Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010037
Liangkai Liu;Yanzhi Wang;Weisong Shi
Predictability is an essential challenge for autonomous vehicles (AVs)‘ safety. Deep neural networks have been widely deployed in the AV's perception pipeline. However, it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks (DNNs) model combinations and system configurations when deploying DNNs in AVs. This paper proposes configurable predictability testbed (CPT), a configurable testbed for quantifying the predictability in AV's perception pipeline. CPT provides flexible configurations of the perception pipeline on data, DNN models, fusion policy, scheduling policies, and predictability metrics. On top of CPT, the researchers can profile and optimize the predictability issue caused by different application and system configurations. CPT has been open-sourced at: https://github.com/Torreskai0722/CPT.
可预测性是自动驾驶汽车（AV）安全性面临的一项重要挑战。深度神经网络已被广泛应用于自动驾驶汽车的感知管道。然而，由于在 AV 中部署深度神经网络时存在数百万种深度神经网络（DNN）模型组合和系统配置，因此如何保证 AV 的感知可预测性仍是一个未决问题。本文提出了可配置可预测性测试平台（CPT），这是一种用于量化 AV 感知管道可预测性的可配置测试平台。CPT 对感知管道的数据、DNN 模型、融合策略、调度策略和可预测性指标进行了灵活配置。在 CPT 的基础上，研究人员可以剖析和优化由不同应用和系统配置引起的可预测性问题。CPT 已开源：https://github.com/Torreskai0722/CPT。
{"title":"CPT: A Configurable Predictability Testbed for DNN Inference in AVs","authors":"Liangkai Liu;Yanzhi Wang;Weisong Shi","doi":"10.26599/TST.2024.9010037","DOIUrl":"https://doi.org/10.26599/TST.2024.9010037","url":null,"abstract":"Predictability is an essential challenge for autonomous vehicles (AVs)‘ safety. Deep neural networks have been widely deployed in the AV's perception pipeline. However, it is still an open question on how to guarantee the perception predictability for AV because there are millions of deep neural networks (DNNs) model combinations and system configurations when deploying DNNs in AVs. This paper proposes configurable predictability testbed (CPT), a configurable testbed for quantifying the predictability in AV's perception pipeline. CPT provides flexible configurations of the perception pipeline on data, DNN models, fusion policy, scheduling policies, and predictability metrics. On top of CPT, the researchers can profile and optimize the predictability issue caused by different application and system configurations. CPT has been open-sourced at: https://github.com/Torreskai0722/CPT.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"87-99"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676407","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010071
Shabeer Ahmad;Jinling Zhang;Ali Nauman;Adil Khan;Khizar Abbas;Babar Hayat
The rise of innovative applications, like online gaming, smart healthcare, and Internet of Things (IoT) services, has increased demand for high data rates and seamless connectivity, posing challenges for Beyond 5G (B5G) networks. There is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas, ensuring higher data rates and uninterrupted connectivity while minimizing costs. Unmanned Aerial Vehicles (UAVs) as Aerial Base Stations (ABSs) offer a promising and cost-effective solution to boost network capacity, especially during emergencies and high-data-rate demands. Nevertheless, integrating UAVs into the B5G networks presents new challenges, including resource scarcity, energy efficiency, resource allocation, optimal power transmission control, and maximizing overall throughput. This paper presents a UAV-assisted B5G communication system where UAVs act as ABSs, and introduces the Deep Reinforcement Learning (DRL) based Energy Efficient Resource Allocation (Deep-EERA) mechanism. An efficient DRL-based Deep Deterministic Policy Gradient (DDPG) mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput maximization. The proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G environment. Through extensive simulations, we validate the performance of the proposed approach, demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.
{"title":"Deep-EERA: DRL-Based Energy-Efficient Resource Allocation in UAV-Empowered Beyond 5G Networks","authors":"Shabeer Ahmad;Jinling Zhang;Ali Nauman;Adil Khan;Khizar Abbas;Babar Hayat","doi":"10.26599/TST.2024.9010071","DOIUrl":"https://doi.org/10.26599/TST.2024.9010071","url":null,"abstract":"The rise of innovative applications, like online gaming, smart healthcare, and Internet of Things (IoT) services, has increased demand for high data rates and seamless connectivity, posing challenges for Beyond 5G (B5G) networks. There is a need for cost-effective solutions to enhance spectral efficiency in densely populated areas, ensuring higher data rates and uninterrupted connectivity while minimizing costs. Unmanned Aerial Vehicles (UAVs) as Aerial Base Stations (ABSs) offer a promising and cost-effective solution to boost network capacity, especially during emergencies and high-data-rate demands. Nevertheless, integrating UAVs into the B5G networks presents new challenges, including resource scarcity, energy efficiency, resource allocation, optimal power transmission control, and maximizing overall throughput. This paper presents a UAV-assisted B5G communication system where UAVs act as ABSs, and introduces the Deep Reinforcement Learning (DRL) based Energy Efficient Resource Allocation (Deep-EERA) mechanism. An efficient DRL-based Deep Deterministic Policy Gradient (DDPG) mechanism is introduced for optimal resource allocation with the twin goals of energy efficiency and average throughput maximization. The proposed Deep-EERA method learns optimal policies to conserve energy and enhance throughput within the dynamic and complex UAV-empowered B5G environment. Through extensive simulations, we validate the performance of the proposed approach, demonstrating that it outperforms other baseline methods in energy efficiency and throughput maximization.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"418-432"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676362","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Indoor localization has been challenging research due to the invalidity of the global navigation satellite system in indoor scenarios. Recent advances in ambient assistive living have shown great power in detecting and locating persons living in their homes, especially using the ON/OFF binary sensors. In this paper, we exploit the Bluetooth low-energy beacons as device-based binary anchors under the lowest transmission power to turn any indoor activity and facility interaction into a binary location indicator. The binary anchors are fused with an extended Kalman filter based pedestrian dead-reckoning using a factor graph optimization, with extra constraints including the normalized magnetic loop closure which is optimized using an attenuation factor, and a rapidly-exploring random tree-based map collision validation. The proposed system provides a cost-effective, scalable, and robust localization for common indoor scenarios. The experimental results show an effective sub-meter precision for the long-term trajectories, and a small amount of anchors is enough for significant calibration in large scenarios.
{"title":"BASIL: Binary Anchor-Based Smart Indoor Localization","authors":"Zhe Yang;Yanjun Li;Yufan Zhang;Yun Pan;Chung Shue Chen;Yi-hua Zhu","doi":"10.26599/TST.2024.9010008","DOIUrl":"https://doi.org/10.26599/TST.2024.9010008","url":null,"abstract":"Indoor localization has been challenging research due to the invalidity of the global navigation satellite system in indoor scenarios. Recent advances in ambient assistive living have shown great power in detecting and locating persons living in their homes, especially using the ON/OFF binary sensors. In this paper, we exploit the Bluetooth low-energy beacons as device-based binary anchors under the lowest transmission power to turn any indoor activity and facility interaction into a binary location indicator. The binary anchors are fused with an extended Kalman filter based pedestrian dead-reckoning using a factor graph optimization, with extra constraints including the normalized magnetic loop closure which is optimized using an attenuation factor, and a rapidly-exploring random tree-based map collision validation. The proposed system provides a cost-effective, scalable, and robust localization for common indoor scenarios. The experimental results show an effective sub-meter precision for the long-term trajectories, and a small amount of anchors is enough for significant calibration in large scenarios.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"1-17"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010001
Zixiang Xian;Rubing Huang;Dave Towey;Chuan Yue
Although Convolutional Neural Networks (CNNs) have achieved remarkable success in image classification, most CNNs use image datasets in the Red-Green-Blue (RGB) color space (one of the most commonly used color spaces). The existing literature regarding the influence of color space use on the performance of CNNs is limited. This paper explores the impact of different color spaces on image classification using CNNs. We compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets, each converted to nine color spaces. We find that color space selection can significantly affect classification accuracy, and that some classes are more sensitive to color space changes than others. Different color spaces may have different expression abilities for different image features, such as brightness, saturation, hue, etc. To leverage the complementary information from different color spaces, we propose a pseudo-Siamese network that fuses two color spaces without modifying the network architecture. Our experiments show that our proposed model can outperform the single-color-space models on most datasets. We also find that our method is simple, flexible, and compatible with any CNN and image dataset.
{"title":"Convolutional Neural Network Image Classification Based on Different Color Spaces","authors":"Zixiang Xian;Rubing Huang;Dave Towey;Chuan Yue","doi":"10.26599/TST.2024.9010001","DOIUrl":"https://doi.org/10.26599/TST.2024.9010001","url":null,"abstract":"Although Convolutional Neural Networks (CNNs) have achieved remarkable success in image classification, most CNNs use image datasets in the Red-Green-Blue (RGB) color space (one of the most commonly used color spaces). The existing literature regarding the influence of color space use on the performance of CNNs is limited. This paper explores the impact of different color spaces on image classification using CNNs. We compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets, each converted to nine color spaces. We find that color space selection can significantly affect classification accuracy, and that some classes are more sensitive to color space changes than others. Different color spaces may have different expression abilities for different image features, such as brightness, saturation, hue, etc. To leverage the complementary information from different color spaces, we propose a pseudo-Siamese network that fuses two color spaces without modifying the network architecture. Our experiments show that our proposed model can outperform the single-color-space models on most datasets. We also find that our method is simple, flexible, and compatible with any CNN and image dataset.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"402-417"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things (IoTs), given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications. Network Dismantling (ND), which is a basic problem, attempts to find an optimal set of nodes that will maximize the connectivity degradation in a network. However, current approaches mainly focus on simple networks that model only pairwise interactions between two nodes, whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world, which can be better modeled as hypernetwork. The structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a hypernetwork. Although some hypernetwork centrality measures (e.g., betweenness) can be used for hypernetwork dismantling, they face the problem of balancing effectiveness and efficiency. Therefore, we propose a betweenness approximation-based hypernetwork dismantling method with a Hypergraph Neural Network (HNN). The proposed approach, called “HND”, trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way, utilizing the well-trained model to approximate the betweenness of the nodes. Extensive experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.
{"title":"Betweenness Approximation for Edge Computing with Hypergraph Neural Networks","authors":"Yaguang Guo;Wenxin Xie;Qingren Wang;Dengcheng Yan;Yiwen Zhang","doi":"10.26599/TST.2023.9010106","DOIUrl":"https://doi.org/10.26599/TST.2023.9010106","url":null,"abstract":"Recent years have seen growing demand for the use of edge computing to achieve the full potential of the Internet of Things (IoTs), given that various IoT systems have been generating big data to facilitate modern latency-sensitive applications. Network Dismantling (ND), which is a basic problem, attempts to find an optimal set of nodes that will maximize the connectivity degradation in a network. However, current approaches mainly focus on simple networks that model only pairwise interactions between two nodes, whereas higher-order groupwise interactions among an arbitrary number of nodes are ubiquitous in the real world, which can be better modeled as hypernetwork. The structural difference between a simple and a hypernetwork restricts the direct application of simple ND methods to a hypernetwork. Although some hypernetwork centrality measures (e.g., betweenness) can be used for hypernetwork dismantling, they face the problem of balancing effectiveness and efficiency. Therefore, we propose a betweenness approximation-based hypernetwork dismantling method with a Hypergraph Neural Network (HNN). The proposed approach, called “HND”, trains a transferable HNN-based regression model on plenty of generated small-scale synthetic hypernetworks in a supervised way, utilizing the well-trained model to approximate the betweenness of the nodes. Extensive experiments on five actual hypernetworks demonstrate the effectiveness and efficiency of HND compared with various baselines.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"331-344"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676406","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2024.9010038
Hongyi Wang;Yang Li;Jing Yang;Daqiang Hu;Zhi Liao
Recent years have witnessed the widespread adoption of mobile applications (apps for short). For quality-of-service and commercial competitiveness, sufficient Graphical User Interface (GUI) testing is required to verify the robustness of the apps. Given that testing with manual efforts is time-consuming and error-prone, automated GUI testing has been widely studied. However, existing approaches mostly focus on GUI exploration while lacking attention to complex interactions with apps, especially generating appropriate text inputs like real users. In this paper, we introduce CamDroid, a lightweight context-aware automated GUI testing tool, which can efficiently explore app activities through (1) a model-based UI-guided testing strategy informed by the context of previous event-activity transitions and (2) a data-driven text input generation approach regarding the GUI context. We evaluate CamDroid on 20 widely-used apps. The results show that CamDroid outperforms non-trivial baselines in activity coverage, crash detection, and test efficiency.
{"title":"CamDroid: Context-Aware Model-Based Automated GUI Testing for Android Apps","authors":"Hongyi Wang;Yang Li;Jing Yang;Daqiang Hu;Zhi Liao","doi":"10.26599/TST.2024.9010038","DOIUrl":"https://doi.org/10.26599/TST.2024.9010038","url":null,"abstract":"Recent years have witnessed the widespread adoption of mobile applications (apps for short). For quality-of-service and commercial competitiveness, sufficient Graphical User Interface (GUI) testing is required to verify the robustness of the apps. Given that testing with manual efforts is time-consuming and error-prone, automated GUI testing has been widely studied. However, existing approaches mostly focus on GUI exploration while lacking attention to complex interactions with apps, especially generating appropriate text inputs like real users. In this paper, we introduce CamDroid, a lightweight context-aware automated GUI testing tool, which can efficiently explore app activities through (1) a model-based UI-guided testing strategy informed by the context of previous event-activity transitions and (2) a data-driven text input generation approach regarding the GUI context. We evaluate CamDroid on 20 widely-used apps. The results show that CamDroid outperforms non-trivial baselines in activity coverage, crash detection, and test efficiency.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"55-67"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-11DOI: 10.26599/TST.2023.9010089
Zhixiong Wu;Yong Cui
Gait recognition has a wide range of application scenarios in the fields of intelligent security and transportation. Gait recognition currently faces challenges: inadequate feature methods for environmental interferences and insufficient local-global information correlation. To address these issues, we propose a gait recognition model based on feature fusion and dual attention. Our model utilizes the ResNet architecture as the backbone network for fundamental gait features extraction. Subsequently, the features from different network layers are passed through the feature pyramid for feature fusion, so that multi-scale local information can be fused into global information, providing a more complete feature representation. The dual attention module enhances the fused features in multiple dimensions, enabling the model to capture information from different semantics and scale information. Our model proves effective and competitive results on CASIA-B (NM: 95.6%, BG: 90.9%, CL: 73.7%) and OU-MVLP (88.1%). The results of related ablation experiments show that the model design is effective and has strong competitiveness.
{"title":"GaitFFDA: Feature Fusion and Dual Attention Gait Recognition Model","authors":"Zhixiong Wu;Yong Cui","doi":"10.26599/TST.2023.9010089","DOIUrl":"https://doi.org/10.26599/TST.2023.9010089","url":null,"abstract":"Gait recognition has a wide range of application scenarios in the fields of intelligent security and transportation. Gait recognition currently faces challenges: inadequate feature methods for environmental interferences and insufficient local-global information correlation. To address these issues, we propose a gait recognition model based on feature fusion and dual attention. Our model utilizes the ResNet architecture as the backbone network for fundamental gait features extraction. Subsequently, the features from different network layers are passed through the feature pyramid for feature fusion, so that multi-scale local information can be fused into global information, providing a more complete feature representation. The dual attention module enhances the fused features in multiple dimensions, enabling the model to capture information from different semantics and scale information. Our model proves effective and competitive results on CASIA-B (NM: 95.6%, BG: 90.9%, CL: 73.7%) and OU-MVLP (88.1%). The results of related ablation experiments show that the model design is effective and has strong competitiveness.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 1","pages":"345-356"},"PeriodicalIF":6.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10676356","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-20DOI: 10.26599/TST.2024.9010056
Long Zhang;Zhiwen Wang;Jingwen Wang;Donglei Du;Chuanwen Luo
This paper studies two scheduling games on identical batching-machines with activation cost, where each game comprises $n$ jobs being processed on $m$ identical batching-machines. Each job, as an agent, chooses a machine (or, more accurately, a batch on a machine) for processing in order to minimize its disutility, which is comprised of its machine's load and the activation cost it shares. Based on previous results, we present the Mixed strategy Nash Equilibria (MNE) for some special cases of the two games. For each game, we first analyze the conditions for the nonexistence of Nash equilibrium, then provide the MNE for the conditions, and offer the efficiency of MNE (mixed price of anarchy).
{"title":"Mixed Strategy Nash Equilibrium for Scheduling Games on Batching-Machines with Activation Cost","authors":"Long Zhang;Zhiwen Wang;Jingwen Wang;Donglei Du;Chuanwen Luo","doi":"10.26599/TST.2024.9010056","DOIUrl":"https://doi.org/10.26599/TST.2024.9010056","url":null,"abstract":"This paper studies two scheduling games on identical batching-machines with activation cost, where each game comprises \u0000<tex>$n$</tex>\u0000 jobs being processed on \u0000<tex>$m$</tex>\u0000 identical batching-machines. Each job, as an agent, chooses a machine (or, more accurately, a batch on a machine) for processing in order to minimize its disutility, which is comprised of its machine's load and the activation cost it shares. Based on previous results, we present the Mixed strategy Nash Equilibria (MNE) for some special cases of the two games. For each game, we first analyze the conditions for the nonexistence of Nash equilibrium, then provide the MNE for the conditions, and offer the efficiency of MNE (mixed price of anarchy).","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"519-527"},"PeriodicalIF":6.6,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640365","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142825964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-08DOI: 10.26599/TST.2023.9010112
Yuhang Jin;Li Ling
Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries. It is essential to differentiate between the unethical act of plagiarism and the legitimate use of open-source resources. Existing copyright protection measures, such as license design, inadequately address copyright ownership and protection issues. Furthermore, they fail to detect plagiarism methods for open-source hardware projects, such as circuit location modification. To address these challenges, this paper proposes a blockchain-based copyright management scheme, which introduces a general originality detection model based on community detection, extracting adjustable granularity digests from code and design files. These digests are stored on a peer-to-peer blockchain, enabling nodes to verify the originality via smart contracts. Additionally, the scheme improves the storage structure, protecting the rights of authors and contributors. Experimental results demonstrate the effectiveness and runtime efficiency of the proposed model in extracting digests for blockchain storage while maintaining verification accuracy. The scheme offers enhanced generality, practical performance, and suitability for distributed development and maintenance, with considerable implications for evidence gathering, fostering innovation and integrity.
{"title":"A Blockchain-Based Copyright Management Scheme for Open-Source Software and Hardware Designs","authors":"Yuhang Jin;Li Ling","doi":"10.26599/TST.2023.9010112","DOIUrl":"https://doi.org/10.26599/TST.2023.9010112","url":null,"abstract":"Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries. It is essential to differentiate between the unethical act of plagiarism and the legitimate use of open-source resources. Existing copyright protection measures, such as license design, inadequately address copyright ownership and protection issues. Furthermore, they fail to detect plagiarism methods for open-source hardware projects, such as circuit location modification. To address these challenges, this paper proposes a blockchain-based copyright management scheme, which introduces a general originality detection model based on community detection, extracting adjustable granularity digests from code and design files. These digests are stored on a peer-to-peer blockchain, enabling nodes to verify the originality via smart contracts. Additionally, the scheme improves the storage structure, protecting the rights of authors and contributors. Experimental results demonstrate the effectiveness and runtime efficiency of the proposed model in extracting digests for blockchain storage while maintaining verification accuracy. The scheme offers enhanced generality, practical performance, and suitability for distributed development and maintenance, with considerable implications for evidence gathering, fostering innovation and integrity.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 3","pages":"1157-1173"},"PeriodicalIF":6.6,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10589541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142905767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the software landscape, understanding component impacts on system reliability is pivotal, especially given the unique complexities of modern software systems. This paper presents a model tailored for software reliability assessment. Our approach introduces the “component influence” to measure a single component's effect on overall system reliability. Additionally, we adapt a state transition model to cater to the diverse architectures of software systems. Using a discrete-time Markov chain, we predict software reliability. We test our model on an actual software system, finding it notably accurate and superior to existing methods. Our work offers a promising direction for those venturing into software reliability enhancement.
{"title":"Software Reliability Assessment: An Architectural and Component Impact Analysis","authors":"Saleh Alyahyan;Mohammed Naif Alatawi;Mrim M. Alnfiai;Shoayee Dlaim Alotaibi;Abdullah Alshammari;Zaid Alzaid;Hathal Salamah Alwageed","doi":"10.26599/TST.2024.9010101","DOIUrl":"https://doi.org/10.26599/TST.2024.9010101","url":null,"abstract":"In the software landscape, understanding component impacts on system reliability is pivotal, especially given the unique complexities of modern software systems. This paper presents a model tailored for software reliability assessment. Our approach introduces the “component influence” to measure a single component's effect on overall system reliability. Additionally, we adapt a state transition model to cater to the diverse architectures of software systems. Using a discrete-time Markov chain, we predict software reliability. We test our model on an actual software system, finding it notably accurate and superior to existing methods. Our work offers a promising direction for those venturing into software reliability enhancement.","PeriodicalId":48690,"journal":{"name":"Tsinghua Science and Technology","volume":"30 2","pages":"908-925"},"PeriodicalIF":6.6,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10577570","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142798052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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