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10th International Conference on Web Research, ICWR 2024

作者： Nazarizadeh, Ali Samavati, Taha Mansouri, Sina Sayyadpour, Minoo Islamic Azad University Central Tehran Branch Department of Computer Engineering Tehran Iran Iran University of Science and Technology Faculty of Computer Engineering Tehran Iran K. N. Toosi University of Technology Faculty of Computer Engineering Tehran Iran Kharazmi University Dept. Math and Computer Science Tehran Iran

ISBN: (纸本)9798350394986

Today, data is more valuable to us than gold. When observing the environment, a substantial amount of data, particularly textual information, can be identified, tagged, prepared, and published in the form of a corpus or datasets. The primary objective of our paper is to gather, prepare, tag, and develop a vast dataset of Fidibo users' opinions regarding educational content and e-books. This dataset enables in-dept. analysis of emotions and opinion mining, particularly within the educational content realm. A common flaw in nearly all similar datasets in the Farsi language is their restriction to user opinions on services and products available on online platforms. The dataset we refer to as LDPSA (A Large Dataset of Persian Sentiment Analysis) offers several advantages over comparable datasets in the Persian language. Notably, this dataset consists of 253,368 comments, each categorized into 5 classes. LDPSA represents the sole extensive Iranian dataset suitable for scrutinizing educational content and e-books. Moreover, significant insights were gleaned from data analysis. For example, during the COVID-19 pandemic, Iranian individuals dedicated more time to studying and engaging with educational platforms significantly. Nearly 80% of users expressed favorable opinions concerning the informational materials available on the Fidibo website. Users' inclination towards utilizing audio books has escalated, along with other analysis referenced in the paper. © 2024 IEEE.

关键词： Sentiment analysis

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Systematic Literature Review of Security Control Assessment Challenges 12

Systematic Literature Review of Security Control Assessment ...

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12th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2022

作者： Abu Othman, Noor Ashitah Norman, Azah Anir Mat Kiah, Miss Laiha Universiti Malaya Faculty of Comp. Science & Information Technology Kuala Lumpur Malaysia Universiti Teknologi MARA Faculty of Computer & Math. Sciences Melaka Malaysia Universiti Malaya Faculty of Computer Science & Information Technology Dept. of Information Systems Kuala Lumpur Malaysia Universiti Malaya Faculty of Computer Science & Information Technology Dept. of Computer System &Technology Kuala Lumpur Malaysia

ISBN: (数字)9781665483391

ISBN: (纸本)9781665483391

The advancement of cybersecurity has called for active effective information security management. Security control (SC) assessment must be empowered to ensure that security implementation is effective and provides expected protection. Lack of comprehensive literature analysis on SC assessment compared to risk and threats assessments are concerning. This research aims to systematically review the trends of SC assessment by identifying, categorizing and analyzing the challenges and available solutions of SC assessment. 34 articles were qualitatively selected with a definite contribution in SC assessment. These articles were reviewed using thematic analysis according to Theme 1: Assessment Challenges, and Theme 2: Proposed Solution. Findings from each theme are systematically categorized to answer research questions. The results of this review are significant in identifying the issues and areas of improvement for future research and can serve as the baseline for SC assessment characteristics. © 2022 IEEE.

关键词： Cybersecurity

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Ease Delivery: A Next-Gen Delivery Management Solution 2

Ease Delivery: A Next-Gen Delivery Management Solution

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Rathore, Saurabh Pratap Singh Kaushik, Priyanka Poonia, Minakshi Sikarwar, Shailendra Singh Singh, Dipti Jain, Deepak Management & Research Icapsr New Delhi India Chandigarh India Madhav Institute of Technology & Science Dept. of Eng. Math's & Computing Gwalior India Jiwaji University Pgv College Dept. of Computer Science Gwalior India Madhav University Dept. of Management Abu Road India Sandip University Iqac Madhubani India

ISBN: (纸本)9798350360523

Delivery Tracker"is a comprehensive delivery management system designed to streamline package delivery services within an educational institution. The primary goal of this initiative is to simplify the process for students by allowing them to effortlessly create, monitor, and confirm the delivery of their packages. Leveraging cutting-edge technologies such as Express, Next. js, React, MongoDB, Paytm UPI, and Socket. io, this system promises efficiency and convenience. With "Delivery Tracker,"senders can initiate the delivery process by furnishing recipient details, specifying package attributes, and supplying essential information. Each package is allocated a unique identifier, and the system promptly notifies a nearby student who is available to collect and deliver the package to the intended recipient's address. Real-time tracking capabilities ensure that deliveries are not only prompt but also secure. Upon successful delivery, recipients can confirm the receipt of their package through the system. Furthermore, the system boasts an integrated payment gateway, streamlining the transaction process. "Delivery Tracker"represents a significant leap forward in enhancing the package delivery experience within educational institutions, prioritizing simplicity, efficiency, and transparency. © 2024 IEEE.

关键词： Efficiency

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Transparent Neighborhood Approximation for Text Classifier Explanation by Probability-Based Editing

Transparent Neighborhood Approximation for Text Classifier E...

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International Conference on Data science and Advanced Analytics (DSAA)

作者： Yi Cai Arthur Zimek Eirini Ntoutsi Gerhard Wunder Dept. of Math. and Comp. Science Freie Universität Berlin Berlin Germany Dept. of Math. and Comp. Science University of Southern Denmark Odense Denmark Research Institute CODE Universität der Bundeswehr München Munich Germany

ISBN: (数字)9798350364941

ISBN: (纸本)9798350364958

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature of texts, thereby improving the quality of explanations. However, the deployed generators are usually implemented via neural networks and lack inherent explainability, sparking arguments over the transparency of the explanation process itself. To address this limitation while preserving neighborhood quality, this paper introduces a probability-based editing method as an alternative to black-box text generators. This approach generates neighboring texts by implementing manipulations based on in-text contexts. Substituting the generator-based construction process with recur-sive probability-based editing, the resultant explanation method, XPROB (explainer with probability-based editing), exhibits com-petitive performance according to the evaluation conducted on two real-world datasets. Additionally, XPROB's fully transparent and more controllable construction process leads to superior stability compared to the generator-based explainers.

关键词： Text categorization Neural networks Prototypes Process control Closed box Data science Market research Generators Stability analysis

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TRANSPARENT NEIGHBORHOOD APPROXIMATION FOR TEXT CLASSIFIER EXPLANATION BY PROBABILITY-BASED EDITING

arXiv

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arXiv 2024年

作者： Cai, Yi Ntoutsi, Eirini Zimek, Arthur Wunder, Gerhard Dept. of Math. and Comp. Science Freie Universität Berlin Berlin Germany Research Institute CODE Universität der Bundeswehr München Munich Germany Dept. of Math. and Comp. Science University of Southern Denmark Odense Denmark

Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to improve synthetic instance quality, especially for explaining text classifiers. These approaches overcome the challenges in neighborhood construction posed by the unstructured nature of texts, thereby improving the quality of explanations. However, the deployed generators are usually implemented via neural networks and lack inherent explainability, sparking arguments over the transparency of the explanation process itself. To address this limitation while preserving neighborhood quality, this paper introduces a probability-based editing method as an alternative to black-box text generators. This approach generates neighboring texts by implementing manipulations based on in-text contexts. Substituting the generator-based construction process with recursive probability-based editing, the resultant explanation method, XPROB (explainer with probability-based editing), exhibits competitive performance according to the evaluation conducted on two real-world datasets. Additionally, XPROB’s fully transparent and more controllable construction process leads to superior stability compared to the generator-based explainers. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Enhancing MQTT Fault Tolerance and Resilience Against Broker Failures and DoS Attacks with Decentralized Blockchain Architecture

Enhancing MQTT Fault Tolerance and Resilience Against Broker...

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Intelligent Cybersecurity Conference (ICSC)

作者： Elisée Toé Fehmi Jaafar Dept. of Math and Computer Science University of Quebec at Chicoutimi Quebec Canada

ISBN: (数字)9798350354775

ISBN: (纸本)9798350354782

The MQTT (Message Queuing Telemetry Transport) protocol has become the standard for IoT (Internet of Things) communication due to its lightweight nature and efficiency. However, its centralized architecture, with a single broker managing message flow, introduces vulnerabilities to failures and Denial of Service (DoS) attacks. This paper proposes a novel decentralized architecture for MQTT systems that leverages blockchain technology and smartcontracts to enhance resilience against broker failures and DoS attacks. Our approach utilizes a three-layer architecture comprising clients, cluster brokers, and a blockchain-based system to securely manage broker transitions and ensure continuous service. Key contributions of this work include a decentralized failover mechanism, secure recording and access control via blockchain, and a robust algorithm for improving system resilience. The use of blockchain and smartcontracts provides a tamper-proof and transparent method for managing broker transitions and access control, enhancing the security and trustworthiness of the system. These findings suggest that integrating blockchain technology with MQTT can mitigate some of the inherent vulnerabilities of traditional centralized architectures, paving the way for more resilient IoT communications.

关键词： Access control Transport protocols Fault tolerance Blockchains Recording Internet of Things Telemetry Standards Resilience Testing

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Creating UML Class Diagrams with General-Purpose LLMs

Creating UML Class Diagrams with General-Purpose LLMs

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IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT)

作者： Mina Shehata Blaire Lepore Hailey Cummings Esteban Parra Dept. of Math Computer Science and Data Science Belmont University Nashville TN USA

ISBN: (数字)9798331528485

ISBN: (纸本)9798331528492

General-purpose large language models (LLMs) have become a versatile tool in software development and maintenance. These models offer support in tasks such as under-standing, writing, and summarizing code. While these LLMs can generate code quickly their use in software modeling is relatively under-explored. This research aims to study ChatGPT's ability to generate class diagrams from source code. To do this, we engineered a prompt that takes in source code to create a UML class diagram for that system. We used this prompt to create class diagrams for 40 systems and assessed the diagrams on their correctness and structure. Our results show that ChatGPT creates class diagrams that accurately capture 90% of the classes and their attributes and 66 % of the associations. While ChatGPT performed nearly flawlessly for smaller projects, the diagrams for larger projects had more issues. We conclude that ChatGPT is best utilized as a complementary tool rather than the sole resource for software modeling and maintenance.

关键词： Visualization Codes Source coding Scalability Unified modeling language Writing Chatbots Software systems Maintenance Software development management

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ON GRADIENT-LIKE EXPLANATION UNDER A BLACK-BOX SETTING: WHEN BLACK-BOX EXPLANATIONS BECOME AS GOOD AS WHITE-BOX

arXiv

引用

arXiv 2023年

作者： Cai, Yi Wunder, Gerhard Dept. of Math. and Comp. Science Freie Universität Berlin Berlin Germany

Attribution methods shed light on the explainability of data-driven approaches such as deep learning models by uncovering the most influential features in a to-be-explained decision. While determining feature attributions via gradients delivers promising results, the internal access required for acquiring gradients can be impractical under safety concerns, thus limiting the applicability of gradient-based approaches. In response to such limited flexibility, this paper presents GEEX (gradient-estimation-based explanation), a method that produces gradient-like explanations through only query-level access. The proposed approach holds a set of fundamental properties for attribution methods, which are math.matically rigorously proved, ensuring the quality of its explanations. In addition to the theoretical analysis, with a focus on image data, the experimental results empirically demonstrate the superiority of the proposed method over state-of-the-art black-box methods and its competitive performance compared to methods with full access. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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An Algorithmic Framework for Constructing Multiple Decision Trees by Evaluating Their Combination Performance Throughout the Construction Process

An Algorithmic Framework for Constructing Multiple Decision ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Keito Tajima Naoki Ichijo Yuta Nakahara Koshi Shimada Toshiyasu math.shima Dept. of Pure and Applied Math. Waseda University Tokyo Japan Center of Data Science Waseda University Tokyo Japan

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Predictions using a combination of decision trees are known to be effective in machine learning. Typical ideas for constructing a combination of decision trees for prediction are bagging and boosting. Bagging independently constructs decision trees without evaluating their combination performance and averages them afterward. Boosting constructs decision trees sequentially, only evaluating a combination performance of a new decision tree and the fixed past decision trees at each step. Therefore, neither method directly constructs nor evaluates a combination of decision trees for the final prediction. When the final prediction is based on a combination of decision trees, it is natural to evaluate the appropriateness of the combination when constructing them. In this paper, we propose a new algorithmic framework that constructs decision trees simultaneously and evaluates their combination performance throughout the construction process. Our framework repeats two procedures. In the first procedure, we construct new candidates of combinations of decision trees to find a proper combination of decision trees. In the second procedure, we evaluate each combination performance of decision trees under some criteria and select a better combination. To confirm the performance of the proposed framework, we experiment with synthetic and benchmark data.

关键词： Benchmark testing Boosting Prediction algorithms Decision trees Bagging Cybernetics

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Managing the Fall Risk in Smart Homes Using Monocular Cameras, Ambient Sensors and an Interactive Social Robot

Managing the Fall Risk in Smart Homes Using Monocular Camera...

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2022 IEEE International Conference on Blockchain, Smart Healthcare and Emerging Technologies, SmartBlock4Health 2022

作者： Lachaux, Killian Maitre, Julien Bouchard, Kevin Lussier, Maxime Bottari, Carolina Couture, Melanie Bier, Nathalie Giroux, Sylvain Gaboury, Sebastien Universite du Quebec À Chicoutimi Dept. of Math. and Computer Science Chicoutimi Canada Universite de Montreal Dept. of Medicine Montreal Canada School of Rehabilitation Universite de Montreal Montreal Canada Center for Research and Expertise in Social Gerontology CIUSSS West-Central Montreal Côte Saint-Luc Canada Universite de Sherbrooke Dept. of Computer Science Sherbrooke Canada

ISBN: (纸本)9781665460149

One of the biggest challenges faced by older adults is the risk of falling. Falls and the fear of falling cause serious physical and mental health issues. They are also a very impacting factor on the independence of the individual. This paper approaches the problem through the view of a population afflicted by the self-neglect syndrome. Therefore, we propose the NAOREA system, using monocular cameras, various ambient sensors and a humanoid social robot, to manage the risk of falling through a step-by-step process. This system achieved up to 100% detection rates of obstacles for proactive fall control in a real-home setting, as well as a 87% detection rates of falls after they happened. © 2022 IEEE.

关键词： Fall detection

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