EAI Endorsed Transactions on Collaborative Computing最新文献

{"title":"A Hybrid Service Ranking Based Collaborative Filtering Model on Cloud Web Service Data","authors":"Suvarna S. Pawar, Y. Prasanth","doi":"10.4108/eai.26-10-2021.171599","DOIUrl":"https://doi.org/10.4108/eai.26-10-2021.171599","url":null,"abstract":"INTRODUCTION: Trust is an important indicator in the cloud computing environment for service selection and recommendation. It is a difficult task to create a composite value-added service from several candidate services for the desired objectives due to the dramatic growth in services that have similar functionalities. OBJECTIVES: This research aims to design a hybrid service feature ranking; cloud service ranking are computed using the advanced contextual service ranking measures. A hybrid collaborative approach is totally based on confidence to the QoS web service prediction. METHODS: A new service ranking similarity computation is optimized for the cloud-based service selection. This collaborative filtering measure is used to check the top k customer selection by performing the top-k customer selection estimation on the cloud service ranking RESULTS: The proposed method is useful in the prediction of QoS values and helps with optimal service ranking. As a result, similar/ relating cloud services are increasing, making it extremely complex to select the best cloud service among the relevant / similar services available CONCLUSION: The state-of the-art approaches are proposed and tested on a mathematical QoS-Aware assessment framework. The use of semantic matching technique and QoS for web service ranking satisfies user requirements for web service recommendations. In addition, users require a web service not only based on functionality, but also based on high quality.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132470892","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}

{"title":"A computing method of predictive value based on fitting function in linear model","authors":"Hao Zhong, Huibing Zhang, Fei Jia","doi":"10.4108/EAI.2-10-2020.166542","DOIUrl":"https://doi.org/10.4108/EAI.2-10-2020.166542","url":null,"abstract":"Linear models are common prediction models in collaborative computing, which mainly generates fitting function to express the relationship between feature vectors and predictive value. In the process of computing the predictive value according to the fitting function and feature vector, this paper mainly conducted the following researches. Firstly, this paper defines a change interval of predictive value according to training set. Secondly, in this paper, the change interval of predictive value corresponding to feature vector in test set is computed. Finally, according to distribution of training set in the changing interval, the predictive values corresponding to feature vectors in test set are computed. Standard data sets are used in experiment, and MAE(Mean Absolute Error) and RMSE(Root Mean Square Error) are used to evaluate the prediction results. The experimental results show that the method proposed in this paper can improve the prediction error to a certain extent. Received on 07 June 2020; accepted on 23 September 2020; published on 02 October 2020","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117168176","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}

{"title":"Seizure Classification Using Person-Specific Triggers","authors":"J. Pordoy, Y. Zhang, N. Matoorian, M. Zolgharni","doi":"10.4108/EAI.4-2-2021.168650","DOIUrl":"https://doi.org/10.4108/EAI.4-2-2021.168650","url":null,"abstract":"Introduction: With advancements in personalised medicine, healthcare delivery systems have moved away from the onesize-fits-all approach, towards tailored treatments that meet the needs of individuals and specific subgroups. As nearly onethird of those diagnosed with epilepsy are classed as refractory and are resistant to antiepileptic medication, there is need for a personalised method of detecting epileptic seizures. Epidemiological studies show that up to 91% of those diagnosed identify one or more epilepsy related trigger as the causation behind their seizure onset. These triggers are person-specific and affect those diagnosed in different ways dependent on their idiosyncratic tolerance and threshold levels. Whilst these triggers are known to induce seizure onset, only a few studies have even considered their use as a preventive component, and whether they could be used as an additional sensing modality for non-EEG detection mechanisms. Objectives: 1. To record person-specific triggers (PST) from participants using IoT-enabled sensors and smart devices. 2. To train and test several dedicated machine learning models using a single participants data, 3. To conduct a comparative analysis and evaluate the performance of each model, 4. Formulate a conclusion as to whether PST could be used to improve on current methods of non-EEG seizure detection. Methodology: This study uses a precision approach combined with machine learning, to train and test several dedicated algorithms that can predict epileptic seizures. Each model is designed for a single participant, enabling a personalised method of classification unseen in non-EEG detection research. Results: Our results show accuracy, sensitivity, and specificity scores of 94.73%, 96.90% and 93.33% for participant 1 and 96.87%, 96.96% and 96.77% for participant 2, respectively. Conclusion: To conclude, this preliminary study has observed a noticeable correlation between the documented triggers and each participants seizure onset, indicating that PST have the potential to be used as an additional non-EEG sensing modality when classifying epileptic seizures.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"154 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131385880","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}

{"title":"VTWM: An Incremental Data Extraction Model Based on Variable Time-Windows","authors":"Weixing Jia, Yang Xu, Jie Liu, Guiling Wang","doi":"10.4108/eai.12-6-2020.166291","DOIUrl":"https://doi.org/10.4108/eai.12-6-2020.166291","url":null,"abstract":"Continuously extracting and integrating changing data from various heterogeneous systems based on an appropriate data extraction model is the key to data sharing and integration and also the key to building an incremental data warehouse for data analysis. The traditional data capture method based on timestamp changes is plagued with anomalies in the data extraction process, which leads to data extraction failure and affects the efficiency of data extraction. To address the above problems, this paper improves the traditional data capture model based on timestamp increments and proposes VTWM, an incremental data extraction model based on variable time-windows, based on the idea of extracting a small number of duplicate records before removing duplicate values. The model reduces the influence of abnormalities on data extraction, improves the reliability of the traditional data extraction ETL processes, and improves the data extraction efficiency.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114369496","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}

{"title":"An Augmented User Model for Personalized Search in Collaborative Social Tagging Systems","authors":"Wenyu Zhao, Dong Zhou, Xuan Wu, S. Lawless, Jianxun Liu","doi":"10.4108/EAI.9-10-2017.154549","DOIUrl":"https://doi.org/10.4108/EAI.9-10-2017.154549","url":null,"abstract":"Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user’s past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user’s past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model utilizes recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted on two real-world collaborative social tagging datasets show that our proposed methods outperform state-of-the-art methods.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128157117","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}

{"title":"Space Searching Algorithms Used by Fungi","authors":"E. Asenova, Eileen Fu, D. V. Nicolau, Hsin-Yu Lin, D. V. Nicolau","doi":"10.4108/eai.3-12-2015.2262591","DOIUrl":"https://doi.org/10.4108/eai.3-12-2015.2262591","url":null,"abstract":"Experimental studies have shown that fungi use a natural program for searching the space available in micro-confined networks, e.g., mazes. This natural program, which comprises two subroutines, i.e., collision-induced branching and directional memory, has been shown to be efficient compared with the suppressing one, or both subroutines. The present contribution compares the performance of the fungal natural program against several standard space searching algorithms. It was found that the fungal natural algorithm consistently outperforms Depth-First-Search (DFS) algorithm, and although it is inferior to informed algorithms, such as A*, this under-performance does not increase importantly with the increase of the size of the maze. These findings encourage a systematic effort to harvest the natural space searching algorithms used by microorganisms, which, if efficient, can be reverse-engineered for graph and tree search strategies.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122871446","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}

{"title":"Group coordination in a biologically-inspired vectorial network model","authors":"Violet Mwaffo, M. Porfiri","doi":"10.4108/eai.3-12-2015.2262389","DOIUrl":"https://doi.org/10.4108/eai.3-12-2015.2262389","url":null,"abstract":"Most of the mathematical models of collective behavior describe uncertainty in individual decision making through additive uniform noise. However, recent data driven studies on animal locomotion indicate that a number of animal species may be better represented by more complex forms of noise. For example, the popular zebrafish model organism has been found to exhibit a burst-and-coast swimming style with occasional fast and large changes of direction. Based on these observations, the turn rate of this small fish has been modeled as a mean reverting stochastic process with jumps. Here, we consider a new model for collective behavior inspired by the zebrafish animal model. In the vicinity of the synchronized state and for small noise intensity, we establish a closed-form expression for the group polarization and through extensive numerical simulations we validate our findings. These results are expected to aid in the analysis of zebrafish locomotion and contribute a new set of mathematical tools to study collective behavior of networked noisy dynamical systems.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134473352","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}

{"title":"Reinforcement Learning with Internal Reward for Multi-Agent Cooperation: A Theoretical Approach","authors":"Fumito Uwano, Naoki Tatebe, Masaya Nakata, K. Takadama, T. Kovacs","doi":"10.4108/eai.3-12-2015.2262878","DOIUrl":"https://doi.org/10.4108/eai.3-12-2015.2262878","url":null,"abstract":"This paper focuses on a multi-agent cooperation which is generally difficult to be achieved without sufficient information of other agents, and proposes the reinforcement learning method that introduces an internal reward for a multi-agent cooperation without sufficient information. To guarantee to achieve such a cooperation, this paper theoretically derives the condition of selecting appropriate actions by changing internal rewards given to the agents, and extends the reinforcement learning methods (Q-learning and Profit Sharing) to enable the agents to acquire the appropriate Q-values updated according to the derived condition. Concretely, the internal rewards change when the agents can only find better solution than the current one. The intensive simulations on the maze problems as one of testbeds have revealed the following implications:(1) our proposed method successfully enables the agents to select their own appropriate cooperating actions which contribute to acquiring the minimum steps towards to their goals, while the conventional methods (i.e., Q-learning and Profit Sharing) cannot always acquire the minimum steps; and (2) the proposed method based on Profit Sharing provides the same good performance as the proposed method based on Q-learning.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114237203","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}

{"title":"Design of Pet Robots with Limitations of Lives and Inherited Characteristics","authors":"Tomoko Yonezawa, Naoto Yoshida, Kento Kuboshima","doi":"10.4108/eai.3-12-2015.2262417","DOIUrl":"https://doi.org/10.4108/eai.3-12-2015.2262417","url":null,"abstract":"In this paper, we propose a framework of life duration and \u0000 \u0000inheritance for pet robots to make them have original characteristics in their limited lives. The purpose of our research \u0000 \u0000is to develop a pet robot that enables the user to treat the \u0000 \u0000robots as though they had real lives from the viewpoint of \u0000 \u0000importance of life and pleasure of birth through the breeding \u0000 \u0000of robots. The characteristics of bodily motions are inheritable by the next generation. The robots also change their \u0000 \u0000behavior corresponding to elapsed time from birth.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132194589","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}

{"title":"Bio-Inspired Routing Protocol Based on Pheromone Diffusion in Mobile Ad Hoc Networks","authors":"Hyun-Ho Choi, Jung-Ryun Lee, Bongsoo Roh, Mijeong Hoh, Hyungseok Choi","doi":"10.4108/eai.3-12-2015.2262499","DOIUrl":"https://doi.org/10.4108/eai.3-12-2015.2262499","url":null,"abstract":"Bio-inspired routing protocols use the principle of swarm intelligence, which finds the optimal path to the destination in a distributed and autonomous way in dynamically changing environments; therefore, they can maximize the routing performance, reduce the control overhead, and recover a path failure quickly according to the change in the network topology. In this paper, we propose a bio-inspired routing protocol for mobile ad hoc networks. The proposed protocol uses a technique of overhearing for obtaining routing information without additional overhead. Through overhearing, a pheromone is diffused around the shortest path between the source and the destination. On the basis of this diffused pheromone, a probabilistic path exploration is executed and the useful alternative routes between the source and the destination are collected. Therefore, the proposed routing protocol can gather up-to-date effective routing information while reducing the control overhead. The simulation results show that the proposed routing protocol outperforms the typical ad hoc on-demand distance vector (AODV) and AntHocNet protocols in terms of the delivery ratio and the end-to-end delay and significantly decreases the routing overhead against AntHocNet.","PeriodicalId":109199,"journal":{"name":"EAI Endorsed Transactions on Collaborative Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128509143","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}