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AI-Powered Network Intrusion Detection: A New Frontier in Cybersecurity

AI-Powered Network Intrusion Detection: A New Frontier in Cy...

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Information Technology (ACIT)

作者： Ali Rachini Charbel Fares Maroun Abi Assaf Buroog Jamal Rida Khatoun Department of Computer Science and Information Technology Holy Spirit University of Kaslik (USEK) Jounieh Lebanon Iraqi Airways Baghdad Iraq Telecom ParisTech Paris France

Intrusion Detection systems (IDS) constitute a critical line of defense in contemporary cybersecurity efforts, designed to identify and counteract unauthorized access and malicious activities within computer networks. Leveraging the capabilities of Machine Learning (ML) algorithms, IDS endeavors to distinguish potentially harmful alterations and security breaches. this study delves into the pivotal question of algorithm selection for optimal performance. Machine Learning-based Intrusion Detection systems (ML-IDS) are designed not only to enhance overall system security but also to strike a balance between minimizing false alarms and maximizing true alarm rates. To address this, we empirically evaluate five ML algorithms and present their performance in the context of network intrusion detection. those algorithms are as follows: Random Forest achieves an impressive accuracy rate of 99.88%, Gradient Boosting demonstrates robust performance at 99.76%, AdaBoost exhibits an accuracy of 90.00%, Decision Tree boasts a noteworthy accuracy of 99.80%, and Extremely Randomized Trees demonstrate substantial proficiency with an accuracy of 99.86%. this empirical exploration enriches our comprehension of these algorithms and offers critical insights to enhance the security of computer systems.

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Multi-level Correlation Matching for Legal Text Similarity Modeling with Multiple Examples 24th

Multi-level Correlation Matching for Legal Text Similarity ...

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24th international conference on Web Information systems Engineering, WISE 2023

作者： Huang, Ting Xie, Xike Liu, Xiufeng Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Technical University of Denmark Copenhagen Denmark

ISBN: (纸本)9789819972531

Legal artificial intelligence (LegalAI) is an emerging field that leverages AI technology to enhance legal services. Similar Case Matching (SCM), which calculates the relevance between a candidate and a target case, is a critical technique in LegalAI to enable diverse legal intelligences. Existing approaches mainly rely the on single query texts or specific keywords for retrieval, yet neglected the domain complexity and multi-faceted nature of queries. thus, a multi-example matching paradigm is motivated where three inherent challenges reveal. 1) Relevance assessment across multiple examples is complex. 2) the inherent lengthy and structured property of legal documents. 3) Lacking datasets containing golden labels for multi-example-based legal text matching. To address these challenges, this paper develops a novel multi-example dataset, and a Multi-level Correlation Semantic Matching (MCSM) is devised to extract similarity between cases given multi-example inputs. the proposed multi-level scheme can be interpreted as two aspects. Firstly, we consider both content and structure correlations to evaluate the relevance. Secondly, by dividing legal documents into distinctive segments, we can hierarchically learn the intra- and inter-segment dependencies to model the long-term dependencies across components of legal documents. An attention mechanism is employed to capture the complex interconnections among these examples and enable an attentive matching aggregation of content and structure. With multiple examples, the MCSM tackles the intricate and diverse nature of legal queries, providing a comprehensive and multi-dimensional description view. Extensive experimental evaluations show that the proposed MCSM outperforms baseline methods. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence

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Diversity-seeking Swap Games in Networks 25

Diversity-seeking Swap Games in Networks

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Proceedings of the 24th international conference on Autonomous Agents and Multiagent systems

作者： Yaqiao Li Lata Narayanan Jaroslav Opatrny Yi Tian Xu Faculty of Comp. Sci. and Control Eng. Shenzhen University of Advanced Technology Shenzhen China Dept of Computer Science and Software Engineering Concordia University Montreal PQ Canada Independent Researcher Shenzhen Canada

ISBN: (纸本)9798400714269

Schelling games use a game-theoretic approach to study the phenomenon of residential segregation. We consider four global measures of diversity, and prove asymptotically tight or almost tight bounds on the price of anarchy with respect to these measures on both general graphs and common specific graphs. In addition we did simulations of our swap games.

关键词： degree of integration colorful edges

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Compositional Safety Rules for Inter-Triggering Hybrid Automata 24

Compositional Safety Rules for Inter-Triggering Hybrid Autom...

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24th ACM international conference on Hybrid systems Computation and control (HSCC)

作者： Rutledge, Kwesi J. Chou, Glen Ozay, Necmiye Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781450383394

In this paper, we present a compositional condition for ensuring safety of a collection of interacting systems modeled by inter-triggering hybrid automata (IthA). IthA is a modeling formalism for representing multi-agent systems in which each agent is governed by individual dynamics but can also interact with other agents through triggering actions. these triggering actions result in a jump/reset in the state of other agents according to a global resolution function. A sufficient condition for safety of the collection, inspired by responsibility-sensitive safety, is developed in two parts: self-safety relating to the individual dynamics, and responsibility relating to the triggering actions. the condition relies on having an over-approximation method for the resolution function. We further show how such over-approximations can be obtained and improved via communication. We use two examples, a job scheduling task on parallel processors and a highway driving example, throughout the paper to illustrate the concepts. Finally, we provide a comprehensive evaluation on how the proposed condition can be leveraged for several multi-agent control and supervision examples.

关键词： Multi agent systems

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An Effective Voltage control Approach for Distribution systems with High Penetration of Renewable Energy Resources

An Effective Voltage Control Approach for Distribution Syste...

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international Middle-East Power System conference, MEPCON

作者： Ahmed Bedawy Yutaka Sasaki Yoshifumi Zoka Naoto Yorino Department of Electrical Engineering Faculty of Engineering South Valley University Qena Egypt Graduate School of Advanced Science and Engineering Hiroshima University Higashi-Hiroshima Japan National Institute of Technology(KOSEN) Kure College Kure Japan

Voltage control becomes a crucial challenge as the penetration of renewable energy sources (RESs) increases in distribution systems. the intermittent and variable nature of RESs like solar and wind power can lead to several voltage problems, which can have detrimental effects on the stability and reliability of the distribution networks. this paper introduces a promising cooperative voltage control (CVC) technique for distribution systems with renewable-based distributed generations (DGs). the objectives of the CVC technique are regulating the voltages within the standard limits, maximizing the DGs’ power generation, and decreasing the number of control operations of the voltage control devices in the distribution systems. the proposed CVC consists of two control stages; the DG prosumers perform the first control stage, whereas the step voltage regulators manage the second stage. this paper describes only the first stage, the DG prosumers control stage, using a proposed target nodal voltage control (TNVC) approach. the TNVC approach aims to maximize the DG’s powers and mitigate the overvoltage problem using the optimized nodal voltage as a control signal. Our approach includes a unique feature that effectively reduces control performance degradation caused by infrequent data exchange among agents. the simulation results confirm the control performance of the proposed TNVC approach in achieving the desired objectives.

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LeCAR: Leveraging Context for Enhanced Automotive Specification Retrieval

LeCAR: Leveraging Context for Enhanced Automotive Specificat...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Kuan-Wei Wu Tz-Huan Hsu Yen-Hao Huang Yi-Shin Chen Ho-Lung Wang Bing-Jing Hsieh Chi-Hung Hsu Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan R.O.C. Department of Computer Science National Tsing Hua University Hsinchu Taiwan R.O.C. AUO Corporation Taiwan R.O.C.

In the domain of automotive manufacturing, specification documents represent intricate descriptions detailing every aspect of a product, design, or service. Conventionally, these specifications demand the deployment of expert teams to manually identify crucial data from the extensive documentation. the need to automate the extraction of candidate information from these documents is increasingly pressing in this industry. this research encounters two central challenges: Firstly, the queries for the specifications input by users are typically concise and ambiguous; secondly, not every word in a query carries the same significance. In response to these challenges, we propose LeCAR, which exploits contextual data to clarify query sentences and concentrate the search scope. Our experiments validate that the proposed method outperforms existing techniques that employ pre-trained language models, all without necessitating additional training data.

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Sequence Separation-Based Modeling of Denoised Implicit Feedback Behavior

Sequence Separation-Based Modeling of Denoised Implicit Feed...

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IEEE international conference on Information Reuse and Integration (IRI)

作者： Shibo Ji Bo Yang Dept. of Computer Science Bowie State University Bowie United States

this paper analyzes Click-through rate prediction (CTR), a critical component within recommender systems aiming to forecast the personalized probability of user-item click events. Recent advancements have shown that incorporating user behavior sequences into CTR prediction models can yield significant performance improvements. However, CTR prediction models primarily rely on implicit positive feedback, such as clicks, from user-item interactions while overlooking the negative feedback, such as unclicks. Moreover, the implicit feedback obtained from users often contains noisy data, which hampers the accuracy of user interest modeling. As a solution, we propose a novel framework for estimating click-through rates, leveraging the modeling of Denoised Implicit feedback Behavior (DIB). DIB integrates the complete implicit feedback behavior of users into the click-through rate estimation task and aims to mitigate the influence of noise in implicit feedback on the model’s accuracy. through extensive experiments conducted on real-world, largescale datasets, we demonstrate that DIB outperforms state-of-the-art models by a substantial margin, resulting in an approximate 5% improvement in Area Under the Curve (AUC).

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RaFID: A Lightweight Approach to Radio Frequency Interference Detection in Time Domain using LSTM and Statistical Analysis

RaFID: A Lightweight Approach to Radio Frequency Interferenc...

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international conference on Mobile Data Management

作者： Luke A. Smith Vishesh Kumar Tanwar Maciej Zawodniok Sanjay Madria Department of Computer Science Missouri University of Science and Technology Rolla USA Department of Electrical and Comuter Engineering Missouri University of Science and Technology Rolla USA

Recently, the utilization of Radio Frequency (RF) devices has increased exponentially over numerous vertical platforms. this rise has led to an abundance of Radio Frequency Interference (RFI) continues to plague RF systems today. the continued crowding of the RF spectrum makes RFI efficient and lightweight mitigation critical. Detecting and localizing the interfering signals is the foremost step for mitigating RFI concerns. Addressing these challenges, we propose a novel and lightweight approach, namely RaFID, to detect and locate the RFI by incorporating deep neural networks (DNNs) and statistical analysis via batch-wise mean aggregation and standard deviation (SD) calculations. RaFID investigates the generation of an expected signal using DNNs within the time domain. We performed the statistical analysis to compare our generated expected signal with the received signal to detect the existence of interference and determine interference frequency. Experimental results show that signal estimation is accurate, with a mean squared error of 0.012 and an average run-time of 0.5 seconds.

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Relaunch of an Industrial Laser control Software – Influence of Perceived Competence on Usability and Acceptance 24th

Relaunch of an Industrial Laser Control Software – Influenc...

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24th international conference on Human-computer Interaction, HCII 2022

作者： Schwaiger, Lena Radziwill, Manuel Wagner-Hartl, Verena Faculty Industrial Technologies Furtwangen University Campus Tuttlingen Kronenstraße 16 Tuttlingen78532 Germany TRUMPF Laser GmbH Volmerstr. 10 Berlin12489 Germany

ISBN: (纸本)9783031064166

Usability and user acceptance play an important role in the development of new systems. In the present study, a new generation of laser control software from TRUMPF Laser GmbH was evaluated regarding its usability and acceptance in a formative remote usability test. Because change processes, such as software relaunches, can lead to a change in the subjectively perceived competence of users, the study focusses on evaluating differences between two competence groups ("low competence" and "high competence" in operating the current laser control software;N = 35). Basic interactions on the prototype of the new generation of laser control software were tested in seven tasks. the results of the study show that the two competence groups do not differ significantly in their performance or acceptance. However, it could also be shown that users with different prior knowledge should be supported differently when transitioning to the new software. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

关键词： Software testing

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Implementation of Individual Educational Trajectories in the Master's Programs

Implementation of Individual Educational Trajectories in the...

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Anna V. Busygina Alexandra Yu. Kim Talgat R. Gazizov Department of Television and Control Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Department of Telecommunications and Fundamentals of Radio engineering Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

the paper discusses the existing approaches to considering individual educational trajectories and ways of their practical implementation in the activities of educational organizations. To develop a graduate's competence model, we present a method that is based on an analysis of industry requirements and professional standards. the proposed method was used to develop an up-to-date list of professional competencies of a radio engineer (master's degree). Moreover, a model of an educational program was developed that supports individual educational trajectories for an enlarged group of training areas and specialties 11.04.00 “Electronics, Radio engineering, and Communication systems”. this model describes the volume of the main structural elements, the content of blocks and modules of the educational program, and tools for building an individual educational trajectory, taking into account the current legislation. the proposed approach to building an individual educational trajectory is employed from the moment a student enters the educational process until the moment he enters the state final attestation.

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