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arXiv 2023年

作者： Kovačević, Mladen Pinelis, Iosif Kountouris, Marios Faculty of Technical Sciences University of Novi Sad Serbia Department of Mathematical Sciences Michigan Technological University United States Department of Communication Systems EURECOM France Department of Computer Science and Artificial Intelligence University of Granada Spain

We revisit the familiar scenario involving two parties in relative motion, in which Alice stays at rest while Bob goes on a journey at speed βc along an arbitrary trajectory and reunites with Alice after a certain period of time. It is a well-known consequence of special relativity that the time that passes until they meet again is different for the two parties and is shorter in Bob’s frame by a factor of √1 − β2. We investigate how this asymmetry manifests from an information-theoretic viewpoint. Assuming that Alice and Bob transmit signals of equal average power to each other during the whole journey, and that additive white Gaussian noise is present at both sides, we show that the maximum number of bits per second that Alice can transmit reliably to Bob is always higher than the one Bob can transmit to Alice. Equivalently, the energy per bit invested by Alice is lower than that invested by Bob, meaning that the traveler is less efficient from the communication perspective, as conjectured by Jarett and *** Codes 83A05, 94A24, 94A40 Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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The Role of Soft Skills in Engineering Education

The Role of Soft Skills in Engineering Education

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Frontiers in Education (FIE) Conference

作者： Marcin Fojcik Martyna Katarzyna Fojcik Adam Ziebinski Bjarte Pollen Anne-Lena Kampen Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Førde Norway Department of Humanities and Education Volda University Collage Volda Norway Department of Distributed Systems and Informatics Devices Silesian University of Technology Gliwice Poland Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The rapid but uneven education development worldwide is creating many challenges. Changing conditions and rules in engineering fields such as programming, automation, and electronics are not always followed by changes in education. Employers expect potential candidates to be current with the latest domain competencies and soft skills. The idea is to hire candidates with both knowledge (theoretical and practical) and experience in reflection and collaboration skills. Universities can not always keep up with the development of technology and the adaptation of curricula because change happens so quickly. Therefore, upgrades are needed in engineering education. Focusing education solely on measurable knowledge and results is a simple but often insufficient approach to presenting the entire curriculum within the time frame of a course. Our study of about 150 engineering students in Poland and Norway included the role of students' experiences in hands-on activities, especially labs. The research aimed to determine what students expect, what influences their results, and what complications may arise. The study results show that students often struggle with time management, prioritizing, and understanding practical tasks. In addition, there is a difference between students with and without practical work experience. Our research showed that both groups of students did not achieve all their intended educational goals for various reasons. Therefore, adapting the current teaching system to the student's needs and requirements is important.

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Awakened at CheckThat! 2022: Fake News Detection using BiLSTM and Sentence Transformer

Awakened at CheckThat! 2022: Fake News Detection using BiLST...

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2022 Conference and Labs of the Evaluation Forum, CLEF 2022

作者： Truică, Ciprian-Octavian Apostol, Elena-Simona Paschke, Adrian Department of Information Technology Uppsala University Lägerhyddsvägen 1 Uppsala75105 Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Splaiul Independentei 313 Bucharest060042 Romania Fraunhofer Institute for Open Communication Systems Berlin10589 Germany

In recent years, online social networks and online news venues have become some of the main news and event-related information spreading mediums. Although using these mediums has facilitated the speed of accessing information, it also created a new phenomenon used for propaganda and disinformation: fake news. As fake news has detrimental consequences to society, new technologies need to be developed in order to stop their harmful effects. In this paper, we propose two Bidirectional Long Short-Term Memory (BiLSTM) architectures with sentence transformers to solve two tasks: (1) a multi-class mono-lingual fake news detection task (i.e., mono-lingual task);and (2) a multi-class cross-lingual fake news detection task (i.e., cross-lingual task). For the mono-lingual task, we train and test a BiLSTM with BART sentence transformers model on an English dataset and obtain an accuracy of ∼ 0.53 and an F1-Score of ∼ 0.32. For the cross-lingual task, we train a BiLSTM with XLM sentence transformers model on an English dataset and test the model using transfer learning on a German dataset. For this task, we obtain an accuracy of ∼ 0.28 and an F1-Score of ∼ 0.19. © 2022 Copyright for this paper by its authors.

关键词： Statistical tests

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Mobility Prediction at the Tactical Edge: A Handover for Centralized/Decentralized Networks

Mobility Prediction at the Tactical Edge: A Handover for Cen...

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International Conference on Military communications and Information systems (ICMCIS)

作者： Merlin von Rechenberg Johannes F. Loevenich Philipp Zißner Paulo H. L. Rettore Roberto Rigolin F. Lopes Peter Sevenich Dept. of Communication Systems Fraunhofer FKIE Bonn Germany Institute of Computer Science 4 University of Bonn Bonn Germany Department of Mathematics/Computer Science University of Osnabrück Bonn Germany Secure Communications & Information (SIX) Thales Deutschland Ditzingen Germany

This paper presents a mobility prediction model as part of a handover mechanism combining centralized and decentralized network control to improve performance and resilience in tactical networks. Our approach utilizes mobility prediction to optimally switch between control modes, minimizing packet loss from live user data flows and maximizing the duration of centralized control connectivity. The prediction model ingests mobility trace files, extracts supplementary mobility features, and employs a Recurrent Neural Network (RNN) to forecast system states representing the network link quality. We evaluate the model using two mobility paradigms: Gauss-Markov and Manhattan-Grid. For the Gauss-Markov model, our method achieves 44% accuracy in predicting network states 60 seconds into the future and up to 72% accuracy for 5-second predictions. Our approach exhibits higher accuracy with the Manhattan-Grid model, attaining 67% and 82% for 60 and 5-second predictions, respectively. A comparative analysis with a previously implemented RSSI-based mechanism demonstrates that our prediction-based mechanism enhances centralized control connectivity duration by 56% and mitigates packet loss by 1.24%. This work contributes to advancing adaptive handover mechanisms for complex tactical networks.

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Cybernated Watermarking Approach on Image with Cryptographic Encryption

Cybernated Watermarking Approach on Image with Cryptographic...

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computer, communication and Control (IC4), International Conference on

作者： R. Sivasankari T Veeramakali S M Mustafa Nawaz P. Prakash P Umadevi A Alvin Ancy Dept. of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamilnadu Dept of Computer Science and Engineering Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Sri Sai Ram Engineering College Department of S & H KPR Institute of Engineering and Technology Coimbatore Dept of Electronics and Communication Engineering Anna University Chennai

Digital watermarking of interactive media content has become an active experimentation field in recent years. In addition to reviewing some of the methods that have been developed for various media types; a general structure for watermark engrafting and identification is outlined. We highlight a few of these application-oriented differences, including copyright protection, substantiation, tamper identification, and data hiding, as well as the technological and system conditions for different media types such as digital photos, digital files, digital audio, and text. We describe a general structure for digital watermarking-based image copyright protection in this approach. In this paper, we outline the pre-processing and implementation of tide marking technique where Twofish algorithm and double encryption is described. The image processing toolbox is also specified by comparing five different image processing techniques. In addition, the post implementation is carried out with the wavelet toolbox and matlab coder to analyze signal, image properties and generate the efficient code for embedding the text.

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A Machine Learning Approach in Predictive Maintenance in the IoT Enabled Industry 4.0

A Machine Learning Approach in Predictive Maintenance in the...

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Smart Electronics and communication (ICOSEC), International Conference on

作者： Sathiyakeerthi Madasamy B. Prabhu Shankar Rakesh Kumar Yadav Jayalakshmi K P Solution Architect Pilvi Systems Inc Lewisville TX USA Department of Computer Science and Engineering Faculty of Engineering and Technology Alliance University Karnataka India Department of Computer Science and Engineering SRM Institute of Science and Technology Ghaziabad Uttar Pradesh India Department of Electronics and Communication Engineering St Joseph Engineering College Mangaluru Karnataka India

Predictive maintenance using machine learning is a powerful technique for industries seeking to enhance their operations with minimize downtime. In an IoT-enabled Industry 4.0 environment, this approach can be taken to a new level by leveraging the vast amounts of data generated by connected devices. To implement a machine learning methodology to projecting conservation in an Industry 4.0 environment, several key steps need to be taken. First, data from IoT devices across the industrial ecosystem should be collected and centralized in a data lake or similar storage system. This data should include information on equipment health, sensor readings, and other relevant metrics. Next, the data should be preprocessed and transformed to ensure its quality and consistency. This may involve cleaning, normalization, and feature engineering to create relevant variables for use in machine learning models. Once the data has been preprocessed, a range of machine learning models can be trained on it to predict equipment failures or other maintenance issues. This may involve ongoing tuning and optimization of model hyperparameters or retraining the models on new data as it becomes available. Finally, the predictions generated by the machine learning models should be integrated into a broader maintenance management system to enable timely action. This may include triggering maintenance requests, generating work orders, or even automating maintenance tasks through the use of robots or other industrial automation technologies. By implementing a machine learning method to projecting preservation in an IoT-enabled Industry 4.0 environment, industries can optimize their operations, minimize downtime, and improve overall equipment effectiveness.

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[dai:si] - A modular dataset retrieval framework with a semantic search for biological data

[dai:si] - A modular dataset retrieval framework with a sema...

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Joint Ontology Workshops 2021 Episode VII: The Bolzano Summer of Knowledge, JOWO 2021

作者： Shafiei, Fateme Löffler, Felicitas Thiel, Sven Opasjumruskit, Kobkaew Grabiger, Denis Rauh, Pauline König-Ries, Birgitta Heinz Nixdorf Chair for Distributed Information Systems Department of Mathematics and Computer Science Friedrich Schiller University Jena Jena Germany Jena Germany Halle-Jena-Leipzig Germany Michael-Stifel-Center for Data-Driven and Simulation Science Jena Germany

Dataset search is receiving increasing attention in a scholar's daily research practice. In biodiversity research, dataset retrieval in particular is a challenging and time-consuming task as most search services in current data portals only offer a simple keyword-based search. In this work we introduce [Dai:Si], a modular framework for dataset retrieval with a semantic search for biological data. [Dai:Si] is based on a former semantic search service developed within the scope of the GFBio project. It allows the expansion of query keywords with related terms using GFBio's Terminology Service. This new version provides an enhanced user interface (UI) with explanations of related semantic terms upon demand. Due to its modular structure, [Dai:Si]'s semantic service can now be used independently of the user interface (UI). © 2021 CEUR-WS. All rights reserved.

关键词： Biodiversity

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An Implementation of Naming Rule Checking Function and Its Applications to Java Programming Codes

An Implementation of Naming Rule Checking Function and Its A...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Wai, Khaing Hsu Funabiki, Nobuo Mentari, Mustika Aung, Soe Thandar Kyaw, Htoo Htoo Sandi Yamaguchi, Shingo Kao, Wen-Chung Graduate School of Science and Engineering Akita University Akita Japan Department of Information and Communication Systems Okayama University Okayama Japan Department of Computer and Information Science Tokyo University of Agriculture and Technology Tokyo Japan Graduate School of Sciences and Technology for Innovation Yamaguchi University Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798331521165

Currently, a lot of universities are offering Java programming courses for teaching the first-step object-oriented programming language. For novice students, to master writing readable codes using proper names for variables, classes, and methods is crucial in Java programming, to improve understandability and maintainability. In this paper, we consider four naming rules to write a readable code and implement the naming rule checking function to check whether the given code follows them. For evaluations, we applied the proposal to 2, 908 source codes submitted in Java programming courses from students in Okayama University, Japan, Malang State Polytechnic, Indonesia, and Yamaguchi University, Japan. The results confirm the effectiveness of the proposal. © 2025 IEEE.

关键词： Java programming language

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Partitioned deep learning of fluid-structure interaction

arXiv

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arXiv 2021年

作者： Totounferoush, Amin Schumacher, Axel Schulte, Miriam Institute for Parallel and Distributed Systems Department of Computer Science University of Stuttgart Stuttgart70569 Germany

We present a partitioned neural network-based framework for learning of fluid-structure interaction (FSI) problems. We decompose the simulation domain into two smaller sub-domains, i.e., fluid and solid domains, and incorporate an independent neural network for each. A library is used to couple the two networks which takes care of boundary data communication, data mapping and equation coupling. Simulation data are used for training of the both neural networks. We use a combination of convolutional and recurrent neural networks (CNN and RNN) to account for both spatial and temporal connectivity. A quasi-Newton method is used to accelerate the FSI coupling convergence. We observe a very good agreement between the results of the presented framework and the classical numerical methods for simulation of 1d fluid flow inside an elastic tube. This work is a preliminary step for using neural networks to speed-up the FSI coupling convergence by providing an accurate initial guess in each time step for classical numerical solvers. © 2021, CC BY-NC-ND.

关键词： Deep neural networks

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Advancements in Multi-Cloud Applications for Enhanced E-Healthcare Services

Advancements in Multi-Cloud Applications for Enhanced E-Heal...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： P Ramesh Naidu Dankan Gowda Parismita Sarma Dinesh Arora Sampathirao Suneetha Sandip Ramesh Patil Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore Karnataka India Department of Electronics & Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India Department of Information Technology Gauhati University Assam Guwahati Assam India Department of Electronics and Communication Chandigarh Engineering College Mohali Punjab India Department of Computer Science & Systems Engineering Andhra University Visakhapatnam Andhra Pradesh India Department of Communication Skill Bharati Vidyapeeth College of Engineering Navi Mumbai India

E-Health Record Security Research on a Cloud-Based Multi-Layer Framework reaches its climax in a string of noteworthy discoveries, demonstrating how the framework may transform cloud health data security. The framework's multi-tiered design proved to be an effective barrier against a wide range of cyber threats, protecting the privacy and security of patients' medical records. An important factor in the varied healthcare industry is the fact that it can be easily scaled and adjusted to meet the needs of healthcare providers of varied sizes and capabilities. Nevertheless, there are obstacles that need to be addressed, according to the report. These include the difficulty of implementation and the ongoing need for upgrades and modifications to address changing cyber threats and technical advances. Healthcare firms must continuously implement educational programs due to the reliance on user compliance and training. With an eye toward the future, this structure lays the groundwork for more sophisticated studies in the subject. Potential research directions include improving cross-platform compatibility, optimizing resource utilization to reduce performance implications, and integrating AI and ML for automated threat response and predictive analytics.

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