In the recent few years, a lot of advancement has been made in Urdu linguistics. There are many portals and news websites that are generating a huge amount of data every day. However, there is still no publicly available dataset nor any framework available for automatic Urdu extractive summary generation. In an automatic extractive summary generation, the sentences with the highest weights are given

Existing collaborative filtering algorithms suffer from the problem of data sparsity. Imputation-based methods are promising algorithms, which alleviate data sparsity without using side information, to solve this problem. However, existing imputation recommendation methods based on matrix factorization only separately factorize the rating matrix and the imputed data matrix, which limits the power of

Cloud storage enables applications to efficiently manage their remote data but facing the risk of being tampered with. This paper utilizes blockchain technique to develop a novel public auditing scheme for verifying data integrity in cloud storage. In the proposed scheme, different from the existing works that involve three participatory entities, only two predefined entities (i.e. data owner and cloud

Document types, where visual and textual information plays an important role in their analysis and understanding, pose a new and attractive area for information extraction research. Although cheques, invoices, and receipts have been studied in some previous multi-modal studies, banking documents present an unexplored area due to the naturalness of the text they possess in addition to their visual richness

Text normalization is the task of transforming lexically variant words to their canonical forms. The importance of text normalization becomes apparent while developing natural language processing applications. This paper proposes a novel technique called Transliteration based Encoding for Roman Hindi/Urdu text Normalization (TERUN). TERUN utilizes the linguistic aspects of Roman Hindi/Urdu to transform

The freedom of expression given by social media has a dark side: the growing proliferation of abusive contents on these platforms. Misogynistic speech is a kind of abusive language, which can be simplified as hate speech targeting women, and it is becoming a more and more relevant issue in recent years. AMI IberEval 2018 and AMI EVALITA 2018 were two shared tasks which mainly focused on tackling the

Social media has influenced socio-political aspects of many societies around the world. It is an effortless way for people to enhance their communication, connect with like-minded people, and share ideas. Online social networks (OSNs) can be used for noble causes by bringing together communities with common shared interests and to promote awareness of various causes. However, there is a dark side to

Given a size-N input string X, a number of algorithms have been proposed to sort the suffixes of X into the output suffix array using the inducing methods. While the existing algorithms eSAIS, DSAIS, and fSAIS presented remarkable time and space results for suffix sorting in external memory, there are still potentials for further improvements. We propose here a new algorithm called nSAIS by reinventing

Identifying health information needs is an important part of user-centered health information behavior research. Qualitative information behavior studies yield deep insights but have limited generalizability. Quantitative studies using questionnaire instruments offer better generalizability, but only if the instruments are properly developed and validated. A systematic review of the questionnaire instruments

Nowadays, e-commerce has become a part of our daily life in such a way that people's decision for buying products or choosing services highly depends on comments, reviews, and rates, which are posted on related businesses’ website and other social media. Because of the importance and prevalence of these sources of information, fraudsters are tempted to use fraudulently opinion sharing platforms in

The number of mass shootings has increased in recent years. Understanding the factors that affect the emotional reactions of the population is very important to help them to cope with the constant sense of threat and fear. Related work has explored sentiment analysis on social media data to derive insights. However, existing solutions are limited to the negative polarity of the sentiment, disregard

Search Engines (SE) have been shown to perpetuate well-known gender stereotypes identified in psychology literature and to influence users accordingly. Similar biases were found encoded in Word Embeddings (WEs) learned from large online corpora. In this context, we propose the Gender Stereotype Reinforcement (GSR) measure, which quantifies the tendency of a SE to support gender stereotypes, leveraging

The hazardous and harmful use of alcohol has become a public health issue worldwide. Social media has emerged as a reliable source to extract information on alcohol consumption at low cost and latency. The automatic classification of tweets related to alcohol consumption can help to understand the factors related to alcohol abuse. In this paper, we propose Drink2Vec, a method aimed at improving the

Neighborhood-based recommenders are a major class of Collaborative Filtering models. The intuition is to exploit neighbors with similar preferences for bridging unseen user-item pairs and alleviating data sparseness, in other words, learn the sub-graph representation of each user in a user graph. Many existing works propose neural attention networks to aggregate neighbors and place higher weights on

Background: Tree-based Long Short Term Memory (LSTM) network has become state-of-the-art for modeling the meaning of language texts as they can effectively exploit the grammatical syntax and thereby non-linear dependencies among words of the sentence. However, most of these models cannot recognize the difference in meaning caused by a change in semantic roles of words or phrases because they do not

Geographical locations of microblog users are essential for user profiling, event localization and target advertising, to name a few. Automatic identification of user locations from the corresponding microblogs has been explored for decades. This paper presents a survey of existing studies on the problem of microblog user geolocalisation. Specifically, we present a general framework for the geolocalisation

The concept of Fake Media or Internet of Fake Media Things (IoFMT) has emerged in different domains of digital society such as politics, news, and social media. Due to the integrity of the media being compromised quite frequently, revolutionary changes must be taken to avoid further and more widespread IoFMT. With today’s advancements in Artificial Intelligence (AI) and Deep Learning (DL), such compromises

Recently, learning-based cross-modal hashing has gained increasing research interests for its low computation complexity and memory requirement. Among existing cross-modal techniques, supervised algorithms can gain better performance. However, due to the cost of acquiring labeled data, unsupervised methods become our choice when faced with large scale unlabeled web images. The label-free nature of

Coreference resolution is one of the fundamental tasks in natural language processing (NLP), and is of great significance to understand the semantics of texts. Meanwhile, resolving coreference is essential for many NLP downstream applications. Existing methods largely focus on pronouns, possessives and noun phrases resolution in the general domain, while little work is proposed for professional domains

In the two recent decades various security authorities around the world acknowledged the importance of exploiting the ever-growing amount of information published on the web on various types of events for early detection of certain threats, situation monitoring and risk analysis. Since the information related to a particular real-world event might be scattered across various sources and mentioned on

Personal health records offer the convenience of accessing medical history and personal health information, but also raise a range of privacy concerns which affect their adoption. In 2018, the Australian nationwide personal health record, My Health Record (MHR), was changed to an opt-out model, meaning that users are automatically enrolled unless they opt out. This significant change sparked wide-ranging

The piecewise convolutional neural network (PCNN) is an important method for distant supervision relation extraction. However, the existing methods based on the PCNN still have the following shortcomings: these methods lack the consideration of the impacts of entity pairs and the sentence context on word encoding and do not distinguish the different contributions of the three segments in PCNN to relation

Multi-source domain adaptation addresses the problem of transferring knowledge from multiple source domains to a target domain. Compared with conventional domain adaptation with single source and target domain, multi-source domain adaptation is more challenging because there are discrepancies not only across the source domains and the target domain, but also in source domains themselves. Hence, previous

Many of the interactions between users on social networks are controversial, specially in polarized environments. In effect, rather than producing a space for deliberation, these environments foster the emergence of users that disqualify the position of others. On news sites, comments on the news are characterized by such interactions. This is detrimental to the construction of a deliberative and democratic

Text summarization is a process of generating a brief version of documents by preserving the fundamental information of documents as much as possible. Although most of the text summarization research has been focused on supervised learning solutions, there are a few datasets indeed generated for summarization tasks, and most of the existing summarization datasets do not have human-generated goal summaries

Diversification of web search results aims to promote documents with diverse content (i.e., covering different aspects of a query) to the top-ranked positions, to satisfy more users, enhance fairness and reduce bias. In this work, we focus on the explicit diversification methods, which assume that the query aspects are known at the diversification time, and leverage supervised learning methods to improve

In this paper, we focus on the problem of discovering internally connected communities in event-based social networks (EBSNs) and propose a community detection method by utilizing social influences between users. Different from traditional social network, EBSNs contain different types of entities and links, and users in EBSNs have more complex behaviours. This leads to poor performance of the traditional

This paper will describe Transformative Computing technologies as a new area of modern information sciences, which plays crucial role in development future IT technologies. Transformative computing allow to join communication technologies, and data processing techniques with advanced AI solutions, which allow to analytically process and manage acquired data. It enhances possibilities of efficient data

Nowadays, it is a common practice for healthcare professionals to spread medical knowledge by posting health articles on social media. However, promoting users’ intention to share such articles is challenging because the extent of sharing intention varies in their eHealth literacy (high or low) and the content valence of the article that they are exposed to (positive or negative). This study investigates

Remote data integrity checking is of great importance to the security of cloud-based information systems. Previous works generally assume a trusted third party to oversee the integrity of the outsourced data, which may be invalid in practice. In this paper, we utilize the blockchain to construct a novel privacy-preserving remote data integrity checking scheme for Internet of Things (IoT) information

A robust value- and time-continuous emotion recognition has enormous potential benefits within healthcare. For example, within mental health, a real-time patient monitoring system capable of accurately inferring a patient’s emotional state could help doctors make an appropriate diagnosis and treatment plan. Such interventions could be vital in terms of ensuring a higher quality of life for the patient

Node clustering on heterogeneous information networks (HINs) plays an important role in many real-world applications. While previous research mainly clusters same-type nodes independently via exploiting structural similarity search, they ignore the correlations of different-type nodes. In this paper, we focus on the problem of co-clustering heterogeneous nodes where the goal is to mine the latent relevance

Finding side effects of drugs, before reaching the animal and clinical test, can decrease the cost and time of developing new drugs. Also, a lot of side effects are reported after going to market and using it by patients (post-marketing). There are some systems to report these events but most of the time, side effects stay hidden. Thus, a lot of computational methods have been developed to predict

Polarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similarity Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly

Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of a document, whilst keeping the original meaning, simplification attempts to reduce the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive

Relation classification is one of the most fundamental tasks in the area of cross-media, which is essential for many practical applications such as information extraction, question&answer system, and knowledge base construction. In the cross-media semantic retrieval task, in order to meet the needs of cross-media uniform representation and semantic analysis, it is necessary to analyze the semantic

Intracerebral hemorrhage (ICH) is the most serious type of stroke, which results in a high disability or mortality rate. Therefore, accurate and rapid ICH region segmentation is of great significance for clinical diagnosis and treatment of ICH. In this paper, we focus on deep neural networks to automatically segment ICH regions. Firstly, we propose an encoder-decoder convolutional neural network (ED-Net)

Search engine optimization allows for users’ needs to be directly met by result snippets or a “knowledge map” without clicking any results. This behavior is called “good abandonment” and is found to frequently occur during mobile searching. Users exhibit such a behavior when they trust the result that addresses their information need without bothering to click it. Therefore, this study examines how

The spreading of misinformation and disinformation is a great problem on microblogs, leading user evaluation of information credibility a critical issue. This study incorporates two message format factors related to multimedia usage on microblogs (vividness and multimedia diagnosticity) with two well-discussed factors for information credibility (i.e., argument quality and source credibility) as a

Real-time processing and learning of conflicting data, especially messages coming from different ideas, locations, and time, in a dynamic environment such as Twitter is a challenging task that recently gained lots of attention. This paper introduces a framework for managing, processing, analyzing, detecting, and tracking topics in streaming data. We propose a model selector procedure with a hybrid

Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be analysed to gain complete insights from social media feeds. This paper proposes an improved

Mouse interaction data contain a lot of interaction information between users and Search Engine Result Pages (SERPs), which can be useful for evaluating search satisfaction. Existing studies use aggregated features or anchor elements to capture the spatial information in mouse interaction data, which might lose valuable mouse cursor movement patterns for estimating search satisfaction. In this paper

Searching the Internet for a certain topic can become a daunting task because users cannot read and comprehend all the resulting texts. Automatic Text summarization (ATS) in this case is clearly beneficial because manual summarization is expensive and time-consuming. To enhance ATS for single documents, this paper proposes a novel extractive graph-based framework “EdgeSumm” that relies on four proposed

Influence diffusion is extensively studied in social networks for product or service promotion and viral-marketing applications. This paper proposes two models for social influence estimation, namely Time Decay Features Cascade Model (TDF-C) and Time Decay Features Cascade Threshold Model (TDF-CT). These models overcome three main existing challenges - first, measure the strength of user's influence

The problem of social spam detection has been traditionally modeled as a supervised classification problem. Despite the initial success of this detection approach, later analysis of proposed systems and detection features has shown that, like email spam, the dynamic and adversarial nature of social spam makes the performance achieved by supervised systems hard to maintain. In this paper, we investigate

A large volume of data flowing throughout location-based social networks (LBSN) gives support to the recommendation of points-of-interest (POI). One of the major challenges that significantly affects the precision of recommendation is to find dynamic spatio-temporal patterns of visiting behaviors, which can hardly be figured out because of the multiple side factors. To confront this difficulty, we

Pseudo-relevance feedback (PRF) is a well-known method for addressing the mismatch between query intention and query representation. Most current PRF methods consider relevance matching only from the perspective of terms used to sort feedback documents, thus possibly leading to a semantic gap between query representation and document representation. In this work, a PRF framework that combines relevance

This study tested how and when leaders’ information seeking from team members influenced team performance and innovation. We developed and examined a model by considering team reflexivity as a mediator and cooperative outcome interdependence as a moderator. The sample comprised 83 work teams and 253 team members from a pharmaceutical firm in China. Multiple-source, time-lagged data were used to test

This paper aims to establish digital forensics and data exploration as a methodology for supporting archival practice and research into a filmmaker's creative processes. We approach this by exploring the digital legacy hard drives of the late artist Stephen Dwoskin (1939–2012), who is recognised as an influential filmmaker at the forefront of the shift from analogue to digital film production. The

Online charitable crowdfunding has become an increasingly popular approach for online fundraising and has attracted widespread concern in society. However, little is known about donation allocation in the implementation of online charitable crowdfunding, especially the purposes involved and the specific flows of these donations. To this end, this study aims to explore project initiators’ fundraising

In recent years, deep learning-based recommender systems have received increasing attention, as deep neural networks can detect important product features in images and text descriptions and capture them in semantic vector representations of items. This is especially relevant for outfit recommendation, since a variety of fashion product features play a role in creating outfits. This work is a comparative

Today, due to a vast amount of textual data, automated extractive text summarization is one of the most common and practical techniques for organizing information. Extractive summarization selects the most appropriate sentences from the text and provide a representative summary. The sentences, as individual textual units, usually are too short for major text processing techniques to provide appropriate

Drawing on the cognitive appraisal theory of emotions and the attribution theory, this study extends existing research by examining how the emotional expressions influence perceived helpfulness of online consumer reviews (OCRs). We include two negative emotions (anger, fear) and two positive emotions (pride, surprise). Each of these four emotions can be described with respect to six emotional appraisal

With the fast growth of e-commerce and the emerging new retail trend—online and offline integration—it is important to recognize the target market and satisfy customers with different needs by analyzing their online search behaviors. Accordingly, we propose sequential search pattern analysis and clustering to analyze consumers’ search behavior throughout the entire shopping process from the perspective

In this paper we introduce HEMOS (Humor-EMOji-Slang-based) system for fine-grained sentiment classification for the Chinese language using deep learning approach. We investigate the importance of recognizing the influence of humor, pictograms and slang on the task of affective processing of the social media. In the first step, we collected 576 frequent Internet slang expressions as a slang lexicon;

Event relations specify how different event flows expressed within the context of a textual passage relate to each other in terms of temporal and causal sequences. There have already been impactful work in the area of temporal and causal event relation extraction; however, the challenge with these approaches is that (1) they are mostly supervised methods and (2) they rely on syntactic and grammatical

Deep hashing has been an important research topic for using deep learning to boost performance of hash learning. Most existing deep supervised hashing methods mainly focus on how to effectively preserve the similarity in hash coding solely depending on pairwise supervision. However, such pairwise similarity-preserving strategy cannot fully explore the semantic information in most cases, which results

Health knowledge graph provides an ideal technical means to integrate heterogeneous data resources and enhance knowledge-based services. There are many challenges for the construction of health knowledge graph such as complex concepts and relationships, various medical standards, heterogeneous data structures, poor data quality, highly accurate and interpretable services, etc. In this paper, firstly

While users’ discontinuance of use has posed a challenge for social media in recent years, there is a paucity of knowledge on the relationships between different dimensions of overload and how overload adversely affects users’ social media discontinuance behaviors. To address this knowledge gap, this study employed the stressor–strain–outcome (SSO) framework to explain social media discontinuance behaviors

Evidently, online voice of customers (VoC) expressed in social media has emerged as quality data for researchers who are willing to conduct customer-driven business intelligence (BI) research. Nevertheless, to the best of authors’ knowledge, there is still a dearth of studies that deal with such remarkable research stream and address various open data (e.g., social media, intellectual property) from