2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)最新文献_第3页

ParaDiS: a Parallel and Distributed framework for Significant pattern mining ParaDiS:用于重要模式挖掘的并行和分布式框架

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00050

Jyoti, S. Kailasam, A. Buzmakov

{"title":"ParaDiS: a Parallel and Distributed framework for Significant pattern mining","authors":"Jyoti, S. Kailasam, A. Buzmakov","doi":"10.1109/CCGridW59191.2023.00050","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00050","url":null,"abstract":"Mining patterns having a high association with a class label is a supervised data mining technique, frequently used in many applications. As we test many patterns using statistical tests to find all interesting patterns, a certain association is likely achieved by chance. The state-of-the-art TopKWY algorithm mines the top-k interesting patterns while controlling the family-wise-error rate (FWER) in the result set. TopKWY is a sequential algorithm that internally uses compute-intensive closed pattern mining. Moreover, it tests several patterns against thousands of permuted class labels to control FWER. To the best of our knowledge, no parallel/distributed implementation exists to address the scalability challenges faced by TopKWY. The tree formed by the explored patterns in TopKWY is inherently irregular and the search strategy used for exploration, namely, the best-first search is non-trivial to emulate in a distributed setup. This paper designs and implements ParaDiS, a novel parallel and distributed framework for mining the top-k statistically significant patterns. We compare its performance with the sequential TopKWY algorithm for real-world datasets and observe a significant reduction in execution time. We further show that our framework achieves good speedup, minimal communication overhead, and faster pruning of non-promising branches by efficient sharing of significance threshold.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126042631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchains’ federation: Developing Personal Health Trajectory-centered health systems 区块链联盟:发展以个人健康轨迹为中心的卫生系统

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00027

J. Rojo, J. García-Alonso, J. Berrocal, L. Foschini, P. Bellavista, Juan Hernández, J. M. Murillo

{"title":"Blockchains’ federation: Developing Personal Health Trajectory-centered health systems","authors":"J. Rojo, J. García-Alonso, J. Berrocal, L. Foschini, P. Bellavista, Juan Hernández, J. M. Murillo","doi":"10.1109/CCGridW59191.2023.00027","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00027","url":null,"abstract":"The current world is a globalized and connected one. Even without moving around, a person interacts with personnel from different institutions treating him as a patient in their daily life. Each of these institutions keeps their patients’ data stored in their own information system, in an isolated way. Due to this, each patient has their data scattered among all these institutions and services with which she interacts along her life. This can complicate the take of the proper decision when the patient is under treatment. To solve this situation, new patient-centered health systems have been proposed as a replacement to the actual institution-centered ones, storing all health information of a patient into a unique global vision. However, some questions have arisen around the actual proposals, as who should store and maintain this vision of a given patient, and how should this information be made available for other systems. The proposal presented in this paper advocate for the achievement of a Personal Health Trajectory that can be useful both for patients and health professionals, using the concept of blockchains’ federation. The proposal has been validated using 5689 records from 50 different institutions, belonging to 1156 actors.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121883549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FL-PSO: A Federated Learning approach with Particle Swarm Optimization for Brain Stroke Prediction 基于粒子群优化的联邦学习脑卒中预测方法

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00020

Nancy Victor, S. Bhattacharya, Praveen Kumar Reddy Maddikunta, Fasial Mohammed Alotaibi, T. Gadekallu, R. Jhaveri

{"title":"FL-PSO: A Federated Learning approach with Particle Swarm Optimization for Brain Stroke Prediction","authors":"Nancy Victor, S. Bhattacharya, Praveen Kumar Reddy Maddikunta, Fasial Mohammed Alotaibi, T. Gadekallu, R. Jhaveri","doi":"10.1109/CCGridW59191.2023.00020","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00020","url":null,"abstract":"Healthcare is one of the significant application areas of Cyber-Physical Systems, wherein massive amounts of sensors and other physical entities are interconnected to each other. Diagnosing and predicting diseases at an early stage is crucial for any healthcare application and machine-learning approaches are widely explored for the same. However, the conventional machine learning approaches can lead to the leakage of sensitive information pertaining to patients. In this study, our primary objective is to develop a machine learning based framework for early brain stroke prediction. Federated Learning (FL) is included in the framework to preserve the privacy of the patient’s data which is used as the basis for brain stroke prediction. The hyperparameters of FL are further optimized using Particle Swarm Optimization (PSO) to yield predictions with enhanced accuracy without compromising with data privacy. The experimental research showed that the suggested FL-PSO framework outperformed its competitors in terms of metrics like accuracy, validating the superiority of the framework.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122121835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BigHOST: Automatic Grading System for Big Data Assignments bigost:大数据作业自动评分系统

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00051

V. Ramesha, Sachin Shankar, Suhas Thalanki, Supreeth Kurpad, Prafullata Auradkar

引用次数: 0

Edge Computing Solutions Supporting Voice Recognition Services for Speakers with Dysarthria 支持语音识别服务的边缘计算解决方案，为患有构音障碍的说话者

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00047

Davide Mulfari, Lorenzo Carnevale, A. Galletta, M. Villari

{"title":"Edge Computing Solutions Supporting Voice Recognition Services for Speakers with Dysarthria","authors":"Davide Mulfari, Lorenzo Carnevale, A. Galletta, M. Villari","doi":"10.1109/CCGridW59191.2023.00047","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00047","url":null,"abstract":"In the framework of Automatic Speech Recognition (ASR), the synergism between edge computing and artificial intelligence has led to the development of intelligent objects that process and respond to human speech. This acts as a key enabler for multiple application scenarios, such as smart home automation, where the user’s voice is an interface for interacting with appliances and computer systems. However, for millions of speakers with dysarthria worldwide, such a voice interaction is impossible because nowadays ASR technologies are not robust to their atypical speech commands. So these people, who also live with severe motor disabilities, are unable to benefit from many voice assistant services that might support their everyday life. To cope with the above challenges, this paper proposes a deep learning approach to isolated word recognition in the presence of dysarthria conditions, along with the deployment of customized ASR models on machine learning powered edge computing nodes. In this way, we work toward a low-cost, portable solution with the potential to operate next to the user with a disability, e.g., in a wheelchair or beside a bed, in an always active mode. Finally, experiments show the goodness (in terms of word error rate) of our speech recognition solution in comparison with other studies on isolated word recognition for impaired speech.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125639579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Spark based Parallel Frequent Pattern Rules for Social Media Data Analytics 基于Spark的社交媒体数据分析并行频繁模式规则

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00039

Shubhangi Chaturvedi, S. Saritha, Animesh Chaturvedi

{"title":"Spark based Parallel Frequent Pattern Rules for Social Media Data Analytics","authors":"Shubhangi Chaturvedi, S. Saritha, Animesh Chaturvedi","doi":"10.1109/CCGridW59191.2023.00039","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00039","url":null,"abstract":"The number of users on social media are increasing, thus the data produced is also increasing tremendously. Social media data mining and analysis can produce a lot of hidden information, which can be helpful in decision-making. Prediction of the co-occurring words with confidence can provide deep insights of social media. The paper presents an applied process to mine social media dataset to retrieve frequent patterns (or rules) in cost effective time. The retrieved patterns can be useful in making decisions related to social media. The experiment is performed on three social media datasets and various rules are analyzed by varying the values of threshold (minimum support and minimum confidence). Experiments are also performed for both Frequent Pattern (FP) Growth and Parallel FP (PFP) Growth using the same datasets. The parallel computation is achieved with the help of a scalable Apache Spark environment. Execution time for both FP-Growth and PFP-Growth on the same datasets is also described. While performing experiments it is found that FP-Growth of SPMF requires preprocessing to convert item-sets into transactional databases. The pre-processing time is required only once, as a result the time required to generate rules is less. Whereas, the PFP-Growth does not require preprocessing on the dataset to generate rules. This saves time to directly generate the association rules using PFP-Growth.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134214261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ML based D3 R: Detecting DDoS using Random Forest 基于ML的D3 R:使用随机森林检测DDoS

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00035

Anagha Ramesh, Ramza Haris, Sumedha Arora

引用次数: 0

Scavenger: A Cloud Service for Optimizing Cost and Performance of DL Training 清道夫:优化深度学习训练成本和性能的云服务

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00081

S. Tyagi

引用次数: 0

TwMiner: Mining Relevant Tweets of News Articles TwMiner:挖掘新闻文章的相关推文

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00052

Roshni Chakraborty, Nilotpal Chakraborty

引用次数: 0

Comparing the Orchestration of Quantum Applications on Hybrid Clouds 混合云上量子应用编排的比较

2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW) Pub Date : 2023-05-01 DOI: 10.1109/CCGridW59191.2023.00069

Rajiv Sangle, Tuhin Khare, P. V. Seshadri, Yogesh L. Simmhan

{"title":"Comparing the Orchestration of Quantum Applications on Hybrid Clouds","authors":"Rajiv Sangle, Tuhin Khare, P. V. Seshadri, Yogesh L. Simmhan","doi":"10.1109/CCGridW59191.2023.00069","DOIUrl":"https://doi.org/10.1109/CCGridW59191.2023.00069","url":null,"abstract":"Early quantum computers are being offered by Cloud providers, with the ability to run Hybrid Quantum-Classical (HQC) applications that span Quantum and Classical (binary) computing resources. These HQC algorithms help benchmark and validate the performance of such near-term quantum hardware. Equally important is the software platform that allows a remote user to program the quantum computer on the cloud. The recent Qiskit Runtime platform from IBM allows a seamless and prioritized access to quantum backends and co-located classical hardware within their hybrid cloud for optimized execution of HQC workloads. In contrast, IBM’s Circuit API offers finer application definition but the classical resources are not co-located but placed farther away. Here, we study and contrast the execution flows of a Variational Quantum Eigensolver (VQE) application defined using Qiskit Runtime, and an equivalent implementation using the Circuit API. We quantify the latencies of the various components in these execution. This offers insights on how to better orchestrate the execution of quantum applications across such hybrid resources.","PeriodicalId":341115,"journal":{"name":"2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW)","volume":"61 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127388347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1