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An Abstractive Summarization Technique with Variable Length Keywords as per Document Diversity

computers, Materials & Continua 2021年第3期66卷 2409-2423页

作者： Muhammad Yahya Saeed Muhammad Awais Muhammad Younas Muhammad Arif Shah Atif Khan M.Irfan Uddin Marwan Mahmoud Department of Software Engineering Government College UniversityFaisalabadFaisalabad38000Pakistan Department of IT and Computer Science Pak-Austria Fachhochschule Institute of Applied Sciences and TechnologyHaripurPakistan Department of Computer Science Islamia College PeshawarPeshawarPakistan Institute of Computing Kohat University of Science and TechnologyKohatPakistan Faculty of Applied Studies King Abdulaziz UniversityJeddahSaudi Arabia

Text Summarization is an essential area in text mining,which has procedures for text *** natural language processing,text summarization maps the documents to a representative set of descriptive ***,the objective of text extraction is to attain reduced expressive contents from the text *** summarization has two main areas such as abstractive,and extractive *** text summarization has further two approaches,in which the first approach applies the sentence score algorithm,and the second approach follows the word embedding *** such text extractions have limitations in providing the basic theme of the underlying *** this paper,we have employed text summarization by TF-IDF with PageRank keywords,sentence score algorithm,and Word2Vec word *** study compared these forms of the text summarizations with the actual text,by calculating cosine ***,TF-IDF based PageRank keywords are extracted from the other two extractive *** intersection over these three types of TD-IDF keywords to generate the more representative set of keywords for each text document is *** technique generates variable-length keywords as per document diversity instead of selecting fixedlength keywords for each *** form of abstractive summarization improves metadata similarity to the original text compared to all other forms of summarized *** also solves the issue of deciding the number of representative keywords for a specific text *** evaluate the technique,the study used a sample of more than eighteen hundred text *** abstractive summarization follows the principles of deep learning to create uniform similarity of extracted words with actual text and all other forms of text *** proposed technique provides a stable measure of similarity as compared to existing forms of text summarization.

关键词： Metadata page rank sentence score word2vec cosine similarity This

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"It’s like an explosion": Cyberwarfare harms for civilian population in Ukraine during the Russian invasion

"It’s like an explosion": Cyberwarfare harms for civilian p...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Kulyk, Oksana Kickbusch, Jari Mayer, Peter IT University of Copenhagen Copenhagen Denmark Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798400713958

Studies on civilian costs of cyberwarfare operations are crucial in understanding how to protect the population in large-scale cyberattacks conducted by state actors. In this preliminary study, we conduct interviews (N=5) focusing on the harms experienced by Ukrainian civilians due to Russian cyberwarfare operations since the start of the full-scale invasion in 2022 as well as mitigation measures for protection of the civil society. The findings include harms such as lack of information, lack of access to essential services, physical harms, anxiety and lack of trust in the government. The study shows that cyberwarfare operations have the potential of causing severe harm to civilians and that there is a need for future studies to be conducted and adopted by institutions in order to develop measures to mitigate, not only in Ukraine but in any countries in risk of such attacks. © 2025 Copyright held by the owner/author(s).

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Enhancement of High-Definition Map for Autonomous Vehicle Application by Light Intensity Equalization Technique

Enhancement of High-Definition Map for Autonomous Vehicle Ap...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications technology (IAICT)

作者： Pattarapoom Nokkaew Nikorn Sutthisangiam Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

This research aims to enhance point cloud data and simulate the operations of autonomous vehicles following data refinement. The study utilizes high-resolution point cloud data generated by a mobile mapping system and employs histogram equalization techniques to broaden the range of intensity levels, enhance contrast, and overall improve intensity values. The findings demonstrate a heightened density of dispersed intensity values and an expanded range of intensity levels compared to previous states. The point cloud data file size contribute to a 21% increase from additional shade values recording format, allows for clearer displays of road details and surrounding areas. while simultaneously increasing the accuracy of coordinates on these maps by 15%, as evaluated through simulations. Additionally, this process can aid in the creation of vector maps, a component of high-resolution maps for autonomous vehicles, thereby enhancing performance in various scenarios such as decision-making at intersections and obstacle detection.

关键词： Point cloud compression Histograms Accuracy Laser radar Roads Vectors Recording

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Impact of Mouse DPI on Wrist Fatigue

Impact of Mouse DPI on Wrist Fatigue

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作者： Zixue Qiu Kaiyan Bi Zhanming Liang Ruoxun Hu Applied Science and Engineering University of Toronto Computer Science and Information Technology Central South University of Forestry and Technology

This Project mainly creates a model to test if the Arm and Wrist muscle is fatigue or not by analyzing the EMG signal,then find if the Muscle Fatigue has some connection to the Mouse DPI.

关键词： IEEE Muscle Fatigue EMG Signal Mouse DPI Machine Learning

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White Leg Shrimp Larvae Counting System

White Leg Shrimp Larvae Counting System

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International Conference on Knowledge and Smart technology (KST)

作者： Vutichai Kamthang Khantharat Anekboon Department of Computer and Information Science Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

White leg shrimp is one of the animals that is popularly consumed and exported in Thailand. A lot of farmers cultivate white leg shrimp in many areas. To raise white leg shrimp larvae into mature white leg shrimp that can be sold in the market for further consumption or export. Before a farm sells white leg shrimp larvae to a farmer, the farm staff needs to count the number of them. The traditional counting method is naked-eye estimation which requires experience and gives a rough estimation. A traditional technique causes time- consuming and human error. Moreover, a farm usually adds some scoops to make sure that the number of white leg shrimp larvae is not less than an order. This situation affects the farm's costs. This study proposes a new system for automated counting white leg shrimp larvae by applying the simple blob detector technique with opening and closing of morphological operations. The experimental result of the proposed system shows that this proposed system gives the best accuracy (94.20%), precision (97.68%), recall (96.35%), and F1 score (96.98 % ) compared with Canny Edge Detection and Blob analysis, morphological operations (opening, dilatation and gamma correction), and the blue sense system.

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On developing deep learning models with particle swarm optimization in the presence of poisoning attacks 23

On developing deep learning models with particle swarm optim...

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23rd International Arab Conference on Information technology, ACIT 2022

作者： Maabreh, Majdi Darwish, Omar Karajeh, Ola Tashtoush, Yahya The Hashemite University Department of Computer Information Systems Zarqa Jordan Eastern Michigan University Information Security and Applied Computing Department MI United States Virginia Polytechnic Institute and State University Department of Computer Science VA United States Jordan University of Science and Technology Department of Computer Science Irbid Jordan

ISBN: (纸本)9798350320244

Deep learning (DL) has demonstrated several successes in a variety of fields, particularly in the era of big data. The process of training a deep learning model entails selecting the ideal learning parameters such as the number of hidden layers and neurons. Particle Swarm Optimization (PSO) is one useful nature-inspired algorithm to set those two influential learning parameters. In this study, two different datasets are used to study and evaluate the particle swarm optimizer with deep learning on datasets of different concentrations of poisoning attack, where some adversarial samples were crafted by attackers to ruin the learning process. The results showed that particle swarm optimization could find settings for deep learning with the existence of poisoned data that maximizes the model accuracy on the unseen testing dataset, and could also offer better recommendations compared to those recommended on all benign samples. This may introduce a concern that optimizers might conceal the existence of data poisoning, which may lead to unreliable learning in the advanced stages of upgrading the model on updated datasets. © 2022 IEEE.

关键词： Particle swarm optimization (PSO)

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Development of a Novel Mechanical Layer Jamming Cable-driven Soft Actuator for Soft Gripper System

Development of a Novel Mechanical Layer Jamming Cable-driven...

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IEEE/SICE International Symposium on System Integration

作者： Chinvudh Uahchinkul Satoshi Muramatsu Katsuhiko Inagaki Course of Science and Technology Tokai University Kanagawa Japan Department of Applied Computer Engineering Tokai University Kanagawa Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

Soft actuators have many advantages, such as flexibility and safe interaction with the environment. Despite these advantages, they still lack the stiffness to carry the high load. The layer jamming mechanism can be applied to the actuator to increase stiffness, and a vacuum can control the stiffness of the mechanism. However, the typical jamming mechanism has a risk of air leakage, and vacuums are bulky to some systems. In order to solve this problem, we have developed a novel mechanical layer jamming for the cable-driven soft actuator in this paper. Our design uses the actuator body to generate the compression force to the layer at the middle of the actuator’s body to increase the actuator’s stiffness. The result shows the effectiveness of our mechanical layer jamming in increasing the stiffness of the actuator when adding the load to the tip of the actuator. In addition, we have applied our actuator to the soft gripper system, which successfully grasped various objects and changed the stiffness of the actuator.

关键词： Actuators Vacuum systems Shape Force Prototypes Mechanical cables System integration Soft robotics Jamming Grippers

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Echo Location Based Bat Algorithm for Energy Efficient WSN Routing

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computers, Materials & Continua 2022年第6期71卷 6351-6364页

作者： Anwer Mustafa Hilal Siwar Ben Haj Hassine Jaber S.Alzahrani Masoud Alajmi Fahd N.Al-Wesabi Mesfer Al Duhayyim Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayil AsirSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the wide range of applications,Wireless Sensor Networks(WSN)are increased in day to day life and becomes *** has marked its importance in both practical and research *** is the most significant resource,the important challenge in WSN is to extend its *** energy reduction is a key to extend the network’s *** of sensor nodes is one of the well-known and proved methods for achieving scalable and energy conserving *** this paper,an energy efficient protocol is proposed using metaheuristic Echo location-based BAT algorithm(ECHO-BAT).ECHO-BAT works in two *** Stage clusters the sensor nodes and identifies tentativeCluster Head(CH)along with the entropy value using BAT *** second stage aims to find the nodes if any,with high residual energy within each *** will be replaced by the member node with high residual energy with an objective to choose the CH with high energy to prolong the network’s *** performance of the proposed work is compared with Low-Energy Adaptive Clustering Hierarchy(LEACH),Power-Efficient Zoning Clustering Algorithm(PEZCA)and Chaotic Firefly Algorithm CH(CFACH)in terms of lifetime of network,death of first nodes,death of 125th node,death of the last node,network throughput and execution *** results show that ECHO-BAT outperforms the other methods in all the considered *** overall delivery ratio has also significantly optimized and improved by approximately 8%,proving the proposed approach to be an energy efficient WSN.

关键词： Wireless sensor networks BAT algorithm energy efficient clustering cluster head energy consumption

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Prior specification for Bayesian matrix factorization via prior predictive matching

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 2910-2960页

作者： Eliezer de Souza da Silva Tomasz Kuśmierczyk Marcelo Hartmann Arto Klami Department of Computer Science Norwegian University of Science and Technology Trondheim Norway and School of Applied Mathematics Fundação Getulio Vargas Rio de Janeiro Brazil Department of Computer Science University of Helsinki Helsinki Finland

The behavior of many Bayesian models used in machine learning critically depends on the choice of prior distributions, controlled by some hyperparameters typically selected through Bayesian optimization or cross-validation. This requires repeated, costly, posterior inference. We provide an alternative for selecting good priors without carrying out posterior inference, building on the prior predictive distribution that marginalizes the model parameters. We estimate virtual statistics for data generated by the prior predictive distribution and then optimize over the hyperparameters to learn those for which the virtual statistics match the target values provided by the user or estimated from (a subset of) the observed data. We apply the principle for probabilistic matrix factorization, for which good solutions for prior selection have been missing. We show that for Poisson factorization models we can analytically determine the hyperparameters, including the number of factors, that best replicate the target statistics, and we empirically study the sensitivity of the approach for the model mismatch. We also present a model-independent procedure that determines the hyperparameters for general models by stochastic optimization and demonstrate this extension in the context of hierarchical matrix factorization models.

关键词： Bayesian modeling prior specification hyperparameter search probabilistic matrix factorization

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Effects of Crystallinity and Defects of Layered Carbon Materials on Potassium Storage:A Review and Prediction

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Electrochemical Energy Reviews 2022年第2期5卷 401-433页

作者： Xiaoxu Liu Tianyi Ji Hai Guo Hui Wang Junqi Li Hui Liu Zexiang Shen Shaanxi Key Laboratory of Green Preparation and Functionalization for Inorganic Materials School of Material Science and EngineeringShaanxi University of Science and TechnologyXi’an 710021ShaanxiChina Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore College of Computer Science and Technology Dalian Minzu UniversityDalian 116650LiaoningChina

Layered carbon materials(LCMs)are composed of basic carbon layer units,such as graphite,soft carbon,hard carbon,and *** they have been widely applied in the anode of potassium-ion batteries,the potassium storage mechanisms and performances of various LCMs are isolated and difficult to relate to each *** importantly,there is a lack of a systematic understanding of the correlation between the basic microstructural unit(crystallinity and defects)and the potas-sium storage *** this review,we explored the key structural factors affecting the potassium storage in LCMs,namely,the crystallinity and defects of carbon layers,and the key parameters(L_(a),L_(c),d_(002),I_(D)/I_(G))that characterize the crystallinity and defects of different carbon materials were extracted from various databases and literature sources.A structure–property database of LCMs was thus built,and the effects of these key structural parameters on the potassium storage properties,including the capacity,the rate and the working voltage plateau,were systematically *** on the structure–prop-erty database analysis and the guidance of thermodynamics and kinetics,a relationship between various LCMs and potas-sium storage properties was ***,with the help of machine learning,the key structural parameters of layered carbon anodes were used for the first time to predict the potassium storage performance so that the large amount of research data in the database could more effectively guide the scientific research and engineering application of LCMs in the future.

关键词： Layered carbon materials Crystallinity and defects Potassium storage properties Machine learning

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