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16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021

作者： Ji, Liangqian Dong, Tingting Lan, Yang Cai, Xingjuan School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan030024 China Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789811912528

In the current cloud computing environment, task scheduling and resource allocation are the key and difficult points in the performance improvement. However, there are numerous problems of workflow, such as Montage, Inspiral, Cybershake etc. They have similar workflow structures, which affect the efficiency of task scheduling and resource distribution. In addition, the result obtained by the traditional evolutionary algorithm is the allocation sequence of the virtual machine in the cloud computing environment only for single task, which is a great waste of resources. Aiming at these problems, the multiple workflow tasks are processed in this paper by using implicit information transfer at the same time, that is, to reasonably use the allocation sequence of each task to exchange information so as to share a better virtual machine allocation. Meanwhile, using the potential relationship and differences between different tasks are better able to make population has better convergence and diversity. We proposed a multifactorial evolutionary algorithm based on combinatorial population (CP-MFEA) for multitasking workflows. This paper constructs nine sets of multi-task combination problems, and compares the method with the traditional single-task evolutionary algorithm, the purpose is to describe the superiority of this method clearly. Through the experimental results, we can notice that CP-MFEA’s ability is much more obvious than single-task evolutionary algorithms. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Designing a Tool to Address the Depression of Children During Online Education 17

Designing a Tool to Address the Depression of Children Durin...

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17th International Conference on Future Networks and Communications / 19th International Conference on Mobile Systems and Pervasive Computing / 12th International Conference on Sustainable Energy information technology, FNC/MobiSPC/SEIT 2022

作者： Alwadei, Asma Alnanih, Reem Department of Computer Science Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Advances in communication and information technology have changed the way humans interact. During the COVID-19 pandemic, the technology for communication has caused depression and anxiety, including among children and teens. Depression among children and teens may go unrecognized and untreated, as parents and teachers may have difficulty recognizing the symptoms. COVID-19 has changed traditional learning methods, forcing children to stay home and connect through online education. Although some children may function reasonably well in less-structured environments, many children with significant depression suffer a noticeable change in social activities, loss of interest in an online school, poor online academic performance, or changes in appearance. Home quarantine has affected children's mental health, and it has become challenging for school counselors to predict depression in many children participating in online education. This study aims to design and develop a tool for predicting depression among children aged 7 to 9 years old by recording students' online classes and sending a note to the child's academic file. The idea of needing this tool arose as an output for applying the design thinking approach to the online education website during COVID-19. This inspired the authors to combine the lecture recordings and the prediction of depression into one tool. Image processing techniques are applied to generate the results predicted by the model on the collected videos. The overall accuracy for classifying depressed and not depressed videos is 89%. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the Conference program Chairs.

关键词： COVID-19

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Whole Slide Image Annotation Support for Estimating Lesion Proportions

Whole Slide Image Annotation Support for Estimating Lesion P...

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International Conference on Cyberworlds

作者： Nana Takano Satoshi Nishiguchi Masahiro Toyoura Graduate School of Information Science and Technology Osaka Institute of Technology Osaka Japan Faculty of Information Science and Technology Osaka Institute of Technology Osaka Japan Department of Computer Science and Engineering University of Yamanashi Yamanashi Japan

ISBN: (数字)9798331527174

ISBN: (纸本)9798331527181

The annotation of medical images for the purpose of segmentation demands a high level of expertise and experience, and the generation of suitable datasets represents a significant challenge. In this study, we propose a method that facilitates annotation on a whole slide image (WSI) without requiring detailed specification of the lesion area. The objective is to enable annotation without the need for specialised knowledge. In particular, the WSI is divided into smaller regions for annotation purposes, with the proportion of lesions in each region being compared with reference images of similar regions, for which the proportion of lesions is known. The proportion of lesions in the reference small region image deemed to be most analogous is then designated as the annotation. The outcomes of an annotation experiment on multiple subjects based on this approach indicated that as the level of experience of the annotator increased, the variability in the accuracy rate diminished. This suggests that the annotators had acquired knowledge and skills through the process of annotation.

关键词： Image segmentation Accuracy Annotations Image annotation Lesions Biomedical imaging

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Implementation and Evaluation of 0.6 µm CMOS Complex RCPF for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and Systems 2025年第1期145卷 25-37页

作者： Sato, Lina Fukagawa, Kanta Shouno, Kazuhiro Tanimoto, Hiroshi Muto, Cosy Moriyama, Seijiro Takahashi, Chikau Yoshino, Michitaka Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan Anagix Corporation 3-22-27 Numama Kanagawa Zushi249-0004 Japan Takamori Corporation Japan Research Center for Micro-Nano Technology Hosei University 3-11-15 Midori-cho Koganei Tokyo184-0003 Japan

This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and its supply voltage is 5 V. The integrated circuit includes a filter under test (FUT), a four-phase square wave generator, several analog switches and differential amplifiers to amplify the high-impedance signals of the RCPF. This FUT is a third-order complex RCPF whose passband is 8 − 40 MHz. This paper describes the design of these circuits in detail. This integrated circuit can select two measurement methods by means of external control signals. The first method uses a four-phase square wave. The frequency characteristics are measured by using an on-chip four-phase square wave generator driven by an external clock generator and by a spectrum analyzer tuned to the fundamental frequency. The second method uses an external network analyzer. Furthermore, this integrated circuit is equipped with a calibration circuit to measure the frequency characteristics of the circuitry except FUT by passing through the input / output signals of the FUT. As a result, the frequency characteristics of the FUT itself can be accurately measured. The effectiveness is confirmed through evaluation of the fabricated integrated circuit. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Differential amplifiers

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Random Number Generator for Securing Data Exchange in Smart Factory: Recent Trends and Best Practices

Random Number Generator for Securing Data Exchange in Smart ...

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computer Technologies (ICCTech), International Conference on

作者： Andi Sama Meyliana Yaya Heryadi Taufik Roni Sahroni Computer Science Department Binus Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Information System Department School of Information System Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Doctor of Computer Science Jakarta Jakarta Indonesia Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350395419

ISBN: (纸本)9798350395426

IT/OT convergence in the form of a Cyber-Physical System (CPS) presents an opportunity to apply recent information technology (IT) advancements to Operational technology (OT). The IT/OT convergence introduces cyber security threats for OT. The OT communication protocols, for example, have limited or no security capabilities for securing data exchange beyond basic authentication. The RNG (Random Number Generator) function is crucial in PLC for producing the encryption keys or the initial seed for security algorithms. The software-based PRNG (Pseudo RNG) is the most common approach for RNG in OT, instead of hardware-based RNG. This study provides a systematic literature review of the most recent evidence in digital transformation to secure data exchange in smart factories, within OT, or across OT and IT settings. It does this by looking at the PRNG on PLC and identifying the most recent trends and best practices. The findings include up-to-date data on author countries' distribution, published journals, research trends, industries, and industry types. The findings also address whether data flow is encrypted or shared in plaintext to ensure secure data transfer between IT and legacy OT environments. This paper concludes that while there is known RNG implementation in the actual embedded system in ICS, there is no evidence in the literature for using RNG in ICS networks for PLC implementation to secure data transfer with encryption.

关键词： Industries Systematics Market research Data transfer Control systems Generators Encryption

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When Zero-Trust Meets Federated Learning

When Zero-Trust Meets Federated Learning

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Xinran Wang, Dan Zhu, Yifei Chen, Weilong Chang, Zheng Han, Zhu The Hong Kong Polytechnic University Department of Computing Hong Kong Shanghai Jiao Tong University UM-SJTU Joint Institute Shanghai China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China University of Jyväskylä Faculty of Information Technology Jyväskylä Finland University of Houston Department of Electrical and Computer Engineering HoustonTX United States Kyung Hee University Department of Computer Science and Engineering Seoul Korea Republic of

ISBN: (纸本)9798350351255

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model's robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients' trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system's betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model's robustness to betrayal. © 2024 IEEE.

关键词： Federated learning

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Evaluation of REAL-ESRGAN Using Different Types of Image Degradation

Evaluation of REAL-ESRGAN Using Different Types of Image Deg...

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International Conference on ICT For Smart Society (ICISS)

作者： Kus Andriadi Muhammad Zarlis Yaya Heryadi Andry Chowanda Computer Science Department Binus Graduate Program Bina Nusantara University Jakarta Indonesia Information System Management Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science BINUS University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

This study evaluates the performance of the REAL-ESRGAN [1] model on images with varying levels of degradation using the DIV2K dataset [2], such as the Wild, the Mild, the Difficult, and the x8 subsets. REAL-ESRGAN was created to solve super-resolution problems and aims to produce high-resolution images from low-resolution images. Experiments were conducted at scales of x2 and x4, and performance was measured using Full-Reference metrics (LPIPS, PSNR, SSIM) and No-Reference metrics (NIQE, MANIQA, CLIPIQA, and PI). The Results were good, especially with the x2 scale; it has higher PSNR and SSIM scores, lower LPIPS and NIQE values, and enhanced visual and perceptual quality. The model faced more significant challenges with the wild and the difficult datasets because they have more complex degradations and compression artifacts; it can be seen with unstable results of Full-Reference and No-Reference metrics. On the contrary, the Mild and x8 datasets yielded better results in both metrics; not only that, even the computational cost for Mild and x8 outperforms the rest of the dataset. This study shows the strengths and limitations of REAL-ESRGAN in handling different levels of image degradation. For future research, the model needs enhancement to tackle the degradation format of the wild and the difficult dataset. It would be good if the REAL-ESRGAN improvement could also maintain the computational cost.

关键词： Degradation Measurement Visualization Computational modeling Superresolution Computational efficiency Image reconstruction

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Convolutional Neural Network Using Res-Net For Organic and Anorganic Waste Classification 4

Convolutional Neural Network Using Res-Net For Organic and A...

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4th IEEE International Conference of computer science and information technology, ICOSNIKOM 2022

作者： Girsang, Abba Suganda Yunanda, Rezky Syahputra, Muhammad Edo Peranginangin, Ezra Binus Graduate Program-Master of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Industrial Engineering Department Jakarta11480 Indonesia

ISBN: (纸本)9798350399073

Convolutional Neural network is state of the art of image recognition or image classification. However to build the robust model using CNN needs many parameters adjusted, and choosing the good combination hyperparameter which impacts taking much computation time. Genetic algorithm is one method metaheuristic which is robust for choosing the combinatorial possible hyperparameter. This model also uses ResNet-50 which is a pretrained model of convolutional neural network that consists 50 layers. By using a pretrained like ResNet-50, it will increase the performance model. CNN-ResNet with efficient genetic algorithm (EGA) to optimize the hyperparameter. The EGA algorithm utilizes transfer learning techniques in its algorithm so that the optimization process on CNN can achieve unified accuracy values quickly. The best performance model optimized using EGA outperformed the ResNet-50 model and the model optimized using GA and VLGA in classifying organic and inorganic materials. The accuracy value obtained from EGA is 97.53% with a loss of 0.08. © 2022 IEEE.

关键词： Convolutional neural networks

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Analyzing the Behavioral Aspects of Implementing Green IT in an Academic Environment 8

Analyzing the Behavioral Aspects of Implementing Green IT in...

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8th International Conference on information Management and technology, ICIMTech 2023

作者： Syamsuar, Dedy Triando, Fero Ulfa, Maria Antoni, Darius Mailangkay, Adele Witarsyah, Deden School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia Universitas Bina Darma Postgradute Department Palembang Indonesia Universitas Bina Darma Information System Study Program Computer Science Faculty Palembang Indonesia Universitas Indo Global Mandiri Faculty of Computer Science Palembang Indonesia School of Industrial Engineering Telkom University Bandung Indonesia

ISBN: (纸本)9798350326093

Environmental awareness has recently emerged as one of the most crucial topics. As a result, various groups advocate for these technologies and research ways to promote their usage in various contexts. This study examines the factors influencing the intention and use of green technology among academics. This study integrates Price Value (PV) and Consideration of future consequence (CFC) to Theory Planned Behavior (TPB) as a theoretical basis. Two hundred five valid replies were gathered and processed through statistical analysis. The results of this study partly support the developed hypotheses. Four hypotheses developed from TPB have presented significant relationships. However, PV and CFC were not. The findings indicate that individuals in this study did not consider CFC or PV of green IT products as critical factors in their decision-making process. Findings also suggest that for implementation success, competent parties must consider the campaign to increase individual awareness and provide financial support regarding environmental policy. © 2023 IEEE.

关键词： Decision making

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The reliability of deep learning models in assessing the shoulder arthroscopic field's visual clarity in relation to bleeding

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JSES International 2025年第3期9卷 864-870页

作者： Tran, Son Quang Bui, Minh Cong Nguyen, Dat Tien Itthipanichpong, Thun Limskul, Danaithep Thamrongskulsiri, Napatpong Tanpowpong, Thanathep Clinical Sciences Program Faculty of Medicine Chulalongkorn University Bangkok Thailand Faculty of Medicine Department of Orthopaedics Cantho University of Medicine and Pharmacy Can Tho Viet Nam Faculty of Computer Science University of Information Technology Ho Chi Minh City National University Ho Chi Minh City Viet Nam Sports Medicine Research Group Faculty of Medicine Chulalongkorn University Bangkok Thailand

Background: The clarity of visualization in shoulder arthroscopy is significantly influenced by intraoperative bleeding. This study aims to develop deep learning models to classify the visual clarity of arthroscopic shoulder images and evaluate their reliability compared to rater assessment. Methods: We retrospectively reviewed videos from 113 shoulder arthroscopies, using 63 patients’ videos to create a 3750-image training dataset and 50 patients’ videos to evaluate the reliability and agreement of the trained models. Images extracted from the videos were assessed for visual clarity with a 3-grade scale. Subsequently, we implemented transfer learning techniques for the pretrained deep learning models involving DensetNet169, DenseNet201, Xception, InceptionResNetV2, VGG16, and ViT. The reliability and agreement of the trained predictive models compared with raters in classifying the visual clarity of shoulder arthroscopic images were reported with percent agreement and weighted kappa coefficients. Similarly, for quantifying the visual clarity of surgical videos, the reliability and agreement were evaluated through intraclass coefficients, bias, and limits of agreement. Results: Most models achieved over 90% accuracy in validation step, with InceptionResNetV2, DenseNet169, and ViT exhibiting good percent agreements of 82.3%, 79.2%, and 77.8%, respectively, and their weighted kappa coefficients above 0.8. DenseNet169 model had the highest reliability for evaluating the visual clarity of surgical videos with an intraclass correlation coefficient of 0.9, a bias of 0.027, and the narrowest limits of agreement. Conclusion: The trained DenseNet169 predictive model was reliable enough to be utilized as an objective measure of the visual clarity of the shoulder arthroscopic field for further research. © 2025 The Authors

关键词： Basic science Study Bleeding Convolutional neural network Deep learning model Grading scale Shoulder arthroscopy Validation of Artificial Intelligence Software Visual clarity Visualization

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