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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9781728163277

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. © 2023 IEEE.

关键词： computer vision

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Disk based pay-as-you-go record linkage

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Frontiers of computer science 2022年第4期16卷 227-229页

作者： Chenchen Sun Derong Shen Key Laboratory of Computer Vision and System(Ministry of Education) Tianjin University of TechnologyTianjin300384China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computer Science and Engineering Northeastern UniversityShenyang110189China

1Introduction Record linkage(RL)groups_records corresponding to the same entities in datasets,and is a long-standing topic in data management and mining communities[1-2].In big data era,real-time data applications become popular,and callfor payas-you-go RL(PRL),which produces as many match pairs as possible in very limited time(much shorter than the overall RLruntime).

关键词： record linkage mining

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Critical Factors of Immersive Learning in Higher Education: A systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Transferable Features from 1D-Convolutional Network for Industrial Malware Classification

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computer Modeling in engineering & sciences 2022年第2期130卷 1003-1016页

作者： LiweiWang Jiankun Sun Xiong Luo Xi Yang School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Shunde Graduate School University of Science and Technology BeijingFoshan528399China Beijing Intelligent Logistics System Collaborative Innovation Center Beijing101149China

With the development of information technology,malware threats to the industrial system have become an emergent issue,since various industrial infrastructures have been deeply integrated into our modern works and *** identify and classify new malware variants,different types of deep learning models have been widely explored ***,sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ***,in current practical applications,an ample supply of data is absent in most specific industrial malware detection *** learning as an effective approach can be used to alleviate the influence of the small sample size *** addition,it can also reuse the knowledge from pretrained models,which is beneficial to the real-time requirement in industrial malware *** this paper,we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning *** experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification,and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.

关键词： Transfer learning malware classification sequence data modeling convolutional network

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Optimizing Web Server Performance: A Comparative Analysis of Central Manager and Round Robin Load Balancing Algorithms 4

Optimizing Web Server Performance: A Comparative Analysis of...

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4th International Conference on Electrical engineering and computer science, ICECOS 2024

作者： Permana Arrasyid, Fahmi Stiawan, Deris Afifah, Nurul Heryanto, Ahmad Azhar, Iman Saladin B. Bardadi, Ali Yudho Suprapto, Bhakti Pahendra Anto Saputra, Iwan Sriwijaya University COMNETS Rerearch Group Faculty of Camp. Science Palembang Indonesia Sriwijaya University Faculty of Comp. Science Computer Engineering Dept. Palembang Indonesia Sriwijaya University Faculty of Comp. Science Information System Dept. Palembang Indonesia Sriwijaya University Faculty of Engineering Electrical Engineering Dept. Palembang Indonesia

ISBN: (纸本)9798350368253

Central manager algorithm has a lower average throughput compared to the round robin algorithm. This is evidenced by a significant decrease in throughput values during measurements. Central manager algorithm provides a faster average response time than round robin algorithm, despite experiencing an increase in response time values over time. Central manager algorithm shows a higher request loss rate and many requests are not served. Under normal performance conditions, both algorithms yield the same average request loss rate, achieving stable values without failed requests. The central manager algorithm ensures relatively balanced resource availability, including CPU and memory usage, across each slave web server compared to the round robin algorithm. The results show that increasing the number of clients and client requests during system testing to better observe server resource availability and load distribution performance. For future research, it is recommended to increase the number of clients in the system testing to enhance server load and to introduce client request actions on the web server to further highlight resource availability on each web server. © 2024 IEEE.

关键词： Response time (computer systems)

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Robotic Anchoring system with Two Locomotion Modes for Preoperative Localization of Colorectal Cancer

Robotic Anchoring System with Two Locomotion Modes for Preop...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Wang, Jiale Ren, Hao Yang, Liu Zhao, Haoxiang Zhang, Ruijia Shen, Yajing Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Kowloon Hong Kong City University of Hong Kong Department of Biomedical Engineering Kowloon Hong Kong Institute of Robotics and Automatic Information System Nankai University China

ISBN: (纸本)9781665481090

At present, colorectal cancer is one of the most prevalent cancers in the world. Colectomy serves as an effective treatment option for this disease. Preoperative localization plays a vital role in the colectomy procedure. However, traditional preoperative localization techniques have a greater risk of infection and lower accuracy. Moreover, it is difficult for the previous anchoring robots to adapt to the unstructured environment of the GI tract because of their limited movement abilities. To address these challenges, this work develops a robotic anchoring system with two locomotion modes which can anchor in the designated position. Driven by the external magnetic field, the robot can achieve slipping and flipping in the stomach. When the gradients of the field increase, the robot is triggered to deploy the anchoring mechanism in the intestine. Two in vitro experiments are conducted in the stomach and intestine phantoms. The first experiment demonstrates that the robot system can achieve both slipping and flipping in the stomach and its average position error is 2.96mm. The second experiment shows that the robot system can anchor in the designated position near the tumor and can resist a peristalsis force of 0.82N from the intestine wall. © 2024 IEEE.

关键词： Diseases

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An Early Prediction of Dropouts for At-risk Scholars in MOOCs using Deep Learning

An Early Prediction of Dropouts for At-risk Scholars in MOOC...

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2022 International Conference on Computing, Communication, Security and Intelligent systems, IC3SIS 2022

作者： Anjali, C.A. Ramani Bai, V. System Engineer Tata Consultancy Services Kochi India Computer Science and Engineering Vidya Academy of Science and Technology Kerala Thrissur India

ISBN: (数字)9781665468831

ISBN: (纸本)9781665468831

In 2012, the world has observed a new approach to educational systems named 'Massive Open Online Courses' which leads to a greater impact on the entire world as it transformed our traditional educational approaches. MOOCs have rapidly moved into a place of prominence within the media, in scholarly publications, and within the mind of the general public Universities. According to recent studies, more than thirty MOOC platforms are providing ethnically diverse courses to scholars across the boundaries however it was also observed that they can hardly follow the subjects until the end of the course. Such conclusions pointed out that in the future it may restrict the flourishing of such platforms and these high dropout rates may diminish the development of MOOCs. The research work focuses on developing an early prediction of a dropout system for at-risk scholars in MOOCs using the deep learning algorithm and simulating a dropout prediction model to construct a ranking system for scholars using their dropout probability for every week. Using this probability value the system could be able to predict the scholar's attitude toward the subject and chances of dropout;thereby this system would help the instructors to guide them at an early stage. The datasets of MITx and HarvardX MOOC courses were used to predict scholar dropouts by analyzing their learning patterns. The Deep Neural Network model could make predictions better than the existing technologies through hyperparameter tuning and optimization. Inducing the benefits of deep learning methods helps to construct an effective dropout prediction model and this new versatile technique helps the tutors to prioritize intervention for those dropouts and can be considered an effective solution to the early dropout students in MOOCs compared to the existing works. © 2022 IEEE.

关键词： Learning algorithms

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Preliminary Exploration on Node-To-Node Fault Tolerance Coordination in Distributed system

Preliminary Exploration on Node-To-Node Fault Tolerance Coor...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Saadoon, Muntadher Hamid, Siti Hafizah Ab. Sofian, Hazrina Altarturi, Hamza Nasuha, Nur Azizul, Zati Hakim Khalid, Haqi Universiti Malaya Faculty of Computer Science and Information Technology Department of Software Engineering Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Department of Artificial Intelligence Kuala Lumpur50603 Malaysia Universiti Putra Malaysia Faculty of Engineering Department of Computer and Communication Selangor 43400 Malaysia

ISBN: (纸本)9798350302684

Internet of Things, edge computing devices, the widespread use of artificial intelligence and machine learning applications, and the extensive adoption of cloud computing pose significant challenges to maintaining fault tolerance in distributed systems. As the volume and complexity of systems continue to grow, the probability of system failures also grows, leading to downtime and poor performance. Fault tolerance is pivotal in modern computing systems by preventing downtime and service disruptions, even during failures. However, adopting fault tolerance comes with additional resource consumption, such as memory, CPU, disk, and network bandwidth usage, impacting system performance. Balancing fault tolerance with system performance is crucial to optimize resource utilization while minimizing the costs associated with detecting and recovering from faults. This paper introduces a node-to-node fault tolerance coordination mechanism for distributed systems to facilitate the tradeoff between fault tolerance and the cost of performance penalties and resource consumption. The proposed mechanism is preliminarily implemented on HDFS cluster. Our proposal enables opportunities to minimize the cost of the fault tolerance impact on performance and resource usage compared to the existing fault tolerance coordination mechanism. © 2023 IEEE.

关键词： Fault tolerance

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An Efficient Graph Accelerator with Distributed On-Chip Memory Hierarchy 22nd

An Efficient Graph Accelerator with Distributed On-Chip Mem...

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Zheng, Ran Jiang, Yingxin Wang, Yibo Su, Yongbo Zheng, Long Yao, Pengcheng Liao, Xiaofei Jin, Hai National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

ISBN: (纸本)9783031226762

Graph processing has evolved and expanded swiftly with artificial intelligence and big data technology. High-Bandwidth Memory (HBM), which delivers terabyte-level memory bandwidth, has opened up new development possibilities for FPGA-based graph accelerators. However, despite the tremendous expansion of underlying hardware capabilities, existing graph accelerators have not benefited too much. In this paper, we observe that the uniformed on-chip memory hierarchy is the key to the low scalability of existing graph accelerators. We present a novel graph accelerator with a distributed on-chip memory hierarchy called GraphS. The on-chip memory of GraphS is divided into numerous tiny blocks, each of which is assigned to only one Processing Element (PE). Different PEs are connected through a scalable network-on-chip (NoC). For realistic graphs with power-law properties, a degree-aware preprocessing method is designed to balance the workload among different PEs. Our results with various graph algorithms demonstrate that GraphS can outperform state-of-the-art ForeGraph by up to 21.84 ×. © 2023, Springer Nature Switzerland AG.

关键词： Network-on-chip

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Unveiling Ethical Challenges and Malpractice in Academic Research and Integrity 3

Unveiling Ethical Challenges and Malpractice in Academic Res...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Singh Bist, Ankur Rawat, Bhupesh Rahardja, Untung Hardini, Marviola Kamal, Mustofa Graphic Era Hill University Faculty of Computer Science & Engineering Uttarakhand India Graphic Era Hill University Faculty of Computing Uttarakhand India University of Raharja Faculty of Information Technology Tangerang Indonesia University of Raharja Faculty of Computer System Tangerang Indonesia

ISBN: (纸本)9798350367492

In the pursuit of knowledge and innovation, academic research stands as a cornerstone of intellectual progress. However, the integrity of this process is susceptible to malpractice, raising profound ethical concerns. This research paper delves into the nuanced landscape of malpractice and ethical considerations within the realm of academic research, aiming to shed light on the challenges that undermine the credibility and reliability of scholarly endeavours. The study explores various forms of malpractice, including plagiarism, data fabrication, and authorship disputes, which have the potential to erode the foundations of academic integrity. Through a combination of literature reviews, case studies, and expert interviews, the research seeks to identify patterns and motivations behind such malpractices. The findings reveal that systematic solutions are necessary to address these issues, and propose the integration of advanced technological tools to enhance ethical oversight. This study contributes to the ongoing discourse on ethics in academic research by providing comprehensive insights and recommendations to uphold ethical standards. © 2024 IEEE.

关键词： Confidentiality Ethics Informed Consent Plagiarism Professional Misconduct

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