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2nd Annual Symposium on Theoretical Aspects of computer science, STACS 1985

作者： Karlsson, Rolf G. Ian Munro, J. Data Structuring Group Department of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9783540139126

Given n keys taken from {1,...,m}×{1,...,m}, this paper presents an O(loglog m) time nearest neighbor query algorithm using O(n) space. An O(log m) time, O(n log m) space semidynamic algorithm and an O(log3/2m) time, O(n log m) space dynamic algorithm are also given. All the results apply under L1 and L∞ metrics. © Springer-Verlag Berlin Heidelberg 1985.

关键词： Nearest neighbor search

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Stress detection system for social media users

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Procedia computer science 2023年 216卷 672-681页

作者： Mochamad Rizky Febriansyah Nicholas Rezki Yunanda Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.

关键词： Machine Learning Stress Detection Social Media Natural Language Processing

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Heuristic assignments of redundant software versions and processors in fault-tolerant computer systems for maximum reliability

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Probability in the Engineering and Informational sciences 1987年第4期1卷 457-479页

作者： Kar Leow, Soo Mcallister, David F. Graduate Program in Operations Research North Carolina State University RaleighNC27695 United States Department of Computer Science North Carolina State University RaleighNC27695 United States

We address the problem of assigning multiple copies of n independently developed versions of a program to a set of m(m > n) possibly heterogeneous processors to maximize system reliability. This problem is viewed as a partition and assignment problem. We first partition the set of processors into n clusters or subgroups. A program version is then assigned to be executed on all the processors in the cluster. This means that each processor in the cluster will execute a copy of the assigned version. The cluster's unreliability is the probability of failure of all its processors. Component i of this system is composed of the copies of version i and the assigned cluster of processors. Copyright © Cambridge University Press 1987.

关键词： Combinatorial optimization

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EFFICIENT ONLINE LABEL CONSISTENT HASHING FOR LARGE-SCALE CROSS-MODAL RETRIEVAL

EFFICIENT ONLINE LABEL CONSISTENT HASHING FOR LARGE-SCALE CR...

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Yi, Jinhan Liu, Xin Cheung, Yiu-Ming Xu, Xing Fan, Wentao He, Yi Department of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Lab. of Computer Vision and Pattern Recognition Fujian Key Lab. of Big Data Intelligence and Security China Department of Computer Science Hong Kong Baptist University Kowloon Hong Kong School of Computer Science and Engineering University of Electronic Science and Technology of China China Provincial Key Laboratory for Computer Information Processing Technology Soochow University China

ISBN: (纸本)9781665438643

Existing cross-modal hashing still faces three challenges: (1) Most batch-based methods are unsuitable for processing large-scale and streaming data. (2) Current online methods often suffer from insufficient semantic association, while lacking flexibility to learn the hash functions for varying streaming data. (3) Existing supervised methods always require much computation time or accumulate large quantization loss to learn hash codes. To address above challenges, we present an efficient Online Label Consistent Hashing (OLCH) for cross-modal retrieval, which aims to incrementally learn hash codes for the current arriving data, while updating the hash functions at a streaming manner. To be specific, an online semantic representation learning framework is designed to adaptively preserve the semantic similarity across different modalities, and a mini-batch online gradient descent approach associated with forward-backward splitting is developed to optimize the hash functions. Accordingly, the hash codes are adaptively learned online with the high discriminative capability, while avoiding high computation complexity to process the streaming data. Experimental results show its outstanding performance in comparison with the-state-of-arts. © 2021 IEEE computer Society. All rights reserved.

关键词： Hash functions

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Sentiment Analysis of Hashtag Activism on Social Media Twitter

Sentiment Analysis of Hashtag Activism on Social Media Twitt...

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

作者： Sidharta, Sidharta Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University BINUS Graduated Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter's API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research. © 2023 IEEE.

关键词： Sentiment analysis

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Constraint programming based large neighbourhood search for energy minimisation in data centres

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10th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2013

作者： Cambazard, Hadrien Mehta, Deepak O'Sullivan, Barry Simonis, Helmut G-SCOP Université de Grenoble CNRS France INSIGHT Centre for Data Analytics Department of Computer Science University College Cork Ireland

ISBN: (纸本)9783319024134

EnergeTIC is a recent industrial research project carried out in Grenoble on optimising energy consumption in data centres. We study the problem formulation proposed by EnergeTIC. The problem focuses on the allocation of virtual machines to servers with time-variable resource demands in data centres in order to minimise energy costs while ensuring service quality. We present a scalable constraint programming-based large neighbourhood search (CP-LNS) method to solving this challenging problem. We present empirical results that demonstrate that the industrial benchmarks can be solved to near optimality using our approach. Our CP-LNS method provides a fast and practical approach for finding high quality solutions for lowering electricity costs in data centres. © 2013 Springer International Publishing.

关键词： Industrial research

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Teaming activities in a freshman engineering and computer science course aimed towards recruitment and retention

Teaming activities in a freshman engineering and computer sc...

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113th Annual ASEE Conference and Exposition, 2006

作者： Rowley, Blair Yelamarthi, Kumar Bazzoli, Thomas Wright State University United States Department of Biomedical Industrial and Human Factors Engineering Freshman Engineering and Computer Science Program

Our university has an open enrollment policy. This means that any person with a high school diploma can enroll in engineering or computer science. As a result entering students have a range of abilities from being excellent in math and science to struggling to understand algebra concepts. The current course, developed over the past five years has seen the overall engineering retention grow from 45% to 70%. This has been accomplished through hands-on experience, establishing a sense of community, developing an understanding of how to be successful in studying, and fostering collaboration among students through cooperative teaming events. This paper describes three major teaming events within the freshman engineering and computer science course that have contributed to fostering collaboration and helped provide an enjoyable experience within the college. data from each event is provided which includes the students' candid feedback, what they like and dislike, and what implementations they would like to see in the future. Students discover that working as a team takes effort and that a team's result depends upon each member's efforts. Overall the teaming activities have been well received. © American Society for Engineering Education, 2006.

关键词： Curricula

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Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

arXiv

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

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A Reduced Features of Public Health Development Index by Using Clustering Methods 4

A Reduced Features of Public Health Development Index by Usi...

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4th International Conference on Informatics and Computing, ICIC 2019

作者： Adi Darmawan, Muhammad Prihandoko Gunadarma University Technology and Engineering Program Depok Indonesia Gunadarma University Department of Computer Science and Information Technology Depok Indonesia

ISBN: (纸本)9781728122076

Public Health Development Index (PHDI) is an index that released by Ministry of Health of The Republic of Indonesia which is used to measure the health of Indonesian people. The PHDI is divided into seven categories and measured by using 30 features. There are 497 cities that contributed to PHDI 2013. The problem emerged is that not all the cities have those 30 features required by PHDI. The objective of this paper is to reduce PHDI features with various Feature Selection For Clustering (FSFC) method. The subset of features resulted from FSFC method is then used to generate two types of clusters, i.e. k-means and DBSCAN clustering. The clusters generated from k-means and DBSCAN techniques will be evaluated by using internal and external validation method. The subset of features is then compared with the original ranking of PHDI. Experimental results show that the results of each FSFC method producing a different rank compared to the original rank of PHDI. This result concludes that all the features that are used to measure PHDI are important to be included in the process of calculation. © 2019 IEEE.

关键词： Feature Selection

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Attentional Sentiment and Confidence Aware Neural Recommender Model 15

Attentional Sentiment and Confidence Aware Neural Recommende...

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15th International Conference on Knowledge Discovery and Information Retrieval, KDIR 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2023

作者： Berkani, Lamia Ighilaza, Lina Dib, Fella Department of Artificial Intelligence & Data Sciences Faculty of Informatics USTHB University Algiers Algeria Department of Computer Science Faculty of Informatics USTHB University Algiers Algeria

ISBN: (纸本)9789897586712

One of the major problems of recommendation systems is the rating data sparseness and information overload. To address these issues, some studies are leveraging review information to construct an accurate user/item latent factor. We propose in this article a neural hybrid recommender model based on attentional hybrid sentiment analysis, using BERT word embedding and deep learning models. An attention mechanism is used to capture the most relevant information. As reviews may contain misleading information (" fake good reviews/fake bad reviews "), a confidence matrix has been used to measure the relationship between rating outliers and misleading reviews. Then, the sentiment analysis module with fake reviews detection is used to update the user-item rating matrix. Finally, a hybrid recommendation is processed by combining the generalized matrix factorization (GMF) and the multilayer perceptron (MLP). The results of experiments on two datasets from the Amazon database show that our approach significantly outperforms state-of-the-art baselines and related work. Copyright © 2023 by SCITEPRESS - science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

关键词： Recommender systems

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