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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 |
مقاله ترجمه شده |