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International IEEE Conference on Intelligent systems, IS

作者： Yaroslav Smitiukh Yuliia Samoilenko Yuliia Kostiuk Olena Kryvoruchko Kateryna Stepashkina department of automation and computer technologies of control systems National University of Food Technologies Kyiv Ukraine department of software engineering and cyber security State University of Trade and Economics Kyiv Ukraine

ISBN: (纸本)9781665492768

The technological process of the churning process in continuous butter manufacture were considered. The qualitative indicator of the water content of butter was modeled on the basis of a set of industrial data using an artificial neural network (ANN). The prototype of an intelligent system for predicting of the water content of butter allows to increase the information support of the operator-technologist. The proposed model predicts the water content of butter with an error of less than 2%. The input variables of the forecast model prototype are cream fat content, cream ripening temperature, cream supply temperature, frequency of revolutions of the stirrer of the whipping device, and consumption of the normalizing component. Further research is aimed at the development of a decision support information system for dairy manufacture and should be integrated into the subsystem of automated control of the technological process to ensure proper functioning in real time.

关键词： Productivity Dairy products Simulation Prototypes Process control Artificial neural networks Predictive models

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Expert Evaluation of Main Measures Proposed for Human Resources Rational Usage for Region Competitiveness Improvement

Expert Evaluation of Main Measures Proposed for Human Resour...

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International Science and Technology Conference FarEastCon, 2021

作者： Kenzhin, Z.B. Zolkin, A.L. Aygumov, T.G. Poskryakov, I.A. West Kazakhstan Agrarian and Technical University Named After Zhangir Khan Uralsk090009 Kazakhstan Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Department of Computer Engineering Software and Automated Systems Dagestan State Technical University Makhachkala367000 Russia Department of Transport Economic and Management Federal State Autonomous Educational Institution for Higher Education Russian Transport University Moscow127994 Russia

ISBN: (纸本)9789811688287

A process of expert evaluation of actions based on complex methodology is reviewed in this article. The methods of questionnaire, expert evaluations, multi-variation problem of optimal alternative were used. Authors explained the methods of expert evaluations, e.g. Delphi method, as the good forecasting tool for region competitiveness improvement measures based on achievement of optimal balance of supply and demand of human resources. As a result of applied methodology, the main measures for rational usage of human resources in region competitiveness improvement were selected. Application of obtained results and proposed methodology allows to improve the region economic management. Authors came to the conclusion that measures for development of human resources as region competitive advantage are referred to quality education, healthcare, effective migration policy as base for labour efficiency improvement and reducing the shortage of skilled manpower. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Personnel

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Transformative Applications and Key Challenges of Generative AI

Transformative Applications and Key Challenges of Generative...

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International IEEE Conference and Workshop: Óbuda on Electrical and Power engineering (CANDO-EPE)

作者： Attila Kovari Jozsef Katona Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary Institute of Digital Technology Faculty of Computer Science Eszterházy Károly Catholic University Eger Hungary Department of Institute of Engineering University of Dunaújváros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemét Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798331531119

ISBN: (纸本)9798331531126

The paper discusses the integration and use of generative artificial intelligence technologies in education. Generative AI, such as OpenAI, in particular the GPT model, has fundamentally changed the way humans interact with machines. Advanced technologies in the field of generative AI in education are mostly used to personalize learning experiences by dynamically customizing learning materials according to the needs of learners, thus enabling the improvement of outcomes. This article explores the use of generative AI tools with the aim of assessing how useful students perceive these tools to be. The findings show mixed receptivity from users, highlighting the critical balance in using innovative AI capabilities through AI.

关键词： Power engineering Accuracy Generative AI Education Chatbots Market research Problem-solving Reliability

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Exploring the effectiveness of surrogate-assisted evolutionary algorithms on the batch processing problem

arXiv

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arXiv 2022年

作者： Variawa, Mohamed Z. Van Zyl, Terence L. Woolway, Matthew University of Johannesburg Academy of Computer Science and Software Engineering Johannesburg South Africa University of Johannesburg Institute for Intelligent Systems Johannesburg South Africa University of Johannesburg Faculty of Engineering and the Built Environment Johannesburg South Africa

Real-world optimisation problems typically have objective functions which cannot be expressed analytically. These optimisation problems are evaluated through expensive physical experiments or simulations. Cheap approximations of the objective function can reduce the computational requirements for solving these expensive optimisation problems. These cheap approximations may be machine learning or statistical models and are known as surrogate models. This paper introduces a simulation of a well-known batch processing problem in the literature. Evolutionary algorithms such as Genetic Algorithm (GA), Differential Evolution (DE) are used to find the optimal schedule for the simulation. We then compare the quality of solutions obtained by the surrogate-assisted versions of the algorithms against the baseline algorithms. Surrogate-assistance is achieved through Probablistic Surrogate-Assisted Framework (PSAF). The results highlight the potential for improving baseline evolutionary algorithms through surrogates. For different time horizons, the solutions are evaluated with respect to several quality indicators. It is shown that the PSAF assisted GA (PSAF-GA) and PSAF-assisted DE (PSAF-DE) provided improvement in some time horizons. In others, they either maintained the solutions or showed some deterioration. The results also highlight the need to tune the hyper-parameters used by the surrogate-assisted framework, as the surrogate, in some instances, shows some deterioration over the baseline algorithm. © 2022, CC BY.

关键词： Machine learning

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Amplifying the Music Listening Experience through Song Comments on Music Streaming Platforms

arXiv

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arXiv 2023年

作者： Chen, Longfei Liu, Qianyu Zhang, Chenyang Huang, Yangkun Peng, Zhenhui Zeng, Haipeng Sun, Zhida Ma, Xiaojuan Li, Quan The School of Information Science and Technology ShanghaiTech University Shanghai Engineering Research Center of Intelligent Vision and Imaging China The Department of Computer Science University of Illinois Urbana-Champaign United States Tandon School of Engineering New York University United States The School of Artificial Intelligence Sun Yat-Sen University China The School of Intelligent Systems Engineering Sun Yat-Sen University China The College of Computer Science and Software Engineering Shenzhen University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Music streaming services are increasingly popular among younger generations who seek social experiences through personal expression and sharing of subjective feelings in comments. However, such emotional aspects are often ignored by current platforms, which affects the listeners' ability to find music that triggers specific personal feelings. To address this gap, this study proposes a novel approach that leverages deep learning methods to capture contextual keywords, sentiments, and induced mechanisms from song comments. The study augments a current music app with two features, including the presentation of tags that best represent song comments and a novel map metaphor that reorganizes song comments based on chronological order, content, and sentiment. The effectiveness of the proposed approach is validated through a usage scenario and a user study that demonstrate its capability to improve the user experience of exploring songs and browsing comments of interest. This study contributes to the advancement of music streaming services by providing a more personalized and emotionally rich music experience for younger generations. © 2023, CC BY.

关键词： Music

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Empowering Personalized Health Data Queries with Knowledge Graph and GPT-Enhanced Voice Assistant

Empowering Personalized Health Data Queries with Knowledge G...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xin Wang Wordh Ul Hasan Juan Li Rasha Hendawi Yang Du Bo Xie Cui Tao Department of Computer Science North Dakota State University Fargo USA Department of Computer Systems and Software Engineering Valley City State University Valley City USA School of Nursing The University of Texas at Austin Austin USA School of Biomedical Informatics University of Texas Health Science Center at Houston Houston USA

The proliferation of digital health technologies has led to an abundance of personal health data. However, querying and retrieving specific health-related information from disparate sources can be challenging and inconvenient, particularly for older individuals unfamiliar with technology. While ChatGPT offers a conversational interface, it lacks domain-specific knowledge, including personalized health information. To address this limitation, we present a novel approach that combines a knowledge graph and GPT to enable personalized health queries. Our solution utilizes a personal knowledge graph as a comprehensive knowledge source and fine-tunes GPT to provide accurate responses. We have implemented a voice assistant mobile app incorporating this knowledge graph-assisted GPT model and conducted initial feasibility testing.

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Uvl: Feature Modelling with the Universal Variability Language

SSRN

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SSRN 2024年

作者： Benavides, David Sundermann, Chico Feichtinger, Kevin Galindo, José A. Rabiser, Rick Thüm, Thomas Department of Computer Languages and Systems Universidad de Sevilla Av. Reina Mercedes Seville41012 Spain Institute of Software Engineering and Programming Languages University of Ulm Albert-Einstein-Allee 11 Ulm89069 Germany CRC 1608 KASTEL – Dependability of Software-intensive Systems Karlsruhe Institute of Technology Am Fasanengarten 5 Karlsruhe76131 Germany Christian Doppler Laboratory VaSiCS LIT CPS Lab Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria

Feature modelling is a cornerstone of software product line engineering, providing a means to represent software variability through features and their relationships. Since its inception in 1990, feature modelling has evolved through various extensions, and after three decades of development, there is a growing consensus on the need for a standardised feature modelling language. Despite multiple endeavours to standardise variability modelling and the creation of various textual languages, researchers and practitioners continue to use their own approaches, impeding effective model sharing. In 2018, a collaborative initiative was launched by a group of researchers to develop a novel textual language for representing feature models. This paper introduces the outcome of this effort: the Universal Variability Language (\UVL), which is designed to be human-readable and serves as a pivot language for diverse software engineering tools. The development of \UVL drew upon community feedback and leveraged established literature in the field of variability modelling. The language is structured into three levels -Boolean, Arithmetic, and Type- and allows for language extensions to introduce additional constructs enhancing its expressiveness. \UVL is integrated into various existing software tools, such as FeatureIDE and FLAMA, and is maintained by a consortium of institutions. © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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A speech-driven pupil response system with affective highlight by virtual lighting

A speech-driven pupil response system with affective highlig...

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作者： Sejima, Yoshihiro Kawamoto, Hiroki Sato, Yoichiro Watanabe, Tomio Faculty of Informatics Kansai University 2-1-1 Ryozenji-cho Takatsuki-shi Osaka569-1095 Japan PEOPLE SOFTWARE CORPORATION 1-7-2 Achi Kurashiki-shi Okayama710-0055 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University 111 Kuboki Soja-shi Okayama719-1197 Japan

In face-to-face communication, talkers perceive various affects and emotions of other talkers as the eye-impressions such as highlight or dilated pupil. In the previous studies, focusing on the eye-impressions in social robots, various expression methods such as changing the LED color or blinking the LED were proposed and developed. However, it is difficult to generate the affective expressions with the highlight that has an optical reflection characteristic on the surface of the eyeball by using the LED. Therefore, in order to enhance the eye-impressions of social robots, it is expected to develop an expressible method with the affective highlight. In this study, we proposed an affective highlight to enhance the eye-impressions of social robots. This method introduces a virtual lighting in virtual space and can generate rich affective expressions by controlling various parameters of the virtual lighting such as brightness or colors. In addition, we developed a speech-driven pupil response system that applied the affective highlight to the pupil response interface which has been developed. This system can generate the affective expressions by the dilation of pupil response that is synchronized with the talker’s speech as well as the highlight expression with talker’s intentional input. The effectiveness of the developed system with the affective highlight was demonstrated by sensory evaluations. © 2022 The Japan Society of Mechanical Engineers.

关键词： Light emitting diodes

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FedMDC: Enabling Communication-Efficient Federated Learning over Packet Lossy Networks via Multiple Description Coding

FedMDC: Enabling Communication-Efficient Federated Learning ...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yixuan Guan Xuefeng Liu Tao Ren Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Federated learning (FL) generally suffers significant communication overhead from high-traffic gradient synchronization. The majority of existing studies on this problem aim at compressing gradients under the premise of reliable transmission. While transmission reliability can be ensured via TCP by default, the notably increased latency and retransmitted packets are prohibitive for most clients in FL. To tackle this issue, we propose FedMDC, a retransmission-free compression framework for FL over packet lossy networks. Given clients’ limited resources, FedMDC adopts multiple description coding to encode gradients into redundant descriptions for erasure resilience simply through multiplying an overcomplete matrix; and then quantizes these descriptions for compression. To further reduce quantization distortion and computational overhead, a reduced decoding algorithm is developed by decoding the aggregation of all clients’ encodings in conjunction with a customized dither quantization design. Besides, FedMDC explicitly supports adaptive bitrates subject to clients’ heterogeneous communication budgets, which maximize resource utilization to facilitate distortion reduction and accelerate model convergence. Theoretical analysis and experimental results both demonstrate the effectiveness of our scheme.

关键词： Quantization (signal) Federated learning Distortion Propagation losses Encoding Decoding Reliability Synchronization Resource management Resilience

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M3OFF: Module-Compositional Model-Free Computation Offloading in Multi-Environment MEC

M3OFF: Module-Compositional Model-Free Computation Offloadin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Tao Ren Zheyuan Hu Jianwei Niu Weikun Feng Hang He State Key Laboratory of Intelligent Game Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Computation offloading is one of the key issues in mobile edge computing (MEC) that alleviates the tension between user equipment's limited capabilities and mobile application's high requirements. To achieve model-free computation offloading when reliable MEC dynamics are unavailable, deep reinforcement learning (DRL) has become a popular methodology. However, most existing DRL-based offloading approaches are developed for a single MEC environment, with invariant system bandwidth, edge capability, task types, etc., while realistic MEC scenarios tend to be of high diversity. Unfortunately, in multi-MEC environments, DRL-based offloading faces at least two challenges, learning inefficiency and interference of offloading experiences. To address the challenges, we propose a DRL-based Multi-environmental Module-compositional Modelfree computation OFFloading (M 3 OFF) framework. M 3 OFF generates offloading policies using module composition instead of a single DRL network so that learning efficiency could be improved by reusing the same modules and learning interference could be reduced by composing different modules. Furthermore, we design multiple module composition-specific training methods for M 3 OFF, including alternate modules-and-composer updates to improve training stability, loss-regularization to avoid module degeneration, and module-dropout to mitigate overfitting. Extensive experimental results on both simulation and testbed demonstrate that M 3 OFF outperforms the performances of most state-of-the-arts in multi-MEC and reaches close to single-MEC.

关键词： Training Visualization Multi-access edge computing Computational modeling Interference Bandwidth Stability analysis

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