Reviewerly/exHarmony huggingface dataset:
The Development of an Ebook Integrated with Learning Management System to Improve Student's Metacognition Ability

This study aims to develop an ebook that is integrated with a Learning Management System (LMS) to improve students' metacognition skills. The research uses the ADDIE development model (Assumption, Design, Development, Implementation, Evaluation). The research data were obtained from 40 students who filled out the questionnaire and took the pretest and posttest. The effectiveness of the ebook on improving metacognitive skills used N-Gain analysis and to determine the difference between the experimental and control classes, a T-test was performed, using One Group Pretest and Post-test Design. The results showed that ebook development was very good. The development of ebooks integrated with LMS can significantly improve students' metacognitive abilities. Thus, this study recommends that lecturers use ebooks that are integrated into the learning management system so that learning can run effectively to improve students' metacognitive abilities.
Analysis of the Efficiency of the Ukrainian Higher Education Institutions Admission Campaign and the Ways to Improve it Through the Information Technology Implementation

The article considers the enrollment campaign of higher education institutions of Ukraine as one of the strategically important areas of their activities. The analysis of modern scientific research that considers the introductory campaign's organization and carrying out and their basic tendencies are defined. One of the priorities of higher education institutions, which needs constant research and improvement, is the search for and implementation of technological innovations, particularly during the introductory campaign. To determine possible ways to implement technologies that would give an effective result for the institution itself and applicants and society as a whole, the concept of the effectiveness of the introductory campaign is analyzed. The main indicators of efficiency are the quantitative and qualitative set of students according to the goals and opportunities of each educational institution. The peculiarities of the interaction of entrants as the target audience and the educational institution to provide the effectiveness of those indicators. To determine the target audience's information needs, authors present an analysis of the statistical data of the admission campaign of the Lviv Polytechnic National University. Dataset included information about the 1st year full-time bachelor's program applicants. The analysis was carried out based on the following indicators: the number of applications for the state budget, the maximum possible number of budget places determined by the university, the priority of applications for admission, and external independent evaluation scores. The analysis allowed to identify current needs for applicants and educational institutions. In order to support the decision-making of applicants and solve the problems facing the university, it was proposed to implement an information system, the main components of which are the recommendation of possible educational programs for admission and evaluation of the success of admission. Following the limitations and requirements of this study and the objectives, set general requirements for the information system.
Using Virtual Reality as Support to the Learning Process of Forensic Scenarios

Virtual Reality (VR) represents a computer technology to create a simulated three-dimensional (3D) environment. Compared to the traditional user interfaces, VR places the user inside an experience. This research aimed to implement and examine modern learning strategies and activities with the VR for teaching crime scene investigation. The target group of this piloting study consisted of all students who enrolled in a crime scene investigation course at Al-Istiqlal University during the Spring semester of 2021. An online item-based questionnaire has been developed. It is used to assess students&#x2019; perceptions towards using cooperative, discussion, field trip and problem-solving learning strategies and activities implemented with the VR technology. This study showed that VR is a helpful tool to practice modern learning strategies and skills in learning forensic science and crime scene investigation. We can conclude that using VR technology for educational purposes supports the learning process.
Centered Error Entropy-Based Variational Bayesian Adaptive and Robust Kalman Filter

In this brief, a centered error entropy based variational Bayesian adaptive and robust Kalman filter (CEEVBKF) is proposed to suppress outlier noise and estimate the unknown noise covariance adaptively. The derived CEEVBKF contains three steps: one-step prediction, centered error entropy (CEE) based outlier suppression, and variational Bayesian (VB) inference. The CEE criterion is first used to suppress outlier noise and obtain rough state estimation value, then they are set as a priori value in VB inference step for accurate a posteriori state estimation. The joint estimation of CEE and VB improves the iterative efficiency and reduces the parameter sensitivity. The simulation results show the effectiveness of CEEVBKF.
Improved Robust High-Degree Cubature Kalman Filter Based on Novel Cubature Formula and Maximum Correntropy Criterion with Application to Surface Target Tracking

Robust nonlinear filtering is an important method for tracking maneuvering targets in non-Gaussian noise environments. Although there are many robust filters for nonlinear systems, few of them have ideal performance for mixed Gaussian noise and non-Gaussian noise (such as scattering noise) in practical applications. Therefore, a novel cubature formula and maximum correntropy criterion (MCC)-based robust cubature Kalman filter is proposed. First, the fully symmetric cubature criterion and high-order divided difference are used to construct a new fifth-degree cubature formula using fewer symmetric cubature points. Then, a new cost function is obtained by combining the weighted least-squares method and the MCC loss criterion to deal with the abnormal values of non-Gaussian noise, which enhances the robustness; and statistical linearization methods are used to calculate the approximate result of the measurement process. Thus, the final fifth-degree divided difference–maximum correntropy cubature Kalman filter (DD-MCCKF) framework is constructed. A typical surface-maneuvering target-tracking simulation example is used to verify the tracking accuracy and robustness of the proposed filter. Experimental results indicate that the proposed filter has a higher tracking accuracy and better numerical stability than other common nonlinear filters in non-Gaussian noise environments with fewer cubature points used.
A Literature Review on the Challenges of Applying Test-Driven Development in Software Engineering

Due to the ongoing trend of digitalization, the importance of software for today’s society is continuously increasing. Naturally, there is also a huge interest in improving its quality, which led to a highly active research community dedicated to this aim. Consequently, a plethora of propositions, tools, and methods emerged from the corresponding efforts. One of the approaches that have become highly prominent is the concept of test-driven development (TDD) that increases the quality of created software by restructuring the development process. However, such a big change to the followed procedures is usually also accompanied by major challenges that pose a risk for the achievement of the set targets. In order to find ways to overcome them, or at least to mitigate their impact, it is necessary to identify them and to subsequently raise awareness. Furthermore, since the effect of TDD on productivity and quality is already extensively researched, this work focuses only on issues besides these aspects. For this purpose, a literature review is presented that focuses on the challenges of TDD. In doing so, challenges that can be attributed to the three categories of people, software, and process are identified and potential avenues for future research are discussed.
Label recovery and label correlation co-learning for multi-view multi-label classification with incomplete labels

Multi-view multi-label learning (MVML) is an important paradigm in machine learning, where each instance is represented by several heterogeneous views and associated with a set of class labels. However, label incompleteness and the ignorance of both the relationships among views and the correlations among labels will cause performance degradation in MVML algorithms. Accordingly, a novel method, label recovery and label correlation co-learning forMulti-ViewMulti-Label classification with incoMpleteLabels (MV2ML), is proposed in this paper. First, a label correlation-guided binary classifier kernel-based is constructed for each label. Then, we adopt the multi-kernel fusion method to effectively fuse the multi-view data by utilizing the individual and complementary information among multiple views and distinguishing the contribution difference of each view. Finally, we propose a collaborative learning strategy that considers the exploitation of asymmetric label correlations, the fusion of multi-view data, the recovery of incomplete label matrix and the construction of the classification model simultaneously. In such a way, the recovery of incomplete label matrix and the learning of label correlations interact and boost each other to guide the training of classifiers. Extensive experimental results demonstrate that MV2ML achieves highly competitive classification performance against state-of-the-art approaches on various real-world multi-view multi-label datasets in terms of six evaluation criteria.
Time Series Model Analysis Using Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) for E-wallet Transactions during a Pandemic

The use of e-wallet can be accessed easily via the internet, this can create a positive impact for economic stability after the Covid-19 pandemic. This can move the wheels of the community's economy, through online shopping and the use of e-wallet among the public. The use of a number of digital services in Indonesia has increased during the Covid-19 pandemic. The first position is occupied by e-commerce and the second position is occupied by digital wallets which increased by 65%. Based on data from the increasing number of e-wallet service users in Indonesia. There are several forms of e-wallet that have a large scale, such as GoPay, OVO, Tokopedia, and Bukalapak. Several types of e-wallets can be analyzed for time series models, so that they can help project e-wallet transactions in the post-pandemic future. The method for obtaining the time series model is using the Autocorrelation Function (ACF) and the Patial Autocorrelation Function (PACF).
A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data

The class imbalance problem has been reported to exist in remote sensing and hinders the classification performance of many machine learning algorithms. Several technologies, such as data sampling methods, feature selection-based methods, and ensemble-based methods, have been proposed to solve the class imbalance problem. However, these methods suffer from the loss of useful information or from artificial noise, or result in overfitting. A novel double ensemble algorithm is proposed to deal with the multi-class imbalance problem of the hyperspectral image in this paper. This method first computes the feature importance values of the hyperspectral data via an ensemble model, then produces several balanced data sets based on oversampling and builds a number of classifiers. Finally, the classification results of these diversity classifiers are combined according to a specific ensemble rule. In the experiment, different data-handling methods and classification methods including random undersampling (RUS), random oversampling (ROS), Adaboost, Bagging, and random forest are compared with the proposed double random forest method. The experimental results on three imbalanced hyperspectral data sets demonstrate the effectiveness of the proposed algorithm.
SFN: A Novel Scalable Feature Network for Vulnerability Representation of Open-Source Codes

Vulnerability detection technology has become a hotspot in the field of software security, and most of the current methods do not have a complete consideration during code characterizing, which leads to problems such as information loss. Therefore, this paper proposes one class of Scalable Feature Network (SFN), a composite feature extraction method based on Continuous Bag of Words and Convolutional Neural Network. In addition, to characterize the source code more comprehensively, we construct multiscale code metrics in terms of semantic-, line-, and function granularity. In order to verify the effectiveness of the SFN, this paper builds a Scalable Vulnerability Detection Model (SVDM) by combining SFN with Bi-LSTM. The experimental results show that the proposed SVDM can obtain precision over 84.3% and recall at 83.4%, respectively, while both FNR and FPR are less than 17%.
HybridRec: A recommender system for tagging GitHub repositories

Abstract Software repositories are increasingly essential to support the management of typical artifacts building up projects, including source code, documentation, and bug reports. GitHub is at the forefront of this kind of platforms, providing developer with a reservoir of code contained in more than 28M repositories. To help developers find the right artifacts, GitHub uses topics, which are short texts assigned to the stored artifacts. However, assigning inappropriate topics to a repository might hamper its popularity and reachability. In our previous work, we implemented MNBN and TopFilter to recommend GitHub topics. MNBN exploits a stochastic network to predict topics, while TopFilter relies on a syntactic-based function to recommend topics. In this paper, we extend our work by building HybridRec, a recommender system based on stochastic and collaborative-filtering techniques to generate more relevant topics. To deal with unbalanced datasets, we employ a Complement Naïve Bayesian Network (CNBN). Furthermore, we apply a preprocessing phase to clean and refine the input data before feeding the recommendation engine. An empirical evaluation demonstrates that HybridRec outperforms three state-of-the-art baselines, obtaining a better performance with respect to various metrics. We conclude that the conceived framework can be used to help developers increase their projects’ visibility.
Speech Sentiment Analysis Using Hierarchical Conformer Networks

Multimodality has been widely used for sentiment analysis tasks, especially for speech sentiment analysis. Compared with the emotion expression of most text languages, speech is more intuitive for human emotion, as speech contains more and richer emotion features. Most of the current studies mainly involve the extraction of speech features, but the accuracy and prediction rate of the models still need to be improved. To improve the extraction and fusion of speech sentiment feature information, we present a new framework. The framework adopts a hierarchical conformer model and an attention-based GRU model to increase the accuracy of the model. The method has two main parts: a local feature learning group and a global feature learning group. The local feature learning group is mainly used to learn the spatio-temporal feature information of speech emotion features through the conformer model, and a combination of convolution and transformer is used to be able to enhance the extraction of long and short-term feature information. The global features are then extracted by the AUGRU model, and the fusion of features is performed by the attention mechanism to access the weights of feature information. Finally, the sentiment is identified by a fully connected network layer, and then classified by a central loss function and a softmax function. Compared with existing speech sentiment analysis models, we obtained better sentiment classification results on the IEMOCAP and RAVDESS benchmark datasets.
Virtual Reality e-Commerce: Contextualization and Gender Impact on User Memory and User Perception of Functionalities and Size of Products

Virtual reality (VR) potential to isolate users from the real world while producing a rich virtual environment where users act similarly to how they would, in reality, is still being investigated in several fields. In this work, we investigated the effects of product contextualisation and gender under an immersive VR application where users can explore in-depth a commercial product with a “hands-on” experience. An experimental between-subjects study was performed with 38 participants between 18 and 28 years. The product tested consisted of a double-door refrigerator equipped with a touchscreen. Two independent variables were studied: Context (the refrigerator was filled with food products and placed in a kitchen), Neutral Context (empty refrigerator displayed in an empty white room), and Gender (Female and Male). As for the dependent variables, we considered how clarified users felt about the product functionalities, its size, the extent users remember details and characteristics of the refrigerator, and the user’s subjective workload. The evidence shows that contextualisation and gender have no impact on any dependent variables. Therefore, we concluded that presenting a product in its context does not benefit significantly benefit it. Thus, opting for a neutral context would be preferable to save computational costs and human resources necessary to build and run the higher complexity environments required to contextualise the product.
Arbitrary scale super resolution network for satellite imagery

Recently, satellite imagery has been widely applied in many areas. However, due to the limitations of hardware equipment and transmission bandwidth, the images received on the ground have low resolution and weak texture. In addition, since ground terminals have various resolutions and real-time playing requirements, it is essential to achieve arbitrary scale super-resolution (SR) of satellite images. In this paper, we propose an arbitrary scale SR network for satellite image reconstruction. First, we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs. Second, we design an edge reinforcement module to enhance the high-frequency details in satellite images through a two-branch network. Finally, extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts.
Transformer with progressive sampling for medical cellular image segmentation

The convolutional neural network, as the backbone network for medical image segmentation, has shown good performance in the past years. However, its drawbacks cannot be ignored, namely, convolutional neural networks focus on local regions and are difficult to model global contextual information. For this reason, transformer, which is used for text processing, was introduced into the field of medical segmentation, and thanks to its expertise in modelling global relationships, the accuracy of medical segmentation was further improved. However, the transformer-based network structure requires a certain training set size to achieve satisfactory segmentation results, and most medical segmentation datasets are small in size. Therefore, in this paper we introduce a gated position-sensitive axial attention mechanism in the self-attention module, so that the transformer-based network structure can also be adapted to the case of small datasets. The common operation of the visual transformer introduced to visual processing when dealing with segmentation tasks is to divide the input image into equal patches of the same size and then perform visual processing on each patch, but this simple division may lead to the destruction of the structure of the original image, and there may be large unimportant regions in the divided grid, causing attention to stay on the uninteresting regions, affecting the segmentation performance. Therefore, in this paper, we add iterative sampling to update the sampling positions, so that the attention stays on the region to be segmented, reducing the interference of irrelevant regions and further improving the segmentation performance. In addition, we introduce the strip convolution module (SCM) and pyramid pooling module (PPM) to capture the global contextual information. The proposed network is evaluated on several datasets and shows some improvement in segmentation accuracy compared to networks of recent years.
Was the ICO boom just a sideshow of the Bitcoin and Ether Momentum?

We investigate whether the market for ICOs in 2017–2018 and 2021 showed signs of contagion from prices of Bitcoin and Ether. During phases of optimism, ICO daily returns display low correlations with those of Bitcoin or Ether. But when the bubble bursts, correlations jump to very high levels, signaling that the ICO market becomes a sideshow of the cryptocurrency dynamics. We demonstrate that this high correlation was not present during the Nasdaq bubble in the 1990s, signaling that the price dynamics of digital tokens seems to be driven by a common factor, much more than in previous bubbles.
Validation of the DigCompEdu Check-in Questionnaire through Structural Equations: A Study at a University in Peru

The technologization of society presents a great challenge for education in the twenty-first century; there is a need to face that challenge to be able to promote quality digital literacy. The use of the teacher’s digital competence in terms of the safe and critical use of technologies is one of the key competencies that can guarantee educational success. The present study analyzes the reliability and validity of the DigCompEdu questionnaire for future teachers within the framework of digital competence improvement. This tool is centered on teacher orientation, with respect to their level of competence, through a self-evaluation of their strengths and needs for improvement in digital learning, according to its different dimensions: digital literacy, communication and collaboration with the organization; search and treatment of data, digital socialization; technological creativity and innovation. For its development, the exploratory and confirmatory factorial analysis (EFA and CFA) technique was utilized via structural equation modeling (SEM). A total of 1659 professors, who are employed at a university in Peru, completed the questionnaire. The analyses that were performed corroborated the reliability and validity of the instrument, as well as the different possibilities guaranteed by the validation method via structural equations. We underline the need to offer training for professors in the area of digital competence and endorse methodologies based on competencies that guarantee the use of valid and reliable tools.