检索结果-内蒙古大学图书馆

13th International Conference on Frontier Computing, FC 2023

作者： Cao, Pu Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819993413

In this study, we propose a novel pre-learning approach for genetic programming (GP) that aims to investigate the effect of the probability of being selected for each operator. Furthermore, we present a technique that combines chaos theory and searches for a relatively good possibility mapping for each operator using one-dimensional chaotic mapping. We conducted several sets of comparative experiments on real-world data to test the viability of the proposal. These experiments included comparisons with conventional GP, examination of the impact of various chaotic mappings on the proposed algorithm, and implementation of different optimization strategies to find the relative optimal probability mapping. The experimental results demonstrate that the proposed method can achieve better results than conventional GP in the tested dataset, without considering the total quantitative calculation amount. Through statistical tests, it has been proven that the proposed method is significantly different from the conventional method. However, the discussion regarding the circumstances under which the proposed method can obtain better results when the total calculation amount is limited is not yet fully explored due to the small-scale nature of the experiments. Our future studies will focus on improving and fully discussing this idea. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic programming

来源：评论

学校读者我要写书评

暂无评论

Exploring the Potential of Discrete Chaotic Evolution Algorithm for Combinatorial Optimization

Exploring the Potential of Discrete Chaotic Evolution Algori...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Yi Meng, Xiang Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu Japan Beijing University of Technology Faculty of Information Beijing100124 China

ISBN: (纸本)9781665410205

We propose an extension of the chaotic evolution algorithm into the discrete domain to address combinatorial optimization problems. In this study, we leverage the discrete chaotic evolution algorithm to tackle the Traveling Salesman Problem (TSP) for assessment purposes. The chaotic evolution algorithm exploits the ergodicity of chaos to facilitate the search process within the optimization algorithm. It incorporates a mathematical mechanism into the iterative evolution process, simulating ergodic motion within a search space based on a simple principle. To manage the discrete mutation operation within the chaotic evolution algorithm, we introduce a specifically designed chaotic operation. This operation is tailored for its application in solving combinatorial optimization problems. The chaotic sequence plays a crucial role in determining the mutation location. Our evaluation involves the comparison of our proposed discrete chaotic evolution algorithm with the outcomes of the simulated annealing algorithm and the tabu search algorithm. The assessment serves to demonstrate and validate that the discrete chaotic evolution algorithm yields superior optimization performance within the discrete domain. © 2024 IEEE.

关键词： Optimization algorithms

来源：评论

学校读者我要写书评

暂无评论

Single-to-Multi Music Track Composition Using Interactive Chaotic Evolution 13th

Single-to-Multi Music Track Composition Using Interactive Ch...

引用

13th International Conference on Frontier Computing, FC 2023

作者： Hung, Ying Kai Pei, Yan Li, Jianqiang Graduate School of Computer Science and Engineering University of Aizu Fukushima Aizuwakamatsu965-8580 Japan Faculty of Information Technology Beijing University of Technology Beijing100124 China

ISBN: (纸本)9789819993413

This research presents a new music generation model and a novel MIDI data format for MIDI music generation. This innovative data format allows us to process MIDI music in a manner analogous to video analysis. Initially, the model employs Convolutional Neural Networks (CNN) as an encoder to effectively capture local and global features within the musical data. Subsequently, we utilize a Transformer as a decoder, leveraging its self-attention mechanism to handle the long-term dependencies present in music data. In the training process, an interactive chaotic algorithm is introduced to update the model’s weights, assisting the model in avoiding entrapment in local optima. This enhances the learning efficiency of the model and improves the quality of the generated output, enabling the model to generate music, including accompaniment, that aligns with human aesthetics from any given melody. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Music

来源：评论

学校读者我要写书评

暂无评论

Deceptive Topographic Path Planning 23

Deceptive Topographic Path Planning

引用

Proceedings of the 22nd Brazilian Symposium on Games and Digital Entertainment

作者： Crhistopher Lenhard Henrique L. Krever Ricardo K. Schlsener Rafael H. Bordini Luis A. L. Silva Graduate Program in Computer Science UFSM Brazil Undergraduate Course in Computer Science UFSM Brazil School of Technology PUCRS Brazil

ISBN: (纸本)9798400716270

Deception is essential to model real-world agent behaviors in entertainment and serious games. This paper approaches the challenge of planning deceptive routes on topographic terrains where topographic path costs and other deceptive path factors are analyzed. The work explores alternative deceptive strategies tuned to explore the terrain costs to determine how deceptive the path search nodes are and to return paths with low topographic costs. In addition to the A* algorithm, the Theta* algorithm is explored in the search for smoother deceptive routes, which better represent routes used by terrestrial agents in real-world adversarial situations. It also analyzes deceptive topographic paths computed according to the Last Deceptive Point (LDP) and Last Topographic Deceptive Point (LDPT) notions, where examples of returned paths are presented with the help of terrain relief scenarios. Experimental results with the proposed methods are statistically analyzed according to various path search metrics, showing that all deceptive strategies with A* compute topographic paths with higher numbers of deceptive nodes on reduced execution times. Moreover, the strategies with Theta* and LDPT return paths that show a useful combination of topographic path quality and number of deceptive nodes.

关键词： Deception Deceptive path planning strategies. Path planning Topographic path planning

来源：评论

学校读者我要写书评

暂无评论

The Effect of WebRTC Jitter Buffer on Cloud Streaming Game QoE

The Effect of WebRTC Jitter Buffer on Cloud Streaming Game Q...

引用

Consumer technology (ICCT-Pacific), International Conference on

作者： Naoyasu Kaneko Toshiro Nunome Department of Engineering Computer Science Program Graduate School of Engineering Nagoya Institute of Technology Nagoya Japan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

This paper considers a cloud streaming game using WebRTC. Since the cloud streaming game consists of a server and a client, the network quality between them affects the game. Among the network quality degradation factors, we focus on delay jitter. We reduce the effect of the jitter by buffering. We conduct a subjective experiment of a single-player action game and evaluate application-level QoS and QoE. We investigate the impact of buffering on the video and audio quality of the user playing the game.

关键词： Degradation Games Quality of service Jitter Streaming media Cloud gaming Delays Quality of experience Servers WebRTC

来源：评论

学校读者我要写书评

暂无评论

Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

引用

computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

来源：评论

学校读者我要写书评

暂无评论

Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Classification of Unbalanced Data Based on Bayesian Optimal Neural Network Model

Journal of Network Intelligence

引用

Journal of Network Intelligence 2024年第3期9卷 1427-1440页

作者： Ye, Li-Zhu Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangzhou511363 China School of Graduate Studies Management and Science University 40100 Shah Alam Selangor Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Mosul41002 Iraq Software Engineering and Digital Innovation Center Management and Science University Shah Alam40100 Malaysia

Aiming at the unbalanced problem of big data classification, this paper proposes a classification method of unbalanced data on the ground of Bayesian optimal neural network model. The method firstly uses Optimized Graph Convolutional Network (OGCN) network to carry out message passing mechanism in data graph, designs neural network model with self-attention pooling and mean maximization pooling, and then uses Bayes optimization to update the posterior probability of optimization function by constantly updating the probability model and evaluating the objective function. Effectively solve the problem of speed and optimal solution of hyperparameter optimization in the neural network model. Secondly, the model is used for data set training to obtain the weight of the entire network. Secondly, the unbalanced data is mixed sampling to reduce the number of negative samples. Finally, the capability of the model was verified on the CiteSeer dataset. Experimental results showed that the accuracy rate, accuracy rate and recall rate of the proposed algorithm were 0.974, 0.982 and 0.973, respectively, which effectively improved the accuracy rate, accuracy rate and recall rate of unbalanced data classification compared with other algorithms. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Features Reduction with PCA Technique for Malware Detection Using Machine Learning 9

Features Reduction with PCA Technique for Malware Detection ...

引用

9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Mantoro, Teddy Ginanjar, Agung Ayu, Media Anugerah Aditiawarman, Umar Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia

ISBN: (纸本)9798350370126

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688. © 2023 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：