Reviewerly/exHarmony huggingface dataset:
Dàgáárè (Central)

Dàgáárè /dàgááɹɪ̀/ (ISO 639-3) is a Mabia language (Bodomo 1997) of Niger-Congo family. It is spoken by about 1.5 million The map in Figure 1 shows the areas, in northwestern Ghana and southern Burkina Faso, where Dàgáárè is spoken predominantly. There are four broad dialects of Dàgáárè. These include Northern Dàgáárè [dàgàɹà], spoken in Lawra /lóɹáː/, Nandom /nàndɔ̀ː/ and Burkina Faso; Southern Dàgáárè /wáːlɪ́/, spoken around Kaleo /kàlèó/ and in Wa /wá/; Western Dàgáárè /bɪ̀ɹɪ̀fɔ̀/ spoken in Lassie /lààsɪ̀ɛ́/, Tuna /tʊ́ːnà/ and along the western side of the Black Volta river in Burkina Faso. The other dialect is Central Dàgáárè. These broad dialects are further divided into sub-dialects, as there are internal variations in these dialect groups (Bodomo 2000). This paper’s primary focus is on Central Dàgáárè, which comprises the varieties spoken in and around Jirapa /ʤɪ̀ɹɛ́bǎː/, Han /hɛ̌ŋ/, Ullo /úlò/, Daffiama /dàfɪ̀ɛ́mɛ́/, Nadowli /nàdòlí/, Charikpong /tʃɛ̀ɹɪ̀kpóŋ/, Sombo /sʊ̀mbɔ́/ and Duong /dùóŋ/.
A Research on Security, Privacy Issues and Privacy Preserving Techniques - Big Data

Information, which is not a fear till 2000, was the precept criteria for man or woman regarding safety and protection a brief time later. these days enormous measure of information is being created for always it is a check to deal with it. it is being used by numerous groups to take large options from the beyond facts to interrupt down thefuture. This tremendous diploma of facts is referred to as as huge statistics. in spite of the fact that there are numerous common calculations in presence, for giving safety and safety in large records it's miles attending to be tough because of its traits (quantity, range and Veracity). This paper demonstrates the investigation of common techniques handy, troubles to protection and safety of big records and protection saving processes like anonymization, randomization, differential safety, word and assent and so on.
Crowd Sourcing-based Deduplication in Big Data Environment

Frequently, in reality, substances have at least two portrayals in databases. Copy records don't share a typical key as well as they contain mistakes that make copy coordinating a troublesome assignment. Mistakes are presented as the consequence of interpretation blunders, inadequate data, absence of standard configurations, or any mix of these components. In big data storage data is excessively enormous and productively store data is troublesome errand. To take care of this issue Hadoop instrument gives HDFS that oversees data by keep up duplication of data however this expanded duplication. In our anticipated strategy bigdata stream is given to the fixed size chunking calculation to make fixed size chunks. In this manuscript, we introduce an exhaustive investigation of the writing on crowd sourcing based big data deduplication technique. In our strategy is to create the guide diminish result after that MapReduce model is connected to discover whether hash esteems and are copy or not. To be familiar with the copy hash esteems MapReduce model contrasted these hash esteems and as of now put away hash esteems in Big data storage space. On the off chance that these hash esteems are now there in the Big data storage space, at that point these can be distinguished as copy. On the off chance that the hash esteems are copied, at that point don't store the data into the Hadoop Distributed File System (HDFS) else then store the data into the HDFS. we additionally spread various deduplication systems in crowd sourcing data's.
GDPR compliance: proposed technical and organizational measures for cloud provider

Purpose The purpose of this paper is to give a brief guidance on what a cloud provider should consider and what further actions to take to comply with General Data Protection Regulation (GDPR). Design/methodology/approach This paper presents in detail the requirements for GDPR compliance of cloud computing environments, presents the GDPR roles (data controller and data processor) in a cloud environment and discusses the applicability of GDPR compliance requirements for each cloud architecture (Infrastructure as a Service, Platform as a Service, Software as a Service), proposes countermeasures for satisfying the aforementioned requirements and demonstrates the applicability of the aforementioned requirements and countermeasures to a PaaS environment offering services for building, testing, deploying and managing applications through cloud managed data centers. The applicability of the method has been demonstrated on in a PaaS environment that offers services for building, testing, deploying and managing applications through cloud managed data centers. Findings The results of the proposed GDPR compliance measures for cloud providers highlight the effort and criticality required from cloud providers to achieve compliance. Originality/value
A Calibration Method for Subaperture Views of Plenoptic 2.0 Camera Arrays

We present a novel methodology to precisely calibrate the subaperture views of an array of plenoptic 2.0 cameras. Such cameras consist of a micro lens array, and the image captured through them is a lenslet image that can be converted to a dense set of pinhole views, the so-called subaperture images. This cam-era array provides several dense multiview images at some sparse points of 3D space. To find the relative position of those views, simply using structure-from-motion creates misalignments due to the small disparities within each set. Additionally, a traditional calibration using calibration patterns will also fail due to the complicated objectives of plenoptic 2.0 cameras and artifacts when they are converted to subaperture views. In this paper, we propose two calibration steps (a) to register the sparse central subaperture views using Structure-from-Motion which makes it robust to artifacts in the subaperture views, and (b) to register all dense multiview sets per plenoptic camera using camera’s lenses specifications, disparity and distance to the scene. These two steps are followed by a novel merging process of the former registrations, to achieve precise calibration parameters for all the subaperture views of the multi-plenoptic array. Experimental results objectively and subjectively demonstrate high accuracy of the calibration. We show a 10% smaller reprojection error than using a naive structure-from-motion approach and verify that our method is suitable for high precision view synthesis applications such as virtual reality and holography.
Global Readiness for Immersive Virtual Space Adoption: The Case of Ohyay

As the world is wholly dependent on information technology, especially in the era of the COVID-19 pandemic, it is expected that hybrid format will become the new norm. This experience indicates that novel ways of combining face-to-face with online activities, including teaching, conferencing, concert, art exhibition, and collaborations, should be explored. Consequently, a new immersive virtual space known as Ohyay was launched in the summer of 2020. Ohyay is a powerful design platform that lets anyone create immersive virtual spaces unlike other video conferencing platforms (e.g., Zoom, Microsoft teams) with a well-defined and prescribed workflow. Even though the platform has been utilized for series of events and activities, empirical studies to ascertain its adoption rate are lacking. Therefore, this study employed the UTAUT2 model infused with self-efficacy and readiness construct to investigate the factors that influence users’ intention of Ohyay adoption. One hundred and forty-two participants across countries completed an online questionnaire. It was found that Performance Expectancy, Facilitating Conditions, Effort Expectancy, Self-Efficacy, Readiness, and Hedonic Motivation predicts the behavioral intention of Ohyay directly, while Self-Efficacy and Hedonic Motivation indirectly predicts behavioral intention through Effort Expectancy and Readiness. The findings of this study contribute to the research applying the UTAUT2 approach with the self-efficacy and readiness construct for the exploration of behavioral intention to utilize an emerging technology such as Ohyay in educational and non-educational activities. As a result of the findings, managerial implications were highlighted, followed by study limitations and proposals for future studies.
Dynamic and Temporal Bayesian Networks

Dynamic Bayesian network models extend BNs to represent the temporal evolution of a certain process. There are two basic types of Bayesian network models for dynamic processes: state based and event based. Dynamic Bayesian networks are state based models that represent the state of each variable at discrete time intervals. Event based models represent the changes in state of each state variable; each temporal variable will then correspond to the time in which a state change occurs. In this chapter we review dynamic Bayesian networks and event networks, including representation, inference and learning. The chapter includes two application examples: dynamic Bayesian networks for gesture recognition and temporal nodes Bayesian networks for HIV mutational pathways prediction.
5. Explanations and user control in recommender systems

Adaptive, personalized recommendations have become a common feature of today’s web and mobile app user interfaces. In most of modern applications, however, the underlying recommender systems are black boxes for the users, and no detailed information is provided about why certain items were selected for recommendation. Users also often have very limited means to influence (e. g., correct) the provided suggestions and to apply information filters. This can potentially lead to a limited acceptance of the recommendation system. In this chapter, we review explanations and feedback mechanisms as a means of building trustworthy recommender and advice giving systems that put their users in control of the personalization process, and we outline existing challenges in the area.
Impact of Human Resource Management Practices on Employees' Turnover Intention in United Arab Emirates (UAE) Health Care Services

The article aims to examine the relationship between the human resource practices and employees' turnover intention in United Arab Emirates health care sector. This quantitative study utilized a structure equation model (SEM) technique with Analysis of Moment Structures (AMOS) 18 software package to analyze data and to investigate the impact of human resources practices on employees' turnover intention in United Arab Emirates health care services. Results of the study showed that human resources (HRM) practices in health care sector (recruitment and selection, performance appraisal, compensation, and career development) have significant relationship with turnover intention. However, this research was limited to the Public Health Care Sector in UAE and it is recommended to include Private health care sector. Moreover, inclusion of health care workers from different emirates such as Dubai and Sharjah will be needed in future researches. Health care facilities management should have a closer look at the significant human resource practices as an initiative to restructure HRM practices, policies, procedures to increase staff satisfaction, reduce employees' turnover intention and retain its valuable health care professionals. This article is among the first and most exhaustive ones carried out in health care sector in UAE. It explores the relationship between the human resource management practices and the employees' turnover intentions.
Acceptance of Biometric Authentication Security Technology on Mobile Devices

The development of mobile devices is quick and changes our daily personal and business lives. Every mobile user wants to be sure about individual data security, and for this reason, biometrics come into existence for mobile devices. Many studies were conducted on the acceptance of biometric authentication technology, but only a few of these studies focused on mobile devices-based biometry and the current study based on the mobile technology. To observe the reliability of the broadcast services, it is essential to offer better security for the biometry mobile phones. The limitations of this study were addressed by proposing a new mobile biometric technology acceptance model (MBTAM) that contains perceived humanness (PH), perceived interactivity (PI), and perceived social presence (PSP). The combined model for this quantitative study was tested on 302 mobile users through the distribution of the survey questionnaire, and examined by using the statistical package for social science (SPS). The results indicate that only one variable of the proposed model is not supported, which calls for further research. Furthermore, the functional elements of the research model become more prominent on the customer’s intention to practice the mobile biometric device than the social elements. The research contributes to academic by suggesting new constructs that join together MBTAM to evaluate the possibility of mobile users to accept biometric authentication technology.
Fitting Security into Agile Software Development

Security objectives in software development are increasingly convergent with the business objectives, as requirements for privacy and the cost of security incidents call for more dependable software products. The development of secure software is accomplished by augmenting the software development process with specific security engineering activities. Security engineering, in contrast to the iterative and incremental software development processes, is characterized by sequential life cycle models: the security objectives are thus to be achieved by conflicting approaches. In this study, to identify the incompatibilities between the approaches, the security engineering activities from Microsoft SDL, the ISO Common Criteria and OWASP SAMM security engineering models are mapped into common agile software development processes, practices and artifacts.
Employing Abstract Meaning Representation to Lay the Last-Mile Toward Reading Comprehension

We propose a Machine reading comprehension (MRC) method based on the Abstract Meaning Representation (AMR) framework and a universal graph alignment algorithm. We combine syntactic, semantic and entity-based graph representations of a question to match it with a combined representation of an answer. The alignment algorithm is applied for combining various representations of the same text as well as for matching (generalization) of two different texts such as a question and an answer. We explore a number of Question Answering (Q/A) configurations and select a scenario where the proposed AMR generalization-based algorithm AMRG detects and rectifies the errors of a traditional neural MRC. When the state-of-the-art neural MRC is applied and delivers the correct answer in almost 90% of cases, the proposed AMRG verifies each answer and if it determines that it is incorrect, attempts to find a correct one. This error-correction scenario boosts the state-of-the-art performance of a neural MRC by at least 4%.
A comparative study of eye tracking and hand controller for aiming tasks in virtual reality

Aiming is key for virtual reality (VR) interaction, and it is often done using VR controllers. Recent eye-tracking integrations in commercial VR head-mounted displays (HMDs) call for further research on usability and performance aspects to better determine possibilities and limitations. This paper presents a user study exploring gaze aiming in VR compared to a traditional controller in an "aim and shoot" task. Different speeds of targets and trajectories were studied. Qualitative data was gathered using the system usability scale (SUS) and cognitive load (NASA TLX) questionnaires. Results show a lower perceived cognitive load using gaze aiming and on par usability scale. Gaze aiming produced on par task duration but lower accuracy on most conditions. Lastly, the trajectory of the target significantly affected the orientation of the HMD in relation to the target's location. The results show potential using gaze aiming in VR and motivate further research.
Obstacle avoidance for multi-UAV path planning based on particle swarm optimization

Abstract Traffic Collision Avoidance System aims to help aircraft to avoid collision with any object or other aircraft. One of the functions of this system is that it avoids threatening UAV to collide, it also addresses each threat separately with the best collision avoidance and the best suitable horizontal separation with other aircraft in the optimal path. In this paper the flight path planning for UAVs was designed to avoid obstacles depending on how the particle swarm was improved. Optimization problems are improved by using swarm dynamics (evolutionary computational technology). This is by describing avoiding obstacles and adapt the path planning for UAVs. The concept of concurrent restructuring has been integrated into path planning to stay away from both static obstacles. This optimization technique designed to decrease processing time and the shortest route of the path planning.
An Enhanced Adaptive Sobel Edge Detector Based on Improved Genetic Algorithm and Non-Maximum Suppression

This paper presents an enhanced adaptive Sobel edge detector based on improved genetic algorithm (GA) and Non-Maximum Suppression. Three key techniques are presented for increasing positioning precision and noise immunity. Considering the best balance between mean value calculation and differential processing, a new 5*5 operator template is defined to effectively match with the filter template and reduce noise. As the edges gotten by Sobel operator are wide and vivid, we use Non-Maximum Suppression algorithm to maintain the local maximum gradient and eliminate all other gradient values to accomplish the effect of edge thinning. Then we propose an improved GA to achieve threshold adaptation and effectively reduce the probability of this approach troubled into the local optimal threshold. The enhanced adaptive Sobel edge detection algorithm is compared with canny, Sobel and Roberts three traditional edge detection operators and eight direction Sobel operator respectively. We evaluate these five different algorithms by comparing the experimental effect of edge detection, PSNR and time cost. The consequence indicate that the proposed enhanced adaptive Sobel edge detector is better than other compared algorithms in accuracy, denoising ability and time cost.
Persian

Abstract There are elliptical constructions in Persian whose properties diverge from their better-studied counterparts in other languages. A type of verb phrase ellipsis removes the non-verbal element and internal arguments of a complex predicate; it can also strand a simple verb that has undergone head movement to a low verbal functional projection. The language does not have obligatory wh-movement, so sluicing is derived instead through a movement operation that is driven from information structure. Gapping, stripping, and fragment answers may arise from the same movement operation, as gapping and stripping allow for their antecedent to be embedded, and stripping and fragment answers are insensitive to island constraints. Persian also has right-node raising and noun phrase ellipsis, though their properties are relatively less understood.
EPSTO-ARIMA: Electric Power Stochastic Optimization Predicting Based on ARIMA

With the advance of the energy industry and the Internet, electricity data sharing has unleashed the full potential for social production during the past decade. However, electricity data inference attack will incur the disclosure of private information and the unavailability of valuable data, thus deteriorating national security and affecting the conversion of energy big data to social public service value.To cope with this challenge in electricity shared data, EPSTO-ARIMA (Electric Power Stochastic optimization Predicting Model Based on Autoregressive Integrated Moving Average) was proposed to increase prediction error of attackers by utilizing the concept of stochastic sampling, optimization, data poisoning and adversarial examples. The generation of adversarial examples is interfered with the prediction effect of EPSTO-ARIMA. The model was validated by seven sets of data from three datasets. Experimental results indicate that EPSTO-ARIMA could increase prediction error. For publicly dataset “Column2”, the proposed EPSTO-ARIMA achieves 61.31% higher prediction error than ARIMA (Autoregressive Integrated Moving Average model), respectively. Simultaneously, the terrific results in other datasets have also been ascertained the viability and generalization ability of our proposed EPSTO-ARIMA.
Application of Mobile Learning Software in College English Vocabulary Teaching

With the deep integration of information technology and education, mobile learning software has been more and more recognized by the majority of university teachers and students because it is not limited by space and time. Based on the mental representation and development process of English vocabulary acquisition, the paper discusses how learners, with the assistance of APP English learning software, realize the transition from the formal stage and L1 lemma mediation stage to L1-L2 integration of semantic, syntax, lexical and formal information. Finally, this paper proposes the strategies of mobile micro-learning teaching of college English vocabulary from three aspects: the transition from vocabulary recognition to in-depth vocabulary development, semantic network construction, chunk awareness, and chunk ability.
Teaching and Learning Abstract Concepts by Means of Social Virtual Worlds

Social Virtual Worlds are increasingly being used in education, as their flexibility can be exploited in order to create heterogeneous groups from all over the world who can collaborate synchronously in different virtual spaces. In this paper, the authors describe the potential of virtual worlds as an educative tool to teach and learn abstract concepts by means of programmable 3D objects. They describe the main experiences carried out recently in the application of these technologies in transnational educational activities that combine the Moodle learning resources and programmable 3D objects in the Second Life virtual world.
Implementation of the Bernstein-Vazirani Quantum Algorithm Using the Qiskit Framework

Abstract This paper describes the basics of quantum computing and then focuses on the implementation of the Bernstein-Vazirani algorithm, which can be seen as an extension of the Deutsch-Josza problem (that solves the question on whether a function is balanced or not). The idea behind the B-V algorithm is that someone can find a secret number (sequence of bits) using only one measurement, unlike the classical counter-part, that requires n measurements, where n is the number of bits of the secret number. The implementation of this algorithm, using the Python programming language, along with the Qiskit framework (an open-source library for quantum operations from IBM), illustrates how to create and simulate a circuit for such an algorithm. The circuit is dynamically generated for the required number (which in practice is received from a different source) and is used to measure the probability of each qubit. The algorithm can also be extended for different types of data and can be used for signal or image processing, as well as applications in cryptography.