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C. Ravindranath Chowdary

Bio: C. Ravindranath Chowdary is an academic researcher from Indian Institute of Technology (BHU) Varanasi. The author has contributed to research in topics: Automatic summarization & Computer science. The author has an hindex of 8, co-authored 43 publications receiving 282 citations. Previous affiliations of C. Ravindranath Chowdary include Indian Institutes of Technology & Indian Institute of Technology Madras.

Papers
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Journal ArticleDOI
TL;DR: The article surveys recent developments on social spam detection and mitigation, its theoretical models and applications along with their qualitative comparison, and presents the state-of-the-art and attempt to provide challenges to be addressed, as the nature and content of spam are bound to get more complicated.
Abstract: Social networking and instant multimedia communication is integral to online existence.Spamming is a new menace in messaging, blogs, video sites, internet telephony etc.The article surveys recent developments on social spam detection and mitigation.A qualitative comparison of different models and their performances are presented.A roadmap on how newer anti-spam techniques can be devised in future is provided. Spam in recent years has pervaded all forms of digital communication.The increase in user base for social platforms like Facebook, Twitter, YouTube, etc., has opened new avenues for spammers. The liberty to contribute content freely has encouraged the spammers to exploit the social platforms for their benefits. E-mail and web search engine being the early victims of spam have attracted serious attention from the information scientists for quite some time. A substantial amount of research has been directed to combat spam on these two platforms. Social networks being quite different in nature from the earlier two, have different kinds of spam and spam-fighting techniques from these domains seldom work. Moreover, due to the continuous and rapid evolution of social media, spam themselves evolve very fast posing a great challenge to the community. Despite being relatively new, there has been a number of attempts in the area of social spam in the recent past and a lot many are certain to come in near future. This paper surveys the recent developments in the area of social spam detection and mitigation, its theoretical models and applications along with their qualitative comparison. We present the state-of-the-art and attempt to provide challenges to be addressed, as the nature and content of spam are bound to get more complicated.

76 citations

Journal ArticleDOI
TL;DR: A survey on the state-of-the-art in group recommender systems concerning various domains is presented and existing systems with respect to their aggregation and user preference models are discussed.
Abstract: Recommender systems are increasingly used in various domains like movies, travel, music, etc. The rise in social activities has increased the usage of recommender systems in general and group recommender systems in particular. A group recommender system is a system that recommends items to a group of users collectively, given their preferences. In addition to the user preferences, using social and behavioural aspects of group members to generate group recommendations will increase the quality of the content recommended in heterogeneous groups. Group recommender systems also address the cold start problem that arises in an individual recommendation system. This paper presents a survey on the state-of-the-art in group recommender systems concerning various domains. We discussed existing systems with respect to their aggregation and user preference models. This organisation is very useful to understand the intricacies with respect to each domain.

76 citations

Journal ArticleDOI
TL;DR: This work proposes A-Stacking and A-Bagging; adaptive versions of stacking and bagging respectively that take into consideration the similarity inherently present in the dataset.
Abstract: Stacking and bagging are widely used ensemble learning approaches that make use of multiple classifier systems. Stacking focuses on building an ensemble of heterogeneous classifiers while bagging constructs an ensemble of homogenous classifiers. There exist some applications where it is essential for learning algorithms to be adaptive towards the training data. We propose A-Stacking and A-Bagging; adaptive versions of stacking and bagging respectively that take into consideration the similarity inherently present in the dataset. One of the main motives of ensemble learning is to generate an ensemble of multiple “experts” that are weakly correlated. We achieve this by producing a set of disjoint experts where each expert is trained on a different subset of the dataset. We show the working mechanism of the proposed algorithms on spoof fingerprint detection. The proposed versions of these algorithms are adaptive as they conform to the features extracted from the live and spoof fingerprint images. From our experimental results, we establish that A-Stacking and A-Bagging give competitive results on both balanced and imbalanced datasets.

55 citations

Journal ArticleDOI
TL;DR: A graph based keyphrase extraction incorporating correlation of terms is proposed that captures the relatedness between words in terms of both mutual information and relevance feedback and is shown to supersede the previously mentioned key phrase extraction algorithms for query expansion significantly.
Abstract: Multimodal Retrieval efficiency can be improved by textual query reformulation.A graph based keyphrase extraction incorporating correlation of terms is proposed.Textual query is expanded with relevant part of narratives and extracted keyphrases.Text and image features are combined using a weightlearning model.The proposed method improves both image and text retrieval efficiency significantly. Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA. While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval.

35 citations

Journal ArticleDOI
TL;DR: The proposed Hungarian Aggregated Method and Least Misery with Priority improves the overall group satisfaction at the cost of a marginal increase in time complexity.
Abstract: Recommendation Systems (RS) are gaining popularity and they are widely used for dealing with information on education, e-commerce, travel planning, entertainment etc. Recommender Systems are used to recommend items to user(s) based on the ratings provided by the other users as well as the past preferences of the user(s) under consideration. Given a set of items from a group of users, Group Recommender Systems generate a subset of those items within a given group budget (i.e. the number of items to have in the final recommendation). Recommending to a group of users based on the ordered preferences provided by each user is an open problem. By order, we mean that the user provides a set of items that he would like to see in the generated recommendation along with the order in which he would like those items to appear. We design and implement algorithms for computing such group recommendations efficiently. Our system will recommend items based on modified versions of two popular Recommendation strategies– Aggregated Voting and Least Misery. Although the existing versions of Aggregated Voting (i.e. Greedy Aggregated Method) and Least Misery perform fairly well in satisfying individuals in a group, they fail to gain significant group satisfaction. Our proposed Hungarian Aggregated Method and Least Misery with Priority improves the overall group satisfaction at the cost of a marginal increase in time complexity. We evaluated the scalability of our algorithms using a real-world dataset. Our experimental results evaluated using a self-established metric substantiates that our approach is significantly efficient.

31 citations


Cited by
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Journal ArticleDOI
TL;DR: This article re-conceptualize coherence assessment as a learning task and shows that the proposed entity-grid representation of discourse is well-suited for ranking-based generation and text classification tasks.
Abstract: This article proposes a novel framework for representing and measuring local coherence. Central to this approach is the entity-grid representation of discourse, which captures patterns of entity distribution in a text. The algorithm introduced in the article automatically abstracts a text into a set of entity transition sequences and records distributional, syntactic, and referential information about discourse entities. We re-conceptualize coherence assessment as a learning task and show that our entity-based representation is well-suited for ranking-based generation and text classification tasks. Using the proposed representation, we achieve good performance on text ordering, summary coherence evaluation, and readability assessment.

754 citations

Journal ArticleDOI
TL;DR: A comprehensive review on existing deep learning techniques for NER is provided in this paper, where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
Abstract: Named entity recognition (NER) is the task to identify text spans that mention named entities, and to classify them into predefined categories such as person, location, organization etc. NER serves as the basis for a variety of natural language applications such as question answering, text summarization, and machine translation. Although early NER systems are successful in producing decent recognition accuracy, they often require much human effort in carefully designing rules or features. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

474 citations

Posted Content
TL;DR: A comprehensive review on existing deep learning techniques for NER, including tagged NER corpora and off-the-shelf NER tools, and systematically categorizes existing works based on a taxonomy along three axes.
Abstract: Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

381 citations

Journal ArticleDOI
TL;DR: A comprehensive review on existing deep learning techniques for NER can be found in this article , where the authors systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder.
Abstract: Named entity recognition (NER) is the task to identify mentions of rigid designators from text belonging to predefined semantic types such as person, location, organization etc. NER always serves as the foundation for many natural language applications such as question answering, text summarization, and machine translation. Early NER systems got a huge success in achieving good performance with the cost of human engineering in designing domain-specific features and rules. In recent years, deep learning, empowered by continuous real-valued vector representations and semantic composition through nonlinear processing, has been employed in NER systems, yielding stat-of-the-art performance. In this paper, we provide a comprehensive review on existing deep learning techniques for NER. We first introduce NER resources, including tagged NER corpora and off-the-shelf NER tools. Then, we systematically categorize existing works based on a taxonomy along three axes: distributed representations for input, context encoder, and tag decoder. Next, we survey the most representative methods for recent applied techniques of deep learning in new NER problem settings and applications. Finally, we present readers with the challenges faced by NER systems and outline future directions in this area.

236 citations

Journal ArticleDOI
TL;DR: This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade.
Abstract: Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

135 citations