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9th International Conference on computer and Communications, ICCC 2023

作者： Tian, Manqing Zhao, Yang Zeng, Ni Kang, Xiaomei Pei, Jihong Yang, Xuan Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China Shenzhen University College of Computer Science and Software Engineering Shenzhen China Huazhong University of Science and Technology Tongji Medical College Department of Reproductive Maternal and Child Health Hospital of Hubei Province Wuhan China

ISBN: (纸本)9798350317251

Semen quality assessment is a very important tool for the timely detection and treatment of infertility disorders. Many artificial intelligence methods for sperm image analysis have emerged in recent years. Sperm detection in sperm microscopy is the first step in semen quality assessment. However, the acquisition of sperm electron microscopy images can produce a variety of different background lighting situations. The performance of established methods is susceptible to changes in background illumination. Therefore, a sperm detection method with heterogeneous Laplacian distribution noise background modeling (HLDNBM) is proposed. In this paper the method uses background illumination surface fitting to eliminate the effect of illumination in a single sperm image, and realizes the segmentation of sperm head and sperm tail with different grayscale by upper and lower segmentation threshold surfaces to achieve complete sperm image segmentation and multi-target detection in a single image. This method effectively avoids the degradation of target detection performance caused by sudden changes in illumination in other background modeling methods. Moreover, this method shows strong adaptability for images with different lighting backgrounds, and is friendly to small datasets as well. The experimental results show that the accuracy and sensitivity of the algorithm for sperm detection in images with different grayscale characteristics are very promising, with high universality and robustness. © 2023 IEEE.

关键词： Lighting

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Verification Tool for Securing RISC-V FPGA-Based Process Control system

Verification Tool for Securing RISC-V FPGA-Based Process Con...

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Intermountain engineering, Technology and Computing (IETC)

作者： Christine Johnson Ann Marie Reinhold Clemente Izurieta Bradley M. Whitaker Brock LaMeres Software Engineering and Cybersecurity Laboratory Montana State University Bozeman MT USA Pacific Northwest National Laboratory Richland WA USA Idaho National Laboratory Idaho Falls ID USA Electrical & Computer Engineering Department Montana State University Bozeman MT USA

ISBN: (数字)9798331512828

ISBN: (纸本)9798331512835

Industrial Control systems (ICSs) are a crucial component of critical infrastructure and a popular target for cyberattacks. While most research focuses on the defense of large-scale networks of ICSs, it is critical to expand research on the small-scale networks of Process Control systems (PCSs), which attackers may target to remain undetectable from the security of the larger network. One potential method of protection for PCS networks is the use of verification tools; however, existing research on verification tools focuses on the detection of attacks without mitigation. This research describes two instantiations of a verification tool for a PCS controlled by a RISC-V computer implemented on an FPGA. The first version of the verification tool provides passive detection, informing an engineering station of an attack, and the second version provides detection with mitigation against unauthorized command messages and malicious software downloads. An experimental testbed was developed to test both versions, consisting of: (1) a circuit to charge a battery; (2) an FPGA controller that controls charge/discharge based on user input; (3) an engineering station that provides control data and updates firmware to the FPGA; and (4) a verification tool that verifies input forwarded by a passive serial tap, connected through the FPGA’s hardware. The experimental data yielded promising results, with the tool successfully providing mitigation and detection against attacks on the serial communication channel between the engineering station and the FPGA. This approach has applicability to common ICS computer devices, such as programmable logic controllers (PLCs).

关键词： Reduced instruction set computing Prevention and mitigation Programmable logic devices Process control Logic gates Malware Protection Field programmable gate arrays Monitoring Microprogramming

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Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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Anomaly Detection for Multivariate Time Series Based on Contrastive Learning and Autoformer

Anomaly Detection for Multivariate Time Series Based on Cont...

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International Conference on computer Supported Cooperative Work in Design

作者： Xuwen Shang Jue Zhang Xingguo Jiang Hong Luo Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing China Department of Computer Science and Engineering Beihang University Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Nowadays, due to the increasingly complex and huge data flow, anomaly detection has become an important technology to ensure the high security and reliability of the systems. It helps identify unusual observations in the data, thereby protecting safety and reducing risk. However, in the field of multivariate temporal anomaly detection, there are still some difficulties in spatio-temporal correlation mining of variables, generalization ability in dealing with complex temporal sequences, and feature learning under different granularity. Therefore, we propose an unsupervised anomaly detection method for multivariate time series based on contrastive learning and Autoformer autoencoder. In order to fully explore the spatio-temporal correlation of time series data, this paper uses Graph Attention Network and Auto-Correlation to jointly learn spatio-temporal correlation, and obtain positive and negative pairs through time-domain and frequency-domain data augmentation. Finally, the Autoformer autoencoder is used for anomaly detection in a predictive manner. In the loss function section, a joint loss function is formed by introducing contrastive learning losses at the time step and time window granularity, combined with prediction loss, to improve the learning effect. This paper investigates the performance of the proposed method through experiments on the SMAP and MSL public datasets, and achieves better results than baseline methods.

关键词： Correlation Computational modeling Time series analysis Contrastive learning computer architecture Stability analysis Safety

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Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Document Ranking and Retrieval

Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Docu...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zeshi Zhang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Ministry of Education Key Laboratory of Computer Vision and System China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

关键词： Time-frequency analysis Privacy Costs Cloud computing security Information retrieval Encryption Delays

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Novel Generation Injection Solution for Security Constrained Optimal Power Flow of Utility Practical Transmission Utility Network 5

Novel Generation Injection Solution for Security Constrained...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Singh, Bhoori Sharma, Avdhesh Garg, Akhil Ranjan Mahela, Om Prakash Khan, Baseem Mbm University Department of Electrical Engineering Jodhpur India Rajasthan Rajya Vidyut Prasaran Nigam Ltd. Power System Planning Division Jaipur India Innovader Research Labs Innovader IITian Padhaiwala LearnlAb Pvt. Ltd. Jaipur India Hawassa University Department of Electrical and Computer Engineering Ethiopia Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhuji311816 China Western Caspian University Department of Technical Sciences Baku Azerbaijan

ISBN: (纸本)9798350376685

The security constrained optimal power flow (SCOPF) optimally configures and dispatches the power system in normal conditions to ensure that steady-state operating conditions do not exceed the emergency ratings when contingency is observed. Suitable methods of SCOPF helps to operate the grid optimally. Hence, this paper discussed a SCOPF considering the transmission loss minimization and minimization of bus voltage deviation (BVD). Study is performed for base case of a practical transmission utility network (TUN) and proposed case TUN by addition of a thermal power plant (TPP). TPP addition improves the voltage profile of the TUN buses, minimizes BVD and transmission losses of TUN. Study is performed using the MiPower software. Proposed study is effective compared to many studies reported in literature for a practical transmission grid. © 2024 IEEE.

关键词： Load flow optimization

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Unsupervised Mapping of Quantitative Measures to Qualitative Characteristics in Hierarchical software Quality Assurance

Unsupervised Mapping of Quantitative Measures to Qualitative...

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Intermountain engineering, Technology and Computing (IETC)

作者： Kaveen Liyanage Ethan L. Gerard Derek Reimanis Ann Marie Reinhold Clemente Izurieta Brock J. LaMeres Bradley M. Whitaker Department of Electrical and Computer Engineering Montana State University Bozeman MT USA Software Engineering and Cybersecurity Laboratory Montana State University Bozeman MT USA Pacific Northwest National Laboratory Richland WA USA Idaho National Laboratory Idaho Falls ID USA

ISBN: (数字)9798331512828

ISBN: (纸本)9798331512835

Ensuring software quality is essential to the software development and deployment process. Evaluating software for known vulnerabilities and weaknesses is a method of assessing the quality of software. However, the existence of a large number of vulnerabilities and weaknesses can hinder decision-making and mitigation. To address this, hierarchical software quality models aggregate quantitative weakness measures into qualitative characteristics to simplify decision making. However, challenges exist when mapping quantitative measures to qualitative characteristics, especially when the relationship between measures and characteristics is ill-defined/unknown or when prior knowledge is absent, thus posing threats to the construct validity of the mapping. This paper presents a pseudo-label-based regression framework to generate qualitative values when given a set of quantitative measurements. To exemplify this research, we created a use case where we applied the framework to binary program analysis.

关键词： Quality assurance Prevention and mitigation Decision making software quality Manuals Machine learning Predictive models Benchmark testing software measurement software development management

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When Sign Language Meets Semantic Communications 35

When Sign Language Meets Semantic Communications

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Kouvakis, Vasileios Trevlakis, Stylianos E. Boulogeorgos, Alexandros-Apostolos A. Tsiftsis, Theodoros Singh, Keshav Qi, Nan InnoCube P.C. Department of Research and Development 17th Noemvriou 79 Thessaloniki55535 Greece University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece University of Thessaly Department of Informatics & Telecommunications Lamia35100 Greece Institute of Communications Engineering National Sun Yat-sen University Kaohsiung804 Taiwan Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Nanjing210016 China

ISBN: (纸本)9798350362244

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system's training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions. © 2024 IEEE.

关键词： Quadrature amplitude modulation

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Towards Survivable In-Memory Stores with Parity Coded NVRAM

Towards Survivable In-Memory Stores with Parity Coded NVRAM

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhixuan Wang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Erasure codes have been widely applied to in-memory key-value storage systems for high reliability and low redundancy. In distributed in-memory key-value storage systems, update operations are relatively frequent, especially the partial-stripe update, which makes data update more challenging. Recently, existing research has been based on appending logs to accelerate parity data write. However, its logs are stored on disks, which decreases the system performance significantly. Therefore, we propose a novel in-memory key-value storage architecture, DNVPL, which utilizes NVRAM to log parity data. Our main idea is to design an appending-only update scheme to tradeoff the memory cost and the update overhead. We implement DNVPL with an in-memory key-value storage prototype, called LogKV. We evaluate it with different workloads. The experiments show that our scheme achieves high update performance from different metrics. Our scheme can reduce update latency by up to 49% and save storage space by 48% compared to the state-of-the-art schemes.

关键词： Privacy Nonvolatile memory system performance Redundancy Random access memory Prototypes computer architecture

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IEEE T-BIOM Editorial Board Changes

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

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