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arXiv 2023年

作者： Truică, Ciprian-Octavian Andrei-Ionut, Stan Apostol, Elena-Simona Department of Information Technology Uppsala University Uppsala Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Text simplification (TS) is the process of generating easy-to-understand sentences from a given sentence or piece of text. The aim of TS is to reduce both the lexical (which refers to vocabulary complexity and meaning) and syntactic (which refers to the sentence structure) complexity of a given text or sentence without the loss of meaning or nuance. In this paper, we present SIMPLEX, a novel simplification architecture for generating simplified English sentences. To generate a simplified sentence, the proposed architecture uses either word embeddings (i.e., Word2Vec) and perplexity, or sentence transformers (i.e., BERT, RoBERTa, and GPT2) and cosine similarity. The solution is incorporated into a user-friendly and simple-to-use software. We evaluate our system using two metrics, i.e., SARI, and Perplexity Decrease. Experimentally, we observe that the transformer models outperform the other models in terms of the SARI score. However, in terms of Perplexity, the Word-Embeddings-based models achieve the biggest decrease. Thus, the main contributions of this paper are: (1) We propose a new Word Embedding and Transformer based algorithm for text simplification;(2) We design SIMPLEX - a modular novel text simplification system - that can provide a baseline for further research;and (3) We perform an in-depth analysis of our solution and compare our results with two state-of-the-art models, i.e., LightLS [19] and NTS-w2v [44]. We also make the code publicly available online. © 2023, CC BY-NC-ND.

关键词： Embeddings

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MISROBÆRTA: TRANSFORMERS VERSUS MISINFORMATION

arXiv

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arXiv 2023年

作者： Truicǎ, Ciprian-Octavian Apostol, Elena-Simona Department of Information Technology Uppsala University Uppsala Sweden Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning proved to be state-of-the-art methods for multiple wellknown natural language processing tasks. In this paper, we propose MisRoBÆRTa, a novel transformer-based deep neural ensemble architecture for misinformation detection. MisRoBÆRTa takes advantage of two state-of-the art transformers, i.e., BART and RoBERTa, to improve the performance of discriminating between real news and different types of fake news. We also benchmarked and evaluated the performances of multiple transformers on the task of misinformation detection. For training and testing, we used a large real-world news articles dataset (i.e., 100,000 records) labeled with 10 classes, thus addressing two shortcomings in the current research: (1) increasing the size of the dataset from small to large, and (2) moving the focus of fake news detection from binary classification to multi-class classification. For this dataset, we manually verified the content of the news articles to ensure that they were correctly labeled. The experimental results show that the accuracy of transformers on the misinformation detection problem was significantly influenced by the method employed to learn the context, dataset size, and vocabulary dimension. We observe empirically that the best accuracy performance among the classification models that use only one transformer is obtained by BART, while DistilRoBERTa obtains the best accuracy in the least amount of time required for fine-tuning and training. However, the proposed MisRoBÆRTa outperforms the other transformer models in the task of misinformation detection. To arrive at this conclusion, we performed ample ablation and sensitivity testing with MisRoBÆRTa on t

关键词： Large dataset

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Applying Transformer-Based Neural Networks to Corpus Linguistic Data for Predictive Text Generation in Multilingual Environments 6

Applying Transformer-Based Neural Networks to Corpus Linguis...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Taufikin Orosoo, Myagmarsuren Rengarajan, Manikandan Fatma, Gulnaz Yuldashev, Dilyorjon Raj, I Infant IAIN Kudus Indonesia Mongolian National University of Education Mongolia Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India University College Al Ardah Jazan University Jazan Saudi Arabia Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K. Ramakrishnan College of Engineering Department of CSE Tamil Nadu Trichy India

ISBN: (纸本)9798350306613

In the subject of corpus linguistics, this study explores the use of transformer-based neural networks to the problems of predictive text production in multilingual contexts. The Transformer architecture presents a viable framework for natural language processing problems because of its reputation for capturing complex patterns and long-range dependencies in sequential data. The research employs a two-phase approach, whereby the Transformer is first pre-Trained using a variety of multilingual corpora. This helps the model learn representations that are independent of language, which makes it easier for it to adapt to various linguistic circumstances. The model is then fine-Tuned using language-specific datasets to improve its ability to produce linguistically nuanced and contextually appropriate text. The study looks into how different model architectures, training methods, and hyperparameters affect how well the suggested multilingual predictive text generation system performs. Evaluation measures are used to evaluate the model's performance in several languages, including linguistic diversity measurements, BLEU scores, and perplexity. The overall accuracy, calculated across all languages, stands at 80.3%, indicating the model's robust cross-lingual generalization and its effectiveness in capturing diverse linguistic nuances The efficacy and generalization of Transformer-based models are improved across a wide range of languages to handle linguistic diversity. © 2024 IEEE.

关键词： Corpus Linguistics Cultural Context Integration Multilingual Text Generation Neural Language Models Transformer-Based Neural Networks

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Evaluation of Differentially Constrained Motion Models for Graph-Based Trajectory Prediction

arXiv

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arXiv 2023年

作者： Westny, Theodor Oskarsson, Joel Olofsson, Björn Frisk, Erik Department of Electrical Engineering Linköping University Sweden Department of Computer and Information Science Linköping University Sweden Department of Automatic Control Lund University Sweden

Given their adaptability and encouraging performance, deep-learning models are becoming standard for motion prediction in autonomous driving. However, with great flexibility comes a lack of interpretability and possible violations of physical constraints. Accompanying these data-driven methods with differentially-constrained motion models to provide physically feasible trajectories is a promising future direction. The foundation for this work is a previously introduced graph-neuralnetwork-based model, MTP-GO. The neural network learns to compute the inputs to an underlying motion model to provide physically feasible trajectories. This research investigates the performance of various motion models in combination with numerical solvers for the prediction task. The study shows that simpler models, such as low-order integrator models, are preferred over more complex ones, e.g., kinematic models, to achieve accurate predictions. Further, the numerical solver can have a substantial impact on performance, advising against commonly used first-order methods like Euler forward. Instead, a second-order method like Heun’s can significantly improve predictions. Copyright © 2023, The Authors. All rights reserved.

关键词： Trajectories

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Ethical Considerations in Explainable AI: Balancing Transparency and User Privacy in English Language-based Virtual Assistants 5

Ethical Considerations in Explainable AI: Balancing Transpar...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Alijoyo, Franciskus Antonius Sandya Sneha Sri, S. Alapati, Purnachandra Rao Yuldashev, Dilyorjon Ponni Valavan, M. Center for Risk Management and Sustainability Indonesia School of Business and Information Technology Stmik Likmi Bandung Indonesia Krishna University Andhra Pradesh India Prasad v Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada Kanuru India Turin Polytechnic University in Tashkent Automatic Control and Computer Engineering Department Tashkent Uzbekistan K.Ramakrishnan College of Engineering Trichy Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350385649

English Language-Based Virtual Assistants (ELB-VAs) are AI-powered systems designed to comprehend and respond to user queries in the English language, exemplified by virtual assistants like Siri or Alexa. The need for balancing transparency and user privacy in ELB-VAs is paramount due to their pervasive integration into daily life. Ensuring transparency imbues user trust, while safeguarding privacy addresses ethical concerns associated with personal data. Existing methods involve clear privacy policies, user-controlled data sharing settings, and encryption. However, drawbacks include user confusion and potential biases. To address these limitations, this study proposes a novel approach. Methodologically, it integrates pre-processing techniques such as lowercasing and tokenization, coupled with a Natural Language Understanding model. This model undergoes intent and entity recognition training, enhancing accuracy, and incorporates privacy-aware response generation, ensuring informative yet privacy-conscious interactions. The implementation of the study's results is carried out using Python tools, showcasing improved metrics and response times. This approach contributes to a more transparent and privacy-respecting user experience, aligning with evolving ethical norms and setting the stage for advancements in ELB-VA technology. This comprehensive exploration bridges existing gaps, emphasizing the ethical imperative of user-centric and privacy-aware AI interactions in ELB- VAs. The proposed NLU model exhibits a substantial increase in accuracy compared to other methods, with an impressive accuracy value of 99.1%• On average, it outperforms the Random Forest and Decision Tree models by 15.7 percentage points, highlighting its superior predictive capabilities in the evaluated task. This comprehensive exploration aligns with evolving ethical norms and establishes a foundation for future advancements in ELB-VA technology. © 2024 IEEE.

关键词： Transparency

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Cloud Architectural Design for Image-based Vehicle Positioning in Traffic Management

Cloud Architectural Design for Image-based Vehicle Positioni...

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International Conference on System Theory, control, and Computing (ICSTCC)

作者： Iosif-Alin Beti Paul-Corneliu Herghelegiu Cristian-Mihai Amarandei Constantin-Florin Caruntu Department of Automatic Control and Applied Informatics Gheorghe Asachi Technical University of Iaṣi Iasi Romania Department of Computer Science and Engineering Gheorghe Asachi Technical University of Iaṣi Iasi Romania

ISBN: (数字)9798350364293

ISBN: (纸本)9798350364309

Vehicle positioning algorithms are essential for improving traffic management and safety by accurately locating vehicles in real-time, and, thus, minimizing congestion and accidents. They also support the development of advanced driver assistance systems and autonomous vehicles, relying on precise positioning data for safe navigation. One of the solutions involves using image processing algorithms, which can have two approaches. One approach is decentralized, in which each vehicle performs its own computing steps and determines its position concerning the other nearby vehicles. The second approach, proposed in this paper, is centralized, where each vehicle sends data to a server that uses cloud computing to process all the data in real-time. As such, vehicles can create a more comprehensive view of the driving conditions in the area by using either of these two approaches, which can help them anticipate potential hazards and make more informed decisions.

关键词： Cloud computing Technological innovation 5G mobile communication Image processing Scalability computer architecture Reliability theory Real-time systems Servers Autonomous vehicles

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Mixed Small Gain and Phase Theorem: A new view using Scale Relative Graphs

arXiv

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arXiv 2025年

作者： Baron-Prada, Eder Anta, Adolfo Padoan, Alberto Dörfler, Florian Austrian Institute of Technology Vienna1210 Austria Automatic Control Laboratory ETH Zurich Zürich8092 Switzerland Austrian Institute of Technology Vienna1210 Austria Department of Electrical & Computer Engineering University of British Columbia Canada Automatic Control Laboratory ETH Zürich Zürich8092 Switzerland

We introduce a novel approach to feedback stability analysis for linear time-invariant (LTI) systems, overcoming the limitations of the sectoriality assumption in the small phase theorem. While phase analysis for single-input single-output (SISO) systems is well-established, multi-input multi-output (MIMO) systems lack a comprehensive phase analysis until recent advances introduced with the small-phase theorem. A limitation of the small-phase theorem is the sectorial condition, which states that an operator’s eigenvalues must lie within a specified angle sector of the complex plane. We propose a framework based on Scaled Relative Graphs (SRGs) to remove this assumption. We derive two main results: a graphical set-based stability condition using SRGs and a small-phase theorem with no sectorial assumption. These results broaden the scope of phase analysis and feedback stability for MIMO systems. © 2025, CC BY.

关键词： MIMO systems

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UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-Generation Framework

UNav-Sim: A Visually Realistic Underwater Robotics Simulator...

引用

International Conference on Advanced Robotics (ICAR)

作者： Abdelhakim Amer Olaya Álvarez-Tuñón Halil İbrahim Uğurlu Jonas Le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical Engineering and Computer Engineering Aarhus University Aarhus Denmark EIVA a/s Skanderborg Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

Underwater robotic surveys can be costly due to the complex working environment and the need for various sensor modalities. While underwater simulators are essential, many existing simulators lack sufficient rendering quality, restricting their ability to transfer algorithms from simulation to real-world applications. To address this limitation, we introduce UNav-Sim, which, to the best of our knowledge, is the first simulator to incorporate the efficient, high-detail rendering of Unreal Engine 5 (UE5). UNav-Sim is open-source 1 1 https://***/open-airlab/UNav-Sim and includes an autonomous vision-based navigation stack. By supporting standard robotics tools like ROS, UNav-Sim enables researchers to develop and test algorithms for underwater environments efficiently.

关键词：

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Enabling IoT Continuous Connectivity in Smart Spaces 22

Enabling IoT Continuous Connectivity in Smart Spaces

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22nd International Symposium on Parallel and Distributed Computing, ISPDC 2023

作者： Andreou, Andreas Mavromoustakis, Constandinos X. Batalla, Jordi Mongay Dobre, Ciprian Markakis, Evangelos Mastorakis, George University of Nicosia University of Nicosia Research Foundation Departement of Computer Science Nicosia Cyprus Warsaw University of Technology Warsaw Poland University Politehnica of Bucharest National Institute for Research and Development in Informatics Automatic Control and Computers Bucharest Romania Hellenic Mediterranean University Department of Electrical and Computer Engineering Heraklion Greece Hellenic Mediterranean University Department of Management Science and Technology Agios Nikolaos Greece

ISBN: (纸本)9798350341270

Smart spaces are a rapidly emerging concept in technology. They result from the convergence of various novel technologies, such as the Internet of Things, Machine Learning and Artificial Intelligence, which allow for greater levels of automation and control within physical environments. The devices which are connected to the IoT network are equipped with sensors to acquire and exchange data. As a result, the IoT has transformed how we live, work, and play. However, the deployment in smart spaces is not always the best due to the issues arising from network node positioning. Therefore, we are investigating solutions to this problem with a novel approach which utilises Voronoi diagrams in conjunction with the algorithmic genetic technique. First, the initial positions of the IoT nodes will be determined by simulating a homogeneous Poisson point process in the smart space environment. Then, after dividing the area into the Voronoi cells, the genetic algorithm will optimise the position towards achieving full network coverage within the smart space. Experimental results prove the 100% network coverage within the specified area. © 2023 IEEE.

关键词： Genetic algorithms

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Security Issues in Software-Defined Networking (SDN) Environments

Security Issues in Software-Defined Networking (SDN) Environ...

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Roedunet International Conference (RoEduNet)

作者： Khirota Gorgees Yalda Diyar Jamal Hamad Nicolae Tapus Ibrahim Taner Okumus Faculty of Automatic Control and Computer Science Politehnica University of Bucharest Bucharest Romania IT department Soran Technical college Erbil Polytechnic University Kurdistan Region Erbil Iraq Computer Engineering Faculty of engineering Kahramanmaras Sutcu Imam University Kahramanmaras Turkey

ISBN: (数字)9798331540388

ISBN: (纸本)9798331540395

Software-Defined Networking (SDN) represents a significant shift in network architecture, providing exceptional programmability, flexibility, and simplified management. However, this paradigm shift introduces a unique set of security challenges that must be addressed to fully realize the potential of SDN. This paper examines the security issues in SDN environments, detailing the threats and vulnerabilities at various layers of the SDN architecture, including the control plane, data plane, and application plane. Through an extensive review of current literature, critical security challenges such as controller attacks, data plane breaches, and vulnerabilities in inter-plane communications are identified. Existing security solutions and mitigation strategies, such as authentication and authorization mechanisms, encryption techniques, and intrusion detection systems, are also explored. Furthermore, the paper discusses recent advances and emerging trends in SDN security, offering insights into ongoing research and future directions. The findings underscore the importance of robust security measures in ensuring the reliability and integrity of SDN deployments, providing a foundation for future innovation and development in this dynamic field.

关键词： Technological innovation Prevention and mitigation Intrusion detection Distributed databases Organizations Robustness Encryption Security Software defined networking computer crime

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