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Hand Gesture Recognition for Multi-Culture Sign Language Using Graph and General Deep Learning Network IEEE Open J. Comput. Soc. Pub Date : 2024-02-28 Abu Saleh Musa Miah, Md. Al Mehedi Hasan, Yoichi Tomioka, Jungpil Shin
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Evaluating Cryptocurrency Market Risk on the Blockchain: An Empirical Study Using the ARMA-GARCH-VaR Model IEEE Open J. Comput. Soc. Pub Date : 2024-02-27 Yongrong Huang, Huiqing Wang, Zhide Chen, Chen Feng, Kexin Zhu, Xu Yang, Wencheng Yang
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Unveiling the Connection Between Malware and Pirated Software in Southeast Asian Countries: A Case Study IEEE Open J. Comput. Soc. Pub Date : 2024-02-09 Asif Iqbal, Muhammad Naveed Aman, Ramkumar Rejendran, Biplab Sikdar
Pirated software is an attractive choice for cybercriminals seeking to spread malicious software, known as malware. This paper attempts to quantify the occurrence of malware concealed within pirated software. We collected samples of pirated software from various sources from Southeast Asian countries, including hard disk drives, optical discs purchased in eight different countries, and online platforms
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CTLA: Compressed Table Look up Algorithm for Open Flow Switch IEEE Open J. Comput. Soc. Pub Date : 2024-02-02 Veeramani Sonai, Indira Bharathi, Muthaiah Uchimucthu, Sountharrajan S, Durga Prasad Bavirisetti
The size of the TCAM memory grows as more entries are added to the flow table of Open Flow switch. The procedure of looking up an IP address involves finding the longest prefix. In order to keep up with the link speed, the IP lookup operation in the forwarding table should also need to be speed up. TCAM's scalability and storage are constrained by its high power consumption and circuit density. The
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Slingshot: Globally Favorable Local Updates for Federated Learning IEEE Open J. Comput. Soc. Pub Date : 2024-01-22 Jialiang Liu, Huawei Huang, Chun Wang, Sicong Zhou, Ruixin Li, Zibin Zheng
Federated Learning (FL), as a promising distributed learning paradigm, is proposed to solve the contradiction between the data hunger of modern machine learning and the increasingly stringent need for data privacy. However, clients naturally present different distributions of their local data and inconsistent local optima, which leads to poor model performance of FL. Many previous methods focus on
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Low Area and Low Power FPGA Implementation of a DBSCAN-Based RF Modulation Classifier IEEE Open J. Comput. Soc. Pub Date : 2024-01-18 Bill Gavin, Tiantai Deng, Edward Ball
This paper presents a new low-area and low-power Field Programmable Gate Array (FPGA) implementation of a Radio Frequency (RF) modulation classifier based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, known as DBCLASS. The proposed architecture demonstrates a novel approach for the efficient hardware realisation of the DBSCAN algorithm by utilising parallelism
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Editorial IEEE Open J. Comput. Soc. Pub Date : 2024-01-01 Song Guo
I have had the great honor of serving as Editor-in-Chief of the IEEE Open Journal of The Computer Society (OJCS) for four years. As described more fully below, I have worked with a great team of advisory board members, associate editors, reviewers, and authors, and together we have raised the profile of OJCS and achieved unprecedented performance. During this period, the OJCS Advisory Board and Editorial
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Prediction of Customer Behavior Changing via a Hybrid Approach IEEE Open J. Comput. Soc. Pub Date : 2023-11-29 Nien-Ting Lee, Hau-Chen Lee, Joseph Hsin, Shih-Hau Fang
This study proposes a hybrid approach to predict customer churn by combining statistic approaches and machine learning models. Unlike traditional methods, where churn is defined by a fixed period of time, the proposed algorithm uses the probability of customer alive derived from the statistical model to dynamically determine the churn line. After observing customer churn through clustering over time
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A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model IEEE Open J. Comput. Soc. Pub Date : 2023-11-20 Chhaya Gupta, Nasib Singh Gill, Preeti Gulia, Sangeeta Yadav, Giovanni Pau, Mohammad Alibakhshikenari, Xiangjie Kong
Computer vision technologies have greatly improved in the last few years. Many problems have been solved using deep learning merged with more computational power. Action recognition is one of society's problems that must be addressed. Human Action Recognition (HAR) may be adopted for intelligent video surveillance systems, and the government may use the same for monitoring crimes and security purposes
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Benchmark for Personalized Federated Learning IEEE Open J. Comput. Soc. Pub Date : 2023-11-13 Koji Matsuda, Yuya Sasaki, Chuan Xiao, Makoto Onizuka
Federated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets. Since data distributions may differ across clients, data heterogeneity is a challenging issue in federated learning. To address this issue, numerous federated learning methods have been proposed to build personalized
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Data Extraction and Question Answering on Chart Images Towards Accessibility and Data Interpretation IEEE Open J. Comput. Soc. Pub Date : 2023-10-31 Shahira K C, Pulkit Joshi, Lijiya A
Graphical representations such as chart images are integral to web pages and documents. Automating data extraction from charts is possible by reverse-engineering the visualization pipeline. This study proposes a framework that automates data extraction from bar charts and integrates it with question-answering. The framework employs an object detector to recognize visual cues in the image, followed
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Finding the Truth From Uncertain Time Series by Differencing IEEE Open J. Comput. Soc. Pub Date : 2023-10-19 Jizhou Sun, Delin Zhou, Bo Jiang
Time series data is ubiquitous and of great importance in real applications. But due to poor qualities and bad working conditions of sensors, time series reported by them contain more or less noises. To reduce noise, multiple sensors are usually deployed to measure an identical time series and from these observations the truth can be estimated, which derives the problem of truth discovery for uncertain
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Towards Reliable Utilization of AIGC: Blockchain-Empowered Ownership Verification Mechanism IEEE Open J. Comput. Soc. Pub Date : 2023-09-18 Chuan Chen, Yihao Li, Zihou Wu, Mingfeng Xu, Rui Wang, Zibin Zheng
With the development of the blockchain technology, a decentralized and de-trusted network paradigm has been constructed, enabling multiple digital assets like NFT, to be permanently recorded and authenticated by blockchain. Also, the uniqueness and verifiability of these assets allows them to flow and generate value between any network entities. With the emergence of AI Generative Content (AIGC), the
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Real-Time High-Quality Visualization for Volumetric Contents Rendering: A Lyapunov Optimization Framework IEEE Open J. Comput. Soc. Pub Date : 2023-09-06 Hankyul Baek, Rhoan Lee, Soyi Jung, Joongheon Kim, Soohyun Park
Real-time volumetric contents streaming on augmented reality (AR) devices should necessitate a balance between end-users' quality of experience (QoE) and the latency requirements. Lowering the quality of the volumetric contents to diminish the latency hinders the user's QoE. Otherwise, setting the quality of volumetric contents relatively high to improve the users' QoE increases the latency, which
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MetaCIDS: Privacy-Preserving Collaborative Intrusion Detection for Metaverse based on Blockchain and Online Federated Learning IEEE Open J. Comput. Soc. Pub Date : 2023-09-05 Vu Tuan Truong, Long Bao Le
Metaverse is expected to rely on massive Internet of Things (IoT) connections so it inherits various security threats from the IoT network and also faces other sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale distributed metaverse, this paper proposes MetaCIDS, a novel collaborative intrusion detection (CID)
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Co-Existence With IEEE 802.11 Networks in the ISM Band Without Channel Estimation IEEE Open J. Comput. Soc. Pub Date : 2023-08-31 Muhammad Naveed Aman, Muhammad Ishfaq, Biplab Sikdar
Any new deployment of networks in the industrial, scientific, and medical (ISM) band, even though it is license-free, has to co-exist with IEEE 802.11 networks. IoT devices are typically deployed in the ISM band, creating a spectrum bottleneck for competing networks. This article investigates the issue of co-existence of wireless networks with WiFi networks. In our scenario, we consider WiFi as the
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An Identity-Based Adaptor Signature Scheme and its Applications in the Blockchain System IEEE Open J. Comput. Soc. Pub Date : 2023-08-29 Zijian Bao, Debiao He, Cong Peng, Min Luo, Kim-Kwang Raymond Choo
Adaptor signature, as a new emerging cryptographic primitive, has become one promising method to mitigate the scalability issue on blockchain. It can transform an incomplete signature into a complete signature by revealing the witness of a pre-set hard relation, which can be applied to atomic swap, payment channel, payment hub, and other blockchain scenarios. Recently, a general transformation for
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A Survey on ChatGPT: AI–Generated Contents, Challenges, and Solutions IEEE Open J. Comput. Soc. Pub Date : 2023-08-16 Yuntao Wang, Yanghe Pan, Miao Yan, Zhou Su, Tom H. Luan
With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at a faster pace and lower cost, based on user-provided
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Twitter Bot Detection Using Neural Networks and Linguistic Embeddings IEEE Open J. Comput. Soc. Pub Date : 2023-08-07 Feng Wei, Uyen Trang Nguyen
Twitter is a web application playing the dual role of online social networking and micro-blogging. The popularity and open structure of Twitter have attracted a large number of automated programs, known as bots. In this article, we propose a Twitter bot detection model using recurrent neural networks, specifically bidirectional lightweight gated recurrent unit (BiLGRU), and linguistic embeddings. To
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Practical Anti-Fuzzing Techniques With Performance Optimization IEEE Open J. Comput. Soc. Pub Date : 2023-08-04 Zhengxiang Zhou, Cong Wang
Fuzzing, an automated software testing technique, has achieved remarkable success in recent years, aiding developers in identifying vulnerabilities. However, fuzzing can also be exploited by attackers to discover zero-day vulnerabilities. To counter this threat, researchers have proposed anti-fuzzing techniques, which aim to impede the fuzzing process by slowing the program down, providing misleading
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Reverse Self-Distillation Overcoming the Self-Distillation Barrier IEEE Open J. Comput. Soc. Pub Date : 2023-06-21 Shuiping Ni, Xinliang Ma, Mingfu Zhu, Xingwang Li, Yu-Dong Zhang
Deep neural networks generally cannot gather more helpful information with limited data in image classification, resulting in poor performance. Self-distillation, as a novel knowledge distillation technique, integrates the roles of teacher and student into a single network to solve this problem. A better understanding of the efficiency of self-distillation is critical to its advancement. In this article
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Disjunctive Threshold Networks for Tabular Data Classification IEEE Open J. Comput. Soc. Pub Date : 2023-06-05 Weijia Wang, Litao Qiao, Bill Lin
While neural networks have been achieving increasingly significant excitement in solving classification tasks such as natural language processing, their lack of interpretability becomes a great challenge for neural networks to be deployed in certain high-stakes human-centered applications. To address this issue, we propose a new approach for generating interpretable predictions by inferring a simple
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A Light-Weight Technique to Detect GPS Spoofing Using Attenuated Signal Envelopes IEEE Open J. Comput. Soc. Pub Date : 2023-05-22 Xiao Wei, Muhammad Naveed Aman, Biplab Sikdar
Global Positioning System (GPS) spoofing attacks have attracted more attention as one of the most effective GPS attacks. Since the signals from an authentic satellite and the spoofer undergo different attenuation, the captured envelope of fake GPS signals exhibits distinctive transmission characteristics due to short transmission paths. This can be utilized for GPS spoofing detection. The existing
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State Space Explosion Mitigation for Large-Scale Attack and Compliance Graphs Using Synchronous Exploit Firing IEEE Open J. Comput. Soc. Pub Date : 2023-05-16 Noah L. Schrick, Peter J. Hawrylak
Attack and compliance graphs are useful tools for cybersecurity and regulatory or compliance analysis. Thgraphs represent the state of a system or a set of systems, and can be used to identify all current or future ways the systems are compromised or at risk of violating regulatory or compliance mandates. However, due to their exhaustiveness and thorough permutation checking, these graphs suffer from
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Backdoor Attacks to Deep Learning Models and Countermeasures: A Survey IEEE Open J. Comput. Soc. Pub Date : 2023-04-14 Yudong Li, Shigeng Zhang, Weiping Wang, Hong Song
Backdoor attacks have severely threatened deep neural network (DNN) models in the past several years. In backdoor attacks, the attackers try to plant hidden backdoors into DNN models, either in the training or inference stage, to mislead the output of the model when the input contains some specified triggers without affecting the prediction of normal inputs not containing the triggers. As a rapidly
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FLIS: Clustered Federated Learning Via Inference Similarity for Non-IID Data Distribution IEEE Open J. Comput. Soc. Pub Date : 2023-03-27 Mahdi Morafah, Saeed Vahidian, Weijia Wang, Bill Lin
Conventional federated learning (FL) approaches are ineffective in scenarios where clients have significant differences in the distributions of their local data. The Non-IID data distribution in the client data causes a drift in the local model updates from the global optima, which significantly impacts the performance of the trained models. In this article, we present a new algorithm called FLIS that
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Promoting the Sustainability of Blockchain in Web 3.0 and the Metaverse Through Diversified Incentive Mechanism Design IEEE Open J. Comput. Soc. Pub Date : 2023-03-23 Daniel Mawunyo Doe, Jing Li, Niyato Dusit, Zhen Gao, Jun Li, Zhu Han
This article explores the role of blockchains in the development of Web 3.0 and the Metaverse. The success of these technologies is dependent on the utilization of decentralized systems like blockchains, which can store and validate data on identities and reputations and facilitate the exchange of virtual assets. Full nodes, which store the entire blockchain state and validate all transactions, are
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Blockchain-Aided Secure Semantic Communication for AI-Generated Content in Metaverse IEEE Open J. Comput. Soc. Pub Date : 2023-03-23 Yijing Lin, Hongyang Du, Dusit Niyato, Jiangtian Nie, Jiayi Zhang, Yanyu Cheng, Zhaohui Yang
The construction of virtual transportation networks requires massive data to be transmitted from edge devices to Virtual Service Providers (VSP) to facilitate circulations between the physical and virtual domains in Metaverse. Leveraging semantic communication for reducing information redundancy, VSPs can receive semantic data from edge devices to provide varied services through advanced techniques
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An Efficient Decentralized Identity Management System Based on Range Proof for Social Networks IEEE Open J. Comput. Soc. Pub Date : 2023-03-16 Xinjie Zhu, Debiao He, Zijian Bao, Min Luo, Cong Peng
Online social networks (OSNs) are becoming more and more popular in people's lives as the demand for online interaction continues to grow. Current OSNs are using centralized identity management system (IDM), which has some problems of single point of failure and privacy data leakage. The emergence of decentralized identity (DID) can solve these problems. However, most existing DID systems have some
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Blockchain-Based Decentralized Application: A Survey IEEE Open J. Comput. Soc. Pub Date : 2023-03-13 Peilin Zheng, Zigui Jiang, Jiajing Wu, Zibin Zheng
Blockchain-based decentralized applications (DApp) draw more attention with the increasing development and wide application of blockchain technologies. A wealth of funds are invested into the crowd-funding of various types of DApp. As reported in August 2022, there are more than 5,000 DApps with more than 1.67 million daily Unique Active Wallets (users). However, the definition, architectures, and
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A Unifying Mathematical Definition of Particle Methods IEEE Open J. Comput. Soc. Pub Date : 2023-03-08 Johannes Pahlke, Ivo F. Sbalzarini
Particle methods are a widely used class of algorithms for computer simulation of complex phenomena in various fields, such as fluid dynamics, plasma physics, molecular chemistry, and granular flows, using diverse simulation methods, including Smoothed Particle Hydrodynamics (SPH), Particle-in-Cell (PIC) methods, Molecular Dynamics (MD), and Discrete Element Methods (DEM). Despite the increasing use
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Towards Area Efficient Logic Circuit: Exploring Potential of Reconfigurable Gate by Generic Exact Synthesis IEEE Open J. Comput. Soc. Pub Date : 2023-03-06 Liuting Shang, Azad Naeemi, Chenyun Pan
In this article, we propose a generic design methodology to achieve area-efficient reconfigurable logic circuits by using exact synthesis based on Boolean satisfiability (SAT) solver. The proposed methodology better leverages the high representation ability of emerging reconfigurable logic gates (RLGs) to achieve reconfigurable circuits with fewer gates. In addition, we propose a fence-based acceleration
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Online Identification Method of Tea Diseases in Complex Natural Environments IEEE Open J. Comput. Soc. Pub Date : 2023-02-22 Senlin Xie, Chunwu Wang, Chang Wang, Yifan Lin, Xiaoqing Dong
An intelligent Internet-of-Things (IoT) hardware system in the field tea plantations was built, comprising collection of tea images by HD zoom cameras in a cluster structure and deployment of the detection model by cluster-head edge computing nodes. Data was sent to customer premise equipment through edge nodes and gateways and then to cloud platforms, which provided a hardware platform for identifying
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Financial Crimes in Web3-Empowered Metaverse: Taxonomy, Countermeasures, and Opportunities IEEE Open J. Comput. Soc. Pub Date : 2023-02-16 Jiajing Wu, Kaixin Lin, Dan Lin, Ziye Zheng, Huawei Huang, Zibin Zheng
At present, the concept of metaverse has sparked widespread attention from the public to major industries. With the rapid development of blockchain and Web3 technologies, the decentralized metaverse ecology has attracted a large influx of users and capital. Due to the lack of industry standards and regulatory rules, the Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes
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Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges IEEE Open J. Comput. Soc. Pub Date : 2023-01-20 Xing An, Celimuge Wu, Yangfei Lin, Min Lin, Tsutomu Yoshinaga, Yusheng Ji
Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination,
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SC-FGCL: Self-Adaptive Cluster-Based Federal Graph Contrastive Learning IEEE Open J. Comput. Soc. Pub Date : 2023-01-11 Tingqi Wang, Xu Zheng, Lei Gao, Tianqi Wan, Ling Tian
As a self-supervised learning method, the graph contrastive learning achieve admirable performance in graph pre-training tasks, and can be fine-tuned for multiple downstream tasks such as protein structure prediction, social recommendation, etc. One prerequisite for graph contrastive learning is the support of huge graphs in the training procedure. However, the graph data nowadays are distributed in
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An Efficient Connected-Component Labeling Algorithm for 3-D Binary Images IEEE Open J. Comput. Soc. Pub Date : 2022-12-30 Xiao Zhao, Yuyan Chao, Hui Zhang, Bin Yao, Lifeng He
Conventional voxel-based algorithms for labeling connected components in 3D binary images use the same mask to process all object voxels. To reduce the number of times that neighboring voxels are checked when object voxels are processed, we propose an algorithm that uses two different masks for processing two different types of object voxels. One type of mask is used when the voxel preceding the object
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Optimized UAV Trajectory and Transceiver Design for Over-the-Air Computation Systems IEEE Open J. Comput. Soc. Pub Date : 2022-12-21 Xiang Zeng, Xiao Zhang, Feng Wang
This article investigates a multi-slot unmanned aerial vehicle (UAV) assisted over-the-air computation (AirComp) system, where the UAV is deployed as a flying base station to compute functional values of data distributed at multiple ground sensors via AirComp. Subject to the power constraints of the UAV and ground sensors, we minimize the computational mean-squared error (MSE) of AirComp, by optimizing
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Local Differential Privacy for Person-to-Person Interactions IEEE Open J. Comput. Soc. Pub Date : 2022-12-14 Yuichi Sei, Akihiko Ohsuga
Currently, many global organizations collect personal data for marketing, recommendation system improvement, and other purposes. Some organizations collect personal data securely based on a technique known as $\epsilon$ -local differential privacy (LDP). Under LDP, a privacy budget is allocated to each user in advance. Each time the user's data are collected, the user's privacy budget is consumed,
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Towards Efficient and Privacy-Preserving Versatile Task Allocation for Internet of Vehicles IEEE Open J. Comput. Soc. Pub Date : 2022-11-29 Zihan Li, Mingyang Zhao, Guanyu Chen, Chuan Zhang, Tong Wu, Liehuang Zhu
Nowadays, task allocation has attracted increasing attention in the Internet of Vehicles. To efficiently allocate tasks to suitable workers, users usually need to publish their task interests to the service provider, which brings a serious threat to users' privacy. Existing task allocation schemes either cannot comprehensively preserve user privacy (i.e., requester privacy and worker privacy) or introduce
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Towards Efficient and Delay-Aware NFV-Enabled Unicasting With Adjustable Service Function Chains IEEE Open J. Comput. Soc. Pub Date : 2022-11-10 Longqu Li, Pengxin Zheng, Quan Chen, Tao Wang, Feng Wang, Yongchao Tao, Jizhou Sun
Network Function Virtualization (NFV) has becoming an emerging technology for ensuring the reliability, security and scalability of data flows. The Virtual Network Function (VNF) embedding problem, which tries to minimize the embedding cost and link connection cost toward customers or maximize network throughput for a given set of NFV-enabled requests, has attracted extensive interests recently. However
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Towards Capacity-Adjustable and Scalable Quotient Filter Design for Packet Classification in Software-Defined Networks IEEE Open J. Comput. Soc. Pub Date : 2022-11-04 Minghao Xie, Quan Chen, Tao Wang, Feng Wang, Yongchao Tao, Lianglun Cheng
Software defined networking (SDN), which can provide a dynamic and configurable network architecture for resource allocation, have been widely employed for efficient massive data traffic management. To accelerate the packet classification process in SDN, the hash-based filters which can support fast approximate membership query have been widely employed. However, the existing Quotient Filters are limited
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Utility-Oriented Computation Scheduling for Energy-Efficient Mobile Edge Computing Networks IEEE Open J. Comput. Soc. Pub Date : 2022-11-04 Ran Bi, Yiwei Sun, Yuexin He, Ting Peng, Meng Han, Guozhen Tan
As a new computing paradigm, mobile edge computing (MEC) enables users to execute computation-intensive tasks at the network edge nodes (ENs) through computation offloading. Energy consumption of computation offloading is envisioned as a significant metric to satisfy the high quality of experience (QoE). In multi-ENs MEC networks, computation scheduling and power control of each user is tightly coupled
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Efficient Video Privacy Protection Against Malicious Face Recognition Models IEEE Open J. Comput. Soc. Pub Date : 2022-11-01 Enting Guo, Peng Li, Shui Yu, Hao Wang
The proliferation of powerful facial recognition systems poses a serious threat to user privacy. Attackers could train highly accurate facial recognition models using public data on social platforms. Therefore, recent works have proposed image pre-processing techniques to protect user privacy. Without affecting people's normal viewing, these techniques add special noises into images, so that it would
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When Digital Economy Meets Web3.0: Applications and Challenges IEEE Open J. Comput. Soc. Pub Date : 2022-10-27 Chuan Chen, Lei Zhang, Yihao Li, Tianchi Liao, Siran Zhao, Zibin Zheng, Huawei Huang, Jiajing Wu
With the continuous development of web technology, Web3.0 has attracted a considerable amount of attention due to its unique decentralized characteristics. The digital economy is an important driver of high-quality economic development and is currently in a rapid development stage. In the digital economy scenario, the centralized nature of the Internet and other characteristics usually bring about
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Cardinality Estimation in Inner Product Space IEEE Open J. Comput. Soc. Pub Date : 2022-10-17 Kohei Hirata, Daichi Amagata, Takahiro Hara
This article addresses the problem of cardinality estimation in inner product spaces. Given a set of high-dimensional vectors, a query, and a threshold, this problem estimates the number of vectors such that their inner products with the query are not less than the threshold. This is an important problem for recent machine-learning applications that maintain objects, such as users and items, by using
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An Attention-Based Neural Network Using Human Semantic Knowledge and Its Application to Clickbait Detection IEEE Open J. Comput. Soc. Pub Date : 2022-10-12 Feng Wei, Uyen Trang Nguyen
Clickbait is a commonly used social engineering technique to carry out phishing attacks, illegitimate marketing, and dissemination of disinformation. As a result, clickbait detection has become a popular research topic in recent years due to the prevalence of clickbait on the web and social media. In this article, we propose a novel attention-based neural network for the task of clickbait detection
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Forecasting TCP's Rate to Speed up Slow Start IEEE Open J. Comput. Soc. Pub Date : 2022-09-22 Ralf Lübben
Selection of the optimal transmission rate in packet-switched best-effort networks is challenging. Typically, senders do not have any information about the end-to-end path and should not congest the connection but at once fully utilize it. The accomplishment of these goals lead to congestion control protocols such as TCP Reno, TCP Cubic, or TCP BBR that adapt the sending rate according to extensive
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Fusion of Building Information Modeling and Blockchain for Metaverse: A Survey IEEE Open J. Comput. Soc. Pub Date : 2022-09-15 Huakun Huang, Xiangbin Zeng, Lingjun Zhao, Chen Qiu, Huijun Wu, Lisheng Fan
Metaverse and blockchain, as the latest buzzwords, have attracted great attention from industry and academia. They will inevitably promote technological innovation in the field of building information modeling (BIM) in the future. BIM organizes various building information into a whole by establishing a virtual three-dimensional model of architectural engineering using digital technology. The metaverse
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Collaborative Federated Learning for Healthcare: Multi-Modal COVID-19 Diagnosis at the Edge IEEE Open J. Comput. Soc. Pub Date : 2022-09-14 Adnan Qayyum, Kashif Ahmad, Muhammad Ahtazaz Ahsan, Ala Al-Fuqaha, Junaid Qadir
Despite significant improvements over the last few years, cloud-based healthcare applications continue to suffer from poor adoption due to their limitations in meeting stringent security, privacy, and quality of service requirements (such as low latency). The edge computing trend, along with techniques for distributed machine learning such as federated learning, has gained popularity as a viable solution
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AI-Driven Energy-Efficient Content Task Offloading in Cloud-Edge-End Cooperation Networks IEEE Open J. Comput. Soc. Pub Date : 2022-09-14 Chao Fang, Xiangheng Meng, Zhaoming Hu, Fangmin Xu, Deze Zeng, Mianxiong Dong, Wei Ni
To tackle a challenging energy efficiency problem caused by the growing mobile Internet traffic, this paper proposes a deep reinforcement learning (DRL)-based green content task offloading scheme in cloud-edge-end cooperation networks. Specifically, we formulate the problem as a power minimization model, where requests arriving at a node for the same content can be aggregated in its queue and in-network
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PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack Detection for AC-Model Transmission Systems IEEE Open J. Comput. Soc. Pub Date : 2022-08-18 Xuefei Yin, Yanming Zhu, Yi Xie, Jiankun Hu
Smart grids are vulnerable to stealthy false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad data detection mechanisms. Methods based on deep learning technology have shown promising accuracy in the detection of SFDIAs. However, most existing methods rely on the temporal structure of a sequence of measurements but do not take account of the spatial structure between buses and
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Leakage-Resilient Certificate-Based Authenticated Key Exchange Protocol IEEE Open J. Comput. Soc. Pub Date : 2022-08-11 Tung-Tso Tsai, Sen-Shan Huang, Yuh-Min Tseng, Yun-Hsin Chuang, Ying-Hao Hung
Certificate-based public key cryptography (CB-PKC) removes the problem of certificate management in traditional public key systems and avoids the key escrow problem in identity-based public key systems. In the past, many authenticated key exchange (AKE) protocols based on CB-PKC systems, called CB-AKE, were proposed to be applied to secure communications between two remote participants. However, these
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Fusing Blockchain and AI With Metaverse: A Survey IEEE Open J. Comput. Soc. Pub Date : 2022-07-04 Qinglin Yang, Yetong Zhao, Huawei Huang, Zehui Xiong, Jiawen Kang, Zibin Zheng
Metaverse as the latest buzzword has attracted great attention from both industry and academia. Metaverse seamlessly integrates the real world with the virtual world and allows avatars to carry out rich activities including creation, display, entertainment, social networking, and trading. Thus, it is promising to build an exciting digital world and to transform a better physical world through the exploration
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Perceived Recovery Trajectories in Post-Earthquake Nepal – A Visual Exploration With Self Organizing Maps IEEE Open J. Comput. Soc. Pub Date : 2022-06-23 Asmita Bhattarai, Thomas J. Cova, Simon C. Brewer
Developing effective recovery plans requires an intricate understanding of the experiences of affected residents following a disaster event. We combined a self-organizing map (SOM) and hierarchical clustering to analyze community perceptions towards disaster response/recovery operations following the 2015 Nepal earthquake. A survey was conducted by the Inter-Agency Common Feedback Project (CFP) that
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Capacity, Coverage and Power Profile Performance Evaluation of a Novel Rural Broadband Services Exploiting TVWS From High Altitude Platform IEEE Open J. Comput. Soc. Pub Date : 2022-06-15 Habib Mohammed Hussien, Konstantinos Katzis, Luzango Pangani Mfupe, Ephrem Teshale Bekele
The coverage and capacity of exploiting TV White Spaces (TVWS) from High Altitude Platforms (HAPs) with multiple antenna payloads using uniform rectangular phased array (URA) antennas are explored in this article. A scenario is suggested in which a HAP and TVWS base station are installed on the HAP within a HAP coverage area with a radius of 100 km and a cell radius of 10.5 km. The formation of HAP
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A Review on Security Issues and Solutions of the Internet of Drones IEEE Open J. Comput. Soc. Pub Date : 2022-06-14 Wencheng Yang, Song Wang, Xuefei Yin, Xu Wang, Jiankun Hu
The Internet of Drones (IoD) has attracted increasing attention in recent years because of its portability and automation, and is being deployed in a wide range of fields (e.g., military, rescue and entertainment). Nevertheless, as a result of the inherently open nature of radio transmission paths in the IoD, data collected, generated or handled by drones is plagued by many security concerns. Since
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A Survey of Sparse Mobile Crowdsensing: Developments and Opportunities IEEE Open J. Comput. Soc. Pub Date : 2022-05-23 Shiting Zhao, Guozi Qi, Tengjiao He, Jinpeng Chen, Zhiquan Liu, Kaimin Wei
Sparse mobile crowdsensing (SMCS) has emerged as a promising sensing paradigm for urban sensing, leveraging the spatial and temporal correlation among data sensed in distinct sub-areas to cut sensing expenses dramatically. It intelligently selects only a tiny portion of the target regions for sensing and accurately infers the data for the remaining unsensed areas. SMCS confronts numerous challenges
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Re-Identification in Differentially Private Incomplete Datasets IEEE Open J. Comput. Soc. Pub Date : 2022-05-20 Yuichi Sei, Hiroshi Okumura, Akihiko Ohsuga
Efforts to counter COVID-19 reaffirmed the importance of rich medical, behavioral, and sociological data. To make data available to many researchers who can conduct statistical analyses and machine learning, personally identifiable information must be excluded to protect individual privacy. It is essential to remove explicit identifiers, sample population data, and apply differential privacy, the de
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Advancing Data for Street-Level Flood Vulnerability: Evaluation of Variables Extracted from Google Street View in Quito, Ecuador IEEE Open J. Comput. Soc. Pub Date : 2022-04-12 Raychell Velez, Diana Calderon, Lauren Carey, Christopher Aime, Carolynne Hultquist, Greg Yetman, Andrew Kruczkiewicz, Yuri Gorokhovich, Robert S. Chen
Data relevant to flood vulnerability is minimal and infrequently collected, if at all, for much of the world. This makes it difficult to highlight areas for humanitarian aid, monitor changes, and support communities in need. It is time consuming and resource intensive to do an exhaustive study for multiple flood relevant vulnerability variables using a field survey. We use a mixed methods approach