Dataset Of Users Research Articles

Using the Context of User Feedback in Recommender Systems

Our work is generally focused on recommending for small or medium-sized e-commerce portals, where explicit feedback is absent and thus the usage of implicit feedback is necessary. Nonetheless, for some implicit feedback features, the presentation context may be of high importance. In this paper, we present a model of relevant contextual features affecting user feedback, propose methods leveraging those features, publish a dataset of real e-commerce users containing multiple user feedback indicators as well as its context and finally present results of purchase prediction and recommendation experiments. Off-line experiments with real users of a Czech travel agency website corroborated the importance of leveraging presentation context in both purchase prediction and recommendation tasks.

Hybrid Soft Computing Approach for Prediction of Cancer in Colon Using Microarray Gene Data

Background: Colon cancer remains among the top perpetrators of deaths linked to cancer. The probability of cancer reaching more parts of the body is extremely high in colorectal cancers. Early detection is hence, highly important for faster treatment. Method: In the current work, a hybrid approach toward the detection of colon cancers through the usage of microarray datasets, is presented. Particle Swarm Optimization (PSO) is utilized for features extraction, while Support Vector Machine (SVM) and Bagging approaches are utilized as classifiers. Results: The Colon Microarray Gene Dataset is used to evaluate minimum Redundancy Maximum Relevance (mRMR), Bagging, SVM, PSO and PSO-SVM with regard to classification accuracy, sensitivity and specificity. The proposed PSO-SVM displays best performance in all categories. Conclusion: Experiments reveal the capabilities of the proposed PSO-SVM to explore features space for the optimal features combination for gene selection from microarray data. Keywords: Colon cancer, microarray data, Support Vector Machine (SVM), Particle Swarm Optimization (PSO), Bagging, minimum Redundancy Maximum Relevancy (mRMR), beam search.

Bayesian Non-Parametric Model to Target Gamification Notifications Using Big Data

I suggest an approach that helps the online marketers to target their Gamification elements to users by modifying the order of the list of tasks that they send to users. It is more realistic and flexible as it allows the model to learn more parameters when the online marketers collect more data. The targeting approach is scalable and quick, and it can be used over streaming data.

Analysis of User’s Behavior on Borrowed Book Record in National Central Library University of Laos

The purpose of this paper is to analyze the student’s behavior of borrowing book in order to find out the frequent group of book items using association rule technique based on the data mining technologies. Data mining is a powerful tool for discovering interesting relationships hidden in large data set. Apriori algorithm can be applied on the university’s library transaction database to improve resource management to find out the relationship which two groups of books nearby together that user like to borrow. In this paper we collected data set of user’s record from Central Library National University of Laos. The number of user’s behavior data for training set was 6,615 record of personal log and the analysis is done by WEKA application software. The study shows that the total of 8 rules are with the confidence of 75%, 64%, 61%, 52%, 45%, 37%, 34%, and 23% respectively, the results about user’s behaviors can be used to manage the library resource.

Assessment of the Latest GPM-Era High-Resolution Satellite Precipitation Products by Comparison with Observation Gauge Data over the Chinese Mainland

The Global Precipitation Mission (GPM) Core Observatory that was launched on 27 February 2014 ushered in a new era for estimating precipitation from satellites. Based on their high spatial–temporal resolution and near global coverage, satellite-based precipitation products have been applied in many research fields. The goal of this study was to quantitatively compare two of the latest GPM-era satellite precipitation products (GPM IMERG and GSMap-Gauge Ver. 6) with a network of 840 precipitation gauges over the Chinese mainland. Direct comparisons of satellite-based precipitation products with rain gauge observations over a 20 month period from April 2014 to November 2015 at 0.1° and daily/monthly resolutions showed the following results: Both of the products were capable of capturing the overall spatial pattern of the 20 month mean daily precipitation, which was characterized by a decreasing trend from the southeast to the northwest. GPM IMERG overestimated precipitation by approximately 0.09 mm/day while GSMap-Gauge Ver. 6 underestimated precipitation by −0.04 mm/day. The two satellite-based precipitation products performed better over wet southern regions than over dry northern regions. They also showed better performance in summer than in winter. In terms of mean error, root mean square error, correlation coefficient, and probability of detection, GSMap-Gauge was better able to estimate precipitation and had more stable quality results than GPM IMERG on both daily and monthly scales. GPM IMERG was more sensitive to conditions of no rain or light rainfall and demonstrated good capability of capturing the behavior of extreme precipitation events. Overall, the results revealed some limitations of these two latest satellite-based precipitation products when used over the Chinese mainland, helping to characterize some of the error features in these datasets for potential users.

Other times, other values: leveraging attribute history to link user profiles across online social networks

Profile linking is the ability to connect profiles of a user on different social networks. Linked profiles can help companies to build psychographics of its potential customers and segment them for targeted marketing in a cost-effective way, can help advertisers target personalized ads and can help security practitioners capture detailed characteristics of malicious/fraudulent users. Existing methods link profiles by observing high similarity between most recent (current) values of the attributes like name and username. However, for a section of users who are observed to evolve their attributes over time and choose dissimilar values across their profiles, these current values have low similarity. Existing methods then falsely conclude that profiles refer to different users. To reduce such false conclusions, we suggest to gather rich history of values assigned to an attribute over time and compare attribute histories to link user profiles across networks. We believe that attribute history highlights user preferences and behavior while creating attribute values on a social network. Coexistence of these preferences across profiles on different social networks results in alike attribute histories that suggests profiles potentially refer to a single user. Through this study, we quantify the importance of attribute history for profile linking on a dataset of real-world users with profiles on Twitter, Facebook, Instagram and Tumblr. We show that attribute history correctly links 48 % more profile pairs with non-matching current values that are incorrectly unlinked by existing methods. We further explore if factors such as longevity and availability of attribute history on either profiles affect linking performance. To the best of our knowledge, this is the first study that explores viability of using attribute history to link profiles on social networks.

A Novel Behavioural Biometric Technique for Robust User Authentication

ABSTRACTIn this paper, we propose a novel approach termed as fingerprint dynamics, targeted for multi-instance authentication system. Inspired by the concept of keystroke dynamics, the technique analyses the behavioural characteristics associated with multi-instance fingerprint acquisition. To capture the user data, an acquisition set-up (hardware) is devised. Data-set of genuine users and imposters are created using time-derived information, collected for unique fixed length sequences by 32 volunteers. Systematic evaluation of features and feature subset selection is performed. Goal of this paper is to determine potential of fingerprint dynamics and establish it as a successful biometric trait for the purpose of robust user authentication. The technique can be implemented as a part of multifactor authentication system or standalone alternative. Statistical analysis of classifier outputs for different kernels, by performing classification cross validation, and assessment of verification accuracies confirms the discriminating capability of proposed technique.

A Novel Hybrid Music Recommendation System using K-Means Clustering and PLSA

We propose a hybrid approach of music recommendation according to which user will get recommendations of the songs searched by the other users alike to the existing user on content basis and also songs listened by that similar set users on context basis. Here we will try to find the similar users on the basis of context using K-Means clustering technique and among those users we will find the most frequent or most listened songs on the basis of content using PLSA technique. To fulfil this task we have created a dummy dataset and taking assumption that it is valid for experimental point of view. In order to evaluate the utility of recommendations produced by using proposed approach of music recommender system, we have computed three metrics i.e. Precision, Recall and F-1 Score. The result of proposed methodology shows the promising result as evaluated by parameters i.e. precision, recall and F1-score on dummy dataset. It can be tested for the real dataset of users from future perspective which will definitely going to take some time because of availability of such data.

Fuzzy rule-based analysis of spatio-temporal ATM usage data for fraud detection and prevention1

This article presents a novel approach for detecting fraudulent behaviors from automated teller machine (ATM) usage data by analyzing geo-behavioral habits of the customers and describe the use of a fuzzy rule-based system capable of classifying suspicious and non-suspicious financial transactions. Firstly, the geographic entropies of ATM cardholders are computed from the spatio-temporal ATM transactions data to form customer classes of mobility. ATM transactions exhibit spatio-temporal properties by inclusion of location information. The transition data can be generated by using transaction data from the current location to the next one. Once, the transition data are generated, statistical outlier detection techniques can be utilized. On top of classical methods, crisp unsupervised methods can easily be used for detecting outliers in the transition data. In addition, fuzzy C-Means algorithm can be implemented to determine outliers. In this study, ATM usage dataset containing around two years’ worth of data, provided by a mid-size Turkish bank was analyzed. It was shown that a significant bulk of ATM users does not leave the vicinity of their living places. Some insightful business rules that can be extracted from geo-tagged ATM transaction data by means of using a fuzzy rule-based system were also presented.

On the efficiency of user identification: a system-based approach

In the Internet era, users’ fundamental privacy and anonymity rights have received significant research and regulatory attention. This is not only a result of the exponential growth of data that users generate when accomplishing their daily task by means of computing devices with advanced capabilities, but also because of inherent data properties that allow them to be linked with a real or soft identity. Service providers exploit these facts for user monitoring and identification, albeit impacting users’ anonymity, based mainly on personal identifiable information or on sensors that generate unique data to provide personalized services. In this paper, we report on the feasibility of user identification using general system features like memory, CPU and network data, as provided by the underlying operating system. We provide a general framework based on supervised machine learning algorithms both for distinguishing users and informing them about their anonymity exposure. We conduct a series of experiments to collect trial datasets for users’ engagement on a shared computing platform. We evaluate various well-known classifiers in terms of their effectiveness in distinguishing users, and we perform a sensitivity analysis of their configuration setup to discover optimal settings under diverse conditions. Furthermore, we examine the bounds of sampling data to eliminate the chances of user identification and thus promote anonymity. Overall results show that under certain configurations users’ anonymity can be preserved, while in other cases users’ identification can be inferred with high accuracy, without relying on personal identifiable information.

Exploring factors of user’s peer-influence behavior in social media on purchase intention: Evidence from QQ

How to leverage the interactions and peer-influences among online friends to motivate users’ purchase willingness and improve marketing performance has been a hot research issue in human behavior based on social media. In our research, we extracted the dataset of users’ purchasing membership services on QQ, the biggest online instant message system in China, to explore the main factors of user’s peer-influence behavior on promoting purchase in social media. Through a series of elaborate experiments, we obtained some valuable results. First, though the high-degree users who have a lot of online friends will influence more followers to purchase the services, the proportion of friends involved in the purchase is actually comparatively lower than some low-degree users. At the same time, the “neighbor-press effect” induced from a user’s connected friends having purchased will bring obvious positive impact on promoting this user’s purchase willingness. Finally, the length of most purchase chains (about 80%) is within two nodes, which verifies that the purchase diffusion is more confined in the small-scale “circle of friends”. Our results indicate that peer-influence purchase is less likely to be due to the influence of higher degree users but is more related to the number of prior adoptions (predecessors) in the user’s social neighborhood. The achievements of this study will provide deep technical supports for strategic planning and implementation in the social marketing practice.

Can Opinion Mining Techniques Help to Select Open Source Software?

People and organizations that are considering the adoption of Open-Source Software (OSS), or that need to choose among different OSS products are interested in knowing the user community’s opinion, since this can provide useful indications about the strengths and limits of the software being evaluated. While several methods for the evaluation of the community size are available, there is no automated support to the extraction of the opinions of the community. In this paper we explore whether it is possible to support the OSS selection process by means of automated sentiment analysis techniques. Our goal is to understand if the actual opinion mining techniques, can be applied to get valuable opinions on OSS software. Our goal will be achieved first developing a web crawler to extract user generated content on OSS, building a data-set of relevant user generated then we apply the opinion mining process the OSS blogs data-set. We collected more than 88K user generated content and we compared the performance of our opinion mining technique with a set of existing opinion mining tools. Results of the application of our technique show that opinion mining can help to evaluate the opinions of OSS products. However, the existing opinion mining tools, even if applicable in different domains, are still not reliable in the domain of OSS, mainly because they are trained on different data-sets, opening new research directions for future work in the opinion mining domain.

The “tipping point” feature of social coupons: An empirical investigation

Groupon has pioneered a new business model that combines the features of daily deals and group buying. Utilizing a large proprietary dataset of Groupon users, the authors formulate three hypotheses about the effects of the tipping point (i.e., whether enough people have purchased the deal before it can be redeemed) on consumer behavior. The results indicate that (1) surprisingly, the tipping point does not stimulate consumers to refer the deal to others, (2) after controlling for detailed deal characteristics, information about the tipping point increases deal purchase probability and accelerates deal purchase speed by removing consumers' uncertainty about whether the deal will eventually tip, and (3) through a comparison of the different effects of prior purchases on purchase likelihood before and after a tipping point, conformity rather than social learning is identified as playing the dominant role in contagious purchases. Taken together, our results support the fact that the tipping point can alter consumer behavior and affect sales. However, recent changes made by Groupon are inconsistent with our empirical results and keeps the company from fully utilizing its potential. This study also provides an example of using web analytics tools to augment clickstream data and consolidate information from other sources.

Toward Robotic Robbery on the Touch Screen

Despite the tremendous amount of research fronting the use of touch gestures as a mechanism of continuous authentication on smart phones, very little research has been conducted to evaluate how these systems could behave if attacked by sophisticated adversaries. In this article, we present two Lego-driven robotic attacks on touch-based authentication: a population statistics--driven attack and a user-tailored attack. The population statistics--driven attack is based on patterns gleaned from a large population of users, whereas the user-tailored attack is launched based on samples stolen from the victim. Both attacks are launched by a Lego robot that is trained on how to swipe on the touch screen. Using seven verification algorithms and a large dataset of users, we show that the attacks cause the system’s mean false acceptance rate (FAR) to increase by up to fivefold relative to the mean FAR seen under the standard zero-effort impostor attack. The article demonstrates the threat that robots pose to touch-based authentication and provides compelling evidence as to why the zero-effort attack should cease to be used as the benchmark for touch-based authentication systems.

Personal network on the Internet: How the socially marginalized stay marginalized in personal network diversity and multiplicity

The Internet by its asynchronous and non-spatial nature may facilitate the diverse and multiple personal networks, but we hypothesize that this network affordance will be heavily influenced by one’s socio-demographic positions. To test this hypothesis, we used a large survey data set of Internet users (n=3,120) and examined how users’ socio-demographic positions interact with Internet access to shape the affordance of network diversity and multiplicity. We found significant interactive effects, while network diversity, as indicated by homogeneity and heterogeneity, was not directly impacted by social positions. The effects of network multiplicity on personal wellbeing also differed by socio-demographic background. We argue that, without an understanding of socio-demographic disparities, a focus on Internet affordance fails to recognize the social reproduction in the creation and the benefits of personal networks.

Extracting Usage Patterns from Power Usage Data of Homes' Appliances in Smart Home using Big Data Platform

Advances in sensing techniques and IOT enabled the possibility to gain precise information about devices in smart home and smart city environments. Data analysis for sensors and devices may help us develop friendlier systems for smart city or smart home. Sequence pattern mining extracts interesting sequence pattern from data. Electricity usage dose follow a sequence of events. In this study the authors investigate this issue and extracted valuable sequence pattern from real appliances' power usage dataset using PrefixSpan. The experiments in this research is implemented on Spark as a novel distributed and parallel big data processing platform on two different clusters and interesting findings are obtained. These findings show the importance of extracting sequence pattern from power usage data to various applications such as decreasing CO2 and greenhouse gas emission by decreasing the electricity usage. The findings also show the needs to bring big data platforms to processing such kind of data which is captured in smart home and smart cities.

Probabilistic evaluation of solar photovoltaic systems using Bayesian networks: a discounted cash flow assessment

AbstractSolar photovoltaic (PV) technology is now a key contributor worldwide in the transition towards low‐carbon electricity systems. To date, PV commonly receives subsidies in order to accelerate adoption rates by increasing investor returns. However, many aleatory and epistemic uncertainties exist with regard to these potential returns. In order to manage these uncertainties, an innovative probabilistic approach using Bayesian networks has been applied to the techno‐economic analysis of domestic solar PV. Empirical datasets from over 600 domestic PV systems, together with national domestic electricity usage datasets, have been used to generate and calibrate prior probability distributions for PV yield and domestic electricity consumption, respectively, for typical urban housing stock. Subsequently, conditional dependencies of PV self‐consumption with regard to PV generation and household electricity consumption have been simulated via stochastic modelling using high temporal resolution demand and PV generation data. A Bayesian network model is subsequently applied to deliver posterior probability distributions of key parameters as part of a discounted cash flow analysis. The results illustrate the sensitivity of PV investment returns to parameters such as PV self‐consumption, PV degradation rates and geographical location and quantify inherent uncertainties when evaluating the impact of sector‐specific PV adoption upon economic indicators. The outcomes are discussed in terms of the value and impact of this new Bayesian approach in terms of supporting robust and rigorous policy and investment decision‐making, especially in post‐subsidy contexts globally. © 2016 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.

Mobile Data Offloading Through Caching in Residential 802.11 Wireless Networks

As the ever growing mobile data traffic challenges the economic viability and performance of cellular networks, innovative solutions that harvest idle user-owned network resources are gaining increasing interest. In this work, we propose leasing wireless bandwidth and cache space of residential 802.11 (WiFi) access points (APs) for offloading mobile data. This solution not only reduces cellular network congestion, but, due to caching, improves also the user-perceived network performance without overloading the backhaul links of the APs. To encourage residential users to contribute their bandwidth and cache resources, we design monetary incentive (reimbursement) schemes. The offered reimbursements directly determine the amounts of available bandwidth and cache space in every AP, which in turn affect the caching policy (where to cache each content file) and the routing policy (where to route each mobile data request). In order to reduce operator’s total cost for serving mobile data requests and leasing resources, we introduce a framework for the joint optimization of incentive, caching, and routing policies. Using a novel WiFi usage dataset collected from 167 residences, we show that in densely populated areas with relatively costly network capacity upgrades, our proposal can halve operator’s total cost, while reimbursing up to 9€ per month each residential user.

Using Visualization Algorithms for Discovering Patterns in Groups of Users for Tutoring Multiple Languages through Social Networking

Social networks are addressed to a very large and heterogeneous audience of people. When trying to incorporate an intelligent language learning system in social networks, a problem of user diversity emerges and thus, user clustering based on their characteristics is necessary. In view of this compelling need, this paper concerns the pattern discovery of user clusters in social networks. In this research, we have modeled the Facebook user characteristics that determine the clustering process. An unsupervised clustering algorithm was used so that coherent groups of users with the same learning styles and capabilities are generated. This algorithm clusters users by taking as input their several fundamental characteristics, such as their age, educational level, number of languages spoken and computer knowledge level. The general objective of this data mining process is to extract important information and to gain knowledge from the user data set and transform it into a manageable and intelligible structure with a view to ameliorating the learning process. These experimental results show that the Facebook user characteristics, which were chosen at the clustering process, seem to be significant determinants for the clusters and the whole learning experience of each user.

Data visualisation in surveillance for injury prevention and control: conceptual bases and case studies

BackgroundThe complexity of current injury-related health issues demands the usage of diverse and massive data sets for comprehensive analyses, and application of novel methods to communicate data effectively to the...