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International Russian Automation Conference (RusAutoCon)

作者： Obadah Hammoud Ivan A. Tarkhanov Engineering Cybernetics Research Faculty National University of Science and Technology Moscow Russia Federal Research Center “Computer Science and Control” of Russian Academy of Sciences State Academic University for Humanities Moscow Russia

One of the key features of blockchain is high reliability, due to its high redundancy nature. While creating many replications across nodes serves well for storing relatively small data entries, this cannot be used with big files, as it might be impossible to replicate many huge files across many nodes. Here we present a DApps-based system that can lead to improving the total reliability of distributed files storage systems by managing the internal processes using DApps instead of a classical centralized load balancer. For the same reason, known reliability calculation methods for distributed file storage systems are not suitable for assessing the reliability of DApps-based systems. They do not take into account the automatic recovery time of files. In this article, we will deduce the reliability method for DApps-based system, then compare the resulting reliability with other existing systems, and discuss the benefits gained in total.

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Systematic Review: Predictive Models for the Winning Team of Super Leagues (SL) 2

Systematic Review: Predictive Models for the Winning Team of...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Muneer, Salman Alvi, Muhammad Bux Al Sakhnani, Mahmoud Raza, Hamamd Ghazal, Taher M. Ahmad, Munir NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan The Islamia University of Bahawalpur Department of Computer Systems Engineering Bahawalpur Pakistan University City Sharjah School of Business Skyline University College Sharjah1797 United Arab Emirates National College of Business Administration & Economics School of Computer Science Lahore Pakistan Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia School of Information Technology Skyline University College University City Sharjah 1797 United Arab Emirates

ISBN: (纸本)9798350335644

The Pakistan Super League (PSL) is one of the most popular cricket leagues in the world, attracting millions of fans and spectators each year. With the 2023 edition of the league just around the corner, the use of predictive models to determine the winning team has gained much attention. This systematic review analyzed the existing literature on predictive models for determining the winning team of PSL 2023. The study examined multiple approaches, including machine learning, statistical modeling, and expert opinion. The results of this review suggest that machine learning models, particularly ensemble models, are the most accurate predictors of PSL match outcomes. These models typically use a combination of features, such as team rankings, player performance statistics, match-winning ratio, and previous match outcomes, to predict the result of a match. This paper discovered that expert opinion-based models could provide valuable insights into the likely winner of a match. These models typically use input from cricket experts and analysts to predict the outcome of a match. © 2023 IEEE.

关键词： Machine learning

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A Study on Reducing Skin Irritation in Multi-Channel TES for Painless Force Feedback

A Study on Reducing Skin Irritation in Multi-Channel TES for...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Jin-Woo Yu Bomin Kim Jinmo Kim Han-Joon Kim Ji-Woong Choi Department of Electrical Engineering & Computer Science DGIST Daegu South Korea Department of Medical IT Convergence Engineering Kumoh National Institute of Technology Gumi South Korea Department of Electrical Engineering & Computer Science Brain Engineering Convergence Research Center DGIST Daegu South Korea

Reducing unwanted skin irritation during transcutaneous electrical stimulation (TES) is essential for providing clear virtual sensations in virtual reality (VR) systems, leading to a more immersive experience. We investigated general characteristics of parameters in the TES, including the number of channels and pulse width of the stimulation signal, which may affect reducing skin irritation with 3D electromagnetic computer simulation with numerical neuron models. The results showed that the increasing number of channels and the declining pulse width attenuated skin irritation to 44.5 %.

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PID Controller-Based Adaptive Federated Learning

PID Controller-Based Adaptive Federated Learning

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IEEE International Conference on Communications in China (ICCC)

作者： Mingjun Dai Genghui Xu Jialong Yuan Sanaz Kianoush Stefano Savazzi Guangdong Provincial Engineering Center for Ubiquitous Computing and Intelligent Networking College of Electronic and Information Engineering Shenzhen University China School of Computer Science and Technology Kashi University China National Research Council (CNR) Institute of Electronics Information Engineering and Telecommunications (IEIIT) Italy

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

For federated learning (FL) optimization, FedAdam algorithm suffers from overshooting issues. To address this problem effectively, this work proposes a solution based on the design principles of PID controllers in the field of feedback control, aiming to explore the connection between the working process of PID controllers and the optimization process of FL models through mathematical methods. More specifically, this paper proposes a federated adaptive PID optimization algorithm, named FedAdamPID, that utilizes historical, current, and future information of client gradients. Extensive experiments on standard datasets validate that the proposed FedAdamPID algorithm significantly outperforms the FedAdam algorithm in terms of convergence speed and accuracy.

关键词： Deep learning Accuracy Federated learning Prevention and mitigation Process control Mathematical models Feedback control

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Developing xApps for Rogue Base Station Detection in SDR-Enabled O-RAN

Developing xApps for Rogue Base Station Detection in SDR-Ena...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Jun-Hong Huang Shin-Ming Cheng Rafael Kaliski Cheng-Feng Hung Dept. of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Dept. of Electrical Engineering National Sun Yat-sen University Kaohsiung Taiwan

In order to support the diverse requirements of 5G communications, a multitude of RAN components are required. To enable multiple vendor support for 5G, each of whom can independently choose components, Open-RAN (O-RAN) defined a set of standards to which the components must adhere. In addition, O-RAN defines the management elements used to manage each component to secure the 5G networks. While the proposed architecture can manage both 4G and 5G environments, including 5G NSA (Non-Standalone), it inherently suffers from the same vulnerabilities found in 4G LTE. Consequently, an attacker can use unprotected signaling and a low-cost Software Defined Radio (SDR) to launch rogue base station (RBS) attacks on the user equipment (UE), even in O-RAN architectures. In this paper, we consider the stability of signals collected from high-quality operational BSs versus cheap RBSs. Using signal stability features, we develop a machine learning (ML) based RBS detector located on the UE. With the aid of an O-RAN xAPP, ML models can be retrained using the data collected from multiple UEs, and the updated model can be delivered to UEs to enable higher detection accuracy. We conduct extensive experiments by implementing an RBS using a USRP B210, enabling O-RAN using E- Release, and data collected from operational BSs. Moreover, the detector is implemented as an Android APP, which realizes the connection to the O-RAN xAPP. The experimental results show that our detector can achieve more than 99% accuracy, precision “recall” and F1 score.

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Disentangled Visual Representations for Extreme Human Body Video Compression

Disentangled Visual Representations for Extreme Human Body V...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Wang, Ruofan Mao, Qi Wang, Shiqi Jia, Chuanmin Wang, Ronggang Ma, Siwei Shenzhen Graduate School Peking University China Communication University of China State Key Laboratory of Media Convergence and Communication Beijing China City University of Hong Kong Department of Computer Science China National Engineering Research Center of Visual Technology School of Computer Science Peking University Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Recent years have witnessed the great promise of deep neural video compression codecs. However, there are still unprecedented challenges ahead when the videos are expected to be encoded with extremely low bitrate. Motivated by recent attempts of layered conceptual image compression, we make the first attempt to leverage the disentangled visual representations for extreme human body video compression. More specifically, to capture the main structure, we adopt the inferred human pose keypoints as the structure code of each frame, thereby deriving the motion information from structure codes of adjacent frames for further compression. To better exploit the texture redundancy, all frames share the same texture codes by incorporating the proposed texture contrastive learning to ensure texture consistency within a video. Two branches are consequently transmitted in a separable manner, and the generator synthesizes the reconstructed video with the combination of all decoded representations at the decoder side. Both qualitative and quantitative experimental results demonstrate that the proposed scheme can produce perceptually pleasing reconstruction results in ultra-low bitrates far below that can be reached by other video codecs. © 2022 IEEE.

关键词： Generative adversarial networks

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Realization of high aspect ratio metalenses by facile nanoimprint lithography using water-soluble stamps

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PhotoniX 2023年第1期4卷 382-392页

作者： Hojung Choi Joohoon Kim Wonjoong Kim Junhwa Seong Chanwoong Park Minseok Choi Nakhyun Kim Jisung Ha Cheng-Wei Qiu Junsuk Rho Heon Lee Department of Materials Science and Engineering Korea UniversitySeoul 02841Republic of Korea Department of Mechanical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Department of Chemical Engineering Pohang University of Science and Technology(POSTECH)Pohang 37673Republic of Korea POSCO-POSTECH-RIST Convergence Research Center for Flat Optics and Metaphotonics Pohang 37673Republic of Korea National Institute of Nanomaterials Technology(NINT) Pohang 37673Republic of Korea ZERC Seoul 02841Republic of Korea

Nanoimprint lithography(NIL)has attracted attention recently as a promising fabrication method for dielectric metalenses owing to its low cost and high throughput,however,high aspect ratio(HAR)nanostructures are required to manipulate the full 2πphase of *** NIL using a hard-polydimethylsiloxane(h-PDMS)mold inevitably incurs shear stress on the nanostructures which is inversely proportional to the surface area parallel to the direction of ***,HAR structures are subjected to larger shear stresses,causing structural ***,we propose a novel wet etching NIL method with no detachment process to fabricate flawless HAR *** water-soluble replica mold is fabricated with polyvinyl alcohol(PVA)which is simpler than an h-PDMS mold,and the flexibility of the PVA mold is suitable for direct printing as its high tensile modulus allows high-resolution patterning of HAR *** diffraction-limited focusing of the printed metalenses demonstrates that it operates as an ideal lens in the visible *** method can potentially be used for manufacturing various nanophotonic devices that require HAR nanostructures at low cost and high throughput,facilitating commercialization.

关键词： Nanoimprint lithography Wet etching Polyvinyl alcohol High fidelity nanofabrication High aspect ratio nanostructure Metalens

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Understanding and Detecting Privacy Leakage Vulnerabilities in Hyperledger Fabric Chaincodes

Understanding and Detecting Privacy Leakage Vulnerabilities ...

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International Symposium on Software Reliability engineering (ISSRE)

作者： Ziming Chen Yue Li Jianbo Gao Jiashuo Zhang Ke Wang Jianbin Hu Zhi Guan Zhong Chen School of Computer Science Peking University Beijing China College of Computer Science and Technology Taiyuan University of Technology Taiyuan China Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong University Beijing China Peking University Chongqing Research Institute of Big Data Chongqing China National Engineering Research Center for Software Engineering Peking University Beijing China

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

The application on a blockchain cannot maintain secrecy because its data is replicated across all peers in the network. To remedy this problem, Hyperledger Fabric introduces private data collection (PDC) into its smart contract (i.e. chaincode) to facilitate applications that require privacy. However, recent studies have revealed that PDC is too complex for chaincode developers to fully understand and use correctly, leading to privacy leaks vulnerabilities. In this paper, we present an empirical study on the prevalence of PDC misuse in chaincodes by extracting privacy leakage cases from StackOverflow posts and Hyperledger Fabric repositories on GitHub. Subsequently, we systematically categorize the misuse of PDC into three categories of vulnerabilities resulting in the leakage of private data and provide formal definitions for them. Furthermore, we develop PDChecker, an automated security analysis framework for identifying the privacy and security vulnerabilities in Fabric chaincodes. We evaluated PDChecker on 956 real-world chaincodes applying PDC and found that 67.78% of them contain at least one privacy leakage vulnerability. In addition, PDChecker uncovered 10 zero-day vulnerabilities documented by the China national Vulnerability Database.

关键词： Privacy Data privacy Distributed ledger Databases Smart contracts Fabrics Blockchains Software reliability Security Software development management

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Region-aware neural graph collaborative filtering for personalized recommendation

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International Journal of Digital Earth 2022年第1期15卷 1446-1462页

作者： Shengwen Li Renyao Chen Chenpeng Sun Hong Yao Xuyang Cheng Zhuoru Li Tailong Li Xiaojun Kang State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhanPeople’s Republic of China School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China School of Future Technology China University of GeosciencesWuhanPeople’s Republic of China National Engineering Research Center of Geographic Information System China University of GeosciencesWuhanPeople’s Republic of China

Personalized recommender systems have been widely deployed in various scenarios to enhance user experience in response to the challenge of information ***,personalized recommendation models based on graph structure have advanced greatly in predicting user ***,geographical region entities that reflect the geographical context of the items is not being utilized in previous works,leaving room for the improvement of personalized *** study proposes a region-aware neural graph collaborative filtering(RA-NGCF)model,which introduces the geographical regions for improving the prediction of user *** approach first characterizes the relationships between items and users with a user-item-region ***,a neural network model for the region-aware graph is derived to capture the higher-order interaction among users,items,and ***,the model fuses region and item vectors to infer user *** on real-world dataset results show that introducing region entities improves the accuracy of personalized *** study provides a new approach for optimizing personalized recommendation as well as a methodological reference for facilitating geographical regions for optimizing spatial applications.

关键词： Collaborative filtering neural graph collaborative filtering geographical region personalized recommendation graph convolution networks

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Intelligent Internet of Things Attack Detection: Novel Approaches and Technologies

Intelligent Internet of Things Attack Detection: Novel Appro...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Bakhtawar Saeed Sobia Arshad Sanay Muhammad Umar Saeed Muhammad Awais Azam Adeel Akram Shehla Gul Computer Engineering Department University of Engineering and Technology Taxila Taxila Pakistan Deep Packet Inspection Lab (DPI) National Center of Cyber Security (NCCS) UET Taxila Pakistan Technology Innovation Research Group School of Information Technology Whitecliffe NZ

The Internet of Things (IoT) influences numerous facets of our daily lives, and artificial intelligence (AI)-based programs and applications are rapidly gaining popularity. Due to the vast adaptability of the IoT and the escalating data privacy concerns, classical AI algorithms may not be applicable in certain practical application domains. Federated learning (FL) has emerged as a viable technique for training distributed machine learning (ML) models while maintaining data privacy and security. In recent years, it has gained attention in the context of IoT security, particularly for detecting attacks on IoT devices and networks. In this article, we examine ML and DL in the context of the IoT and IoT attack detection. Then, we evaluate the most recent FL applications in IoT networks and IoT attack detection, investigating their potential to deliver a variety of IoT services, including security and privacy. Our work on detecting FL-IoT attacks has taught us a lot, and at the end, we suggest some directions for future research in this quickly growing field.

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