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Question-Answering Pair Matching Based on Question Classification and Ensemble Sentence Embedding

computer Systems science & Engineering 2023年第9期46卷 3471-3489页

作者： Jae-Seok Jang Hyuk-Yoon Kwon Department of Computer Science and Engineering Seoul National University of Science and TechnologySeoul01811Korea Department of Industrial Engineering/Graduate School of Data Science Seoul National University of Science and TechnologySeoul01811Korea

Question-answering(QA)models find answers to a given *** necessity of automatically finding answers is increasing because it is very important and challenging from the large-scale QA data *** this paper,we deal with the QA pair matching approach in QA models,which finds the most relevant question and its recommended answer for a given *** studies for the approach performed on the entire dataset or datasets within a category that the question writer manually *** contrast,we aim to automatically find the category to which the question belongs by employing the text classification model and to find the answer corresponding to the question within the *** to the text classification model,we can effectively reduce the search space for finding the answers to a given ***,the proposed model improves the accuracy of the QA matching model and significantly reduces the model inference ***,to improve the performance of finding similar sentences in each category,we present an ensemble embedding model for sentences,improving the performance compared to the individual embedding *** real-world QA data sets,we evaluate the performance of the proposed QA matching *** a result,the accuracy of our final ensemble embedding model based on the text classification model is 81.18%,which outperforms the existing models by 9.81%∼14.16%***,in terms of the model inference speed,our model is faster than the existing models by 2.61∼5.07 times due to the effective reduction of search spaces by the text classification model.

关键词： Question-answering text classification model data augmentation text embedding

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Target Detection Algorithm in Foggy Scenes Based on Dual Subnets

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computers, Materials & Continua 2024年第2期78卷 1915-1931页

作者： Yuecheng Yu Liming Cai Anqi Ning Jinlong Shi Xudong Chen Shixin Huang School of Computer Science Jiangsu University of Science and TechnologyZhenjiang212100China Information Department China Merchants Heavy IndustryNantong226116China

Under the influence of air humidity,dust,aerosols,etc.,in real scenes,haze presents an uneven *** this way,the image quality and contrast will *** this case,It is difficult to detect the target in the image by the universal detection ***,a dual subnet based on multi-task collaborative training(DSMCT)is proposed in this ***,in the training phase,the Gated Context Aggregation Network(GCANet)is used as the supervisory network of YOLOX to promote the extraction of clean information in foggy *** the test phase,only the YOLOX branch needs to be activated to ensure the detection speed of the ***,the deformable convolution module is used to improve GCANet to enhance the model’s ability to capture details of non-homogeneous ***,the Coordinate Attention mechanism is introduced into the Vision Transformer and the backbone network of YOLOX is *** this way,the feature extraction ability of the network for deep-level information can be *** experimental results on artificial fog data set FOG_VOC and real fog data set RTTS show that the map value of DSMCT reached 86.56%and 62.39%,respectively,which was 2.27%and 4.41%higher than the current most advanced detection *** DSMCT network has high practicality and effectiveness for target detection in real foggy scenes.

关键词： Target detection fog target detection YOLOX twin network multi-task learning

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Improving DNS Server Resilience Against DDoS Attacks Through Anycast Routing 6

Improving DNS Server Resilience Against DDoS Attacks Through...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Dzaky, Julian Daffa Saputra, Jason Adrianto Singale, Nicodemus Nathanael Saputri, Hanis Amalia Sari, Azani Cempaka Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

Domain Name System (DNS) is the critical backbone of the internet, translating human-readable domain names into machine-readable IP addresses. DDoS attacks are a type of distributed denial-of-service (DDoS) attacks where the attacker attempts to make a victim's resources unavailable by flooding the server with bots that have high bandwidth which results in a slow server or outright a server shutdown. Anycast routing is a crucial technology in internet services such as DNS servers. This research paper aims to test the features of anycast routing in DNS to evaluate its resilience against DDoS attacks. A quantitative and Black-box technique approach is used, using experiments that simulate DDoS attacks on DNS servers in various scenarios. data was collected from nine popular websites in Indonesia that use DNS servers with and without Anycast routing. The results showed that Anycast routing, especially with Cloudflare's WARP feature, improved DNS resilience against DDoS attacks by maintaining service availability with an average response time of 172.44 ms for before the attack and 117.66 ms after the DDoS attack. However, this experiment also found the need to use additional protections, such as WARP, to effectively manage and mitigate DDoS attacks. This research also emphasizes the importance of combining Anycast routing with additional protection to improve the resilience of DNS servers against external threats such as DDoS attacks. © 2024 IEEE.

关键词： Denial-of-service attack

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Web-Based Application for GUI programming Learning Through Reverse Engineering Method

Web-Based Application for GUI Programming Learning Through R...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Tunardi, Yovita Yulianto, Budi Layona, Rita Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331511470

GUI (Graphical User Interface) programming is the process of developing (coding) a graphical interface application. GUI-based application, which replaces TUI (Textual User Interface), allows users to interact with a program interactively. computer science students are required to master in creating GUI-based applications because of industry needs. Despite the importance of mastering GUI programming, students still have obstacles when learning it. Students who fail the GUI programming course will potentially experience difficulties in two things. First, it is difficult to finish the final project. Second, it is difficult to get a programmer job after graduation. This research aims to develop a web-based application for GUI programming learning using a reverse engineering method that can produce sourcecode based on GUI designed by users. The application development method used is the Waterfall Method with the stages of Requirement Collection, Analysis & Design, Development & Testing, Implementation, and Maintenance. The conclusion of this research is that the application helps users understand GUI programming learning through the reverse engineering method. © 2024 IEEE.

关键词： Students

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Exploring the Ethical Landscape: Safeguarding data Privacy in the Realm of Virtual Reality

Exploring the Ethical Landscape: Safeguarding Data Privacy i...

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2024 International Conference on Informatics Electrical and Electronics, ICIEE 2024

作者： Tionanda, Leandro Satyawira Sin, Bun Jak Achmad, Said Sutoyo, Rhio School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331519216

As VR becomes more immersive and widespread, the fact that it collects vast amounts of personal and sensitive data from users, have raised concerns about how this information is managed and protected. This paper delves into the complex landscape of VR, focusing on the collection, management, and protection of user data. Through a systematic literature review using the PRISMA framework filtered by inclusion and exclusion criteria which are based on our research questions, this research identifies key privacy concerns, evaluates current security measures, and examines the ethical implications of VR usage. Our findings reveal several critical issues: the potential for misuse of personal data, insufficient transparency in data handling practices, and the lack of robust security protocols to safeguard user information. We also explore the ethical dimensions of VR, such as the potential for manipulation of user behavior and the responsibilities of VR companies to protect user welfare. To address these challenges, we recommend the development of comprehensive privacy policies, implementation of more advanced security measures, and establishment of clear ethical guidelines. Additionally, it is essential for VR companies to prioritize privacy and ethic in their products. By prioritizing these aspects, VR industry can create a safer and more trustworthy virtual environment for users which would in turn ensure a secure and ethical future for this technology. This paper aims to provide insights and practical recommendations for policymakers, developers, and researchers, encouraging a collaborative effort to enhance data privacy and ethical standards in the rapidly evolving field of Virtual Reality. © 2024 IEEE.

关键词： Differential privacy

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Comparison of Different Machine Learning Algorithms for Predicting Loan Risk Categories

Comparison of Different Machine Learning Algorithms for Pred...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Sudjono, Salomo Hendrian Adrian, Favian H.N. Sunarya, Caroline Angelina Ariyanto, Gabrielle Felicia Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

There are many cases where borrowed money by debtors is not returned. It is because the company misjudged in determining the risk of lending. Thus, debtors cannot repay their debts and end up in losses on the company's side. Thus, this research aims to create an accurate classification model for determining loan risk categories to minimize business losses. We used the public dataset from kaggle website. Then we perform several data transformation and analysis methods are performed to obtain information from the data. We built 2 classification models to predict the original label. Unfortunately, the desired results did not achieve based on the f1-score. Therefore, the clustering method using K-Means is applied to make a new label for the dataset and determine the behavior of each cluster. Each cluster is labeled then build a model to perform cluster classification. We created several classification models and found that gradient tree boosting is the most accurate predictor of the loan risk category based on the accuracy. We used clustering to create a classification model that can have high accuracy. The classification model we built can have high accuracy because it uses clustering. The empirical results of this study and others pave the way for further in-depth future work to guarantee that the algorithm performs acceptably when confronted with more complicated real data. © 2023 IEEE.

关键词： K-means clustering

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Performance Analysis of Regression Models on Predicting Movie Ratings 2

Performance Analysis of Regression Models on Predicting Movi...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Kennard, Ignatius Hartono, Hansel Lucky, Henry Suhartono, Derwin School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350351613

This research explores the application and performance comparison of various machine learning for movie ratings. This study aims to make a comprehensive comparison in performance between various machine learning models such as XGBoost, Random Forest Regressor, K Nearest Neighbor Regression, Support Vector Regressor and Linear Regression. The dataset used is MovieLens 20M which includes a rating dataset and a movie dataset. We employ a content-based recommendation system in conjunction to applying machine learning models to predict movie ratings based on user meta data. Our methodology involves splitting the data into 70:30 train-test data and evaluating them using Mean Absolute Error (MAE), Mean Square Error (MSE), and Root Mean Squared Error (RMSE). The findings of our research conclude that XGBoost is the superior model which is shown from the RMSE value of XGBoost being the lowest (0.8727). © 2024 IEEE.

关键词： Adversarial machine learning

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Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge Computing Networks

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IEEE Transactions on Mobile Computing 2025年第07期24卷 6293-6314页

作者： Asheralieva, Alia Niyato, Dusit Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering 1088 Xueyuan Avenue Shenzhen China Nanyang Technological University College of Computing and Data Science 50 Nanyang Avenue Singapore639798 Singapore

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs;ii) dynamic, heterogeneous edge environment;iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs. © 2002-2012 IEEE.

关键词： Federated learning

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Unleashing the potential of ChatGPT in education: Revolutionizing learning and collaboration

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IEEE Potentials 2024年第6期43卷 13-22页

作者： Chandrasekaran, Balaji Nair, Smrithy Girijakumari Sreekantan College of Engineering Guindy Anna University Department of Computer Science and Engineering Chennai600025 India Vellore Institute of Technology School of Computer Science and Engineering Chennai600127 India

ChatGPT, an advanced language model powered by artificial intelligence, has emerged as a transformative tool in the field of education. This article explores the potential of ChatGPT in revolutionizing learning and collaboration within educational settings. By leveraging natural language processing and machine learning, ChatGPT offers an interactive and personalized learning experience, enabling students to engage in dynamic conversations and receive instant feedback. Moreover, ChatGPT may facilitate collaborative learning by promoting effective communication and knowledge sharing among students and educators. This article examines the various applications of ChatGPT in education, including virtual tutoring, adaptive learning, and intelligent chatbots. It also discusses the benefits and considerations associated with implementing ChatGPT, such as ethical considerations, data privacy, and potential biases. While acknowledging the limitations and challenges, this article emphasizes the promising potential of ChatGPT in transforming education by enhancing student engagement, fostering critical thinking skills, and expanding access to quality education. © 1988-2012 IEEE.

关键词： Adversarial machine learning

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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2024年第17期84卷 1-45页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud Computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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