Cloud storage services have promising characteristics for personal and corporate use as consumers' data can be stored on shared pools of storage hosted by cloud providers, while consumers can access and manage the...
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Cloud storage services have promising characteristics for personal and corporate use as consumers' data can be stored on shared pools of storage hosted by cloud providers, while consumers can access and manage their data anywhere via an Internet connection. Nevertheless, protection of data privacy is one of the major issues, and at the time of storage service selection, prospective consumers are concerned with how the cloud storage providers handle their personal data. To help a consumer with storage service selection, this paper proposes a methodology to assess the risk of privacy loss when consumer data are stored with a particular cloud storage service. The risk of privacy loss is viewed from two aspects. First, sensitivity of the personal data to be stored in the cloud and sensitivity of consumers' personal data requested at the time of service registration can contribute to the risk. Second, lack of privacy control transparency can be a risk factor if the cloud storage provider inadequately show the necessary privacy principles that are practiced. The proposed methodology can assist the consumers when determining the risk levels of privacy loss of different cloud storage services. We present the application of the methodology to a case of an organization selecting a cloud storage service to host its corporate data.
Spammer detection on social network is a challenging problem. The rigid anti-spam rules have resulted in emergence of "smart" spammers. They resemble legitimate users who are difficult to identify. In this p...
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With the rapid popularization of explosive smart terminals due to the increase in broadband, a high speed transfer rate can realize the need for 5G mobile technology. Future mobile service technology can accommodate m...
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This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of m...
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This study proposes the use of a computational approach based on machine learning (ML) algorithms to build predictive models using eye tracking data. Our intention is to provide results that may support the study of medical investigation in the decision-making process in clinical bioethics, particularly in this work, in cases of euthanasia. The data used in the approach were collected from 75 students of the nursing undergraduate course using an eye tracker. The available data were processed through feature selection methods, and were later used to create models capable of predicting the euthanasia decision through ML algorithms. Statistical experiments showed that the predictive model resultant from the multilayer perceptron (MLP) algorithm led to the best performance compared with the other tested algorithms, presenting an accuracy of 90.7% and a mean area under the ROC curve of 0.90. Interesting knowledge (patterns and rules) for the studied bioethical decision-making was extracted using simulations with MLP models and inspecting the obtained decision-tree rules. The good performance shown by the obtained MLP predictive model demonstrates that the proposed investigation approach may be used to test scientific hypotheses related to visual attention and decision-making.
The cross-plane thermal conductivities of InGaZnO (IGZO) thin films in different morphologies were measured on three occasions within 19 months, using the 3ω method at room temperature 300 K. Amorphous (a-), semi-cry...
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We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brai...
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We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three human subjects to collaborate and solve a task using direct brain-to-brain communication. Two of the three subjects are designated as "Senders" whose brain signals are decoded using real-time EEG data analysis. The decoding process extracts each Sender’s decision about whether to rotate a block in a Tetris-like game before it is dropped to fill a line. The Senders’ decisions are transmitted via the Internet to the brain of a third subject, the "Receiver," who cannot see the game screen. The Senders’ decisions are delivered to the Receiver’s brain via magnetic stimulation of the occipital cortex. The Receiver integrates the information received from the two Senders and uses an EEG interface to make a decision about either turning the block or keeping it in the same orientation. A second round of the game provides an additional chance for the Senders to evaluate the Receiver’s decision and send feedback to the Receiver’s brain, and for the Receiver to rectify a possible incorrect decision made in the first round. We evaluated the performance of BrainNet in terms of (1) Group-level performance during the game, (2) True/False positive rates of subjects’ decisions, and (3) Mutual information between subjects. Five groups, each with three human subjects, successfully used BrainNet to perform the collaborative task, with an average accuracy of 81.25%. Furthermore, by varying the information reliability of the Senders by artificially injecting noise into one Sender’s signal, we investigated how the Receiver learns to integrate noisy signals in order to make a correct decision. We found that like conventional social networks, BrainN
It is common knowledge that the decision of an individual regarding adoption of a product or technology is, more often than not, heavily influenced by their friends and acquaintances. In real world, there are differen...
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ISBN:
(纸本)9781467385800
It is common knowledge that the decision of an individual regarding adoption of a product or technology is, more often than not, heavily influenced by their friends and acquaintances. In real world, there are different competing products and innovations that try to garner as many loyal followers as possible. Over the past few years, there has been a significant interest in the research community to study social network problems with a flavor of competition. Such problems often focus on identification of a set of people in a given social network by the competing players in order to achieve some goal. In this paper, we introduce the weighted Segregating Vertex Set (wSVS) problem, in which we are given a weighted undirected graph with a subset of nodes identified as the seedset of the first player and the goal for the second player is to identify a subset of nodes (firewall) of minimum cumulative weight, such that the total weight of the nodes reachable by the first player is strictly less than the total weight of the nodes not reachable by the first player. Thus, the second player tries to contain the reach of the first player within the social network community. This problem is also relevant for containment of disease in epidemiology, containment of forest fire and several other domains. We prove that this problem is NP-complete and provide an optimal solution through the use of Mixed Integer Linear programming. We also provide a heuristic solution for the wSVS problem and show its efficacy through detailed experimentation. Our heuristic solution delivers near optimal solution in lesser time compared to that needed to find the optimal solution.
Background: The progressive and chronic course of COPD, characterized by difficulty in breathing, can be aggravated by periods of increased symptoms (exacerbation). The treatment often involves in-hospital care and am...
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Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enh...
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The purpose of this study is to create an application which functions automatically with high accuracy when analyzing bank customer data. This needed due to non-performing loans occurring frequently caused by the inac...
The purpose of this study is to create an application which functions automatically with high accuracy when analyzing bank customer data. This needed due to non-performing loans occurring frequently caused by the inaccuracy of credit analysts in the assessment of creditworthiness. This can be seen in the incident occurred in a public bank located in Bandung. This bank does not have the database that serves to accommodate data history and the method used in assessing creditworthiness is merely based on the simple statistical analysis. This leads to reduced accuracy and speed in the decision-making process. This research applies Naïve Bayes Classifier (NBC) method, a Data Mining technique. This helps credit analysts to select customers who are truly eligible to be given credit so that non-performing loan can be avoided. NBC calculates the probability of one class from each group of attributes and determines which class is most optimal. The accuracy of the NBC sampling test from 501 data is 94% compared to the decision made by a credit analyst. It can be concluded that this application is very helpful for credit analysts in recommending customers who are eligible for a loan to the bank's decision maker.
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