Reviewerly/exHarmony huggingface dataset:
Statistical Validation of the “ECODIES” Questionnaire to Measure the Digital Competence of Colombian High School Students in the Subject of Mathematics

Education in the 21st century faces the challenge of digitalization; therefore, the acquisition and development of digital skills in students is indispensable, not only for their learning processes but also for their lives. This study aims to validate the test “ECODIES”, which was used to assess the level of development of digital competence in students in a public high school in Bogotá (Colombia). The test is based on the DigCom model and was administered to a sample of 777 students aged between 11 and 18. The results obtained in the exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and reliability analysis show the quality of the test. Therefore, in this study it is concluded that “ECODIES” is a test with the reliability and validity to assess digital competence in the Colombian context; in this way, we hope to gain enough research about this topic to contribute to the development of digital competence in Colombian students. We conducted an instrumental study for the analysis of the psychometric properties of the questionnaire.
Cluster-Based Structural Redundancy Identification for Neural Network Compression

The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years. Existing works typically compress models based on importance, removing unimportant filters. This paper reconsiders model pruning from the perspective of structural redundancy, claiming that identifying functionally similar filters plays a more important role, and proposes a model pruning framework for clustering-based redundancy identification. First, we perform cluster analysis on the filters of each layer to generate similar sets with different functions. We then propose a criterion for identifying redundant filters within similar sets. Finally, we propose a pruning scheme that automatically determines the pruning rate of each layer. Extensive experiments on various benchmark network architectures and datasets demonstrate the effectiveness of our proposed framework.
Fast Training Set Size Reduction Using Simple Space Partitioning Algorithms

The Reduction by Space Partitioning (RSP3) algorithm is a well-known data reduction technique. It summarizes the training data and generates representative prototypes. Its goal is to reduce the computational cost of an instance-based classifier without penalty in accuracy. The algorithm keeps on dividing the initial training data into subsets until all of them become homogeneous, i.e., they contain instances of the same class. To divide a non-homogeneous subset, the algorithm computes its two furthest instances and assigns all instances to their closest furthest instance. This is a very expensive computational task, since all distances among the instances of a non-homogeneous subset must be calculated. Moreover, noise in the training data leads to a large number of small homogeneous subsets, many of which have only one instance. These instances are probably noise, but the algorithm mistakenly generates prototypes for these subsets. This paper proposes simple and fast variations of RSP3 that avoid the computationally costly partitioning tasks and remove the noisy training instances. The experimental study conducted on sixteen datasets and the corresponding statistical tests show that the proposed variations of the algorithm are much faster and achieve higher reduction rates than the conventional RSP3 without negatively affecting the accuracy.
Mobile Robot Tracking with Deep Learning Models under the Specific Environments

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.
Eye Pupil Localisation and Labeling Using a Small Size Database and YOLOv4 Object Detection Algorithm

Eye-related research has shown that eye gaze data are very important for applications that are essential to human daily life. Eye gaze data has been used in research and systems for eye movements, eye tracking and eye gaze tracking. Eye pupil localization, labelling and tracking are challenging problems in computer science. This article discusses and explores that problem․ The YOLOv4 (“You only look once”) object detection algorithm which is an evolution of the YOLOv3 model has been evaluated in a tiny database consisting of 103 eye images. The YOLOv4 algorithm was created by Alexey Bochkovskiy, Chien-Yao Wang and Hong-Yuan Mark Liao [3]. It is twice as fast as EfficientDet with comparable performance. The main purpose of this article is to test the YOLOv4 algorithm, to find out its effectiveness in process of localization and labelling of eye pupils and find out (determine) the effectiveness of the algorithm when training with a tiny database and with a relatively small number of iterations.
Delivery Order Information System in Raya Family Restaurant Based On Android Application

The development of information technology and communication facilitates and accelerates access to information we need as well as facilitates corporate or individual transactions for business purposes. The development of the culinary world resulted in entrepreneurs competing to open restaurants or restaurants. Family Raya restaurant is a field of business or company that is still developing so it needs to be designed an information system. The information system is useful for providing management information in making decisions and also for carrying out company operations. The purpose of this research is to make it easier for customers who want to order a food menu simply by accessing the application with an Android-based cellphone. It is hoped that more customers will later buy Family Raya cuisine and of course the benefits will be even greater. Therefore, an Android-based Application information system is needed. The method used in this study is the waterfall model which has several stages, namely design, analysis, design, coding, test and maintenance. The supporting software used is MIT App Inventor, Java, CSS3, HTML5. Based on the results of the implementation it was concluded that this application can facilitate customers to order food and increase profits at Rumah Raya Family Restaurant.
De-redundancy Relative Discrimination Criterion-based Feature Selection for Text Data

High dimensionality of text data would degrade the performance of text classification for the existence of irrelevant terms. Thus, it is necessary to perform feature selection to remove irrelevant terms. As an effective feature selection method, Relative Discrimination Criterion (RDC) can identify the irrelevant terms according to discriminant information, but it is incapable of capturing redundancy between terms. Therefore, this paper proposes a novel text feature selection based on RDC, named De-redundancy Relative Discrimination Criterion (DRDC), which takes into account the redundancy between terms when assessing their importance. For one thing, DRDC takes advantage of RDC to measure the relevance of terms to categories. For another, DRDC utilizes mutual information to measure the redundancy between terms. During iterations, we separately normalize the scores of RDC and mutual information for balancing them and reducing the impact of marginal probabilities on mutual information. In the procedure of feature selection, DRDC iteratively picks up the term that has the maximum relevance to categories and the minimum redundancy with terms already in the current feature subset. In such a way, DRDC can find an optimal term subset. The effectiveness and efficiency of DRDC is clarified by experiments on R8 and 20Newsgroups data sets using three widely used classifiers.
Game AI Planning Analytics: The Case of Three First-Person Shooters

We present a general framework for Game Artificial Intelligence Planning (AIP) Analytics. The objective is to provide analytic tools to study and improve AIP components and their use in video-games. Extraction and formatting of AI data is first described and discussed. Then AIP metrics are listed with examples and illustrations from three popular First-Person Shooters: F.E.A.R. (2005), KillZone 3 (2011) and Transformers 3: Fall of Cybertron (2012). The patterns we discovered in our study clearly show the AIP component is called more often by the game over the years.
Evaluation Model of E-learning Systems Implementation by using Factor Analysis and Regression Analysis Method

To achieve the target of implementing an electronic learning system or known as e-learning, it is necessary to evaluate the performance of the system on a regular basis. In response to this challenge, schools using the system have substantially increased the use of the system, through initiatives to disseminate information to teachers and students to use e-learning and support the teaching and learning process in every subject in the school. Careful evaluation of the acceptability and suitability of the technology is essential to ensure the implementation of the system and the continuity of the effective learning process. This study aims to evaluate the performance of e-learning implemented in the Kepulauan Seribu region of DKI Jakarta. The method used is factor analysis that can be used to find factors in successful implementation and regression analysis to build an e-learning evaluation model. The results obtained indicate that there are several main factors, namely system availability, instructor style, learning culture, helpdesk response and the latest technology can then be presented in the form of a mathematical model that can explain the performance of the system that is currently running with a value of 5.765 (from a scale of 0.000 -10,000), and can be used to build a system development plan in the future with a value of 8,328 (on a scale of 0.000 -10,000)
Detecting and Refactoring Feature Envy Based on Graph Neural Network

As one of the most common code smells, feature envy reduces the cohesion of classes and increases the coupling between classes, thus leading to difficulty of software maintainability. Though many studies have made good achievements on feature envy detection, they often despise or even ignore the inherent calling relationships between methods, causing unimpressive detection efficiency. To address this problem, we propose a Graph Neural Network (GNN) based approach towards feature envy detection. We first collect code metrics and calling relationships, and then convert them to the form of a graph, where the node represents the code metrics of a method and the edge represents the calling relationship between methods. Particularly, considering the unbalance of positive and negative samples, we introduce a graph augmenter to obtain an enhanced graph. Finally, we feed the enhanced graph into a GNN model for training and predicting. We conducted extensive experiments on a dataset containing five open-source software projects. The result shows that our approach achieves 78.90% in terms of average F1-score, which is 37.98% absolutely higher than the best comparison approach. Besides, we propose a refactoring recommendation approach based on calling strength. It achieves 61.44% of accuracy, which is 5.13% absolutely higher than the best competitive. Our code and datasets are available at https://github.com/HduDBSI/Feature-Envy-Detection.
Opinion Spam Detection: A New Approach Using Machine Learning and Network-Based Algorithms

E-commerce is the fastest-growing segment of the economy. Online reviews play a crucial role in helping consumers evaluate and compare products and services. As a result, fake reviews (opinion spam) are becoming more prevalent and negatively impacting customers and service providers. There are many reasons why it is hard to identify opinion spammers automatically, including the absence of reliable labeled data. This limitation precludes an off-the-shelf application of a machine learning pipeline. We propose a new method for classifying reviewers as spammers or benign, combining machine learning with a message-passing algorithm that capitalizes on the users' graph structure to compensate for the possible scarcity of labeled data. We devise a new way of sampling the labels for the training step (active learning), replacing the typical uniform sampling. Experiments on three large real-world datasets from Yelp.com show that our method outperforms state-of-the-art active learning approaches and also machine learning methods that use a much larger set of labeled data for training.
DarkLoc: Attention-based Indoor Localization Method for Dark Environments Using Thermal Images

Image-based localization is an essential component for many applications such as autonomous driving, virtual reality. Many researchers focus on developing methods for daytime via RGB images. Few research study the methods for night condition. The main reason is that the RGB-based image localization methods fail to work in dark scenes due to the low illumination. Thermal images capture temperature information instead of texture, making it well suitable for dark environments. However, thermal image-based localization methods are not well studied. To address it, we propose an attention-based localization method for night condition (DarkLoc) using thermal images. The proposed method introduce the attention mechanism in a deep learning-based framework to extract key information from low quality thermal images. The attention model can enforce the whole network focus on geometry meaningful feature in thermal images and thus improve the localization accuracy. We perform extensive experiment on the thermal image dataset. The results show that the attention model can enforce the whole network to learn geometry meaningful feature from thermal images and effectively locate the thermal image in real-time.
The Implementation of TEYL in Indonesia Revisited: A Study at Primary Schools in Bandung

This study explored Teaching English to Young Learners (TEYL) implementation in elementary schools in Bandung, Indonesia. Surveys, interviews, observation, and document analysis were used to obtain data. The survey was distributed to 30 TEYL teachers from 30 schools within the region. Interviews were conducted with four teachers from four different schools. The data were triangulated and descriptively examined to assess TEYL in primary schools. The findings showed a mismatch between the government's policy on English instruction in primary schools and TEYL's implementation. In the 30 schools investigated, English was taught as local content in 21 schools (70%) and extracurricular in 7 schools (23%). Meanwhile, only 2 schools (7%) did not conduct English in their teaching-learning process. Due to the lack of government supervision, teachers are free to construct their own TEYL course content. According to the interviews, adopting TEYL is challenging due to varying teacher abilities and the availability of school’s infrastructure. The discussion suggests some recommendations on how to utilize the TEYL curriculum in Indonesian's primary schools effectively.
Street Floor Segmentation for a Wheeled Mobile Robot

In urban cities, the information about the type or class of street floors enables a wheeled mobile robot to perform many tasks ranging from traversability region identification, localization and the choice of wheel control strategy.In this paper, we considered a new task named as street floor segmentation (SFS) using an RGB camera.The SFS can be considered as the generalized problem of the existing traversability region identification problem in urban situations.Our SFS has two special classes for the possible application to the traversability region identification and they are traversable and non-traversable curbs.The SFS using an RGB camera is implemented using a real-time semantic segmentation (SS) network.A booster module named as Dynamic Context-based Refinement Module (DCRM) was developed to enhance the performance of the SFS.Our network was applied to real-world applications, and its validity is demonstrated through experiment.
Ranking-Based Deep Hashing Network for Image Retrieval

In large-scale image retrieval, the deep learning-based hashing methods have significantly progressed. However, most of the existing deep hashing methods still have the problems of low feature learning efficiency and weak ranking relationship discrimination. To remedy these problems, a novel Ranking-based Deep Hashing Network (RDHN) is proposed for image retrieval in this paper, which integrates the feature learning module and hash learning module into a unified deep hashing network framework to jointly learn a powerful hash function so that the raw images can be mapped to discrete hash codes with significant discrimination. Specifically, a novel difference convolution is designed based on edge detection operators, and then it is uniquely applied to the first convolutional layer of the convolutional neural network (CNN), which can take advantage of the sensitive characteristics of edge detection operators for edge information to extract richer image edge information. Meanwhile, in hash learning, the ranking metric Mean Average Precision (MAP) is optimized using the idea of scaling, and then a ranking loss function based on MAP is carefully designed to enhance the neighborhood ranking capabilities of the hash codes. Furthermore, to reduce the quantification error, a quantization loss function is also designed. Finally, the ranking loss function is combined with the quantization loss function to form the objective function. The proposed method can generate high-quality discrete hash codes while learning to preserve ranking information, effectively improving retrieval performance. Extensive experimental results on three widely used benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art hashing approaches.
Design of integrated network security management system based on situation awareness

At present, network centers in universities must try to efficiently, accurately and intelligently locate network attacks, and automatically handle security events. In this paper, we extend the concept of situational awareness based on the OODA Loop theory, and apply network security situation awareness (NSSA) to four stages: observe, orientation, decision, and action, which consist of a control process loop. We also propose the network security management and control platform based on NSSA concept to provide enough support for building an integrated campus network security management system.
Information Fusion-Based Meta-Learning for Few-Shot Fault Diagnosis Under Different Working Conditions

Abstract With the development of deep learning and information technologies, intelligent fault diagnosis has been further developed, which achieves satisfactory identification of mechanical faults. However, the lack of labeled samples and complex working conditions can hinder the improvement of diagnostics models. In this article, a novel method called Information Fusion-based Meta-Learning (IFML) is explored for fault diagnosis with few-shot problems under different working conditions. Firstly, an information fusion and embedding module is applied to perform both data- and feature-level fusion of multi-source. The embedding module only contains one input layer and multiple convolutions, residual and batch normalization (BN) layers, which has the advantage of low computational cost and high generalization. Then the prototypical module is proposed to reduce the influence of domain-shift caused by different working conditions using the fusion representation, which can improve the performance of fault diagnosis. The approach is verified on artificial and real faults under 4 different working conditions from the KAt-DataCenter at Paderborn University. For the 3-way 1-shot classification on Task T1, the average testing accuracy of the proposed method is 97.14%. For the K-shot classification on different tasks, the proposed method achieves the highest average testing accuracy of 94.21%. The results show the proposed method outperforms other typical meta-learning methods in terms of testing accuracy and generalization capability.
Low Cost Hardware Design of ECC Scalar Multiplication

Elliptic curve cryptography (ECC) is one of the most popular asymmetric key cryptography techniques used in secured data communications. The scalar multiplication is the most expensive operation in the ECC. In this paper, we propose a low cost hardware for scalar multiplication in Affine coordinates based ECC. Here, we use a reconfigurable Galois field (GF) arithmetic circuit, which performs various GF arithmetic operations such as addition, multiplication, inverse, and fused multiply add (FMA) using a same set of hardware circuit. Instead of using a number of multipliers and adders in point addition/doubling, we have used only one reconfigurable GF arithmetic circuit. The existing and proposed designs are implemented in 45 nm CMOS technology using Cadence. The synthesis results show that the affine co-ordinate based proposed $$GF(2^{163})$$ scalar multiplier achieves $$69\%$$ of reduction in the switching power dissipation as compared with the Lopez-Dahab projective co-ordinates based conventional design in 45 nm CMOS technology.