Reviewerly/exHarmony huggingface dataset:
Detecting Fraud Apps using Sentiment Research

With the increase in the number of mobile applications in the day to day life, it is important to keep track as to which ones are safe and which ones aren’t. One can’t judge how safe and true each application is based only on the reviews that are mentioned for each application. Hence it is a need to keep track and develop a system to make sure the apps present are genuine or not. The objective is to develop a system in detecting fraud apps before the user downloads by using sentimental analysis and data mining. Sentimental analysis is to help in determining the emotional tones behind words which are expressed in online. This method is useful in monitoring social media and helps to get a brief idea of the public’s opinion on certain issues. The user cannot always get correct or true reviews about the product on the internet. We can check for user’s sentimental comments on multiple application. The reviews may be fake or genuine. Analyzing the rating and reviews together involving both user and admins comments, we can determine whether the app is genuine or not. Using sentimental analysis and data mining, the machine is able to learn and analyze the sentiments, emotions about reviews and other texts. The manipulation of review is one of the key aspects of App ranking fraud. By using sentimental analysis and data mining, analyzing reviews and comments can help to determine the correct application for both Android and iOSplatforms.
The mediating effects of innovation culture on leadership traits, leadership style, and firm performance

Knowledge The rapid change in economy and technology require quick responses by the organization to cope with this dynamic transformation.Many organizations neglect to pay attention to the wind of the transformation, leading them to stay behind and sometimes fail or die.Oman is an oil-based economy for several decades.The Omani economy has dramatically transformed for the past 7 years mainly attributable to falling oil prices (Trading Economics, 2018).All the recent events have necessitated Oman to be a diversified non-oil-based economy.Studies indicate that countries that have a well-diversified economy lead to low risk, can sustain during the times of turbulence and can lead to sustainable growth in GDP (Dissart, 2003;Pece et al., 2015).Therefore, this study will focus on three main potential industries in Oman including construction, manufacturing and information technology.Construction sectors in Oman is growing steadily and sharply.In the last 2 years, the building industry has grown by 10.4% in 2016.The construction sector is considered one of the main pillars of Oman's government (Oxford Business Group, 2018), and it is currently achieving high performance.The construction sector contributed around 5 -6 % to the GDP in 2016 and it is expected to rise to 10% by 2020 (Trading Economics, 2018).The construction sector is working also towards enhancing its competitiveness by streamlining the activities streamline, decreasing expenses, increasing efficiency through adopting the latest technology.As for the manufacturing sectors, it has become the key drivers of non-oil field and economic diversification.This particular sector
The Technology to Increase the Level of Education at the University in the Context of the Development of Society Informatization

The article presents the problem of eliminating the writing blocks as a process of finding ways, means, and conditions for success in teaching students. The study involved 852 students (of which 84 students of the faculty of oriental studies and history of Pacific State University participated in the formative experiment; while 83 students of the faculty of philology, translation studies and intercultural communication, and faculty of psychology, social and humanitarian technologies made up the control group). The authors propose to unlock the potential of student learning psychology by using qualitative methods of teaching students to understand the phenomenon of writing blocks to reduce them in order to increase the motivation for success in writing a scientific text.
The Future is Here: Library Services for Mobile Devices

&lt;p&gt;"What does your library website look like on a cell phone? What library services do your patrons want to access on a mobile device? The first question is easily answered. Unless your patron is using an iPhone or iPod Touch, your library website as viewed on their phone will have little resemblance to the carefully crafted site that displays on a laptop or desktop computer. The answer to the second question will depend very much on the types of users that frequent your library."&lt;/p&gt;
Pengembangan Multimedia Interaktif IPA SD Kelas IV Materi Makan dan Dimakan Antar Makhluk Hidup

This development aims to produce interactive multimedia as a learning medium consisting of text, graphic arts, sound, animation, and video with the sub-discussion of the relationship between eating and eating between living things for the fourth grade natural education subject. This interactive multimedia can be used as an alternative in learning so that students are interested and motivated in learning. Interactive multimedia is presented in the form of an Exe for use via a personal computer. Details of the sub-discussion of the material and its appearance design are arranged in a User Friendly system to make it easier to use. This interactive multimedia research and development method uses the Sugiyono model. Response from media experts and material experts on interactive multimedia received a positive response. The trial of the use of interactive multimedia was also carried out on fourth grade elementary school students. Interactive multimedia learning is beneficial for teachers and students during learning activities, because the role of learning media is to help teachers and students in explaining and studying material in a straightforward and interesting way, interactive multimedia is worth using in learning.
Factors Affecting Knowledge Sharing in the Administrative Work Environment

The study aims at contributing in identifying the factors affecting the sharing of knowledge in the administrative work environment, starting from realizing the concept of knowledge sharing among administrators in the various administrative sectors of the university, identifying their opinions, attitudes, behaviors and practices towards sharing knowledge in the work environment, and depicting the expected benefits of sharing knowledge at the university. It relied on the descriptive approach using the survey method, where an electronic questionnaire was distributed to a random sample of administrators within the University. The study concluded that the most influential demographic data on Knowledge sharing is the employee’s years of experience, the more the experience increases, the more knowledge sharing will be. The study attributes this result to trust and its impact on work practice. The cognitive type of knowledge that administrators share most is modern information, administrative processes and procedures, and social activities. The study also revealed that sharing knowledge pushes them towards positive relationships that bind the employee with other administrators working at the university and finding strong relationships with employees who have common jobs and relationships depending on trusting others led to increased knowledge sharing. Moreover, sharing administrative knowledge increases productivity within the university, supports innovation among administrators, helps develop university work procedures, and increases the efficiency of employee’s cooperation, and helps colleagues within the university work to find solutions to problems and that the university's organizational culture promotes knowledge sharing among administrators, and a culture of teamwork. The study indicated that the most relevant applications that help administrators share knowledge are specialized training programs, work teams and groups of practice in the same profession.
Polarized light compass-aided inertial navigation under discontinuous observations environment

In recent years, integrated polarized light/inertial heading measurement systems have been widely used to obtain autonomous heading measurements of small unmanned combat platforms in the case of satellite navigation rejection. However, existing polarized light/inertial heading measurement systems have certain limitations. For example, they can only measure the heading angle in environments where continuous observations can be obtained. When encountering a complex environment with trees and/or tall buildings, the measured heading angle will contain sharp noise which greatly affects its accuracy. In particular, when encountering an underpass, it will lead to the complete loss of lock of the polarized light compass signal. Therefore, for the problem of sharp noise arising from a complex environment, a robust Cubature Kalman filter (CKF) data-fusion algorithm is proposed and verified by experiments. It is proved that the robust CKF algorithm has a certain ability to filter out the effects of poor measurements. After application of the robust CKF algorithm, the Root Mean Square Error (RMSE) of the heading angle reaches 0.3612°. This method solves the problem of low precision and poor stability of the polarized light/inertial system when high buildings and/or trees are contained in a complex environment. Next, in view of the problem that the polarized light compass signal is completely lost due to passing through an underground passage, a random forest regression (RFR) neural network model is established and introduced into the combined system. Simulated and outdoor experiments are carried out to verify the designed model using data obtained with a vehicle. The RMSE of the heading angle obtained in the experiment is 1.1894°, which solves the problem that the polarized light/inertial system cannot utilize discontinuous observations when attempting to detect the carrier heading angle.
Multiple Instance Learning through Explanation by Using a Histopathology Example

A new method for solving the multiple instance learning (MIL) problem, which is based on ideas of the black-box model prediction explanation, is proposed. The explanation aims to show instances (pixels, patches) which have the highest contribution into the image (bag) classes and to automatically annotate instances in bags. Three ideas behind the method are used. First, the surrogate black-box model is implemented as the Siamese neural network which is trained on pairs of whole images. Second, patches in each image are changed by using their dynamic fill or noise. Third, noisy images are compared with the original image by using the Siamese neural network such that Euclidean distances between outputs of the network depending on the noise level form a shape function for every patch. The shape function is interpreted from its contribution into the image class. Numerical experiments with the real Breast Cancer Cell Segmentation dataset illustrate the method.
Quantum rotator and Josephson junction: Compact vs. extended phase and dissipative quantum phase transition

This paper reassesses the old dilemma “compact vs. extended phase” in the quantum theory of the rotator and the Josephson junction and the analogy of these two systems with a particle moving in a periodic potential. This dilemma is in fact the dilemma of whether the states with the phases φ and φ + 2π are distinguishable, or not. In the past it was widely accepted that in the Josephson junction these states are distinguishable, as in the case of a particle moving in a periodic potential. This paper argues that the states with the phases φ and φ + 2π are indistinguishable as in a pendulum (a particular example of the quantum rotator). However, this does not lead to revision of the conclusions of the conventional theory predicting the transition from the superconducting to the insulating state in the small Josephson junction.
Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning

The amount of secure data being stored on mobile devices has grown immensely in recent years. However, the security measures protecting this data have stayed static, with few improvements being done to the vulnerabilities of current authentication methods such as physiological biometrics or passwords. Instead of these methods, behavioral biometrics has recently been researched as a solution to these vulnerable authentication methods. In this study, we aim to contribute to the research being done on behavioral biometrics by creating and evaluating a user authentication scheme using behavioral biometrics. The behavioral biometrics used in this study include touch dynamics and phone movement, and we evaluate the performance of different single-modal and multi-modal combinations of the two biometrics. Using two publicly available datasets - BioIdent and Hand Movement Orientation and Grasp (H-MOG), this study uses seven common machine learning algorithms to evaluate performance. The algorithms used in the evaluation include Random Forest, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Logistic Regression, Multilayer Perceptron, and Long Short-Term Memory Recurrent Neural Networks, with accuracy rates reaching as high as 86%.
Media security framework inspired by emerging challenges in fake media and NFT

Advances in deep neural networks (DNN) and distributed ledger technology (DLT) have shown major influence on media security, authenticity and privacy. Current deepfake techniques can produce near realistic media content which can be used in both good and bad intended use cases. At the same time, DLTs are finding their way in the industry as fair, transparent and reliable means for content distribution. In particular non-fungible tokens (NFTs) are emerging in the digital art market. However, such new developments also introduce new challenges, including the need for robust and reliable metadata, a mechanism to secure the media and associated metadata, means to verify authenticity and interoperability between various stakeholders. This paper identifies emerging challenges in fake media and NFT, and proposes a novel framework to effectively cope with secure media applications allowing for a structured, systematic, and interoperable solution. The framework relies on an architecture that is modular, flexible, extensible, and scalable in the sense that it can be implemented in both lighter as well as more feature-rich and more complex configurations depending on the underlying application, needed features and available resources, while enabling products and services in various ecosystems with desired trust and security capabilities. The framework is inspired by activities and developments within JPEG standardisation related to security, authenticity and privacy.
Pengembangan Multimedia Interaktif Materi Sistem Peredaran Darah Manusia Untuk Kelas V Sekolah Dasar

Interactive multimedia is a learning medium that presents the material with a combination of text, images, audio, video, and animation. The purpose of this research and development is to produce an interactive multimedia product that is valid, feasible, and effective for learning activities on the human circulatory system material at the elementary school level. The method used in this research is Research and Development (R&amp;D) with the Lee &amp; Owens development model consists of 5 stages as follows: (1) analysis, (2) planning, (3) development, (4) implementation, (5) evaluation. The validation stage was carried out on media experts and material experts and then tested on 19 grade V students of SDN Tamiajeng. The results of interactive multimedia development were declared valid with the percentage of media experts was 98.61%, 79.16% of material experts, 82.98% small group trials, 84.36% large group trials. Interactive multimedia is declared effective for learning by obtaining a percentage of 84.21%. Because of this finding, it can be concluded that interactive multimedia material on the human circulatory system produces valid, feasible, and effective categories to be used for learning.
Machine Learning Trivializing Maps: A First Step Towards Understanding How Flow-Based Samplers Scale Up

A trivializing map is a field transformation whose Jacobian determinant exactly cancels the interaction terms in the action, providing a representation of the theory in terms of a deterministic transformation of a distribution from which sampling is trivial.Recently, a proof-of-principle study by Albergo, Kanwar and Shanahan [Phys.Rev. D 100 034515 (2019)] demonstrated that approximations of trivializing maps can be 'machine-learned' by a class of invertible, differentiable neural models called normalizing flows.By ensuring that the Jacobian determinant can be computed efficiently, asymptotically exact sampling from the theory of interest can be performed by drawing samples from a simple distribution and passing them through the network.From a theoretical perspective, this approach has the potential to become more efficient than traditional Markov Chain Monte Carlo sampling techniques, where autocorrelations severely diminish the sampling efficiency as one approaches the continuum limit.A major caveat is that it is not yet understood how the size of models and the cost of training them is expected to scale.As a first step, we have conducted an exploratory scaling study using two-dimensional 4 with up to 20 2 lattice sites.Although the scope of our study is limited to a particular model architecture and training algorithm, initial results paint an interesting picture in which training costs grow very quickly indeed.We describe a candidate explanation for the poor scaling, and outline our intentions to clarify the situation in future work.
Teaching Presence vs. Student Perceived Preparedness for Testing in Higher Education Online English Courses During a Global Pandemic? Challenges, Tensions, and Opportunities

In the context of a global pandemic that started in 2020, the Chilean higher education institution Universidad Andrés Bello (UNAB) faced the challenge of giving continuity to its already established blended program for English courses while also starting the implementation of a high-stakes certification assessment for its students using the Test of English for International Communication (TOEIC) Bridge. This study sought to evaluate how much of a mediating factor online teaching presence could be in the context of test preparation within a language course in aspects related to autonomous learning and perceived learning outcomes. A mixed-methods approach was used. It included a survey applied to 1,642 eligible students of the English program. These quantitative data were complemented with students’ comments and teacher interviews. After triangulating quantitative and qualitative data, teaching presence was clearly perceived to be a relevant aspect of the online experience in the studied courses. However, both students’ and teachers’ voices evidenced pervasive challenges and tensions that hinder the potentially transformative benefits that online learning is expected to bring about.
Afan-Oromo Named Entity Recognition Using Bidirectional RNN

Objectives: This work aims about the development of Afan-Oromo language named entity recognition which widely used in question answering, information extraction and information retrieval aimed at categorizing and predicting tokens of a given corpus into predefined named entity classes like organization, location person and others (non-named entity tags).Methods: In this work, a bidirectional long-short term memory technique is used to model the Afan-Oromo language NER system to recognize and classify words into their named entity classes.Findings: While we evaluated the experiment in cross-validation, we attained a result of precision, recall and f1-measure values 96.7%, 96.2% and 97.3% respectively.We have collected the data from Ethiopian broadcasting Corporation (Afan-Oromo program).Therefore, a newly annotated dataset having 12,479 instances is used for this study.Novelty: Finally we have contributed by boosting a NER system for Afan-Oromo language which is independent of other natural language processing tasks.We proved bidirectional long-short term memory approach can be extended, trained can work for Afan-Oromo language.
Internet of Things and Blockchain-Based Framework for Coronavirus (COVID-19) Disease

The COVID-19 is a highly contagious infection which is already had a catastrophic effect on the most serious diseases, including death, around the world. Blockchain-based healthcare systems are being introduced to help simplify mobile and telehealth services, reducing patient stress and the cost of critical clinical services. shared the advantages of blockchain for building a cutting-edge authentication infrastructure and detecting COVID-19 suspicious cases. The authors presented a blockchain-based design for developing a real-time cellular health monitoring system for COVID-19 patients in this paper. This study identifies clinical problems and electronic diagnoses for people with COVID-19 infectious diseases and provides a framework for them. Any mobile application can be configured on digital devices such as smartphones. COVID-19 patients may benefit from such applications. Smartphone apps are designed to save time and money while increasing the efficiency of infectious patients. IoT and Blockchain strategies are presented in the four-layer structure.
Use Case Realization in Software Reverse Engineering

The Use Case Diagram (UCD) is a visual form of system design that helps software developers comprehend the system behavior. Maintaining and updating the system can be a difficult task when it has no visualization of a system behavior or software requirement specification document. Reverse engineering is an approach used to extract software requirement specifications from the existing systems. Research in reverse engineering has shown various techniques in which the processes are not fully understood. This study analyzes the University Community Services Information System (UCSIS) as the existing system in three processes: identifying the system domain process, elaborating system features by implementing the event table, and constructing the use case realization. The results showed that a UCD could be generated through the reverse engineering process on the existing system. Furthermore, a new feature for system improvement can also be detected using this method. It is expected that the reverse engineering approach in this study can be used as guidance for the software development team in extracting the use case diagram from the existing systems.
TrajectoryVis: a visual approach to explore movement trajectories

Social networks are a dominant data source for sharing, participation, and exchanging information. For example, Twitter is a microblogging site that enables users to express opinions by transmitting brief messages (i.e., Tweets). Tweets can be used to extract information on users’ movements or trajectories over time. Information visualization (InfoVis) is helpful to understand, analyze, and make decisions about these trajectories. To better understand and compare existing visual encoding methods in InfoVis, we propose TrajectoryVis, a generic trajectory visualization tool to represent social network datasets (e.g., Twitter). Individual and aggregated trajectories can be visualized using different visual coding approaches. Our approach is assessed using a user and a COVID-19 case study to prove its effectiveness.