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检索条件"任意字段=5th International Conference on the Art, Science, and Engineering of Programming, Programming 2021"

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Pedestrian Intention Prediction via Depth Augmented Scene Restoration

Pedestrian Intention Prediction via Depth Augmented Scene Re...

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conference on Vehicle Control and Intelligence (CVCI)

作者： Dazhi Zhang Feifan Shi Yu Meng Yan Xu Xiaofeng Xiao Wenbo Li School of Mechanical Engineering University of Science and Technology Beijing Beijing China College of MVE Chongqing University Chongqing China

ISBN: (纸本)9781665408486

Minimizing traffic accidents between vehicles and pedestrians is of great importance in constructing intelligent transportation systems. Pedestrian behavior prediction is one of the major solutions to achieve this goal. the current methods are all extracting key information in the two-dimensional plane of image (e.g., the scene map around pedestrians, semantic map), and there is no specific use of the unique information in the three-dimensional space. Besides, the relative distance information that accounts for the interaction between the target pedestrian and the scene has not been properly utilized, and this information is missing in the current behavioral benchmark dataset. To solve these challenges, we introduce a new view for pedestrian intention prediction. the distance from each pixel to the camera is firstly estimated by the method of monocular depth estimation, so that the two-dimensional pixels are remapped to the three-dimensional space. then, a new deep learning variant model is proposed to adequately fuse information from different perspectives. In particular, the experiments based on large-scale traffic dataset JAAD [1] and PIE [2] show that the multi-view architecture has an outstanding performance than state-of-the-art (SOTA) methods.

关键词： Deep learning Visualization Fuses Semantics Estimation Benchmark testing Robustness

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Deep Leaf Disease Prediction Framework (DLDPF) with Transfer Learning for Automatic Leaf Disease Detection

Deep Leaf Disease Prediction Framework (DLDPF) with Transfer...

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international conference on Computing Methodologies and Communication (ICCMC)

作者： T. Vijaykanth Reddy K Sashi Rekha Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Agriculture in India plays vital role in the economy and growth of the country. However, technology driven innovation in this field known as Precision Agriculture (PA) is still in its infancy. Nevertheless, there are significant improvements with technology innovations. With the emergence of deep learning as element of artificial Intelligence (AI), it is made possible to bring about technology into agriculture activities. One of the activities that can be automated is leaf disease detection. With scalable access to cloud computing resources, this area of the research has attracted many academia and researchers. the existing approaches with Convolutional Neural Network (CNN) exhibited shortcomings in terms of adaptation and reuse of learned outcomes. this paper has used CNN with pre-trained deep models with transfer learning to fill this gap. A framework known as Deep Leaf Disease Prediction Framework (DLDPF) has been proposed by integrating CNN with AlexNet and GoogLeNet cascade inception. the underlying algorithm is known as Cascade Inception based Deep CNN with Transfer Learning (CIDCNN-TL). Keras and TensorFlow along with Python data science platform are used for implementation of the proposed framework. the proposed framework DLDPF is compared with many deep learning models such as AlexNet, GoogLeNet, VGGNet-16 and ResNet-20. Apple leafs datasets is use for empirical study. the experimental outcome exposed that the DLDPF outperforms the shape of the art deep learning model for automated prediction of lead diseases.

关键词： Deep learning Technological innovation Shape Computational modeling Biological system modeling Transfer learning Predictive models

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Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers 26th

Feature Selection on Epistatic Problems Using Genetic Algor...

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26th international conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Carvalho, Pedro Ribeiro, Bruno Rodrigues, Nuno M. Batista, João E. Vanneschi, Leonardo Silva, Sara University of Coimbra Centre for Informatics and Systems of the University of Coimbra Department of Informatics Engineering Coimbra Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development LASI - Intelligent Systems Associate LAboratory Polytechnic of Porto Rua Dr. António Bernardino de Almeida 431 Porto4249-015 Portugal LASIGE Department of Informatics Faculty of Sciences University of Lisbon Lisbon Portugal Universidade Nova de Lisboa Campus de Campolide Lisboa1070-312 Portugal

ISBN: (纸本)9783031302282

Feature selection is becoming an essential part of machine learning pipelines, including the ones generated by recent AutoML tools. In case of datasets with epistatic interactions between the features, like many datasets from the bioinformatics domain, feature selection may even become crucial. A recent method called SLUG has outperformed the state-of-the-art algorithms for feature selection on a large set of epistatic noisy datasets. SLUG uses genetic programming (GP) as a classifier (learner), nested inside a genetic algorithm (GA) that performs feature selection (wrapper). In this work, we pair GA with different learners, in an attempt to match the results of SLUG with less computational effort. We also propose a new feedback mechanism between the learner and the wrapper to improve the convergence towards the key features. Although we do not match the results of SLUG, we demonstrate the positive effect of the feedback mechanism, motivating additional research in this area to further improve SLUG and other existing feature selection methods. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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Assessing Self-Efficacy Method for Enhancing Computational thinking in Educational Game Environment

Assessing Self-Efficacy Method for Enhancing Computational T...

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Technology Innovation Management and engineering science international conference (TIMES-iCON)

作者： Ratchanon Nobnop Nacha Chondamrongkul Punnarumol Temdee Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand

ISBN: (数字)9798350362602

ISBN: (纸本)9798350362619

Self-efficacy is critical for improving computational thinking in gaming. However, conventional assessment methods like questionnaires can produce disengaged responses or require additional work to understand the questions and have limitations in the assessment of dynamic self-efficacy. To address this challenge, this study proposes a method using in-game features to assess self-efficacy, which can change over the playtime. the employed in-game feature is the Planning Stage, in which learners strategize and prepare their approach before engaging with challenges. We collect data on the time spent in this stage, which is newly proposed in this study. the experiment was conducted with twenty-five students in the Multimedia Technology and Animation Major playing Tenacity-P to accomplish tasks from a computer programming course. To validate the method, we compared self-efficacy results from the game-based approach with those from conventional questionnaires. the findings highlight the need for strategic planning in managing difficult tasks. As challenges increased, performance measures varied more, underscoring the importance of strong problem-solving skills. Efficient strategic planning directly improved self-efficacy, as participants with better planning skills maintained higher self-efficacy despite increasing task difficulty.

关键词： Games Strategic planning Innovation management Animation Particle measurements Real-time systems Problem-solving

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Advancements in High-Performance Computing: Case Studies in Fast Fourier Transform with Graphical Processing Unit Acceleration

Advancements in High-Performance Computing: Case Studies in ...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Gaylord Ogbonna Asoronye Jude Emenike Okorie Augustine Chidiebere Onuora Emmanuel Okekenwa Computer Engineering Department Akanu Ibiam Federal Polytechnic Unwana Afikpo Ebonyi State Nigeria Electrical Electronic Engineering Department Akanu Ibiam Federal Polytechnic Unwana Afikpo Ebonyi State Nigeria Computer Science Department Akanu Ibiam Federal Polytechnic Unwana Afikpo Ebonyi State Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

In several scientific and engineering applications, the need for fast computational processes is a key factor considering the vast amount of work involved to simulate real-world physical phenomena. this is also the case for the Fast Fourier Transform which is a fundamental method for solving the N-point Discrete Fourier Transform (DFT). However, current DFT implementations on Central Processing Units (CPUs) become extremely slow as the point number grows due to the high number of operations involved in the evaluation of the FFT. For this reason, this study will explore the major improvements of High-Performance Computing (HPC) using the capabilities of Graphical Processing Units (GPUs) to speed up the FFT algorithms. In this project, the FFT algorithm will be executed in parallel on an NVIDIA Quadro K420 GPU using the programming toolkit CUDA (Compute Unified Device Architecture). the results clearly show that, comparing to the CPU implementation, by using the GPU as an accelerator to process the FFT we are able to reduce the time involved in the computational process, which consequently improves the performance scalability as the FFT length increases. For an 8-point FFT, execution time is reduced by 150 times. the performance scales as the input size increases, with GPUs being particularly effective for high-performance computing tasks. the results provide a strong foundation for optimizing FFT algorithms in scientific and engineering applications.

关键词： Performance evaluation Humanities Fast Fourier transforms High performance computing Scalability Discrete Fourier transforms Graphics processing units programming Parallel processing Central Processing Unit

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Real Time-Based Face Recognition, Tracking, Counting, and Calculation of Spent Time of Person Using OpenCV and Centroid Tracker Algorithms

Real Time-Based Face Recognition, Tracking, Counting, and Ca...

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IEEE international conference on Computer Communication and the Internet (ICCCI)

作者： Md. Rahatul Islam Keiichi Horio Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology Wakamatsu Kitakyushu Japan

the study of human vision is one of the current controversial subjects in the computer vision consortium. Real-time tracking, face recognition, and counting of persons from video footage and CCTV or Webcam are almost new in flexible activities. Face recognition is often the first impression when used in video tracking, interfaces, and facial recognition. In this paper, we resolve to implement human face recognition, tracking, and counting, and spent time calculating in real-time using OpenCV and Python programming. the pilot method was implemented by using different types of OpenCV libraries like face recognition, Imutils, DateTime, and Centroid Tracker algorithm which we acquired the exact and absolute outcome for face recognition, tracking, counting, and spent time calculation depending on real-time. Face recognition algorithms is recognized faces in video files or webcams by showing people's identities. People are tracked and assigned an Individual ID using OpenCV's centroid tracking algorithm. then the system calculates spent time for indicates the person was in the frame. After that, counting the number of people with the help of different personal IDs of other people which were provided by the tracker. the live frame captures the number of people (Live person count) and the total number of people (Total people count). Real-time and date are also displayed for security reasons. through this research, we have been able to identify person through face detection, track and count all individuals with spent time calculating in real-time, which plays an important role in the security of important public and private institutions, especially banks, shopping malls, universities, etc.

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G†eographic Flighting for the Tennis Competitors of Dallas: A Case Study in Optimizing Drive Time and Competition in Sports League Design 22

G†eographic Flighting for the Tennis Competitors of Dallas:...

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Proceedings of the 5th international conference on Information Management and Management science

作者： Zhuo J. Chen Operations Research & Engineering Management Southern Methodist University USA

ISBN: (纸本)9781450396721

Club sports (also known as recreational team sports) are prevalent in the metropolitan areas of the United States nowadays. However, there is a key concern for organizers: how to reduce the time players spend driving to and from matches while keeping league divisions competitive. We adopt a three-step approach to solve this problem. Initially, we analyze the drive time data between the clubs’ locations to determine geographic regions for the league. Next, clubs are assigned to divisions based on their ranking within in the league and their home facility's geographic region. Finally, divisions are further subdivided to minimize drive time. Applying the approach to the Tennis Competitors of Dallas, a large and well-established sports league in Texas, USA, we demonstrate that it can rearrange the existing divisions in a way that not only shortens the drive time for players, but also maintains an acceptable level of competition.

关键词： sports analytics integer programming

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Hybrid Collaborative Filtering-Based API Recommendation

Hybrid Collaborative Filtering-Based API Recommendation

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IEEE international conference on Software Quality, Reliability and Security (QRS)

作者： Yongchao Wang Yu Zhou Taolue Chen Jingxuan Zhang Wenhua Yang Zhiqiu Huang College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Lab. for Novel Software Technology Nanjing University China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology China University of London Birkbeck UK

ISBN: (纸本)9781665458146

Automatic API recommendations can liberate software developers from labor-intensive programming tasks. Collaborative filtering (CF) techniques, which have been proved to be superior to other classic techniques, are widely used in recommendation tasks such as music, book, and goods recommendations, but are rarely used in the recommendation of APIs. In this paper, we employ the hybrid of CF techniques to build an API recommendation system. More precisely, We treat the API recommendation task as an item recommendation problem, where method declarations are regarded as users, API calls are regarded as items. First, we use the memory-based CF technique to find the most similar projects, collect the most similar declarations, and take API calls used by the considered declarations together to generate a rating matrix. Next, we use the model-based CF technique to complete the missing values in the rating matrix, then a ranked list of APIs is generated based on the completed rating matrix and sent to the developers as a recommendation result. Experimental results show that compared with the state-of-the-art work, the proposed approach can achieve better performance in terms of a comprehensive set of metrics, such as Success Rate, Precision, Recall, MRR, and NDCG for the top-1, top-3 and top-5 recommended APIs.

关键词： Measurement Weight measurement Collaborative filtering Software quality programming Reliability engineering Software reliability

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Virtual Machine Placement Optimization Supporting Performance SLAs

Virtual Machine Placement Optimization Supporting Performanc...

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IEEE international conference on Cloud Computing Technology and science (CloudCom)

作者： Ankit Anand J. Lakshmi S.K. Nandy Department of Computer Science and Engineering Indian Institute of Technology Delhi India Supercomputer Education and Research Centre Indian Institute of Science Bangalore India

Cloud computing model separates usage from ownership in terms of control on resource provisioning. Resources in the cloud are projected as a service and are realized using various service models like IaaS, PaaS and SaaS. In IaaS model, end users get to use a VM whose capacity they can specify but not the placement on a specific host or with which other VMs it can be co-hosted. Typically, the placement decisions happen based on the goals like minimizing the number of physical hosts to support a given set of VMs by satisfying each VMs capacity requirement. However, the role of the VMM usage to support I/O specific workloads inside a VM can make this capacity requirement incomplete. I/O workloads inside VMs require substantial VMM CPU cycles to support their performance. As a result, placement algorithms need to include the VMM's usage on a per VM basis. Secondly, cloud centers encounter situations wherein change in existing VM's capacity or launching of new VMs need to be considered during different placement intervals. Usually, this change is handled by migrating existing VMs to meet the goal of optimal placement. We argue that VM migration is not a trivial task and does include loss of performance during migration. We quantify this migration overhead based on the VM's workload type and include the same in placement problem. One of the goals of the placement algorithm is to reduce the VM's migration prospects, thereby reducing chances of performance loss during migration. this paper evaluates the existing ILP and First Fit Decreasing (FFD) algorithms to consider these constraints to arrive at placement decisions. We observe that ILP algorithm yields optimal results but needs long computing time even with parallel version. However, FFD heuristics are much faster and scalable algorithms that generate a sub-optimal solution, as compared to ILP, but in time-scales that are useful in real-time decision making. We also observe that including VM migration overheads in the p

关键词： Vectors Virtualization Virtual machine monitors Linear programming Resource management Bandwidth Measurement

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Multi-UPF: A Multi-Protocol Forwarding UPF towards 6G

Multi-UPF: A Multi-Protocol Forwarding UPF towards 6G

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Future Communications and Networks (FCN), international conference on

作者： Ying Zhao Xingxing Liao Min Wang School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories Nanjing China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

Currently, the User Plane Function (UPF) in the 5th Generation Core Network (5GC) only supports forwarding for GTP (GPRS Tunneling Protocol). On the one hand, the emergence of technologies like network slicing reflects the growing demand for customized services, exposing deficiencies in GTP, such as its limited underlying network awareness. Some performance-efficient protocols (e.g., SRv6) are regarded as alternative solutions. On the other hand, the rigidity of single IP network architecture fails to meet the diversified and intelligent user requirements of ubiquitous 6G scenarios. the polymorphic smart network is considered an effective solution and a prominent trend in 6G network architecture research. However, the current research on 6G polymorphic networks primarily focuses on the overall network architecture design, lacking specific polymorphic capability design for particular network components. thus, this paper proposes a polymorphic UPF—Multi-UPF, which achieves support for multiple communication protocols’ forwarding on a single UPF through P4 programming. the experiment tests Multi-UPF’s forwarding capabilities and latency for various communication protocols in 5GC. the results indicate that our approach can well support multiple communication protocol forwarding, with latency within feasible ranges. We also design the visual interface of Multi-UPF for the convenience of users. Multi-UPF provides initial validation of SRv6’s compatibility and potential replacement for GTP, simultaneously expanding the choice of communication protocols. this work lays the groundwork for polymorphic capability in subsequent 6G architecture design.

关键词： 6G mobile communication Visualization Protocols Scalability Network slicing programming Tunneling

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