Reviewerly/exHarmony huggingface dataset:
Primary School Teachers' Determinants of Integrated Teaching for Realistic Math Education

&lt;p style="text-align:justify"&gt;The purpose of this study was to explore the factor structure of a measurement and to evaluate its internal reliability. Overall, 525 math-majoring elementary school teachers volunteered to participate in this study by answering online survey questions via Google Form. These samples were randomly partitioned into 262 participants for exploratory factor analysis (EFA) and 263 observations for confirmatory factor analysis (CFA). The EFA tended to largely prefer a four-factor solution, which was proven to explain over 68% of the variation in the data. Awareness, effectiveness, engagement, and opportunity were the provisional labels for these hidden variables. The CFA results verified and validated the four-factor model, with all test measures exceeding the specified thresholds, suggesting an acceptable and excellent fit. The results of this study, on the one hand, provide four key areas for realistic math teachers, educators, and policymakers to discuss as opposed to examining individual indicators, and on the other hand, they serve as a foundation for interested researchers to conduct additional analyses, such as multivariate linear regression or complement for cluster analysis.&lt;/p&gt;
Optimization Algorithms and Classical Training Algorithms

This chapter explains the application of different optimization algorithms and training algorithms for training artificial neural network models (ANN). The different optimization algorithms are reliable tools for training ANN models. These algorithms use advanced operators to train the ANN models. The ANN parameters, such as bias and weight, are unknown. Thus, robust optimization algorithms can be used for adjusting ANN parameters. This chapter explains the structure of different optimization algorithms for solving optimization problems. The advantages and disadvantages of different optimization algorithms are explained in this chapter. The optimization algorithms use advanced operators to adjust the ANN parameters.
Transformer-based difference fusion network for RGB-D salient object detection

RGB-D salient object detection (SOD) can usually be divided into three stages: feature extraction, feature fusion, and feature prediction. Most approaches treat the feature information extracted by the backbone network identically in the final two stages of detection, neglecting the fact that various modalities and different hierarchical features play distinct roles in SOD, resulting in poor detection results. To solve this problem, we propose a transformer-based difference fusion network (TDF-Net) for RGB-D SOD that treats modal features and hierarchical features differently in the feature fusion and feature prediction stages, respectively. First, we adopt the pyramid vision transformer as a feature extractor to obtain hierarchical features from the input RGB images and depth images, respectively. Second, we propose a differential interactive fusion module, in which the RGB modality and the depth modality learn modality-specific features independently, and the two modalities guide each other to fuse features. Finally, we divide the hierarchical features after cross-modal fusion into high-level and low-level features and propose three types of cross-layer fusion modules to discriminately integrate features from different layers to predict the salient maps. Extensive experiments on five benchmark datasets confirm that our proposed TDF-Net outperforms the state-of-the-art methods.
RMFRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection

In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the performance of classification and clustering tasks. RMFRASL integrates three models (robust matrix factorization, adaptive structure learning, and structure regularization) into a unified framework. More specifically, a robust matrix factorization-based feature selection (RMFFS) model is proposed by introducing an indicator matrix to measure the importance of features, and the L21-norm is adopted as a metric to enhance the robustness of feature selection. Furthermore, a robust adaptive structure learning (RASL) model based on the self-representation capability of the samples is designed to discover the geometric structure relationships of original data. Lastly, a structure regularization (SR) term is designed on the learned graph structure, which constrains the selected features to preserve the structure information in the selected feature space. To solve the objective function of our proposed RMFRASL, an iterative optimization algorithm is proposed. By comparing our method with some state-of-the-art unsupervised feature selection approaches on several publicly available databases, the advantage of the proposed RMFRASL is demonstrated.
THE CONCEPT OF INFORMATIONAL COMPETENCE

The article deals with the concepts of «competence» and «competence». The author also determines the main essence of «information competence» in the system of higher education
Cloud Solutions in Global Health Solutions

Digital innovations and their applications in healthcare have changed the current scenario of delivering healthcare services and storing data. Cloud computing is one such technological adjunct that has the capability of storing volumes of medical data that can be accessed anytime through internet from anywhere. Today all smart devices and health related apps designed for lifestyle changes and monitoring of medical problems use cloud-based programs. These applications result in positive reinforcement for inculcating regular monitoring and counselling. The paper critically discusses and summarizes the cloud-aided performance and applications in health and research. Moreover, it outlines the integration of cloud technology in handling drug theft and counterfeiting. The paper explores misconfigurations and associated risk of current technology to comprehend future scope and applications.
A Delegation Attack Method on Attribute-Based Signatures and Probable Solutions

Attribute-based signature (ABS) assures the verifier that the message is endorsed by a signer whose attributes satisfy the claimed attribute policy (predicate); thus, it can provide identity authentication with privacy preservation in scenarios like anonymous communication and access control. However, we have found that the inherent delegatibility of attribute-based cryptography, which enables the utilization of relationship between policies, could make most of the existing ABS constructions not satisfy the unforgeability requirement under the common security model. In this paper, we dig into the delegatibility property of ABS for the first time and propose the potential delegation attack to break the unforgeability of the existing ABS constructions under the common security model. We also give two attack instances on a typical ABS construction to demonstrate the feasibility of the proposed delegation attack. Finally, we present two solutions to improve the above issue and give a further discussion about the delegatibility property of ABS.
Mandatory lane-changing decision making and planning in emergency situations

Abstract By considering mandatory lane-changing as a collision avoidance measure, this paper presented the corresponding lane-change decision making and trajectory planning algorithm under an emergency scenario. Different from the traditional algorithm in which lane-change decision making and trajectory planning are separated, they are here coupled in a proposed algorithm and the related parameters are dynamically adjusted in the whole process. In addition to lane-change collision avoidance feasibility analysis, lane-change time instance and duration time are obtained by solving the constrained convex quadratic optimization programme. By taking lane-change time instance and duration time as inputs, the algorithm then proceeded to propose a kinematic model-based high-order polynomial lane change trajectory. By giving the simulation result compassion with the related algorithm, it is proved that the proposed algorithm has a good robustness and high efficiency.
Image Perceptual Hashing for Content Authentication Based on Geometric Invariant Vector Distance

Image perceptual hashing is broadly applied in image content authentication, recognition, retrieval, and social media hotspot event detection. An image authentication algorithm is put forward based on the Itti visual saliency model and geometric invariant vector distance. To begin with, the image is preprocessed and weighted by the Itti model and contourlet transform. After that, the weighted image is randomly divided into blocks, and the image feature vector is constructed by calculating the geometric invariant vector distance on both Hu invariant moment vector and maximum singular value vector of the random blocks. In the end, the feature vector is quantized and encrypted to generate the ultimate hash. Experimental results illustrate that when the threshold T = 70, the true positive rate <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">R</mi> </mrow> </msub> </math> for duplicate images stands at 0.96574, while the false rate <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msub> <mrow> <mi>P</mi> </mrow> <mrow> <mi mathvariant="normal">F</mi> <mi mathvariant="normal">P</mi> <mi mathvariant="normal">R</mi> </mrow> </msub> </math> of different images is merely 0.0224, with the total error rate reaching the minimum value (0.0566). Furthermore, the AUC value of the proposed algorithm is 0.9951, which is higher than that of the comparison algorithms, indicating that the algorithm has better performance than other state-of-the-art algorithms in terms of various visual content-preserving attacks.
A systematic mapping study on machine learning methodologies for requirements management

Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.
Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Recent advances in the cutting-edge technologies of biomedical sensing and image processing tools provide us with big data of biomedical and various types of images that can’t be processed within a finite period by professional clinicians. Various techniques for processing biomedical images comprise mathematical algorithms that extract vital diagnostic features from biomedical information and biological data. Because of the complexity and big size of the data computation, intelligence techniques have been applied in processing, visualizing, diagnostic, and classification tasks. This study will explore the effectiveness of the variously artificial intelligence approaches on biomedical signal and image processing applications. The researchers and community entirely will benefit from this study as a guide to the state-of-the-art artificial intelligence techniques for biomedical signal and image processing applications.
An Information-Reserved and Deviation-Controllable Binary Neural Network for Object Detection

Object detection is a fundamental task in computer vision, which is usually based on convolutional neural networks (CNNs). While it is difficult to be deployed in embedded devices due to the huge storage and computing consumptions, binary neural networks (BNNs) can execute object detection with limited resources. However, the extreme quantification in BNN causes diversity of feature representation loss, which eventually influences the object detection performance. In this paper, we propose a method balancing Information Retention and Deviation Control to achieve effective object detection, named IR-DC Net. On the one hand, we introduce the KL-Divergence to compose multiple entropy for maximizing the available information. On the other hand, we design a lightweight convolutional module to generate scale factors dynamically for minimizing the deviation between binary and real convolution. The experiments on PASCAL VOC, COCO2014, KITTI, and VisDrone datasets show that our method improved the accuracy in comparison with previous binary neural networks.
PREVENTION OF COMPUTER CRIME THROUGH KNOWLEDGE OF THE CONCEPT OF CYBER SECURITY

The purpose of this article is to make the reader aware of the concept of cyber security and of the importance of establishing cyber data protection systems that we currently manage. Cyber-attacks have experienced a continuous upward trend in recent years, being targeted at both personal computer systems, computer systems managed by companies or computer systems managed by state institutions, as such cyber security has become a necessity for any user, provider, or consumer of cyber data.
TRUST: A Novel Framework for Vehicle Trajectory Recovery from Urban-Scale Videos

We study a new type of path inference query against urban-scale video databases. Given a vehicle image query, our goal is to recover its historical trajectory from the footprints captured by surveillance cameras deployed across the road network. The problem is challenging because visual matching inherently suffers from object occlusion, low camera resolution, varying illumination conditions, and viewing angles. Furthermore, with limited computation resources, only a fraction of video frames can be ingested and indexed, causing severe data sparsity issues for visual matching. To support efficient and accurate trajectory recovery, we develop a select-and-refine framework in a heterogeneous hardware environment with both CPUs and GPUs. We construct a proximity graph from the top-k visually similar frames and propose holistic scoring functions based on visual and spatial-temporal coherence. To avoid enumerating all the paths, we also propose a coarse-grained scoring function with monotonic property to reduce search space. Finally, the derived path is refined by examining raw video frames to fill the missing cameras. For performance evaluation, we construct two largest-scale video databases generated from cameras deployed upon real road networks. Experimental results validate the efficiency and accuracy of our proposed trajectory recovery framework.
TERTIAN: Clinical Endpoint Prediction in ICU via Time-Aware Transformer-Based Hierarchical Attention Network

Accurately predicting the clinical endpoint in ICU based on the patient's electronic medical records (EMRs) is essential for the timely treatment of critically ill patients and allocation of medical resources. However, the patient's EMRs usually consist of a large amount of heterogeneous multivariate time series data such as laboratory tests and vital signs, which are produced irregularly. Most existing methods fail to effectively model the time irregularity inherent in longitudinal patient medical records and capture the interrelationships among different types of data. To tackle these limitations, we propose a novel time-aware transformer-based hierarchical attention network (TERTIAN) for clinical endpoint prediction. In this model, a time-aware transformer is introduced to learn the personalized irregular temporal patterns of medical events, and a hierarchical attention mechanism is deployed to get the accurate patient fusion representation by comprehensively mining the interactions and correlations among multiple types of medical data. We evaluate our model on the MIMIC-III dataset and MIMIC-IV dataset for the task of mortality prediction, and the results show that TERTIAN achieves higher performance than state-of-the-art approaches.
The Design of Mental Health Intelligent Evaluation System in Big Data Technology

In order to cultivate the healthy development of all-round senior talents, knowledge skills and mental health education is very important, the arrival of big data makes people's thinking habits and lifestyle changes, but also to the university mental health education work brought opportunities and challenges, to the mental health work also brought opportunities and challenges, big data and college mental health education work is a mainstream trend.This paper takes the characteristics of the big data era as the starting point, analyzes the impact of the mental health education of college students, and puts forward relevant countermeasures, aiming to enhance the effectiveness of college students' psychological education, so as to better adapt to the continuous development of education information.
Research on Modular Design of Wearable Device for Elderly Pet Dogs

In recent years, with the rapid development of artificial intelligence technology, wearable devices have gradually entered the operation stage.[1] The design focus of wearable devices has shifted from technological breakthroughs to personalization and customization in use and experience.Based on the analysis of the development status of wearable devices, this paper puts forward the modular design method of wearable devices.In this paper, React front-end interaction technology is adopted, and MVC development mode in ASP.NET [2] is adopted at the back end.Infrared spectrum sensor module hc-sr501 is used to realize information transmission, realizing a friendly graphical interactive interface.Caretakers (pet owners and linked accounts) are better connected and act as a bridge between the two.
Research on Multi-head Self-attention Aspect Term Extraction with Multi-level Features Encoding

With the development of social media and e-commerce, aspect-based sentiment analysis technique is more and more urgently needed.Aspect term extraction is an important part of aspect-based sentiment analysis, and the quality of the extracted aspect words directly determines the quality of aspect-based sentiment analysis.To solve the problems of current research, in this paper, we design and implement a multi-head self-attention aspect term extraction fusing with multilevel features model (MSA-FMF).In addition, we conduct experiments on three public datasets, and the proposed model outperforms the comparison models, proving the effectiveness of our model.
A Survey of College Students’ Reading Situation Against the Background of Internet Plus

College students' reading habits have changed with the arrival era of Internet plus.This investigation focuses on the college students' reading situation against this background: their cognition and view of traditional reading and digital reading, the number of books they read, their digital reading time, paper reading time and time allocation, the access to electronic resources and reading methods, and their evaluation of reading quantity and situation.The survey shows that students' reading has a diversified pattern.Traditional paper reading and digital reading are nearly equally divided, yet paper book will not be replaced by e-book.Fragmentation reading through mobile phones has become the mainstream and most students have positive evaluation of fragmented reading.However, compared with the national grow-up readers, college students' reading amount is not large and reading time is not long.Most students are not satisfied with their reading situation.Therefore, some concrete measures are put forward to encourage students to read more books.
On Neural Machine Translation Based on Cloud Platform in the Context of Artificial Intelligence

The research of machine translation has gone through the era of machine translation based on grammar rules, case data and statistical methods.Up to now, it has become a neural machine translation designed with coding and decoding as the basic framework and using neural network to model the translation process.It is illustrated how the human-computer co-translation can be applied based on translation technology cloud platforms so as to greatly enhance the translation productivity.
Upaya Meningkatkan Hasil Belajar Bahasa Inggris Materi Menulis Procedure Text melalui Penerapan Metode Demonstrasi pada Siswa Kelas X TDPIB -1 SMK Negeri 2 Karang Baru Aceh Tamiang

The purpose of this study is: To Improve English Learning Outcomes Writing Materials for Procedure Text in Class X TDPIB-1 Students of SMK Negeri 2 Karang Baru Aceh Tamiang Regency Through the Application of the Demonstration Method. This study used two rounds of action research. Each round consists of four stages: design, activity, observation, and reflection. The target of this study were students of Class X TDPIB-1 at SMK Negeri 2 Karang Baru, Aceh Tamiang Regency. The data obtained is in the form of formative test results, observation sheets of teaching and learning activities. From the results of the analysis, it was found that student learning achievement increased from cycle I first meeting to cycle II second meeting namely, cycle I the first meeting 45%, cycle I the second meeting 65%, and cycle II the first meeting 75%, cycle II the second meeting 90% , classically students achieve mastery of learning outcomes. The conclusions from this research are: Through the Application of the Demonstration Method it can improve English Learning Outcomes of Procedure Text Writing Material for Class X TDPIB-1 Students of SMK Negeri 2 Karang Baru Aceh Tamiang Regency. These results can be seen from the increase in learning, namely the first cycle at the first meeting 45% and the second meeting 65% of the second cycle of the first meeting 75% and the second meeting 90% (classical complete).
Improving end-to-end deep learning methods for Arabic handwriting recognition

The number of Arabic handwriting documents has increased greatly. Studies conducted in the Arabic handwriting recognition field have progressed significantly in recent years in different areas. The existing studies for Arabic language compared to others such as Latin remains insufficient. During the last decade, neural networks (NNs) have become the de facto standard for deep learning, and the combination of two or more NNs has proved its ability to learn complex objects, such as handwriting, and emulate human brains. We proposed an approach combining convolutional NN and bidirectional long short-term memory for recognition. This unique approach makes it possible to recognize Arabic handwriting words without segmentation. The proposed architecture is very efficient in terms of accuracy for Arabic word recognition. The hyperparameters set for our model were chosen based on a structured process of randomness and grid search to increase the accuracy of the model.