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A Survey of Spark Scheduling Strategy Optimization Techniques and Development Trends

computers, Materials and Continua 2025年第3期83卷 3843-3875页

作者： Li, Chuan Wen, Xuanlin Department of Computer Science and Technology School of Computer Science Xi’an University of Posts and Telecommunications Xi’an710100 China

Spark performs excellently in large-scale data-parallel computing and iterative processing. However, with the increase in data size and program complexity, the default scheduling strategy has difficulty meeting the demands of resource utilization and performance optimization. Scheduling strategy optimization, as a key direction for improving Spark’s execution efficiency, has attracted widespread attention. This paper first introduces the basic theories of Spark, compares several default scheduling strategies, and discusses common scheduling performance evaluation indicators and factors affecting scheduling efficiency. Subsequently, existing scheduling optimization schemes are summarized based on three scheduling modes: load characteristics, cluster characteristics, and matching of both, and representative algorithms are analyzed in terms of performance indicators and applicable scenarios, comparing the advantages and disadvantages of different scheduling modes. The article also explores in detail the integration of Spark scheduling strategies with specific application scenarios and the challenges in production environments. Finally, the limitations of the existing schemes are analyzed, and prospects are envisioned. Copyright © 2025 The Authors.

关键词： Cluster computing

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"Nyctophy": Assessing the effectiveness of serious game for nyctophobia based on virtual reality by usability and heart rate monitoring 9th

"Nyctophy": Assessing the effectiveness of serious game for ...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Ardiyanto, Elshad Ryan Alexander, Patrick Putra, Edyth Novian Ramdhan, Dimas Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nyctophobia is a phobia of the dark and is common among children but also found in adults. While the phobia itself is commonly known, the diversity of its treatment is still minimal. As technology has reached its highest point, numerous inventions and innovations have emerged in the medical sector. This study aimed to examine the usability of Virtual Reality and Keyboard and Mouse as an interface using the Extended Reality Game Usability Questionnaire (XR-GUQ) in Nyctophobia therapy and to analyze the heart rate response, impact, and implications using t-test Paired/Wilcoxon Signed Rank. Thirty-four Bina Nusantara University students from various backgrounds completed the test using interfaces and a smartwatch. They were divided into two groups, high- and low fear, using the modified Acrophobia Questionnaire (AQ), which resulted in a significant difference. The XR-GUQ result was insignificant in all variables, which indicates similar usability scores on both interfaces. The t-test Paired/Wilcoxon Signed Rank test revealed a significant difference between the high- and low-fear groups when using the Virtual Reality interface compared to the Keyboard and Mouse interface. This significant difference was found within each group regardless of the interface used. This research shows similar scores in terms of usability but a significant difference in heart rate response with the help of a serious game to give a new sensation of phobia therapy. © 2024 The Authors.

关键词： Serious games

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OSFS-Vague: Online streaming feature selection algorithm based on vague set

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1451-1466页

作者： Jie Yang Zhijun Wang Guoyin Wang Yanmin Liu Yi He Di Wu School of Physics and Electronic Science Zunyi Normal UniversityZunyiChina Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Computer Science Old Dominion UniversityNorfolkVirginiaUSA College of Computer and Information Science Southwest UniversityChongqingChina

Online streaming feature selection(OSFS),as an online learning manner to handle streaming features,is critical in addressing high-dimensional *** real big data-related applications,the patterns and distributions of streaming features constantly change over time due to dynamic data generation ***,existing OSFS methods rely on presented and fixed hyperparameters,which undoubtedly lead to poor selection performance when encountering dynamic *** make up for the existing shortcomings,the authors propose a novel OSFS algorithm based on vague set,named *** main idea is to combine uncertainty and three-way decision theories to improve feature selection from the traditional dichotomous method to the trichotomous ***-Vague also improves the calculation method of correlation between features and ***,OSFS-Vague uses the distance correlation coefficient to classify streaming features into relevant features,weakly redundant features,and redundant ***,the relevant features and weakly redundant features are filtered for an optimal feature *** evaluate the proposed OSFS-Vague,extensive empirical experiments have been conducted on 11 *** results demonstrate that OSFS-Vague outperforms six state-of-the-art OSFS algorithms in terms of selection accuracy and computational efficiency.

关键词： feature selection online feature selection three-way decision vague set

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Improved Harmony Search Algorithm for SDN Controller Placement

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 167-179页

作者： Aldeen, Yousra Abdul Alsahib S. Sadiq, Ahmed T. Abed, Abeer E. Hussain, Omar F. Madni, Syed Hamid Hussain Department of Computer Science College of Science for Women University of Baghdad Iraq Department of Computer Science University of Technology Iraq Office of the Vice President for Scientific University of Baghdad Iraq Quality Assurance and Academic Performance Department University of Baghdad Iraq School of Electronics and Computer Science University of Southampton Malaysia Johor Bahru Malaysia

The Software-Defined Networking (SDN) paradigm decouples the control and the data plane. One of the most significant challenges in this paradigm is SDN controller placement optimization, since improper placement may dramatically influence latency, load balancing, and network resilience. The paper proposes the Improved Harmony Search Algorithm (IHSA) as a new approach for the placement of SDN controllers. To overcome the disadvantages of conventional optimization methods like HSA, GA, and PSO, adaptive parameter tuning, dynamic harmony memory management, and updating rules for enhanced memory are incorporated into the IHSA. The complete simulations of IHSA over small, medium, and large-scale SDN topologies have shown its robust supremacy in performance compared to the benchmark algorithms, which provide up to 23.5% less latency along with significant improvements in load-balancing and convergence-time. The optimization results of IHSA are well supported by the statistical validity based on the p-values and confidence intervals. The algorithm is scalable, adaptive, and very efficient. Hence, it makes a more realistic solution for real-time and dynamic SDN environments. The results show that IHSA has the potential to improve network performance in latency-sensitive applications such as IoT, cloud computing, and telecommunications, thus opening up resilient and high-performance network infrastructures. This is a practical application of IHSA, and subsequently, the recommendation given through this study is for its integration with datasets and machine learning techniques from real world to further improve optimization. © 2025 The Author(s).

关键词： Resource allocation

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

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computer Modeling in Engineering & sciences 2023年第2期134卷 909-926页

作者： Passent El-kafrawy Maie Aboghazalah Abdelmoty M.Ahmed Hanaa Torkey Ayman El-Sayed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Math and Computer Science Department Faculty of ScienceMenoufia UniversityMenoufia32511Egypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Computer Science and Engineering Department Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common *** ofmedical images is very important to secure patient *** these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a *** this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the *** the other hand,a decoder was used to reproduce the original image back after the vector was received and *** convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and *** hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding *** this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in *** first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification *** second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 *** third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

关键词： Auto-encoder cloud image encryption IoT healthcare

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Multivariate stock market forecasting using lstm on Indonesian banking stock market 9th

Multivariate stock market forecasting using lstm on Indonesi...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Kenrick Ivancio, Steffano Claus Isabellini, Elisabeth Adhi, Canggih Gelar Setyo Prasetyo, Anang Computer Science Department School of Computer Science Universitas Bina Nusantara Semarang Campus Indonesia Computer Science Department School of Computer Science Universitas Bina Nusantara Jakarta Indonesia

Forecasting stock market prices and trends can be a major challenge for investors and traders because of its volatility and various factors that affects it. Because of that, it is crucial for investors to be able to forecast the stock market alongside its trends as accurately as possible to minimize risk and increase profit, given the complexities and uncertainties inherent in the market. This research aims in digging the potential of LSTM models to forecast the open and close price of the 6 Indonesia bank stock market which consist of 3 national bank (BNI, BRI, and Mandiri) and 3 private bank (CIMB, BCA, and OCBC) in which it also helps in enhancing investment strategies in the banking sector, making it a very helpful tools for investors to gain more profit and minimize loss while also test and evaluate its error rate and accuracy while also evaluate the LSTM performance based on its accuracy and error rates. The historical stock data from April 30, 2019, to April 30, 2024, that's used in this study as the main dataset was acquired from Yahoo Finance. The provided model average accuracy and error rate when forecasting the open and close prices can be seen with an exceptional accuracy and very low error rates which can be proven with the predicted open's MSE: 0.000303, RMSE: 0.018430, MAE: 0.013380, and R2: 0.967834, as well as predicted close's MSE: 0.000511, RMSE: 0.022322, MAE: 0.017260, and R2: 0.942172, making it considered as a robust, reliable, and accurate model for investors and traders to forecast the close and open price of the stock market in the future. The LSTM model performance highlights its capabilities to capture important multivariate patterns of the tested bank's stock market data, which offer more advantages over traditional methods. © 2024 The Authors.

关键词： Marketplaces

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Towards Long-Tailed Recognition for Graph Classification via Collaborative Experts

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IEEE Transactions on Big data 2023年第6期9卷 1683-1696页

作者： Yi, Si-Yu Mao, Zhengyang Ju, Wei Zhou, Yong-Dao Liu, Luchen Luo, Xiao Zhang, Ming School of Statistics and Data Science Nankai University Tianjin300071 China School of Computer Science Peking University Beijing100871 China Department of Computer Science University of California Los AngelesCA90095 United States

Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution. In fact, most real-world graph data naturally presents a long-tailed form, where the head classes occupy much more samples than the tail classes, it thus is essential to study the graph-level classification over long-tailed data while still remaining largely unexplored. However, most existing long-tailed learning methods in visions fail to jointly optimize the representation learning and classifier training, as well as neglect the mining of the hard-to-classify classes. Directly applying existing methods to graphs may lead to sub-optimal performance, since the model trained on graphs would be more sensitive to the long-tailed distribution due to the complex topological characteristics. Hence, in this paper, we propose a novel long-tailed graph-level classification framework via Collaborative Multi-expert Learning (CoMe) to tackle the problem. To equilibrate the contributions of head and tail classes, we first develop balanced contrastive learning from the view of representation learning, and then design an individual-expert classifier training based on hard class mining. In addition, we execute gated fusion and disentangled knowledge distillation among the multiple experts to promote the collaboration in a multi-expert framework. Comprehensive experiments are performed on seven widely-used benchmark datasets to demonstrate the superiority of our method CoMe over state-of-the-art baselines. © 2023 IEEE.

关键词： Learning systems

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User Sentiment Analysis of Online Transportation Platforms Using K-Means and K-Nearest Neighbor 6

User Sentiment Analysis of Online Transportation Platforms U...

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

作者： Yuhanas, Adila Nashira Salazar, Golda Clarabella Fredyan, Renaldy Ibrahim, Muhammad Amien Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798331539603

Online transportation has been one of the most used type of applications in this day and age, dominated by the platforms Go-Jek and Grab. In order to improve their service and user satisfaction, this research aims to determine the user sentiment analysis on both application using the K-Nearest Neighbor (KNN) method, a supervised learning algorithm used to classify data according to their nearest neighbors, and the K-Means method, an unsupervised learning algorithm used to group data into clusters according to their similarities. Both methods will be implemented using RapidMiner. The data used are 199 usernames and ratings on both applications, collected from Kaggle. The KNN method resulted in a 100 percent accuracy and the K-Means method proved that the cluster with the most items is the one which had the ratings above 4.5. This proves that the user sentiment analysis of online transportation platforms, such as Go-Jek and Grab, using K-Nearest Neighbor (KNN) and K-Means reveals that the sentiments are, on average, positive sentiments. © 2024 IEEE.

关键词： Self-supervised learning

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A Systematic Literature Review: Challenges of Security Optimization while Maintaining a Good Accessibility in Internet of Things 3

A Systematic Literature Review: Challenges of Security Optim...

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3rd International Conference on Electronic and Electrical Engineering and Intelligent System, ICE3IS 2023

作者： Tampubolon, Devin Gunadi Widodo, Gerald Nathaniel Margaretha, Janet Sweetmay Gunawan, Alexander Agung Santoso Edbert, Ivan Sebastian School of Computer Science Bina Nusantara University Cyber Security Program Computer Science Department Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350327762

The Internet of Things (IoT), which has had a tremendous development in recent years, has altered many aspects of daily life by giving rise to commercial tools, home automation, wearable technology, and the framework for smart cities. The modern fantasy of having remote control over several parts of life has been realized because to this continuous connectivity and the ability of devices to acquire and interpret information. However, the various features and functionalities of IoT devices are a double-edged sword because they introduce a number of security flaws and dangers. Through a Systematic Literature Review (SLR), we explore vulnerabilities and their mitigation strategies based on prior research in the field. Notably, while the importance of security in IoT is paramount, especially considering the sensitivity of data involved, there exists a challenge in striking a balance between robust security and user convenience. It's imperative that while bolstering security, we do not compromise on user accessibility. This paper presents a comprehensive discourse addressing the dual challenges of security and accessibility in the IoT landscape. Our findings underscore the urgent need for layered security protocols, encryption, and user authentication to counteract the escalating cyber threats. We conclude by proposing a balanced solution that ensures both security and accessibility in the IoT ecosystem. © 2023 IEEE.

关键词： Internet of things

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