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Assessing and Mitigating Bias in Artificial Intelligence: A Review

Recent Advances in computer science and Communications 2024年第1期17卷 1-10页

作者： Sinha, Akruti Sapra, Devika Sinwar, Deepak Singh, Vijander Raghuwanshi, Ghanshyam North Carolina State University RaleighNC27695 United States Department of Computer and Communication Engineering Manipal University Jaipur303007 India Department of Computer Science and Engineering Manipal University Jaipur Jaipur India

There has been an exponential increase in discussions about bias in Artificial Intelligence (AI) systems. Bias in AI has typically been defined as a divergence from standard statistical patterns in the output of an AI model, which could be due to a biased dataset or biased assumptions. While the bias in artificially taught models is attributed able to bias in the dataset provided by hu-mans, there is still room for advancement in terms of bias mitigation in AI models. The failure to detect bias in datasets or models stems from the "black box" problem or a lack of understanding of algorithmic outcomes. This paper provides a comprehensive review of the analysis of the approach-es provided by researchers and scholars to mitigate AI bias and investigate the several methods of employing a responsible AI model for decision-making processes. We clarify what bias means to different people, as well as provide the actual definition of bias in AI systems. In addition, the paper discussed the causes of bias in AI systems thereby permitting researchers to focus their efforts on minimising the causes and mitigating bias. Finally, we recommend the best direction for future research to ensure the discovery of the most accurate method for reducing bias in algorithms. We hope that this study will help researchers to think from different perspectives while developing unbiased systems. © 2024 Bentham science Publishers.

关键词： Decision making

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A Novel Insertion Solution for the Travelling Salesman Problem

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computers, Materials & Continua 2024年第4期79卷 1581-1597页

作者： Emmanuel Oluwatobi Asani Aderemi Elisha Okeyinka Sunday Adeola Ajagbe Ayodele Ariyo Adebiyi Roseline Oluwaseun Ogundokun Temitope Samson Adekunle Pragasen Mudali Matthew Olusegun Adigun Department of Computer Science Landmark UniversityOmu Aran251103Nigeria SDG 11 Group Landmark UniversityOmu Aran251103Nigeria Department of Computing MiVA UniversityAbuja900211Nigeria Department of Computer Science Ibrahim Badamasi Babangida UniversityLapai911101Nigeria Department of Computer Science University of ZululandKwadlangezwa3886South Africa Department of Computer&Industrial Production Engineering First Technical UniversityIbadan200243Nigeria Department of Multimedia Engineering Kaunas University of TechnologyKaunasLT-44249Lithuania Department of Computer Science Colorado State UniversityFort Collins80523USA

The studypresents theHalfMax InsertionHeuristic (HMIH) as a novel approach to solving theTravelling SalesmanProblem (TSP). The goal is to outperform existing techniques such as the Farthest Insertion Heuristic (FIH) andNearest Neighbour Heuristic (NNH). The paper discusses the limitations of current construction tour heuristics,focusing particularly on the significant margin of error in FIH. It then proposes HMIH as an alternative thatminimizes the increase in tour distance and includes more nodes. HMIH improves tour quality by starting withan initial tour consisting of a ‘minimum’ polygon and iteratively adding nodes using our novel Half Max *** paper thoroughly examines and compares HMIH with FIH and NNH via rigorous testing on standard TSPbenchmarks. The results indicate that HMIH consistently delivers superior performance, particularly with respectto tour cost and computational efficiency. HMIH’s tours were sometimes 16% shorter than those generated by FIHand NNH, showcasing its potential and value as a novel benchmark for TSP solutions. The study used statisticalmethods, including Friedman’s Non-parametric Test, to validate the performance of HMIH over FIH and *** guarantees that the identified advantages are statistically significant and consistent in various situations. Thiscomprehensive analysis emphasizes the reliability and efficiency of the heuristic, making a compelling case for itsuse in solving TSP issues. The research shows that, in general, HMIH fared better than FIH in all cases studied,except for a few instances (pr439, eil51, and eil101) where FIH either performed equally or slightly better thanHMIH. HMIH’s efficiency is shown by its improvements in error percentage (δ) and goodness values (g) comparedto FIH and NNH. In the att48 instance, HMIH had an error rate of 6.3%, whereas FIH had 14.6% and NNH had20.9%, indicating that HMIH was closer to the optimal solution. HMIH consistently showed superior performanceacross many benchmarks,

关键词： Nearest neighbour heuristic farthest insertion heuristic half max insertion heuristic tour construction travelling salesman problem

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Breaking the Edge: Enabling Efficient Neural Network Inference on Integrated Edge Devices

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IEEE Transactions on Cloud Computing 2025年第2期13卷 694-710页

作者： Zhang, Feng Zhang, Chenyang Guan, Jiawei Zhou, Qiangjun Chen, Kuangyu Zhang, Xiao He, Bingsheng Zhai, Jidong Du, Xiaoyong School of Information Beijing100872 China Tsinghua University Department of Computer Science and Technology Beijing100084 China University of California Department of Computer Science and Engineering RiversideCA92507 United States National University of Singapore School of Computing 119077 Singapore

Edge computing has gained widespread attention in cloud computing due to the increasing demands of AIoT applications and the evolution of edge architectures. One prevalent application in this domain is neural network inference on edge for computing and processing. This paper presents an in-depth exploration of inference on integrated edge devices and introduces EdgeNN, a groundbreaking solution for inference specifically designed for CPU-GPU integrated edge devices. EdgeNN offers three key innovations. First, EdgeNN adaptively employs zero-copy optimization by harnessing unified physical memory. Second, EdgeNN introduces an innovative approach to CPU-GPU hybrid execution tailored for inference tasks. This technique enables concurrent CPU and GPU operation, effectively leveraging edge platforms’ computational capabilities. Third, EdgeNN adopts a finely tuned adaptive inference tuning technique that analyzes complex inference structures. It divides computations into sub-tasks, intelligently assigning them to the two processors for better performance. Experimental results demonstrate EdgeNN’s superiority across six popular neural network inference processing. EdgeNN delivers average speed improvements of 3.97×, 4.10×, 3.12×, and 8.80× when compared to inference on four distinct edge CPUs. Furthermore, EdgeNN achieves significant time advantages compared to the direct execution of original programs. This improvement is attributed to better unified memory utilization (44.37%) and the innovative CPU-GPU hybrid execution approach (17.91%). Additionally, EdgeNN exhibits superior energy efficiency, providing 29.14× higher energy efficiency than edge CPUs and 5.70× higher energy efficiency than discrete GPUs. © 2013 IEEE.

关键词： Mobile edge computing

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IDCE:Integrated Data Compression and Encryption for Enhanced Security and Efficiency

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computer Modeling in engineering & sciences 2025年第4期143卷 1029-1048页

作者： Muhammad Usama Arshad Aziz Suliman A.Alsuhibany Imtiaz Hassan Farrukh Yuldashev Department of Cyber Security Pakistan Navy Engineering CollegeNational University of Sciences and TechnologyKarachi75350Pakistan Department of Computer Science Main CampusIqra UniversityKarachi75500Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Informatics and Its Teaching Methods Tashkent State Pedagogical UniversityTashkent100170Uzbekistan

Data compression plays a vital role in datamanagement and information theory by reducing ***,it lacks built-in security features such as secret keys or password-based access control,leaving sensitive data vulnerable to unauthorized access and *** the exponential growth of digital data,robust security measures are *** encryption,a widely used approach,ensures data confidentiality by making it unreadable and unalterable through secret key *** their individual benefits,both require significant computational ***,performing them separately for the same data increases complexity and processing *** the need for integrated approaches that balance compression ratios and security levels,this research proposes an integrated data compression and encryption algorithm,named IDCE,for enhanced security and *** on 128-bit block sizes and a 256-bit secret key *** combines Huffman coding for compression and a Tent map for ***,an iterative Arnold cat map further enhances cryptographic confusion *** analysis validates the effectiveness of the proposed algorithm,showcasing competitive performance in terms of compression ratio,security,and overall efficiency when compared to prior algorithms in the field.

关键词： Chaotic maps security data compression data encryption integrated compression and encryption

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Recent advances in multipactor physics and mitigation

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High Voltage 2023年第6期8卷 1095-1114页

作者： Asif Iqbal De‐Qi Wen John Verboncoeur Peng Zhang Department of Electrical and Computer Engineering Michigan State UniversityEast LansingMichiganUSA Department of Computational Mathematics Scienceand EngineeringMichigan State UniversityEast LansingMichiganUSA

Recent progress made in the prediction,characterisation,and mitigation of multipactor discharge is reviewed for single‐and two‐surface ***,an overview of basic concepts including secondary electron emission,electron kinetics under the force law,multipactor susceptibility,and saturation mechanisms is provided,followed by a discus-sion on multipactor mitigation *** strategies are categorised into two broad areas–mitigation by engineered devices and engineered radio frequency(rf)*** approach is useful in different *** advances in multipactor physics and engineering during the past decade,such as novel multipactor prediction methods,un-derstanding space charge effects,schemes for controlling multipacting particle trajec-tories,frequency domain analysis,high frequency effects,and impact on rf signal quality are *** addition to vacuum electron multipaction,multipactor‐induced ioni-zation breakdown is also reviewed,and the recent advances are summarised.

关键词： multip kinetics susceptibility

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A General Scalable Approach for Knowledge Selection based on Iterative Hybrid Encoding and Re-ranking

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Data Intelligence 2025年第1期7卷 124-142页

作者： Zhisheng Huang Xudong Jia Tao Chen Zhongwei Zhang Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen 529020GuangdongChina College of Engineering and Computer Science California State UniversityNorthridge CA 91330USA

Knowledge selection is a challenging task that often deals with semantic drift issues when knowledge is retrieved based on semantic similarity between a fact and a question. In addition, weak correlations embedded in pairs of facts and questions and gigantic knowledge bases available for knowledge search are also unavoidable issues. This paper presents a scalable approach to address these issues. A sparse encoder and a dense encoder are coupled iteratively to retrieve fact candidates from a large-scale knowledge base. A pre-trained language model with two rounds of fine-tuning using results of the sparse and dense encoders is then used to re-rank fact candidates. Top-k facts are selected by a specific re-ranker. The scalable approach is applied on two textual inference datasets and one knowledge-grounded question answering dataset. Experimental results demonstrate that (1) the proposed approach can improve the performance of knowledge selection by reducing the semantic drift;(2) the proposed approach produces outstanding results on the benchmark datasets. The code is available at https://***/hhhhzs666/KSIHER.

关键词： Knowledge selection Textual inference Semantic drift Coarse-to-Fine

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Looking Backwards (and Forwards): NSF Secure and Trustworthy Computing 20-Year Retrospective Panel Transcription

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IEEE Security and Privacy 2023年第2期21卷 32-42页

作者： Landwehr, Carl Reiter, Michael K. Williams, Laurie Tsudik, Gene Jaeger, Trent Kohno, Tadayoshi Kapadia, Apu University of Michigan Electrical Engineering Ann ArborMI48109-2122 United States Duke University Computer Science and Electrical and Computer Engineering DurhamNC27708 United States North Carolina State University Computer Science Department of the College of Engineering RaleighNC27513 United States University of California Computer Science Irvine United States The Pennsylvania State University Computer Science and Engineering University ParkPA16802 United States University of Washington Paul G. Allen School of Computer Science and Engineering SeattleWA98195 United States Indiana University Bloomington Department of Computer Science BloomingtonIN47408 United States

The U.S. National science Foundation (NSF) celebrated the 20th anniversary of its research funding programs in cybersecurity, and more generally, secure and trustworthy computing, with a panel session at its conference held in June, 2022. The panel members, distinguished researchers in different research areas of trustworthy computing, were asked to comment on what has been learned, what perhaps should be 'unlearned what still needs to be learned, and the status of education and training in their respective areas of expertise. Laurie Williams covered enterprise security and measuring security, Gene Tsudik commented on cryptographic security, Trent Jaeger addressed computing infrastructure security, Tadayoshi Kohno reviewed security in cyberphysical systems, and Apu Kapadia provided insights on human-centered security. Michael K. Reiter chaired the panel and moderated questions from the audience. This report provides a brief summary of NSF's research programs in the area and an edited transcript of the panel discussion. © 2003-2012 IEEE.

关键词： Personnel training

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Superior efficiency and stability of YbO_(x)-enhanced perovskite solar cells

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science China Materials 2024年第4期67卷 1356-1358页

作者： Qifeng Zhang Guozhong Cao Department of Electrical and Computer Engineering North Dakota State UniversityFargoND 58108-6050USA Department of Materials Science and Engineering University of WashingtonSeattleWA 98195-2120USA

Perovskite solar cells represent a revolutionary class of photovoltaic devices that have gained substantial attention for their exceptional performance and potential to provide an affordable and efficient solution for harnessing solar energy. These cells utilize perovskite-structured materials, typically hybrid organicinorganic lead halide compounds, as the light-absorbing layer.

关键词： energy perovskite stability

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

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Artificial Intelligence in Agriculture 2024年第4期14卷 99-114页

作者： Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad School of Electrical and Computer Engineering University of TehranTehranIran Department of Biosystems and Agricuttural Engineering Michigan State UniversityMIUSA Department of Computer Engineering Khatam UniversityTehranIran Department of Computer Science and Engineering Michigan State UniversityEast LansingMIUSA Department of Agronomy Faculty of AgricultureUniversity of JafinaKilinochchSri Lanka Department of Agronomy Kansas State UniversityManhattanKSUA Feed the Future Sustainable Intensification Innovation Lab Kansas State UniversityManhattanKSUA

In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.

关键词： Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning

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Energy-Efficient Functional Safety Design Methodology Using ASIL Decomposition for Automotive Cyber-Physical Systems

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IEEE Transactions on Reliability 2024年 1-23页

作者： Xie, Guoqi Peng, Hao Huang, Jing Li, Renfa Li, Keqin Department of Computer Engineering College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Key Laboratory for Embedded and Cyber-Physical Systems of Hunan Province Changsha 410082 China College of Computer Science and Electronic Engineering Hunan University Changsha 410082 China and also with the Department of Computer Science State University of New York New Paltz NY 12561 USA

Automotive cyber-physical systems (ACPS) are typical cyber-physical systems because of the joint interaction between the cyber part and physical part. Functional safety requirement (including response time and reliability requirements) for an ACPS function must be assured for safe driving. Auto industry is cost-sensitive, power-sensitive, and environment-friendly. Energy consumption affects the development efficiency of the ACPS and the living environment of people. This paper solves the problem of optimizing the energy consumption for an ACPS function while assuring its functional safety requirement (i.e., energy-efficient functional safety for ACPS). However, implementing minimum response time, maximum reliability, and minimum energy consumption is a conflicting problem. Consequently, solving the problem is a challenge. In this paper, we propose a three-stage design process toward energy-efficient functional safety for ACPS. The topic problem is divided into three sub-problems, namely, response time requirement verification (first stage), functional safety requirement verification (second stage), and functional safety-critical energy consumption optimization (third stage). The proposed energy-efficient functional safety design methodology is implemented by using automotive safety integrity level decomposition, which is defined in the ACPS functional safety standard ISO 26262. Experiments with real-life and synthetic ACPS functions reveal the advantages of the proposed design methodology toward energy-efficient functional safety for ACPS compared with state-of-the-art algorithms. IEEE

关键词： Energy utilization

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