Vietnam Journal of Computer Science

Periodic frequent patterns are frequent patterns which occur at periodic intervals in databases. They are useful in decision making where event occurrence intervals are vital. Traditional algorithms for discovering periodic frequent patterns, however, often report a large number of such patterns, most of which are often redundant as their periodic occurrences can be derived from other periodic frequent patterns. Using such redundant periodic frequent patterns in decision making would often be detrimental, if not trivial. This paper addresses the challenge of eliminating redundant periodic frequent patterns by employing the concept of deduction rules in mining and reporting only the set of non-redundant periodic frequent patterns. It subsequently proposes and develops a Non-redundant Periodic Frequent Pattern Miner (NPFPM) to achieve this purpose. Experimental analysis on benchmark datasets shows that NPFPM is efficient and can effectively prune the set of redundant periodic frequent patterns.

This paper presents the use of gamified crowdsourcing for knowledge content validation. Constructing a high-quality knowledge base is crucial for building an intelligent system. We develop a refinement process for the knowledge base of our word retrieval assistant system, where each piece of knowledge is represented as a triple. To validate triples acquired from various sources, we introduce yes/no quizzes and present them to many casual users for their inputs. Only the triples voted “yes” by a sufficient number of users are incorporated into the main knowledge base. Users are incentivized by rewards based on their contribution to the validation process. To ensure transparency of the reward-giving process, blockchain is utilized to store logs of the users’ inputs from which the rewards are calculated. Different strategies are also proposed for selecting the next quiz. The simulation results indicate that the proposed approach has the potential to validate knowledge contents. This paper is a revised version of our conference paper presented at the 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020).

Analysis of the sentence writing test is conducted in this paper to support diagnostics of the Parkinsons disease. Drawing and writing tests digitization has become a trend where synergy of machine learning techniques on the one side and knowledge base of the neurology and psychiatry on the other side leading sophisticated result in computer aided diagnostics. Such rapid progress has a drawback. In many cases, decisions made by machine learning algorithm are difficult to explain in a language human practitioner familiar with. The method proposed in this paper employs unsupervised learning techniques to segment the sentence into the individual characters. Then, feature engineering process is applied to describe writing of each letter using a set of kinematic and pressure parameters. Following feature selection process applicability of different machine learning classifiers is evaluated. To guarantee that achieved results may be interpreted by human, two major guidelines are established. The first one is to keep dimensionality of the feature set low. The second one is clear physical meaning of the features describing the writing process. Features describing amount and smoothness of the motion observed during the writing alongside with letter size are considered. Resulting algorithm does not take into account any semantic information or language particularities and therefore may be easily adopted to any language based on Latin or Cyrillic alphabets.

Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs.

This work identifies the strong dominant features by its location and extracts the image features for the purpose of automatic desire focusing on prominent structure and artistic stylization of images. At the pre-processing level, dataset image is treated using refined structure preserving image abstraction framework which can deliver the best effectual structure preserved abstracted results by utilizing visual attributes from 2D color image. The presented framework efficiently conserves the structural characteristics in the foreground of an input image by exhaustively amalgamate the series of non-photorealistic rendering image filters over meticulous investigational work and it also reduces the background substance of an image. The framework assesses image and object space details to generate structure preserved image abstraction thus distinguishing the accentuated elements of an enhanced structures using Harris key-point feature detector and chooses the 100 major unique dominant feature locations among available features. This work automatically selects the unique location from the extracted features using polynomial region of interest and unselected image regions and its background are blurred using Gaussian motion blurring with point spread function. Deblurring the selected region using wiener filtering to get the desire focusing on prominent structure followed by color quantization and flow-based bilateral filtering is applied over focused structural region to achieve artistic stylization. Efficiency of the framework has been validated by carrying out the trials on the selected Flickr repository, David Mould and Ruixing Wang dataset. In addition, user’s visual opinion and the image quality estimation methods were also utilized to appraise the proposed pre-processing framework. This work lists the structure preserving image abstraction framework applications, limitation, execution difficulties and future work in the field of Non-photorealistic rendering domain.

Online user activities are tracked for many purposes. In e-commerce, cross-domain tracking is used to quantify and pay for web-traffic generation. Our previous research studies have shown that HTTP cookie-based tracking process, though reliable, can fail due to technical reasons, as well as through fraudulent manipulation by traffic generators. In this research study, we evaluate which of the previously published tracking mechanisms are still functional. We assess the efficacy and utility of those methods to create a robust tracking mechanism for e-commerce. A failsafe and robust tracking mechanism does not need to translate into further privacy intrusions. Many countries are rushing to introduce new regulations, which can have a negative impact on the development of robust technologies in an inherently stateless eco-system. We used a multi-domain, purpose-built simulation environment to experiment common tracking scenarios, and to describe the parameters that define the minimum tracking requirement use-cases, and practices that result in invading privacy of users. This study will help practitioners in their implementations, and policy developers and regulators to draw up policies that would not curtail the development of robust tracking technologies that are needed in e-commerce activities, while safeguarding the privacy of internet users.