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6th International Conference on Next Generation data-Driven Networks, NGDN 2024

作者： Du, Yan Ding, Zhiming Faculty of Information Technology Beijing University of Technology Beijing China Center of Spatial-Temporal Data Management and Data Sciecne Institute of Software Chinese Academy of Science Beijing China

ISBN: (纸本)9798350388374

The rapid growth in the scale of distributed databases has led to a deeper impact of the multi-join query optimization problem on data query efficiency. The increase in the number of relations causes a significant increase in the search space complexity of distributed database multi-join optimization, which greatly affects the efficiency of finding the optimal execution plan, thus hindering the further optimization of multi-join queries. In this regard, a genetic multiple bee colony algorithm combined with fuzzy logic is proposed to solve the problem of further optimizing multi-join queries of distributed databases. Firstly, the fast convergence property of the genetic algorithm is utilized to obtain a set of relatively optimal solutions in order to overcome the blindness of the artificial bee algorithm's pre-search, and then the parallel multiple bee algorithm is introduced, which makes use of the information interaction between multiple bee colonies to better prevent the algorithm from falling into the local optimal situation as well as the parallelization of the execution of the multiple bee colonies to improve the search efficiency and convergence speed of the algorithm. Secondly, the fuzzy logic system is also used to optimize the parameters of the algorithm and set the corresponding algorithmic control parameters for different dimensional queries. Finally, a comparison experiment is conducted to test the average execution cost and algorithm execution time of the Top-K query plans output by the algorithm in the case of six query statements involving different lengths, and the experimental results show that the K optimal query plans generated by the method proposed in this paper have lower query excution cost and algorithm execution time, and can effectively improve the query efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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REINFORCING PRE-TRAINED MODELS USING COUNTERFACTUAL IMAGES 31

REINFORCING PRE-TRAINED MODELS USING COUNTERFACTUAL IMAGES

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31st IEEE International Conference on Image Processing, ICIP 2024

作者： Li, Xiang Togo, Ren Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Graduate School of Information Science and Technology Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan Data-Driven Interdisciplinary Research Emergence Department Hokkaido University Japan

ISBN: (纸本)9798350349399

This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. Deep learning classification models are often trained using datasets that mirror real-world scenarios. In this training process, because learning is based solely on correlations with labels, there is a risk that models may learn spurious relationships, such as an overreliance on features not central to the subject, like background elements in images. However, due to the black-box nature of the decision-making process in deep learning models, identifying and addressing these vulnerabilities has been particularly challenging. We introduce a novel framework for reinforcing the classification models, which consists of a two-stage process. First, we identify model weaknesses by testing the model using the counterfactual image dataset, which is generated by perturbed image captions. Subsequently, we employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model. Through extensive experiments on several classification models across various datasets, we revealed that fine-tuning with a small set of counterfactual images effectively strengthens the model. © 2024 IEEE.

关键词： Deep reinforcement learning

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Zero-shot Controllable Music Generation from Videos Using Facial Expressions 13

Zero-shot Controllable Music Generation from Videos Using Fa...

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Liu, Shilin Kamikawa, Kyohei Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido University Graduate School of Information Science and Technology Japan Hokkaido University Data-Driven Interdisciplinary Research Emergence Department Japan Hokkaido University Faculty of Information Science and Technology Japan

ISBN: (纸本)9798350355079

This paper proposes a method to control a music generation process from videos using users' facial expressions, aligning the music with their emotions. Unlike previous works, this method considers users' facial expressions, which reflect their emotions, to create more resonant music. We establish a functional relationship between facial expressions and music attributes and integrate these functions into the music generation process in a generation phase. Then zero-shot controllable music generation with facial expressions becomes feasible. To address the challenge of objectively verifying the generated music's emotional correspondence, we introduce a novel evaluation metric that compares video, music, and emotions within a common latent space. The experimental results show the effectiveness of our method. © 2024 IEEE.

关键词： Music

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to data Clustering

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Computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Application of Machine Learning in Mental Diseases Diagnosis 25

Application of Machine Learning in Mental Diseases Diagnosis

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25th International Arab Conference on information technology, ACIT 2024

作者： Alsawi, Rahaf Alazaidah, Raed Samara, Ghassan Alzoui, Haneen Alidmat, Omar Elsoud, Esraa Abu Talat Khouj, Mohammed Katrawi, Anwar Alluwaici, Mo'Ath Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient's way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person's mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method. © 2024 IEEE.

关键词： Adversarial machine learning

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A Quantum Tanimoto Coefficient Fidelity for Entanglement Measurement

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 439-450页

作者： Yangyang Zhao Fuyuan Xiao Masayoshi Aritsugi Weiping Ding the School of Big Data and Software Engineering Chongqing UniversityChongqing 401331China IEEE the Faculty of Advanced Science and Technology Kumamoto UniversityKumamoto 860-8555Japan the School of Information and Technology Nantong UniversityNantong 223019China

Fidelity plays an important role in quantum information processing,which provides a basic scale for comparing two quantum *** present,one of the most commonly used fidelities is Uhlmann-Jozsa(U-J)***,U-J fidelity needs to calculate the square root of the matrix,which is not trivial in the case of large or infinite density ***,U-J fidelity is a measure of overlap,which has limitations in some cases and cannot reflect the similarity between quantum states ***,a novel quantum fidelity measure called quantum Tanimoto coefficient(QTC)fidelity is proposed in this *** other existing fidelities,QTC fidelity not only considers the overlap between quantum states,but also takes into account the separation between quantum states for the first time,which leads to a better performance of ***,we discuss the properties of the proposed QTC *** fidelity is compared with some existing fidelities through specific examples,which reflects the effectiveness and advantages of QTC *** addition,based on the QTC fidelity,three discrimination coefficients d_(1)^(QTC),d_(2)^(QTC),and d_^(3)^(QTC)are defined to measure the difference between quantum *** is proved that the discrimination coefficient d_(3)^(QTC)is a true ***,we apply the proposed QTC fidelity-based discrimination coefficients to measure the entanglement of quantum states to show their practicability.

关键词： Distance measure entanglement measurement fidelity measure quantum Tanimoto coefficient(QTC) similarity measure uncertainty

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Generative dataset Distillation Based on Large Model Pool 13

Generative Dataset Distillation Based on Large Model Pool

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13th IEEE Global Conference on Consumer Electronic, GCCE 2024

作者： Li, Longzhen Li, Guang Togo, Ren Maeda, Keisuke Ogawa, Takahiro Haseyama, Miki Hokkaido University Graduate School of Information Science and Technology Japan Hokkaido University Education and Research Center for Mathematical and Data Science Japan Hokkaido University Faculty of Information Science and Technology Japan Hokkaido University Data-Driven Interdisciplinary Research Emergence Department Japan

ISBN: (纸本)9798350355079

Distilling knowledge into generative models is a technique to extract knowledge from a large dataset and embed it into a generative model, which effectively reduces redeployment costs and boosts dataset distillation efficiency. In this paper, we propose a novel approach to enhance the performance of generative models by expanding the size of the model pool. Our approach strives to improve the diversity of models in the model pool so that more distinctive information can be considered when matching the prediction logits. We also verified the proposed method is effective on the CIFAR-10 dataset. © 2024 IEEE.

关键词： Generative adversarial networks

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Cloud Cost Prediction with LSTM on Parametric data 16th

Cloud Cost Prediction with LSTM on Parametric Data

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： San, Khin Pyae Phyo Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

In the distributed computing paradigm, cloud computing provides a wide range of software, platforms and computing infrastructure. It has ability sharable elastic resource pools in dynamic. In this way, cloud computing offers many benefits. It reduces the cost of deployment in applications. It makes easy to increase the capacity of local infrastructures. And it can reduce power consumption by sharing resources. There are challenges over the benefits. The virtual instances needed to be shared equally distributed across different availability zones per demand. That can be categorized into operational and business challenges. In this paper, we implement the cloud prediction system using historical billing data and study the measurement of the model accuracy using Mean Absolute Error and Root Mean Squared Error. The trained model evaluates by the predicted and targeted value graph in this study. By leveraging historical financial data, this method aims to aid organizations in better foreseeing and managing AWS expenditures, leading to improved cost control and financial planning for cloud infrastructure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Optimizing Clustering Approaches in Cloud Environments

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第19期17卷 70-94页

作者： Al-Ghuwairi, Abdel-Rahman Al-Fraihat, Dimah Sharrab, Yousef Kreishan, Yazeed Alsarhan, Ayoub Idhaim, Hasan Qahmash, Ayman Department of Software Engineering Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Software Engineering Faculty of Information Technology Isra University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Isra University Amman Jordan Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Information Systems Computer Science College King Khalid University Abha Saudi Arabia

This study focuses on the challenge of developing abstract models to differentiate various cloud resources. It explores the advancements in cloud products that offer specialized services to meet specific external needs. The study proposes a new approach to request processing in clusters, improving downtime, load distribution, and overall performance. A comparison of three clustering approaches is conducted: local single cluster, local multiple clusters, and multiple cloud clusters. Performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness are evaluated through experiments with 50 requests. All three approaches achieve a 100% success rate, but processing times vary. The local single cluster has the longest duration, while the local multiple clusters and multiple cloud clusters perform better and offer faster processing, scalability, fault tolerance, and availability. From a cost perspective, the local single cluster and local multiple clusters incur capital and operational expenses, while the multiple cloud clusters follow a pay-as-you-go model. Overall, the local multiple clusters and multiple cloud clusters outperform the local single cluster in terms of performance, scalability, fault tolerance, resource allocation, availability, and cost-effectiveness. These findings provide valuable insights for selecting appropriate clustering strategies in cloud environments. © 2023 by the authors of this article. Published under CC-BY.

关键词： Fault tolerance

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Evaluating Pipeline Architecture with Apache Kafka and Apache Flink: data-Driven Architecture 16th

Evaluating Pipeline Architecture with Apache Kafka and Apach...

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16th International Conference on Genetic and Evolutionary Computing, ICGEC 2024

作者： Kyaw, Chan Min Thein, Nwe Nwe Myint Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar Faculty of Computer Science Data Distribution Management Lab University of Information Technology Parami Road Hlaing Campus Yangon Myanmar

ISBN: (纸本)9789819615308

Nowadays, the ability to process, generate and collect various data in real-time has become more relevant for faster reactions to important events in technical enterprises. The numerous distributed stream processing engines is developed to provide the advance large streaming applications. Apache Kafka and Flink also become more popular in the processing and analyzing of large amount of data. Apache Flink is best suited for data analytics, event-driven and data pipeline applications. Applying Apache Kafka to Flink provides low idle time and low latency, and the most demanding stream processing applications in the world. This paper investigates the effective pipeline architecture for developing data driven analytics. Confirming the experimental results, the system proves to optimize the maximum latency, average latency, and idle time of the pipeline architecture. Additionally, further experiments were performed by implementing different levels of parallelism while streaming data, allowing for a comprehensive analysis of maximum latency and average latency of the pipeline. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： data streams

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