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Big Data, Machine Learning, and Applications 1

引用

丛书名： Lecture Notes in Electrical engineering

1000年

作者： Malaya Dutta Borah Dolendro Singh Laiphrakpam Nitin Auluck Valentina Emilia Balas

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The sensitivity of language models and humans to Winograd schema perturbations 58

The sensitivity of language models and humans to Winograd sc...

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58th Annual Meeting of the Association for Computational Linguistics, ACL 2020

作者： Abdou, Mostafa Ravishankar, Vinit Barrett, Maria Belinkov, Yonatan Elliott, Desmond Søgaard, Anders Department of Computer Science University of Copenhagen Department of Informatics University of Oslo John A. Paulson School of Engineering and Applied Sciences Harvard University Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology

ISBN: (纸本)9781952148255

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of commonsense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues. © 2020 Association for Computational Linguistics

关键词： Computational linguistics

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Micro Frontend Architecture

Micro Frontend Architecture

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Vladimír Kunštnár Patrik Podhorský Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350349153

ISBN: (纸本)9798350349160

Our paper focuses on software architectures in web application environments, specifically their evolution in client interface development. We will focus on implementing client interfaces with Micro Frontends, which are based on the principle of microservices. We will explain how to implement this architecture and integrate multiple Micro Frontends into a single web application. Additionally, we will evaluate the efficiency and performance of Micro Frontends compared to monolithic client interfaces. Finally, we will compare the performances of the two architectures and propose an improvement to implementing Micro Frontends. Our improvement proposal will explain how Micro Frontends can be implemented to create more modular and reusable user interfaces using well-known methodologies and design patterns.

关键词： Technological innovation Software architecture Design methodology Microservice architectures computer architecture User interfaces Proposals

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Are you (Google) home? Detecting users’ presence through traffic analysis of smart speakers 4

Are you (Google) home? Detecting users’ presence through tr...

引用

4th Italian Conference on Cyber Security, ITASEC 2020

作者： Caputo, D. Verderame, L. Merlo, A. Ranieri, A. Caviglione, L. Computer Security Lab Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Italy Institute for Applied Mathematics and Information Technologies National Research Council of Italy Italy

Smart speakers and voice-based virtual assistants are core building blocks of modern smart homes. For instance, they are used to retrieve information, interact with other devices, and command a variety of Internet of Things (IoT) nodes. To this aim, smart speakers and voice-based assistants typically take advantage of cloud architectures: vocal commands of the user are sampled, sent through the Internet to be processed and transmitted back for local execution, e.g., to perform an automation task or activate an IoT device. Even if privacy and security is enforced by means of encryption, features of the traffic, such as the throughput, the size of protocol data units or the IP addresses, can leak important information about the habits of the users as well as the number and the type of IoT nodes deployed. In this perspective, the paper showcases risks of machine learning techniques to develop black-box models to automatically classify traffic and implement privacy leaking attacks. We prove that such traffic analysis allows to detect the presence of a person in a house equipped with a Google Home device, even if the same person does not interact with the smart device. Experimental results collected in a realistic scenario are presented and possible countermeasures are discussed. Copyright © 2020 for this paper by its authors.

关键词： Internet of things

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Using Web Server Logs to Identify and Comprehend Anomalous User Activity

Using Web Server Logs to Identify and Comprehend Anomalous U...

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International Conference on Telecommunications (ConTEL)

作者： Lenka Benova Ladislav Hudec Faculty of Informatics and Information Technologies Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

This research paper presents a study for identifying user anomalies in large datasets of web server requests. Using a cybersecurity company's network of web servers as a case study, we propose a technique for analyzing user activity in NGINX logs. The proposed method does not require a labeled dataset and is capable of efficiently identifying different user anomalies in large datasets with millions of daily requests. The results of the analysis provided a deeper understanding of user behavior when seeking updates through web requests and aided in interpreting the findings. Clustering the anomalies helped to produce typical clusters and further supported the interpretation of the results. This work provides valuable insights into user behavior in web server networks and highlights the importance of efficient anomaly detection techniques in large datasets. The findings have potential real-world applications in the field of cybersecurity, particularly in providing network security analysts with an automated and more objective approach to threat analysis. This study showcases the importance of automated methods for analyzing user activity in web server networks and provides a more objective and efficient approach to detecting user anomalies in large datasets. This approach contributes to the development of more effective and precise cybersecurity systems, ultimately improving the protection of network infrastructures from malicious attacks.

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Development of a remote access control laboratory using xPC Target and virtual reality modeling language

Development of a remote access control laboratory using xPC ...

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2010 International Conference on Intelligent and Advanced Systems, ICIAS 2010

作者： Lee, Ka Wai Truong, Nguyen-Vu Rhodes, Brad McLaren, James Wang, Liuping School of Electrical and Computer Engineering RMIT University Melbourne Australia Institute of Applied Mechanics and Informatics Vietnam Academy of Science and Technology Ho Chi Minh city Viet Nam

ISBN: (纸本)9781424466238

In this paper, we present the development of a remote access control laboratory at RMIT University which allows users to perform experiments on real hardware through the internet. The web based system consists of an internal server linked to an xPC Target real time machine which communicates with a DC Motor control module via relevant DAQ interfaces. In this set up, we attempt to replace the expensive setup of IP camera or video server by a realistic virtual reality model. This enhances the experiment's flexibility and reduces the cost of implementation while providing more interactive visualization and reality feeling of the real system to the users. In addition, the software also features with multiple login database system allowing students to observe or perform experiments under supervision remotely from the admin server.

关键词： DC motors

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Estimating Mutual Information for Link Adaptation in Generalized Spatial Modulation Systems with Neural Networks 26

Estimating Mutual Information for Link Adaptation in General...

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on Systems, Communications, and Coding, SCC 2023

作者： Bailon, Daniel Nicolas Kühn, Volker Shavgulidze, Sergo Freudenberger, Jürgen Institute for System Dynamics HTWG Konstanz University of Applied Sciences Germany Institute of Communications Engineering Faculty of Computer Science and Electrical Engineering University of Rostock Germany Faculty of Informatics and Control Systems Georgian Technical University Georgia Germany

ISBN: (纸本)9783800760510

Spatial modulation (SM) is a low-complexity multiple-input/multiple-output transmission technique that combines index modulation and quadrature amplitude modulation for wireless communications. In this work, we consider the problem of link adaption for generalized spatial modulation (GSM) systems that use multiple active transmit antennas simultaneously. Link adaption algorithms require a real-time estimation of the link quality of the time-variant communication channels, e.g., by means of estimating the mutual information. However, determining the mutual information of SM is challenging because no closed-form expressions have been found so far. Recently, multilayer feedforward neural networks were applied to compute the achievable rate of an index modulation link. However, only a small SM system with two transmit and two receive antennas was considered. In this work, we consider a similar approach but investigate larger GSM systems with multiple active antennas. We analyze the portions of mutual information related to antenna selection and the IQ modulation processes, which depend on the GSM variant and the signal constellation. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Complex networks

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RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

RerrFact: Reduced Evidence Retrieval Representations for Sci...

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2022 Workshop on Scientific Document Understanding, SDU 2022

作者： Rana, Ashish Khanna, Deepanshu Ghosal, Tirthankar Singh, Muskaan Singh, Harpreet Rana, Prashant Singh School of Business Informatics and Mathematics University of Mannheim Germany Department of Electronics & Communication Engineering Thapar Institute of Engineering & Technology India Institute of Formal and Applied Linguistics Faculty of Mathematics and Physics Charles University Czech Republic Department of Computer Science & Engineering Thapar Institute of Engineering & Technology India

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. © 2022 Copyright for this paper by its authors.

关键词： Abstracting

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dAUTObase: Mining gems on autoimmune diseases utilizing web visualization technologies

dAUTObase: Mining gems on autoimmune diseases utilizing web ...

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10th International Conference on Information Technology and Applications in Biomedicine: Emerging Technologies for Patient Specific Healthcare, ITAB 2010

作者： Antoniou, Dimitris I. Georgitsi, Marianthi A. Gkantouna, Vassiliki A. Patrinos, George P. Poulas, Konstantinos A. Tsakalidis, Athanasios K. Tzimas, Giannis E. Viennas, Emmanouil S. Technological Educational Institute of Messolonghi Department of Applied Informatics in Management and Finance Messolonghi 30200 Greece Department of Computer Engineering and Informatics Faculty of Engineering University of Patras Patras 26504 Greece Department of Pharmacy School of Health Sciences University of Patras Patras 26504 Greece

ISBN: (纸本)9781424465606

An autoimmune disorder is a condition that occurs when the immune system mistakenly attacks and destroys healthy body parts. The epidemiology of these diseases is a matter of study and discussion, with many published results worldwide. We have critically collected more than 100 publications, analyzing autoimmune diseases' frequencies in various populations and ethnic groups. Simultaneously, we developed a web application in order to host the collected data. By utilizing Microsoft Silverlight technology, we have built a multimedia web frontend, in order to support a high level visualization of the data and the mining process. In parallel we continue the data mining process and the update of our database. © 2010 IEEE.

关键词： Data visualization

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