IEEE 2017 – IEEE has available to assist engineering students with their final-year projects.IEEE – IEEE Resources for Final-Year Engineering Projects. The Institute of Electrical and Electronics Engineers (IEEE) is a professional association with its corporate office in New York City.

Personal Web Revisitation by Context and Content Keywords with Relevance Feedback

Introduction:
Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human’s natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPage prev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual’s memory strength and revisitation habits. Our 6-month user study shows that: (1) Compared with the existing web revisitation tool Memento, History List Searching method, and Search Engine method, the proposed WebPage prev delivers the best re-finding quality in finding rate (92.10%), average F1-measure (0.4318) and average rank error (0.3145). (2) Our dynamic management of context and content memories including decay and reinforcement strategy can mimic users’ retrieval and recall mechanism. With relevance feedback, the finding rate of WebPagePrev increases by 9.82%, average F1-measure increases by 47.09%, and average rank error decreases by 19.44% compared to stable memory management strategy. Among time, location, and activity context factors in WebPagePrev, activity is the best recall cue, and context+content based re-finding delivers the best performance, compared to context based re-finding and content based re-finding.

Reference IEEE paper:
“Personal Web Revisitation by Context and Content Keywords with Relevance Feedback”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID -SBI1042

DomainDATA MINING

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PPRank: Economically Selecting Initial Users for Influence Maximization in Social Networks

Introduction:
This paper focuses on seeking a new heuristic scheme for an influence maximization problem in social networks: how to economically select a subset of individuals (so-called seeds) to trigger a large cascade of further adoptions of a new behavior based on a contagion process. Most existing works on selection of seeds assumed that the constant number k seeds could be selected, irrespective of the intrinsic property of each individual’s different susceptibility of being influenced (e.g., it may be costly to persuade some seeds to adopt a new behaviour). In this paper, a price-performance-ratio inspired heuristic scheme, PPRank, is proposed, which investigates how to economically select seeds within a given budget and meanwhile try to maximize the diffusion process. Our paper’s contributions are threefold. First, we explicitly characterize each user with two distinct factors: the susceptibility of being influenced (SI) and influential power (IP) representing the ability to actively influence others and formulate users’ SIs and IPs according to their social relations, and then, a convex price-demand curve-based model is utilized to properly convert each user’s SI into persuasion cost (PC) representing the cost used to successfully make the individual adopt a new behaviour. Furthermore, a novel cost-effective selection scheme is proposed, which adopts both the price performance ratio (PC-IP ratio) and user’s IP as an integrated selection criterion and meanwhile explicitly takes into account the overlapping effect; finally, simulations using both artificially generated and real-trace network data illustrate that, under the same budgets, PPRank can achieve larger diffusion range than other heuristic and brute-force greedy schemes without taking users’ persuasion costs into account.

Reference IEEE paper:
“PPRank: Economically Selecting Initial Users for Influence Maximization in Social Networks”, IEEE SYSTEMS JOURNAL, 2017.

Unique ID -SBI1043

DomainDATA MINING

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QDA: A Query Driven Approach to Entity Resolution

Introduction:
This paper addresses the problem of query-aware data cleaning in the context of a user query. In particular, we develop a novel Query-Driven Approach (QDA) that systematically exploits the semantics of the predicates in SQL-like selection queries to reduce the data cleaning overhead. The objective of QDA is to issue the minimum number of cleaning steps that are necessary to answer a given SQL-like selection correctly. The comprehensive empirical evaluation of QDA demonstrates outstanding results – that is QDA is significantly better compared to traditional ER techniques, especially when the query is very selective.

Reference IEEE paper:
“QDA: A Query Driven Approach to Entity Resolution”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID -SBI1044

DomainDATA MINING

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Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data

Introduction:
Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships and semantic matching approaches are among those favoured by previous research. However, user profiles which only contain a user’s past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach outperforms traditional techniques, including existing non-personalized and personalized query expansion methods.

Reference IEEE paper:
“Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2017.

Unique ID -SBI1045

DomainDATA MINING

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RAPARE: A Generic Strategy for Cold Start Rating Prediction Problem

Introduction:
In recent years, recommender system is one of indispensable components in many e-commerce websites. One of the major challenges that largely remains open is the cold-start problem, which can be viewed as a barrier that keeps the cold-start users/items away from the existing ones. In this paper, we aim to break through this barrier for cold-start users/items by the assistance of existing ones. In particular, inspired by the classic Elo Rating System, which has been widely adopted in chess tournaments; we propose a novel rating comparison strategy (RAPARE) to learn the latent profiles of cold-start users/items. The center-piece of our RAPARE is to provide a fine-grained calibration on the latent profiles of cold-start users/items by exploring the differences between cold-start and existing users/items. As a generic strategy, our proposed strategy can be instantiated into existing methods in recommender systems. To reveal the capability of RAPARE strategy, we instantiate our strategy on two prevalent methods in recommender systems, i.e., the matrix factorization based and neighborhood based collaborative filtering. Experimental evaluations on five real data sets validate the superiority of our approach over the existing methods in cold-start scenario.

Reference IEEE paper:
“RAPARE: A Generic Strategy for Cold Start Rating Prediction Problem”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID -SBI1046

DomainDATA MINING

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SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors

Introduction:
Mass media sources, specifically the news media, have traditionally informed us of daily events. In modern times, social media services such as Twitter provide an enormous amount of user-generated data, which have great potential to contain informative news-related content. For these resources to be useful, we must find a way to filter noise and only capture the content that, based on its similarity to the news media, is considered valuable. However, even after noise is removed, information overload may still exist in the remaining data—hence, it is convenient to prioritize it for consumption. To achieve prioritization, information must be ranked in order of estimated importance considering three factors. First, the temporal prevalence of a particular topic in the news media is a factor of importance, and can be considered the media focus (MF) of a topic. Second, the temporal prevalence of the topic in social media indicates its user attention (UA). Last, the interaction between the social media users who mention this topic indicates the strength of the community discussing it, and can be regarded as the user interaction (UI) toward the topic. We propose an unsupervised framework—SociRank—which identifies news topics prevalent in both social media and the news media, and then ranks them by relevance using their degrees of MF, UA, and UI. Our experiments show that SociRank improves the quality and variety of automatically identified news topics.

Reference IEEE paper:
“SociRank: Identifying and Ranking Prevalent News Topics Using Social Media Factors”, IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, 2017.

Unique ID -SBI1047

DomainDATA MINING

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Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

Introduction:
The increase of interest in using social media as a source for research has motivated tackling the challenge of automatically geolocating tweets, given the lack of explicit location information in the majority of tweets. In contrast to much previous work that has focused on location classification of tweets restricted to a specific country, here we undertake the task in a broader context by classifying global tweets at the country level, which is so far unexplored in a real-time scenario. We analyse the extent to which a tweet’s country of origin can be determined by making use of eight tweet-inherent features for classification. Furthermore, we use two datasets, collected a year apart from each other, to analyse the extent to which a model trained from historical tweets can still be leveraged for classification of new tweets. With classification experiments on all 217 countries in our datasets, as well as on the top 25 countries, we offer some insights into the best use of tweet-inherent features for an accurate country-level classification of tweets. We find that the use of a single feature, such as the use of tweet content alone – the most widely used feature in previous work – leaves much to be desired. Choosing an appropriate combination of both tweet content and metadata can actually lead to substantial improvements of between 20% and 50%. We observe that tweet content, the user’s self-reported location and the user’s real name, all of which are inherent in a tweet and available in a real-time scenario, are particularly useful to determine the country of origin. We also experiment on the applicability of a model trained on historical tweets to classify new tweets, finding that the choice of a particular combination of features whose utility does not fade over time can actually lead to comparable performance, avoiding the need to retrain. However, the difficulty of achieving accurate classification increases slightly for countries with multiple commonalities, especially for English and Spanish speaking countries.

Reference IEEE paper:
“Towards Real-Time, Country-Level Location Classification of Worldwide Tweets”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID -SBI1048

DomainDATA MINING

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Trajectory Community Discovery and Recommendation by Multisource Diffusion Modeling

Introduction:
In this paper, we detect communities from trajectories. Existing algorithms for trajectory clustering usually rely on simplex representation and a single proximity-related metric. Unfortunately, additional information markers (e.g., social interactions or semantics in the spatial layout) are ignored, leading to the inability to fully discover the communities in trajectory database. This is especially true for human-generated trajectories, where additional fine-grained markers (e.g., movement velocity at certain locations, or the sequence of semantic spaces visited) are especially useful in capturing latent relationships among community members. To overcome this limitation, we propose TODMIS, a general framework for Trajectory-based cOmmunity Detection by diffusion modeling on Multiple Information Sources. TODMIS combines additional information with raw trajectory data and construct the diffusion process on multiple similarity metrics. It also learns the consistent graph Laplacians by constructing the multi-modal diffusion process and optimizing the heat kernel coupling on each pair of similarity matrices from multiple information sources. Then, dense sub-graph detection is used to discover the set of distinct communities (including community size) on the coupled multi-graph representation. At last, based on the community information, we propose a novel model for online recommendation. We evaluate TODMIS and our online recommendation methods using different real-life datasets. Experimental results demonstrate the effectiveness and efficiency of our methods.

Reference IEEE paper:
“Trajectory Community Discovery and Recommendation by Multi-source Diffusion Modeling” , IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID -SBI1049

DomainDATA MINING

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Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method

Introduction:

Recently, collaborative filtering-based methods are widely used for service recommendation. QoS attribute value based collaborative filtering service recommendation includes two important steps. One is the similarity computation, and the other is the prediction for the QoS attribute value, which the user has not experienced. In some previous studies, the similarity computation methods and prediction methods are not accurate. The performances of some methods need to be improved. In this paper, we propose a ratio-based method to calculate the similarity. We can get the similarity between users or between items by comparing the attribute values directly. Based on our similarity computation method, we propose a new method to predict the unknown value. By comparing the values of a similar service and the current service that are invoked by common users, we can obtain the final prediction result. The performance of the proposed method is evaluated through a large data set of real web services. Experimental results show that our method obtains better prediction precision, lower mean absolute error (MAE) and faster computation time than various reference schemes considered.

Reference IEEE paper:

“Collaborative Filtering Service Recommendation Based on a Novel Similarity Computation Method”, IEEE TRANSACTIONS ON SERVICE COMPUTING, VOL.10, NO.3, May-June 2017.

Unique ID – SBI1090

Domain – SERVICE COMPUTING (WEB SERVICES)

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Improving Automated Bug Triaging with Specialized Topic Model

Introduction:

Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modelling algorithm named multi-feature topic model (MTM) which extends Latent Dirichlet Allocation (LDA) for bug triaging. MTM considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named TopicMiner which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair TopicMiner with MTM (TopicMiner MTM). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that TopicMinerMTM can achieve top-1 and top-5 prediction accuracies of 0.4831 – 0.6868, and 0.7686 – 0.9084, respectively. We also compare TopicMinerMTM with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.’s approach. The results show that TopicMinerMTM on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48% and 53.22%, LDA-KL by 262.91% and 105.97%, SVM-LDA by 205.89% and 110.48%, LDA-Activity by 377.60% and 176.32%, and Yang et al.’s approach by 59.88% and 13.70%, respectively.

Reference IEEE paper:

“Improving Automated Bug Triaging with Specialized Topic Model”, IEEE Transactions on Software Engineering, 2017.

Unique ID – SBI1089

Domain – SOFTWARE ENGINEERING

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A System for Profiling and Monitoring Database Access Patterns by Application Programs for Anomaly Detection

Introduction :

Database Management Systems (DBMSs) provide access control mechanisms that allow database administrators (DBAs) to grant application programs access privileges to databases. Though such mechanisms are powerful, in practice finer-grained access control mechanism tailored to the semantics of the data stored in the DMBS is required as a first class defense mechanism against smart attackers. Hence, custom written applications which access databases implement an additional layer of access control. Therefore, securing a database alone is not enough for such applications, as attackers aiming at stealing data can take advantage of vulnerabilities in the privileged applications and make these applications to issue malicious database queries. An access control mechanism can only prevent application programs from accessing the data to which the programs are not authorized, but it is unable to prevent misuse of the data to which application programs are authorized for access. Hence, we need a mechanism able to detect malicious behavior resulting from previously authorized applications. In this paper, we present the architecture of an anomaly detection mechanism, DetAnom, that aims to solve such problem. Our approach is based the analysis and profiling of the application in order to create a succinct representation of its interaction with the database. Such a profile keeps a signature for every submitted query and also the corresponding constraints that the application program must satisfy to submit the query. Later, in the detection phase, whenever the application issues a query, a module captures the query before it reaches the database and verifies the corresponding signature and constraints against the current context of the application. If there is a mismatch, the query is marked as anomalous. The main advantage of our anomaly detection mechanism is that, in order to build the application profiles, we need neither any previous knowledge of application vulnerabilities nor any example of possible attacks. As a result, our mechanism is able to protect the data from attacks tailored to database applications such as code modification attacks, SQL injections, and also from other data-centric attacks as well. We have implemented our mechanism with a software testing technique called concolic testing and the PostgreSQL DBMS. Experimental results show that our profiling technique is close to accurate, requires acceptable amount of time, and the detection mechanism incurs low run-time overhead.

Reference IEEE paper :

“A System for Profiling and Monitoring Database Access Patterns by Application Programs for Anomaly Detection”, IEEE Transactions on Software Engineering, 2017.

Unique ID – SBI1088

Domain – SOFTWARE ENGINEERING

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Two Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic Topic Model

Introduction:

Precise friend recommendation is an important problem in social media. Although most social websites provide some kinds of auto friend searching functions, their accuracies are not satisfactory. In this paper, we propose a more precise auto friend recommendation method with two stages. In the first stage, by utilizing the information of the relationship between texts and users, as well as the friendship information between users, we align different social networks and choose some “possible friends”. In the second stage, with the relationship between image features and users we build a topic model to further refine the recommendation results. Because some traditional methods such as variational inference and Gibbs sampling have their limitations in dealing with our problem, we develop a novel method to find out the solution of the topic model based on series expansion. We conduct experiments on the Flickr dataset to show that the proposed algorithm recommends friends more precisely and faster than traditional methods.

Reference IEEE paper:

“Two Stage Friend Recommendation Based on Network Alignment and Series-Expansion of Probabilistic Topic Model”, IEEE Transactions on Multimedia, 2017.

Unique ID – SBI1087

Domain – MULTIMEDIA

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Transactional Behaviour Verification in Business Process as a Service Configuration

Introduction :

Business Process as a Service (BPaaS) is an emerging type of cloud service that offers configurable and executable business processes to clients over the Internet. As BPaaS is still in early years of research, many open issues remain. Managing the configuration of BPaaS builds on areas such as software product lines and configurable business processes. The problem has concerns to consider from several perspectives, such as the different types of variable features, constraints between configuration options, and satisfying the requirements provided by the client. In our approach, we use temporal logic templates to elicit transactional requirements from clients that the configured service must adhere to. For formalizing constraints over configuration, feature models are used. To manage all these concerns during BPaaS configuration, we develop a structured process that applies formal methods while directing clients through specifying transactional requirements and selecting configurable features. The Binary Decision Diagram (BDD) analysis is then used to verify that the selected configurable features do not violate any constraints. Finally, model checking is applied to verify the configured service against the transactional requirement set. We demonstrate the feasibility of our approach with several validation scenarios and performance evaluations.

Reference IEEE paper :

“Transactional Behaviour Verification in Business Process as a Service Configuration”, IEEE TRANSACTIONS ON SERVICE COMPUTING 2017.

Unique ID – SBI1091

Domain – SERVICE COMPUTING (WEB SERVICES)

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FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data

Introduction :

Spatial data have wide applications, e.g., location-based services, and geometric range queries (i.e., finding points inside geometric areas, e.g., circles or polygons) are one of the fundamental search functions over spatial data. The rising demand of outsourcing data is moving large-scale datasets, including large-scale spatial datasets, to public clouds. Meanwhile, due to the concern of insider attackers and hackers on public clouds, the privacy of spatial datasets should be cautiously preserved while querying them at the server side, especially for location-based and medical usage. In this paper, we formalize the concept of Geometrically Searchable Encryption, and propose an efficient scheme, named FastGeo, to protect the privacy of clients’ spatial datasets stored and queried at a public server. With FastGeo, which is a novel two-level search for encrypted spatial data, an honest-but-curious server can efficiently perform geometric range queries, and correctly return data points that are inside a geometric range to a client without learning sensitive data points or this private query. FastGeo supports arbitrary geometric areas, achieves sublinear search time, and enables dynamic updates over encrypted spatial datasets. Our scheme is provably secure, and our experimental results on real-world spatial datasets in cloud platform demonstrate that FastGeo can boost search time over 100 times.

Reference IEEE paper :

“FastGeo: Efficient Geometric Range Queries on Encrypted Spatial Data”, IEEE Transactions on Dependable and Secure Computing, 2017.

Unique ID – SBI1068

Domain – SECURE COMPUTING

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Efficient and Privacy-preserving Min and k-th Min Computations in Mobile Sensing Systems

Introduction :

Protecting the privacy of mobile phone user participants is extremely important for mobile phone sensing applications. In this paper, we study how an aggregator can expeditiously compute the minimum value or the k-th minimum value of all users’ data without knowing them. We construct two secure protocols using probabilistic coding schemes and a cipher system that allows homomorphic bitwise XOR computations for our problems. Following the standard cryptographic security definition in the semi-honest model, we formally prove our protocols’ security. The protocols proposed by us can support time-series data and need not to assume the aggregator is trusted. Moreover, different from existing protocols that are based on secure arithmetic sum computations, our protocols are based on secure bitwise XOR computations, thus are more efficient.

Reference IEEE paper :

“Efficient and Privacy-preserving Min and k-th Min Computations in Mobile Sensing Systems”, IEEE Transactions on Dependable and Secure Computing 2017.

Unique ID – SBI1067

Domain – SECURE COMPUTING

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A Credibility Analysis System for Assessing Information on Twitter

Introduction :

Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.

Reference IEEE paper:

“A Credibility Analysis System for Assessing Information on Twitter”, IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, 2017.

Unique ID – SBI1066

Domain – SECURE COMPUTING

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iASK: A Distributed Q&A System Incorporating Social Community and Global Collective Intelligence

Introduction:

Traditional web-based Question and Answer (Q&A) websites cannot easily solve non-factual questions to match askers’ preference. Recent research efforts begin to study social-based Q&A systems that rely on an asker’s social friends to provide answers. However, this method cannot find answerers for a question not belonging to the asker’s interests. To solve this problem, we propose a distributed Q&A system incorporating both social community intelligence and global collective intelligence, named as iASK. iASK improves the response latency and answer quality in both the social domain and global domain. It uses a neural network based friend ranking method to identify answerer candidates by considering social closeness and Q&A activities. To efficiently identify answerers in the global user base, iASK builds a virtual server tree that embeds the hierarchical structure of interests, and also maps users to the tree based on user interests. To accurately locate the cooperative experts, iASK has a fine-grained reputation system to evaluate user reputation based on their cooperativeness and expertise, and uses a reputation based reward strategy to encourage users to be cooperative. To further improve the performance of iASK, we propose a weak tie assisted social based potential answerer location algorithm and an interest coefficient based uncategorized question forwarding algorithm. To further improve the response quality and cooperativeness, we propose a reputation based reward strategy that motivates users to answer questions from unknown users. Experimental results from large-scale trace-driven simulation and real-world daily usages of the iASK prototype show the superior performance of iASK. It achieves high answer quality with 24% higher accuracy, short response latency with 53% less delay and effective cooperative incentives with 16% more answers compared to other social-based Q&A systems. The results also show the effectiveness of the enhancement algorithms in improving the performance of iASK.

Reference IEEE paper :

“iASK: A Distributed Q&A System Incorporating Social Community and Global Collective Intelligence”, IEEE Transactions on Parallel and Distributed Systems, 2017.

Unique ID – SBI1065

Domain – PARALLEL AND DISTRIBUTED SYSTEMS

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SUPERMAN: Security Using Pre Existing Routing for Mobile Ad hoc Networks

Introduction :

The flexibility and mobility of Mobile Ad hoc Networks (MANETs) have made them increasing popular in a wide range of use cases. To protect these networks, security protocols have been developed to protect routing and application data. However, these protocols only protect routes or communication, not both. Both secure routing and communication security protocols must be implemented to provide full protection. The use of communication security protocols originally developed for wireline and WiFi networks can also place a heavy burden on the limited network resources of a MANET. To address these issues, a novel secure framework (SUPERMAN) is proposed. The framework is designed to allow existing network and routing protocols to perform their functions, whilst providing node authentication, access control, and communication security mechanisms. This paper presents a novel security framework for MANETs, SUPERMAN. Simulation results comparing SUPERMAN with IPsec, SAODV and SOLSR are provided to demonstrate the proposed frameworks suitability for wireless communication security.

Reference IEEE paper :

“SUPERMAN: Security Using Pre Existing Routing for Mobile Ad hoc Networks”, IEEE TRANSACTIONS ON MOBILE COMPUTING, 2017.

Unique ID – SBI1064

DomainMOBILE COMPUTING

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Searching Trajectories by Regions of Interest

Introduction:

With the increasing availability of moving-object tracking data, trajectory search is increasingly important. We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in many popular applications such as trip planning and recommendation, and location based services in general. TSR query processing faces three challenges: how to model the spatial-density correlation between query regions and data trajectories, how to effectively prune the search space, and how to effectively schedule multiple so-called query sources. To tackle these challenges, a series of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.

Reference IEEE paper :

“Searching Trajectories by Regions of Interest”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2017.

Unique ID – SBI1063

Domain – MOBILE COMPUTING

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Quantifying Interdependent Privacy Risks with Location Data

Introduction :

Co-location information about users is increasingly available online. For instance, mobile users more and more frequently report their co-locations with other users in the messages and in the pictures they post on social networking websites by tagging the names of the friends they are with. The users’ IP addresses also constitute a source of co-location information. Combined with (possibly obfuscated) location information, such co-locations can be used to improve the inference of the users’ locations, thus further threatening their location privacy: As co-location information is taken into account, not only a user’s reported locations and mobility patterns can be used to localize her, but also those of her friends (and the friends of their friends and so on). In this paper, we study this problem by quantifying the effect of co-location information on location privacy, considering an adversary such as a social network operator that has access to such information. We formalize the problem and derive an optimal inference algorithm that incorporates such co-location information, yet at the cost of high complexity. We propose some approximate inference algorithms, including a solution that relies on the belief propagation algorithm executed on a general Bayesian network model, and we extensively evaluate their performance. Our experimental results show that, even in the case where the adversary considers co-locations of the targeted user with a single friend, the median location privacy of the user is decreased by up to 62% in a typical setting. We also study the effect of the different parameters (e.g., the settings of the location-privacy protection mechanisms) in different scenarios.

Reference IEEE paper :

“Quantifying Interdependent Privacy Risks with Location Data”, IEEE Transactions on Mobile Computing, 2017.

Unique ID SBI1062

DomainMOBILE COMPUTING

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Detecting Mobile Malicious Webpages in Real Time

Introduction :

Mobile specific webpages differ significantly from their desktop counterparts in content, layout and functionality. Accordingly, existing techniques to detect malicious websites are unlikely to work for such webpages. In this paper, we design and implement kAYO, a mechanism that distinguishes between malicious and benign mobile webpages.  kAYO makes this determination based on static features of a webpage ranging from the number of iframes to the presence of known fraudulent phone numbers. First, we experimentally demonstrate the need for mobile specific techniques and then identify a range of new static features that highly correlate with mobile malicious webpages. We then apply kAYO to a dataset of over 350,000 known benign and malicious mobile webpages and demonstrate 90% accuracy in classification. Moreover, we discover, characterize and report a number of webpages missed by Google Safe Browsing and VirusTotal, but detected by kAYO. Finally, we build a browser extension using kAYO to protect users from malicious mobile websites in real-time. In doing so, we provide the first static analysis technique to detect malicious mobile webpages.

Reference IEEE paper :

“Detecting Mobile Malicious Webpages in Real Time”, IEEE Transactions on Mobile Computing, 2017.

Unique ID -SBI1061

DomainMOBILE COMPUTING

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A Proxy based Collaboration System to Minimize Content Download Time and Energy Consumption

Introduction:

Mobile collaborative community (MCC) is an emerging technology that allows multiple mobile nodes (MNs) to perform a resource intensive task, such as large content download, in a cooperative manner. In this paper, we introduce a proxy-based collaboration system for the MCC where a content proxy (CProxy) determines the amount of chunks and the sharing order scheduled to each MN, and the received chunks are shared among MNs via Wi-Fi Direct. We formulate a multi-objective optimization problem to minimize both the collaborative content download time and the energy consumption in an MCC, and propose a heuristic algorithm for solving the optimization problem. Extensive simulations are carried out to evaluate the effects of the number of MNs, the wireless bandwidth, the content size, and dynamic channel conditions on the content download time and the energy consumption. Our results demonstrate that the proposed algorithm can achieve near-optimal performance and significantly reduce the content download time and has an energy consumption comparable to that of other algorithms.

Reference IEEE paper :

“A Proxy-based Collaboration System to Minimize Content Download Time and Energy Consumption”, IEEE Transactions on Mobile Computing, 2017.

Unique ID -SBI1060

Domain – MOBILE COMPUTING

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SWEET: Serving the Web by Exploiting Email Tunnels

Introduction :

Open communications over the Internet pose serious threats to countries with repressive regimes, leading them to develop and deploy censorship mechanisms within their networks. Unfortunately, existing censorship circumvention systems do not provide high availability guarantees to their users, as censors can easily identify, hence disrupt, the traffic belonging to these systems using today’s advanced censorship technologies. In this paper, we propose Serving the Web by Exploiting Email Tunnels (SWEET), a highly available censorship-resistant infrastructure. SWEET works by encapsulating a censored user’s traffic inside email messages that are carried over public email services like Gmail and Yahoo Mail. As the operation of SWEET is not bound to any specific email provider, we argue that a censor will need to block email communications all together in order to disrupt SWEET, which is unlikely as email constitutes an important part of today’s Internet. Through experiments with a prototype of our system, we find that SWEET’s performance is sufficient for Web browsing. In particular, regular Websites are downloaded within couple of seconds.

Reference IEEE paper :

“SWEET: Serving the Web by Exploiting Email Tunnels”, IEEE/ACM TRANSACTIONS ON NETWORKING, 2017.

Unique ID – SBI1059

Domain – NETWORKING

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Routing in Accumulative Multi-Hop Networks

Introduction :

This paper investigates the problem of finding optimal paths in single-source single-destination accumulative multi-hop networks. We consider a single source that communicates to a single destination assisted by several relays through multiple hops. At each hop, only one node transmits, while all the other nodes receive the transmitted signal, and store it after processing/decoding and mixing it with the signals received in previous hops. That is, we consider that terminals make use of advanced energy accumulation transmission/reception techniques, such as maximal ratio combining reception of repetition codes, or information accumulation with rate less codes. Accumulative techniques increase communication reliability, reduce energy consumption, and decrease latency. We investigate the properties that a routing metric must satisfy in these accumulative networks to guarantee that optimal paths can be computed with Dijkstra’s algorithm. We model the problem of routing in accumulative multi-hop networks, as the problem of routing in a hyper graph. We show that optimality properties in a traditional multi-hop network (monotonicity and isotonicity) are no longer useful and derive a new set of sufficient conditions for optimality. We illustrate these results by studying the minimum energy routing problem in static accumulative multi-hop networks for different forwarding strategies at relays.

Reference IEEE paper :

“Routing in Accumulative Multi-Hop Networks”, IEEE/ACM TRANSACTIONS ON NETWORKING, 2017.

Unique ID -SBI1058

DomainNETWORKING

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Privacy and Integrity Preserving Top-k Query Processing for Two Tiered Sensor Networks

Introduction :

Privacy and integrity have been the main road block to the applications of two-tiered sensor networks. The storage nodes, which act as a middle tier between the sensors and the sink, could be compromised and allow attackers to learn sensitive data and manipulate query results. Prior schemes on secure query processing are weak, because they reveal non-negligible information, and therefore, attackers can statistically estimate the data values using domain knowledge and the history of query results. In this paper, we propose the first top-k query processing scheme that protects the privacy of sensor data and the integrity of query results. To preserve privacy, we build an index for each sensor collected data item using pseudo-random hash function and Bloom filters and transform top-k queries into top range queries. To preserve integrity, we propose a data partition algorithm to partition each data item into an interval and attach the partition information with the data. The attached information ensures that the sink can verify the integrity of query results. We formally prove that our scheme is secure under IND-CKA security model. Our experimental results on real-life data show that our approach is accurate and practical for large network sizes.

Reference IEEE paper :

“Privacy and Integrity Preserving Top-k Query Processing for Two Tiered Sensor Networks”, IEEE/ACM TRANSACTIONS ON NETWORKING, 2017.

Unique ID – SBI1057

DomainNETWORKING

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Network Capability in Localizing Node Failures via End-to-End Path Measurements

Introduction :

We investigate the capability of localizing node failures in communication networks from binary states (normal/failed) of end-to-end paths. Given a set of nodes of interest, uniquely localizing failures within this set requires that different observable path states associate with different node failure events. However, this condition is difficult to test on large networks due to

the need to enumerate all possible node failures. Our first contribution is a set of sufficient/necessary conditions for identifying a bounded number of failures within an arbitrary node set that can be tested in polynomial time. In addition to network topology and locations of monitors, our conditions also incorporate constraints imposed by the probing mechanism used. We consider three probing mechanisms that differ according to whether measurement paths are: (i) arbitrarily controllable; (ii) controllable but cycle-free; or (iii) uncontrollable (determined by the default routing protocol). Our second contribution is to quantify the capability of failure localization through: 1) the maximum number of failures (anywhere in the network) such that failures within a given node set can be uniquely localized and 2) the largest node set within which failures can be uniquely localized under a given bound on the total number of failures. Both measures in 1) and 2) can be converted into the functions of a per-node property, which can be computed efficiently based on the above sufficient/necessary conditions. We demonstrate how measures 1) and 2) proposed for quantifying failure localization capability can be used to evaluate the impact of various parameters, including topology, number of monitors, and probing mechanisms.

Reference IEEE paper :

“Network Capability in Localizing Node Failures via End-to-End Path Measurements”, IEEE/ACM TRANSACTIONS ON NETWORKING, 2017.

Unique ID -SBI1056

DomainNETWORKING

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FaceChange: Attaining Neighbour Node Anonymity in Mobile Opportunistic Social Networks With Fine-Grained Control

Introduction :

In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Anonymizing real IDs among neighbour nodes solves such concerns. However, this prevents nodes from collecting real ID-based encountering information, which is needed to support MOSN services. Therefore, in this paper, we propose FaceChange that can support both anonymizing real IDs among neighbour nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to ensure the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine-grained control over what information is shared with the encountered node based on attribute similarity (i.e., trust), which is calculated without disclosing attributes. Advanced extensions for sharing real IDs between mutually trusted nodes and more efficient encountering evidence collection are also proposed. Extensive analysis and experiments show the effectiveness of FaceChange on protecting node privacy and meanwhile supporting the encountering information collection in MOSNs. Implementation on smartphones also demonstrates its energy efficiency.

Reference IEEE paper :

“FaceChange: Attaining Neighbor Node Anonymity in Mobile Opportunistic Social Networks With Fine-Grained Control”, IEEE/ACM TRANSACTIONS ON NETWORKING, 2017.

Unique ID -SBI1055

DomainNETWORKING

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User Centric Similarity Search

Introduction:

User preferences play a significant role in market analysis. In the database literature there has been extensive work on query primitives, such as the well known top-k query that can be used for the ranking of products based on the preferences customers have expressed. Still, the fundamental operation that evaluates the similarity between products is typically done ignoring these preferences. Instead products are depicted in a feature space based on their attributes and similarity is computed via traditional distance metrics on that space. In this work we utilize the rankings of the products based on the opinions of their customers in order to map the products in a user-centric space where similarity calculations are performed. We identify important properties of this mapping that result in upper and lower similarity bounds, which in turn permit us to utilize conventional multidimensional indexes on the original product space in order to perform these user-centric similarity computations. We show how interesting similarity calculations that are motivated by the commonly used range and nearest neighbor queries can be performed efficiently, while pruning significant parts of the data set based on the bounds we derive on the user-centric similarity of products.

Reference IEEE paper:

“User-Centric Similarity Search”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID – SBI1052

DomainDATA MINING

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User Vitality Ranking and Prediction in Social Networking Services: a Dynamic Network Perspective

Introduction:

Social networking services have been prevalent at many online communities such as Twitter.com and Weibo.com, where millions of users keep interacting with each other every day. One interesting and important problem in the social networking services is to rank users based on their vitality in a timely fashion. An accurate ranking list of user vitality could benefit many parties in social network services such as the ads providers and site operators. Although it is very promising to obtain a vitality-based ranking list of users, there are many technical challenges due to the large scale and dynamics of social networking data. In this paper, we propose a unique perspective to achieve this goal, which is quantifying user vitality by analyzing the dynamic interactions among users on social networks. Examples of social network include but are not limited to social networks in microblog sites and academical collaboration networks. Intuitively, if a user has many interactions with his friends within a time period and most of his friends do not have many interactions with their friends simultaneously, it is very likely that this user has high vitality. Based on this idea, we develop quantitative measurements for user vitality and propose our first algorithm for ranking users based vitality. Also we further consider the mutual influence between users while computing the vitality measurements and propose the second ranking algorithm, which computes user vitality in an iterative way. Other than user vitality ranking, we also introduce a vitality prediction problem, which is also of great importance for many applications in social networking services. Along this line, we develop a customized prediction model to solve the vitality prediction problem. To evaluate the performance of our algorithms, we collect two dynamic social network data sets. The experimental results with both data sets clearly demonstrate the advantage of our ranking and prediction methods.

Reference IEEE paper:

“User Vitality Ranking and Prediction in Social Networking Services: a Dynamic Network Perspective”, IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2017.

Unique ID – SBI1051

DomainDATA MINING

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Understand Short Texts by Harvesting and Analyzing Semantic Knowledge

Introduction:
Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modelling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. We argue that semantic knowledge is required in order to better understand short texts. In this work, we build a prototype system for short text understanding which exploits semantic knowledge provided by a well-known knowledge base and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labelling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.

Reference IEEE paper:
“Understand Short Texts by Harvesting and Analyzing Semantic Knowledge”, IEEE Transactions on Knowledge and Data Engineering, 2017.

Unique ID -SBI1050

DomainDATA MINING

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