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Due to advancements in data collection, storage, and processing techniques, machine learning has become a thriving and dominant paradigm. However, one of its main shortcomings is that the classical machine learning paradigm acts in isolation without utilizing the knowledge gained through learning from related tasks in the past. To circumvent this, the concept of Lifelong Machine Learning (LML) has been proposed, with the goal of mimicking how humans learn and acquire cognition. Human learning research has revealed that the brain connects previously learned information while learning new information from a single or small number of examples. Similarly, an LML system continually learns by storing and applying acquired information. Starting with an analysis of how the human brain learns, this paper shows that the LML framework shares a functional structure with the brain when it comes to solving new problems using previously learned information. It also provides a description of the LML framework, emphasizing its similarities to human brain learning. It also provides citation graph generation and scientometric analysis algorithms for the LML literatures, including information about the datasets and evaluation metrics that have been used in the empirical evaluation of LML systems. Finally, it presents outstanding issues and possible future research directions in the field of LML. [ABSTRACT FROM AUTHOR]

Due to the enormous amount of user-generated content being generated on the web, labeling such data is a time-consuming and expensive endeavor. As a result, we have limited annotated data and the vast majority of data are unlabeled. Analysis reveals that extracting (external) knowledge from unlabeled data and integrating it with knowledge extracted from labeled data is a beneficial task for text information processing, in particular text classification. In this paper, we present a hybrid approach for classifying sentiments that employs external knowledge, which is categorized as either general-purpose sentiment knowledge or domain-related knowledge. General-purpose sentiment knowledge is extracted from sentiment lexicons, whereas domain-related knowledge is extracted from unlabeled data from the same or related domains. Similar domains for a given domain are identified based on their similarity score in terms of overlapping features. The proposed approach utilizes both forms of external knowledge and combines them with logistic regression to train an improved classification model. The classification model uses the conventional gradient descent algorithm for optimization, and its convergence analysis indicates that it is convex and converges to the global optimum. The proposed approach is empirically evaluated and compared to three baselines and one state-of-the-art method using standard performance evaluation metrics on a multi-domain sentiment dataset. The experiment results are encouraging, demonstrating that the proposed approach considerably outperforms the baseline approaches and outperforms the state-of-the-art approach by up to 2% in terms of both f-score and accuracy. • A hybrid approach for document-level sentiment classification that makes use of external knowledge. • A domain-specific knowledge extraction approach from unlabeled text documents. • A detailed convergence analysis of the proposed optimization model used in the proposed approach. [ABSTRACT FROM AUTHOR]

In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users' perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets, i.e., users who are susceptible to be trapped by the socialbots. We model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active, reactive, and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis, we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate, multimedia ratio, and follower rate are most relevant to segregate different categories of the socialbot targets. We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users. [ABSTRACT FROM AUTHOR]

Online Social Networks (OSNs) are the modern communication media at the peak of Internet technology that facilitate users' ability to connect with friends and celebrities, and to disseminate breaking news and updates on real-life events. Connections and interactions among OSN users generate a massive amount of data containing a rich collection of knowledge that could be useful to various realworld data modeling and predictive analytics problems like open-source intelligence, business intelligence, event prediction, and product recommendations. On the Web, adversaries explore and target products and services for misuse and vulnerabilities, and OSNs are no exception. Their large user base, easy-to-use functionality, and open nature further attract antisocial elements to these OSNs. In OSNs, cybercrimes and illicit activities are generally committed using various forms of fake profiles, such as clone profiles,1 sybil,2 and bots. [ABSTRACT FROM AUTHOR]

Twitter is a popular microblogging platform, which facilitates users to express views and thoughts on day-to-day events using short texts limited to a maximum of 280 characters. However, it is generally targeted by socialbots for political astroturfing, advertising, spamming, and other illicit activities due to its open and real-time information sharing and dissemination nature. In this paper, we present a socialbots analysis-driven graph-based approach for identifying coordinated campaigns among Twitter users. To this end, we first present statistical insights derived from the analysis of logged data of 98 socialbots, which were injected in Twitter and associated with top-six Twitter using countries. In the analysis, we study and present the impact of socialbots' profile features, such as age and gender on infiltration. We also present a multi-attributed graph-based approach to model the profile attributes and interaction behavior of users as a similarity graph for identifying different groups of synchronized users involved in coordinated campaigns. The proposed approach is experimentally evaluated using four different evaluation parameters over a real dataset containing socialbots' trapped user profiles. The evaluation of identified campaigns in the form of clusters reveals the traces of spammers, botnets, and other malicious users. [ABSTRACT FROM AUTHOR]

Community detection from networks is one of the long standing and challenging tasks in the field of complex network research. Detection of communities poses numerous challenges in terms of their overlapping and hierarchical nature, dynamics of networks and underlying communities, scalability of detection algorithms on large scale networks to mention a few. Traditional community detection methods are not readily scalable to large networks mainly due to the computation of global network metrics. This paper presents a novel scalable overlapping community detection approach for large scale networks by presenting a MapReduce framework based implementation of a density-based local community detection method. The method is divided in two stages where the first stage uses a MapReduce approach to identify a mutual-core connected subgraph of the underlying network. The second stage uses an existing connected component detection method, implemented via MapReduce, to identify connected components in the mutual-core connected subgraph generated in the first stage. A community is then taken as the union of the core-nodes in a connected component and the respective density-based neighborhood of each core-node in the connected component. The resulting approach is among the first scalable overlapping community detection methods proposed in literature. [ABSTRACT FROM AUTHOR]

This paper introduces a novel thresholding and morphology-based skull stripping method for different MRI modalities. The proposed method is designed in a way which is easy to use and generates satisfactory results with minimal parameter adjustments. The method is evaluated on three different benchmark datasets and compared with nine state-of-the-art skull stripping methods. The experimental results suggest that the proposed method generates comparable results to some of the best methods in literature. However, unlike many other methods, it works well on different types of MRI scans. Moreover, this method generates the skull mask along with the brain mask that can be used to study various skull pathologies. [ABSTRACT FROM AUTHOR]

Due to proliferation of competitive online Business-to-Consumer (B2C) models, it is becoming a challenging task for new users to choose best products, based on existing users' reviews residing on different e-commerce websites. On analysis, it is found that the opinions of the existing customers play an important role for new customers in making appropriate purchase decisions. Though there are some online websites that provide aggregation of basic product information from multiple sources, there is a negligible research effort in the direction of opinion-based product ranking. In this paper, we propose an Opinion-based Multi-Criteria Ranking (OMCR) approach, which amalgamates structural and content-based features of review documents to rank different alternatives of the online products. It uses a total number of five features based on reviews' meta-data and contents to rank different alternatives using multi-criteria decision making approaches. OMCR also incorporates a sentiment analysis and visualization approach to determine sentiment polarity values and visualize them in a comprehendible manner. Experiments are conducted over two different real datasets, and efficacy of OMCR is assessed using set intersection method, which is generally used to compare two ranked lists in terms of their overlapping score. [ABSTRACT FROM AUTHOR]

Recommender systems aim to estimate item ratings and recommend items based on the users' interests. The traditional recommender systems generally consider user–item rating information for rating prediction, but they suffer from various limitations, such as data sparsity , black-box recommendation , and cold-start problems. As a result, researchers have proposed amalgamating contextual information with rating data to provide effective recommendations. Although user-generated data in the form of reviews are a rich source of contextual information, they are rarely utilized in recommender algorithms. This study presents a hybrid recommendation technique, called RecTE, using rating data and topic embedding, which is an amalgamation of word embedding and topic modeling techniques. The novelty of RecTE lies in predicting item ratings using topic embeddings learned by incorporating local and global contextual information and integrating them with user-based collaborative filtering. RecTE is empirically evaluated over three real-world datasets – YelpNYC, YelpZip and TripAdvisor. This technique performs significantly better in comparison to nine baselines and five state-of-the-art recommendation techniques. On empirical analysis, we found that incorporating topic embedding in RecTE makes it capable of performing significantly better and handle cold-start problems effectively in comparison to the existing recommendation approaches. • A novel recommendation approach to handle the cold-start problem. • Incorporating rating data and topic embedding into UBCF for rating prediction. • Assessing the impact of topic embedding on effective recommendation. • Comparative evaluation of the proposed approach with 9 baselines and 5 SOTA. • Assessing efficacy of the proposed approach to handle the cold-start problem. [ABSTRACT FROM AUTHOR]

Social network analysis ( SNA) is a multidisciplinary field dedicated to the analysis and modeling of relations and diffusion processes among various objects in nature and society, and other information/knowledge processing entities with an aim of understanding how the behavior of individuals and their interactions translates into large-scale social phenomenon. Because of exploding popularity of online social networks and availability of huge amount of user-generated content, there is a great opportunity to analyze social networks and their dynamics at resolutions and levels not seen before. This has resulted in a significant increase in research literature at the intersection of the computing and social sciences leading to several techniques for social network modeling and analysis in the area of machine learning and data mining. Some of the current challenges in the analysis of large-scale social network data include social network modeling and representation, link mining, sentiment analysis, semantic SNA, information diffusion, viral marketing, and influential node mining. WIREs Data Mining Knowl Discov 2013, 3:408-444. doi: 10.1002/widm.1105 Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. [ABSTRACT FROM AUTHOR]

The rapid growth of the biological text data repository makes it difficult for human beings to access required information in a convenient and effective manner. The problem arises due to the fact that most of the information is embedded within unstructured or semi-structured text that computers cannot interpret very easily. In this paper we have presented an ontology-based Biological Information Extraction and Query Answering (BIEQA) System, which initiates text mining with a set of concepts stored in a biological ontology, and thereafter mines possible biological relations among those concepts using NLP techniques and co-occurrence-based analysis. The system extracts all frequently occurring biological relations among a pair of biological concepts through text mining. A mined relation is associated to a fuzzy membership value, which is proportional to its frequency of occurrence in the corpus and is termed a fuzzy biological relation. The fuzzy biological relations extracted from a text corpus along with other relevant information components like biological entities occurring within a relation, are stored in a database. The database is integrated with a query-processing module. The query-processing module has an interface, which guides users to formulate biological queries at different levels of specificity. [Copyright &y& Elsevier]

Word based searches for relevant information from texts retrieve a huge collection and burden the user with information overload. Ontology based text information retrieval can perform concept-based search and extract only relevant portions of text containing concepts that are present in the query or those that are semantically linked to query concepts. While these systems have better precision of retrieval than general-purpose search engines, problems arise with those domains where ontological concepts cannot be unambiguously described using precise property descriptors. Besides, the ontological descriptors may not exactly match text descriptions or the user given descriptors in query. In such situations, uncertainty based reasoning principles can be applied to find approximate matches to user queries. In this paper we have presented a framework to enhance traditional ontological structures with fuzzy descriptors. The fuzzy ontology structure has been used to locate and extract both precise and imprecise descriptions of concepts from Web documents and then store them in a structured knowledge base. The design of the structured knowledge base, which in our case is a database, is also derived from the underlying fuzzy ontology representing the domain. User queries are processed in two stages. In the first stage, precise SQL queries are formulated and processed over the knowledge base to find a possible answer set. In the second stage, fuzzy reasoning is applied to compute the relevance of the answers in the answer set with respect to the query. We have provided experimental validation of the approach through knowledge-extraction and query processing executed over a diverse set of domains. [ABSTRACT FROM AUTHOR]

In this paper, we propose a unified framework, HOCTracker, for tracking the evolution of hierarchical and overlapping communities in online social networks. Unlike most of the dynamic community detection methods, HOCTracker adapts a preliminary community structure towards dynamic changes in social networks using a novel density-based approach for detecting overlapping community structures, and automatically tracks evolutionary events like birth, growth, contraction, merge, split, and death of communities. It uses a novel and efficient log-based approach to map evolutionary relations between communities identified at two consecutive time-steps of a dynamic network, which considerably reduces the number of community comparisons. Moreover, it does not require an ageing function to remove old interactions for identifying community evolutionary events. HOCTracker is applicable to diirected/undirected and weighted/unweighted networks. Experimental results have shown that community structures identified by HOCTracker on some well-known benchmark networks are significant and in general better that the community structures identified by the state-of-the-art methods. [ABSTRACT FROM PUBLISHER]