Reviewerly/exHarmony huggingface dataset:
A Deep Concept-aware Model for predicting and explaining restaurant future status

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).
ADAPTATION OF HUMANITIES STUDENTS TO DISTANCE LEARNING AS A PROBLEM OF MODERN PEDAGOGY IN A PANDEMIC CONDITION

The article analyzes the problems of adaptation of humanities students to distance learning in a pandemic. The main elements, advantages and disadvantages of distance education are considered and analyzed, as well as the prospects of its development in modern Ukraine are forecasted. Based on the theoretical analysis of the study, it is proved that the effectiveness of distance learning increases with the use of the latest information and communication technologies (ICT), educational services and distance learning platforms that use free learning in a pandemic COVID 19: ED-ERA, MOODLE, PADLET, MY CLASS, CLASSTIME , EQUITY MAPS, GOOGLE CLASSROOM, GOOGLE SITES, GOOGLE FORMS. Application of webinars, video clips, audio scripts, forums, charts, ICQ, Skype, online testing, online tutorials and boards. Based on the study of the problems of adaptation of humanities students to the conditions of distance learning, we conducted a study on the educational platform Google Classroom using Blank Quiz. The sample consisted of 182 first- and fourth-year students of the Faculty of Linguistics and Social Communications of the National Aviation University, specialties 029 "Information, Library and Archival Affairs", 035 "Philology", 052 "Political Science", 053 "Psychology", 054 "Sociology", 231 Social work". The results of the study showed that most students during distance learning have mastered the skills of independent work, improved their level of knowledge. The biggest problem that arose in this issue - the lack of live communication with teachers, classmates and work in the university audience. Instead, mobility and accessibility were positively noted. The potential and efficiency of remote technologies is highly rated by respondents. The ways of successful adaptation of students of humanitarian specialties to the conditions of distance learning are determined: carrying out of the actions promoting interactive participation of students in educational process and extracurricular activities; students' work should be well planned, it helps to create a favorable social environment; high self-organization of students, which increases the creative and intellectual potential; aspiration and motivation to acquire knowledge; ability to interact with computer equipment, mastering the latest information technologies; readiness of pedagogical staff to organize online learning.
Boundary Deformation Measurement by Mesh-Based Digital Image Correlation Method

Digital image correlation (DIC) is a popular photomechanics method for deformation measurement. The conventional subset-based DIC method obtains the displacement vectors at the subset centers, but cannot calculate the deformation on the specimen edges, which may contain very useful information because specimens usually have greater deformation on edges, especially on curved edges due to stress concentration. In this study, the capability of a mesh-based DIC method using 8-node quadrilateral elements (Q8-mesh-DIC) in boundary deformation measurement was investigated and highlighted for specimens with non-uniform deformation. The results were compared with those obtained by some conventional subset-based DIC methods, and the accuracy of the boundary deformation measurement was verified through simulated and real experiments. The Q8-mesh-DIC appears to be more suitable for the boundary deformation measurement of non-uniform deformation fields.
Conversation Analysis of Muhammad (PBUH) for exploring his Effective Use of Nonverbal Communication including Paralinguistic Features

This paper examined the proficiency of Holy Prophet Muhammad (pbuh) in nonverbal communication and particularly its sub-fields like Kinesics, Haptics, and Proxemics during conversation in the light of the Hadiths. The Holy Quran attests it and every Muslim believes it that Muhammad (pbuh) was sent by Allah to serve as a role-model in the sphere of refined and excellent manners. We look up to his conversation manners too and his model of conversation, for holding successful conversation. Communication experts and linguists have come up with new perspectives over the dynamics of conversation, according to which people normally assume that the transmission of meaning to the listener and the creation of impression on the mind of the listener is dependent mostly upon words. But in reality words, instead of working in isolation, work in unison with the nonverbal language. In fact, the share of nonverbal language in conveying meaning is more than the verbal language during an interaction. The Holy Prophet kept his volume and tone low and polite; his eye contact was never irritating; his facial expressions were inviting; his smiles and jolliness were befriending; his body contact was soothing, his manner of speaking made his words understandable and memorable; his use of hands was enlightening; his silence was for lenience, the roughness of his conversation opponent led to increase in his politeness, his address to different participants of the gathering was based on consideration and fairness.
Fast Training of Deep Neural Networks Robust to Adversarial Perturbations

Despite their promising performance, deep neural networks have shown sensitivities to perturbations of their inputs (e.g., adversarial examples) and their learned feature representations are often difficult to interpret, raising concerns about their true capability and trustworthiness. Recent work in adversarial training, a form of robust optimization in which the model is optimized against adversarial examples, demonstrates the ability to improve performance sensitivities to perturbations and yield feature representations that are more interpretable. Adversarial training, however, comes with an increased computational cost over that of standard (i.e., nonrobust) training, rendering it impractical for use in large-scale problems. Recent work suggests that a fast approximation to adversarial training shows promise for reducing training time and maintaining robustness in the presence of perturbations bounded by the infinity norm. In this work, we demonstrate that this approach extends to the Euclidean norm and preserves the human-aligned feature representations that are common for robust models. Additionally, we show that using a distributed training scheme can further reduce the time to train robust deep networks. Fast adversarial training is a promising approach that will provide increased security and explainability in machine learning applications for which robust optimization was previously thought to be impractical.
Combination of LSTM and CNN for Article-Level Propaganda Detection in News Articles

Propaganda is a way of disseminating information, regardless of whether the information is true or not. Propaganda usually uses bias in obscuring the understanding of the propaganda targets. News articles are one of the media that is often used in spreading propaganda. Text classification in the form of propaganda detection in news articles is a crucial thing to do in relation to preventing the spread of the propaganda. Long Short-Term Memory (LSTM) is a variant of the Recurrent Neural Network (RNN) which has been widely used in text classification. However, LSTM has a weakness in the form of a tendency to high bias in extracting context from information through word order. Convolutional Neural Network (CNN) in text analysis can perform important feature extraction through the use of convolutional layers but is weak when assigned to context extraction. This research tries to compare LSTM, CNN and the combination of the two methods in text classification in the form of propaganda detection in news articles. The combination of each method is proved to improve classification performance and also shorten the required running time.
A Clustering Analysis Method Based on Wilcoxon-Mann-Whitney Testing

As the core step of clustering analysis, the results of distance measurements will influence the clustering accuracy.The existing measurements are mostly based on the information about cluster features.However, the cluster features may be not sufficient enough and would result in losing data information about clusters containing a number of objects.To improve the measurement accuracy, we make full use of the distribution characteristics of objects in clusters, so we use the descriptive statistics and the Wilcoxon-Mann-Whitney rank sum test in nonparametric statistics to measure distances during clustering.Furthermore, a two-stage clustering is proposed to improve the performance of clustering analysis, from the aspects ofavoiding assuming the number of clusterspreliminarily, discovering clusters of arbitrary shapes andimproving clustering accuracy.The experiments on multiple datasets compared with other clustering algorithms illustrate the accuracy and efficiency of the proposed clustering algorithm.
The Hard Teacher’s Leadership Coping to the COVID-19 Pandemic

Most teachers in Mexico are not experts on Information and Communication Technologies, some rural areas lack a good internet connectivity or even electricity. This context led us to determine: How can teachers keep the pace of educational leadership? and How they cope their teaching task with the COVID-19 pandemic? The sample included 329 teachers from urban and rural zones, 71.1% female and 28.9% male, with a mean age of 38.8 years, working in public (71.7%) and private (28.3%) schools. A self-evaluation template was used to assess the planning, didactical sequence analysis and evaluation competence from the teachers. Our aim was to sketch a teacher’s leadership competences profile, specifically in these pandemic times. The results showed than 75.7% of the teachers had an internet access between Good and Very good; on the contrary, 78.4% of the teachers considered that most of their students had between “not very good” to “very bad” internet access. Only a few teachers addressed the didactic planning or followed its development and assessment: I have elaborated and shared with the students indicators of achievement from the didactical sequence (32.8%); I have stimulated processes of reflection upon learning through an instrument (22.5%); I have regularly incorporated and used digital tools and Internet (31.9%); at last, I have established and conducted moments of evaluation, self and formative co-evaluation in which the students have been able to make changes based on the feedback received (30.1%). However, teachers are coping with this pandemic time and it may involve a change in educational strategies towards the future.
Lecturers' attitude toward and anxiety in writing English academic papers for international publications

This study was aimed at investigating attitude and anxiety of Indonesian lecturers in writing English papers for international publications, together with the influence of gender and functional grades on them. It involved lecturers of a private university in Semarang, Central Java, Indonesia (n=62) who were attending a training on English academic writing especially designed to help them prepare manuscripts for international publications. Data were collected by questionnaires and analyzed using t-test and ANOVA. The study found that the attitude toward writing was positive. Three aspects of attitude namely behavioral, cognitive, and affective were all positive. The anxiety in writing English papers was found to be moderate and somatic anxiety was the type of anxiety the lecturers suffered the most. There was no difference on the attitude and level of anxiety across gender but there was a difference on the level of anxiety across functional grades. As attitude and anxiety predict the success of writing efforts, which may then affect the success of publication, the issue of attitude and anxiety needs to be addressed in the effort of increasing the productivity of Indonesian lecturers in making international publications.
Edge Machine Learning for Animal Detection, Classification, and Tracking

A research field currently advancing is the use of machine learning on camera trap data, yet few explore deep learning for camera traps to be run in real-time. A camera trap has the purpose to capt ...
Pattern Recognition and Neural Network-Driven Roller Track Analysis via 5G Network

Roller skating is an important and international physical exercise, which has beautiful body movements to be watched. However, the falling of roller athletes also happens frequently. Upon the roller athletes’ fall, it means that the whole competition is over and even the roller athletes are perhaps injured. In order to stave off the tragedy, the roller track can be analyzed and be notified the roller athlete to terminate the competition. With such consideration, this paper analyzes the roller track by using two advanced technologies, i.e., pattern recognition and neural network, in which each roller athlete is equipped with an automatic movement identifier (AMI). Meanwhile, AMI is connected with the remote video monitor referee via the transmission of 5G network. In terms of AMI, its function is realized by pattern recognition, including data collection module, data processing module, and data storage module. Among them, the data storage module considers the data classification based on roller track. In addition, the neural network is used to train the roller tracks stored at AMI and give the further analysis results for the remote video monitor referee. Based on NS3, the devised AMI is simulated and the experimental results reveal that the prediction accuracy can reach 100% and the analyzed results can be used for the falling prevention timely.
Analysis and use of Cryptography techniques in programming language C#: Coding and Decoding

Cryptography is an old idea and science, but its approach exists and plays a large role in modernization today. Conventional cryptographic techniques form the basis of today's cryptographic algorithm. The different categories of algorithms have their respective features; internally, in performance and implementation. Cryptographic schemes and mechanisms have undergone continuous improvement. The application of cryptography has grown increasingly, ranging from limited use in state institutions to widespread use by private individuals and companies. The increased use of the Internet has significantly influenced the nature of applications and the way we communicate. Data security dictates the use of different cryptographic techniques. For this reason, we analyze in detail the various coding techniques by evaluating their performance and efficiency. Regarding the new paradigms in cryptography there are also new cryptographic schemes whose application requires detailed study and analysis. The classical cryptography algorithm is the oldest algorithm that was used long before the cryptographic system was discovered. Currently, the system has been widely applied to secure data, and using new methods in a way to improve existing methods. In this thesis the use of cryptographic methods using the C # programming language will be discussed.
IEEE Access Special Section: Intelligent Data Sensing, Collection, and Dissemination in Mobile Computing

Data are an important parts of building applications and providing services to users. With the development of modern technology, the requirements for data quantity, quality, and processing capabilities have become more stringent. Sufficient high-quality data are the foundation for most mobile applications. It is important to collect enough data to effectively build your application. In addition to ensure the amount of data collected, the quality of the data collected is also important. Otherwise, a large amount of data redundancy will occur, which will cause trouble for data processing. At the same time, large amounts of data also mean the need for data processing capabilities. Traditional processing methods cannot meet the growing demand for data processing. Mobile computing is a promising solution to meet the growing computing needs of various mobile applications. The advancement and application of mobile computing require more intelligent ways to sense and collect data, which constantly poses new challenges to researchers.
The Importance Of Metacognitive Knowledge In Engineering Education: Strategy And Experience

For engineering education today it is very important to respond to new requests related to changes in the object of engineering, methods, the resulting risks.Metacognitive knowledge allows reconstructing and managing self-understanding during transition to a new engineering paradigm of activity.This study proposes an educational methodology aimed at building metacognitive knowledge and skills based on discursive practices, the ability to identify a metaphysical meaning, problem.Its content is knowledge, methodology, model tasks in philosophy.Philosophical disciplines play a big role in metacognitive knowledge and skills shaping due to the emphasis on the practices of thinking (analysis, synthesis, conceptualization, etc.), as well as the implementation of constant conscious reflection behind them to achieve self-esteem, self-regulation by knowledge.Particular attention is paid to the practice of "round thinking", aimed at identifying an idea, meaning, as well as the practice of statement a problem.The educational strategy of teaching philosophical disciplines in engineering specialties presented in the study was successfully tested at the Yuri Gagarin State Technical University of Saratov.
Image Compression and Noise Reduction through Algorithms in Wavelet Domain

Image compression and noise reduction are two important application in the field of digital image processing. The use of transformed domains plays an important role in both mentioned applications, offering a different representation of the data, in which the main characteristics to be processed become more distinct. Image compression aims to reduce the number of bits needed for the digital representation of the image with or without an acceptable loss of image quality. The underlying idea behind the use of linear transforms in signal processing is a more efficient quantization of the coefficients corresponding to the transformed domain, compared to the case of the coefficients corresponding to the primary space. From this point of view, the wavelet transforms allow, due to the multi-resolution representation that they accomplish and the correlation between the sub-images of this representation, a more efficient quantization and entropic encoding of the coefficients corresponding to the transformed domain. In the field of image compression, the multi-resolution analysis property of the wavelet transform allows progressive quality-scalable and spatially scalable transmissions, very useful in applications that require interactive searching or when bandwidth transmission channels are available. The wavelet transform focuses most of the signal energy in the low pass sub-band with the lowest resolution, the coefficients of this sub-band having high absolute values. The rest of the coefficients obtained after decomposition have small absolute values, being canceled after the quantization process. A good compression rate can be obtained in this case if the absolute values of the coefficients and their locations are encoded separately.
Block cipher four implementation on field programmable gate array

Block ciphers are used to protect data in information systems from being leaked to unauthorized people. One of many block cipher algorithms developed by Indonesian researchers is the BCF (Block Cipher-Four) - a block cipher with 128-bit input/output that can accept 128-bit, 192-bit, or 256-bit keys. The BCF algorithm can be used in embedded systems that require fast BCF implementation. In this study, the design and implementation of the BCF engine were carried out on the FPGA DE2. It is the first research on BCF implementation in FPGA. The operations of the BCF machine were controlled by Nios II as the host processor. Our experiments showed that the BCF engine could compute 2,847 times faster than a BFC implementation using only Nios II / e. Our contribution presents the description of new block cipher BCF and the first implementation of it on FPGA using an efficient method.
Machine Learning to Assist in Pneumonia Decision Making: A Systematic Review of the Literature

Clinical decision support systems is a research area in which Machine Learning (ML) techniques can be applied. Nevertheless, specifically in assisting pneumonia decision making, the use of ML has not been so expressive. To help matters, this work aims to contribute to the evolution of the intersection of such areas by presenting a Systematic Review of the Literature. It provides results which may help to identify, interpret and evaluate how ML techniques have been applied and some research enhancements yet to be done.
Review of the Personalized Learning In China

Teaching students in accordance with their aptitude and individualized training are the focus of the world’s attention, and it is also one of the eight education concepts in my country. Based on the CSSCI database, this research sorts out the concept and connotation of personalized learning and teaching in China, and analyzes the theoretical research progress of personalized learning and teaching, learning analysis technology, personalized recommendation technology, personalized learning path planning, and personalized learning system It summarizes the research focus and characteristics of domestic personalized learning and teaching theory from the perspectives of theory, practical application, and technology development, and analyzes the development trend of personalized learning and teaching in the era of artificial intelligence.