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28th ACM SIGPLAN Annual Symposium on Principles and Practice of parallel Programming, PPoPP 2023

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Lu, Yunping School of Computer Science Fudan University China Institute of Big Data Fudan University China State Key Laboratory of Mathematical Engineering and Advanced Computing China Parallel Processing Institute Fudan University China Department of Computer Science Tulane University United States

ISBN: (纸本)9798400700156

Concurrent B+trees have been widely used in many systems. With the scale of data requests increasing exponentially, the systems are facing tremendous performance pressure. GPU has shown its potential to accelerate concurrent B+trees performance. When many concurrent requests are processed, the conflicts should be detected and resolved. Prior methods guarantee the correctness of concurrent GPU B+trees through lock-based or software transactional memory (STM)-based approaches. However, these methods complicate the request processing logic, increase the number of memory accesses and bring execution path divergence. They lead to performance degradation and variance in response time increasing. Moreover, previous methods do not guarantee linearizability among concurrent requests. In this paper, we design a combined-based concurrency control framework, called Eirene, for GPU B+tree to reduce the overhead of conflict detection and resolution. First, a combining-based synchronization method is designed to combine and issue requests. It combines the requests with the same key, constructs their dependence, decides the issued request, and determines their return values. Since only one request for each key is issued, key conflicts are eliminated. Then, an optimistic STM method is used to reduce structure conflicts. The query and the update requests are partitioned into different kernels. For the update kernels, STM is involved only when the number of the retry reaches a threshold. Finally, a locality-aware warp reorganization optimization is proposed to improve memory behavior and reduce conflicts by exploiting the locality among requests. Evaluations on an NVIDIA A100 GPU show that Eirene is efficient (a throughput of 2.4 billion per second) and can guarantee linearizability. Compared to the state-of-the-art GPU B+tree, it can achieve a speedup of 7.43X and reduce the response time variance from 36% to 5%. © 2023 ACM.

关键词： Graphics processing unit

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The Role of Deep Learning in Parking Space Identification and Prediction Systems

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computers, Materials & Continua 2023年第4期75卷 761-784页

作者： Faizan Rasheed Yasir Saleem Kok-Lim Alvin Yau Yung-Wey Chong Sye Loong Keoh School of Physics Engineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK Department of Computer Science Aberystwyth UniversityAberystwyth SY233FLUK Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul RahmanKajang43200SelangorMalaysia National Advanced IPv6 Centre(NAv6) Universiti Sains MalaysiaPenang11800Malaysia School of Computing Science University of Glasgow Singapore737729Singapore

In today’s smart city transportation,traffic congestion is a vexing issue,and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40%of traffic *** parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives,resulting in the driver’s frustration and aggravating traffic jams while searching for another parking *** explains the need to predict the availability of parking ***,deep learning(DL)has been shown to facilitate drivers to find parking spaces efficiently,leading to a promising performance enhancement in parking identification and prediction ***,no work reviews DL approaches applied to solve parking identification and prediction *** by this gap,the purpose of this work is to investigate,highlight,and report on recent advances inDLapproaches applied to predict and identify the availability of parking *** of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature,and by doing so,the salient and supportive features of different DL techniques for providing parking solutions are ***,several open research challenges are *** work identifies that there are various DL architectures,datasets,and performance measures used to address parking identification and prediction ***,there are some open-source implementations available that can be used directly either to extend existing works or explore a new *** is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart *** study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits,the convergence of these two types of systems and DL brings about new i

关键词： Convolutional neural network deep learning neural networks parking identification parking prediction smart city

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Harnessing Machine Learning-Based Classification Techniques to Optimize Crop and Fertilizer Recommendations 7th

Harnessing Machine Learning-Based Classification Techniques ...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Kumar, Sachin Muthaiah, U. Vinod Roy, Ram Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Peggy and Charles Stephenson Cancer Center Science Centre University of Oklahoma Health Oklahoma CityOK73104 United States

ISBN: (纸本)9783031699856

With machine learning, agriculture which is essential to the world’s food security is experiencing a revolutionary change. Using methods such as XG Boost, Support Vector Machine (SVM), and Decision Tree, this research investigates three important agricultural domains: crop prediction, fertilizer management, and crop disease diagnosis. Accurate crop forecast that maximizes resource allocation with an average accuracy of 94%. Fertilizer management increases crop quality by 15%, lowers expenses by 20%, and increases output by 25% when it is based on soil parameters, historical data, and meteorological considerations. With XG Boost, a Crop Recommendation System that takes into account the specific climatic circumstances of the area provides customized guidance on crop selection and fertilizer application, guaranteeing a 99% average recommendation accuracy. Machine learning can revolutionize Indian agriculture, promote informed decision-making, and increase agricultural productivity while maintaining traditional methods. To fully realize this promise, more study on improving accessibility is essential. © IFIP International Federation for Information Processing 2024

关键词： Decision Tree Fertilizers SVM XG Boost

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Open-source Hardware: Different Approaches to Softcore implementation 6

Open-source Hardware: Different Approaches to Softcore imple...

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6th IEEE International Conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Hiremath, Sneha Chickerur, Satyadhyan Dandin, Joel Patil, Mihir Muddinkoppa, Bhoomi Adakoli, Shreerama Kle Technological University Centre for High Performance Computing School of Computer Science and Engineering Hubballi India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Since the last few years, open-source hardware is preferred in the landscape of hardware development. Originally proprietary hardware was only developed by big multinational companies like AMD, Intel, and NVIDIA, which were expensive and general-purpose. Due to this, people started developing their own hardware. Open-source hardware has support from the opensource community, is developed at low cost, and has presently got a lot of support from developers and enthusiasts. Softcore is a microprocessor that can be implemented on FPGA using logic synthesis and can be fabricated as open-source hardware. This paper focuses on how various approaches to softcore implementation can be done on FPGA which would involve logic cells, CLBs, LUTs, RAM, etc. The soft cores which are built upon the RISC-V ISA, namely VexRISCV and RISC-V BOOM, as well as the softcores adhering to the POWER ISA, such as Microwatt and LibreSoC, are the main topics of discussion. These softcores are installed and simulated. To test the working of the ISA, some standard test cases and test suites are executed. Hardware implementation is done by porting the softcore onto a Xilinx Nexys A7-100T FPGA board. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Multispace Evolutionary Search for Large-Scale Optimization with Applications to Recommender Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第1期4卷 107-120页

作者： Feng, Liang Shang, Qingxia Hou, Yaqing Tan, Kay Chen Ong, Yew-Soon College of Computer Science Chongqing University Chongqing400044 China College of Computer Science and Technology Dalian University of Technology Dalian116024 China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong Centre for Frontier AI Research A STAR Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Large-scale optimization is vital in today's artificial intelligence (AI) applications for extracting essential knowledge from huge volumes of data. In recent years, to improve the evolutionary algorithms used to solve optimization problems involving a large number of decision variables, many attempts have been made to simplify the problem solution space of a given problem for the evolutionary search. In the literature, the existing approaches can generally be categorized as decomposition-based methods and dimension-reduction-based methods. The former decomposes a large-scale problem into several smaller subproblems, while the latter transforms the original high-dimensional solution space into a low-dimensional space. However, it is worth noting that a given large-scale optimization problem may not always be decomposable, and it is also difficult to guarantee that the global optimum of the original problem is preserved in the reduced low-dimensional problem space. This article, thus, proposes a new search paradigm, namely the multispace evolutionary search, to enhance the existing evolutionary search methods for solving large-scale optimization problems. In contrast to existing approaches that perform an evolutionary search in a single search space, the proposed paradigm is designed to conduct a search in multiple solution spaces that are derived from the given problem, each possessing a unique landscape. The proposed paradigm makes no assumptions about the large-scale optimization problem of interest, such as that the problem is decomposable or that a certain relationship exists among the decision variables. To verify the efficacy of the proposed paradigm, comprehensive empirical studies in comparison to five state-of-the-art algorithms were conducted using the CEC2013 large-scale benchmark problems as well as an AI application in e-commerce, i.e., movie recommendation. © 2022 IEEE.

关键词： Evolutionary algorithms

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Optimizing Drug Discovery for Breast Cancer in a Laboratory Environment Using Machine Learning 9

Optimizing Drug Discovery for Breast Cancer in a Laboratory ...

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Borkhade, Gauri Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Kuwar, Vishakha Tiwar, Mohit Bharati Vidyapeeth College of Engineering Navi Mumbai India Alliance School of Advanced Computing Alliance University Bangalore India School of Computer Science Engineering and Technology Bennett University Greater Noida India Delhi-NCR Department of Computer Science Ghaziabad India Dr. D y Patil Vidyapeeth Centre for Online Learning Pune India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering Delhi India

ISBN: (纸本)9798350350845

Breast cancer therapy can be greatly enhanced by the proposed method that combines experimental and computational techniques. Employing a state-of-the-art in vitro system, we evaluated biopsy tissues at different cancer stages, monitoring them for 48 hours. Later on, our investigation involved the application of machine learning models including naïve Bayes (NB), artificial neural networks (ANN), random forest (RF), and decision trees (DT). Surprisingly, these models reached high test accuracies - ANN 93.2%, NB 90.4%, DT 87.8%, and RF 85.9%. The dataset's impedance dynamics data provide evidence for treatment efficacy. Therapeutic strategies need to be adjusted for particular patients and their stage of cancer since the results underscore the usefulness of personalized breast cancer therapy. This study will significantly contribute to new tailored treatment options available for breast cancer patients. © 2024 IEEE.

关键词： Decision trees

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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Unveiling Effective Heuristic Strategies: A Review of Cross-Domain Heuristic Search Challenge Algorithms

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computer Modeling in engineering & sciences 2025年第2期142卷 1233-1288页

作者： Mohamad Khairulamirin Md Razali MasriAyob Abdul Hadi Abd Rahman Razman Jarmin Chian Yong Liu Muhammad Maaya Azarinah Izaham Graham Kendall Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Center for Artificial Intelligence Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Medicine Universiti Kebangsaan Malaysia Medical CentreCheras56000Kuala LumpurMalaysia School of Engineering and Computing MILA UniversityNilai71800Negeri SembilanMalaysia School of Computer Science University of Nottingham-Malaysia CampusSemenyih43500SelangorMalaysia

The Cross-domain Heuristic Search Challenge(CHeSC)is a competition focused on creating efficient search algorithms adaptable to diverse problem *** hyper-heuristics are a class of algorithms that dynamically choose heuristics during the search *** selection hyper-heuristics have different imple-mentation ***,comparisons between them are lacking in the literature,and previous works have not highlighted the beneficial and detrimental implementation methods of different *** question is how to effectively employ them to produce an efficient search ***,the algorithms that competed in the inaugural CHeSC have not been collectively *** work conducts a review analysis of the top twenty competitors from this competition to identify effective and ineffective strategies influencing algorithmic performance.A summary of the main characteristics and classification of the algorithms is *** analysis underlines efficient and inefficient methods in eight key components,including search points,search phases,heuristic selection,move acceptance,feedback,Tabu mechanism,restart mechanism,and low-level heuristic parameter *** review analyzes the components referencing the competition’s final leaderboard and discusses future research directions for these *** effective approaches,identified as having the highest quality index,are mixed search point,iterated search phases,relay hybridization selection,threshold acceptance,mixed learning,Tabu heuristics,stochastic restart,and dynamic *** are also compared with recent trends in *** work enhances the understanding of selection hyper-heuristics,offering valuable insights for researchers and practitioners aiming to develop effective search algorithms for diverse problem domains.

关键词： Hyper-heuristics search algorithms optimization heuristic selection move acceptance learning diversification parameter control

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis 40

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Gao, Junhui Wang, Qianru Zhang, Xin Shi, Juan Zhao, Xiang Han, Qingye Pan, Yan School of Computer Science Northwestern Polytechnical University China School of Computer Science and Technology Xidian University China Air Force Engineering University China National University of Defense Technology Laboratory for Big Data and Decision China School of Management Science and Real Estate Chongqing University China National University of Defense Technology National Key Laboratory of Information Systems Engineering China National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing China

ISBN: (纸本)9798350317152

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Smart Grid Security Framework for Data Transmissions with Adaptive Practices Using Machine Learning Algorithm

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computers, Materials & Continua 2025年第3期82卷 4339-4369页

作者： Shitharth Selvarajan Hariprasath Manoharan Taher Al-Shehari Hussain Alsalman Taha Alfakih School of Built Environment Engineering and ComputingLeeds Beckett UniversityLeedsLS13HEUK Department of Computer Science and Engineering Chennai Institute of TechnologyChennai602109India Centre for Research Impact & Outcome Chitkara UniversityChandigarh140401India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Self-Development Skill Common First Year DeanshipKing Saud UniversityRiyadh11362Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

This research presents an analysis of smart grid units to enhance connected units’security during data *** major advantage of the proposed method is that the system model encompasses multiple aspects such as network flow monitoring,data expansion,control association,throughput,and *** addition,all the above-mentioned aspects are carried out with neural networks and adaptive optimizations to enhance the operation of smart grid ***,the quantitative analysis of the optimization algorithm is discussed concerning two case studies,thereby achieving early convergence at reduced *** suggested method ensures that each communication unit has its own distinct channels,maximizing the possibility of accurate *** results in the provision of only the original data values,hence enhancing *** power and line values are individually observed to establish control in smart grid-connected channels,even in the presence of adaptive settings.A comparison analysis is conducted to showcase the results,using simulation studies involving four scenarios and two case *** proposed method exhibits reduced complexity,resulting in a throughput gain of over 90%.

关键词： Machine learning power systems security smart grid

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