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14th International Conference on Cloud Computing, data science and Engineering, Confluence 2024

作者： Bushra, S.Nikkath Syed Ismail, A. Sasigresa, P. Edwin Singh, C. Srm Institute of Scienece and Technology Department of Computing Technologies Chengalpattu Kattankulathur India Srm Institute of Scienece and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Veltech University Department: Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department: Computer Science and Engineering India

ISBN: (纸本)9798350344837

data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner. © 2024 IEEE.

关键词： Authentication

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Joint Wireless and Computing Resource Management with Optimal Slice Selection in in-network-edge Metaverse System 15

Joint Wireless and Computing Resource Management with Optima...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Rashid, Sulaiman Muhammad Aliyu, Ibrahim Isah, Abubakar Lee, Jihoon Oh, Sangwon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligent Electronics and Computer Engineering Gwangju Korea Republic of Astana It University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798350364637

This paper presents an approach to joint wireless and computing resource management in slice-enabled metaverse networks, addressing the challenges of inter-slice and intra-slice resource allocation in the presence of in-network computing. We formulate the problem as a mixed-integer nonlinear programming (MINLP) problem and derive an optimal solution using standard optimization techniques. Through extensive simulations, we demonstrate that our proposed method significantly improves system performance by effectively balancing the allocation of radio and computing resources across multiple slices. Our approach outperforms existing benchmarks, particularly in scenarios with high user demand and varying computational tasks. © 2024 IEEE.

关键词： Mixed-integer linear programming

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Extracting key challenges in achieving sobriety through shared subspace learning 12th

Extracting key challenges in achieving sobriety through shar...

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12th International Conference on Advanced data Mining and Applications, ADMA 2016

作者： Harikumar, Haripriya Nguyen, Thin Rana, Santu Gupta, Sunil Kaimal, Ramachandra Venkatesh, Svetha Centre for Pattern Recognition and Data Analytics Deakin University Geelong Australia Computer Science and Engineering Department Amrita University Kollam India

ISBN: (纸本)9783319495859

Alcohol abuse is quite common among all people without any age restrictions. The uncontrolled use of alcohol affects both the individual and society. Alcohol addiction leads to a huge increase in crime, suicide, health related problems and financial crisis. Research has shown that certain behavioral changes can be effective towards staying abstained. The analysis of behavioral changes of quitters and those who are at the beginning phase of quitting can be useful for reducing the issues related to alcohol addiction. Most of the conventional approaches are based on surveys and, therefore, expensive in both time and cost. Social media has lend itself as a source of large, diverse and unbiased data for analyzing social behaviors. Reddit is a social media platform where a large number of people communicate with each other. It has many different sub-groups called subreddits categorized based on the subject. We collected more than 40,000 self reported user’s data from a subreddit called ‘/r/stopdrinking’. We divide the data into two groups, short-term with abstinent days less than 30 and long-term abstainers with abstinent days greater than 365 based on badge days at the time of post submission. Common and discriminative topics are extracted from the data using JS-NMF, a shared subspace non-negative matrix factorization method. The validity of the extracted topics are demonstrated through predictive performance. © Springer International Publishing AG 2016.

关键词： Non-negative matrix factorization

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Utilizing Machine Learning Techniques for Forecasting Agricultural Commodity Prices 2

Utilizing Machine Learning Techniques for Forecasting Agricu...

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2nd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Benjamin, Minu Inba Shanthini Watson Gowtham, D. Reddy, S. Anand Kumar Krishna, D. Durga Radha Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology Department of Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798350327533

Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linear regression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informed decisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting. © 2024 IEEE.

关键词： Bayesian regression Decision tree Ensemble methods exploratory data analysis hybrid models K-Nearest Neighbors linear regression Machine learning Random Forest XGBoost

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On Computing the Exact Determinant of Matrices with Polynomial Entries

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Journal of the ACM (JACM) 1975年第1期22卷 38-50页

作者： Horowitz, E. Sahni, S. Computer Science Program University of Southern California Los Angeles CA United States Department of Computer Information and Control Sciences University of Minnesota Minneapolis MN United States

The problem of computing the determinant of a matrix of polynomials is considered. Four algorithms are compared- expansion by minors, Gausslan elimination over the integers, a method based on evaluation and interpolation, and a procedure which computes the characteristic polynomial of the matrix. Each method m analyzed with respect to its computing time and storage requirements using several models for polynomial growth. First, the asymptotic time and storage is developed for each method within each model. In addition to these asymptotm results, the analysis is done exactly for certain especially small, yet practical and important cases Then the results of empirical studies are given which support conclusions about which of the methods will work best within an actual computing environment. © 1975, ACM. All rights reserved.

关键词： characteristic polynomial determinants expansion by minors Gaussian elimination matrix of polynomials

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Ensemble Learning Based Employment Recommendation Under Interaction Sparsity for College Students 9th

Ensemble Learning Based Employment Recommendation Under Inte...

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19th International Conference on Advanced data Mining and Applications, ADMA 2023

作者： Zhu, Haiping Zhao, Yifei Wu, Yuchen Chen, Yan Li, Wenhao Zheng, Qinghua Tian, Feng Department of Computer Science and Technology Xi’an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an China

ISBN: (纸本)9783031466632

Recommendation systems play a crucial role in helping college students find job opportunities. However, the sparsity of interactions in employment recommendation for college students poses a challenge for models based on historical user preferences. To address this issue, we propose a novel model called Ensemble Learning based Employment Recommendation under Interaction Sparsity for College Students (EERIS). The model comprises two components: a similarity information component that uses pooled users to determine the nearest neighbor in user similarity measurement, and a global interaction component that uses interaction vectors of user groups to enhance interactions. To evaluate the missing interactions, we propose a loss function called CellLoss. These components are combined based on ensemble learning to improve the model’s generalization and scalability. Our experiments on two real-world datasets demonstrate the superior performance of the EERIS model. Ablation experiments further confirm that each component positively contributes to the model’s performance. Additionally, we design a revised metric for better model testing. Overall, the proposed EERIS model effectively addresses the interaction sparsity in employment recommendation for college students and provides satisfactory recommendations to students. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Learning systems

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Using location dependence to manage mobile data

Using location dependence to manage mobile data

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2006 3rd Annual International Conference on Mobile and Ubiquitous Systems, MobiQuitous

作者： Crawl, Daniel Dunn, Joseph Bennett, John Bhatnagar, Avneesh Speight, Evan Department of Computer Science University of Colorado Verizon Data Services Inc. Future Systems Group IBM Austin Research Laboratory

ISBN: (纸本)1424404991

Fragmentation, (portions of data located on many separate devices), and versioning, (different versions of the same datum located on different devices), of user data are increasingly prevalent and important problems as users work on a more diverse array of mobile computing devices. A common solution to these problems is to cache data on the current device. This paper describes and evaluates a mechanism for addressing fragmentation and versioning using affinity relationships. Affinity relationships that represent the current data interests of users provide hints as to what data to cache on mobile computing devices. Trace data collected over a 15 month period has been used to inform the design of the affinity mechanism. Trace-based simulation demonstrates the effectiveness of an affinity-directed approach to reduce fragmentation and versioning with low overhead. © 2006 IEEE.

关键词： Mobile computing

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Efficiently updating materialized views 86

Efficiently updating materialized views

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1986 ACM SIGMOD International Conference on Management of data, SIGMOD 1986

作者： Blakeley, Josh A. Larson, Per-Ake Tompa, Blank Wm Data Structurmg Group Department of Computer Science University of Waterloo WaterlooONN2L 3Gl Canada

ISBN: (纸本)0897911911

Query processmg can be sped up by keeping frequently accessed users' views materlahzed However, the need to access base relations in response to queues can be avoided only If the materlahzed view ls adequately maintained We propose a method in which all database updates to base relations are first filtered to remove from consideration those that cannot possibly affect the view The condltlons given for the detection of updates of this type, called arrelevant updates, are necessary and sufficient and are mdependent of the database state For the remammg database updates, a dzfferentlal algonthm can be apphed to re-evaluate the view expression The algonthm proposed exploits the knowledge provided by both the view defimtlon expression and the database update operations. © 1986 ACM.

关键词： database systems

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Multi Label Classification Based on Ensemble Learning: Investigative Study 25

Multi Label Classification Based on Ensemble Learning: Inves...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Nasir, Ola Alazaidah, Raed Hadi, Wael Samara, Ghassan Al Daoud, Essam Owaida, Suhaila Abu Elrashidi, Ali Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan Zarqa University Faculty of Information Technolog Department of Computer Science Zarqa Jordan Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of It Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

In the big data technology nowadays, activities associated with Multi-Label Classification (MLC) pose big and complex challenges, receiving terrific interest in diverse fields. Existing MLC algorithms suffer from low overall performance. This paper considers four well-known Problem Transformation Methods (PTMs). Two of these PTMs are based on the frequency of the labels (MFL and LFL) while the last two PTMs are based on the positive correlations among labels. These PTMs have been implemented and compared against four multi label datasets of various sizes to identify the best one that fits the nature of MLC datasets. Moreover, six popular ensemble learning models have been carried out and compared on the four datasets with respect to several evaluation metrics such as Accuracy, Hamming loss, and Exact Match and using SMO, DecisionStump, Random Forest, and Random Commission classifiers. The results revealed that MFL accomplished the quality performance amongst the four PTMs, and Stacking SVM is the best ensemble learning technique. © 2024 IEEE.

关键词： Contrastive Learning

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Enhanced explicit congestion notification (EECN) in TCP with P4 programming

Enhanced explicit congestion notification (EECN) in TCP with...

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2020 International Conference on Green and Human Information Technology, ICGHIT 2020

作者： Shahzad, Shahzad Jung, Eun-Sung Chung, Joaquin Kettimuthu, Rajkumar Department of Electronics and Computer Engineering Hongik University Korea Republic of Data Science and Learning Division Argonne National Laboratory United States

ISBN: (纸本)9781728162959

In current TCP/IP networks, TCP mainly relies on packet drops as an indication of congestion. With the emergence of Explicit Congestion Notification (ECN), TCP can detect the congestion through packets with marked bits instead of dropped packets. ECN thus reduces the unnecessary delay due to packet drops, especially for bandwidth-delay sensitive connections. However, ECN still relies on the receiver for congestion indication, which incurs a round trip time (RTT) before the sender can react to the congestion. In network environments with a high bandwidth-delay product (BDP), this could be problematic. In this paper, we present an enhanced ECN mechanism for the early detection of congestion using P4 programming. In our proposed scheme, the sender does not have to wait for the receiver to indicate congestion because switches in the network can indicate congestion. We evaluate our scheme comparing our proposed solution with standard TCP and TCP with the conventional ECN. Experimental results show that our scheme outperforms ECN as the RTT grows. © 2020 IEEE.

关键词： Drops

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