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An Empirical Study on Group Fairness Metrics of Judicial Data

IEEE ACCESS 2021年 9卷 149043-149049页

作者： Li, Yanjun Huang, Huan Guo, Xinwei Yuan, Yuyu Beijing Univ Posts & Telecommun Software Coll Beijing 100876 Peoples R China Nanjing Univ Shenzhen Res Inst Shenzhen 518000 Peoples R China China Justice Big Data Inst Co Ltd Beijing 100070 Peoples R China China Natl Accreditat Serv Conform Assessment Beijing 100062 Peoples R China

Group fairness means that different groups have an equal probability of being predicted for one aspect. It is a significant fairness definition, which is conducive to maintaining social harmony and stability. Fairness is a vital issue when an artificial intelligence software system is used to make judicial decisions. Either data or algorithm alone may lead to unfair results. Determining the fairness of the dataset is a prerequisite for studying the fairness of algorithms. This paper focuses on the dataset to research group fairness from both micro and macro views. We propose a framework to determine the sensitive attributes of a dataset and metrics to measure the fair degree of sensitive attributes. We conducted experiments and statistical analysis of the judicial data to demonstrate the framework and metric approach better. The framework and metric approach can be applied to datasets of other domains, providing persuasive evidence for the effectiveness and availability of algorithmic fairness research. It opens up a new way for the research of the fairness of the dataset.

关键词： Measurement software algorithms Benchmark testing Machine learning algorithms Gaussian distribution Training software systems Data fairness sensitive attributes metric judicial data

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Application of a Simple, Spiking, Locally Competitive Algorithm to Radionuclide Identification

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2021年第3期68卷 292-304页

作者： Carson, Merlin Woods, Walt Reynolds, Sebastian Wetzel, Mark Morton, Adam J. Hecht, Adam A. Osinski, Marek Teuscher, Christof Portland State Univ Dept Elect & Comp Engn Portland OR 97201 USA Legacy Hlth Dow Neurosci Labs Portland OR 97232 USA Univ New Mexico Dept Nucl Engn Albuquerque NM 87131 USA Univ New Mexico Ctr High Technol Mat Albuquerque NM 87106 USA

Many radionuclide identification algorithms use statistical inference to collect a variety of features from gamma-ray spectra to deduce the presence of particular radionuclides. More modern algorithms require large amounts of data to learn and use latent features from spectra for classification. Both approaches are computationally expensive, which is reflected in their power consumption, and require large amounts of user intervention to prepare. In this article, we introduce a low-power, neuromorphic algorithm for the real-time identification of radionuclides which simultaneously considers the entire shape of a gamma-ray spectrum. Utilizing the output of a traditional gamma-ray detector, our spiking, locally competitive algorithm uses sparse coding optimization to compare global patterns in a gamma-ray spectrum with a dictionary of radionuclide templates. This approach allows us to model informative global features resulting from both photoelectric absorption and Compton scattering. For the purpose of radiation threat reduction, the dictionary consists of data from the Nuclear Wallet Cards, a list of radionuclides and their properties compiled by the National Nuclear Data Center. To test our algorithm, we use a variety of gamma-ray spectra created using radionuclides measured under laboratory conditions with varying durations, distances, activity levels, and backgrounds, resulting in a wide range of signal-to-noise ratios. We have created test sets for three different gamma-ray detector types, with Co-57, Cs-137, Eu-152, Co-60, Pu-239, and U-235 sources, to quantify the effect of resolution, efficiency, and background on the accuracy of the algorithm. We demonstrate a true positive accuracy of 91% with a high-resolution detector and 89% with a low-resolution detector on the corresponding test sets. Experimenting with the same radionuclides included in the test sets in a variety of special nuclear material (SNM) masking configurations, we show that our algorithm is c

关键词： Gamma-rays Detectors software algorithms Prediction algorithms Dictionaries Feature extraction Shape Compton scatter gamma-ray identification locally competitive algorithm (LCA) memristor neuromorphic power efficient radionuclide simple spiking LCA (SSLCA) sparse coding spectrum spiking architecture

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An Efficient Algorithm for Finding Modules in Fault Trees

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IEEE TRANSACTIONS ON RELIABILITY 2021年第3期70卷 862-874页

作者： Chen, Mei Xiao, Ning-Cong Zuo, Ming J. Ding, Yi Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Peoples R China Univ Alberta Dept Mech Engn Edmonton AB T6G 2G8 Canada Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Zhejiang Univ Coll Elect Engn Hangzhou 310007 Peoples R China

A module of a fault tree is an independent subtree that has no input from the rest of the tree and no output to the rest, except the top events. Modularization is an important technique to reduce the computation cost for large, complex fault tree analysis. This article presents a new linear-time algorithm that is more efficient and easier to code for finding modules existing in fault trees. Two main stages are included in the proposed algorithm: branching and transforming. To demonstrate the efficiency and applicability of the proposed algorithm, comparisons are performed between the proposed algorithm and other linear-time algorithms for finding modules in fault trees. Results have shown the superiority and effectiveness of the proposed algorithm.

关键词： Fault trees Logic gates software algorithms Parallel algorithms Indexes Terminology Reliability Coherent fault trees linear-time algorithm lowest common ancestor modules

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Energy Efficiency Analysis of Post-Quantum Cryptographic algorithms

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IEEE ACCESS 2021年 9卷 71295-71317页

作者： Roma, Crystal Andrea Tai, Chi-En Amy Hasan, M. Anwar Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Univ Waterloo Dept Management Sci Waterloo ON N2L 3G1 Canada

Classical cryptographic schemes in use today are based on the difficulty of certain number theoretic problems. Security is guaranteed by the fact that the computational work required to break the core mechanisms of these schemes on a conventional computer is infeasible;however, the difficulty of these problems would not withstand the computational power of a large-scale quantum computer. To this end, the post-quantum cryptography (PQC) standardization process initiated by the National Institute of Standards and Technology (NIST) is well underway. In addition to the evaluation criteria provided by NIST, the energy consumption of these candidate algorithms is also an important criterion to consider due to the use of battery-operated devices, high-performance computing environments where energy costs are critical, as well as in the interest of green computing. In this paper, the energy consumption of PQC candidates is evaluated on an Intel Core i7-6700 CPU using PAPI, the Performance API. The energy measurements are categorized based on their proposed security level and cryptographic functionality. The results are then further subdivided based on the underlying mechanism used in order to identify the most energy-efficient schemes. Lastly, IgProf is used to identify the most energy-consuming subroutines within a select number of submissions to highlight potential areas for optimization.

关键词： Cryptography NIST Energy consumption Digital signatures software algorithms Measurement software Post-quantum cryptography energy consumption digital signature key encapsulation mechanism public-key cryptography

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Efficient Classification of Enciphered SCADA Network Traffic in Smart Factory Using Decision Tree Algorithm

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IEEE ACCESS 2021年 9卷 154892-154901页

作者： Ahakonye, Love Allen Chijioke Nwakanma, Cosmas Ifeanyi Lee, Jae-Min Kim, Dong-Seong Kumoh Natl Inst Technol Networked Syst Lab It Convergence Engn Gumi 39177 South Korea Kumoh Natl Inst Technol Dept IT Convergence Engn Gumi 39177 South Korea

Vulnerability detection in Supervisory Control and Data Acquisition (SCADA) network of a Smart Factory (SF) is a high-priority research area in the cyber-security domain. Choosing an efficient Machine Learning (ML) algorithm for intrusion detection is a huge challenge. This study performed an investigative analysis into the classification ability of various ML models leveraging public cyber-security datasets to determine the best model. Based on the performance evaluation, all adaptions of Decision Tree (DT) and KNN in terms of accuracy, training time, MCE, and prediction speed are the most suitable ML for resolving security issues in the SCADA system.

关键词： Security Training Smart manufacturing SCADA systems software algorithms Computational modeling Testing algorithms artificial intelligence machine learning SCADA systems

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Design and Implementation of Autonomous Underwater Vehicles’ software Stack

Design and Implementation of Autonomous Underwater Vehicles...

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Power, Instrumentation, Energy and Control (PIECON), International Conference on

作者： Disha Singh Kulsoom Masood Nabeel Jamshed Yahya Farooq Yusuf Hasan Huzaif Ahmad Z.H. College of Engg. & Tech Aligarh Muslim University India Faculty of science Aligarh Muslim University India

The autonomous underwater vehicle (AUV) is the subject of extensive research to operate more effectively in unsound environments. In the industry, autonomous underwater vehicles are becoming more prevalent and are proving to be very helpful in many practical fields, including mapping and oceanographic research, military reconnaissance, emergency response, data collection, and commercial usage. The software stack has been designed for maneuvering a vehicle with in-depth perception. This involves underwater object detection and sensor data fusion for navigation and maintaining stability. With their great accuracy, machine learning algorithms provide much faster solutions to the perception and detection parts of the problem. With YOLOv7’s quicker inference time and higher accuracy, object detection has become more convenient, even underwater, and faster after using the Jetson Xavier NX. Intel’s Depth Sense camera has been used for its better depth perception capabilities. Pixhawk is also used for communication with the micro-controllers for the navigation part.

关键词： Pressure sensors Autonomous underwater vehicles Machine learning algorithms Navigation software algorithms Full stack Object detection

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How Different is Test Case Prioritization for Open and Closed Source Projects?

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IEEE TRANSACTIONS ON software ENGINEERING 2021年第7期48卷 2526-2540页

作者： Ling, Xiao Agrawal, Rishabh Menzies, Tim North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA

Improved test case prioritization means that software developers can detect and fix more software faults sooner than usual. But is there one "best" prioritization algorithm? Or do different kinds of projects deserve special kinds of prioritization? To answer these questions, this article applies nine prioritization schemes to 31 projects that range from (a) highly rated open-source Github projects to (b) computational science software to (c) a closed-source project. We find that prioritization approaches that work best for open-source projects can work worst for the closed-source project (and vice versa). From these experiments, we conclude that (a) it is ill-advised to always apply one prioritization scheme to all projects since (b) prioritization requires tuning to different project types.

关键词： Testing software Open source software software development management Measurement software algorithms History software testing regression testing test case prioritization open-source software

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Bi-Objective Optimization of Data-Parallel Applications on Heterogeneous HPC Platforms for Performance and Energy Through Workload Distribution

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2021年第3期32卷 543-560页

作者： Khaleghzadeh, Hamidreza Fahad, Muhammad Shahid, Arsalan Manumachu, Ravi Reddy Lastovetsky, Alexey Univ Coll Dublin Sch Comp Sci Dublin 4 Ireland

Performance and energy are the two most important objectives for optimization on modern parallel platforms. In this article, we show that moving from single-objective optimization for performance or energy to their bi-objective optimization on heterogeneous processors results in a tremendous increase in the number of optimal solutions (workload distributions) even for the simple case of linear performance and energy profiles. We then study full performance and energy profiles of two real-life data-parallel applications and find that they exhibit shapes that are non-linear and complex enough to prevent good approximation of them as analytical functions for input to exact algorithms or optimization software for determining the Pareto front. We, therefore, propose a solution method solving the bi-objective optimization problem on heterogeneous processors. The method's novel component is an efficient and exact global optimization algorithm that takes as an input performance and energy profiles as arbitrary discrete functions of workload size, which accurately and realistically take into account resource contention and NUMA inherent in modern parallel platforms, and returns the Pareto-optimal solutions (generally speaking, load imbalanced). To construct the input discrete energy functions, the method employs a methodology that accurately models the energy consumption by a hybrid data-parallel application executing on a heterogeneous HPC platform containing different computing devices using system-level power measurements provided by power meters. We experimentally analyse the proposed solution method using three data-parallel applications, matrix multiplication, 2D fast Fourier transform (2D-FFT), and gene sequencing, on two connected heterogeneous servers consisting of multicore CPUs, GPUs, and Intel Xeon Phi. We show that it determines a superior Pareto front containing the best load balanced solutions and all the load imbalanced solutions that are ignored by load bala

关键词： Optimization Program processors Energy consumption Heuristic algorithms Multicore processing software algorithms Sequential analysis Heterogeneous platforms data-parallel applications workload partitioning performance optimization energy optimization bi-objective optimization workload distribution multicore CPU GPU Intel Xeon Phi

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A Multiobjective Optimization of PCB Prototyping Assembly With OFA Based on the Similarity of Intuitionistic Fuzzy Sets

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第7期29卷 2054-2061页

作者： Zhang, Wei-Bo Zhu, Guang-Yu Fuzhou Univ Sch Mech Engn & Automat Fuzhou 350108 Peoples R China

Shop scheduling is a significant factor affecting the ability of manufacturers of printed circuit board (PCB) prototyping assemblies to remain profitable and meet the needs of customers. This scheduling is complicated and involves various factors;thus, the shop scheduling problem of a PCB prototyping assembly is regarded as a multiobjective permutation flow shop scheduling problem (MOPFSP). A mathematical model with four objective functions is built based on the characteristics of a PCB prototyping assembly shop. In this article an aggregation-based approach is designed to aggregate multiple objective function values into the similarity of intuitionistic fuzzy sets (sIFSs). This approach is adopted as a fitness function assignment strategy and is combined with the optimal foraging algorithm (OFA) to build a multiobjective evolutionary algorithm: the OFA based on sIFSs (OFA-sIFSs). To verify the performance of OFA-sIFSs, six congress on evolutionary computation benchmark test functions, ten MOPFSP benchmark instances and a practical problem for a PCB prototyping assembly are solved by OFA-sIFSs and three state-of-the-art algorithms. Three types of similarity measures are evaluated. During the process of testing, four performance metrics, statistical analysis, and business software are employed. The results of this study suggest that OFA-sIFSs can be used to solve the MOPFSP and have better performances compared with the other three algorithms.

关键词： Fuzzy sets Optimization Job shop scheduling Linear programming Mathematical model software algorithms Intuitionistic fuzzy sets (IFSs) multiobjective optimization multiobjective permutation flow shop scheduling problem (MOPFSP) optimal foraging algorithm (OFA) printed circuit board (PCB) prototyping assembly

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Implementation of Decentralized Reinforcement Learning-Based Multi-Quadrotor Flocking

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IEEE ACCESS 2021年 9卷 132491-132507页

作者： Abichandani, Pramod Speck, Christian Bucci, Donald Mcintyre, William Lobo, Deepan Newark Coll Engn NCE New Jersey Inst Technol Dept Elect & Comp Engn Robot & Data Lab RADLab Newark NJ 07103 USA Lockheed Martin Adv Technol Labs Cherry Hill NJ 08002 USA

Enabling coordinated motion of multiple quadrotors is an active area of research in the field of small unmanned aerial vehicles (sUAVs). While there are many techniques found in the literature that address the problem, these studies are limited to simulation results and seldom account for wind disturbances. This paper presents the experimental validation of a decentralized planner based on multi-objective reinforcement learning (RL) that achieves waypoint-based flocking (separation, velocity alignment, and cohesion) for multiple quadrotors in the presence of wind gusts. The planner is learned using an object-focused, greatest mass, state-action-reward-state-action (OF-GM-SARSA) approach. The Dryden wind gust model is used to simulate wind gusts during hardware-in-the-loop (HWIL) tests. The hardware and software architecture developed for the multi-quadrotor flocking controller is described in detail. HWIL and outdoor flight tests results show that the trained RL planner can generalize the flocking behaviors learned in training to the real-world flight dynamics of the DJI M100 quadrotor in windy conditions.

关键词： Wind Heuristic algorithms Atmospheric modeling software algorithms Mathematical models Licenses Birds Cooperative systems design for experiments unmanned aerial vehicles multi-agent systems motion planning supervised learning

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