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International Conference on information Technology, information Systems and Electrical engineering (ICITISEE)

作者： Fairuz Iqbal Maulana Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter.

关键词： COVID-19 Sentiment analysis Analytical models Data analysis Social networking (online) Blogs Data visualization

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Enhancing Security in SDN: Systematizing Attacks and Defenses from a Penetration Perspective

TechRxiv

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TechRxiv 2024年

作者： Kim, Jinwoo Seo, Minjae Lee, Seungsoo Nam, Jaehyun Yegneswaran, Vinod Porras, Phillip Gu, Guofei Shin, Seungwon The School of Software Kwangwoon University Seoul01897 Korea Republic of The Graduate School of Information Security KAIST Daejeon34141 Korea Republic of The Department of Computer Science and Engineering Incheon National University Incheon22012 Korea Republic of The Department of Computer Engineering Dankook University Gyeonggi-do Yongin16890 Korea Republic of SRI International Menlo ParkCA94025 United States Texas A&M University College StationTX77843 United States The School of Electrical Engineering KAIST Daejeon34141 Korea Republic of

Over the last 15 years, Software-Defined Networking (SDN) has gained widespread support from both research communities and the industry owing to its open nature and programmability. This paradigm empowers stakeholders, including researchers, practitioners, and developers, to craft innovative networking services using robust APIs and a global network view, free from reliance on vendor-dependent control planes. However, the flexible architecture of SDN has introduced a plethora of security challenges absent in traditional network environments. While many surveys have presented existing attacks, there has been a lack of systematization of attacks from a penetration perspective, which is crucial for understanding attacks and their root causes. This paper aims to analyze previous literature that has disclosed attack cases in SDN, scrutinizing their vulnerabilities, penetration routes, and root causes. Additionally, we provide an in-depth and comprehensive discussion of the underlying problems associated with these attacks and present defenses proposed by researchers to mitigate them, analyzing how the root causes are remedied. Through this study, we aim to illuminate existing security issues within the current SDN architecture, prompting a reevaluation of various security problems and providing a future research guideline for SDN security. © 2024, CC BY.

关键词： Network intrusion

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Adaptive Drop Approaches to Train Spiking-YOLO Network for Traffic Flow Counting

Research Square

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Research Square 2024年

作者： Zhang, Bin Wu, Liangshun Liu, Peilin Juan, Hu Luo, Meizhen Peng, Ling School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Computer City University of Hong Kong 999077 Hong Kong Xinjiang University of Political Science and Law Tumushuke150060 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China College of Huanggang Normal University Huanggang China Graduate School Binary University of Managment & Entrepreneurship Malaysia Huanggang Real Estate Registration Center Huanggang Land and Resources Bureau Huanggang China

Traffic flow counting is an object detection problem. YOLO (" You Only Look Once ") is a popular object detection network. Spiking-YOLO converts the YOLO network from ANN(artificial neural network) to SNN (spiking neural network), which consumes less power than YOLO. However, Spiking-YOLO is difficult to train and has poor accuracy because of overfitting. Dropout and DropConnect are two methods to regularize neural network models and have produced cutting-edge outcomes in many benchmarks. The two drop approaches are first applied to Spiking-YOLO. Considering that a higher membrane potential of a biological neuron implies a higher probability of neural activation, we propose three adaptive drop algorithms—Spiking-YOLO with adaptive Dropout, Spiking-YOLO with adaptive DropConnect, and Spiking-YOLO with group adaptive Drop, which adaptively adjusts the keep probability for training SNNs. The experimental results show that the adaptive algorithms have fast convergence speed with high detection accuracy and mAP value, and work well in the case of high-speed streaming video, making them suitable for online learning scenarios. © 2024, CC BY.

关键词： Object detection

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Building a Smart Agricultural Product Classification System with Security Mechanism Using 5G Network

Building a Smart Agricultural Product Classification System ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Arun Kumar Sangaiah Mu Yen Chen Hsin-Te Wu Department of International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan (R.O.C) Department of Engineering Science National Cheng Kung University Tainan Taiwan (R.O.C) Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan (R.O.C)

Many countries worldwide aim to proactively conduct digital transformation in conventional industries as these businesses require a massive workforce to classify, inspect, and package, causing high costs. Moreover, manual approaches might lead to defective products due to human errors and productivity limits. This article develops an Internet of Things (IoT) system with an edge computing function to detect agricultural products' quality and classify them. The proposed system in this research detects the luster, weight, and appearance of agricultural products to judge the quality; hence, the equipment needs sensors and a camera that connects to the Artificial Intelligence (AI) module. The features of our research are as follows: 1. The system analyzes agricultural products' appearance, luster, and weight; when the equipment detects an object, the AI module will be activated to analyze the item; 2. The edge computing function in the system allows it to pass the task to the next step directly after the IoT development board completes the process without centralized operations on a server; 3. The 5G network used in this study ensures data transmission and reduces unnecessary circuits between devices; 4. The network safety mechanism built into the system also protects data transmission security. Additionally, this study creates a small-scale conveyor to experiment with the design and test the system's practical feasibility.

关键词：

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Artificial intelligence for geoscience:Progress,challenges,and perspectives

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The Innovation 2024年第5期5卷 136-160,135页

作者： Tianjie Zhao Sheng Wang Chaojun Ouyang Min Chen Chenying Liu Jin Zhang Long Yu Fei Wang Yong Xie Jun Li Fang Wang Sabine Grunwald Bryan MWong Fan Zhang Zhen Qian Yongjun Xu Chengqing Yu Wei Han Tao Sun Zezhi Shao Tangwen Qian Zhao Chen Jiangyuan Zeng Huai Zhang Husi Letu Bing Zhang Li Wang Lei Luo Chong Shi Hongjun Su Hongsheng Zhang Shuai Yin Ni Huang Wei Zhao Nan Li Chaolei Zheng Yang Zhou Changping Huang Defeng Feng Qingsong Xu Yan Wu Danfeng Hong Zhenyu Wang Yinyi Lin Tangtang Zhang Prashant Kumar Antonio Plaza Jocelyn Chanussot Jiabao Zhang Jiancheng Shi Lizhe Wang Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China School of Computer Science China University of GeosciencesWuhan 430078China State Key Laboratory of Mountain Hazards and Engineering Resilience Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu 610299China Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China Data Science in Earth Observation Technical University of Munich80333 MunichGermany The National Key Laboratory of Water Disaster Prevention Yangtze Institute for Conservation and DevelopmentHohai UniversityNanjing 210098China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China Soil Water and Ecosystem Sciences DepartmentUniversity of FloridaPO Box 110290GainesvilleFLUSA Materials Science Engineering Program Cooperating Faculty Member in the Department of Chemistry and Department of Physics Astronomy University of CaliforniaCaliforniaRiversideCA 92521USA Institute of Remote Sensing and Geographical Information System School of Earth and Space SciencesPeking UniversityBeijing 100871China Key Laboratory of Computational Geodynamics University of Chinese Academy of SciencesBeijing 100049China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China College of Geography and Remote Sensing Hohai UniversityNanjing 211100China Department of Geography The University of Hong KongHong Kong 999077SARChina Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Nanjing 210044China School of Environmental Science and Engineering Nanjing University of Information Science&TechnologyNanjing 210044China Collaborative Inno

This paper explores the evolution of geoscientific inquiry,tracing the progression from traditional physics-based models to modern data-driven approaches facilitated by significant advancements in artificial intelligence(AI)and data collection *** models,which are grounded in physical and numerical frameworks,provide robust explanations by explicitly reconstructing underlying physical ***,their limitations in comprehensively capturing Earth’s complexities and uncertainties pose challenges in optimization and real-world *** contrast,contemporary data-driven models,particularly those utilizing machine learning(ML)and deep learning(DL),leverage extensive geoscience data to glean insights without requiring exhaustive theoretical *** techniques have shown promise in addressing Earth science-related ***,challenges such as data scarcity,computational demands,data privacy concerns,and the“black-box”nature of AI models hinder their seamless integration into *** integration of physics-based and data-driven methodologies into hybrid models presents an alternative *** models,which incorporate domain knowledge to guide AI methodologies,demonstrate enhanced efficiency and performance with reduced training data *** review provides a comprehensive overview of geoscientific research paradigms,emphasizing untapped opportunities at the intersection of advanced AI techniques and *** examines major methodologies,showcases advances in large-scale models,and discusses the challenges and prospects that will shape the future landscape of AI in *** paper outlines a dynamic field ripe with possibilities,poised to unlock new understandings of Earth’s complexities and further advance geoscience exploration.

关键词： Earth utilizing landscape

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Composite FORCE Learning of Chaotic Echo State Networks for Time-Series Prediction

arXiv

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arXiv 2022年

作者： Li, Yansong Hu, Kai Nakajima, Kohei Pan, Yongping School of Artificial Intelligence Sun Yat-sen University Zhuhai519000 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China Graduate School of Information Science and Technology University of Tokyo Tokyo113-8656 Japan

Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights. First-order reduced and controlled error (FORCE) learning is an online supervised training approach that can change the chaotic activity of ESNs into specified activity patterns. This paper proposes a composite FORCE learning method based on recursive least squares to train ESNs whose initial activity is spontaneously chaotic, where a composite learning technique featured by dynamic regressor extension and memory data exploitation is applied to enhance parameter convergence. The proposed method is applied to a benchmark problem about predicting chaotic time series generated by the Mackey-Glass system, and numerical results have shown that it significantly improves learning and prediction performances compared with existing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Distinguishing Working State by Palm Orientation

Distinguishing Working State by Palm Orientation

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IEEE Global Conference on Life sciences and Technologies (LifeTech)

作者： Kenta Hayashi Shingo Kumazoe Shigemi Ishida Yutaka Arakawa Graduate School/Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan School of Electrical Engineering and Computer Science Kyushu University Fukuoka Japan

ISBN: (数字)9781665418751

ISBN: (纸本)9781665448086

When working from home, self-management becomes of paramount importance due to the absence of a boss or colleagues. As a result, individuals tend to waste time surfing the internet and playing with our smartphones. We propose a wrist-worn sensor-based system that identifies whether a desk worker is working or not for self-management and productivity. Our main hypothesis is that the identification of the various tasks that occur during desk work, such as using computers, reading books, manipulating a smartphone, and writing, can be simply distinguished by the direction of the palm. In this paper, to verify our hypothesis, we measure various tasks with the wrist-worn sensor attached to clarify the relationship between hand orientation and each task. At the same time, we develop a machine learning-based classifier to distinguish between the states of `working' and `not-working' using the obtained hand orientation data. We performed 10-fold cross-validation and Leave-One-Person-Out validation and we found that it was possible to distinguish whether a desk worker is working or not with an F1-value of 0.8 or higher.

关键词： Wrist Productivity Writing Life sciences Task analysis Smart phones Monitoring

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Loss Distillation via Gradient Matching for Point Cloud Completion with Weighted Chamfer Distance

arXiv

引用

arXiv 2024年

作者： Lin, Fangzhou Liu, Haotian Zhou, Haoying Hou, Songlin Yamada, Kazunori D. Fischer, Gregory S. Li, Yanhua Zhang, Haichong K. Zhang, Ziming Department of Robotics Engineering Worcester Polytechnic Institute WorcesterMA01609 United States Dell Technologies HopkintonMA01748 United States Graduate School of Information Sciences Tohoku University Sendai980-8579 Japan Department of Computer Science Worcester Polytechnic Institute WorcesterMA01609 United States Department of Electrical & Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States

3D point clouds enhanced the robot’s ability to perceive the geometrical information of the environments, making it possible for many downstream tasks such as grasp pose detection and scene understanding. The performance of these tasks, though, heavily relies on the quality of data input, as incomplete can lead to poor results and failure cases. Recent training loss functions designed for deep learning-based point cloud completion, such as Chamfer distance (CD) and its variants (e.g. HyperCD [1]), imply a good gradient weighting scheme can significantly boost performance. However, these CD-based loss functions usually require data-related parameter tuning, which can be time-consuming for data-extensive tasks. To address this issue, we aim to find a family of weighted training losses (weighted CD) that requires no parameter tuning. To this end, we propose a search scheme, Loss Distillation via Gradient Matching, to find good candidate loss functions by mimicking the learning behavior in backpropagation between HyperCD and weighted CD. Once this is done, we propose a novel bilevel optimization formula to train the backbone network based on the weighted CD loss. We observe that: (1) with proper weighted functions, the weighted CD can always achieve similar performance to HyperCD, and (2) the Landau weighted CD, namely Landau CD, can outperform HyperCD for point cloud completion and lead to new state-of-the-art results on several benchmark datasets. Our demo code is available at https://***/Zhang-VISLab/ IROS2024-LossDistillationWeightedCD. © 2024, CC0.

关键词： Deep learning

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities

Learner Action Patterns in the Problem-Solving Process Relat...

引用

International Conference on Informatics and Computing (ICIC)

作者： Aulia Akhrian Syahidi Ahmad Afif Supianto Tsukasa Hirashima Yutaka Watanobe Smart City Information System Applied Undergraduate Program Politeknik Negeri Banjarmasin Banjarmasin City Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency (BRIN) Bandung City Indonesia Learning Engineering Laboratory Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Higashihiroshima Japan Department of Computer Science and Engineering Division of Information Systems University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (纸本)9798350345728

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the “if-then” rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process.

关键词： Visualization Codes Media Data models Recording Data mining Problem-solving

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Smartphone Application for Badminton sports community 2

Smartphone Application for Badminton sports community

引用

2nd International Conference on computer, science, engineering, and Technology, ICComSET 2019

作者： Anwar, Nizirwan Hendroasali, Eric Heryanto Budiyanto, Teddy Hendyan, Willy Warnars, Harco Leslie Hendric Spits Faculty of Computer Science Esa Unggul University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, there are so many people in Indonesia particularly young people who are interested in playing badminton, but at the end of the day, this game will make boring for young people particularly with the booming of the smartphone game. Literally, it will make the young generation too lazy for outing activity or doing sports and busy with their digital technology, particularly their smartphone. Obviously, we are interested in using the mobile application which can be used to bridge the boring of doing sports mainly badminton sport, wherewith this mobile application is not just only for field badminton or coach booking, but can extend their friendship and community in positive ways. Hopefully, with this mobile application not just only booking for Badminton fields, but beyond that, there will be a mobile application that supports the fans of the badminton sports community when can know each other from different places and make friendship and competition among the members. At the end of the day, we hope this mobile application will support young people to stay away from trouble and drug and to have a positive life with the sport, particularly badminton sport as Indonesian's favorite sport. © Published under licence by IOP Publishing Ltd.

关键词： Sports

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