynamic landscape of cybersecurity threats. We propose a student-centered learning methodology by developing comprehensive hands-on learning modules. Specifically, we introduce labware modules designed to automate static security analysis, empowering learners to identify known vulnerabilities efficiently. These modules offer a structured learning experience with pre-lab, hands-on, and post-lab sections, guiding students through DevOps concepts and security challenges. In this paper, we introduce hands-on learning modules that familiarize students with recognizing known security flaws through the application of Git Hooks. Through prac-tical exercises with real-world code examples containing security flaws, students gain proficiency in detecting vulnerabilities using relevant tools. Initial evaluations conducted across educational institutions indicate that these hands-on modules foster student interest in software security and cybersecurity and equip them with practical skills to address DevOps security vulnerabilities.

关键词： Learning systems DevOps Codes Education Static analysis Dynamic scheduling software

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Unleashing the Power of Multi-Task Learning: A Comprehensive Survey Spanning Traditional, Deep, and Pretrained Foundation Model Eras

arXiv

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arXiv 2024年

作者： Yu, Jun Dai, Yutong Liu, Xiaokang Huang, Jin Shen, Yishan Zhang, Ke Zhou, Rong Adhikarla, Eashan Ye, Wenxuan Liu, Yixin Kong, Zhaoming Zhang, Kai Yin, Yilong Namboodiri, Vinod Davison, Brian D. Moore, Jason H. Chen, Yong Department of Computer Science and Engineering Lehigh University United States Department of Biostatistics Epidemiology and Informatics University of Pennsylvania United States Department of Industrial and Systems Engineering Lehigh University United States Department of Statistics University of Missouri United States School of Software Shandong University China Department of Computer Science University of Hong Kong Hong Kong Department of Computer Science and Engineering South China University of Technology China Department of Community and Population Health Lehigh University United States Department of Computational Biomedicine Cedars-Sinai Medical Center United States

Multi-Task Learning (MTL) is a learning paradigm that effectively leverages both task-specific and shared information to address multiple related tasks simultaneously. In contrast to Single-Task Learning (STL), MTL offers a suite of benefits that enhance both the training process and the inference efficiency. MTL's key advantages encompass streamlined model architecture, performance enhancement, and cross-domain generalizability. Over the past twenty years, MTL has become widely recognized as a flexible and effective approach in various fields, including computer vision, natural language processing, recommendation systems, disease prognosis and diagnosis, and robotics. This survey provides a comprehensive overview of the evolution of MTL, encompassing the technical aspects of cutting-edge methods from traditional approaches to deep learning and the latest trend of pretrained foundation models. Our survey methodically categorizes MTL techniques into five key areas: regularization, relationship learning, feature propagation, optimization, and pre-training. This categorization not only chronologically outlines the development of MTL but also dives into various specialized strategies within each category. Furthermore, the survey reveals how the MTL evolves from handling a fixed set of tasks to embracing a more flexible approach free from task or modality constraints. It explores the concepts of task-promptable and -agnostic training, along with the capacity for zero-shot learning, which unleashes the untapped potential of this historically coveted learning paradigm. Overall, we hope this survey provides the research community with a comprehensive overview of the advancements in MTL from its inception in 1997 to the present in 2023. We address present challenges and look ahead to future possibilities, shedding light on the opportunities and potential avenues for MTL research in a broad manner. This project is publicly available at https://***/junfish/Awesome-Multi

关键词： Learning systems

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Depth Based Region Proposal: Multi-Stage Real- Time Object Detection

SSRN

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SSRN 2022年

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Computer Science Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Information Systems Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

The majority of real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in full-dimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a twodimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified into the particular scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-bypixel accurate localization of objects. © 2022, The Authors. All rights reserved.

关键词： Object detection

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Intelligent Gen: An Efficient On-Demand Access Protocol in MANETs using Network Simulator - 2

Intelligent Gen: An Efficient On-Demand Access Protocol in M...

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International Conference on Computing for Sustainable Global Development (INDIACom)

作者： T. Ganesan N. Murali S. Palani Murugan M. Chandrakumar Peter P. Anand Raj S. Manikandan Department of Computer Science & Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence & Data Science E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Business Systems E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India

Mobile Ad-Hoc network is a self-configuring and multi-hop collection of mobile devices for making successful communication. The wireless and mobile communication process are encountered based on bandwidth, process control and energy conception. Energy conception is considered and dynamically changed to rely on life time of batteries and power. The life time of battery get exhausted very quickly the nodes are moved and changed rapidly across the network. This paper noticed the problem of life time of power, batteries and energy consumption problem in various routing stages. The proposed Intelligent Gen Algorithm is used to get optimal solution for ensuring high robustness, flexibility and Energy consumption factor. It presents a common framework to balance various routing requirements in MANETs dynamically. The following are the main contributions includes, i. introducing Intelligent GEN Algorithms to classify the throughout bandwidth, delay, data ratio. ii. Various routing performance are evaluated and viewed by speed, packetize and simulation time. The comparison evaluated by select routing algorithm using NS-2. The simulation results are recorded and the performance is monitored by using on demand access protocl. The result clearly demonstrated the proposed Intelligent Gen algorithm used in self-organized manner by wireless channel.

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