Reviewerly/exHarmony huggingface dataset:
Cloud Computing Based Masked Face Recognition Application

Face recognition-based applications have been widely used in security, health, education, military and industrial fields, especially in recent years. Studies such as face recognition-based personal assistant applications, security forces to identify criminals quickly and accurately, students `feelings from facial images while listening to lessons, and follow-up of employees' entrances and exits are becoming more and more common. Because of the epidemic we have experienced today, it is important for people to wear masks to reduce the spread of the disease. Within the scope of the study, people's masking situations are determined by using cloud computing-based software. In addition, there are identity information, emotional state, age and gender of the people identified in the study. Using the proposed system, masking situations in shopping malls, streets, streets, schools and crowded places can be examined according to age and gender characteristics.
STRUCTURAL SIMILARITY MEASURE OF USERS PROFILES BASED ON A WEIGHTED BIPARTITE GRAPHS

Abstract. The user profile is a very important tool in several fields such as recommendation systems, customization systems etc., it is used to narrow the number of data or results provided for a specific user, also to minimize the cost and the time of processing of multiple systems. Whatever the user profile model used, it’s updating and enrichment is a very essential step in the information research process in order to obtain more interesting and satisfactory results, which lead the information systems to develop several techniques aiming to enrich them based especially on similarity methods between user profiles. The similarity methods are used for several tasks such as the detection of duplicate profiles in online social network, also to answer the problem of cold start, and to predict users who can become friends as well as their future intentions, etc. In this paper, we propose a new approach to express the similarity between users profiles by developing a structural similarity measure to calculate the similarity between user profiles based on SimRank measure or similarity ,and the properties of bipartite graphs, in order to take advantage of the information provided by the relational structure between user profiles and their interests, our method is characterized by the similarity propagation between graph's nodes over iterations from source nodes to their successors, so our method finds profiles similar to the query profile, whether the links are direct or indirect between profiles.
Occlusion Data Augmentation for Object Detectors based on Random Erasing and Bounding Box Tailoring

Recent convolutional object detectors have showed remarkable improvements, but occlusion has been a major obstacle to apply them to the real-world. More specifically, in military domain, cover and concealment of objects are common, and building occlusion-tolerable object detector is important. In this paper, we introduce bounding box tailoring approach based on bivariate standard normal distribution. The main idea of our approach is tailoring bounding boxes to fit tighter to remaining object area after random erasing. Our experiments on military vehicle data set show that our approach outperforms previous random erasing data augmentation techniques.
Online Arabic Handwriting Characters Recognition using Deep Learning

Recent research shows that in a wide variety of fields, such as computer vision, speech recognition and natural language processing, deep learning has produced noticeably promising results.Automatic recognition of handwriting is a significant component for many applications in different fields.It is a complex subject that has gained a great deal of attention in the past three decades.Research has focused on the recognition of Latin languages' handwriting, fewer studies have been done for the Arabic language, a few problems still wait to be solved for Arabic handwritten characters.We presented a Convolutional Neural Network (CNN) model for the recognition of Arabic handwritten characters in this paper.The dataset is pre-processed before feeding it to the CNN model, it applied on database that contain 16800 of handwritten Arabic characters.The accuracy was raised to 96% as a test accuracy showing better results than other methods using the same database.
Role of psychology in human resource management

Organization and psychology are inter-related.A few trends have emerged in the field of strategic Human Resource Management over the past few years.Here, we will describe, why psychology is important in organizational performance.This paper considers the two major areas that comprise the broader domain of the field i.e. (personnel psychology and organizational psychology).The integration between the psychology and human behavior's management is arguing the main factor depicting HRM theory and practice from its more traditional personnel management roots.The human factor is considered as the main point in the psychology of the management, as its importance and a core thing.Studying the psychology management, we study the psychology of the person, his various and the most important thing -influence of person's mental action, on activity; and influence of that activity on psychology and behavior of that person.The psychology of management is the branch of psychology, studying mental features of an individual and its behavior in the course of planning, organizing, managing and controlling the joint activity.
A smart method for spark using neural network for big data

Apache spark, famously known for big data handling ability, is a distributed open-source framework that utilizes the idea of distributed memory to process big data. As the performance of the spark is mostly being affected by the spark predominant configuration parameters, it is challenging to achieve the optimal result from spark. The current practice of tuning the parameters is ineffective, as it is performed manually. Manual tuning is challenging for large space of parameters and complex interactions with and among the parameters. This paper proposes a more effective, self-tuning approach subject to a neural network called Smart method for spark using neural network for big data (SSNNB) to avoid the disadvantages of manual tuning of the parameters. The paper has selected five predominant parameters with five different sizes of data to test the approach. The proposed approach has increased the speed of around 30% compared with the default parameter configuration.
Files cryptography based on one-time pad algorithm

The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the use of the AES algorithm; thus, the protection record becomes very high. The Huffman algorithm is then used for data compression to reduce the size of the output file. A set of files are encrypted and decrypted using our methodology. The experiments demonstrate the flexibility of our method, and it’s successful without losing any information.
CONSTRAINTS ANALYSIS OF STUDENTS IN ONLINE LEARNING OF BIOLOGICAL MATERIALS AT SMP NEGERI 3 BANTUL

Changes in learning methods from face-to-face learning to online learning experienced several disturbances ranging from inadequate internet quotas, unstable networks to students' understanding of the material presented. This study aims to examine the convenience of students in learning online material at SMP Negeri 3 Bantul. This type of research is a descriptive survey research with a qualitative approach. The research was conducted on Saturday, September 19, 2020. The research data were obtained from 27 students of class IX A, 22 people of class IX B, 26 people of class IX C, and 19 people of class IX D so that the total was 94 people. The data technique in this study was a questionnaire, while the instrument used in this study was an online questionnaire distributed via the WhatsApp application. The data analysis technique used by researchers is descriptive data analysis. The results showed that the students of SMP Negeri 3 Bantul felt the most pleasant was their lack of understanding of the material presented. Keywords: Constraints, Online Learning, Biological Materials
Use of Manipulative Media as A Stimulation Of Ability To Understand The Concept of Early Children's Age

Every child has the right and needs for stimulation of his development. One of them is cognitive. Aspects of cognitive development can be stimulated through mathematics learning. But children can find it difficult to understand mathematical concepts because reasoning and logic are needed, whereas mathematical concepts are not concrete. For this reason, concrete media need to be used to demonstrate or illustrate the concept, the media are manipulative. The purpose of this study is to analyze articles and documents resulting from research on the use of manipulative media as a stimulation of the ability to recognize the concept of child numbers. Method: This research uses a literature review method. There are criteria in searching journals so that 20 journals are found to be analyzed based on population, sample, variables, data analysis, type of research design, and research results. Results and discussion: Literature review shows that the use of manipulative media in early childhood education varies greatly in terms of media material, its play, and its effectiveness. The manipulative media in question such as grain media, number blocks, clock puzzles, container marbles, picture cards, congklak numbers cards, numbers fishing games, and others. While the ability to recognize the concept of numbers in question such as the meaning of the symbol number of concepts a lot a little, counting, and others. Statistical analysis shows the application of manipulative media can stimulate the ability to recognize the concept of numbers in children, increase the activities of children and teachers, and found the response of children who are happy with the use of manipulative media. Conclusion: Manipulative media can stimulate the ability to recognize the concept of child numbers.
A Cognitive Agent Framework in Information Retrieval: Using User Beliefs to Customize Results

This paper presents a framework for a cognitive agent in information retrieval that customizes the list of returned documents based on what the agent believes about the user knowledge. Throughout the interactions between the agent and the user, the agent builds its beliefs by extracting the content of the documents examined by the user. The agent's belief base consists of "simple beliefs" represented by the document's keywords as well as "contextual rules" that allow the agent to derive new beliefs about the user knowledge. The agent is therefore able to compare its own beliefs about the user knowledge with the knowledge conveyed by a given document, and thus understand if the document really contains useful information for the user or not. Finally, in case of inconsistency, the agent revises its belief base to restore consistency.
Sensor Ontology Metamatching with Heterogeneity Measures

The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.
Introduction to the Special Issue on Interface Architects: The Evolution of Human–Computer Interaction

The articles in this special section focus on the evolution of human-computer interaction. The articles include a review article and three case studies that examine the theme of human–computer interaction (HCI), exploring how humans and computers relate to each other. The issue analyzes the history of human–computer interfaces through interdisciplinary approaches, involving scholars from distinct areas of research, including computing history, design, art history, and media history. It also broadens the focus from the US to European cases.
Controlled Digital Lending

Controlled digital lending is an intriguing model for libraries to make books available digitally. Building on fair use and the first sale doctrine, libraries digitize their print books, put the print books in dark storage, and lend one electronic copy for each print copy on a platform that prevents users from copying or redistributing electronic versions. The concept empowers libraries to digitize in-copyright books when there are no alternatives available in the e-book licensing market. AMBS Library experimented with a small pilot controlled digital lending collection using Internet Archive’s established digitization and controlled digital lending services. This session reported on the results of that experiment.
Cross-Lingual Domain Adaptation for Dependency Parsing

We show how we can adapt parsing to low-resource domains by combining treebanks across languages for a parser model with treebank embeddings.We demonstrate how we can take advantage of in-domain treebanks from other languages, and show that this is especially useful when only out-of-domain treebanks are available for the target language.The method is also extended to low-resource languages by using out-of-domain treebanks from related languages.Two parameter-free methods for applying treebank embeddings at test time are proposed, which give competitive results to tuned methods when applied to Twitter data and transcribed speech.This gives us a method for selecting treebanks and training a parser targeted at any combination of domain and language.
Applying of Cooperative Integrated Reading and Composition (CIRC) Strategy on Students’ Reading Comprehension

The article was to analyse Cooperative Integrated Reading and Composition (CIRC) Strategy Effective on student’s reading comprehensio, especially in recount text. This research was conducted at tenth grade in second semester at tenth grade SMA 10 Bandar Lampung The design of this research was quantitative research. The subject of this research was 32 students at tenth grade of SMA 10 Bandar Lampung. The result of the research showed that the significant influence was clarified by the assumption of the level of the coefficients correlation between Cooperative Integrated Reading and Composition (CIRC) Strategy and students’ reading comprehension which was signing positive. Directing to positive correlation means that if the level of Cooperative Integrated Reading and Composition (CIRC) Strategy is high, the level of students’ reading comprehension is also high. In contrast, if the level of Cooperative Integrated Reading and Composition (CIRC) Strategy is low, the level of students’ reading comprehension is also low. Moreover, based on the result of hypothesis testing result (table of correlations) demonstrated that r observed is higher than r table with N = 32, it can be looked at significant either for level 5 % or 1 %. In the table also demonstrated that the level of significant was 1,696 &lt; 10,56 &gt; 2,040. It means that the correlation between Cooperative Integrated Reading and Composition (CIRC) Strategy and students’ reading comprehension was significant.&#x0D; Keyword: Reading Comprehension, CIRC Strategy, Reading and Composition (CIRC) Strategy
CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output

It is often infeasible or impossible to obtain ground truth labels for medical data. To circumvent this, one may build rule-based or other expert-knowledge driven labelers to ingest data and yield silver labels absent any ground-truth training data. One popular such labeler is CheXpert, a labeler that produces diagnostic labels for chest X-ray radiology reports. CheXpert is very useful, but is relatively computationally slow, especially when integrated with end-to-end neural pipelines, is non-differentiable so can't be used in any applications that require gradients to flow through the labeler, and does not yield probabilistic outputs, which limits our ability to improve the quality of the silver labeler through techniques such as active learning. In this work, we solve all three of these problems with $\texttt{CheXpert++}$, a BERT-based, high-fidelity approximation to CheXpert. $\texttt{CheXpert++}$ achieves 99.81\% parity with CheXpert, which means it can be reliably used as a drop-in replacement for CheXpert, all while being significantly faster, fully differentiable, and probabilistic in output. Error analysis of $\texttt{CheXpert++}$ also demonstrates that $\texttt{CheXpert++}$ has a tendency to actually correct errors in the CheXpert labels, with $\texttt{CheXpert++}$ labels being more often preferred by a clinician over CheXpert labels (when they disagree) on all but one disease task. To further demonstrate the utility of these advantages in this model, we conduct a proof-of-concept active learning study, demonstrating we can improve accuracy on an expert labeled random subset of report sentences by approximately 8\% over raw, unaltered CheXpert by using one-iteration of active-learning inspired re-training. These findings suggest that simple techniques in co-learning and active learning can yield high-quality labelers under minimal, and controllable human labeling demands.
On the Relation Between Smooth Variable Structure and Adaptive Kalman Filter

This article is addressed to the topic of robust state estimation of uncertain nonlinear systems. In particular, the smooth variable structure filter (SVSF) and its relation to the Kalman filter is studied. An adaptive Kalman filter is obtained from the SVSF approach by replacing the gain of the original filter. Boundedness of the estimation error of the adaptive filter is proven. The SVSF approach and the adaptive Kalman filter achieve improved robustness against model uncertainties if filter parameters are suitably optimized. Therefore, a parameter optimization process is developed and the estimation performance is studied.
Role of Cloud Computing in Service Quality, Information Quality &amp; Low Costs: An Empirical Study on Jordanian Customs

The study seeks to define the role of cloud computing in service quality, information quality and low costs.To achieve this research purpose, 207 employees were surveyed and there were 187 questionnaires valid for statistical analysis, adopting the descriptive approach.Questionnaires were used to measure the cloud computing impact on Jordanian customs.The study concluded that there was a high usage level of cloud computing and high level of application of service quality and information quality.In addition, there was a statistically significant correlation between cloud computing and service quality, information quality, and low costs.In light of the aforementioned results, Implementations for cloud computing and service quality, information quality, and low costs are discussed.
A Statewide Study of Perceptions of Directors on the Availability of Online Student Support Services at Postsecondary Institutions

As a state university system planned for growth in the availability of distance education degrees, the presidents and the provosts decided to include consideration for the availability of student support services. To ensure availability of student support services for online students, college and university systems in the state developed and implemented a self-reporting tool, the Online Student Support Scorecard to measure the availability of those services at both the college and the university levels. Although institutions were offering many of the services identified in the scorecard as essential, institutions were struggling to provide some of the services. Differences also were identified between the types of services available at the state college system compared with the university system.
Augmented Reality as a Classroom Teaching and Learning Tool: Teachers’ and Students’ Attitude

The use of Information Communication and Technology in teaching learning has been emphasized a lot in the recent times as an effective instructional tool and it will only grow and develop further. Augmented Reality is one of the futuristic pedagogy that will offer great opportunities in the incorporation of information technology in teaching and learning. Augmented Reality refers to the introduction of virtual elements in the real world. This will supplement students’ learning through the use of technology. This study assessed the students’ and teachers’ attitude towards the use of Augmented Reality as a learning tool after they experimented and experienced the use of augmented reality through mobile application. For the teachers, a three-hour professional development session on Augmented Reality was conducted. Teachers were taught how to use mobile application to create Augmented Reality. For the students, research team created the Augmented Reality using a mobile application and let students experience it in a biology lesson. After the sessions, their attitudes towards the use of Augmented Reality was assessed through statements of agreement on a 5-point Likert scale. SPSS has been used for statistical analysis. The results show that both the teachers and students have overwhelming support-positive attitude towards the use of Augmented Reality in the classroom teaching and learning.
Generative Adversarial Network for Class-Conditional Data Augmentation

We propose a novel generative adversarial network for class-conditional data augmentation (i.e., GANDA) to mitigate data imbalance problems in image classification tasks. The proposed GANDA generates minority class data by exploiting majority class information to enhance the classification accuracy of minority classes. For stable GAN training, we introduce a new denoising autoencoder initialization with explicit class conditioning in the latent space, which enables the generation of definite samples. The generated samples are visually realistic and have a high resolution. Experimental results demonstrate that the proposed GANDA can considerably improve classification accuracy, especially when datasets are highly imbalanced on standard benchmark datasets (i.e., MNIST and CelebA). Our generated samples can be easily used to train conventional classifiers to enhance their classification accuracy.