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检索条件"任意字段=21st International Symposium on Computer Architecture and High Performance Computing"

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Face Anti-Spoofing in Face Recognition: Using Color Texture and Corner Features with Mtcnn and Facenet on Raspberry PI 21

Face Anti-Spoofing in Face Recognition: Using Color Texture ...

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21st international computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Lamsis, Almer A. Osmena, John Daniel M. Linsangan, Noel B. School of Electrical Electronics and Computer Engineering Mapúa University Manila Philippines

ISBN: (纸本)9798331519254

Face recognition, a dependable biometric technology, utilizes facial information to verify an individual's identity. In the field of research, the emphasis of face recognition has been on enhancing the recognition rate. Spoofing attacks pose serious threats to traditional face recognition systems, which rely primarily on face detection and identification. The study's objective is to apply an anti-spoofing model in an MTCNN and FaceNet face recognition model utilizing the limited computing resources of Raspberry Pi. To achieve this, three specific objectives must be met;(1) To implement an MTCNN and FaceNet face recognition model that detects face input from a Raspberry camera (2) To implement a Color Texture and Corner Feature anti-spoofing detection model that identifies and prevents spoofing attacks in the face recognition model (3) Determine the accuracy of the facial recognition model with Anti-Spoofing Detection using the ratio of the False Reject Rate (FRR) and False Accept Rate (FAR). This study successfully implemented face image anti-spoofing, face detection, and identification on a Raspberry Pi system. At threshold 0.5, the anti-spoofing model achieves an accuracy of 94.94%, an FRR of 7.78%, and a low FAR of 1.47%. The face identification model gave the best results at a threshold of 0.1, with 100% Accuracy, 0% FAR, and a low FRR of 5.26%. The system allowed usability balancing with a very low FAR and FRR overall. Further improvements toward the real-time performance of the face recognition and anti-spoofing model are recommended for future studies. © 2024 IEEE.

关键词： Face recognition

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A Semantic-Driven Generative Information Extraction Framework Oriented to Patent Text 21

A Semantic-Driven Generative Information Extraction Framewor...

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21st IEEE international symposium on Parallel and Distributed Processing with Applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhao, Yushan Wang, Tengke Zhang, Shunxiang Anhui University of Science and Technology School of Mathematics and Big Data Huainan China Anhui University of Science and Technology School of Computer Science and Engineering Huainan China

ISBN: (纸本)9798350329223

Generative information extraction for patent text relies on a text-to-structure encoder-decoder framework, facilitating the automatic construction of patent knowledge bases. Existing models challenge to capture the long-range semantic dependencies due to the decentralized semantic context within the patent text, diminishing the accuracy of information extraction. In this work, we present a Semantic-driven Generative Information Extraction (SGIE) framework oriented to patent texts by enhancing and associating semantic context, leading to an improved information extraction performance. Information networks obtained from the pre-trained language model are used as information representations in the encoder layer, and based on an AMR (Abstract Meaning Representation) parser combined with patent schema knowledge, the patent-AMR graphs are created and viewed as semantic representations. The semantic-enhanced information representations are acquired by alignment from the same level of semantic and information representations, so then the global information representation is gained by gated fusing all semantic-enhanced information representations. A patent scheme knowledge-constrained decoder is employed beam search to generate information based on the semantic order decided, so patent technical information is extracted. Experimental results show that the proposed model is highly accurate and efficient in patent texts and is promising. © 2023 IEEE.

关键词： Semantics

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Research on the Application of Blockchain Digital Forensics architecture Based on the DIKW Graph Model in the Police Forensic Process 25

Research on the Application of Blockchain Digital Forensics ...

引用

25th IEEE international Conferences on high performance computing and Communications, 9th international Conference on Data Science and Systems, 21st IEEE international Conference on Smart City and 9th IEEE international Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Liu, Tingwei Cheng, Hexiang Liu, Yanfei Duan, Yucong Wang, Zhenhua Chongqing Police College Department of Public Security ChongQing China Administrative Law School Southwest University of Political Science and Law Chong Qing China Tianjin University Chongqing Police College Department of Intelligence and Computing Department of Information Security ChongQing China School of Computer Science and Technology Hainan University Haikou China Chongqing Police College Department of Tactics ChongQing China

ISBN: (纸本)9798350330014

This study explores the application of a blockch-ain-based digital forensics architecture using the DIKW (Data, Information, Knowledge, Wisdom) graph in police investigations. Current digital intelligence systems usually plan a route from Data Governance to Wisdom Governance, but ignore or lack I nformation Governance. From a conceptual semantic perspectiv -e [1], the concepts underlying these domains include Data, Information, Knowledge, Wisdom(DIKW) [2]. With the development of blockchain technology and the Internet of Things, digital forensics plays a crucial role in criminal investigations. Existing methods face challenges in terms of flexibility in storing evidence on the blockchain and efficient handling of large-scale and multimodal forensic demands. To address these issues, the DIKW (Data, Information, Knowledge, Wisdom) graph model is introduced. The DIKW graph is a multi-layered hierarchical structure for processing typed resources, consisting of data graph, information graph, knowledge graph, and wisdom graph [3]. It can be used to optimize the efficiency of storage, trans mission, and computation integration [4], as well as for privacy data protection [5]. The methods include the integration of the Internet of Thi -ngs (IoT) and blockchain, as well as the design of a blockcha -in-based digital forensics architecture using the DIKW (Data, Information, Knowledge, Wisdom) graph. The data layer is responsible for collecting and storing data generated b y IoT devices. The results show that this architecture im proves the efficiency and accuracy of digital forensics an d provides strong decision support for law enforcement. © 2023 IEEE.

关键词： Blockchain

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Interference-Aware Workload Placement for Improving Latency Distribution of Converged HPC/Big Data Cloud Infrastructures 21st

Interference-Aware Workload Placement for Improving Latency ...

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21st international Conference on Embedded computer Systems - architectures, Modeling, and Simulation (SAMOS)

作者： Tzenetopoulos, Achilleas Masouros, Dimosthenis Xydis, Sotirios Soudris, Dimitrios Natl Tech Univ Athens Athens Greece Harokopio Univ Athens Athens Greece

ISBN: (纸本)9783031045806;9783031045790

Recently, high performance, Big Data, and Cloud computing worlds tend to converge in terms of workload deployment with containerization technology acting as an enabler towards this direction. In such cases of application diversity and multi-tenancy, a universal scheduler able to satisfy the end-user needs for seamless, yet, efficient application deployment is required. While Kubernetes container orchestrator seems to be the answer that enables application-agnostic deployment, it still depends highly on coarse system metrics for its scheduling policies, thus, neglecting the performance degradation due to resource contention in the underlying system. In this paper, we design and implement an interference-aware modular framework, able to balance incoming workload based on low-level metrics monitoring. We evaluate our proposed solution over different workload mixes and co-location scenarios showing that against the state-of-art, but interference unaware Kubernetes scheduler the proposed framework significantly improves the latency distribution of the converged cloud infrastructure, improving median latency up to 27% and reducing standard deviation up to 25%.

关键词： Resource management Kubernetes Interference-aware high-performance computing Cloud computing

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Analysis of Chinese traditional opera based on the Western music genre classification methods 25

Analysis of Chinese traditional opera based on the Western m...

引用

作者： Li, Yue Peng, Zhengwei Liufu, Ying Li, Guanghao Zhu, Huanyan School of Computer Science and Engineering South China University of Technology Guangzhou China Shenzhen Futu Network Technology Co. Ltd. Shenzhen China

ISBN: (纸本)9798350330014

At a time of rapid development of Internet technology, AI capabilities are gradually penetrating into the audio field. Although the study of traditional music has a long history, the mainstream music research is currently limited to Western classical music. But besides Western music, there is other traditional classical music, Chinese opera is a representative of traditional music, and it is one of the six traditional music genres. Cantonese opera is an important part of the Chinese opera atlas. It is one of the few local operas included in the World Intangible Cultural Heritage among the more than 360 kinds of Chinese operas, so the operas processed in this experiment are all Cantonese opera excerpts. A total of 1000 audios from Cantonese Opera Dataset and G TZAN Dataset are collected and processed in this experiment, and the classification methods of western traditional music are applied to analyze Chinese operas. A music classification network based on deep learning is designed and implemented in which the most classical methods in music classification are used to conduct some research on Cantonese opera. Finally, the connections and differences between Chinese opera and western music are analyzed and obtained. © 2023 IEEE.

关键词： Classification (of information)

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Cell Manipulation Attack Against Onion Services 25

Cell Manipulation Attack Against Onion Services

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作者： Lin, Jianjun Yue, Yinliang Zhang, Yijing Liu, Xiaoyu Yang, XiaoDu Gai, Weilin Sun, Bo Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Zhongguancun Laboratory Beijing China Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Beijing China

ISBN: (纸本)9798350330014

In this study, we proposed OREA-OS, a cell manipulation attack devised to compromise Onion Services. Through the manipulation of cells, two collaborating nodes within a circuit exchange information to unveil the anonymity of the targeted Onion Service. We conducted an analysis of existing de-anonymization scenarios and effectively addressed the associated challenges, thereby achieving successful de-anonymization of Onion Services. To assess the efficacy of our attacks, we conducted experiments on both private and public Tor networks. The experimental results validate the effectiveness of our approach, as we accomplished 100% accurate identification of the relationship between domain names and Internet Protocol addresses, with no false positives. Within our simulated private network, the recall rate neared 100 %. On the public Tor network, the de-anonymization rate (98.6 %) exceeded the successful download rate (98.2 %). Furthermore, 99.2 % of success attacks took less than 10 seconds to confirm. © 2023 IEEE.

关键词： Molecular biology

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TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System 25

TPMARL-MSS Thought-Perception Multi-Agent Reinforcement Lear...

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作者： Zhang, Jinsong Mao, Xiaoxuan Li, Lu Wu, Hanqian Joint Graduate School Southeast University Nanjing China College of Software Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Key Laboratory of Computer Network and Information Integration of Ministry of Education Nanjing China

ISBN: (纸本)9798350330014

In today's evolving society, the increasing complexity and frequency of meetings necessitate advanced scheduling systems. Traditional methods are constrained by rigid prede-fined strategies, lack intelligent negotiation mechanisms, and often compromise user privacy. Addressing these challenges, we introduce the Thought-Perception Multi-Agent Reinforcement Learning Meeting Scheduling System (TPMARL-MSS). Unlike conventional systems, TPMARL-MSS autonomously learns and refines its strategies through continuous feedback. It features automated negotiation and adaptive decision-making, offering a more nuanced scheduling approach. Importantly, our Thought-Perception module protect privacy, allowing the agent to deduce preferences from participant behavior without revealing personal data. Evaluations on the real-world dataset shows that TPMARL-MSS surpasses traditional methods in efficiency and schedule quality, highlighting its practical applicability. © 2023 IEEE.

关键词： Decision making

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Investigating Layer-2 Scalability Solutions for Blockchain Applications 25

Investigating Layer-2 Scalability Solutions for Blockchain A...

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作者： Mandal, Mahen Chishti, Mohd Sameen Banerjee, Amit South Asian University Department of Computer Science New Delhi India Bennett University Scset Greater Noida India

ISBN: (纸本)9798350330014

Scalability is one of the trilemma of blockchain technology that hinders its adoption for large-scale decentralized applications (dApps). Researchers have proposed several layer-1 and layer-2 solutions to address the issue. This paper focuses on layer-2 solutions or protocols operating on top of the primary blockchain to enhance scalability. Previous works surveying these solutions mainly focused on a particular layer-2 solution, like rollup, or a specific application, such as smart manufacturing. However, in this paper, we present a comprehensive analysis of layer-2 scalability solutions, such as state channels, side-chain, and rollup, from the perspectives of various functional requirements of block chain applications. More precisely, the goal is to evaluate the applicability of the layer-2 scalability solutions in terms of the application patterns that depict the transactional dependencies, interoperability, decentralization, and security. To further illustrate these features, the paper evaluates the applicability of the layer-2 solutions on the case studies of major blockchain applications, including supply chain management and healthcare. Our analysis suggests that in terms of throughput, application pattern is the most crucial aspect in selecting the scaling solutions, in conjecture with other features as per the application demand. © 2023 IEEE.

关键词： Scalability

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False Positive Detection for Text-Based Person Retrieval 21st

False Positive Detection for Text-Based Person Retrieval

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21st Pacific Rim international Conference on Artificial Intelligence, PRICAI 2024

作者： Cao, Yan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9789819601189

Text-based person retrieval (TBPR) is a challenging topic in cross-modal retrieval tasks, aiming to query corresponding person images based on textual descriptions. This task is complicated by noisy correspondences between images and text due to incorrect text annotations and low-quality images. Although many robust noise learning methods have been proposed with satisfactory performance, they tend to directly filter out a portion of the noise in the dataset, which decreases the ability to distinguish the noise as the number of training epochs increases. This paper proposes a noise-robust learning strategy based on false positive detection (FPD). Precisely, FPD consists of two main components: 1) The key-value library (KVL) module utilizes a set of clean samples, assigns confidence weights to each training sample during the training process, and adaptively adjusts the contribution of each sample to reduce the accumulation of noise. 2) To improve the performance of TBPR, this paper designs a local token-based selection method, which utilizes the contribution of each local token to the weights in the self-attention matrix and selects the local tokens with vital information as global representations. Extensive experiments are conducted on three datasets (CUHK-PEDES, ICFG-PEDES, and RstPReID) to evaluate the performance and robustness of FPD under noisy conditions. The experimental results show that the FPD has better results at high noise ratios, and all the metrics outperform the state-of-the-art method under the RstPReID dataset with a noise ratio of 80%, where Rank-1 outperforms the best method by 2.28%, 1.64% for mAP, and 1.92% for mINP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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The Simplex 3-partite Hypergraph-based Resource Allocation (S3H-RA) Method for Platooning Communications 25

The Simplex 3-partite Hypergraph-based Resource Allocation (...

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作者： Huang, Chung-Ming Lam, Dan-Nguyen Dao, Duy-Tuan National Cheng Kung University Dept of Computer Science and Information Engineering Tainan Taiwan The University of Danang-University of Science and Technology Faculty of Electronics and Telecommunications Engineering Viet Nam

ISBN: (纸本)9798350330014

This work proposed the Simplex 3-partite Hypergraph-based Resource Allocation (S3H-RA) method for multi-platoon. Using S3H-RA, the subchannels that Platoon Leader Vehicles (PLV) can use are assigned at first, for which these subchannels cannot be shared with other entities. Then, a 3-partite hypergraph, in which a 3-partite hyperedge links (1) a cluster of Platoon Member Vehicles (PMV) that use the same subchannel, (2) an individual cellular user and (3) a subchannel, was adopted to devise the PMV s' resource allocation algorithm. The simulation results shown that the proposed S3H-RA method can have the better performance in terms of spectral efficiency and the sum of transmission rate comparing with the other methods. © 2023 IEEE.

关键词： Autonomous vehicles

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