With the rapid growth of Twitter in recent years, there has been a tremendous increase in the number of tweets generated by users. Twitter allows users to make use of hashtags to facilitate effective categorization and retrieval of tweets. Despite the usefulness of hashtags, a major fraction of tweets do not contain hashtags. Several methods have been proposed to recommend hashtags based on lexical

Medication recommendation is attracting enormous attention due to its promise in effectively prescribing medicines and improving the survival rate of patients. Among all challenges, drug–drug interactions (DDI) related to undesired duplication, antagonism, or alternation between drugs could lead to fatal side effects. Previous researches usually provide models with DDI knowledge to achieve DDI reduction

The volume of data generated by internet and social networks is increasing every day, and there is a clear need for efficient ways of extracting useful information from them. As this information can take different forms, it is important to use all the available data representations for prediction; this is often referred to multi-view learning. In this paper, we consider semi-supervised classification

In this paper, we propose a sequence-to-sequence affinity metric for the data association of near-online multi-object tracking. The proposed metric learns the affinity between track sequence consisting of the already associated detections and hypothesis sequence consisting of detections in the near future. With the potential hypothesis sequences, we leverage the idea that if a track sequence has a

Aspect-based sentiment analysis can predict the sentiment polarity of specific aspect terms in the text. Compared to general sentiment analysis, it extracts more useful information and analyzes the sentiment more accurately in the comment text. Many previous approaches use long short-term memory networks with attention mechanisms to directly learn aspect-specific representations and model comment text

Trading strategies are constantly being employed in the financial markets in order to increase consistency, reduce human errors of judgment and boost the probability of taking profitable market positions. In this work, we attempt to transfer the knowledge of several different types of trading strategies to deep learning models. The trading strategies are applied on price data of foreign exchange trading

One-class collaborative filtering (OCCF) is a fundamental research problem in a myriad of applications where the preferences of users can only be implicitly inferred from their one-class feedback (e.g., click an ad or purchase a product). The main challenges of OCCF lie in the sparsity of user feedback and the ambiguity of unobserved preferences. To effectively address the above two challenges, side

Recommender systems provide personalized recommendations to the users from a large number of possible options in online stores. Matrix factorization is a well-known and accurate collaborative filtering approach for recommender system, which suffers from cold-start problem for new users and items. When new users join the system, it will take some time before they enter some ratings in the system, until

In recent years, point of interest (POI) recommendation has gained increasing attention all over the world. POI recommendation plays an indispensable role in assisting people to find places they are likely to enjoy. The exploitation of POIs recommendation by existing models is inadequate due to implicit correlations among users and POIs and cold start problem. To overcome these problems, this work

The triangular fuzzy multiplicative preference relation (TFMPR) has attracted the attention of many scholars. This paper investigates the geometric consistency of TFMPR and applies it to group decision making (GDM). Firstly, by introducing two parameters, a triangular fuzzy number is transformed into an interval. According to the geometric consistency of interval multiplicative preference relation

There is a large amount of data on the web in tabular form, such as Excel sheets, CSV files, and web tables. Often, tabular data is meant for human consumption, using data layouts that are difficult for machines to interpret automatically. Previous work uses the stylistic features of tabular cells (such as font size, border type, and background color) to classify tabular cells by their role in the

The collective behavior describing spontaneously emerging social processes and events is ubiquitous in both physical society and online social media. The knowledge of collective behavior is critical in understanding and predicting social movements, fads, riots, and so on. However, detecting, quantifying, and modeling the collective behavior in cascading systems at large scale are seldom explored. In

Research teams have been well recognized as the norm in modern scientific discovery. Rather than a loose collection of researchers, a well-performing research team is composed of a number of researchers, each of them playing a particular role (i.e., principal investigator, sub-investigator or research staff) for a short- or long-term effort. Role analysis for research teams would help gain insights

In Big Data cubes with hundreds of dimensions and billions of tuples, the indexing and query operations are a challenge and the reason is the time-space exponential complexity when a full cube is computed. Therefore, solutions based on RAM may not be practical and the solutions based on hybrid memory (RAM and disk) become viable alternatives. In this paper, we propose a hybrid approach, named bCubing

Non-traditional optimization tools have proved their potential in solving various types of optimization problems. These problems deal with either single objective or multiple/many objectives. Bonobo Optimizer (BO) is an intelligent and adaptive metaheuristic optimization algorithm inspired from the social behavior and reproductive strategies of bonobos. There is no study in the literature to extend

Relation extraction is one of the most important tasks in information extraction. The traditional works either use sentences or surface patterns (i.e., the shortest dependency paths of sentences) to build extraction models. Intuitively, the integration of these two kinds of methods will further obtain more robust and effective extraction models, which is, however, ignored in most of the existing works

The structural information of data contains useful prior knowledge and thus is important for designing classifiers. Extreme learning machine (ELM) has been a potential technique in handling classification problems. However, it only simply considers the prior class-based structural information and ignores the prior knowledge from statistics and geometry of data. In this paper, to capture more structural

The software system design phase has recently received increasing attention due to continuous growth in both the size and complexity of software systems. As a key concept of this phase, software architecture plays an important role in the software extension cycle to the extent that the success of a software project is often determined by the degree of its design efficiency. In addition, software architecture

Cloud computing has become a widely used environment for database querying. In this context, the goal of a query optimizer is to satisfy the needs of tenants and maximize the provider’s benefit. Resource allocation is an important step toward achieving this goal. Allocation methods are based on analytical formulas and statistics collected from a catalog to estimate the cost of various possible allocations

The MULTIMOORA method is better than some of the existing decision making methods. However, it has not been improved to process Pythagorean fuzzy sets (PFSs). The decision results of the MULTIMOORA method greatly depend on the distance measure and score function. Although there are many studies focusing on proposing distance measures and score functions for PFSs, they still show some defects. In this

High utility itemset mining is an important extension of frequent itemset mining which considers unit profits and quantities of items as external and internal utilities, respectively. Since the utility function has not downward closure property, an overestimated value of utility is obtained using an anti-monotonic upper bound of utility function to prune the search space and improve the efficiency

Integrating social networks data in the process of promoting business and marketing applications is widely addressed by several researchers. However, regarding the isolation between social network platforms managing such data has become a challenging task facing data scientist. In this respect, the present paper is designed to put forward a special semantic data integration approach, whereby a unified

We consider the task of discovering functional dependencies in data for target attributes of interest. To solve it, we have to answer two questions: How do we quantify the dependency in a model-agnostic and interpretable way as well as reliably against sample size and dimensionality biases? How can we efficiently discover the exact or \(\alpha \)-approximate top-k dependencies? We address the first

Long short-term memory (LSTM) is a powerful deep learning technique that has been widely used in many real-world data-mining applications such as language modeling and machine translation. In this paper, we aim to minimize the latency of LSTM inference on cloud systems without losing accuracy. If an LSTM model does not fit in cache, the latency due to data movement will likely be greater than that

Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities,

The application of edge clouds is becoming more and more widespread. The resource optimization is one of the important research contents of edge cloud. Generally, the edge cloud has limited computing resources and energy. Resource optimization can make tasks perform efficiently and reduce costs. Therefore, achieving high energy efficiency while ensuring a satisfying user experience is critical. This

The task of temporal slot filling (TSF) is to extract values of specific attributes for a given entity, called “facts”, as well as temporal tags of the facts, from text data. While existing work denoted the temporal tags as single time slots, in this paper, we introduce and study the task of Precise TSF (PTSF), that is to fill two precise temporal slots including the beginning and ending time points

In this paper, we present two algorithms to obtain the convex hull of a set of points that are stored in the compact data structure called \(k^2\)-\(tree\). This problem consists in given a set of points P in the Euclidean space obtaining the smallest convex region (polygon) containing P. Traditional algorithms to compute the convex hull do not scale well for large databases, such as spatial databases

Analyzing texts from social media encounters many challenges due to their unique characteristics of shortness, massiveness, and dynamic. Short texts do not provide enough context information, causing the failure of the traditional statistical models. Furthermore, many applications often face with massive and dynamic short texts, causing various computational challenges to the current batch learning

The increasing number of researchers and scientists participating in online communities has induced big challenges for users who are looking for researchers who are interested. As a result, finding potential collaborators among the huge amount of online information is going to be even much more important in the future. Collaborator recommendation is a kind of expert recommendation in scientific fields

Directed acyclic graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information

Supervised machine learning methods depend highly on the quality of the training dataset and the underlying model. In particular, neural network models, that have shown great success in dealing with natural language problems, require a large dataset to learn a vast number of parameters. However, it is not always easy to build a large (labelled) dataset. For example, due to the complex nature of tweets

As data become an increasingly important asset for organizations, so does the access control policies that protect aforesaid data. Many subjects (public, researchers, etc.) are interested in accessing these data, leading to the desire for simple access control. However, some scenarios use vague concepts, such as the “researcher’s expertise”, when making access control decisions. Therefore, access control

Nowadays, many R&I institutions are presently implementing mechanisms to measure and rate their scientific production so as to comply with current legislation and to support research management and decision making. In many cases, they rely on the implementation of current research information systems (CRIS). This is a challenging task that often requires major human intervention and supervision to

Autonomy is a key factor in the quality of life of a person. With the aging of the population, an increasing number of people suffers from a reduced level of autonomy. That compromises their capacity of performing their daily activities and causes safety issues. The new concept of ambient assisted living (AAL), and more specifically its application in smart homes for supporting elderly people, constitutes

Time-to-event prediction has been an important practical task for longitudinal studies in many fields such as manufacturing, medicine, and healthcare. While most of the conventional survival analysis approaches suffer from the presence of censored failures and statistically circumscribed assumptions, few attempts have been made to develop survival learning machines that explore the underlying relationship

Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing

In traditional facility location recommendations, the objective is to select the best locations which maximize the coverage or convenience of users. However, since users’ behavioral habits are often influenced by time, the temporal impacts should not be neglected in recommendation. In this paper, we study the problem of time-aware facility location recommendation problem, taking the time factor into

Crowdsourcing services provide an efficient and relatively inexpensive approach to obtain substantial amounts of labeled data by employing crowd workers. It is obvious that the labeling qualities of crowd workers directly affect the quality of the labeled data. However, existing label aggregation strategies seldom consider the differences in the quality of workers labeling different instances. In this

The widespread use of position-tracking devices leads to vast volumes of spatial–temporal data aggregated in the form of the trajectory data streams. Extracting useful knowledge from moving object trajectories can benefit many applications, such as traffic monitoring, military surveillance, and weather forecasting. Most of the knowledge gleaned from the trajectory data illustrates different kinds of

Representing the knowledge learned by neural networks in the form of interpretable rules is a prudent technique to justify the decisions made by neural networks. Heretofore many algorithms exist to extract symbolic rules from neural networks, but among them, a few extract rules from deep neural networks trained using deep learning techniques. So, this paper proposes an algorithm to extract rules from

A big challenge in the knowledge discovery process is to perform data pre-processing, specifically feature selection, on a large amount of data and high dimensional attribute set. A variety of techniques have been proposed in the literature to deal with this challenge with different degrees of success as most of these techniques need further information about the given input data for thresholding,

Generally, users are reserved in describing their search intention when submitting queries into the search engine. Therefore, a large number of search queries are usually short, ambiguous and tend to have multiple interpretations. With the gigantic size of the web, ignoring the information needs underlying such queries can misguide the search engine. To mitigate these issues, an effective approach

Social media have a great potential to improve information dissemination in our society, yet they have been held accountable for a number of undesirable effects, such as polarization and filter bubbles. It is thus important to understand these negative phenomena and develop methods to combat them. In this paper, we propose a novel approach to address the problem of breaking filter bubbles in social

In the published article, Figure 9(a) and Figure 9(b) are the same figure.

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics

In the modern digital era, information systems must operate in increasingly interconnected and dynamic environments, which force them to be changeable yet consistent. Such modern information systems are usually decision- and knowledge-intensive. A recently introduced standard, the decision model and notation (DMN), has been adopted in both industry and academia as a suitable method for modelling decisions

Unsupervised domain adaptation (UDA) serves to transfer specific knowledge from massive labeled source domain data to unlabeled target domain data via mitigating domain shift. In this paper, we propose a discriminative relevance regularization term (DRR) to enhance the performance of UDA by reducing the domain shift from the aspect of semantic relevance across domains. In particular, DRR is formulated

Data processing is increasingly becoming the subject of various policies and regulations, such as the European General Data Protection Regulation (GDPR) that came into effect in May 2018. One important aspect of GDPR is informed consent, which captures one’s permission for using one’s personal information for specific data processing purposes. Organizations must demonstrate that they comply with these

Online social media has become an ideal place in spreading rumor events with its convenience in communication and information dissemination, which raises the difficulty in debunking rumor events automatically. To deal with such a challenge, traditional classification approaches relying on manually labeled features have to face a daunting number of human efforts. With the consideration of the realness

Frequent itemset mining is considered a popular tool to discover knowledge from transactional datasets. It also serves as the basis for association rule mining. Several algorithms have been proposed to find frequent patterns in which the apriori algorithm is considered as the earliest proposed. Apriori has two significant bottlenecks associated with it: first, repeated scanning of input dataset and

In this paper, we propose PAIRS3: a possibilistic classification approach based on artificial immune recognition system (AIRS) and the possibility theory. PAIRS3 is applied to address shortcomings in student attainment rates of course learning outcomes by predicting effective remedial actions through learning from assessment rubrics instances. For most of assessment rubric instances, it is difficult

Bayesian network classifiers are, functionally, an interesting class of models, because they can be learnt out-of-core, i.e. without needing to hold the whole training data in main memory. The selective K-dependence Bayesian network classifier (SKDB) is state of the art in this class of models and has shown to rival random forest (RF) on problems with categorical data. In this paper, we introduce an

Given a social network with diffusion probabilities as edge weights and a positive integer k, which k nodes should be chosen for initial injection of information to maximize the influence in the network? This problem is popularly known as the Social Influence Maximization Problem (SIM Problem). This is an active area of research in computational social network analysis domain, since one and half decades

Recommender systems are a fundamental component of contemporary social-media platforms and require feedback submitted from users in order to fulfill their goal. On the other hand, the raise of advocacy about user-controlled data repositories supports the selective submission of data by user through intelligent software agents residing at the user end. These agents are endowed with the task of protecting

Identification of the influential users is one of the most practical analyses in social networks. The importance of this analysis stems from the fact that such users can affect their followers “/friends” viewpoints. This study aims at introducing two new indices to identify the most influential users in the Twitter social network. Four sets of features extracted from user activities, user profile,

Extracting discriminative features and reducing the dimensionality of data are two main objectives of manifold learning. Among different techniques, nonlinear manifold learning methods have been proposed in order to extract features from data which are not linearly distributed. Kernel trick is one of the famous nonlinear techniques which helps to project the data without an explicit mapping which can

The increasing amount and complexity of Cyber security attacks in recent years have made text analysis and data mining techniques an important factor in discovering features of such attacks and detecting future security threats. In this paper, we report on the results of a recent case study that involved the analysis of a community data set collected from five small and medium companies in Korea. The

Existing distance metric learning methods require optimisation to learn a feature space to transform data—this makes them computationally expensive in large datasets. In classification tasks, they make use of class information to learn an appropriate feature space. In this paper, we present a simple supervised dissimilarity measure which does not require learning or optimisation. It uses class information

We present Lexifield, a fully automatic language-independent system for building domain-specific lexicons from a short list of terms defining the domain. Lexifield relies on a pre-trained word embedding model, a definition dictionary and a dictionary of synonyms. To evaluate this system, four lexicons have been generated: one lexicon in French for the topic “son” (“sound”) and three lexicons in English