Vietnam Journal of Computer Science

Wireless Sensor Network (WSN) is gaining popularity day by day in a large area of applications. However, the operation of WSN is facing a multitude of challenges, mainly in terms of energy consumption since WSN nodes operate with battery power and changing the batteries is a complicated task, as networks may include hundreds to thousands of nodes. In this context, it is very crucial to know the remaining energy value in the battery of the sensor node to take required actions before losing sensor’s function. Sending these measurements is very expensive in terms of energy and reduces the battery lifetime of the sensor and thus of the entire network. In this paper, we are interested in defining a probabilistic approach which aims to estimate these monitoring energy values and optimize energy consumption in WSN. Our approach is based on hidden Markov chains and includes two phases namely a learning phase and a prediction phase. Our approach is implemented as a web service. We illustrate our approach with a sensor-based health-care monitoring case study for COVID-19 patients. To evaluate our approach, we carry out experimentations based on the AvroraZ simulator with a test for different types of applications and for different energy models: μAMPS-specific model, Mica2-specific model, and Mica2-specific model with actual measurements. These experimentations demonstrate the accuracy and efficiency of our approach. Our results show that periodic WSN applications i.e. applications which send monitoring data periodically, tested with the μAMPS-specific model perform an accuracy of 98.65%. In addition, our approach can perform a gain up to 75% of the battery charge of the sensor with an estimation of three-quarters of the remaining energy values.

Text readability is a measure of how easy or difficult it is to read a text. This readability factor plays a crucial role in the processes of drafting and comprehending the texts, affecting the choice of proper texts for reading. While text readability has been a focus in research on computational linguistics for English and other resource-rich languages, there is still little work on this subject in understudied languages like Vietnamese. In this study, we propose a semi-automated method to build a corpus for text readability purposes for the Vietnamese language. This method performs the classification of new documents based on comparing the correlation readability with the documents that have been manually pre-assessed by language experts. The evaluation results of experts show that the classification accuracy of documents in the corpus is over 90%. The experimental results on other available corpus also show that our proposed method can not only address the lack of corpora that are graded on various difficulty levels in Vietnamese, but it can also be extended to the analysis of other resource-poor languages without requiring major adjustments.

Mining frequent patterns (FPs) received a lot of research interest which is a fundamental problem in the field of data mining. It has been studied on many database types with various applications in intelligent systems. However, the traditional methods using a given threshold to mine FPsterns suffer from a crucial drawback, that is, the number of resulting patterns is uncontrollable, sometimes very large. Mining top-rank-$k$ FPs is one of the current research directions to solve the aforementioned disadvantage of mining FPs. Many methods have been proposed to mine effectively top-rank-$k$ frequent patterns. However, these methods also have many limitations regarding memory consumption and suboptimal mining time. Therefore, this study proposes a new method based on $N$-list structure using the Priority Queue, the Hash Table structure, and some fast pruning strategies for efficient mining top-rank-$k$ FPs from binary transaction databases. The experimental results on many datasets demonstrate that the proposed method is more effective than the previous methods in the terms of runtime and memory usage.

Information filtering and information retrieving applications are based on web page classification methods. Usually, web pages serve different functionalities or develop different topics or subjects. The diversity of web page content increases the need for automatic web page classification, making it a challenging task at the same time. Considering that the main component of the content of a web page is most often represented by the text and the classification of the text is a problem intensively studied in the last years, with researchers reporting state-of-the-art results for various methods, the idea of applying these methods on the text extracted from web pages could lead to important results. In this work, we revisit our experimental study on convolutional neural networks for multi-label multi-language web page classification with a new approach that consists of dividing the classification problem into functional classification and subject classification of web pages. From the experimental evaluation, one may conclude that the separation of the functional and subject classification of web pages leads to an improvement of the overall results.

A rehabilitation robot is a device that has been proving its positive effectiveness in the process of helping patients recover quickly after a stroke. Researching, designing, and manufacturing robot models in general and upper limb rehabilitation robots in particular are very practical. In this study, we proposed to combine the use of an algorithm and a physical modeling method to shorten the calculation process and design an upper limb rehabilitation robot. First, an exoskeleton upper limb rehabilitation robot model (UExosVN) was briefly described. Next, in turn, all the important problems including inverse kinematics, inverse dynamics for this robot model were proposed and solved by using optimization algorithms and physical modeling methods. The model was evaluated in the critical movement of daily operations. The results after the testing process have proven the accuracy and effectiveness of the proposed methods.

Time series is acknowledged as one of the most common crucial data types in our daily lives. Among the time series mining tasks, rule discovery is important to provide valuable knowledge that brings us a profound insight view of relationships between different objects through time. One challenge is that when the number of objects and their lengths increase, it easily leads to a combinatorial explosion. Therefore, we propose a temporal inter-object association rule mining algorithm, NPTR, to discover new informative temporal inter-object association rules from time series and overcome the challenge with parallelization. Another remarkable point is that NPTR defines a concurrent approach by performing the frequent pattern mining process and rule mining one simultaneously. From the experiments on real-world data, NPTR returns the rules exactly with less time and memory costs than others do. Those rules can be further utilized for other tasks such as prediction, classification, and clustering.

Currently, there are many research studies that apply and improve mutation testing techniques including traditional mutation testing or first-order mutation testing, and higher-order mutation testing (HOMT) for evaluating the quality of the set of test data in particular, and the quality of test suites in general. The results of those studies have proven the effectiveness of mutation testing in the field of software testing. Mutation testing allows the quality of test cases to be automatically evaluated, thereby helping the testers to improve the quality in the design and execution of the software testing. Besides, these studies have also pointed out the main barriers in applying mutation testing techniques in practice. However, we are the first to introduce a method that can reduce the cost, but keep the quality of testing activity based on evaluating the quality of the mutation operator as well as the quality of the test cases. In this paper, we concentrate on two problems regarding higher-order mutation testing: Evaluating the quality of mutation operators as well as generated mutants and prioritizing test cases based upon its capability of killing mutants. This may help developers allocate suitably their resources during testing phase. The study of this paper is an extended version of our previous study titled “Evaluating Mutation Operator and Test Case Effectiveness by Means of Mutation Testing”, which is published in the proceedings of the 13th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2021 (V. N. Do, Q. V. Nguyen and T. B. Nguyen, Evaluating Mutation Operator and Test Case Effectiveness by Means of Mutation Testing, in Intelligent Information and Database Systems. ACIIDS 2021, eds. N. T. Nguyen, S. Chittayasothorn, D. Niyato and B. Trawiński. Lecture Notes in Computer Science, Vol. 12672 (Springer, Cham), https://doi.org/10.1007/978-3-030-73280-6_66) to confirm the usefulness of our proposed method.