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 only time- or space-efficient algorithms for improvement are the subject of several research studies. In order to boost performance even further, this study focuses on time and space efficient algorithms. To strike a balance between speedy data access and efficient storage, this study proposes a combination of compression and a quick look-up mechanism to satisfy the space and speed requirements of the Open Flow switch. As the data is compressed, performance improves because less memory is required to store the look-up table and fewer bits are required to search. The look up complexity of proposed approach is $O(log,(log;n/2))$ and average space reduction is 61%.
随着开放式流量交换机流量表条目的增加，TCAM 内存的大小也会随之增加。查找 IP 地址的过程包括查找最长的前缀。为了跟上链路速度，转发表中的 IP 查找操作也需要加快速度。TCAM 的可扩展性和存储能力受到其高能耗和电路密度的限制。目前仅有的时间或空间高效改进算法是多项研究的主题。为了进一步提高性能，本研究侧重于时间和空间高效算法。为了在快速数据访问和高效存储之间取得平衡，本研究提出了压缩和快速查找机制相结合的方法，以满足开放流交换机对空间和速度的要求。随着数据被压缩，性能也随之提高，因为存储查找表所需的内存更少，搜索所需的位数也更少。拟议方法的查找复杂度为 $O(log,(log;n/2))$，平均空间减少了 61%。
{"title":"CTLA: Compressed Table Look up Algorithm for Open Flow Switch","authors":"Veeramani Sonai;Indira Bharathi;Muthaiah Uchimucthu;Sountharrajan S;Durga Prasad Bavirisetti","doi":"10.1109/OJCS.2024.3361710","DOIUrl":"10.1109/OJCS.2024.3361710","url":null,"abstract":"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 only time- or space-efficient algorithms for improvement are the subject of several research studies. In order to boost performance even further, this study focuses on time and space efficient algorithms. To strike a balance between speedy data access and efficient storage, this study proposes a combination of compression and a quick look-up mechanism to satisfy the space and speed requirements of the Open Flow switch. As the data is compressed, performance improves because less memory is required to store the look-up table and fewer bits are required to search. The look up complexity of proposed approach is \u0000<inline-formula><tex-math>$O(log,(log;n/2))$</tex-math></inline-formula>\u0000 and average space reduction is 61%.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"73-82"},"PeriodicalIF":0.0,"publicationDate":"2024-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10419009","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139952219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 mitigating objective inconsistency. Although local objective consistency can be guaranteed when the number of communication rounds is infinite, we should notice that the accumulation of global drift and the limitation on the potential of local updates are non-negligible in those previous methods. In this article, we study a new framework for data-heterogeneity FL, in which the local updates in clients towards the global optimum can accelerate FL. We propose a new approach called Slingshot . Slingshot's design goals are twofold, i.e., i) to retain the potential of local updates, and ii) to combine local and global trends. Experimental results show that Slingshot helps local updates become more globally favorable and outperforms other popular methods under various FL settings. For example, on CIFAR10, Slingshot achieves 46.52% improvement in test accuracy and 48.21× speedup for a lightweight neural network named SqueezeNet .
{"title":"Slingshot: Globally Favorable Local Updates for Federated Learning","authors":"Jialiang Liu;Huawei Huang;Chun Wang;Sicong Zhou;Ruixin Li;Zibin Zheng","doi":"10.1109/OJCS.2024.3356599","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3356599","url":null,"abstract":"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 mitigating objective inconsistency. Although local objective consistency can be guaranteed when the number of communication rounds is infinite, we should notice that the accumulation of global drift and the limitation on the potential of local updates are non-negligible in those previous methods. In this article, we study a new framework for data-heterogeneity FL, in which the local updates in clients towards the global optimum can accelerate FL. We propose a new approach called \u0000<italic>Slingshot</i>\u0000. Slingshot's design goals are twofold, i.e., i) to retain the potential of local updates, and ii) to combine local and global trends. Experimental results show that \u0000<italic>Slingshot</i>\u0000 helps local updates become more globally favorable and outperforms other popular methods under various FL settings. For example, on CIFAR10, \u0000<italic>Slingshot</i>\u0000 achieves 46.52% improvement in test accuracy and 48.21× speedup for a lightweight neural network named \u0000<italic>SqueezeNet</i>\u0000.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"39-49"},"PeriodicalIF":0.0,"publicationDate":"2024-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10411043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139715219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-18DOI: 10.1109/OJCS.2024.3355693
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, a bespoke sorting algorithm, and eliminating memory access. The design achieves 100% classification accuracy with lab-captured RF data above 8 dB signal-to-noise ratio(SNR) whilst exhibiting an improvement of latency in comparison to the next quickest design by a factor of 7.5, a reduction in terms of total FPGA resources used in comparison to the next smallest complete system by a factor of 3.65, and a reduction in power consumption over the next most efficient by a factor of 4.75. The proposed design is well suited for resource-constrained applications, such as mobile cognitive radios and spectrum monitoring systems.
{"title":"Low Area and Low Power FPGA Implementation of a DBSCAN-Based RF Modulation Classifier","authors":"Bill Gavin;Tiantai Deng;Edward Ball","doi":"10.1109/OJCS.2024.3355693","DOIUrl":"https://doi.org/10.1109/OJCS.2024.3355693","url":null,"abstract":"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, a bespoke sorting algorithm, and eliminating memory access. The design achieves 100% classification accuracy with lab-captured RF data above 8 dB signal-to-noise ratio(SNR) whilst exhibiting an improvement of latency in comparison to the next quickest design by a factor of 7.5, a reduction in terms of total FPGA resources used in comparison to the next smallest complete system by a factor of 3.65, and a reduction in power consumption over the next most efficient by a factor of 4.75. The proposed design is well suited for resource-constrained applications, such as mobile cognitive radios and spectrum monitoring systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"50-61"},"PeriodicalIF":0.0,"publicationDate":"2024-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10404057","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139750051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-15DOI: 10.1109/OJCS.2023.3239731
{"title":"IEEE Open Journal of the Computer Society Information for Authors","authors":"","doi":"10.1109/OJCS.2023.3239731","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3239731","url":null,"abstract":"","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"C3-C3"},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10361940","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138678677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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, the proposed method segmented customers into four behaviors: new, short-term, high-value, and churn, and selected machine learning models to predict the churned customers. This combination reduces the risk to be misjudged as churn for customers with longer consumption cycles. Two public datasets were used to evaluate the hybrid approach, an online retail of U.K. gift sellers and the largest E-Commerce of Pakistan. Based on the top three learning models, the recall ranged from 0.56 to 0.72 in the former while that ranged from 0.91 to 0.95 in the latter. Results show that the proposed approach enables companies to retain important customers earlier by predicting customer churn. The proposed hybrid method requires less data than existing methods.
{"title":"Prediction of Customer Behavior Changing via a Hybrid Approach","authors":"Nien-Ting Lee;Hau-Chen Lee;Joseph Hsin;Shih-Hau Fang","doi":"10.1109/OJCS.2023.3336904","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3336904","url":null,"abstract":"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, the proposed method segmented customers into four behaviors: new, short-term, high-value, and churn, and selected machine learning models to predict the churned customers. This combination reduces the risk to be misjudged as churn for customers with longer consumption cycles. Two public datasets were used to evaluate the hybrid approach, an online retail of U.K. gift sellers and the largest E-Commerce of Pakistan. Based on the top three learning models, the recall ranged from 0.56 to 0.72 in the former while that ranged from 0.91 to 0.95 in the latter. Results show that the proposed approach enables companies to retain important customers earlier by predicting customer churn. The proposed hybrid method requires less data than existing methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"27-38"},"PeriodicalIF":0.0,"publicationDate":"2023-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10334013","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-20DOI: 10.1109/OJCS.2023.3334528
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. This paper proposes a deep learning-based HAR model, i.e., a 3-dimensional Convolutional Network with multiplicative LSTM. The suggested model makes it easier to comprehend the tasks that an individual or team of individuals completes. The four-phase proposed model consists of a 3D Convolutional neural network (3DCNN) combined with an LSTM multiplicative recurrent network and Yolov6 for real-time object detection. The four stages of the proposed model are data fusion, feature extraction, object identification, and skeleton articulation approaches. The NTU-RGB-D, KITTI, NTU-RGB-D 120, UCF 101, and Fused datasets are some used to train the model. The suggested model surpasses other cutting-edge models by reaching an accuracy of 98.23%, 97.65%, 98.76%, 95.45%, and 97.65% on the abovementioned datasets. Other state-of-the-art (SOTA) methods compared in this study are traditional CNN, Yolov6, and CNN with BiLSTM. The results verify that actions are classified more accurately by the proposed model that combines all these techniques compared to existing ones.
{"title":"A Real-Time 3-Dimensional Object Detection Based Human Action Recognition Model","authors":"Chhaya Gupta;Nasib Singh Gill;Preeti Gulia;Sangeeta Yadav;Giovanni Pau;Mohammad Alibakhshikenari;Xiangjie Kong","doi":"10.1109/OJCS.2023.3334528","DOIUrl":"https://doi.org/10.1109/OJCS.2023.3334528","url":null,"abstract":"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. This paper proposes a deep learning-based HAR model, i.e., a 3-dimensional Convolutional Network with multiplicative LSTM. The suggested model makes it easier to comprehend the tasks that an individual or team of individuals completes. The four-phase proposed model consists of a 3D Convolutional neural network (3DCNN) combined with an LSTM multiplicative recurrent network and Yolov6 for real-time object detection. The four stages of the proposed model are data fusion, feature extraction, object identification, and skeleton articulation approaches. The NTU-RGB-D, KITTI, NTU-RGB-D 120, UCF 101, and Fused datasets are some used to train the model. The suggested model surpasses other cutting-edge models by reaching an accuracy of 98.23%, 97.65%, 98.76%, 95.45%, and 97.65% on the abovementioned datasets. Other state-of-the-art (SOTA) methods compared in this study are traditional CNN, Yolov6, and CNN with BiLSTM. The results verify that actions are classified more accurately by the proposed model that combines all these techniques compared to existing ones.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"14-26"},"PeriodicalIF":0.0,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10323158","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139060146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-13DOI: 10.1109/OJCS.2023.3332351
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 models for clients, referred to as personalized federated learning. Nevertheless, no studies comprehensively investigate the performance of personalized federated learning methods in various experimental settings such as datasets and client settings. Therefore, in this article, we aim to benchmark the performance of existing personalized federated learning methods in various settings. We first survey the experimental settings in existing studies. We then benchmark the performance of existing methods through comprehensive experiments to reveal their characteristics in computer vision and natural language processing tasks which are the most popular tasks based on our survey. Our experimental study shows that (i) large data heterogeneity often leads to highly accurate predictions and (ii) standard federated learning methods (e.g. FedAvg) with fine-tuning often outperform personalized federated learning methods.
{"title":"Benchmark for Personalized Federated Learning","authors":"Koji Matsuda;Yuya Sasaki;Chuan Xiao;Makoto Onizuka","doi":"10.1109/OJCS.2023.3332351","DOIUrl":"10.1109/OJCS.2023.3332351","url":null,"abstract":"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 models for clients, referred to as personalized federated learning. Nevertheless, no studies comprehensively investigate the performance of personalized federated learning methods in various experimental settings such as datasets and client settings. Therefore, in this article, we aim to benchmark the performance of existing personalized federated learning methods in various settings. We first survey the experimental settings in existing studies. We then benchmark the performance of existing methods through comprehensive experiments to reveal their characteristics in computer vision and natural language processing tasks which are the most popular tasks based on our survey. Our experimental study shows that (i) large data heterogeneity often leads to highly accurate predictions and (ii) standard federated learning methods (e.g. FedAvg) with fine-tuning often outperform personalized federated learning methods.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"5 ","pages":"2-13"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10316561","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135612465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-31DOI: 10.1109/OJCS.2023.3328767
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 by text recognition. Mask-RCNN for plot element detection achieves a mean average precision of 95.04% at a threshold of 0.5 which decreases as the Intersection over Union (IoU) threshold increases. A contour approximation-based approach is proposed for extracting the bar coordinates, even at a higher IoU of 0.9. The textual and visual cues are associated with the legend text and preview, and the chart data is finally extracted in tabular format. We introduce an extension to the TAPAS model, called TAPAS++, by incorporating new operations and table question answering is done using TAPAS++ model. The chart summary or description is also produced in an audio format. In the future, this approach could be expanded to enable interactive question answering on charts by accepting audio inquiries from individuals with visual impairments and do more complex reasoning using Large Language Models.
{"title":"Data Extraction and Question Answering on Chart Images Towards Accessibility and Data Interpretation","authors":"Shahira K C;Pulkit Joshi;Lijiya A","doi":"10.1109/OJCS.2023.3328767","DOIUrl":"10.1109/OJCS.2023.3328767","url":null,"abstract":"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 by text recognition. Mask-RCNN for plot element detection achieves a mean average precision of 95.04% at a threshold of 0.5 which decreases as the Intersection over Union (IoU) threshold increases. A contour approximation-based approach is proposed for extracting the bar coordinates, even at a higher IoU of 0.9. The textual and visual cues are associated with the legend text and preview, and the chart data is finally extracted in tabular format. We introduce an extension to the TAPAS model, called TAPAS++, by incorporating new operations and table question answering is done using TAPAS++ model. The chart summary or description is also produced in an audio format. In the future, this approach could be expanded to enable interactive question answering on charts by accepting audio inquiries from individuals with visual impairments and do more complex reasoning using Large Language Models.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"314-325"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10302417","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135263025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-19DOI: 10.1109/OJCS.2023.3326150
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 time series data. Several algorithms have been proposed, but they mainly focus on minimizing the error between the estimated truth and the observations. In our study, we aim at minimizing the noise in the estimated truth. To solve this optimization problem, we first find out the level of noise produced by each sensor based on differenced time series, which can help estimating the truth wisely. Then, we propose a quadratic optimization model to minimize the noise of the estimated truth. Further, a post process is introduced to refine the result by iteration. Experimental results on both real world and synthetic data sets verify the effectiveness and efficiency of our proposed methods, respectively.
{"title":"Finding the Truth From Uncertain Time Series by Differencing","authors":"Jizhou Sun;Delin Zhou;Bo Jiang","doi":"10.1109/OJCS.2023.3326150","DOIUrl":"10.1109/OJCS.2023.3326150","url":null,"abstract":"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 time series data. Several algorithms have been proposed, but they mainly focus on minimizing the error between the estimated truth and the observations. In our study, we aim at minimizing the noise in the estimated truth. To solve this optimization problem, we first find out the level of noise produced by each sensor based on differenced time series, which can help estimating the truth wisely. Then, we propose a quadratic optimization model to minimize the noise of the estimated truth. Further, a post process is introduced to refine the result by iteration. Experimental results on both real world and synthetic data sets verify the effectiveness and efficiency of our proposed methods, respectively.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"303-313"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10288191","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135056637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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 ownership of models and generative contents, which are also digital assets, has not been well protected. Both because the black-box nature of neural networks makes it difficult to mark models' ownership and because the lack of a reliable third-party verification platform. Meanwhile, the existing model-attack threat and raising ethical problems driven the research on model watermark embedding for traceability and verification, and thus the reliable basic algorithm and the verification platform are needed. In this survey, while emphasizing the importance and reason of the ownership protection in AIGC and summarizing the recent research using model watermarking, we will also introduce the achievements of blockchain in copyright in order to summarize the research history and point out future direction of model copyright validation from both the underlying technology and the supporting platform.
{"title":"Towards Reliable Utilization of AIGC: Blockchain-Empowered Ownership Verification Mechanism","authors":"Chuan Chen;Yihao Li;Zihou Wu;Mingfeng Xu;Rui Wang;Zibin Zheng","doi":"10.1109/OJCS.2023.3315835","DOIUrl":"10.1109/OJCS.2023.3315835","url":null,"abstract":"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 ownership of models and generative contents, which are also digital assets, has not been well protected. Both because the black-box nature of neural networks makes it difficult to mark models' ownership and because the lack of a reliable third-party verification platform. Meanwhile, the existing model-attack threat and raising ethical problems driven the research on model watermark embedding for traceability and verification, and thus the reliable basic algorithm and the verification platform are needed. In this survey, while emphasizing the importance and reason of the ownership protection in AIGC and summarizing the recent research using model watermarking, we will also introduce the achievements of blockchain in copyright in order to summarize the research history and point out future direction of model copyright validation from both the underlying technology and the supporting platform.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"4 ","pages":"326-337"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10254223","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135502429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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