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8th International Conference on Information Management and Technology, ICIMTech 2023

作者： Prabowo, Yulius Denny Fernando, Erick Gate, Jullend Binus Online Learning Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Universitas Multimedia Nusantara Faculty of Engineering and Informatics Department of Information System Tangerang Indonesia Institut Teknologi Dan Bisnis Kalbis Faculty of Computer Science and Design Department of Information System Jakarta Indonesia

ISBN: (纸本)9798350326093

This study's objective is to evaluate the system's level of maturity by adopting the COBIT Framework 5. The COBIT Framework is one of the ISACA-issued frameworks that defines the concept of information technology governance. The BAI (Build, Acquire, and Implement) domain is the subject of this investigation. The BAI domain is one of the COBIT 5 Framework domains responsible for program and project management, description of needs, identification of solutions, building change availability and capacity, change acceptance and transition, knowledge, assets, and configuration. This study employs qualitative methods to perform literature reviews and question-and-answer sessions, and then collects quantitative analytical data through the formulation of statements or inquiries. There were 5 respondents who responded to the statement, and they implemented the system. The BAI domain of the CBT Examination system in Jakarta's Senior High School XYZ has been calculated and determined to be at level 4, indicating that the system is functioning appropriately. There are subdomains with a lower maturity level in BAI01, BAI02, BAI03, BAI04, BAI06, BAI07, and BAI08, specifically at a rate between 3.8 and 3.96. Additionally, BAI05, BAI09, and BAI10 contain subdomains with a higher level of maturity, with a value range of 4.07 to 4.15. All BAI subdomains culminate at level 4, the system's maturity level, with a value of 3.93. Level 4 indicates that the level of development for Managed and Measurable is fairly good. It can also provide recommendations for improvements, such as paying attention to project management methods before beginning implementation, being able to estimate the time required for the implementation process, and paying attention to the role of trained stakeholders. © 2023 IEEE.

关键词： Project management

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Real Time Flower Identification by Artificial Intelligence 4

Real Time Flower Identification by Artificial Intelligence

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Rahman, Moshiur Joy, Taushik Ahmed Akter, Samia Sattar, Abdus Daffodil International University Department of Management Information System Daffodil Smart City Dhaka Bangladesh North South University Department of Electrical and Computer Engineering Bashundhara Dhaka1229 Bangladesh Daffodil International University Department of Computer Science and Engineering Daffodil Smart City Dhaka Bangladesh

ISBN: (纸本)9798350343670

In the realm of flower-rich Bangladesh, the presence of these blossoms enriches our everyday experiences, whether encountered during leisurely strolls, along railway tracks, or within our gardens. However, the beauty of these flowers often remains unexplored due to our limited knowledge about their names and attributes. To address this, a project was initiated to close this gap and acquaint people with these unfamiliar yet frequently encountered blooms. Our endeavor involves an innovative mobile application that employs real-time camera recognition to identify flowers, powered by neural networks, particularly the Tensorflow-based image classifier on the Android platform. Machine learning's expansive applications in computer science have propelled our interest in this arena, specifically focusing on Convolutional Neural Networks (CNN) and Tensorflow for image classification. While our current application marks the inception, aspiration to further enrich and refine our system for the future. Our ultimate aim is to share the benefits of our work, enabling individuals to gain profound insights into the enchanting floral world that envelops them daily. © 2024 IEEE.

关键词： Artificial Intelligence Data science Deep Learning Machine Learning Monitoring Smartphone Sensors

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Marching Page Walks: Batching and Concurrent Page Table Walks for Enhancing GPU Throughput 31

Marching Page Walks: Batching and Concurrent Page Table Walk...

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31st IEEE International Symposium on High Performance computer Architecture, HPCA 2025

作者： Lee, Jiwon Ko, Gun Yoon, Myung Kuk Jeong, Ipoom Oh, Yunho Ro, Won Woo Ewha Womans University Department of Computer Science and Engineering Seoul Korea Republic of Yonsei University Department of System Semiconductor Engineering Seoul Korea Republic of Yonsei University Department of Electrical and Electronic Engineering Seoul Korea Republic of Korea University School of Electrical Engineering Seoul Korea Republic of

ISBN: (纸本)9798331506476

Virtual memory, with the support of address translation hardware, is a key technique in expanding programmability and memory management in GPUs. However, the nature of the GPU execution model heavily pressures its translation hardware, particularly due to a discrepancy in the behavior of page table walkers and thousands of concurrently running threads. In GPU workloads, multiple threads simultaneously access a number of pages necessitating a substantial number of translations whereas each walker handles only a single walk request at a time. Such a limitation significantly increases the queueing latency of walk requests, which we observe as a major bottleneck for servicing page table walks in GPUs. To tackle this challenge, we investigate a design of page walkers that facilitates multiple walk requests to be handled together in batches. Then, we make the following observations: 1) allowing a page walker to issue beyond a single memory request significantly improves the throughput of walkers, and 2) GPU applications tend to concurrently access pages in wide address ranges. By leveraging the above implications, we propose Marching Page Walks (MPW) that effectively mitigate the contention in GPU page table walkers. MPW scans pending walk requests to identify ones that can be grouped together. Then, MPW batches these requests and concurrently handles them by issuing multiple memory instructions. Experiments show that MPW reduces the queueing latency of page walks by 86.7% and improves GPU performance by 55.6% over the baseline design. © 2025 IEEE.

关键词： Graphics processing unit

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Stock Monitoring and Localization systems for FIFO Method Using RFID Based Pressure Sensing Tag and AI

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and systems 2024年第9期144卷 894-900页

作者： Hadi, Danang Kumara Song, Zequn Rahmadya, Budi Sun, Ran Takeda, Shigeki Graduate School of Science and Engineering Ibaraki University 4-12-1 Nakanarusawa Ibaraki Hitachi316-8511 Japan Department of Agroindustrial Technology Universitas Muhammadiyah Jember Jember68124 Indonesia Computer System Department Faculty of Information Technology Andalas University Padang25175 Indonesia

This research introduces real-time monitoring and localizing product stock using the First-In-First-Out (FIFO) method with radio frequency identification (RFID) pressure sensing tags. The proposed FIFO system has RFID pressure sensing tags that detect pressure changes, generate sensor codes, and estimate a stock location. Integrating artificial intelligence (AI) improves location estimation accuracy of up-to-date stock visibility. The ability of RFID pressure sensing tags and AI to detect stock from sensor code data enables more effective asset management, optimizes stock management, reduces product waste, and contributes to better efficiency in product inventory management. © 2024 The Institute of Electrical Engineers of Japan.

关键词： Inventory control

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High-Resolution Data on Human Behavior for Effective COVID-19 Policy-Making—Wuhan City,Hubei Province,China,January 1–February 29,2020

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China CDC weekly 2023年第4期5卷 76-81,I0004-I0009页

作者： Jingyuan Wang Honghao Shi Jiahao Ji Xin Lin Huaiyu Tian School of Computer Science and Engineering Beihang UniversityBeijingChina Pengcheng Laboratory Shenzhen CityGuangdong ProvinceChina School of Economics and Management Beihang UniversityBeijingChina State Key Laboratory of Remote Sensing Science Center for Global Change and Public HealthCollege of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina

Introduction:High-resolution data is essential for understanding the complexity of the relationship between the spread of coronavirus disease 2019(COVID-19),resident behavior,and interventions,which could be used to inform policy responses for future prevention and ***:We obtained high-resolution human mobility data and epidemiological data at the community *** propose a metapopulation Susceptible-Exposed-Presymptomatic-Infectious-Removal(SEPIR)compartment model to utilize the available data and explore the internal driving forces of COVID-19 transmission dynamics in the city of ***,we will assess the effectiveness of the interventions implemented in the smallest administrative units(subdistricts)during the ***:In the Wuhan epidemic of March 2020,intra-subdistrict transmission caused 7.6 times more infections than inter-subdistrict *** the city was closed,this ratio increased to 199 *** main transmission path was dominated by population activity during peak evening ***:Restricting the movement of people within cities is an essential measure for controlling the spread of ***,it is difficult to contain intra-street transmission solely through citywide mobility restriction *** can only be accomplished by quarantining communities or buildings with confirmed cases,and conducting mass nucleic acid testing and enforcing strict isolation protocols for close contacts.

关键词： district utilize Wuhan

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SAM-COD+: SAM-Guided Unified Framework for Weakly-Supervised Camouflaged Object Detection

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IEEE Transactions on Circuits and systems for Video Technology 2025年第5期35卷 4635-4647页

作者： Chen, Huafeng Wei, Pengxu Guo, Guangqian Gao, Shan Northwestern Polytechnical University Unmanned System Research Institute Xi’an710072 China National Key Laboratory of Unmanned Aerial Vehicle Technology Xi’an710072 China Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Peng Cheng Laboratory Shenzhen518066 China

Most Camouflaged Object Detection (COD) methods heavily rely on mask annotations, which are time-consuming and labor-intensive to acquire. Existing weakly-supervised COD approaches exhibit significantly inferior performance compared to fully-supervised methods and struggle to simultaneously support all the existing types of camouflaged object labels, including scribbles, bounding boxes, and points. Even for Segment Anything Model (SAM), it is still problematic to handle the weakly-supervised COD and it typically encounters challenges of prompt compatibility of the scribble labels, extreme response, semantically erroneous response, and unstable feature representations, producing unsatisfactory results in camouflaged scenes. To mitigate these issues, we propose a unified COD framework in this paper, termed SAM-COD, which is capable of supporting arbitrary weakly-supervised labels. Our SAM-COD employs a prompt adapter to handle scribbles as prompts based on SAM. Meanwhile, we introduce response filter and semantic matcher modules to improve the quality of the masks obtained by SAM under COD prompts. To alleviate the negative impacts of inaccurate mask predictions, a new strategy of prompt-adaptive knowledge distillation is utilized to ensure a reliable feature representation. To validate the effectiveness of our approach, we have conducted extensive empirical experiments on three mainstream COD benchmarks. The results demonstrate the superiority of our method against state-of-the-art weakly-supervised and even fully-supervised methods. Our source codes and trained models will be publicly released. © 1991-2012 IEEE.

关键词： Object recognition

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DD-UNet: Densely Dilated U-Net for Curvilinear Structure Segmentation in Fundus Image

DD-UNet: Densely Dilated U-Net for Curvilinear Structure Seg...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Yindong Chen, Jie Wang, Li Chen, Miaohong Zhang, Guoming Li, Jianqiang Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Eye Hospital China

ISBN: (纸本)9798350337488

Retinopathy of Prematurity (ROP) is a retina disorder that affects premature infants with lower weights. If the patient cannot get the treatment in time when the illness reaches the last stage, irreversible vision loss will be caused. Nevertheless, there has been relatively little consideration given to the segmentation of the ridge, the key clinical characteristic of the illness. Additionally, existing research has not adequately addressed several segmentation issues, such as fragmentary topology, class imbalance, and false positives. This paper proposes a Densely Dilated U-Net (DD-UNet) improved from U-Net to tackle these challenges. Furthermore, the post-processing techniques based on the spatial relationship between vessels and ridges, along with the relative pixel counts of ridges and false positive results is integrated to mitigate false positive results in the predicted ridge. To enhance the precision of thin vessel, a sliding window sampling method is introduced for refined training. Compared with the state-of-the-art models in medical image segmentation, DD-UNet performs well in curvilinear structure segmentation of fundus image. For instance, our DD-UNet outperforms the Attention U-Net by 6.26% in terms of sensitivity and exhibits a 1.85% higher dice score in ridge segmentation. © 2023 IEEE.

关键词： curvilinear structure segmentation fundus image Retinopathy of Prematurity (ROP) ridge segmentation vessel segmentation

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An Effective and Head-Friendly Long-Tailed Object Detection Network with Improving Data Enhancement and Classifier 9

An Effective and Head-Friendly Long-Tailed Object Detection ...

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9th International Conference on computer and Communications, ICCC 2023

作者： Gong, Huiyun Yao, Jingxiu Liu, Yanhong Beihang University School of Computer Science and Engineering Beijing China Beihang University School of Reliability and System Engineering Beijing China Beihang University Beijing Institute of Metrology Beijing China

ISBN: (纸本)9798350317251

Supervised learning methods achieve good results in large amounts of data and balanced data scenarios. However, the real world often has unbalanced data, such as long-tail distribution data sets, where the number of head categories is far exceeds the number of tail categories. Because the amount of tail data is small, using traditional supervised training methods will cause the model to be biased towards the head category. Therefore, when optimizing model performance, we often consider preventing bias from causing difficulty in improving model performance. A common approach is to balance the performance of the head and tail categories to achieve the purpose of improving the performance of the tail category and the overall performance of the model. Such approaches make a complex trade-off between the head and tail categories. Based on the above observations, we propose a head category-friendly long-tail object detection network (EHFL), including a data enhancement module (DEM) and a classifier enhancement module (CEM), and propose a decoupled performance adjustment strategy (DAS) for training, which can decouple the optimization of head category and tail category performance. In DEM, we propose using label information to generate training data to improve some categories' performance. In CEM, we propose a classifer enhancement loss function to improve overall performance. Experimental results show that it reaches 26.48% on the LVIS-v0.5 data set, improving overall performance without reducing the performance of the head category, which is better than state-of-the-art methods. © 2023 IEEE.

关键词： Object detection

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AFaVS: Accurate Yet Fast Version Switching for Graph Processing systems 39

AFaVS: Accurate Yet Fast Version Switching for Graph Process...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zheng, Long Ye, Xiangyu Liu, Haifeng Wang, Qinggang Huang, Yu Gui, Chuangyi Yao, Pengcheng Liao, Xiaofei Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Laboratory Wuhan430074 China Zhejiang Lab Hangzhou311121 China Unsw School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798350322279

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating"version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value"that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency. © 2023 IEEE.

关键词： Efficiency

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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