Reviewerly/exHarmony huggingface dataset:
Analyzing the Role of Class Rebalancing Techniques in Software Defect Prediction

Predicting software defects is an important task during software testing phase, especially for allocating appropriate resources and prioritizing testing tasks. Typically, classification algorithms are used to accomplish this task by using previously collected datasets. However, these datasets suffer from imbalanced label distribution where clean modules outnumber defective modules. Traditional classification algorithms cannot handle this nature in defect datasets because they assume the datasets are balanced. Failing to address this problem, the classification algorithm will produce a prediction biased towards the majority label. In the literature, there are several techniques designed to address this problem and most of them focus on data re-balancing. Recently, ensemble class imbalance techniques have emerged as an opposing approach to data rebalancing approaches. Regarding the software defect prediction, there are no studies examining the performance of ensemble class imbalance learning against data re-balancing approaches. This paper investigates the efficiency of ensemble class imbalance learning for software defect prediction. We conducted a comprehensive experiment that involved 12 datasets, six classifiers, nine class imbalance techniques, and 10 evaluation metrics. The experiments showed that ensemble approaches, particularly the Under Bagging technique, outperform traditional data re-balancing approaches, particularly when dealing with datasets that have high defect ratios.
OTRN-DCN: An optimized transformer-based residual network with deep convolutional network for action recognition and multi-object tracking of adaptive segmentation using soccer sports video

In today’s era, video analysis is immensely involved in recognizing the sport-related movement that has become a significant part of human’s life. The intent of this approach is to know about the player’s activities with prior information of tracking objects. It also analyzes the player potential or capacity to lead the winning team. When the player frequently changes their location, object tracking and action recognition will become a quite challenging task. Over the game, various athletes or different objects are considered to assist the system to easily recognize the respective actions of the player. Most of the previous models have been implemented, yet, it faces such consequences to provide promising performance. To meet the pre-requisite, a new multi-athlete tracking model for action recognition in soccer sports is designed with deep learning approaches. Initially, the multi-object tracking video is offered as the input to pre-processing phase. Here, occlusion and background clutter removal and contrast enhancement techniques are utilized to perform pre-processing in the videos. Then, the pre-processed video is offered to the multi-object tracking phase, where the jersey number is observed during multi-object tracking to avoid the identity switch problem. Then, effective multi-object tracking is performed by adaptive YOLOv5. The parameters presented in the improved adaptive YOLOv5 are tuned by proposing a new algorithm as the Random-based Cheetah Red Deer Algorithm (RCRDA). Next, in the action recognition phase, the tracked object from the video is taken based on the Region of Interest (ROI) that is subjected to an action recognition model named Optimized Transformer-based Residual Network with Deep Convolutional Network (OTRN-DCN). At first, ROI is offered as the input to TRN for attaining the feature vectors. Then, the optimal weighted vector extraction is performed, where the weight is tuned by the developed RCRDA. Finally, the attained optimally weighted vectors are given to the DCN phase for attaining recognized action as output. Hence, the developed multi-object tracking and action recognition model will secure an improved recognition rate than the traditional framework.
Observing the Performance of the TextRank Algorithm on Automatic Text Summarization for Bahasa Indonesia

The research about automatic text summarization is common in English text.According to the previous study, automatic text summarization in Bahasa Indonesia is still challenging due to research in this area, especially the research which discusses TextRank algorithm performance, which is still meagerly.Accordingly, this research observes the performance of the TextRank algorithm to summarize the text in Bahasa Indonesia.The TextRank algorithm summarizes a text by sorting out the essential words and relevant sentences regardless of the source language.This algorithm uses a vertex to represent a word.The similarity measurement process will calculate the overlapping words (the same word between two vertices).These overlapping words are represented by the edge, which connects the vertices.Thus, the text forms a graph.This research focuses on the similarity measurement process to determine relevant sentences in a text.As the similarity measurement is critical for the summarization result, this research switches the original process to the Levenshtein Distance algorithm and observes its performance.This research uses the human-produced summarized text by the expert in Bahasa Indonesia linguistics to evaluate the result.The evaluation method is conducted by using ROUGE-1 and ROUGE-2.The result shows that the average of ROUGE-1 and ROUGE-2 for the TextRank algorithm is 0.439 and 0.3186, respectively.Meanwhile, the modified TextRank obtains 0.3999 and 0.2805, respectively.Both of the algorithms have not shown satisfactory results as expected.
Quantitative Analysis of Student Emotion Based on Face Recognition Technology

In order to solve the problem that the classroom learning state is difficult to quantify and evaluate, a quantitative analysis method of student emotion based on face recognition technology is proposed. In this paper, the images of student faces in the natural state are collected in a non-intrusive way, and the real-time expression characteristics of students are obtained by using cascade regression tree and hog algorithm. Meanwhile, a quantitative emotion model is established to evaluate the emotion score of students, and active intervention strategies is used to improve the teaching environment. The results show that after the intervention, the learning state of students increases significantly, which proves that appropriate intervention strategies can effectively improve the quality of education. The research content and results provide data and method support for quantifying the situation of classroom learning.
A Study on the Maintenance of Distributed Lag Model in Time Series Prediction Model

An ambiguity in time series prediction analysis is that the distributed lag model can be transformed due to AR, differencing, MA, and seasonality adjustment. This means that the time series prediction model can make predictions without considering distributed lag model, which is a characteristic of time series. This study is to confirm whether the time series prediction model maintains the distributed lag model. Analysis tools were used cross-correlation and Granger causality test. Time series forecast models were used statistics-based ARIMA model and RNN-based LSTM model. And we used monthly terror attack data and monthly time series data of international oil prices from 1996 to 2020. The research method is four stages. First, we performed cross-correlation and Granger causality tests. Second, the ARIMA model and LSTM model were applied to the time series. Thirdly, we reperformed cross-correlation and Granger causality tests on the predicted results. Fourth, in the conclusion, the first and third steps results were compared to confirm whether the prediction model maintained distributed lag model. As a result of this research method, the ARIMA model maintained the distributed lag model in the cross-correlation, and the LSTM model maintained the distributed lag model in the Granger causality test.
On the use of Query by Committee for Human-in-the-Loop Named Entity Recognition

Named Entity Recognition is a relevant task for extracting information from textual data. Traditional methods for training NER models assume that humans annotate entities manually, identifying entities in predefined categories. This strategy presents a great human effort, mainly in more specific application domains. To address these challenges, we consider Human in the Loop (HITL), which can be understood as a set of strategies to incorporate human knowledge and experience into machine learning, while accelerating model training. In this paper, we investigate a classic method called Query by Committee (QBC), which helps to select informative instances for data labeling. Traditionally, QBC selects instances with a high level of disagreement between different models of a committee. We present heuristics for QBC relaxation to also consider some level of agreement. We showed that taking advantage of some committee agreement for pre-labeling of instances is promising to speed up human feedback and increase the training set. Experimental results showed that our method is able to preserve the performance of models compared to traditional QBC, while reducing human labeling effort.
CHALLENGES OF TIME IN LEARNING A FOREIGN LANGUAGE BY STUDENTS OF NON-LINGUIST SPECIALTIES IN HIGHER EDUCATION INSTITUTIONS

The article considers the possibilities of using modern information technologies in the process of learning foreign languages by students of non-linguistic majors under different conditions. Considerable attention is paid to e-learning and identification of problems in higher education institutions. To conduct the research, the authors used the survey method in order to collect and analyze information about the respondents. The collected information expressed the opinion of students regarding learning a foreign language in the process of implementing e-learning, their proper preparation for its use in classes. A survey of students of various faculties of Ternopil Volodymyr Hnatiuk National Pedagogical University demonstrated the following disadvantages: e-learning requires more time than traditional courses due to the use of e-mail or discussion boards; slow internet connection causing many problems for students. At the same time, the results of the conducted research showed that e-learning has its advantages: it helps students to understand their educational materials in foreign language classes by watching online videos with the participation of native speakers; use of personal devices, such as a mobile phone, mp3 player, etc.; allows them to use various computer programs; helps students learn the course (material) and get more information; provides an opportunity to share one's thoughts with one's peers on the forums. The authors consider the views of modern specialists regarding the use of information technologies in teaching foreign languages, and also give their own examples of the implementation of e-learning in the educational process; reveal the practical aspect of using an educational Internet resource in the framework of teaching a foreign language; emphasize the role of the teacher as an individual consultant. In addition, the authors outline further directions for the development and implementation of mixed and e-learning, note the need to develop educational and methodological complexes and update educational programs with elements of mixed learning. Key words: distance learning, time challenges, mixed learning, information technologies, foreign language, higher education institution, student.
Design and Practice of a Classroom Feedback Teaching Model with Skewness-Peakiness Test Method

Entering the metaverse era, immersive learning, as a unique and chic form of learning, has gradually become a normalized way of learning.Based on the flow theory, this research constructs a all-time, new-field learning metaverse teaching model to empower traditional classrooms, and analyzes the classroom effects of this teaching model using data generated from teaching practices as samples using SPSS24.0software.The results show that the overall effect of the all-time, new-field learning metaverse model in the classroom is significant, and there is a positive relationship between learners' engagement in the all-time, newfield learning metaverse classroom and their immersion experience.There is a positive effect on the exercise of learners' thinking and the cultivation of their creative abilities.
A Federated Attention-Based Multimodal Biometric Recognition Approach in IoT

The rise of artificial intelligence applications has led to a surge in Internet of Things (IoT) research. Biometric recognition methods are extensively used in IoT access control due to their convenience. To address the limitations of unimodal biometric recognition systems, we propose an attention-based multimodal biometric recognition (AMBR) network that incorporates attention mechanisms to extract biometric features and fuse the modalities effectively. Additionally, to overcome issues of data privacy and regulation associated with collecting training data in IoT systems, we utilize Federated Learning (FL) to train our model This collaborative machine-learning approach enables data parties to train models while preserving data privacy. Our proposed approach achieves 0.68%, 0.47%, and 0.80% Equal Error Rate (EER) on the three VoxCeleb1 official trial lists, performs favorably against the current methods, and the experimental results in FL settings illustrate the potential of AMBR with an FL approach in the multimodal biometric recognition scenario.
Design of an Interactive Educational Information System Based on the SECI Model

The construction of an educational informatization application system has become an important content of current educational informatization construction.Based on SECI knowledge management model, this paper discusses the training of teachers and the construction of a learning mechanism, and the construction of an interactive teaching platform based on SECI model has become the key to realizing IITC.At present, this kind of education and teaching means and teaching platform is extremely rare, and the construction of information-based colleges and universities has become an inevitable way for the development of schools.Therefore, starting from the reality of our school, it is an urgent problem to build a teaching platform that applies IITC, covers a wide range of teaching contents, supports diversified teaching, and is suitable for interactive classroom teaching mode.
A Lightweight Attentional Shift Graph Convolutional Network for Skeleton-Based Action Recognition

In the field of skeleton-based human behavior recognition, graph convolutional neural networks have made remarkable achievements. However, high precision networks are often accompanied by numerous parameters and computational cost, and their application in mobile devices has considerable limitations. Aiming at the problem of excessive spatiotemporal complexity of high-accuracy methods, this paper further analyzes the lightweight human action recognition model and proposes a lightweight architecture attentional shift graph convolutional network. There are three main improvements in this model. Firstly, shift convolution is a lightweight convolution method that can be combined with graph convolution to effectively reduce its complexity. At the same time, a shallow architecture for multi-stream early fusion is designed to reduce the network scale by merging multi-stream networks and reducing the number of network layers. In addition, the efficient channel attention module is introduced into the model to capture the underlying characteristic information in the channel domain. Experiments are conducted on the three existing skeleton datasets, NTU RGB+D, NTU-120 RGB+D, and Northwestern-UCLA. Results demonstrate that the proposed model is not only competitive in accuracy, but also outperforms current mainstream methods in parameter count and computational cost, and supports running in some devices with limited computing and storage resources.
The Degree of Administrators’ Involvement in Change Management at Jordanian Universities over the COVID-19 Pandemic

This study aimed to identify the degree to which administrators of Jordanian universities are involved in change management (comprising planning, decision-making, organization, and guidance) over the COVID-19 pandemic and as a function of gender, qualification, years of experience, and specialization. To this end, the descriptive-analytical method was used, where a questionnaire consisting of 45 items distributed to 4 domains was developed. The study population included all administrators (college deans, heads of academic departments, and managers of administrative units) of the public and private Jordanian universities. The total number of participants was 1685 administrators, among whom 321 (19 percent) were selected as the sample group. The sample group worked in three public universities (Yarmouk University, University of Sciences and Technology, and Al Al-Bayt University), and three private universities (Jadara University, Irbid Private University, Jerash Private University) in the academic year 2020-2021. Results showed that there exist statistically no significant differences in the means of responses in relation to administrators’ involvement in change management at Jordanian universities as a function of gender and in all domains, and as a function of specialization in the domains of change in planning and change in decision-making. However, there do exist statistically significant differences in the domains of change in planning and change in decision-making in favor of higher education studies. There are also significant differences in terms of years of experience in the domains of change in planning, change in decision-making, and change in organization in favor of those with many and average years of experience. Also there are significant differences in terms of specialization in the domains of change in planning and change in guidance, with natural sciences showing higher degrees.
RFFsNet-SEI: A multidimensional balanced-RFFs deep neural network framework for specific emitter identification

Existing specific emitter identification (SEI) methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages, which reduce the identification accuracy of emitters and complicate the procedures of identification. In this paper, we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints (RFFs), namely, RFFsNet-SEI. Particularly, we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition (VMD) and Hilbert transform (HT). The physical RFFs and I-Q data are formed into the balanced-RFFs, which are then used to train RFFsNet-SEI. As introducing model-aided RFFs into neural network, the hybrid-driven scheme including physical features and I-Q data is constructed. It improves physical interpretability of RFFsNet-SEI. Meanwhile, since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end, it accelerates SEI implementation and simplifies procedures of identification. Moreover, as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI, identification accuracy is improved. Finally, we compare RFFsNet-SEI with the counterparts in terms of identification accuracy, computational complexity, and prediction speed. Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
DCTR U-Net: automatic segmentation algorithm for medical images of nasopharyngeal cancer in the context of deep learning

Nasopharyngeal carcinoma (NPC) is a malignant tumor that occurs in the wall of the nasopharyngeal cavity and is prevalent in Southern China, Southeast Asia, North Africa, and the Middle East. According to studies, NPC is one of the most common malignant tumors in Hainan, China, and it has the highest incidence rate among otorhinolaryngological malignancies. We proposed a new deep learning network model to improve the segmentation accuracy of the target region of nasopharyngeal cancer. Our model is based on the U-Net-based network, to which we add Dilated Convolution Module, Transformer Module, and Residual Module. The new deep learning network model can effectively solve the problem of restricted convolutional fields of perception and achieve global and local multi-scale feature fusion. In our experiments, the proposed network was trained and validated using 10-fold cross-validation based on the records of 300 clinical patients. The results of our network were evaluated using the dice similarity coefficient (DSC) and the average symmetric surface distance (ASSD). The DSC and ASSD values are 0.852 and 0.544 mm, respectively. With the effective combination of the Dilated Convolution Module, Transformer Module, and Residual Module, we significantly improved the segmentation performance of the target region of the NPC.
Voice Conversion in C-E Translation

Language is a social phenomenon; each language reflects a unique culture.There are many differences between Chinese language and English.Chinese lay stress on parataxis while English pay more attention to hypo taxis.In English language, much more sentences with passive forms can be found, so voice conversion must be taken into consideration in C-E translation according to two different sentence structures and distinguishing patterns of thinking.The solution to voice conversion is to integrate culture with sentence expression.In this thesis, the author puts forward some concrete solutions on this.
Hot Spot and Development Trend of Adaptive Learning in China Based on CiteSpace Software

This paper mainly uses citespace 6.1.R6software to take the literature related to "adaptive learning" in CNKI as research samples, draw knowledge map, and analyze the research hotspot and development trend of adaptive learning in China.The results show that in the era of irreversible AI development, the number of studies on adaptive learning is increasing year by year.In recent years, the research topics of adaptive learning are mainly neural network, deep learning, reinforcement learning and artificial intelligence.In general, there are a wide range of research fields involving adaptive learning, especially in education, and the quantity and quality of research continue to improve and reach a peak in recent years.Similarly, the research on this topic has core experts and scholars, but lacks mutual cooperation and no systematic and stable research team.However, in the artificial intelligence environment such as ChatGPT, there is still a lack of empirical research on cultivating students' interest in adaptive learning at various educational stages, which also provides the future development direction for the subsequent research on adaptive learning.