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2nd Annual International Conference on Information and sciences, AiCIS 2020

作者： Ramadhan, Rebaz N. Manguri, Kamaran H. Manguri, Balen H. Koya University Software Engineering Department Koya Iraq University of Raparin Computer Science Department Rania Iraq Soran University Computer Science Department Soran Iraq

ISBN: (纸本)9781728191690

Clearance is a mandatory process in almost every university. Either graduated or undergraduate students must undertake the procedure to grant the permission to participate in the final exams. In addition to this, students need stamped clearance paper to receive their final exam results. Over the last decades, clearance has been carried out manually. With the manual clearance system, students endure a long and exhausting process of signatures and stamps. In the Iraqi educational system, the process involves visiting many departments including library, registrar, accounting, accommodation, laboratory, storage, sport and artistic activity departments, and the dean's office;only to have the clearance certified. The aim of this project is to propose an open source clearance system. In simple words, the proposed system will have several advantages over the old system for instance time and effort, cost, speed, and data safety. Languages such as HTML, CSS, and JavaScript are used to design and implement the front-end side. On the other hand, its back-end is created by the help of PHP scripts and MySQL for the database. Generally, the system has four equally important major tasks to be fulfilled in order to have the clearance. The tasks are registration, unit administrator (to confirm a clearance status as Cleared/Not Cleared), Exam committee (to check whether student's clearance status), and finally, a system administrator (to manage the entire system). Furthermore, system admin can add or remove any unit administrator or any student to/from the system. This proposed system is available online and can be found at the following link: ***/hvwNY © 2020 IEEE.

关键词： Students

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User Acceptance of New HCI Technologies

User Acceptance of New HCI Technologies

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Jozsef Katona Attila Kovari Kandó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Faculty of Computer Science Institute of Digital Technology Eszterházy Károly Catholic University Eger Hungary Department of Natural Sciences Institute of Engineering University of Dunaujvaros Dunaujvaros Budapest Hungary

Human-computer interaction (HCI) is an evolving field of research that focuses on understanding and improving the communication and interaction between humans and computers. Over the past decades, we have seen many significant advances in this field, which have contributed to the widespread adoption and integration of technology into our daily lives. HCI research and development aims to design information technology systems to meet the needs and preferences of users. Usability, efficiency, accessibility and user satisfaction are important considerations in HCI design. This paper presents the results of a pilot study on user acceptance of HCIs. The results show that users are positive about and willing to use HCIs.

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GroPipe: A Grouped Pipeline Hybrid Parallel Method for Accelerating DCNNs Training

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IEEE Transactions on computers 2025年第7期74卷 2487-2500页

作者： Liu, Bin Ma, Yongyao Hu, Zijian Ji, Zeyu He, Zhenli Li, Keqin Northwest A&F University College of Information Engineering Shaanxi Yangling712100 China Shaanxi Engineering Research Center of Agricultural Information Intelligent Perception and Analysis Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650091 China Yunnan University School of Software Kunming650091 China State University of New York Department of Computer Science New PaltzNY12561 United States

Training large Deep Convolutional Neural Networks (DCNNs) with increasingly large datasets to improve model accuracy has become extremely time-consuming. Distributed training methods, such as data parallelism (DP) and pipeline model parallelism (PMP), offer potential solutions but face challenges like load imbalance and significant communication overhead. This paper introduces GroPipe, a novel architecture that synergistically integrates PMP and DP, markedly improving training speeds. GroPipe employs an automatic model partitioning algorithm based on a performance projection technique, ensuring load balance and facilitating quantitative performance evaluation in PMP. Additionally, it adopts a group-based delayed asynchronous communication strategy to efficiently reduce communication overhead in DP. Using the ResNet and VGG models with the ImageNet dataset, extensive experiments are performed on an 8-GPU server and demonstrate GroPipe’s effectiveness. GroPipe achieves substantial improvements in time to accuracy, showing an average improvement of 42.2% and 14.0% on the ResNet series, and 79.2% and 43.9% on the VGG series, without compromising Top-1 accuracy. © 1968-2012 IEEE.

关键词： Training Computational Modeling Parallel Processing Load Modeling Pipelines Data Models computers Memory Management Asynchronous Communication Resource Management Delayed Asynchronous Communication Deep Convolutional Neural Networks Hybrid Parallelism Pipelined Model Parallelism Deep Convolutional Neural Network Training Methods Training Speed Load Balancing Image Net Dataset Parallel Data Communication Overhead Partition Model Partitioning Algorithm Top 1 Accuracy Pipeline Model Load Imbalance Learning Rate Training Time Resource Utilization Parallelization Model Convergence Memory Usage Training Efficiency Balanced Distribution Backward Pass Multiple GP Us Forward Pass Multiple Partitions Vertical Dimension Parallel Efficiency VGG 19 Model Throughput Experiments Parallel Training Idle Time

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Personalization of Job Circular Announcement to Applicants Using Decision Tree Classification Algorithm

Personalization of Job Circular Announcement to Applicants U...

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2020 IEEE International Conference for Innovation in Technology, INOCON 2020

作者： Javed Mehedi Shamrat, F.M. Tasnim, Zarrin Ghosh, Pronab Majumder, Anup Hasan, Md. Zahid Daffodil International University Department of Software Engineering Dhaka Bangladesh Jahangirnagar University Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9781728197449

While looking for jobs, job seekers like to register themselves in different job portal that has a huge number of job circulars. As from all the job circulars, it is easier to look for the job of their liking. At the same time, these job portals gather some information on the jobseekers that they use to apply to different jobs. Whenever a new job circular is posted in the portal, it is advertised to the job seekers through email notification. To make the email notification more personalized and relevant to the job seekers, a decision tree algorithm is implemented. This decision tree makes the decision of which job circular should be advertised to which job seekers based on the job seeker's field of interest, expected salary and experience. This helps jobseekers relate to the notification and be more engaged in the job portal. © 2020 IEEE.

关键词： Decision trees

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Diagnosis and Identification of citrus Canker Growth Rate Using Machine Learning

Diagnosis and Identification of Citrus Canker Growth Rate Us...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Naureen Zainab Hammad Afzal Fahim Arif Abdul Ghafoor Naima Iltaf Muhammad Zakria Muhammad Javed Hyder Rafia Ahsan Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan National Agricultural Research Centre (NARC) Park Road Crop Diseases Research Institute (CDRI) Islamabad Pakistan Department of Mechanical Engineering Capital University of Science and Technology (CUST) Islamabad Pakistan

citrus canker is a bacterial disease caused by Xanthomonas citri pv citri. Mainly the research in the field of citrus canker has been done on fruits; early detection of disease on leaves can help in adopting preventive measures at early stage. The present study focuses on the detection of citrus canker in leaves and classification of six different growth stages/levels of disease: water soaking, yellow initiation, chlorosis, and blister formation, canker infection (50%), canker infection (100%). The primary dataset was developed by taking images of citrus leaves infected by X. citri for the specified time. The proposed strategy is based on the detection of the lesion spot on citrus leaves and the classification of citrus disease stages/level was done to evaluate the growth rate of citrus canker through six distinct growth levels and evaluate affected area of leaf with each passing day. The entire processing includes Pre-processing, Segmentation, Features extraction and perform Classification using four different classifiers which include SVM, KNN, Naive Bayes, and Neural Network. The efficiency of different classifiers was 96.77% by SVM, followed by KNN 90%, NB 84% and NN 82% respectively. The lowest execution time was achieved by NB 0.011s, followed by KNN 0.015s, SVM 0.021s, and N.N. 0.216s.

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Refining the Unseen: Self-supervised Two-stream Feature Extraction for Image Quality Assessment

Refining the Unseen: Self-supervised Two-stream Feature Extr...

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IEEE International Conference on Data Mining (ICDM)

作者： Yiwei Lou Yanyuan Chen Dexuan Xu Doudou Zhou Yongzhi Cao Hanpin Wang Yu Huang Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University Beijing China School of Software & Microelectronics Peking University Beijing China Department of Biostatistics Harvard T.H. Chan School of Public Health Boston USA National Engineering Research Center for Software Engineering Peking University Beijing China

The inadequacy of labeled datasets for image quality assessment has led to the development and popularity of self-supervised approaches. However, most existing self-supervised methods primarily focus on content and fidelity features extracted with convolutional neural networks, overlooking the crucial importance of structural features in quality assessment. To address this problem, we present a novel self-supervised two-stream feature extraction and representation approach. In our approach, the first stream leverages a contrastive learning framework to extract image fidelity features, while the second stream emphasizes structural features by incorporating an attention mechanism. This innovative combination results in a comprehensive feature representation for quality assessment. Moreover, our proposed method facilitates transfer learning, allowing the pre-trained two-stream model in the source domain to be seamlessly applied to target domains for quality regression. This compatibility with transfer learning enhances the adaptability and generalization of the model. Extensive experiments are carried out on three synthetic distortion datasets to validate the effectiveness of our approach. The results demonstrate that our work not only competes with state-of-the-art self-supervised methods but also outperforms some supervised approaches.

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Robust Image Ordinal Regression with Controllable Image Generation

arXiv

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arXiv 2023年

作者： Cheng, Yi Ying, Haochao Hu, Renjun Wang, Jinhong Zheng, WenHao Zhang, Xiao Chen, Danny Wu, Jian School of Software Technology Zhejiang Univerisity China School of Public Health Zhejiang University China Alibaba Group China College of Computer Science and Technology Zhejiang University China School of Computer Science and Technology Shandong University China Department of Computer Science and Engineering University of Notre Dame United States Second Affiliated Hospital School of Medicine Zhejiang University China

Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called CIG based on controllable image generation to directly tackle these two issues. Our main idea is to generate extra training samples with specific labels near category boundaries, and the sample generation is biased toward the less-represented categories. To achieve controllable image generation, we seek to separate structural and categorical information of images based on structural similarity, categorical similarity, and reconstruction constraints. We evaluate the effectiveness of our new CIG approach in three different image ordinal regression scenarios. The results demonstrate that CIG can be flexibly integrated with off-the-shelf image encoders or ordinal regression models to achieve improvement, and further, the improvement is more significant for minority categories. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Application of soft computing algorithms for hybrid modular multilevel inverters

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Measurement: Sensors 2024年 31卷

作者： Gireesh Kumar, Devineni Venkata Sireesha, Nagineni Bhoopal, Neerudi Kumar Gatla, Ranjith Kotb, Hossam AboRas, Kareem M. Elrashidi, Ali Alqarni, Mohammed Yasin Ghadi, Yazeed Oubelaid, Adel Department of Electrical & Electronics Engineering B V Raju Institute of Technology Narsapur502313 India Department of Information Technology Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical & Electronics Engineering Institute of Aeronautical Engineering Hyderabad500043 India Department of Electrical Power and Machines Faculty of Engineering Alexandria University Alexandria21544 Egypt Electrical Engineering Department University of Business and Technology Ar Rawdah Jeddah23435 Saudi Arabia Engineering Mathematics Department Faculty of Engineering Alexandria University Alexandria21544 Egypt Department of Computer Science and Software Engineering Al Ain University Abu Dhabi15322 United Arab Emirates Laboratoire de Technologie Industrielle et de L'Information Faculté de Technologie Université de Bejaia Bejaia06000 Algeria

This study utilized Harris Hawk optimization to analyze the harmonic distortions of a symmetric capacitor based multilevel converter. The proposed multilevel converter uses identical and symmetric DC sources in its input. The proposed topology for a multilevel converter uses fewer switches than typical cascaded designs. It can output 9-level outputs and can be scaled up to higher levels. The proposed multilevel converter can be improved by implementing a low frequency pulse width modulation technique to reduce the stress on the switches. The optimization of the switching angles using Harris Hawk was able to solve the non-linear aspects of the Selective Harmonic Elimination Pulse Width Modulation. The output voltage's THD was compared with that of other optimization methods, such as ant colony and particle swarm. The comparison shows that the output THD of the Harris Hawk optimizer is lower than that of other methods, which is about 5 %, according to the standards of IEEE-519. © 2023

关键词： Pulse width modulation

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Real time Detection and Localization of Colorectal Polyps from Colonoscopy Images: A Deep Learning Approach

Real time Detection and Localization of Colorectal Polyps fr...

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IEEE International WIE Conference on Electrical and computer engineering (WIECON-ECE)

作者： Md. Al Amin Bikash Kumar Paul Nasima Islam Bithi Department of Software Engineering Daffodil International University (DIU) Dhaka Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology Univeristy Tangail Bangladesh Department of Computer Science and Engineering Daffodil International University (DIU) Dhaka Bangladesh

The growth of malignant colon polyps is a serious threat to both men’s and women’s lives, and image analysis performed during a colonoscopy is the procedure that is utilized the most frequently to test for benign and malignant polyps. Even though colorectal cancer is a major problem on a global scale, the mortality rate can be significantly lowered with the early diagnosis of polyps. This study made use of a model for the early detection of polyps. The model began by gathering data from the Kvasir-SEG dataset, and then proceeded to apply data augmentation techniques in order to boost the effectiveness of the dataset. The data were divided as follows: 80% percent for training and 20% for testing, including 20% of the training data utilized for validation to prevent overfitting. The study examined the dataset with YOLOv5 (You Only Look Once), specifically three variants of the model called YOLOv5s, YOLOv5m, and YOLOv5l. This allowed them to monitor each model’s performance while contrasting the results of the various models.. Results showed that YOLOv5l had the highest average testing IoU of 86.25% compared to the other employed models.

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Serverless Framework for Efficient Resource Management in Docker Environment 10th

Serverless Framework for Efficient Resource Management in Do...

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10th International Conference on computer science and its Applications, CSA 2018 and the 13th KIPS International Conference on Ubiquitous Information Technologies and Applications, CUTE 2018

作者： Han, Sangwook Chae, Minsu Lee, HwaMin Department of Computer Science Soonchunhyang University Asan Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan Korea Republic of

ISBN: (纸本)9789811393402

APIs provided by the Docker is executed through the container engine. Thus, it is a reality that the speed of creation and deletion of containers or requests for information is very slow. The load is even worse when many containers sending API requests at once. In this paper, we propose a method to apply module related to resource management in a container to reduce the load on API request generated in serverless environment. And we propose a new framework that manages resources of several containers or multiple Docker serves. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Containers

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