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نتیجه جستجو - BSN

تعداد مقالات یافته شده: 10
ردیف عنوان نوع
1 Enhancing context specifications for dependable adaptive systems: A data mining approach
افزایش مشخصات زمینه برای سیستمهای انطباقی قابل اعتماد: رویکرد داده کاوی-2019
Context: Adaptive systems are expected to cater for various operational contexts by having multiple strategies in achieving their objectives and the logic for matching strategies to an actual context. The prediction of relevant contexts at design time is paramount for dependability. With the current trend on using data mining to support the requirements engineering process, this task of understanding context for adaptive system at design time can benefit from such techniques as well. Objective: The objective is to provide a method to refine the specification of contextual variables and their rela- tion to strategies for dependability. This refinement shall detect dependencies between such variables, priorities in monitoring them, and decide on their relevance in choosing the right strategy in a decision tree. Method: Our requirements-driven approach adopts the contextual goal modelling structure in addition to the operationalization values of sensed information to map contexts to the system’s behaviour. We propose a design time analysis process using a subset of data mining algorithms to extract a list of relevant contexts and their related variables, tasks, and/or goals. Results: We experimentally evaluated our proposal on a Body Sensor Network system (BSN), simulating 12 re- sources that could lead to a variability space of 4096 possible context conditions. Our approach was able to elicit subtle contexts that would significantly affect the service provided to assisted patients and relations between contexts, assisting the decision on their need, and priority in monitoring. Conclusion: The use of some data mining techniques can mitigate the lack of precise definition of contexts and their relation to system strategies for dependability. Our method is practical and supportive to traditional requirements specification methods, which typically require intense human intervention.
Keywords: Self-adaptive system | Context uncertainty | Data mining | Design time | Goal modelling | Dependability
مقاله انگلیسی
2 The role of location and social strength for friendship prediction in location-based social networks
نقش مکان و قدرت اجتماعی برای پیش بینی دوستی در شبکه های اجتماعی مبتنی بر مکان-2018
Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.
keywords: Location-based social networks| Link prediction| Friendship recommendation| Human mobility| User behavior
مقاله انگلیسی
3 A hybrid collaborative filtering model for social influence prediction in event-based social networks
یک مدل فیلتربندی مشترک ترکیبی برای پیش بینی نفوذ اجتماعی در شبکه های اجتماعی مبتنی بر رویداد-2017
Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Understanding users’ social influences in social networks can benefit many applications, such as social recommendation and social marketing. In this paper, we focus on the problem of predicting users’ social influences on upcoming events in EBSNs. We formulate this prediction problem as the estimation of unobserved entries of the constructed user-event social influence matrix, where each entry represents the influence value of a user on an event. In particular, we define a users social influence on a given event as the proportion of the users friends who are influenced by him/her to attend the event. To solve this problem, we present a hybrid collaborative filtering model, namely, Matrix Factorization with Event-User Neighborhood (MF-EUN) model, by incorporating both event-based and user-based neighborhood methods into matrix factorization. Due to the fact that the constructed social influence matrix is very sparse and the overlap values in the matrix are few, it is challenging to find reliable similar neighbors using the widely adopted similarity measures (e.g., Pearson correlation and Cosine similarity). To address this challenge, we propose an additional information based neighborhood discovery (AID) method by considering both event-specific and user-specific features in EBSNs. The parameters of our MF-EUN model are determined by minimizing the associated regularized squared error function through stochastic gradient descent. We conduct a comprehensive performance evaluation on real-world datasets collected from DoubanEvent. Experimental results show that our proposed hybrid collaborative filtering model is superior than several alternatives, which provides excellent performance with RMSE and MAE reaching 0.248 and 0.1266 respectively in the 90% training data of 10 000 users dataset.
Keywords: Event-based social network | Social influence | Matrix factorization | Neighborhood method | Collaborative filtering
مقاله انگلیسی
4 An adaptive hawkes process formulation for estimating time-of-day zonal trip arrivals with location-based social networking check-in data
فرمول سازمانی فرآیند سازگار با هوس برای برآورد زمان ورود روزانه منطقه ای با داده های ثبت نام شبکه های اجتماعی مبتنی بر مکان-2017
Location-Based Social Networking (LBSN) services, such as Foursquare, Facebook check-ins, and Geo-tagged Twitter tweets, have emerged as new secondary data sources for studying individual travel mobility patterns at a fine-grained level. However, the differences between human social behavioral and travel patterns can cause significant sampling bias for travel demand estimation. This paper presents a dynamic model to estimate time-of day zonal trip arrival patterns. In the proposed model, the state propagation is formulated by the Hawkes process; the observation model implements LBSN sampling. The proposed model is applied to Foursquare check-in data collected from Austin, Texas in 2010 and cal ibrated with Origin-Destination (OD) data and time of day factor from Capital Area Metropolitan Planning Organization (CAMPO). The proposed model is compared with a simple aggregation model of trip purposes and time of day based on a prior daily OD esti mation model using the LBSN data. The results illustrate the promising benefits of applying stochastic point process models and state-space modeling in time-of-day zonal arrival pat tern estimation with the LBSN data. The proposed model can significantly reduce the num ber of parameters to calibrate in order to reduce the sampling bias of LBSN data for trip arrival estimation.
Keywords: Location-based social networking | Dynamic trip arrival estimation | Human activity patterns | Hawkes process | State space model
مقاله انگلیسی
5 VRer: Context-Based Venue Recommendation using embedded space ranking SVM in location-based social network
VRer: توصیه محل برگزاری مبتنی بر محتوا با استفاده از SVM رتبه بندی فضای جاسازی شده در شبکه اجتماعی مبتنی بر مکان-2017
Venue recommendation has attracted a lot of research attention with the rapid development of Location Based Social Networks. The effectiveness of venue recommendation largely depends on how well it cap tures users’ contexts or preferences. However, it is quite difficult, if not impossible, to capture the whole information about users’ preferences. In addition, users’ preferences are often heterogeneous (i.e., some preferences are static and common to all users while some preferences are dynamic and diverse). Exist ing venue recommendation does not well address the aforementioned issues and often recommends the most popular, the cheapest, or the closest venues based on simple contexts. In this paper, we cast the venue recommendation as a ranking problem and propose a recommendation framework named VRer (Context-Based Venue Recommendation using embedded space ranking SVM) employing an embedded space ranking SVM model to separate the venues in terms of different charac teristics. Our proposed approach makes use of ‘check-in’ data to capture users’ preferences and utilizes a machine learning model to tune the importance of different factors in ranking. The major contribu tion of this paper are: (1) VRer combines various contexts (e.g., the temporal influence and the category of locations) with the check-in records to capture individual heterogeneous preferences; (2) we propose an embedded space ranking SVM optimizing the learning function to reduce the time consumption of training the personalized recommendation model for each group or user; (3) we evaluate our proposed approach against a real world LBSN and compare it with other baseline methods. Experimental results demonstrate the benefits of our proposed approach.
Keywords: LBSNs| Preference | Check-in | Context-based | Ranking SVM
مقاله انگلیسی
6 Using check-in features to partition locations for individual users in location based social network
استفاده از ویژگی های چک در مکان های پارتیشن برای کاربران شخصی در شبکه اجتماعی مبتنی بر مکان-2017
With location-based social network (LBSN) flourishing, location check-in records offer us sufficient in formation resource to do relative mining. Among locations visited by a user, those attracting relatively more visits from that user can serve as a support for further mining and improvement for location-based services. Therefore, great significance lies in the partition for visited locations based on a user’s visiting frequency. The aim of our paper is to partition locations for individual users by utilizing classification in machine learning, categorizing the location for a user once he or she makes initial check-in there. After feature extraction for each initial check-in record, we evaluate the contribution of three feature categories. The results show the contribution of different feature categories varies in classification, where social fea tures appear to offer the least contribution. At last, we do a final test on the whole sample, comparing the results with two baselines based on majority voting respectively. The results largely outperform the baselines in general, demonstrating the effectiveness of classification.
Keywords: Location-based social network | Classification | Prediction
مقاله انگلیسی
7 Genetic variants of the DNA repair genes from Exome Aggregation Consortium (EXAC) database: significance in cancer
انواع ژنتیک ژن های بازیابی DNA از پایگاه داده Consortium Aggregation Exome (EXAC): اهمیت در سرطان-2017
DNA repair pathway is a primary defense system that eliminates wide varieties of DNA damage. Any deficiencies in them are likely to cause the chromosomal instability that leads to cell malfunctioning and tumorigenesis. Genetic polymorphisms in DNA repair genes have demonstrated a significant association with cancer risk. Our study attempts to give a glimpse of the overall scenario of the germline polymorphisms in the DNA repair genes by taking into account of the Exome Aggregation Consortium (ExAC) database as well as the Human Gene Mutation Database (HGMD) for evaluating the disease link, particularly in cancer. It has been found that ExAC DNA repair dataset (which consists of 228 DNA repair genes) comprises 30.4% missense, 12.5% dbSNP reported and 3.2% ClinVar significant variants. 27% of all the missense variants has the deleterious SIFT score of 0.00 and 6% variants carrying the most damaging Polyphen-2 score of 1.00, thus affecting the protein structure and function. However, as per HGMD, only a fraction (1.2%) of ExAC DNA repair variants was found to be cancer-related, indicating remaining variants reported in both the databases to be further analyzed. This, in turn, may provide an increased spectrum of the reported cancer linked variants in the DNA repair genes present in ExAC database. Moreover, further in silico functional assay of the identified vital cancer-associated variants, which is essential to get their actual biological significance, may shed some lights in the field of targeted drug development in near future.
Keywords: DNA repair | Germline variants | Cancer | ExAC database | DNA repair variants status | HGMD
مقاله انگلیسی
8 A deep dive into location-based communities in social discovery networks
شیرجه رفتن عمیق به جوامع مبتنی بر مکان در کشف شبکه های اجتماعی-2017
Location-based social discovery networks (LBSD) is an emerging category of location-based social net works (LBSN) that are specifically designed to enable users to discover and communicate with nearby people. In this paper, we present the first measurement study of the characteristics and evolution of location-based communities which are based on a social discovery network and geographic proximity. We measure and analyse more than 176K location-based communities with over 1.4 million distinct members of a popular social discovery network and more than 46 million locations. We characterise the evolution of the communities and study the user behaviour in LBSD by analysing the mobility features of users belonging to communities in comparison to non-community members. Using observed spatio-temporal similarity features, we build and evaluate a classifier to predict location-based community membership solely based on user mobility information.
Keywords: Human mobility | Link prediction | Social discovery networks
مقاله انگلیسی
9 Secure hitch in location based social networks
اتصال امن در شبکه های اجتماعی مبتنی بر مکان-2017
Location based services are increasingly popular, partly due to the trend of smartphone and online social network service adoption. However, it is important for location-based service provider (LBSP) to ensure user location privacy in the provision of such services. In this paper, we present a secure hitch service in location based social networks (LBSNs). To provide such a service, we propose a privacy-preserving proximity based location query (PPLQ) protocol, which is based on the hierarchical predicate encryption technique and the prefix membership verification technique. There are two types of users in this system, namely: the querier and the publisher. Our protocol allows a querier to query the location of publish ers using multi-dimensional search, and it enforces distance based access control in the location queries. In order to improve the efficiency of our protocol, we use the multi-scale technique to represent user’s location information in the query condition and searchable index. The proposed protocol is designed to achieve multi-dimensional keyword search and bilateral private proximity testing simultaneously. Our protocol enables each user to independently define his/her own location policy for private proximity test ing. In particular, we propose some solutions to reduce the search time cost of the CSP so that the time cost is acceptable for queriers. Finally, we demonstrate the utility of the protocol using simulated data on the map of the city area of Changsha and a U.S. census dataset.
Keywords: Location based social networks | Location privacy | Private proximity detection | Multi-keyword dimensional search
مقاله انگلیسی
10 مروری برروی شبکه بدنی بی‌سیم : فناوری‌ امنیتی و مسائل روش طراحی آن
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 16
شبکه‌های حسگر بدنی بی‌سیم (WBANs) محبوبیت بیشتری یافتند و پتانسیل بسیار زیادی در نظارت بلادرنگ برروی بدن انسان نشان دادند. برنامه‌های کاربردی مانیتورینگ مستمر مقرون به صرفه، بدون مزاحمت و بدون نظارت مانند دوربین‌های مدار بسته، و به همین ترتیب وعده WBANها در مراقبت‌های بهداشتی طیف وسیعی از فعالیت‌ها و سیستم توانبخشی را به خود جذب کرده است. بااین حال، مزایای WBAN، که از مسائل چالش برانگیز استفاده می‌کنند باید حل شوند. علاوه بر باز کردن مسئله برای استانداردسازی، مسائل کارآمدی انرژی WBAN و کیفیت خدمات (QoS)، امنیت و حفظ حریم شخصی نگرانی‌های بزرگی هستند. داده‌های مهم زندگی سیستم‌های پوشیدنی باید حفاظت شوند، از این رو کنترل می‌شوند. بااین اوصاف، این سیستم‌ها با سختی‌های پرداختن به امنیت مواجهه هستند. WBAN بیشتر چالش‌های امنیتی را از شبکه‌های حسگر بی‌سیم (WSN) به ارث برده است. بااین حال، WBAN، علائمی مانند محدودیت شدید منابع و شرایط سخت محیطی، چالش‌های امنیتی و حفظ حریم شخصی برای تظاهر به حمایت بیشتر از افراد را مشخص می‌سازد. در این مقاله، مسائل امنیتی و حفظ حریم شخصی و حملات بالقوه WBAN مورد بررسی قرار می‌گیرند. علاوه براین، این مسئله حل نشده که برای کیفیت سرویس‌ها مسئله امنیت جدی است که دارای پتانسیل زیادی برای تبادلات در WBAN هستند توضیح داده خواهد شد، و سپس برروی جهت‌های پژوهش‌های آینده بحث خواهیم کرد.
کلمات کلیدی: WBAN | BSN | UH | ECC | DES | ARQ | PSKA | EKG
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