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检索条件"任意字段=6th International Conference on the Art, Science, and Engineering of Programming, Programming 2022"

共 1074 条记录，以下是271-280 订阅

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Integration of Neural Network Models in Spatial Data Analysis Systems 6

Integration of Neural Network Models in Spatial Data Analysi...

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6th international Scientific conference on Information, Control, and Communication Technologies, ICCT 2022

作者： Yamashkin, Stanislav A. Yamashkina, Ekaterina O. Yamashkin, Anatoliy A. National Research Mordovia State University Institute of Electronics and Lighting Engineering Saransk Russia Geography Faculty National Research Mordovia State University Saransk Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

the article presents a practical solution in the field of integration of neural network models in spatial data analysis systems. the article shows that the deep neural network model can to be differentiated by topology that defines availability in her structure layers certain type and connections between them and architecture neural networks defines format representation input data and view weekend signal models, parameters functioning and strategies learning. Component visual design neural network models allows visualize neural network models in form graph - diagrams with opportunity interactive online editing them topology and architecture. For efficient analysis spatial data implemented broadcast graphic representation models in performance on the language Python based on the application frame deep machine learning Keras, and also on the language MATLAB and package applied modules deep Learning toolbox. For solutions tasks broadcasts graphic representation neural network models in program the code her structure described oriented acyclic graph, and key algorithm transformations graphic representation models in instructions on the language programming general destination is topological sorting. © 2022 IEEE.

关键词： Neural network models

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Classroom Student Action Recognition Based on Dynamic Head 6

Classroom Student Action Recognition Based on Dynamic Head

引用

6th Annual international conference on Data science and Business Analytics, ICDSBA 2022

作者： Qu, Xiudong Bao, Wenxing Jing, Yongjun Chen, Xu Li, Chuanming North Minzu University School of Computer Science and Engineering Yinchuan750021 China Hefei University of Technology School of Computer and Information Hefei230009 China

ISBN: (纸本)9781665454506

Students' behavior reflects their status and the degree of participation in the classroom. therefore, it is particularly crucial to use the classroom camera to automatically detect the student's behavior. However, there are situations where students are densely distributed and there are many small objects in the classroom. To solve this problem, the dynamic head was introduced to effectively detect student behavior. Firstly, we collect a large number of videos from real university classrooms for data annotation to form a student behavior dataset and treat student behavior recognition as an object detection task. Secondly, we propose a model called YOLOv5-Dy, which includes a dynamic head. the dynamic head includes scale, space, and task attention mechanisms, which focus on the importance of different features from different perspectives. Experimental results show that the improved model is more adaptable to the student behavior detection task. Finally, we compare our method with state-of-the-art detectors, and it turns out that the proposed approach performs better on the student behavior dataset. © 2022 IEEE.

关键词： Object detection

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Evolving Ranking-Based Failure Proximities for Better Clustering in Fault Isolation 7

Evolving Ranking-Based Failure Proximities for Better Cluste...

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37th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Song, Yi Xie, Xiaoyuan Zhang, Xihao Liu, Quanming Gao, Ruizhi Wuhan Univ Sch Comp Sci Wuhan Peoples R China Sonos Inc Santa Barbara CA USA

ISBN: (纸本)9781450394758

Failures that are not related to a specific fault can reduce the effectiveness of fault localization in multi-fault scenarios. To tackle this challenge, researchers and practitioners typically cluster failures (e.g., failed test cases) into several disjoint groups, with those caused by the same fault grouped together. In such a fault isolation process that requires input in a mathematical form, ranking-based failure proximity (R-proximity) is widely used to model failed test cases. In R-proximity, each failed test case is represented as a suspiciousness ranking list of program statements through a fingerprinting function (i.e., a risk evaluation formula, REF). Although many offthe-shelf REFs have been integrated into R-proximity, they were designed for single-fault localization originally. To the best of our knowledge, no REF has been developed to serve as a fingerprinting function of R-proximity in multi-fault scenarios. For better clustering failures in fault isolation, in this paper, we present a genetic programming-based framework along with a sophisticated fitness function, for evolving REFs with the goal of more properly representing failures in multi-fault scenarios. By using a small set of programs for training, we get a collection of REFs that can obtain good results applicable in a larger and more general scale of scenarios. the best one of them outperforms the state-of-the-art by 50.72% and 47.41% in faults number estimation and clustering effectiveness, respectively. Our framework is highly configurable for further use, and the evolved formulas can be directly applied in future failure representation tasks without any retraining.

关键词： Failure proximity Clustering Fault isolation Parallel debugging Search-based software engineering

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Mining Vocational Educational Data to Analyse the Effectiveness of a Blended Learning Model in programming Courses

Mining Vocational Educational Data to Analyse the Effectiven...

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Novel Intelligent and Leading Emerging sciences conference (NILES)

作者： Aml Zakaria Mohamed AbdelRaheem Taysir Hassan A. Soliman IT Department Egyptian-German Colleague of Technology Misr International Technological University Assiut EGYPT Electrical Engineering Department Assiut University Assiut EGYPT Information Science Department Faculty of Computers and Information Assiut University Assiut EGYPT

ISBN: (数字)9798350378511

ISBN: (纸本)9798350378528

this study investigates the impact of a blended learning strategy on programming education, combining online and face-to-face instruction. A station rotation model was implemented in a JavaScript course for secondary vocational school students, with an intervention group using blended learning and a control group using traditional face-to-face instruction. the course was designed using problem-based learning and Bloom's taxonomy to promote higher-order thinking skills. Educational Data Mining (EDM) was employed to analyze the effectiveness of the blended learning strategy. the results showed the superiority of applying blended learning model over the traditional teaching one in terms of student performance. Also, the adopted model improve the knowledge retention and engagement level of the students

关键词： Analytical models Adaptation models Correlation Education Collaboration Data models Workstations Hybrid learning Data mining programming profession

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Design and Implementation of a Digital Twin to Control the Industrial Robot Mitsubishi RV-12SD 6th

Design and Implementation of a Digital Twin to Control the I...

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6th IFToMM Asian Mechanisms and Machine science conference, Asian MMS 2021

作者： Vu, Minh Duc Nguyen, the Nguyen My, Chu Anh Department of Aerospace Engineering Le Quy Don Technical University Hanoi Viet Nam Institute of Simulation Technology Le Quy Don Technical University Hanoi Viet Nam

ISBN: (纸本)9783030918910

this paper presents a case study of using a digital twin model to control and monitor a real industrial robot. the digital twin and user interfaces are designed and implemented in the Unity 3D environment. the inverse kinematics of the robot is analytically calculated. Both the digital twin and the physical model (the robot Mitsubishi RV-12SD) is successfully integrated, and the data flow can be exchanged one another to control both the models to work together. the real system integration and the collaborating scenarios demonstrate the potential and advantage of the proposed method to develop a smarter programming, controlling and monitoring system for industrial robots. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Inverse kinematics

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SOFTWARE engineering TEACHER NETWORKS - WHAT MOTIVATES TEACHERS TO COLLABORATE? 50

SOFTWARE ENGINEERING TEACHER NETWORKS - WHAT MOTIVATES TEACH...

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50th Annual conference of the European Society for engineering Education, SEFI 2022

作者： Kilamo, T. Knutas, A. Korhonen, A. Poranen, T. Sievi-Korte, O. Tampere University Tampere Finland LUT University Lappeenranta Finland Aalto University Espoo Finland

ISBN: (纸本)9788412322262

Teaching collaboration between universities is becoming increasingly important. Student intakes are increasing, and students are including MOOC courses from other universities to their studies. In Finland, there has been a long tradition in national teaching collaboration in computing education field. Our main research question is what motivates software engineering teachers to collaborate among universities. In this paper, we first give a short overview of national teaching collaboration from 1990's to the present day. then, we present findings from a questionnaire that had respondents from active network participants. Main factors and practices that motivated teachers to participate in networking activities were existing project funding, active leaders and enough participants, interesting topics such as new innovations in learning technology, regular meetings and remote participation. © 2022 SEFI 2022 - 50th Annual conference of the European Society for engineering Education, Proceedings. All rights reserved.

关键词： Software engineering

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Combining Dynamic programming and GPU to accelerate k-Distance Discord Detection in Time Series Data 23

Combining Dynamic Programming and GPU to accelerate k-Distan...

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Proceedings of the 6th international conference on Machine Learning and Machine Intelligence

作者： Nguyen Trong Nhan Duong Tuan Anh Jiri Dvorsky Center for Applied Information Technology Ton Duc Thang University Vietnam and Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University Czech Republic Department of IT Ho Chi Minh City University of Foreign Languages and Information Technology Vietnam Department of Computer Science Faculty of Electrical Engineering and Computer Science VSB Technical University Czech Republic

ISBN: (纸本)9798400709456

Detecting anomalous subsequences plays a vital role in time series analysis. Traditional approaches often rely on sliding windows to extract subsequences and identify anomalies. However, these methods encounter two difficulties: i) they are unable to address the "twin freak" problem and ii) they incur high computational costs when dealing with large time series datasets. We introduce a new method called DP_KBF_GPU to address the aforementioned challenge. Our method is designed for identifying K-Distance discords and effectively handle the "twin freak" problem commonly encountered. this method combines Dynamic programming technique and Graphics Processing Units (GPUs) parallelization to enhance its performance. By employing Dynamic programming with the Euclidean distance metric and leveraging the parallel processing capabilities of GPUs, our method achieves efficient computations. Experimental results on six benchmark time series datasets have demonstrated the high efficiency of our approach in timely detecting the most anomalous subsequences while maintaining high accuracy.

关键词： Discord detection Dynamic programming Graphics Processing Unit (GPU) K-Distance discord Time Series

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Research on Optimal Operation of Cascade Small Hydropower Based on Runoff Simulation 6

Research on Optimal Operation of Cascade Small Hydropower Ba...

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6th IEEE conference on Energy Internet and Energy System Integration, EI2 2022

作者： Zhang, Hongqiang Song, Xingrong Tian, Haiping Wang, Yike Long, Yuannan Song, Xinyi State Grid Hunan Electric Power Co. Ltd. Research Institute Hunan Province Key Laboratory of Efficient & Clean Power Generation Technology Changsha China State Grid Hunan Electric Power Co. Ltd. Research Institute Changsha China Hohai University College of Water Conservancy and Hydropower Engineering Nanjing China School of Hydraulic and Environmental Engineering Changsha University of Science & Technology Changsha China

ISBN: (纸本)9798350347159

As an important part of rural energy, small hydropower plays a crucial role in driving rural economic development, as well as promoting energy conservation and emission reduction. Taking the Mengdong River basin, a secondary tributary of the Yuanjiang River as an example, this paper establishes a cascade optimal operation model of small hydropower based on runoff simulation. In terms of runoff simulation, based on the principle of production and confluence of the three water sources-Xin 'an River, the river confluence calculation is carried out, and a semi-distributed hydrological model is constructed to simulate the process of inflow runoff. In terms of optimal operation, the successive approximation dynamic programming method is used to obtain the optimal scheduling scheme. the results show that the Xin 'an River model has a good fitting effect in this basin. the changing pattern of simulated and measured runoff processes is basically consistent, and the Nash efficiency coefficient between series is 0.86. the optimal scheduling model takes the maximum total generating capacity and minimum waste water as the optimal scheduling objectives, achieving a 1.5% increase in the total power generation and a 56.7% reduction in the waste water, and the change in the upstream water level of the hydropower stations meets the safety requirements. © 2022 IEEE.

关键词： Rivers

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Language Resource Building and English-to-Mizo Neural Machine Translation Encountering Tonal Words 6

Language Resource Building and English-to-Mizo Neural Machin...

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6th Workshop on Indian Language Data: Resources and Evaluation, WILDRE 2022

作者： Khenglawt, Vanlalmuansangi Laskar, Sahinur Rahman Pal, Santanu Pakray, Partha Khan, Ajoy Kumar Department of Computer Engineering Mizoram University Mizoram India Department of Computer Science and Engineering National Institute of Technology Silchar India Wipro Limited Bengaluru India

ISBN: (纸本)9791095546870

Multilingual country like India has an enormous linguistic diversity and has an increasing demand towards developing language resources such that it will outreach in various natural language processing applications like machine translation. Low-resource language translation possesses challenges in the field of machine translation. the challenges include the availability of corpus and differences in linguistic information. this paper investigates a low-resource language pair, English-to-Mizo exploring neural machine translation by contributing an Indian language resource, i.e., English-Mizo corpus. In this work, we explore one of the main challenges to tackling tonal words existing in the Mizo language, as they add to the complexity on top of low-resource challenges for any natural language processing task. Our approach improves translation accuracy by encountering tonal words of Mizo and achieved a state-of-the-art result in English-to-Mizo translation. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

关键词： Neural machine translation

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Fast Traffic engineering by Gradient Descent with Learned Differentiable Routing 18

Fast Traffic Engineering by Gradient Descent with Learned Di...

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18th international conference on Network and Service Management (CNSM) - Intelligent Management of Disruptive Network Technologies and Services

作者： Rusek, Krzysztof Almasan, Paul Suarez-Varela, Jose Cholda, Piotr Barlet-Ros, Pere Cabellos-Aparicio, Albert AGH Univ Sci & Technol Inst Telecommun Krakow Poland Univ Politecn Cataluna Barcelona Neural Networking Ctr Barcelona Spain

ISBN: (纸本)9783903176515

Emerging applications such as the metaverse, telesurgery or cloud computing require increasingly complex operational demands on networks (e.g., ultra-reliable low latency). Likewise, the ever-faster traffic dynamics will demand network control mechanisms that can operate at short timescales (e.g., sub-minute). In this context, Traffic engineering (TE) is a key component to efficiently control network traffic according to some performance goals (e.g., minimize network congestion). this paper presents Routing By Backprop (RBB), a novel TE method based on Graph Neural Networks (GNN) and differentiable programming. thanks to its internal GNN model, RBB builds an end-to-end differentiable function of the target TE problem (MinMaxLoad). this enables fast TE optimization via gradient descent. In our evaluation, we show the potential of RBB to optimize OSPF-based routing (similar to 25% of improvement with respect to default OSPF configurations). Moreover, we test the potential of RBB as an initializer of computationally-intensive TE solvers. the experimental results show promising prospects for accelerating this type of solvers and achieving efficient online TE optimization.

关键词： Traffic engineering Routing Optimization Graph-based Deep Learning Gradient Descent

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