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distributed Online Computer-Based Test System for English Exam 23

Distributed Online Computer-Based Test System for English Ex...

引用

23rd international Electronics symposium, IES 2021

作者： Ilyas, Muhajirin Ida Yuwono, Wiratmoko Winarno, Idris Alimudin, Akhmad Politeknik Elektronika Negeri Surabaya Dept.Information and Computer Surabaya Indonesia Toyohashi University of Technology Dept.Computer Science and Engineering Tempaku441-8580 Japan

ISBN: (纸本)9781665443463

Nowadays, computer-based tests are commonly used since a pandemic made educational institutions hold exams from home and it made computer-based tests application demand increasing time by time. This situation leads to the need for applications that can run on any device, secure from cheating and compatible with poor internet connection. Our proposed system able to improve security and deception issues in the examination. In this paper we also introduce distributed online computer-based test for English test that made our software can share questions-data to another instance securely. © 2021 IEEE.

关键词： software testing

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A Real-Time Machine Learning Module for Motion Artifact Detection in fNIRS

A Real-Time Machine Learning Module for Motion Artifact Dete...

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IEEE international symposium on Circuits and systems (ISCAS)

作者： Renas Ercan Yunjia Xia Yunyi Zhao Rui Loureiro Shufan Yang Hubin Zhao HUB of Intelligent Neuro-Engineering (HUBIN)(UCL) & Department of Physics (University of Cambridge) University of Cambridge Cambridge UK HUB of Intelligent Neuro-engineering (HUBIN) DSIS University College London(UCL) London UK Division of Surgery & Interventional Science(DSIS) University College London(UCL) London UK Institute of Medical & Biological Engineering School of Mechanical Engineering University of Leeds Leeds UK

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Functional Near-Infrared Spectroscopy (fNIRS) is a neuroimaging method which can be implemented with a wearable form factor. However, the data of fNIRS can be affected by motion artifact, which is conventionally processed offline using MATLAB-based software package via a bulky PC. This study trains a Support Vector Machine (SVM) algorithm and proposes a hardware design approach based on an FPGA to achieve the first real-time fNIRS motion artifact detection. The SVM hardware architecture proposed here utilizes a partially sequential–partially parallel implementation of the classification algorithm where Support Vector channels are consolidated into a single oversampled channel. A high classification accuracy of 97.42%, low FPGA resource utilization of 38,354 look-up tables and 6024 flip-flops with 10.92 us latency is achieved, outperforming conventional CPU SVM methods. These results show that an FPGA-based fNIRS motion artifact detector can be exploited whilst meeting real-time and resource constraints that are crucial in high-performance reconfigurable hardware systems.

关键词： Support vector machines Machine learning Computer architecture Real-time systems Hardware Vectors Classification algorithms

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Knowledge-aware Multi-view Cross Learning for Edge-based Collaborative Recommendation

Knowledge-aware Multi-view Cross Learning for Edge-based Col...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yang Dai Shunmei Meng Huanhuan Gu Nan Liu Longchuan Tu School of Cyberspace Security NanJing University of Science and Technology Nanjing China State Key Lab. for Novel Software Technology NanJing University Nanjing China Department of Computer Science and Engineering NanJing University of Science and Technology Nanjing China Nanjing Sinovatio Technology Co. Ltd. Nanjing China

In order to effectively overcome the sparsity and cold start challenges of collaborative filtering in actual scenarios, and provide more accurate suggestions, knowledge graphs are commonly utilized in recommender systems. Graph neural networks are increasingly used in knowledge graph-based recommender systems. Nevertheless, conventional recommender systems overlook the relationship information between items and entities as well as the sparse supervision signal, potentially leading to a decline in the actual recommendation performance. In this investigation, we propose KMCL, a Knowledge-aware Multi-view Cross-learning framework for edge-based collaborative recommendation. In contrast to the typical manner of dealing with knowledge graphs, our approach investigates three different graph views to improve users’ and items’ embedding representations, respectively. Firstly, the merged user-item KG graph is utilized to integrate the multi-hop neighbor information of each user, with the Graph Attention Network assigning various weights to different neighbors. Secondly, Graph Neural Network collects the top-x neighbor nodes for learning in the collaborative view of user-item graph and item-entity graph. Finally, a cross module is developed to share high-level information between embeddings with the same features. In addition, we also introduce the idea of edge computing, which preprocesses and calculates the locally obtained user preferences first, and then inputs them into different views to improve the recommendation efficiency. Experimental results demonstrate that KMCL delivers better suggestions than state-of-the-art baseline models in delivering superior recommendations. Furthermore, our approach maintains higher accuracy in predicting outcomes in less familiar interaction scenarios.

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7th international symposium on Dependable software engineering: Theories, Tools, and Applications, SETTA 2021

7th International Symposium on Dependable Software Engineeri...

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7th international symposium on Dependable software engineering: Theories, Tools, and Applications, SETTA 2021

ISBN: (纸本)9783030912642

The proceedings contain 16 papers. The special focus in this conference is on Dependable software engineering: Theories, Tools, and Applications. The topics include: Predicting and Monitoring Bug-Proneness at the Feature Level;CSFL: Fault Localization on Real software Bugs Based on the Combination of Context and Spectrum;a distributed Simplex Architecture for Multi-agent systems;OURS : Over- and Under-Approximating Reachable Sets for Analytic Time-Invariant Differential Equations;ESampler: Efficient Sampling of Satisfying Assignments for Boolean Formulas;API Usage Pattern Search Based on Model Checking;DeepGlobal: A Global Robustness Verifiable FNN Framework;leveraging Event-B Theories for Handling Domain Knowledge in Design Models;reasoning About Iteration and Recursion Uniformly Based on Big-Step Semantics;Trace Semantics and Algebraic Laws for MCA ARMv8 Architecture Based on UTP;Formal Analysis of 5G AKMA;verifying the Correctness of distributed systems via Mergeable parallelism;mutation Testing of Reinforcement Learning systems;AIdetectorX: A Vulnerability Detector Based on TCN and Self-attention Mechanism.

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Electric Drive Assignment Strategies Optimization for Plugin Hybrid Urban Buses on Tailored Emissions Mapping

Electric Drive Assignment Strategies Optimization for Plugin...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： José Miguel Aragón-Jurado Marina Díaz-Jiménez Bernabé Dorronsoro Pablo Pavón-Domínguez Marcin Seredynski Patricia Ruiz School of Engineering University of Cadiz Cadiz Spain Director of Research E-bus Competence Center Livange Luxembourg

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Traffic congestion, noise and tailpipe emissions are negative externalities from transportation that need to be reduced to make cities more livable. Electric buses can help significantly reducing noise and tailpipe emissions. However, they provide nowadays limited range due to battery capacity, affecting their operating flexibility. Plug-in electric hybrid buses are a versatile solution that offer certain zero-emission capability, which de-pends on the battery capacity and how electric drive is distributed over a route. This is managed by an electric drive assignment system that determines the optimal locations to use the electric motor. Today's assignment systems are in their early development stages, and do not exploit the full potentials. This work introduces a novel combinatorial optimization problem to find optimal electric drive assignment strategies for a number of bus lines under the presence of zero- and restricted-emissions zones. The latter is a new concept introduced here where tailpipe emissions are allowed but restricted to a predefined limit. The goal is to maximize the electric range of the buses and minimize the total pollution emitted, while at the same time complying with the mandatory zero-emission zones and the restricted-emission zones. CCMOCell, a parallel Cooperative Co-evolutionary version of the well-known Multi-objective Cellular genetic algorithm, MOCell, is proposed to solve the problem, and solutions are compared against those found by MOCell itself, and validated using a heuristic from the literature, GreenK. Six lines from Barcelona's bus network (Spain), are considered, and real topological data obtained from administrations and digital elevation models are used. The two multi-objective algorithms reduced pollutant emissions by up to 20% with respect to GreenK. Also, CCMOCell outperformed it in terms of electric range by up to 6.26%.

关键词： Electric potential Pollution Noise Urban areas Transportation Drives Batteries

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C++ and Interoperability Between Libraries: The GraphBLAS C++ Specification

C++ and Interoperability Between Libraries: The GraphBLAS C+...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Benjamin Brock Scott McMillan Aydın Buluç Timothy G. Mattson José E. Moreira Parallel Computing Lab Intel Software Engineering Institute Carnegie Mellon University Computational Research Department Lawrence Berkeley National Laboratory EECS Department University of California Berkeley IBM Thomas J. Watson Research Center

Interoperability between libraries is often hindered by incompatible data formats, which can necessitate creating new copies of data when transferring data back and forth between different libraries. This additional data movement incurs additional runtime costs, particularly for sparse applications, where the costs of data movement often dwarf compute costs. In this paper, we investigate interoperability in the context of the C++ GraphBLAS Specification, where C++ concepts allow GraphBLAS algorithms to accept any matrix type as long as it follows the matrix interface defined in the GraphBLAS matrix concept. We first develop non-owning, lazily evaluated adapted views for a number of external data structures, including two categories of graphs defined in the Northwest Graph Library (NWGraph) and traditional pointer-based CSR data structures. These adapted views fulfill the C++ GraphBLAS matrix concept, allowing them to be used inside GraphBLAS algorithms. We then evaluate the performance of these adapted views across two kernels, matrix reduction and sparse times dense matrix multiplication (SpMM), where the performance achieved using a single generic implementation with these views largely matches the performance achieved operating directly on the original data structures, with a slight performance loss in one case. We then propose a mechanism for automatically discovering the availability of these views, allowing algorithms to directly accept external data structures. We also discuss potential extensions to the C++ GraphBLAS specification that might eliminate the small performance dip observed for one of the views.

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A QoE-Aware Adaptive Energy-Efficient Transmission Scheduling Method

A QoE-Aware Adaptive Energy-Efficient Transmission Schedulin...

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international Conference on parallel and distributed systems (ICPADS)

作者： Yankun Yuan Lin Wang Chonghui Xiao Zijuan Liu Fan Dang Xu Wang Haitian Zhao Xin Miao School of Information Science and Engineering Yanshan University National Computer Network Emergency Response Technical Team/Coordination Center of China Department of Automation and BNRist Tsinghua University Global Innovation Exchange Tsinghua University School of Software and BNRist Tsinghua University

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

In this paper, we propose a dynamic data transmission strategy for smart home environments that aims to optimize the Quality of Experience (QoE) by adaptively adjusting the data upload frequency based on the predicted trends in sensor data. Using the home wireless sensors monitoring dataset, we implement a deep learning model for accurate time series forecasting. In addition, an anomaly detection mechanism is used to identify critical events, requiring more frequent data uploads when important changes are detected. The QoE is quantified through a weighted average of several influencing factors, including data timeliness, timely upload of critical events, and transmission frequency. Our optimization objective is to maximize QoE while minimizing the number of transmissions, with an emphasis on reducing energy consumption through intelligent scheduling. The results demonstrate that our approach effectively balances data timeliness, transmission efficiency, and energy savings, leading to improved user satisfaction in smart home applications.

关键词： Wireless communication Wireless sensor networks Energy consumption Data centers Time-frequency analysis Time series analysis Smart homes Quality of experience Data communication Optimization

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Stability Analysis of a Wind Park Based on DFIG Technology Supported by A Battery Energy Storage System

Stability Analysis of a Wind Park Based on DFIG Technology S...

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international symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Belmin Memisevic Mirza Saric Jasna Hivziefendic Department of Electrical and Electronics Engineering International Burch University Sarajevo Bosnia and Herzegovina

Power system stability plays a significant role in the overall power system analysis. With the high penetration level of distributed generation (DG), especially large-scale wind farms, this problem needs to be addressed. This study investigates the system stability in case of a wind park (WP) integration using doubly fed induction generators (DFIGs) to transmission grid, while focusing on WP fault ride-through ability. The system was modelled for time-domain simulations. The results indicate that WP parallel operation with the high voltage network is possible if specific conditions are met, with fault clearance time being crucial. This is shown through scenarios, in which each of the overhead lines (OHL) was disconnected due to three-phase short circuit symmetrical fault, and the network parameters were observed for each case. The predefined control and protection configurations in the DFIG-based wind farm model simplify the analysis. The introduction of a battery energy storage system (BESS) with P and Q control strategies, improves WP stability during faults. Professional software tools, PSSE, and EMTP-RV, were employed for the analysis. The study showed that simulated WP and BESS connected to a real network, paired with appropriate fault clearance time and protection settings, can operate effectively while maintaining overall system stability. This research is significant for power system planning, especially with the growing integration of large-scale wind generation.

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IGOR: Accelerating Byzantine Fault Tolerance for Real-Time systems with Eager Execution 27

IGOR: Accelerating Byzantine Fault Tolerance for Real-Time S...

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30th IEEE international symposium on software Reliability engineering (ISSRE)

作者： Loveless, Andrew Dreslinski, Ronald Kasikci, Baris Linh Thi Xuan Phan Univ Michigan Ann Arbor MI 48109 USA Univ Penn Philadelphia PA 19104 USA NASA Johnson Space Ctr Houston TX 77058 USA

ISBN: (纸本)9781665403863

Critical real-time systems like spacecraft and aircraft commonly use Byzantine fault-tolerant (BFT) state machine replication (SMR) to mask faulty processors and sensors. Unfortunately, existing BFT SMR techniques require replicas to reach agreement on redundant sensor data and perform source selection before executing, which adds unavoidable latency to every execution and inevitably decreases control performance. The standard way to reduce the latency of BFT SMR in non-real-time systems is to use speculation, forgoing agreement on inputs altogether, and repeating executions when faults occur. However, this approach is not suitable for real-time systems, since its worst-case latency when faults occur can be even higher than that of non-speculative systems. This paper presents IGOR, a new speculative BFT SMR approach that leverages multi-core processors to avoid the added latency inherent to traditional BFT SMR techniques in both the absence and presence of faults. The key idea of IGOR is to eagerly execute on data from redundant sensors in parallel. While these executions are underway, the replicas reach agreement on which execution will determine the system's final state. As a result, IGOR's latency is reduced to the time taken by the executions or by the agreement process, whichever is longer. Our evaluation shows that IGOR reduces latency by up to 1.75x and improves schedulability by 1.88-3.22x compared to the state of the art. We also show that when used to execute spacecraft guidance, navigation, and control software during a dynamic mission phase, IGOR noticeably increases vehicle stability.

关键词： Byzantine fault tolerance state machine replication speculative execution multi-core real-time systems distributed systems

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mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave Radar

mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave R...

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international Conference on parallel and distributed systems (ICPADS)

作者： Ziheng Mao Yuan He Jia Zhang Yimiao Sun Yadong Xie Xiuzhen Guo School of Software & BNRist Tsinghua University China College of Control Science and Engineering Zhejiang University China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method.

关键词： Accuracy Target tracking Heart beat Signal processing algorithms Millimeter wave radar Harmonic analysis User experience Millimeter wave communication Monitoring Wearable sensors

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