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Modern trend of automation and digitalization in law application

AIP Conference Proceedings 2023年第1期2910卷

作者： O. Karpenko T. Aygumov A. Zolkin A. Gureva I. Poskryakov JSC Samaragorenergosbyt Samara 443079 Russia Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala Russia Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia Transport college Federal State Budgetary Institution of Higher Education “Admiral Ushakov Maritime State University” Novorossiysk 353918 Russia Department «Economics and management of transport» Russian University of Transport (MIIT) Obraztsova Street 9 str. 9 Moscow 127994 Russia

The main points and manifestations of the modern trend of automation and digitalization in law application are considered in the article. The positive and negative aspects of the considered phenomena are highlighted. Attention is paid to informatization of court activities, optimization, introduction of new technologies, videoconferencing, provision of legal Internet services, audio recording, etc. Informatization in the field of legal relations and law application is designed to meet the modern needs of all subjects of legal relations. It affects lawmaking, law application and law enforcement activities, expert practice, legal education and increasing the legal literacy of the population, and includes government agencies, the judiciary system. It also enables citizens to access up-to-date modern legal information.

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A Model to Measure the Spread Power of Rumors

arXiv

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arXiv 2020年

作者： Jahanbakhsh-Nagadeh, Zoleikha Feizi-Derakhshi, Mohammad-Reza Ramezani, Majid Akan, Taymaz Asgari-Chenaghlu, Meysam Nikzad–Khasmakhi, Narjes Feizi-Derakhshi, Ali-Reza Ranjbar-Khadivi, Mehrdad Zafarani-Moattar, Elnaz Balafar, Mohammad-Ali Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Naghadeh Branch Islamic Azad University Naghadeh Iran Department of Computer Science and Research Branch Islamic Azad University Tehran Iran Department of Software Engineering Istanbul Topkapi University Istanbul Turkey Clinical Informatics Louisiana State University Health Sciences Center Shreveport Shreveport United States Department of Computer Engineering Shabestar Branch Islamic Azad University Shabestar Iran Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Department of Computer Engineering University of Tabriz Iran

With technologies that have democratized the production and reproduction of information, a significant portion of daily interacted posts in social media has been infected by rumors. Despite the extensive research on rumor detection and verification, so far, the problem of calculating the spread power of rumors has not been considered. To address this research gap, the present study seeks a model to calculate the Spread Power of Rumor (SPR) as the function of content-based features in two categories: False Rumor (FR) and True Rumor (TR). For this purpose, the theory of Allport and Postman will be adopted, which it claims that importance and ambiguity are the key variables in rumor-mongering and the power of rumor. Totally 42 content features in two categories"importance" (28 features) and"ambiguity" (14 features) are introduced to compute SPR. The proposed model is evaluated on two datasets, Twitter and Telegram. The results showed that (i) the spread power of False Rumor documents is rarely more than True Rumors. (ii) there is a significant difference between the SPR means of two groups False Rumor and True Rumor. (iii) SPR as a criterion can have a positive impact on distinguishing False Rumors and True Rumors. © 2020, CC BY.

关键词： Cell proliferation

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Analysis and improvement of the management system on the example of the organization of the aerospace industry

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AIP Conference Proceedings 2023年第1期2700卷

作者： V. V. Kuleshov A. L. Zolkin T. G. Aygumov A. S. Bityutskiy V. N. Malikov 1Department of Industrial Ecology and Safety FSBEI HE Omsk State Technical University Omsk 644050 Russia 2Computer and Information Sciences Department Povolzhskiy State University of Telecommunications and Informatics Samara 443010 Russia 3Department of computer engineering software and automated systems Dagestan State Technical University Makhachkala 367000 Russia 4"Invent Technology" LLP Almaty A10E5P4 Kazakhstan 5Department of General and Experimental Physics Altai State University Barnaul 656057 Russia

This study has been carried out in the organization of the aerospace industry. The scientific literature has been analysed in the article. Article shows the relevance of ongoing research on the selected topic. It was noted that there is an insufficient attention of the heads of the organization to the workers occupational health and safety. Due to the analysis of the existing occupational safety and health management system, its significant shortcomings have been identified: the use of an ineffective method of analysis of occupational risks, as well as the development of measures only on the basis of accidents and occupational diseases (lagging indicators). To improve the efficiency of the occupational safety and health management system, it was proposed to switch to proactive indicators of safety culture. For the assessment of professional risks, the “Fine Kinney” professional risk assessment method was recommended and implemented. At the same time, in order to increase the efficiency of monitoring the labour conditions management system, an audit system has been introduced, and the levels of management in the organization have been revised. The study made it possible to increase the efficiency of the occupational safety and health management system of the organization significantly. It will reduce the level of industrial injuries.

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EntreCompOnto: An Ontology for Semantic Representation of Entrepreneurship Competences

EntreCompOnto: An Ontology for Semantic Representation of En...

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2018 IEEE International Conference on Teaching, Assessment, and Learning for engineering, TALE 2018

作者： Piedra, Nelson Caro, Edmundo Tovar Knowledge-Based Systems Research Group Universidad Técnica Particular de Loja Loja Ecuador Department of Computer Languages and Systems and Software Engineering Technical University of Madrid Madrid Spain

ISBN: (纸本)9781538665220

The development of citizens' and organizations' entrepreneurial capacity is a key policy objective of any country. The information required for monitoring development of curricula and learning activities fostering entrepreneurship as a competence is obtained from different data sources in different information systems. The semantic interoperability between these systems with resources relevant to the development of entrepreneurship stimulates more productive sharing of information among all stakeholders. Building semantic interoperable systems, ensuring in this way a collaborative performance, is not without difficulties. This paper presents EntreCompOnto, an ontology based on the EntreComp framework that could support European citizens and organizations in attaining entrepreneurship competence. EntreCompOnto sets the ground for identifying the knowledge, skills and attitudes that comprise 'entrepreneurial mindset', the ultimate aim being to facilitate exchange among users of EntreComp's adaptation families and eventually to positively impact the mobility, employability and participation of citizens in society and the economy. © 2018 IEEE.

关键词： Curricula

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Flexible production systems: Automated generation of operations plans based on ISA-95 and PDDL

arXiv

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arXiv 2019年

作者： Wally, Bernhard Vyskočil, Jiří Novák, Petr Huemer, Christian Šindelář, Radek Kadera, Petr Mazak, Alexandra Wimmer, Manuel CDL for Model-Integrated Smart Production Department of Business Informatics Software Engineering Johannes Kepler University Linz Linz4040 Austria Department of Intelligent Systems for Industry and Smart Distribution Networks Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague160 00 Czech Republic Business Informatics Group Institute of Information Systems Engineering Technische Universitat Wien Vienna1040 Austria

Model-driven engineering (MDE) provides tools and methods for the manipulation of formal models. In this letter, we leverage MDE for the transformation of production system models into flat files that are understood by general purpose planning tools and that enable the computation of "plans", i.e., sequences of production steps that are required to reach certain production goals. These plans are then merged back into the production system model, thus enriching the formalized production system knowledge. Copyright © 2019, The Authors. All rights reserved.

关键词： Factory automation

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The use of public-private partnership in the machine-building industry

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AIP Conference Proceedings 2022年第1期2467卷

作者： Oksana Bondarskaya Tatyana Bondarskaya Sergey Pichugin Anna Drozdova Elena Avksentieva Sergey Yekimov 1Tambov State Technical University Tambov Russian 2S. P. Korolev Rocket and Space Corporation Energia Moscow Russia 3Faculty of Business Informatics and Integrated Systems Management Department of Industrial Economics and Management National Research Nuclear University MEPhI Moscow Russia 4Faculty of Software Engineering and Computer Systems ITMO University St. Peterburg Russia 5Publishing House “Education and Science” Prague The Czech Republic

The process of reengineering business processes, in our opinion, is an iterative procedure in the process of enterprise management. Each subsequent business process in the business process chain is a logical continuation of the previous one. The reengineering procedure is characterized by the presence of some feedback about the impact of each individual business process on the overall set of business processes. During the implementation of reengineering, traditional business approaches may undergo revision. Often, many business rules need to be improved and are outdated. The use of public-private partnerships, in our opinion, can be considered as elements of reengineering, since partnerships with the state can open up new business development prospects for an enterprise, and many business processes used at this enterprise can be rethought and updated. The production of high-quality and high-performance agricultural machinery can be of great importance for solving the problem of ensuring food security of the state. The use of various forms of public-private partnership allows us to speed up the solution of this problem. The implementation of economic modernization requires the full use of the economic potential of the state. This can be achieved only if public-private partnerships are effectively implemented, since they allow combining the resources of the state and private business to achieve common goals.

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Machine Learning-Driven Prediction of Optical and Physical Properties in Lanthanum and Gold-Doped Zinc Borotellurite Glasses for Optoelectronic Applications

引用

Ceramics International 2025年

作者： S.N. Nazrin Camellia Doroody Liyana Adilla Burhanuddin Neesha Jothi N. Ibrahim Halimah Badioze Zaman M.H.M. Tahir Manzoore Elahi M. Soudagar S. Ramesh Sagar Shelare Shubham Sharma Institute of Informatics and Computing in Energy (IICE) College of Engineering Universiti Tenaga Nasional 43000 Kajang Selangor Malaysia Institute of Sustainable Energy (ISE) Universiti Tenaga Nasional (UNITEN @ The Energy University) Putrajaya Campus Jalan IKRAM-UNITEN 43000 Kajang Selangor Malaysia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura-140401 Punjab India Zhejiang Xingyu Mechanical and Electrical Technology Company Ltd. Tongqin Wuyi County Jinhua Zhejiang 311400 China Department of Electronic Systems Engineering (ESE) Malaysia–Japan International Institute of Technology (MJIIT) Universiti Teknologi Malaysia Department of Computing College of Computing and Informatics Universiti Tenaga Nasional (UNITEN) Malaysia Dept. of Digital Economy Technology Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman 31900 Kampar Perak Department of English Language and Literature Faculty of Languages and Communication Universiti Pendidikan Sultan Idris 35900 Tanjong Malim Perak Malaysia Center of Advanced Manufacturing and Materials Processing (AMMP) Faculty of Engineering University of Malaya Kuala Lumpur Chitkara Centre for Research and Development Chitkara University Himachal Pradesh 174103 India Division of Research and Development Lovely Professional University Phagwara Punjab 144411 India Department of Applied Chemistry Priyadarshini College of Engineering Nagpur Maharashtra 440019 India University Centre for Research and Development Chandigarh University Mohali Punjab 140413 India Department of Technical Sciences Western Caspian University Baku Azerbaijan Jadara University Research Center Jadara University Jordan

Recent advancements in glass science have focused on optimizing optical and structural properties through the incorporation of rare oxide nanoparticles, such as lanthanum and gold oxide, to enhance transparency, refractive index, and band gap energy for photonic applications. Additionally, the integration of machine learning techniques in material design accelerates the prediction and optimization of these properties, enabling more efficient discovery and development of advanced glass systems for optoelectronic and photonic devices. The melt-quenching technique was employed to fabricate zinc borotellurite glasses doped with lanthanum oxide and gold oxide. The glass's amorphous nature was confirmed through structural analysis, which detected absorption bands corresponding to TeO 3 , BO 4 , and BO 3 units. A drop in molar volume with an increase in gold oxide quantity prompted a suggestion for structural reorganisation. Optical investigations indicated an increase in the optical band gap energy and a blue shift in the absorption edge. Four machine learning models—Random Forest, Gradient Boosting, XGBoost, and Artificial Neural Networks—were employed to forecast critical optical and physical parameters, such as refractive index, density, molar volume, optical band gap, and Urbach energy, to expedite the discovery process. This data-driven approach illustrates the transformative capabilities of machine learning in materials science, facilitating the swift modification of glass compositions to enhance optical performance for photonic and optoelectronic applications. The enhanced optical properties of the synthesized glass system make it a promising candidate for photonic and optoelectronic applications, including optical fibers, solid-state lasers, and planar waveguides , where its tunable refractive index and improved transparency can optimize signal transmission and device efficiency. Additionally, the presence of gold oxide nanoparticles suggests potential use in nonl

关键词： Artificial neural networks (ANN) Energy Gold oxide Gradient boosting (GB) Random forest (RF) XGBoost (XGB)

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Portable Blended MOOC Laboratory

Portable Blended MOOC Laboratory

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Learning With MOOCS (LWMOOCS)

作者： Pedro Plaza Elio Sancristobal German Carro Manuel Blazquez Antonio Menacho Felix García-Loro Clara Perez Javier Muñoz Edmundo Tovar JIm Sluss Russ Meier Manuel Castro Electrical and Computer Engineering Department (DIEECTQAI) UNED Madrid Spain Plaza Robotica Madrid Spain Languages Computer Systems and Software Engineering Department UPM Madrid Spain Dean Tuksa Graduate College School of Electrical and Computer Engineering Tulsa OK USA Electrical Engineering and Computer Science Department Milwaukee School of Engineering Milwaukee WI USA Director Electrical and Computer Engineering Department (DIEECTQAI) UNED Madrid Spain

Two types of teaching coexist today: traditional face-to-face methods and teaching based on self-learning where the student has ubiquitous access to course contents. This paper shows how tests have been made of a type of learning that is becoming more common in educational environments. This hybrid teaching method combines the Massively Open Online Course (MOOC) and students' face-to-face work. On the one hand, the advantages of face-to-face learning are taken advantage of: teacher in the classroom, social bonds and daily work sessions. On the other hand, the advantages of distance learning are integrated: flexibility, accessible material and wide variety. Also included in the paper is a description of the equipment and the way in which it has been used with students. Finally, the paper details the educational experience carried out with students and the outcomes obtained. These pilot tests are framed within the education of STEM topics and robotics.

关键词： Computer aided instruction Robots Electronic learning Tools Light emitting diodes Training

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A cyber-enabled mission-critical system for post-flood response: Exploiting TV white space as network backhaul links

A cyber-enabled mission-critical system for post-flood respo...

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作者： Rahman, M.D. Arafatur Asyhari, A. Taufiq Azad, Saiful Hasan, M.D. Munirul Munaiseche, Cindy P.C. Krisnanda, Made Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Kuantan26300 Malaysia IBM Center of Excellence and Earth Resources and Sustainability Center Universiti Malaysia Pahang Kuantan26300 Malaysia School of Computing Electronics and Mathematics Coventry University CoventryCV1 5FB United Kingdom Telkom University Jalan Telekomunikasi Terusan Buah Batu Bandung40257 Indonesia Faculty of Engineering Department of Informatics Universitas Negeri Manado Tondano95618 Indonesia Faculty of Engineering Department of Information and Communication Technology Education Universitas Negeri Manado Tondano95618 Indonesia

A crucial problem in post-flood recovery actions is the ability to rapidly establish communication and collaboration among rescuers to conduct timely and effective search and rescue (SAR) mission given disrupted telecommunication infrastructure to support the service. Aimed at providing such proximity service (ProSe) for mission-critical data exchange in the post-flood environment, the majority of existing solutions rely heavily upon ad-hoc networking approaches, which suffer from restricted communication range and the limited scope of interaction. As an effort to broaden the ProSe coverage and expand integrated global-local information exchange in the post-flood SAR activities, this paper proposes a novel network architecture in the form of a cyber-enabled mission-critical system (CEMCS) for acquiring and communicating post-flood emergency data by exploiting TV white space spectrum as network backhaul links. The primary method of developing the proposed system builds upon a layered architecture of wireless local, regional and wide-area communications, and incorporates collaborative network components among these layers. The desirable functionalities of CEMCS are showcased through formulation and the development of an efficient global search strategy exploiting a wide range of collaboration among network agents. The simulation results demonstrate the capability of CEMCS to provide ProSe in the post-flood scenarios as reflected by reliable network performance (e.g., packet delivery ratio nearing 80%–90%) and the optimality of efficient search algorithm. © 2017 Oxford University Press. All rights reserved.

关键词： Cyber Physical System

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MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for Computer Vision

arXiv

引用

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

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