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International conference on Information Technologies in engineering Education (Inforino)

作者： Sergey Kovalev Anna Kolodenkova Evgenia Muntyan Rostov branch JSC NIIAS Rostov-on-Don Russia Dept. of Information Technologies Samara State Technical University Samara Russia Dept. of Computer Engineering Southern Federal University Taganrog Russia

ISBN: (数字)9781728148106

ISBN: (纸本)9781728148113

The generalization and systematization of various scientific views of both domestic and foreign researchers on the problem of educational data mining (EDM) and their application to support decision-making on the educational process management. Particular attention is paid to the problems of automatic extraction of interpreted knowledge from educational data in order to transfer it to users and facilitate the processes of interactive computing and communication among users. The proposed methods and models allow increasing the effectiveness of decision-making process at all stages of the educational process management. The novelty of the work results is a new approach to the extraction of interpretable knowledge based on the use of a special class of neural network models, as well as the hybridization of several approaches developed as part of data mining into a single learning environment in order to implement an end-to-end learning strategy, including the steps of data collecting and analyzing, knowledge extracting and management. The paper presents the fragments of the developed software that provides EDM by individual methods.

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Crime information collection system using chatbot

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AIP conference Proceedings 2023年第1期2587卷

作者： M. S. Antony Vigil Akash Rai Shashwat Sharma Praveen Kumar Murugesan Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India

Artificial intelligence and Machine learning advancements in stimulation of human knowledge to computers has led to the software based application / program called chatbot. A chatbot acts as a conventional agent which uses natural languages for communicating with operators. The chatbots are aware of self learning algorithms, for example natural language processing (NLP). These software programs are made to help people and have a one-on-one interaction with them (either in text or speech format). The proposed chatbot provides a platform for crime registration and also getting information about various types of crimes. We can find chatbots in the field of entertainment, query clarification, social media, e-commerce sites etc. but rarely can we find chatbots serving the purpose of security and crime related functions. Our proposed system collects various information from the victims so that the authorities could cross-verify the information provided. It gives a stage for people to report about the crimes, getting data about different sorts of violations. It gathers different check archives from the casualties so the specialists could cross-confirm the data given. The chatbot works on the principle of natural language processing algorithm.

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Sidekicks and Superheroes: A Look into Student Reasoning about Concurrency with Threads versus Actors

Sidekicks and Superheroes: A Look into Student Reasoning abo...

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software engineering: software engineering Education and Training (ICSE-SEET), IEEE/ACM International conference on

作者： Aubrey Lawson Eileen T. Kraemer Clemson University Clemson SC

ISBN: (数字)9781450371247

ISBN: (纸本)9781728165257

When computations consist of different processes or threads that may execute at the same time or in an interleaved manner, we say the computation is concurrent or that it exhibits concurrency. University students often struggle to reason about concurrency. Researchers have presented students with natural language descriptions of scenarios involving concurrency and have evaluated students' natural language responses to explore their prior knowledge and to determine characteristics of student reasoning about concurrency. However, natural language responses are necessarily abstract and pertain to the design stage of software development, while studies in which students are asked to implement solutions provide a more concrete view of student reasoning and reveal difficulties that arise when putting design into practice. In this work we build upon prior work and ask: What are common and problematic features in student approaches to solving concurrency-related problems? How do these features differ from the design phase to the implementation phase of the software development life cycle? In this study we asked students enrolled in a jointly offered upper division undergraduate / MS level course on “Programming with Concurrency” to implement a solution to the same problem (a side-kick/superhero version of the party matching problem) using two different approaches: Threads-based in Java, and Actors-based in Scala. We performed qualitative feature analysis of their implementations and their written reflections to better understand student reasoning when programming concurrent solutions. The feature of additional complexity was found in our earlier natural language study in which students described their designs. Additional complexity was also represented in student implementations in this study and adversely impacted student success as their programs became cumbersome and hard to trace. Student reflections provided insight into their resistance to modeling prior to implementa

关键词： Concurrent computing Programming Cognition Instruction sets software Reflection Natural languages

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Transmission of Multimedia Content between Social Networks without Reloading 5

Transmission of Multimedia Content between Social Networks w...

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5th IEEE conference on knowledge based engineering and Innovation, KBEI 2019

作者： Nikkar, Kousha Parseh, Mohammad Javad Ebrahim Samie, Mohammad Department of Computer Engineering Jahrom University Jahrom Iran

ISBN: (纸本)9781728108728

Nowadays, while increasing variety of online social networks (OSNs), content management has become harder. The paper suggests social media pages' managers to publish their contents, including the composition of image, video or text, only once on a OSN and then contents will simply transfer to other OSNs with an add-on software without having to re-use the internet traffic. Suggested method has more high performance than similar ones on any OSN. Also, the proposed technique makes it possible to transmit content between OSNs belonged to different companies. In this study, due to the availability of suitable facilities in both OSNs of Telegram and Instagram, an intermediary software, was implemented under the Android operating system and this action was carried out for the transmission media from the Instagram OSN to the Telegram channels or groups. This study contains three innovations than related researches. The first one is transmission content with the least usage of Internet traffic between OSNs which are not supported by one company. The next innovation is a caching software system to reduce processes of extracting contents from source OSN's network. The third item is transmission delay time decreased sharply. As a result, the trial version has served users and managers of pages and channels, including the official Telegram channel of a TV program named 90 and a news feed named CANNews (the analytical aviation industry news feed). Overall, this method not only has 98.81% correct action, but also contents are fitted into the post format of the destination OSN. © 2019 IEEE.

关键词： Social networking (online)

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An adventure serious game for teaching effort estimation in software engineering

An adventure serious game for teaching effort estimation in ...

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2019 Joint conference of the International Workshop on software Measurement and the International conference on software Process and Product Measurement, IWSM-Mensura 2019

作者： Marín, Beatriz Vera, Matías Giachetti, Giovanni Universidad Diego Portales Facultad de Ingeniería y Ciencias Santiago Chile Universidad Tecnológica de Chile INACAP Santiago Chile

Imagine that it is possible to learn effort estimation concepts and its application in an attractive manner, where complex and technical knowledge are presented through a playful experience. The serious game Back to Penelope face this challenge, in which the student is trying to save the life of a young girl astronaut from a desolated world. To do this, the player must correctly estimate the development effort for the vital systems of the spaceship of the astronaut. The game is based on the COSMIC measuring method for estimation. This paper presents the results obtained from a systematic review performed to identify the main features of the existent serious games related to estimation. Moreover, this paper presents the design of the game developed, which has elements to improve the motivation of the students that use Back to Penelope. Finally, we present the promising results obtained from the validation of the game when teaching software engineering courses. Copyright © 2019 for this paper by its authors.

关键词： Serious games

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Keynote Speech III: Intelligence Science for Smart Detection and Diagnosis of Corona Virus

Keynote Speech III: Intelligence Science for Smart Detection...

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International conference on Computer Theory and Applications (ICCTA)

作者： Abdel-Badeeh M. Salem Artificial Intelligence and Knowledge Engineering Research Labs Egypt Faculty of Computer and Information Science Ain Shams University Cairo

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Intelligent science(IS) is an interdisciplinary subject which dedicates to joint research on informatics and technology of intelligence by brain science, artificial intelligence(AI), cognitive science, data science and others. IS is devoted to create intelligent computer software that models the human behavior. The main goal of IS is to make computers smarter by creating intelligent algorithms that will allow a computer to mimic some of the functions of the human brain in selected applications. All of these applications employ knowledge base and inferencing techniques to solve problems or help make decisions in specific domains. This talk discusses the potential role of intelligence science, AI methodologies, knowledge engineering and intelligent computing paradigms, for smart detection and diagnosis of Corona virus, COVID-19. The talk presents the following issues; (a) detection intelligent software for people who have Corona virus, (b) super computing power resources to assist professors with research on the corona virus, (c) intelligent medical imaging evaluation systems, (d) facial recognition intelligent algorithms and thermal imaging temperature measurement technology, and (e) deep learning technique for detection of COVID-19 using X-Ray images. Moreover, the talk presents a proposal for case-based reasoning expert system for corona, COVID-19 diagnosis. In addition, the talk presents the research directions and some examples of the developed intelligent healthcare systems by the author and his colleagues at AIKER-Labs.

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knowledge management for problem solving using semistructured contradiction matrix based on physical quantity description 22

Knowledge management for problem solving using semistructure...

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22nd International conference on engineering Design, ICED 2019

作者： Murakami, Tamotsu University of Tokyo Department of Mechanical Engineering Japan

A Contradiction Matrix of TRIZ that classifies problems to solve as contradictions of features is an effective framework of knowledge management for problem solving. The features, however, may have a problem of completeness because they may not cover contradictions about all physical phenomena. In addition, rigidly structured Contradiction Matrix may have a problem of searchability because a relevant contradiction may not be properly searched if a recorder and a retriever describe it differently. To solve these problems, this paper proposes a semistructured contradiction matrix using not TRIZ features but physical quantities in SI unit. To enable not only exact match but also partial match in searching for relevant contradictions, dimensional similarity and qualitative value similarity of physical quantity and similarity between contradictions are defined. The proposed method is implemented as software in Python and contradictions are described in XML and stored in a semistructured matrix. From the result of similarity calculation between stored contradictions, possible effectiveness of the proposed method is confirmed. © 2019 Design Society. All rights reserved.

关键词： knowledge management

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Research on deep learning image recognition technology in garbage classification

Research on deep learning image recognition technology in ga...

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Communications Technology and Computer Science (ACCTCS), Asia-Pacific conference on

作者： Qiang Guo Yuliang Shi Shikai Wang School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9781665447034

Nowadays, with the rapid development of industry, people's daily life is becoming more and more rich and diverse. However, while enjoying the pleasure brought by material life, a large number of garbage are produced. They are various, and people lack the knowledge related to garbage classification, which leads to the difficulty of manual or robot classification. This paper studies a garbage classification algorithm model based on deep learning convolutional neural network Efficientnet to help identify garbage classification. In this research, data augmentation and normalization are carried out to solve the problem of small amount of data sets and different sizes of pictures. Efficientnet is used to extract the features of images. In order to solve the problem that BN has no obvious effect on small batches in the network, we replace BN with group normalization (GN). In order to prevent some irrelevant information in the image from affecting the training of the model, we add attention mechanism after the output of Efficientnet to emphasize or select the important information of the target processing object, and suppress some irrelevant details, so that the model can focus on the key features and better identify the image; according to the above process, we use softmax to classify the spam image and divide it into four categories (Recyclables, Kitchen garbage, Hazardous garbage, Other garbage) The results show that the model can effectively extract the features of the input garbage image, and get accurate judgment, and identify the types of garbage. The experimental results show that the average accuracy of the algorithm model is high, and has good classification performance and robustness. In the practical significance of the research, this reliable model can help people quickly know the type of garbage, or can be applied to robot sorting, to help detect the types of garbage for robot judgment and sorting, so it has very important application scenarios and significanc

关键词： Deep learning Training Service robots Feature extraction Robustness Classification algorithms Mobile applications

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Training Feed-forward Neural Networks using Asexual Reproduction Optimization (ARO) Algorithm 5

Training Feed-forward Neural Networks using Asexual Reproduc...

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IEEE 5th conference on knowledge based engineering and Innovation (KBEI)

作者： Hashemi, Seyyed Mohammad. R. Kozegar, Ehsan Deramgozin, Mohammad Mahdi Minaei-Bidgoli, Behrouz Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran Univ Guilan Fac Engn Rasht Iran Tosan Techno Corp Payment Software Serv Team Tehran Iran Iran Univ Sci & Technol Dept Comp Engn Tehran Iran

ISBN: (纸本)9781728108728

Artificial neural networks have been increasingly used in many problems of data classification because of their learning capacity, robustness and extendibility. Training in the neural networks accomplished by identifying the weight of neurons which is one of the main issues addressed in this field. The process of network learning by back-propagation algorithm which is based on gradient, commonly fall into a local optimum. Due to the importance of weights and neural network structure, evolutionary neural networks have been emerged to obtain suitable weight set. This paper will concentrate on training a feed-forward networks by a modified evolutionary algorithm based on asexual reproduction optimization (ARO) in order to data classification problems. The idea is to use real representation (rather the binary) for adjusting weights of the network. Experimental results show a better result in terms of speed and accuracy compared with other evolutionary algorithms including genetic algorithms, simulated annealing and particle swarm optimization.

关键词： Neural Network Asexual reproduction optimization Real representation Local optimum

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Hardware and software for Learning Analog-toDigital Converters in engineering Education 10

Hardware and Software for Learning Analog-toDigital Converte...

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10th National conference with International Participation, ELECTRONICA 2019

作者： Shehova, Daniela Lyubomirov, Slavi Asparuhova, Katya Faculty of Physics and Engineering Technologies University of Plovdiv 'Paisii Hilendarski' Smolyan4700 Bulgaria Department of Electronics Faculty of Electronic Engineering and Technologies Technical University of Sofia 8 Kliment Ohridski blvd. Sofia1000 Bulgaria

ISBN: (纸本)9781728136226

The paper presents an open source code-based module designed to help undergraduate students on higher education engineering programmes to learn about analogue-to-digital conversion (ADC). The developed Successive Approximation ADC (SAR ADC) was implemented with integrated circuits DAC 7821 and TL 082 CP and Arduino Mega *** proposed strategy for researching and teaching ACDs provides new opportunities for knowledge organization and new ways of learning aimed at achieving better quality of learning outcomes in engineering education. The results of the experimental studies are shown and discussed. © 2019 IEEE.

关键词： engineering education

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