The social media technologies are open to users who are intended in creating a community and publishing their opinions of recent incidents. The participants of the online social networking sites remain ignorant of the criticality of disclosing personal data to the public audience. The private data of users are at high risk leading to many adverse effects like cyberbullying, identity theft, and job

Querying a search engine is one of the most frequent activities performed by Internet users. As queries are submitted, the server collects and aggregates them to build detailed user profiles. While user profiles are used to offer personalized search services, they may also be employed in behavioral targeting or, even worse, be transferred to third parties. Proactive protection of users' privacy in

Time evolving networks have some properties in common with complex networks, while some characteristics are specific to their time evolving nature. A number of interesting properties have been observed in time-varying complex networks such as densification power-law, shrinking diameter, scale-free degree distribution, big clustering coefficient and the emergence of community structure. Existing generative

With the access devices that are densely deployed in multi-access edge computing environments, users frequently switch access devices when moving, which causes the imbalance of network load and the decline of service quality. To solve the problems above, a seamless handover scheme for wireless access points based on perception is proposed. First, a seamless handover model based on load perception is

NoSQL technologies have become a common component in many information systems and software applications. These technologies are focused on performance, enabling scalable processing of large volumes of structured and unstructured data. Unfortunately, most developments over NoSQL technologies consider security as an afterthought, putting at risk personal data of individuals and potentially causing severe

The minibatching technique has been extensively adopted to facilitate stochastic first-order methods because of their computational efficiency in parallel computing for large-scale machine learning and data mining. Indeed, increasing the minibatch size decreases the iteration complexity (number of minibatch queries) to converge, resulting in the decrease of the running time by processing a minibatch

The rapid, increasing adoption of businesses to deliver their services in Software as a Service (SaaS) products in the marketplace presents selection challenges to users. Recently, major cloud service providers such as Amazon Web Services and Microsoft Azure have introduced well-architected frameworks that assess SaaS products in different pillars (also referred to herein as features). Furthermore

Fisher’s Linear Discriminant Analysis (LDA) has been widely used for linear classification, feature selection, and metrics learning in multivariate data analytics. To ensure high classification accuracy while optimally discovering predictive features from the data, this paper studied \(\mathbf {CDA}\), namely Combinatorial Discriminant Analysis that intends to combinatorially select a subset of features

Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a result, many data stream learning algorithms have been proposed during the past decades and achieve great success in various fields. This paper aims to explore a specific type of challenge in learning evolving data streams, called concept evolution

In text categorization, Vector Space Model (VSM) has been widely used for representing documents, in which a document is represented by a vector of terms. Since different terms contribute to a document’s semantics in various degrees, a number of term weighting schemes have been proposed for VSM to improve text categorization performance. Much evidence shows that the performance of a term weighting

The standard machine learning tasks often assume that the training (source domain) and test (target domain) data follow the same distribution and feature space. However, many real-world applications suffer from the limited number of training labeled data and benefit from the related available labeled datasets to train the model. In this way, since there is the distribution difference across the source

The location selection is a strategic decision that significantly influences revenue, level of competition, and success of companies and countries. This study aims to propose a hybrid approach for the location selection, to evaluate the potential location for the automotive manufacturing plant of Turkey, and to reveal a comprehensive analysis of weighting and multiple criteria decision-making (MCDM)

In this paper, we propose a generic boosting framework for multiple object tracking (MOT). Unlike other works tracking objects from zero, our framework uses their results (tracklets) and makes further optimizations. The motivation of us derives from the observation that most modern MOT trackers have been acceptable performance and can yield relatively reliable tracklets; accordingly, we straight focus

Most existing algorithms of anomaly detection are suitable for static data where all data are available during detection but are incapable of handling dynamic data streams. In this study, we proposed an improved iLOF (incremental local outlier factor) algorithm based on the landmark window model, which provides an efficient method for anomaly detection in data streams and outperforms conventional methods

The Internet plays an important role in society as a whole. One of the services that arose from the Internet was collaborative systems, in which several users create the content of the systems by means of personal experiences. One of the many collaborative systems existing today is those of sharing gastronomic recipes. The area of information retrieval on the Web has increased interest in regard to

In the research area of time series classification, the ensemble shapelet transform algorithm is one of the state-of-the-art algorithms for classification. However, its high time complexity is an issue to hinder its application since its base classifier shapelet transform includes a high time complexity of a distance calculation and shapelet selection. Therefore, in this paper we introduce a novel

Academic collaborations improve research efficiency and spur scientific innovation. However, scholarly big data has hindered scholars from finding suitable collaborators. Although some studies have involved the prediction problem of academic collaborations, they neglect the rich dynamic information of the heterogeneous academic network. In this paper, we propose a prediction model for academic collaborations

Ensemble methods represent an effective way to solve supervised learning problems. Such methods are prevalent for learning from evolving data streams. One of the main reasons for such popularity is the possibility of incorporating concept drift detection and recovery strategies in conjunction with the ensemble algorithm. On top of that, successful ensemble strategies, such as bagging and random forest

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively

Mining patterns of temporal sequence data is an important problem across many disciplines. Under appropriate preprocessing procedures, a structured temporal sequence can be organized into a probability measure or a time series representation, which grants a potential to reveal distinctive temporal pattern characteristics. In this paper, we propose a nested two-stage clustering method that integrates

Successful project is determined based on its effective performance and prioritization of all unavoidable software project risks. In this paper, the risk evaluation in software projects is done through developing a new hybridized fuzzy-based risk evaluation framework. During decision making process, this proposed scheme has determined and ranked all the significant project risks. Software project risks

Mining itemsets for association rule generation is a fundamental data mining task originally stemming from the traditional market basket analysis problem. However, enumerating all frequent itemsets, especially in a dense dataset, or with low support thresholds, remains costly. In this paper, a novel theorem builds the relationship between frequent closed itemsets (FCIs) and frequent generator itemsets

This work studies the location-privacy preserving location update in the context of data-centric people mobility applications. The mobility model involves an urban area annotated city network (ACN) over which the users move and record/report their locations at non-regular intervals. The ACN is modeled as a directed weighted graph. Since the data receiver (e.g., an LBS provider) is curious in our privacy

With the advance of information technology, many fields have begun using data clustering to reveal data structures and obtain useful information. Most of the existing clustering algorithms are susceptible to outliers and noises as well as the initial solution. The fuzzy c-ordered-means (FCOM) method can handle outlier and noise problems by using Huber’s M-estimators and Yager’s OWA operator to enhance

Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related features are widely used for chart pattern matching in financial

There is a certain belief among data science researchers and enthusiasts alike that clustering can be used to improve classification quality. Insofar as this belief is fairly uncontroversial, it is also very general and therefore produces a lot of confusion around the subject. There are many ways of using clustering in classification and it obviously cannot always improve the quality of predictions

We focus on the automatic detection of communities in large networks, a challenging problem in many disciplines (such as sociology, biology, and computer science). Humans tend to associate to form families, villages, and nations. Similarly, the elements of real-world networks naturally tend to form highly connected groups. A popular model to represent such structures is the clique, that is, a set of

Visualization strategies facilitate streaming network analysis by allowing its exploration through graphical and interactive layouts. Depending on the strategy and the network density, such layouts may suffer from a high level of visual clutter that hides meaningful temporal patterns, highly active groups of nodes, bursts of activity, and other important network properties. Edge sampling improves layout

Tourism is one of the largest service industries and a popular leisure activity participated by people with friends or family. A significant problem faced by the tourists is how to plan sequences of points of interest (POIs) that maintain a balance between the group preferences and the given temporal and spatial constraints. Most traditional group itinerary recommendation methods adopt predefined preference

The cross-lingual topic analysis aims at extracting latent topics from corpora of different languages. Early approaches rely on high-cost multilingual resources (e.g., a parallel corpus), which is hard to come by in many real cases. Some works only require a translation dictionary as a linkage between languages; however, when given an inappropriate dictionary (e.g., small coverage of dictionary), the

Massive open online courses (MOOCs) have emerged as a great resource for learners. Numerous challenges remain to be addressed in order to make MOOCs more useful and convenient for learners. One such challenge is how to automatically extract a set of keyphrases from MOOC video lectures that can help students quickly identify the right knowledge they want to learn and thus expedite their learning process

Metro passenger counting and density estimation are crucial for traffic scheduling and risk prevention. Although deep learning has achieved great success in passenger counting, most existing methods ignore fundamental appearance information, leading to density maps of low quality. To address this problem, we propose a novel counting method called “dilated-transposed fully convolution neural network”

So far, multiple classifier systems have been increasingly designed to take advantage of hardware features, such as high parallelism and computational power. Indeed, compared to software implementations, hardware accelerators guarantee higher throughput and lower latency. Although the combination of multiple classifiers leads to high classification accuracy, the required area overhead makes the design

Tracking of document collections over time (or across domains) is helpful in several applications such as finding dynamics of terminologies, identifying emerging and evolving trends, and concept drift detection. We propose a novel ‘Cluster Association-aware’ Non-negative Matrix Factorization (NMF)-based method with graph-based visualization to identify the changing dynamics of text clusters over time/domains

Stream mining considers the online arrival of examples at high speed and the possibility of changes in its descriptive features or class definitions compared with past knowledge (i.e., concept drifts). The fast detection of drifts is essential to keep the predictive model updated and stable in changing environments. For many applications, such as those related to smart sensors, the high number of features

The G-Skyline (GSky) query is formulated to report optimal groups that are not dominated by any other group of the same size. Particularly, a given group \(G_1\) dominates another group \(G_2\) if for any point \(p\in G_1\), p dominates or equals to points \(p{'}\in G_2\); at the same time, there is at least one point p dominating \(p{'}\). Most existing group skyline queries need to calculate an aggregate

The literature on the modeling and management of data generated through the lifecycle of a manufacturing system is split into two main paradigms: product lifecycle management (PLM) and product, process, resource (PPR) modeling. These paradigms are complementary, and the latter could be considered a more neutral version of the former. There are two main technologies associated with these paradigms:

Generalized from image and language translation, the goal of graph translation or transformation is to generate a graph of the target domain on the condition of an input graph of the source domain. Existing works are limited to either merely generating the node attributes of graphs with fixed topology or only generating the graph topology without allowing the node attributes to change. They are prevented

Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition

A co-location pattern indicates a group of spatial features whose instances are frequently located together in proximate geographic area. Spatial co-location pattern mining (SCPM) is valuable for many practical applications. Numerous previous SCPM studies emphasize the equal participation per feature. As a result, the interesting co-locations with rare features cannot be captured. In this paper, we

In the last decades, temporal networks played a key role in modelling, understanding, and analysing the properties of dynamic systems where individuals and events vary in time. Of paramount importance is the representation and the analysis of Social Media, in particular Social Networks and Online Communities, through temporal networks, due to their intrinsic dynamism (social ties, online/offline status

Least squares kernel-based methods have been widely used in regression problems due to the simple implementation and good generalization performance. Among them, least squares support vector regression (LS-SVR) and extreme learning machine (ELM) are popular techniques. However, the noise sensitivity is a major bottleneck. To address this issue, a generalized loss function, called \(\ell _s\)-loss,

When considering the problem of influence maximization from information cascades, one essential component is influence estimation. Traditional approaches for influence estimation generally follow a two-stage framework, i.e., learn a hypothetical diffusion model from information cascades and then calculate the influence spread according to the learned diffusion model via Monte Carlo simulation or heuristic

Traditionally, community detection and network embedding are two separate tasks. Network embedding aims to output a vector representation for each node in the network, and community detection aims to find all densely connected groups of nodes and well separate them from others. Most of the existing approaches do community detection and network embedding in a separate manner, and ignore node attributes

Many applications require the collection of data on different variables or measurements over many system performance metrics. We term those broadly as measures or variables. Often data collection along each measure incurs a cost, thus it is desirable to consider the cost of measures in modeling. This is a fairly new class of problems in the area of cost-sensitive learning. A few attempts have been

Within the field of process mining, several different trace clustering approaches exist for partitioning traces or process instances into similar groups. Typically, this partitioning is based on certain patterns or similarity between the traces, or driven by the discovery of a process model for each cluster. The main drawback of these techniques, however, is that their solutions are usually hard to

The software engineering field is continuously making an effort to improve the effectiveness of the software development process. This improvement is performed by developing quantitative measures that can be used to enhance the quality of software products and to more accurately describe, better understand and manage the software development life cycle. Even if the ontology engineering field is constantly

A discriminative itemset is a frequent itemset in the target data stream with much higher frequency than that of the same itemset in the rest of the data streams in the dataset. The discriminative itemsets describe the distinguishing features between data streams. Mining discriminative itemsets in data streams is very important, where continuously arriving transactions can be inserted in fast speed

Most works of recommender systems focus on providing users with highly accurate item predictions based on the assumption that accurate suggestions can best satisfy users. However, accuracy-focused models also create great system bias towards popular items and, as a result, unpopular items rarely get recommended and will stay as “cold items” forever. Both users and item providers will suffer in such

Fine-grained entity typing (FGET) is to classify the mentions of entities into hierarchical fine-grained semantic types. There are two main issues with existing FGET approaches. Firstly, the process of training corpora for FGET is normally to label the data automatically, which inevitably induces noises. Existing approaches either directly tweak noisy labels in corpora by heuristics or algorithmically

MapReduce has been widely recognized as an efficient tool for large-scale data analysis. It achieves high performance by exploiting parallelism among processing nodes while providing a simple interface for upper-layer applications. However, there are many existing applications maintaining their data in a distributed database. It is costly to export those data into the storage system of MapReduce (normally

Knowledge discovery systems are nowadays supposed to store and process very large data. When working with big time series, multivariate prediction becomes more and more complicated because the use of all the variables does not allow to have the most accurate predictions and poses certain problems for classical prediction models. In this article, we present a scalable prediction process for large time

Histograms are widely used in selectivity estimation for one-dimensional data. Using the one-dimensional histograms to estimate the selectivity of the multidimensional queries will result in a high estimation error, unless the assumption of attribute independence is true. Constructing a multidimensional histogram also brings great challenges. The storage of a multidimensional histogram exponentially

The notion of Textual Entailment (TE) is an established indicator of text connectedness. It captures semantic relationships between texts. Recently, it has been used successfully for determining sentence salience in many text summarization methods. However, it has been reported in previous works that the standard textual entailment is not ideal for measuring sentence salience. This is because textual

Many fundamental problems in machine learning require some form of dimensionality reduction. To this end, two different strategies were used: manifold learning and feature selection. Manifold learning (or data embedding) attempts to compute a subspace from original data by feature recombination/transformation. Feature selection aims to select the most relevant features in the original space. In this

In everyday life, we often observe unusually frequent interactions among people before or during important events, e.g., people send/receive more greetings to/from their friends on holidays than regular days. We also observe that some videos or hashtags suddenly go viral through people’s sharing on online social networks (OSNs). Do these seemingly different phenomena share a common structure? All these

Studies on named entity recognition (NER) often require a substantial amount of human-annotated training data. This makes technical domain-specific NER from industry data especially challenging as labelled data are scarce. Despite English as the surface language, technical jargon and writing conventions used in technical documents render the low-resource language challenges where techniques such as

Natural language question answering over knowledge graph has received widespread attention. However, the existing methods always aim to improve every phase of natural language question answering and neglect the defects; namely, not all query intentions can be identified and mapped to the correct SPARQL statement. In contrast, keyword search relies on the links among multiple keywords regardless of

SPARQL 1.1 offers a type of navigational query for RDF systems, called regular path query (RPQ). A regular path query allows for retrieving node pairs with the paths between them satisfying regular expressions. Regular path queries are always difficult to be evaluated efficiently because of the possible large search space. Thus there has been no scalable and practical solution so far. In this paper

At present, You only look once (YOLO) is the fastest real-time object detection system based on a unified deep neural network. During training, YOLO divides the input image to \(S \times S \) gird cells and the only one grid cell that contains the center of an object, takes charge of detecting that object. It is not sure that the cell corresponding to the center of the object is the best choice to