arXiv - CS - Emerging Technologies最新文献_第3页

Colaboot: A Cloud-based Diskless PC Booting Mechanism Colaboot：基于云的无盘电脑启动机制

arXiv - CS - Emerging Technologies Pub Date : 2024-08-30 DOI: arxiv-2408.17045

Aditya Mitra, Anisha Ghosh, Sibi Chakkaravarthy Sethuraman, Devi Priya V S

引用次数: 0

Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework 利用亲子区块游戏协议和注意力增强型 GCN-xLSTM 混合深度学习框架提高自闭症谱系障碍的早期检测能力

arXiv - CS - Emerging Technologies Pub Date : 2024-08-29 DOI: arxiv-2408.16924

Xiang Li, Lizhou Fan, Hanbo Wu, Kunping Chen, Xiaoxiao Yu, Chao Che, Zhifeng Cai, Xiuhong Niu, Aihua Cao, Xin Ma

{"title":"Enhancing Autism Spectrum Disorder Early Detection with the Parent-Child Dyads Block-Play Protocol and an Attention-enhanced GCN-xLSTM Hybrid Deep Learning Framework","authors":"Xiang Li, Lizhou Fan, Hanbo Wu, Kunping Chen, Xiaoxiao Yu, Chao Che, Zhifeng Cai, Xiuhong Niu, Aihua Cao, Xin Ma","doi":"arxiv-2408.16924","DOIUrl":"https://doi.org/arxiv-2408.16924","url":null,"abstract":"Autism Spectrum Disorder (ASD) is a rapidly growing neurodevelopmental\u0000disorder. Performing a timely intervention is crucial for the growth of young\u0000children with ASD, but traditional clinical screening methods lack objectivity.\u0000This study introduces an innovative approach to early detection of ASD. The\u0000contributions are threefold. First, this work proposes a novel Parent-Child\u0000Dyads Block-Play (PCB) protocol, grounded in kinesiological and neuroscientific\u0000research, to identify behavioral patterns distinguishing ASD from typically\u0000developing (TD) toddlers. Second, we have compiled a substantial video dataset,\u0000featuring 40 ASD and 89 TD toddlers engaged in block play with parents. This\u0000dataset exceeds previous efforts on both the scale of participants and the\u0000length of individual sessions. Third, our approach to action analysis in videos\u0000employs a hybrid deep learning framework, integrating a two-stream graph\u0000convolution network with attention-enhanced xLSTM (2sGCN-AxLSTM). This\u0000framework is adept at capturing dynamic interactions between toddlers and\u0000parents by extracting spatial features correlated with upper body and head\u0000movements and focusing on global contextual information of action sequences\u0000over time. By learning these global features with spatio-temporal correlations,\u0000our 2sGCN-AxLSTM effectively analyzes dynamic human behavior patterns and\u0000demonstrates an unprecedented accuracy of 89.6% in early detection of ASD. Our\u0000approach shows strong potential for enhancing early ASD diagnosis by accurately\u0000analyzing parent-child interactions, providing a critical tool to support\u0000timely and informed clinical decision-making.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy 将大型语言模型作为治疗工具：比较提示技术以改进 GPT 提供的问题解决疗法

arXiv - CS - Emerging Technologies Pub Date : 2024-08-27 DOI: arxiv-2409.00112

Daniil Filienko, Yinzhou Wang, Caroline El Jazmi, Serena Xie, Trevor Cohen, Martine De Cock, Weichao Yuwen

{"title":"Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy","authors":"Daniil Filienko, Yinzhou Wang, Caroline El Jazmi, Serena Xie, Trevor Cohen, Martine De Cock, Weichao Yuwen","doi":"arxiv-2409.00112","DOIUrl":"https://doi.org/arxiv-2409.00112","url":null,"abstract":"While Large Language Models (LLMs) are being quickly adapted to many domains,\u0000including healthcare, their strengths and pitfalls remain under-explored. In\u0000our study, we examine the effects of prompt engineering to guide Large Language\u0000Models (LLMs) in delivering parts of a Problem-Solving Therapy (PST) session\u0000via text, particularly during the symptom identification and assessment phase\u0000for personalized goal setting. We present evaluation results of the models'\u0000performances by automatic metrics and experienced medical professionals. We\u0000demonstrate that the models' capability to deliver protocolized therapy can be\u0000improved with the proper use of prompt engineering methods, albeit with\u0000limitations. To our knowledge, this study is among the first to assess the\u0000effects of various prompting techniques in enhancing a generalist model's\u0000ability to deliver psychotherapy, focusing on overall quality, consistency, and\u0000empathy. Exploring LLMs' potential in delivering psychotherapy holds promise\u0000with the current shortage of mental health professionals amid significant\u0000needs, enhancing the potential utility of AI-based and AI-enhanced care\u0000services.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"410 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustainable Data Democratization: A Multifaceted Investment for an Equitable Future 可持续的数据民主化：为公平的未来进行多方面投资

arXiv - CS - Emerging Technologies Pub Date : 2024-08-26 DOI: arxiv-2408.14627

Michela Taufer, Valerio Pascucci, Christine R. Kirkpatric, Ian T. Foster

引用次数: 0

Overcoming the Barriers of Using Linked Open Data in Smart City Applications 克服在智慧城市应用中使用关联开放数据的障碍

arXiv - CS - Emerging Technologies Pub Date : 2024-08-26 DOI: arxiv-2408.14315

Javier Conde, Andres Munoz-Arcentales, Johnny Choque, Gabriel Huecas, Álvaro Alonso

引用次数: 0

Security Concerns in IoT Light Bulbs: Investigating Covert Channels 物联网灯泡的安全问题：调查隐蔽渠道

arXiv - CS - Emerging Technologies Pub Date : 2024-08-26 DOI: arxiv-2408.14613

Ravisha Rohilla, Janvi Panwar

{"title":"Security Concerns in IoT Light Bulbs: Investigating Covert Channels","authors":"Ravisha Rohilla, Janvi Panwar","doi":"arxiv-2408.14613","DOIUrl":"https://doi.org/arxiv-2408.14613","url":null,"abstract":"The proliferation of Internet of Things (IoT) devices has raised significant\u0000concerns regarding their security vulnerabilities. This paper explores the\u0000security risks associated with smart light systems, focusing on covert\u0000communication channels. Drawing upon previous re-search highlighting\u0000vulnerabilities in communication protocols and en-cryption flaws, the study\u0000investigates the potential for exploiting smart light systems for covert data\u0000transmission. Specifically, the paper repli-cates and analyzes an attack method\u0000introduced by Ronen and Shamir, which utilizes the Philips Hue White lighting\u0000system to create a covert channel through visible light communication (VLC).\u0000Experimental re-sults demonstrate the feasibility of transmitting data covertly\u0000through subtle variations in brightness levels, leveraging the inherent\u0000functional-ity of smart light bulbs. Despite limit. ations imposed by device\u0000constraints and communication protocols, the study underscores the need for\u0000heightened awareness and security measures in IoT environment. Ultimately, the\u0000findings emphasize the importance of implementing robust security practices and\u0000exercising caution when deploying networked IoT devices in sensitive\u0000environment.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-Faceted Evaluation of Modeling Languages for Augmented Reality Applications -- The Case of ARWFML 对用于增强现实应用的建模语言进行多方面评估 -- ARWFML 案例

arXiv - CS - Emerging Technologies Pub Date : 2024-08-26 DOI: arxiv-2408.14137

Fabian Muff, Hans-Georg Fill

引用次数: 0

Generative Blockchain: Transforming Blockchain from Transaction Recording to Transaction Generation through Proof-of-Merit 生成式区块链：通过权益证明将区块链从交易记录转变为交易生成

arXiv - CS - Emerging Technologies Pub Date : 2024-08-23 DOI: arxiv-2408.13367

Haozhao Zhang, Zhe Zhang, Zhiqiang Zheng, Varghese Jacob

{"title":"Generative Blockchain: Transforming Blockchain from Transaction Recording to Transaction Generation through Proof-of-Merit","authors":"Haozhao Zhang, Zhe Zhang, Zhiqiang Zheng, Varghese Jacob","doi":"arxiv-2408.13367","DOIUrl":"https://doi.org/arxiv-2408.13367","url":null,"abstract":"This paper proposes a new paradigm: generative blockchain, which aims to\u0000transform conventional blockchain technology by combining transaction\u0000generation and recording, rather than focusing solely on transaction recording.\u0000Central to our design is a novel consensus mechanism, Proof-of-Merit (PoM),\u0000specifically crafted for environments where businesses must solve complex\u0000problems before transactions can be recorded. PoM integrates the generation and\u0000recording of transactions within a unified blockchain system, fundamentally\u0000differing from prevailing consensus mechanisms that primarily record existing\u0000transactions. We demonstrate PoM on a ride service on-demand platform, where\u0000the task of solving complex transaction-generating problems is delegated to a\u0000pool of independent problem solvers. These solvers generate transactions, and\u0000their solutions are selected based on merit. The winning solvers then register\u0000these transactions onto the blockchain and are rewarded accordingly. We\u0000introduce a Decentralized Control Parameter (DCP) to balance two key\u0000performance metrics: efficiency and equity. The applicability of our generative\u0000blockchain is illustrated through a ridesharing context, where matchers\u0000(solvers) are tasked with matching riders to drivers. We demonstrate PoM's\u0000performance and nuanced properties using agent-based simulation, exploring how\u0000to find the optimal DCP value to achieve a desirable balance of efficiency and\u0000equity in a generative blockchain.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QuCLEAR: Clifford Extraction and Absorption for Significant Reduction in Quantum Circuit Size QuCLEAR：克利福德抽取和吸收，显著缩小量子电路尺寸

arXiv - CS - Emerging Technologies Pub Date : 2024-08-23 DOI: arxiv-2408.13316

Ji Liu, Alvin Gonzales, Benchen Huang, Zain Hamid Saleem, Paul Hovland

{"title":"QuCLEAR: Clifford Extraction and Absorption for Significant Reduction in Quantum Circuit Size","authors":"Ji Liu, Alvin Gonzales, Benchen Huang, Zain Hamid Saleem, Paul Hovland","doi":"arxiv-2408.13316","DOIUrl":"https://doi.org/arxiv-2408.13316","url":null,"abstract":"Quantum computing carries significant potential for addressing practical\u0000problems. However, currently available quantum devices suffer from noisy\u0000quantum gates, which degrade the fidelity of executed quantum circuits.\u0000Therefore, quantum circuit optimization is crucial for obtaining useful\u0000results. In this paper, we present QuCLEAR, a compilation framework designed to\u0000optimize quantum circuits. QuCLEAR significantly reduces both the two-qubit\u0000gate count and the circuit depth through two novel optimization steps. First,\u0000we introduce the concept of Clifford Extraction, which extracts Clifford\u0000subcircuits to the end of the circuit while optimizing the gates. Second, since\u0000Clifford circuits are classically simulatable, we propose Clifford Absorption,\u0000which efficiently processes the extracted Clifford subcircuits classically. We\u0000demonstrate our framework on quantum simulation circuits, which have\u0000wide-ranging applications in quantum chemistry simulation, many-body physics,\u0000and combinatorial optimization problems. Near-term algorithms such as VQE and\u0000QAOA also fall within this category. Experimental results across various\u0000benchmarks show that QuCLEAR achieves up to a $77.7%$ reduction in CNOT gate\u0000count and up to an $84.1%$ reduction in entangling depth compared to\u0000state-of-the-art methods.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SAM-SP: Self-Prompting Makes SAM Great Again SAM-SP：自我提示让 SAM 再次伟大

arXiv - CS - Emerging Technologies Pub Date : 2024-08-22 DOI: arxiv-2408.12364

Chunpeng Zhou, Kangjie Ning, Qianqian Shen, Sheng Zhou, Zhi Yu, Haishuai Wang

{"title":"SAM-SP: Self-Prompting Makes SAM Great Again","authors":"Chunpeng Zhou, Kangjie Ning, Qianqian Shen, Sheng Zhou, Zhi Yu, Haishuai Wang","doi":"arxiv-2408.12364","DOIUrl":"https://doi.org/arxiv-2408.12364","url":null,"abstract":"The recently introduced Segment Anything Model (SAM), a Visual Foundation\u0000Model (VFM), has demonstrated impressive capabilities in zero-shot segmentation\u0000tasks across diverse natural image datasets. Despite its success, SAM\u0000encounters noticeably performance degradation when applied to specific domains,\u0000such as medical images. Current efforts to address this issue have involved\u0000fine-tuning strategies, intended to bolster the generalizability of the vanilla\u0000SAM. However, these approaches still predominantly necessitate the utilization\u0000of domain specific expert-level prompts during the evaluation phase, which\u0000severely constrains the model's practicality. To overcome this limitation, we introduce a novel self-prompting based\u0000fine-tuning approach, called SAM-SP, tailored for extending the vanilla SAM\u0000model. Specifically, SAM-SP leverages the output from the previous iteration of\u0000the model itself as prompts to guide subsequent iteration of the model. This\u0000self-prompting module endeavors to learn how to generate useful prompts\u0000autonomously and alleviates the dependence on expert prompts during the\u0000evaluation phase, significantly broadening SAM's applicability. Additionally,\u0000we integrate a self-distillation module to enhance the self-prompting process\u0000further. Extensive experiments across various domain specific datasets validate\u0000the effectiveness of the proposed SAM-SP. Our SAM-SP not only alleviates the\u0000reliance on expert prompts but also exhibits superior segmentation performance\u0000comparing to the state-of-the-art task-specific segmentation approaches, the\u0000vanilla SAM, and SAM-based approaches.","PeriodicalId":501168,"journal":{"name":"arXiv - CS - Emerging Technologies","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142213975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0