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Contribution to Improvement of Distance learning Based on Zeroconf Protocol and an Interactive IPTV 2nd

Contribution to Improvement of Distance Learning Based on Ze...

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22nd international conference on Interactive Collaborative learning (ICL) / 48th IGIP international conference on engineering Pedagogy - Impact of the 4th Industrial Revolution on engineering Education

作者： Seck, Massamba Diatta, Baboucar Ouya, Samuel Mendy, Gervais Gaglou, Kokou Univ Alioune Diop Bambey Bambey Senegal Univ Cheikh Anta DIOP Higher Polytech Sch Lab LIRT Dakar Senegal

ISBN: (纸本)9783030402747;9783030402730

In the context of widespread practice of distance education to solve problems of standardization of learners, multimedia flows are increasingly being used in teaching-learning situations. However, this use is often made with difficulties related to the scalability of platforms due to the unicast transmission mode of the exchanged data and especially to the many configurations on the terminals on the users' side. In this article we propose an interactive IPTV multicast platform for distance learning using a VPN in bridge mode, Zeroconf, SAP protocols to facilitate automatic discovery and access to multimedia flows and WebRTC technology to enable interactivity between actors. This multicast IPTV solution is deployed on a public network, unlike the multicast IPTV solutions of telecommunications operators on their dedicated networks. Our solution contributes to the improvement of distance learning by helping universities to provide their students with quality interactive education and thus helps to reduce the feeling of isolation that is a factor among distance learning students who drop out of the instruction.

关键词： Multicast VPN in bridge mode Interactivity Zeroconf WebRTC Elearning

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MOBA Game Analysis System Based on Neural Networks 22nd

MOBA Game Analysis System Based on Neural Networks

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22nd international conference on Web Information Systems engineering (WISE)

作者： Li, Kangwei Li, Mengwei Tian, Jia Cao, Xiaobo Nie, Tiezheng Kou, Yue Shen, Derong Northeastern Univ Shenyang 110819 Peoples R China Beijing Syst Design Inst Electromech Engn Beijing 100039 Peoples R China

ISBN: (纸本)9783030915605;9783030915599

The domain of knowledge contained in Multiplayer Online Battle Arena (MOBA) is quite complex, which is of great research value. With the rapid development of E-sports, the impact of data analysis on MOBA games is increasing. For example, data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games. This paper proposes a novel MOBA game analysis system. The system includes three individual modules, namely lineup recommendation, real-time win rate prediction, and trend forecasting. The lineup module is implemented using NSGA-II algorithm to recommend hero combinations according to the enemy lineup. Win rate module is a neural network for predicting the quantitative advantage between teams. Trend module is a sequence-to-sequence model that forecasts the future team gold and exp. Finally, the system is applied to Dota 2, one of the most popular MOBA games. Experiments on a large number of professional match replays show that the system works well on arbitrary matches.

关键词： MOBA game analytics Deep learning in E-sports Real-time win rate prediction Time-series forecasting Lineup recommendation

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Class-Targeted Poisoning Attacks against DNNs

Class-Targeted Poisoning Attacks against DNNs

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IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Jian Chen Jingyao Wu Hao Yin Qiang Li Wensheng Zhang Chen Wang Hubei Key Laboratory of Smart Internet Technology Huazhong University of Science and Technology Wuhan China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China Research Institute of Information Technology Tsinghua University Beijing China School of Information Science and Engineering Shandong University Qingdao China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In recent years, the emergence of targeted cleanlabel poisoning attacks, which maliciously influence the training data without controlling over the labeling process to manipulate the behavior of the predictive model, is shown to be crucial threats to compromise deep learning systems. Prior targeted clean-label poisoning attacks have been demonstrated to target only one sample at a time, which is not always applicable in restricted real-world situations. In this paper, we explore targeted clean-label poisoning attacks on a per-class basis, which refers to misclassify samples from a victim class to the desired class while maintaining the classification accuracy of samples on other classes in multi-class classification tasks. To achieve this, we present the first class-targeted clean-label poisoning attack, called CTCL, which firsts craft clean label poisons along with multiple directions in the target feature space and enhance the attacking capability of poisons by reducing their the feature information of the target class. We illustrate the effectiveness of the proposed CTCL on various deep neural network models. The experiment results demonstrate that our attack is effective, with the attacking success rate over 80% compared to the other two baseline attacks on average, while the detection accuracy of state-of-the-art defenses is lower than 65% illustrating that CTCL can escape the detection of existing defenses readily.

关键词： Deep learning data privacy Toxicology Training data Process control Predictive models data models

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MULTIPLE: Multi-level User Preference learning for List Recommendation 22nd

MULTIPLE: Multi-level User Preference Learning for List Reco...

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22nd international conference on Web Information Systems engineering (WISE)

作者： Li, Beibei Jin, Beihong Dong, Xinzhou Zhuo, Wei Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China MX Media Co Ltd Singapore Singapore

ISBN: (纸本)9783030915605;9783030915599

On many streaming platforms, several individual items with certain common correlations are organized into lists, making list recommendation a distinctive and significant task. Multiple data including user-list interactions, user-item interactions and the list-item hierarchical structure are available for list recommendation. However, existing list recommendation models fail to conduct in-depth analyses of user preferences implied in the interactions and also fail to make use of the sequential information among interactions. In this paper, we propose a model named MULTIPLE, which recognizes the structural and compositional complexity of user preferences and explicitly learns user preferences from different perspectives, i.e., list-level, item-level and dual-level. In particular, MULTIPLE designs a gated and attentive sequence learning module to identify the drift of user preferences. By taking the list-item hierarchical structure as a bridge, MULTIPLE designs an attention network to distill essential items from the constituent items of lists and help learn the dual-level user preferences. We conduct extensive experiments on real-world datasets. The performance enhancement on recallAK and ndCGAK verifies the effectiveness of the model.

关键词： Recommender system List recommendation Multi-level user preferences

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Inconsistent Interest Profiles of Students of engineering Disciplines: Recruitment Potential for Other Technical Study Courses? 2nd

Inconsistent Interest Profiles of Students of Engineering Di...

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作者： Koehler, Marcel Leon, Andreas Schmechtig, Nelly Abele, Stephan Tech Univ Dresden Inst Vocat Educ & Vocat Didact Dresden Germany Tech Univ Dresden Ctr Teacher Educ & Educ Res Dresden Germany

ISBN: (纸本)9783030402716;9783030402709

The shortage of teachers in technical subjects at vocational schools has been known in Germany for several years. However, there are hardly any empirical studies available on the question of whether the professional interests of students can serve to assess the recruitment potential of engineering study courses for teacher training in these subjects. In this article, data collected since 2015 from teacher training students at the TU Dresden (N = 422) and the engineering sciences at the University of Stuttgart (N = 254) are examined with regard to the consistency of professional interest profiles. The results suggest that inconsistent interest profiles can be a basis for the development of engineering degree programmes as a recruiting source for reducing the shortage of teachers in technical subjects.

关键词： Shortage of teachers Technical studies Interest profiles RIASEC

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

引用

IEEE international conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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Mentoring Across Cultures and Professions: Approaches and Methods 2nd

Mentoring Across Cultures and Professions: Approaches and Me...

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作者： Osipov, Petr Ziyatdinova, Julia Irismetova, Indira Kazan Natl Res Technol Univ Kazan Russia

ISBN: (纸本)9783030402716;9783030402709

Mentoring is an informal process of transferring knowledge within an organization through internalization and socialization. Mentoring has a long history being one of the earliest forms of learning through apprenticeship. Recent years, however, have seen many publications on new forms of mentoring with different terms applied, e.g. tutoring, coaching, facilitation, shadowing and etc. The topic is interesting for researchers in engineering education from different countries, yet there has been no complex analysis done to compare approaches and methods of mentoring in various cultures and professions. Such an analysis can show similarities and differences and help in finding the best approaches to apply mentoring for engineering university and engineering workplace practice worldwide.

关键词： Mentoring Cultures Professional development engineering education

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Engagement in learning Through Design and Experimentation with Robots 2nd

Engagement in Learning Through Design and Experimentation wi...

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作者： Perez, Huberth Verner, Igor Technion Israel Inst Technol IL-32000003 Haifa Israel

ISBN: (纸本)9783030402716;9783030402709

In this research we characterize students' engagement in learning in robotic environments and factors that influence engagement. We observed robotics activities of a group of 20 eleventh graders from a technological school, majoring in mechanical engineering. We searched for repeated learning behaviors and elicited patterns of student's engagement. For the analysis of these patterns we applied Goldin's theory of engagement structures. To determine the factors affecting students' engagement, we took teachers' explanations of possible reasons behind the observed students' behaviors. The explanations and student interviews were used to determine factors of student engagement. Our study explores and extends the group of known engagement structures related to learning in robotic environment. The knowledge of factors of student engagement can facilitate selecting proper strategies of robotics education.

关键词： Robotics education Student engagement Engagement structures

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learning Bezier-Durrmeyer Type Descriptors for Classifying Curves – Preliminary Studies 1

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22nd international conference on Artificial Intelligence and Soft Computing, ICAISC 2023

作者： Krzyżak, Adam Rafajlowicz, Wojciech Rafajlowicz, Ewaryst Department of Computer Science Concordia University 1455 De Maisonneuve West MontrealQCH3G 1M8 Canada Szczecin Poland Faculty of Information and Comunication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783031425059

ISBN: (纸本)9783031425042

We aim to propose a new approach for generating descriptors for functional data that are represented by curves that may not be functions but rather parametrically described curves in 2D, 3D, etc. The idea of generating these descriptors is based on the Bezier curves, but instead of using classical control points, we propose to generalize the Durrmeyer approach for parametrically defined vector curves. The Durrmeyer-type descriptors are then estimated from noisy samples of the underlying curve and serve as input vectors to a selected classifier that is learned to recognize from which class of curves noisy observations come. The Bezier curves are not rapidly convergent. However, our aim is not to reconstruct functions but to recognize them, maintaining the shape-preserving and variation-diminishing properties of these curves that increase the classification accuracy in the presence of noise without invoking pre-filtering procedures. For more complicated curves, one can directly apply our approach to their parts in a similar way as polynomial splines are used. The proposed algorithm for learning descriptors of the Bezier-Durrmeyer type and training classifiers on them was tested on synthetic, but interesting per se, data from two families of the Lissajous curves observed with high amplitude noise. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Polynomial approximation

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Operationally-informed hospital-wide discharge prediction using machine learning 22

Operationally-informed hospital-wide discharge prediction us...

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22nd IEEE international conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Ward, Andrew Mann, Ariana Vallon, Jacqueline Escobar, Gabriel Bambos, Nicholas Schuler, Alejandro Stanford University Department of Electrical Engineering CA United States Stanford University Department of Management Science and Engineering United States Kaiser Permanente Division of Research Oakland United States

ISBN: (纸本)9781728162676

Accurate patient discharge time estimates are invaluable for hospital operations management. They are vital for efficient and effective scheduling of hospital resources including beds and staff. Unexpected discharges place strain on the patient families and care providers, in addition to causing hospital inefficiencies. Due to the increasing availability of electronic health record data, predictive models can be leveraged to not only offer clinical decision support, but also to optimize hospital operations. In this work, we incorporate clinical knowledge from operational leaders at Kaiser Perma-nente Northern California to design a predictive model for patient discharge using a novel dataset that contains hourly data from the electronic health records of 14 different Kaiser Permanente hospitals. We train and test several algorithms with varying complexity to predict patient-level discharges for the following day at operationally relevant times on the hospital-centric timescale. The highest AUC we achieve is 0.729 with a gradient boosted model, which significantly outperforms both the current estimates deployed in these 14 facilities and the baseline model without hourly data. A feature permutation importance assessment is performed and we conclude that the majority of the improvement is due to the inclusion of the detailed, hourly data. © 2021 IEEE.

关键词： Hospitals

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