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Formation of project competence of future environmentalists

Formation of project competence of future environmentalists

2020 International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters, ICSF 2020

作者： Horbatiuk, Roman Voitovych, Oksana Voitovych, Ihor Volodymyr Hnatyuk Ternopil National Pedagogical University Department of Mechanical Engineering and Transport 2 M. Kryvonosa Str Ternopil46027 Ukraine Rivne State University of Humanities Department of Ecology Geography and Tourism 12 Stepana Bandery Str Rivne33028 Ukraine Rivne State University of Humanities Department of Information and Communication Technologies and Methods of Teaching Computer Science 12 Stepana Bandery Str Rivne33028 Ukraine

We have gained successful experience in conducting educational projects with studentsenvironmentalists of various thematic areas. The topics of ecological projects was related to a practical, professionally oriented block of the curriculum and are intended to deepen the knowledge of individual students in a certain area in order to differentiate the learning process. Most often, project topics relate to a specific practical issue that is relevant to real life. At the same time, it requires the acquisition of students' knowledge from different subjects stimulates systematic creative thinking, turning up the research skills and opens opportunities for effective using of ICT in the project activity. © The Authors, published by EDP sciences, 2020.

关键词： Students

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Research on Data Extraction and Analysis of Software Defect in IoT Communication Software

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computers, Materials & Continua 2020年第11期65卷 1837-1854页

作者： Wenbin Bi Fang Yu Ning Cao Wei Huo Guangsheng Cao Xiuli Han Lili Sun Russell Higgs School of Computer and Software Dalian Neusoft University of InformationDalian116023China School of Information Engineering Qingdao Bin Hai UniversityQingdao266555China School of Internet of Things and Software Technology Wuxi Vocational College of Science and TechnologyWuxi214028China Public Teaching Department Neuedu Software Talent Training SchoolQingdao266000China Public Teaching Department Qingdao Technical CollegeQingdao266555China School of Information Engineering Sanming UniversitySanming365004China School of Mathematics and Statistics University College DublinDublinIreland

Software defect feature selection has problems of feature space dimensionality reduction and large search *** research proposes a defect prediction feature selection framework based on improved shuffled frog leaping algorithm(ISFLA).Using the two-level structure of the framework and the improved hybrid leapfrog algorithm's own advantages,the feature values are sorted,and some features with high correlation are selected to avoid other heuristic algorithms in the defect prediction that are easy to produce local The case where the convergence rate of the optimal or parameter optimization process is relatively *** framework improves generalization of predictions of unknown data samples and enhances the ability to search for features related to learning *** the same time,this framework further reduces the dimension of the feature *** the contrast simulation experiment with other common defect prediction methods,we used the actual test data set to verify the framework for multiple iterations on Internet of Things(IoT)system *** experimental results show that the software defect prediction feature selection framework based on ISFLA is very effective in defect prediction of IoT communication *** framework can save the testing time of IoT communication software,effectively improve the performance of software defect prediction,and ensure the software quality.

关键词： Improved shuffled frog leaping algorithm defect prediction feature selection framework Internet of Things

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Shooting-projection method for a small object moving under the influence of a force 10

Shooting-projection method for a small object moving under t...

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202 10th International Conference on Engineering Mathematics and Physics, ICEMP 2021

作者： Filipov, S.M. Faragó, I. Avdzhieva, A. Department of Computer Science Faculty of Chemical and System Engineering University of Chemical Technology and Metallurgy 8 St. Kliment Ohridski Blvd. Sofia1756 Bulgaria Department of Differential Equations Institute of Mathematics Budapest University of Technology and Economics MTA-ELTE Research Group Egry József u. 1 Budapest1111 Hungary Department of Numerical Methods Faculty of Mathematics and Informatics Sofia University St. Kliment Ohridski 5 James Bourchier Blvd. Sofia1164 Bulgaria

We consider a small object in 3D moving under the influence of a force that may depend explicitly on time, on the position of the object, and on its velocity. The equations of motion of classical mechanics are assumed to hold. If the position of the object is specified at some initial and some final time, obtaining the trajectory of the object requires the solution of a two-point boundary value problem. To solve the problem various numerical technics can be applied. This paper extends the recently proposed shooting-projection method to 3D. We introduce a Lagrangian from which, applying the principle of least action, the projection trajectory is derived. Analysis of the action reveals the meaning of the projection trajectory. Using the shooting-projection method, the considered two-point boundary value problem is solved for the case of a projectile motion in the presence of air resistance and wind. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Trajectories

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Semi-supervised deep neural networks for left atrial segmentation from cardiac MRI

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European Journal of Preventive Cardiology 2023年第SUPPLEMENT_1期30卷

作者： Qiu, Y Meng, J Loughborough University Department of Computer Science Loughborough United Kingdom of Great Britain & Northern Ireland Lancashire Teaching Hospitals NHS Foundation Trust Preston United Kingdom of Great Britain & Northern Ireland

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Occlusion-Resistant Instance Segmentation of Piglets in Farrowing Pens Using Center Clustering Network

arXiv

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arXiv 2022年

作者： Huang, Endai Mao, Axiu Hou, Junhui Wu, Yongjian Xu, Weitao Ceballos, Maria Camila Parsons, Thomas D. Liu, Kai Department of Computer Science City University of Hong Kong Hong Kong Department of Infectious Diseases and Public Health City University of Hong Kong Hong Kong Department of Production Animal Health Faculty of Veterinary Medicine University of Calgary CalgaryAB Canada Swine Teaching and Research Center School of Veterinary Medicine University of Pennsylvania Kennett SquarePA United States

computer vision enables the development of new approaches to monitor the behavior, health, and welfare of animals. Instance segmentation is a high-precision method in computer vision for detecting individual animals of interest. This method can be used for in-depth analysis of animals, such as examining their subtle interactive behaviors, from videos and images. However, existing deep-learning-based instance segmentation methods have been mostly developed based on public datasets, which largely omit heavy occlusion problems;therefore, these methods have limitations in real-world applications involving object occlusions, such as farrowing pen systems used on pig farms in which the farrowing crates often impede the sow and piglets. In this paper, we adapt a Center Clustering Network originally designed for counting to achieve instance segmentation, dubbed as CClusnet-Inseg. Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M), and Remain-Centers-to-Mask (RC2M) algorithms. In all, 4,600 images were extracted from six videos collected from three closed and three half-open farrowing crates to train and validate our method. CClusnet-Inseg achieves a mean average precision (mAP) of 84.1 and outperforms other methods compared in this study. We conduct comprehensive ablation studies to demonstrate the advantages and effectiveness of core modules of our method. In addition, we apply CClusnet-Inseg to multi-object tracking for animal monitoring, and the predicted object center that is a conjunct output could serve as an occlusion-resistant representation of the location of an object. Copyright © 2022, The Authors. All rights reserved.

关键词： computer vision

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A Novel Virtual Traffic Light Algorithm Based on V2V for Single Intersection in Vehicular Networks 1

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13th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2019

作者： Guo, Longjiang Wang, De Li, Peng Zhang, Lichen Ren, Meirei Liu, Hong Wang, Ana Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Computer Science Shaanxi Normal University Xi’an710119 China School of Computer Science and Technology Heilongjiang University Harbin150080 China

ISBN: (数字)9783030364120

ISBN: (纸本)9783030364113

For the vehicle passing priority, the existing virtual traffic light algorithm only considered the factor of the arrival time, a group of vehicles won’t transfer the wayleave until they all pass completely. This method cannot achieve the maximum road traffic throughput capacity. This paper designs a queuing chain model and proposes a virtual traffic lights algorithm for Single Intersection (for short SVTL) based on the queuing chain model, considering comprehensively three parameters: the waiting time of each lane, the length of queue chain and the number of queues. The SVTL algorithm eliminates the phenomenon of waiting in vain basically, saves the waiting time of the vehicle, and avoids unnecessary parking conditions. The simulation shows that the SVTL shortens the average waiting time when the traffic density is high, compared with the Common Traffic Lights (CTL) and the existing virtual traffic light algorithm DVTL, the average speed is increased by 36.9% and 16.2% respectively. © 2019, Springer Nature Switzerland AG.

关键词： Vehicles

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Production scheduling of open pit mine using sequential branch-and-cut and longest path algorithm: An application from an African copper mine

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Journal Europeen des Systemes Automatises 2020年第5期53卷 629-636页

作者： Joshi, Devendra Satpathy, Susanta Kumar University Teaching Department Chhattisgarh Swami Vivekanand Technical University Bhilai Chhattisgarh491107 India Department of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi A.P.522213 India

Open pit mine production scheduling assigns mining blocks in different production periods for maximising profits after satisfying geotechnical and operational constraints. In this paper, two Open pit mine production scheduling models were applied in an African copper deposit. The first model is a traditional model with more tight resource constraints;the second model is a more robust model where resource constraints are relaxed by penalizing the objective function. Both the models were solved using two step algorithms: (a) year wise production scheduling using a sequential branch-and-cut algorithm;and (b) an iterative longest path algorithm to improve the solution generated from branch-and-cut. Results demonstrated that due to the tight constraints in Model 1, the optimizer was unable to generate a feasible solution after the first period, therefore the lower limit metal production constraint was eliminated to generate a feasible solution;however, Model 2 was able to generate a feasible solution for all periods. Results show that both the models generated nearly the same amount of ore, waste, metal content, and mine life. Model 2 generates relatively more net present value as compared to Model 1, whereas, the computational time required for solving the scheduling problem is relatively less for Model 1 than for Model 2. © 2020 Lavoisier. All rights reserved.

关键词： Integer programming

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Inhibition of the inferior parietal lobe triggers state-dependent network adaptations

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Heliyon 2024年第21期10卷 e39735页

作者： Williams, Kathleen A. Numssen, Ole Guerra, Juan David Kopal, Jakub Bzdok, Danilo Hartwigsen, Gesa Lise Meitner Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Methods and Development Group “Brain Networks” Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany The Neuro - Montreal Neurological Institute (MNI) McConnell Brain Imaging Centre Department of Biomedical Engineering Faculty of Medicine School of Computer Science McGill University Montreal Canada Mila - Quebec Artificial Intelligence Institute Montreal QC Canada Wilhelm Wundt Institute for Psychology Leipzig University Germany

The human brain comprises large-scale networks that flexibly interact to support diverse cognitive functions and adapt to variability in daily life. The inferior parietal lobe (IPL) is a hub of multiple brain networks that sustain various cognitive domains. It remains unclear how networks respond to acute regional perturbations to maintain normal function. To provoke network-level adaptive responses to local inhibition, we combined offline transcranial magnetic stimulation (TMS) over left or right IPL with neuroimaging during attention, semantic and social cognition tasks, and rest. Across tasks, TMS specifically affected task-active network activity with inhibition and facilitation. Network interaction responses differed between rest and tasks. After TMS over both IPL regions, large-scale network interactions were exclusively facilitated at rest, but mainly inhibited during tasks. Overall, responses to TMS primarily occurred in and between domain-general default mode and frontoparietal subnetworks. These findings elucidate short-term adaptive plasticity in response to network node inhibition. © 2024 The Authors

关键词： Cognition cTBS fMRI Functional connectivity Resting state TMS

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A recognition method of hand gesture based on stacked denoising autoencoder 5th

A recognition method of hand gesture based on stacked denois...

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5th Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2018

作者： Ma, Miao Gao, Ziang Wu, Jie Chen, Yuli Zhu, Qingqing Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an China

ISBN: (纸本)9783030037659

In order to avoid the complex preprocessing, this paper proposes a recognition method based on stacked denoising autoencoder (SDAE), in which the structure and the strategies including the number of hidden units, the number of hidden layers, the level of noise and the regularization are carefully considered and analyzed for American Sign Language Dataset (ASL). Specifically, with the increasing number of hidden units and hidden layers, the optimal structure of SDAE is gradually determined, whose performance is simply measured by the recognition accuracy on testing samples. And then, the influences of the noisy strength and the regularization methods on the performance of the designed SDAE are analyzed and compared. Finally, an effective SDAE network is suggested for ASL Dataset. Experiment results show that, compared with stacked autoencoder (AE), deep belief network (DBN) and convolutional neural network (CNN) etc., the designed SDAE shows a better performance, the accuracy in ASL Dataset is up to 98.07% while the training time is reduced to 1 h. © Springer Nature Switzerland AG 2019.

关键词： Gesture recognition

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Learning discrete representations via constrained clustering for effective and efficient dense retrieval

arXiv

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arXiv 2021年

作者： Zhan, Jingtao Mao, Jiaxin Liu, Yiqun Guo, Jiafeng Zhang, Min Ma, Shaoping Department of Computer Science and Technology Institute for Artificial Intelligence Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

Dense Retrieval (DR) has achieved state-of-the-art first-stage ranking effectiveness. However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space. Therefore, we present RepCONC, a novel retrieval model that learns discrete Representations via CONstrained Clustering. RepCONC jointly trains dual-encoders and the Product Quantization (PQ) method to learn discrete document representations and enables fast approximate NNS with compact indexes. It models quantization as a constrained clustering process, which requires the document embeddings to be uniformly clustered around the quantization centroids and supports end-to-end optimization of the quantization method and dual-encoders. We theoretically demonstrate the importance of the uniform clustering constraint in RepCONC and derive an efficient approximate solution for constrained clustering by reducing it to an instance of the optimal transport problem. Besides constrained clustering, RepCONC further adopts a vector-based inverted file system (IVF) to support highly efficient vector search on CPUs. Extensive experiments on two popular ad-hoc retrieval benchmarks show that RepCONC achieves better ranking effectiveness than competitive vector quantization baselines under different compression ratio settings. It also substantially outperforms a wide range of existing retrieval models in terms of retrieval effectiveness, memory efficiency, and time efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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