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distributed Music Comments Mining System Based on BGLL Algorithm

Distributed Music Comments Mining System Based on BGLL Algor...

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international conference on Network and Information Systems for Computers (ICNISC)

作者： Yawei Peng Chengzhang Qu Department of Electronic Information Engineering Wuhan Bussiness University Wuhan China

ISBN: (纸本)9781665453523

this paper aims to establish a distributed music comments mining system based on BGLL algorithm. With the massive music resource and users of NetEase Cloud Music, our system employs big data technology and machine learning technology to analyze data from the perspective of comments, discover users' attitudes towards the song or the music. the whole system is built based on the Hadoop ecosystem, and it includes four main modules: data acquisition, data storage, data analysis, and data visualization. A python based web crawler is used for data acquisition on NetEase Cloud Music resource, then the distributed HDFS system is built for data processing and analyzing. A refined mining algorithm based on BGLL is implemented for topic mining procedure. At last, a word cloud map is generated based on javaweb technology. Experiments show that our system can perfome well on the comment topic mining application, due to the distributed architecture, our system has a potential capability on dealing with large scale of data.

关键词： Learning systems Sentiment analysis Machine learning algorithms Social networking (online) Data acquisition distributed databases Memory

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An Adaptive Tiering Scheme for an Autonomous distributed Storage System 21

An Adaptive Tiering Scheme for an Autonomous Distributed Sto...

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8th international conference on Applied computing and Information Technology, ACIT 2021

作者： Nunome, Atsushi Hirata, Hiroaki Kyoto Institute of Technology Faculty of Information and Human Sciences Kyoto Japan

ISBN: (纸本)9781450384933

Our previous works showed a distributed storage system capable to migrate data blocks autonomously across storage tiers. In this paper, we present an adaptive method for constructing storage tiers in the autonomous distributed storage system. this method can reduce task for tuning runtime parameters. the task normally depends on the configuration of storage devices and the behavior of process, so it should be done periodically by system administrators. the proposed method fine-tunes the configuration of the storage tiers during execution to match the execution environment, thus eliminating the need for manual parameter adjustments by the system administrator. the simulation results show that the proposed method can reduce the execution time by up to about 23% compared to the previous method. © 2021 ACM.

关键词： Virtual storage

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SDN-DDoSNet: A Deep Learning Framework for DDoS Attack Detection in Software-Defined Networks

SDN-DDoSNet: A Deep Learning Framework for DDoS Attack Detec...

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conference on Information and Communication Technology (CICT)

作者： Abhilash Ojha Ashok Yadav Vrijendra Singh Department of Computer Science Tezpur University Tezpur Assam India Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India

ISBN: (数字)9798331531539

ISBN: (纸本)9798331531546

distributed Denial of Service (DDoS) attack is a malicious attempt to disrupt normal web traffic, posing an increasing threat to network infrastructures due to the growing frequency and sophistication of such attacks. this necessitates the development of robust and adaptive defense mechanisms. In this research, we propose a deep learning-based approach for DDoS attack detection, leveraging a Software-Defined Net-working dataset. Our study explores three models: Long-short-term memory,convolutional neural network, and hybrid CNN-LSTM architecture, each designed to capture the temporal and spatial features inherent in network traffic data. the data set includes diverse attack scenarios, including UDP, ICMP, and SYN protocol. the LSTM model achieved an accuracy of 99. 61% in the SDN- DDOS data set, followed by the hybrid model at 99.24%, and the CNN model at 98.86%.

关键词： Deep learning Protocols Neural networks Telecommunication traffic Denial-of-service attack Data models Information and communication technology Computer crime Software defined networking Long short term memory

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A Blockchain-Based IoT Data Secure Vickery Auction System 8th

A Blockchain-Based IoT Data Secure Vickery Auction System

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8th international conference on Artificial Intelligence and Security , ICAIS 2022

作者： Wang, Haohui Chen, Xiubo Ahmad, Haseeb Xu, Gang Yang, Yixian Guizhou Provincial Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang550025 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad37610 Pakistan School of Information Science and Technology North China University of Technology Beijing100144 China

ISBN: (纸本)9783031067631

Traditional centralized architectures for IoT data suffer from single points of failure in terms of data storage and access control, and IoT data does not fully realize its value as a digital asset. In response to the above challenges, we propose a decentralized storage and lightweight access control method for IoT data based on blockchain technology, and put forward an auction system to commercialize the IoT data. Firstly, a blockchain-based data storage method proposed using Inter Planetary File System (IPFS), which ensures the data is immutable and traceable. Secondly, in this paper, we propose a distributed lightweight Capability-Based Access Control (CapBAC) based on blockchain, in which a time dimension is added so that no additional token revocation operation is required. thus, lightweight access control is achieved under IoT devices with limited computing power. Finally, we come up with a blockchain-based Vickery auction system to sell the data to the highest bidder in demand. this auction effectively protects the privacy of bidders and does not require third-party intermediary fees. And it can ensure fairness and non-repudiation of the auction. By analyzing the entire system process, we guarantee secure storage of IoT data. Besides, we can achieve lightweight access control and realize the value of the data based on the blockchain technology. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Access control

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Towards Accurate and Efficient Super Spreader Detection with Sketching

Towards Accurate and Efficient Super Spreader Detection with...

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IEEE international conference on Data Science in Cyberspace (DSC)

作者： Shouyou Song Pu Wang Yangchun Li Nanyang Wang Wei Jiang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education BUPT Beijing China Chinese Academy of Cyberspace Studies Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT Beijing China School of Computer and Information Engineering Chuzhou University Anhui China

Super spreaders are the flow that have a large number of distinct connections (also called spread), which related with many threats to networks. Estimating flow spread is the crucial step in super spreader detection. However, existing methods cannot achieve flow spread estimation in terms of accurate, efficient, and reversible simultaneously. All these characteristics is highly required for high-speed network measurement. In this paper, we propose MorphSketch, a new data structure that estimates flow spread for super spreader detection with high accuracy, memory efficiency, high throughput and reversibility. MorphSketch combines hashing with sampling to process packets in order to improve throughput. It uses self-morph bitmap to record spread information, which can adaptively enlarge the upper bound of spread estimation under limited memory usage to ensure accuracy and memory efficiency. Moreover, MorphSketch can track candidate super spreader by comparing corresponding spread information, which realizes reversibility in super spreader detection. We perform a series of performance evaluations on real world traffic trace. Experiment results demonstrate that under same memory usage, the MorphSketch significantly outperforms existing work in terms of accuracy and efficiency.

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A Novel Approach to Feature Extraction in MI - Based BCI Systems

A Novel Approach to Feature Extraction in MI - Based BCI Sys...

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international conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Arun Kumar S L Anand Anil Kannur Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Computer Science and Engineering BLDEA'S V.P. Dr. P.G. Halakatti College of Engineering and Technology Vijayapura

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Brain-computer interfaces based on motor imagery (MI-BCI) enable communication between the brain and the outside world. It accomplishes this by capturing and examining the EEG data generated when a person visualizes moving a certain limb. Based on this investigation; to enhance categorization precision, we enhance the attribute extraction process. Once the EEG signals have been pre-processed and deconstructed into distinct bands of frequencies, we apply similar spatial algorithms for every sub-band. After that, we use Blind Source Separation (BSS) and spatial filter techniques to extract attributes directly from the sources of the brain instead of only from channels. Ultimately, by choosing the most relevant attributes as inputs for the categorization stage, we distinguish between the envisioned movements. To assess our recommended approach, we utilized data set IVa from the BCI competition III. the outcomes from diverse experiments indicate that the system achieves an average accuracy of 98.8%, with 100% sensitivity across all subjects. these results illustrate that the suggested approach effectively improves MI-based BCI.

关键词： Training Sensitivity Accuracy System performance Spatial filters Electroencephalography Brain-computer interfaces Blind source separation Reliability Standards

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An Energy-Aware Multi-Criteria Federated Learning Model for Edge computing 8

An Energy-Aware Multi-Criteria Federated Learning Model for ...

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8th international conference on Future Internet of things and Cloud, FiCloud 2021

作者： Al-Saedi, Ahmed A. Casalicchio, Emiliano Boeva, Veselka Blekinge Institute of Technology Department of Computer Science Karlskrona Sweden Sapienza University of Rome Departement of Computer Science Italy

ISBN: (纸本)9781665425742

the successful convergence of Internet of things (IoT) technology and distributed machine learning have leveraged to realise the concept of Federated Learning (FL) with the collaborative efforts of a large number of low-powered and small-sized edge nodes. In Wireless Networks (WN), an energy-efficient transmission is a fundamental challenge since the energy resource of edge nodes is restricted. In this paper, we propose an Energy-aware Multi-Criteria Federated Learning (EaMC-FL) model for edge computing. the proposed model enables to collaboratively train a shared global model by aggregating locally trained models in selected representative edge nodes (workers). the involved workers are initially partitioned into a number of clusters with respect to the similarity of their local model parameters. At each training round a small set of representative workers is selected on the based of multi-criteria evaluation that scores each node representativeness (importance) by taking into account the trade-off among the node local model performance, consumed energy and battery lifetime. We have demonstrated through experimental results the proposed EaM C-Flmodel is capable of reducing the energy consumed by the edge nodes by lowering the transmitted data. © 2021 IEEE.

关键词： Edge computing

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Twitter Tweet: Sentiment Analysis on Illegal Investment using Naive Bayes Algorithm

Twitter Tweet: Sentiment Analysis on Illegal Investment usin...

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international conference on computing, Engineering, and Design (ICCED)

作者： Anisa Sholihat Fahurachman Bei Regina Ainaya Falentino Sembiring Amy Lattu Universitas Nusa Putra Sukabumi Indonesia

ISBN: (纸本)9781665453905

In today’s era, investment is in demand by society because of its vast profits. In recent months, Indonesia has been shocked by illegal investment platforms or online trading platforms that are touted as fraudulent investments. Overcoming this problem, sentiment analysis was carried out with the help of social media Twitter. this study aims to find out and analyze people’s sentiments. It is then classified into positive, neutral, and negative sentiments. this study used the naïve Bayes method with orange application. Data collection was taken in March 2022. the survey results obtained 152 positive sentiment data, 244 negative sentiment data, and 175 neutral sentiment data with an accuracy rate of 81.069%. this means that the level of negative sentiment is greater than that of positive sentiment. With this research, it is hoped that it can benefit the public to be more careful in choosing investments.

关键词： Sentiment analysis Social networking (online) Blogs Data collection Naive Bayes methods Investment

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Factors Influencing Security, Trust and Customer Continuance Usage Intention of Cloud based Electronic Payment System in Indonesia 8

Factors Influencing Security, Trust and Customer Continuance...

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8th international conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2021

作者： Gui, Anderes Dwi Putra, Ananda Bilal Sienarto, Axel Geovanni Andriawan, Hary Made Karmawan, I Gusti Permatasari, Angelina Bina Nusantara University Information Systems Study Program School of Information Systems Jakarta Indonesia

ISBN: (纸本)9781665439985

Due to the increase in electronic payment system use, this study explores the factors influencing continuance usage intention of cloud based electronic payment system in Indonesia. A total of 385 respondents are collected using go ogle form and analyzed using SmartPLS 3.3. Questionnaire distributed using snowball technique by using social media such as line and telegram from 1 May until 21 June 2021. the result show only 3 hypotheses rejected which security has not impact to perceived security, technical protection has not impact to perceived trust and security statement has not impact to perceived trust. Another hypotheses supported such as transaction procedure, technical protection and usability have positive influence to perceived security;transaction procedure and usability have positive influence to perceived trust;perceived security and trust affect continuance usage intention of electronic payment system in Indonesia. To increase customer intention, both management of electronic payment system and merchant need to improve their security and trust. © 2021 IEEE.

关键词： Trusted computing

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distributed Misbehavior Detection System for Cooperative Driving Networks

Distributed Misbehavior Detection System for Cooperative Dri...

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IEEE international conference on Fog and Edge computing (ICFEC)

作者： Abdullahi Modibbo Abdullahi Wassila Lalouani Department of Computer and Information Science Towson University Towson Maryland USA

ISBN: (数字)9798350361353

ISBN: (纸本)9798350361360

Cooperative Intelligent Transportation Systems (C-ITS) are foundational evolution of vehicular networks, emphasizing the seamless coordination of Vehicle-to-Vehicle communication for exchanging vital information. In C-ITS, vehicles collaborate by distributing V2V messages throughout the network. However, the potential transmission of deceptive or inaccurate information by a malicious vehicle poses a significant risk to road safety. Detecting such malicious data promptly is crucial for taking appropriate actions. Current Misbehavior Detection Systems (MBS) operate with the assumption of a centralized MBS existence, but the challenge lies in empowering vehicles to make local MBS decisions as early as possible. this approach avoids the exchange of all messages with the infrastructure, reducing excessive communication overhead. In response to this challenge, we introduce an innovative distributed misbehavior detection system designed to protect vehicular networks from potential attacks. Our proposed system harnesses the scalability benefits of Federated Learning, allowing vehicles to collaboratively learn a shared model for detecting faulty and malicious messages. Recognizing the impracticality of assuming that all vehicles possess fully labeled data in real-world settings, we propose Fisher-Discriminant Analysis and model regularization to generate an effective pseudo-labeling mechanism. the model regularization proves effective when multiple models from different vehicles produce less-confidence predictions for a given sequence of Basic Safety Messages. Our approach demonstrates efficacy in identifying a broad spectrum of faults and attacks, including rare types, by learning the inherent data distribution and essential network traffic characteristics.

关键词： Training Adaptation models Analytical models Vehicular ad hoc networks Transportation Predictive models Data models Safety Vehicle dynamics Vehicle-to-everything

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