Vietnam Journal of Computer Science

In this paper, we present results of employing DBpedia and YAGO as lexical databases for answering questions formulated in the natural language. The proposed solution has been evaluated for answering class 1 and class 2 questions (out of 5 classes defined by Moldovan for TREC conference). Our method uses dependency trees generated from the user query. The trees are browsed for paths leading from the root of the tree to the question subject. We call those paths fibers and they represent the user intention. The question analysis consists of three stages: query analysis, query breakdown and information retrieval. The aim of those stages is the detection of the entities of interest and its attributes, indicating the users’ domain of interest. The user query is then converted into a SPARQL query and sent to the DBpedia and YAGO databases. The baseline and the extended methods are presented and the quality of the solution is evaluated and discussed.

The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. The process of extraction of association rules is based on the lexical quality of the text and on the minimum support set by the user. In this paper, we implemented a platform named “IDETEX” capable of extracting itemsets from textual data and using it for the experimentation in different types of clustering methods, such as $K$-Medoids and Hierarchical clustering. The experiments conducted demonstrate the potential of the proposed approach for defining similarity between segments.

An improved version of Alpha-N, a self-powered, wheel-driven Automated Delivery Robot (ADR), is presented in this study. Alpha-N-V2 is capable of navigating autonomously by detecting and avoiding objects or obstacles in its path. For autonomous navigation and path planning, Alpha-N uses a vector map and calculates the shortest path by Grid Count Method (GCM) of Dijkstra’s Algorithm. The RFID Reading System (RRS) is assembled in Alpha-N to read Landmark determination with Radio Frequency Identification (RFID) tags. With the help of the RFID tags, Alpha-N verifies the path for identification between source and destination and calibrates the current position. Along with the RRS, GCM, to detect and avoid obstacles, an Object Detection Module (ODM) is constructed by Faster R-CNN with VGGNet-16 architecture that builds and supports the Path Planning System (PPS). In the testing phase, the following results are acquired from the Alpha-N: ODM exhibits an accuracy of $89.25\%$, RRS shows $93.2\%$ accuracy and the PPS maintains the accuracy of $87.5\%$. This proposed version of Alpha-N shows significant improvement in terms of performance and usability compared with the previous version of Alpha-N.

This paper presents an analysis of scientific collaboration through graph theory, based on a previous study focused on the collaborative work of researchers within an institution. This proposal also exposes the representation of inter-institutional collaboration of research groups, combining graph theory and data mining. The state of the art relates the concepts of scientific production, digital repositories, interoperability between repositories, the law of Open Science in Mexico, the theory of graphs and their use in previous studies for the analysis of scientific collaboration, and the definition of research groups in Mexico. Furthermore, the methodology uses elements of knowledge extraction for data mining, involving recovery, processing and visualization. Results present the collaboration status at the Universidad Autónoma de Yucatán, internally and externally, by the research groups. Internally, 22 groups were found and each researcher collaborates with six other professors within the institution, on average. In addition, consolidated research groups are those with the highest level of production and collaboration at national and international level, compared to the groups with less consolidation.

Clustering is a key method in unsupervised learning with various applications in data mining, pattern recognition and intelligent information processing. However, the number of groups to be formed, usually notated as $k$ is a vital parameter for most of the existing clustering algorithms as their clustering results depend heavily on this parameter. The problem of finding the optimal $k$ value is very challenging. This paper proposes a novel idea for finding the correct number of groups in a dataset based on data depth. The idea is to avoid the traditional process of running the clustering algorithm over a dataset for $\sqrt{n}$ times and further, finding the $k$ value for a dataset without setting any specific search range for $k$ parameter. We experiment with different indices, namely CH, KL, Silhouette, Gap, CSP and the proposed method on different real and synthetic datasets to estimate the correct number of groups in a dataset. The experimental results on real and synthetic datasets indicate good performance of the proposed method.

The influence of acute severe stress or extreme emotion based on a Network-Oriented modeling methodology has been addressed here. Adaptive temporal causal network model is an approach to address the phenomena with complexity which cannot be or hard to be explained in a real-world experiment. In the first phase, the suppression of the existing network connections as a consequence of the acute stress modeled and in the second phase relaxing the suppression by giving some time and starting a new learning of the decision making in accordance to presence of stress starts again.

Often, uncertainty is present in processes that are part of our routines. Having tools to understand the consequences of unpredictability is convenient. We introduce a general framework to deal with uncertainty in the realm of distribution sets that are descriptions of imprecise probabilities. We propose several non-biased refinement strategies to obtain sensible forecasts about results of uncertain processes. Initially, uncertainty on a system is modeled as the non-deterministic choice of its possible behaviors. Our refinement hypothesis translates non-determinism into imprecise probabilistic choices. Imprecise probabilities allow us to propose a notion of uncertainty refinement in terms of set inclusions. Later on, unpredictability is tackled through a strategic approach using uncertainty profiles and angel/daemon games ($𝔞∕𝔡$-games). Here, imprecise probabilities form the set of mixed strategies and Nash equilibria corresponds to natural uncertainty refinements. We use this approach to study the performance of Web applications — in terms of response times — under stress conditions.