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Blockchain-based Internet of Musical Things

Blockchain(Research and Applications) 2022年第3期3卷 60-69页

作者： Luca Turchet Chan Nam Ngo Department of Information Engineering and Computer Science University of TrentoTrento38122Italy Faculty of Mathematics Informaticsand Mechanics of the University of WarsawWarsaw00-927Poland

Blockchain technology is impacting several industries,including the creative industries and those operating in the Internet of Things(IoT).Lately,researchers'attention has been devoted to the application of blockchain in the recorded music ***,thus far,no research has investigated the use of such technology in the Internet of Musical Things(IoMusT).The IoMusT is a new area emerging in industry and academy as an extension of the IoT to the musical *** IoMusT itself,as the IoT,is a distributed network of musical things,which are objects augmented with information and communication technologies serving a musical *** IoMusT vision requires,above all,IoT features such as decentralization,seamless authentication,transparency,data integrity and privacy,and self-maintenance,as well as the musical domain features such as efficient handling of copyrights and speed of royalties *** features can be brought by *** this paper,we investigate the integration of blockchain technology with the IoMusT,and we name such synthesis“Blockchain-based IoMusT”.We present a vision for this new paradigm in terms of the novel opportunities that are enabled,and we propose a set of application scenarios enabled by technological ***,we outline the open research directions in this promising area.

关键词： Internet of Musical Things Blockchain Music industry Privacy Security Internet of Things

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Multi-input address incremental clustering for the Bitcoin blockchain based on Petri net model analysis

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Digital Communications and Networks 2022年第5期8卷 680-686页

作者： Fangchi Qin Yan Wu Fang Tao Lu Liu Leilei Shi Anthony J.Miller Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace School of Computer Science and Telecommunication EngineeringJiangsu UniversityChina Birmingham Business School University of BirminghamUK School of Informatics University of LeicesterUK

Bitcoin is a cryptocurrency based on *** historical Bitcoin transactions are stored in the Bitcoin blockchain,but Bitcoin owners are generally *** is the reason for Bitcoin's pseudo-anonymity,therefore it is often used for illegal *** addresses are related to Bitcoin users'*** Bitcoin addresses have the potential to be analyzed due to the behavior patterns of Bitcoin ***,existing Bitcoin analysis methods do not consider the fusion of new blocks'data,resulting in low efficiency of Bitcoin address *** order to address this problem,this paper proposes an incremental Bitcoin address cluster method to avoid re-clustering when new block data is ***,a heuristic Bitcoin address clustering algorithm is developed to improve clustering accuracy for the Bitcoin *** results show that the proposed method increases Bitcoin address cluster efficiency and accuracy.

关键词： Bitcoin Blockchain Petri net Incremental clustering

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Evaluation of the security of password-protected encrypted RAR3 and RAR5 archives

Evaluation of the security of password-protected encrypted R...

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2022 International Conference on Networks, Communications and Information Technology, NCIT 2022

作者： Krupalija, Ehlimana Mrdović, Saša Cogo, Emir Prazina, Irfan Bećirović, Šeila Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9783800760190

Roshal Archive (RAR) format is one of the most widely used data archive formats, enabling users to reduce the size of data and protect it with the desired password before the data is transferred to its intended recipients over the network. This work focuses on the security of encrypted RAR archives and various different approaches for their decryption. Two different datasets composed of randomly generated and real-world user passwords were used for deploying brute force and dictionary attacks on password-protected RAR archives. Two available and widely used tools, John the Ripper and Hashcat, were used for cracking passwords of encrypted RAR3 and RAR5 archives. Experimental results indicate that both brute force and dictionary attacks were unsuccessful for RAR archives protected with randomly generated passwords, even of very small length. Real-world user passwords were successfully cracked only partially by brute force attacks, whereas dictionary attacks were very successful. The success rate for RAR5 archives was only slightly lower than for RAR3 archives and processing times were similar, indicating that this new version of the RAR format does not significantly improve data security. Instead, the security of RAR archives can be increased by using longer passwords more similar to randomly generated data, which are not present in commonly used dictionaries, as indicated by the experimental results. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Cryptography

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Customized toolbox in VR Design 26

Customized toolbox in VR Design

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Drampalou, Georgia Kourniatis, Nikolaos Voyiatzis, Ioannis Department of Informatics and Computer Engineering University of West Attica Greece Department of Civil Engineering University of West Attica Greece

ISBN: (纸本)9781450398541

Designing in an environment of Virtual Reality is a field that gains ground. Although more and more applications are constantly released, the majority of designers have not yet been introduced into Virtual Reality. A widely used tool for designing is Rhino which has been released by McNeel and Rhino Developer encourages the users to create plug-ins for custom use of the software. In parallel, Meta's developing tools offer their users the chance to customize their own applications. Towards the direction of Virtual Reality integration, both have already released new products for their users. RhinoVR is an open source plug-in for Rhino, while at the same time Meta provides Oculus hardware and platform solutions for users to turn their concept into reality. What is yet to be developed is a model that encourages the customization of a VR design platform, focusing mainly on developing a personalized toolbox for designing. © 2022 ACM.

关键词： Virtual reality

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Bounded and Uniform Energy-based Out-of-distribution Detection for Graphs 41

Bounded and Uniform Energy-based Out-of-distribution Detecti...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Shenzhi Liang, Bin Liu, An Gui, Lin Yao, Xingkai Zhang, Xiaofang School of Computer Science and Technology Soochow University Suzhou China Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Department of Informatics King's College London London United Kingdom

Given the critical role of graphs in real-world applications and their high-security requirements, improving the ability of graph neural networks (GNNs) to detect out-of-distribution (OOD) data is an urgent research problem. The recent work GNNSAFE (Wu et al., 2023) proposes a framework based on the aggregation of negative energy scores that significantly improves the performance of GNNs to detect node-level OOD data. However, our study finds that score aggregation among nodes is susceptible to extreme values due to the unboundedness of the negative energy scores and logit shifts, which severely limits the accuracy of GNNs in detecting node-level OOD data. In this paper, we propose NODESAFE: reducing the generation of extreme scores of nodes by adding two optimization terms that make the negative energy scores bounded and mitigate the logit shift. Experimental results show that our approach dramatically improves the ability of GNNs to detect OOD data at the node level, e.g., in detecting OOD data induced by Structure Manipulation, the metric of FPR95 (lower is better) in scenarios without (with) OOD data exposure are reduced from the current SOTA by 28.4% (22.7%). The code is available via https://***/ShenzhiYang2000/NODESAFE. Copyright 2024 by the author(s)

关键词： Graph neural networks

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Private Blockchain-Based Platform for Cool Box Truck Based on LoRa Communication Protocol 1

Private Blockchain-Based Platform for Cool Box Truck Based o...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Prayudi, Agus Hakim, Oktafian Sultan Zainuddin, Muhammad Agus Sukaridhoto, Sritrusta Budiarti, Rizqi Putri Nourma Politeknik Elektronika Negeri Surabaya Department of Electrical Engineering Surabaya Indonesia Politeknik Elektronika Negeri Surabaya Department of Informatics and Computer Engineering Surabaya Indonesia Universitas Nadhlatul Ulama Surabaya Department of Information System Surabaya Indonesia

ISBN: (纸本)9798350365191

The Internet of Things utilizes software, embedded system, sensor, and network connectivity to control and monitor business processes such as supply chains. One of the implementations of IoT is LoRa (Long Range) which enables data transactions in long-range without using radio frequency. Currently, a combination of IoT and blockchain is able to increase the security level of IoT applications. Implementation of IoT in the supply chain has been done in several research by implementing blockchain and LoRa. Approximately 6.43% of the Gross Domestic Product (GDP) is attributed to logistics, with cold and hot logistics constituting around 11%. Among these, cold chain logistics, a specialized form, accounts for 9.7% of tonnage. Cold chain logistics, characterized by comprehensive temperature control, is a sophisticated system closely intertwined with advanced technology. Despite its complexity, there are still technological drawbacks leading to significant material losses on an annual basis. In this research, to overcome that problem, we proposed Blockchain-Based Monitoring System on Cool Box Trucks using LoRa. The proposed system implemented LoRa for data communication and Blockchain as decentralized databases. We simulated the LoRa environment by using FLoRa framework to obtain the optimal parameters such as Transmission Power (TP), Packet Delivery Ratio (PDR), and Spread Factor (SF) that used in TTGO T-Beam V1. © 2024 IEEE.

关键词： Trucks

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On-Camera Content Filtering for Real-Time Video Analytics 23

On-Camera Content Filtering for Real-Time Video Analytics

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Chen, Fahao Li, Peng Wu, Celimuge School of Computer Science and Engineering The University of Aizu Japan Graduate School of Informatics and Engineering The University of Electro-Communications Japan

ISBN: (纸本)9781665494571

In the past several years, pervasive surveillance cameras have generated massive video records continually, and the video records can be used for several applications (e.g., tracking and object detection). Machine Learning (ML), especially Deep Learning, is a powerful method extensively used for video analytics. Typically, cameras do not have enough computational power to analyze videos locally. A commonly used strategy is gathering videos from cameras and processing them in the cloud or edge server. However, limited bandwidth between cameras and the server results in the inefficiency of existing central-based video analytics systems. In this paper, we effectively reduce the bandwidth demands by proposing a novel object filtering method while keeping the resolution of the video. Specifically, we adopt the moving object detection algorithm on the camera side to find regions in the video and encode these regions into high-resolution video. Through extensive experiments, our system can reduce the bandwidth demands of the video to be analyzed by at least 36.2% without losing analysis for main objects. © 2021 IEEE.

关键词： Filtering Visual analytics Surveillance Object detection Bandwidth Streaming media Filtering algorithms

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RGB-D Video Mirror Detection

RGB-D Video Mirror Detection

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mingchen Xu Peter Herbert Yu-Kun Lai Ze Ji Jing Wu School of Computer Science and Informatics Cardiff University School of Engineering Cardiff University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Mirror detection aims to identify mirror areas in a scene, with recent methods either integrating depth information (RGB-D) or making use of temporal information (video). However, utilizing both data is still under-explored due to the lack of a high-quality dataset and an effective method for the RGB-D Video Mirror Detection (DVMD) problem. To the best of our knowledge, this is the first work to address the DVMD problem. To exploit depth and temporal information in mirror segmentation, we first construct a large-scale RGB-D Video Mirror Detection Dataset (DVMD-D), which contains 17977 RGB-D images from 273 diverse videos. We further develop a novel model, named DVMDNet, which can first locate the mirrors based on triple consistencies: local consistency, cross-modality consistency and global consistency, and then refine the mirror boundaries through content discontinuity, taking the temporal information within videos into account. We conduct a comparative study on the DVMD dataset, evaluating 12 state-of-the-art models (including single-image mirror detection, single-image glass detection, RGB-D mirror detection, video shadow detection, video glass detection, and video mirror detection methods). Code is available from https://***/UpChen/2025_DVMDNet.

关键词： Image segmentation computer vision Codes Glass Benchmark testing Mirrors

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Smart Paddy Rice Plant Monitoring System Based on Soil and Leaf Quality Integrated with PadiSense Mobile Apps 11

Smart Paddy Rice Plant Monitoring System Based on Soil and L...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Rahmadewi, Reni Latifa, Ulinnuha Padilah, Tesa Nur Sutiyana, Agus Husna, Fitria Nurul Sandi Syafri, Edi College of Singaperbangsa Karawang Departement of Electrical Engineering Karawang Indonesia College of Singaperbangsa Karawang Departement of Informatics Engineering Karawang Indonesia College of Pertanian Negeri Payakumbuh Departement of Agricultural and Computer Science Lima Puluh Kota Indonesia

ISBN: (纸本)9798350389654

Indonesia is well known as a country that relies on agricultural sector as the main source of livelihood. Over time, the management of agricultural products has evolved towards more advanced conventional practices, incorporating new technologies. Therefore, this research examined the measurement process of soil pH, soil moisture, and identification of fertilizer requirements through a scanning system equipped with color sensors integrated into the PadiSense mobile applications. The purposive sampling method with a pH sensor and instrument measurement of 6.4 percent and 7.5 percent were used to conduct this research. The pH sensor-based measurement yielded an average pH of 7.6 percent and within the Neutral scale, meeting the criteria for optimal rice growth and development. The results obtained from measuring soil moisture proved optimal for the growth of rice stems, with an average of 71.7 percent to 74.1 percent at a minimal measurement error of 0.047 percent. In conclusion, the requirements for nitrogen were identified using color sensors, which showed the BWD scale, with an average measurement corresponding to BWD 4. © 2024 IEEE.

关键词： Smart agriculture

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Spices Image Classification Using Support Vector Machine 10

Spices Image Classification Using Support Vector Machine

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10th International Conference on Cyber and IT Service Management, CITSM 2022

作者： Al-Amin, Muhammad Insan Sidiq, Fahmi Nur Ramdania, Diena Rauda Fajar, Nurmalik Gerhana, Yana Aditia Harika, Maisevli Uin Sunan Gunung Djati Bandung Department of Informatics Bandung Indonesia Politeknik Negeri Bandung Computer Engineering and Informatics Department Bandung Indonesia

ISBN: (数字)9781665460743

ISBN: (纸本)9781665460743

Spices are ingredients that are commonly used as food flavoring in various types of food in the world. There are many types of spices that exist. Not everyone can distinguish the types of these spices. A Spice classifier is needed to address this problem. This study performs the classification of spice images using the Support Vector Machine (SVM) algorithm, which is a method of mapping data into certain categories. There are 5 spices identified, namely ginger, cinnamon, candlenut, turmeric and pepper. In this study, the SVM model for image identification produced an average accuracy of 38.7%. This model has the greatest accuracy in identifying cinnamon by 65.3% and the smallest accuracy in identifying ginger by 24.5%. © 2022 IEEE.

关键词： Support vector machines

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