Reviewerly/exHarmony huggingface dataset:
Flying Drone Controller by Hand Gesture Using Leap Motion

The Leap Motion is the mechanism to Flying Drone Controller by Hand Gesture Using Leap Motion.It has a sensor for hand gesture to fly a drone.This undertaking consists of a few hardware and software that are microcontroller Arduino, drone-like SYMA X5SC Explorer 2, Microsoft Visual Studio 2019, gesture sensor Leap motion and digital to analog MCP 4725.Leap Motion is taught by the manual motion and the Arduino instruction.Arduino receives the Leap Motion instructions, and the remote controller receives data from Arduino and sends signal to a drone immediately.Based on the hand gesture instruction, Drone flies into the air.There are three instructional conditions: throttle, pitch and roll.This system is designed to create a hand gesture instruction and to develop the program code of the features of the Leap Mo-tion using the Microsoft Visual Studio 2019.This technology has been explored and researched and many new things can be learned.Leap Motion was selected to operate a drone with a touchless condition for this task.There were three purposes in this task after investigations of the control system of hand gesture were made, particularly for drones.The planning is designed to ensure that the system fulfills its goals.It is suitable for farming, photography and military applications.Therefore, drone handling can be improved in future without shifting the key or pressing any control button.
An Efficient Hybrid Approach for Twitter Sentiment Analysis based on Bidirectional Recurrent Neural Networks

Many optimization problems from various applications have been solved by many algorithms such as Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), Whale Optimization Algorithm (WOA) and Particle Swarm Optimization (PSO).Sentiment Analysis (SA) is used to evaluate the polarity of reviews.In SA, feature selection phase is an important, as the best way to solve all optimization problems not happen yet so this paper proposes a hybrid approach that combines three modified hybrid algorithms [ (GWO), (PSO) and (GA) ], its name (HWPG) .To reduce the search space filter features selection, Information Gain (IG) has been used.(HWPG) used to select the best features for training Bidirectional Recurrent Neural Networks (BRNN) classifier.Arabic benchmark dataset which was collected from twitter on different topics used in our experimental.The proposed algorithm is compared with three well-known optimization algorithms the experiments and comparisons result to evaluate the quality and effectiveness of the (HWPG)
Sustaining Linked Ancient World Data

a workshop funded by the US National Endowment For the Humanities.This makes it a good time to take stock of the Ancient World Linked Data initiatives that have been around for some time, as well as some that have foundered and some that are new.What makes for sustainable Linked Open Data?Why do some initiatives thrive while others fail?What resources do successful LOD sites need, and how may they be obtained?The promise of LOD is that it frees our information from the silos in which it is housed, permitting cross-system interactions that improve the quality and usefulness of the information in any single system.This article will take the broader view of the definition of Linked Data suggested by Tim Berners-Lee's foundational "Linked Data -Design Issues" paper, as encompassing more types of data than simply RDF and other "Semantic Web" technologies.This view of LOD is pragmatic and leverages the strengths of semantic technologies while avoiding their weaknesses.
Sentence Representation

Abstract Sentence is an important linguistic unit of natural language. Sentence Representation has remained as a core task in natural language processing, because many important applications in related fields lie on understanding sentences, for example, summarization, machine translation, sentiment analysis, and dialogue system. Sentence representation aims to encode the semantic information into a real-valued representation vector, which will be utilized in further sentence classification or matching tasks. With large-scale text data available on the Internet and recent advances on deep neural networks, researchers tend to employ neural networks (e.g., convolutional neural networks and recurrent neural networks) to learn low-dimensional sentence representations and achieve great progress on relevant tasks. In this chapter, we first introduce the one-hot representation for sentences and the n -gram sentence representation (i.e., probabilistic language model). Then we extensively introduce neural-based models for sentence modeling, including feedforward neural network, convolutional neural network, recurrent neural network, and the latest Transformer, and pre-trained language models. Finally, we introduce several typical applications of sentence representations.
GazeR: A Package for Processing Gaze Position and Pupil Size Data

Eye-tracking is widely used throughout the scientific community, from vision science and psycholinguistics, to marketing and human-computer interaction. Surprisingly, there is little consistency and transparency in preprocessing steps, making replicability difficult. To increase replicability and transparency, a package in R (a free and widely used statistical programming environment) called gazeR was created to read in and preprocess two types of data from the SR EyeLink eye tracker: gaze position and pupil size. For gaze position data, gazeR has functions for: reading in raw eye-tracking data, formatting it for analysis, converting from gaze coordinates to areas of interest, and binning and aggregating data. For data from pupillometry studies, the gazeR package has functions for: reading in and merging multiple raw pupil data files, removing observations with too much missing data, eliminating artifacts, blink identification and interpolation, subtractive baseline correction, and binning and aggregating data. The package is open-source and freely available for download and installation: https://github.com/dmirman/gazer. We provide step
Prospects for Digital Footprint Usage in the Higher Education

The article analyzes possible directions of using the digital footprint in the higher education system of the Russian Federation. The author considers the phenomenon of a digital footprint as an array of information, large data (Big Data) left by subjects of the educational process (primarily students and teachers) in a global information network that can be analyzed, processed and used to improve the educational space and improve the quality of teaching. The main research methods are analysis, systematization and modelling. On their basis, the author identifies three promising areas of applying digital footprint technology in the educational context, which are interdependent: ensuring continuity and integration of educational levels; organization and management of the educational process; management of the educational system as a whole (education management).
Literature Review of Job Description: Meta-analysis

A job description document is a formal document that exists in the organization that usually has a formal social structure. Whether it is a small-scale organization to a large-scale organization, every formal organization usually has this document. The study presented in this paper tries to present the summary of the research related to the job description document. In the previous research, there is less attention has been given to explore the role of a job description in the various aspects of decision making in the company. Therefore, the importance and the role of the job description to the company, especially in supporting decision-making in the company, is explored in this study. In this study, we reviewed 43 papers published between 2001 and 2021. Several aspects related to job description such as 1) Definition of the job description; 2) Development of job descriptions; and 3) Benefits of job descriptions based on four reviewed papers are explained. The selected articles are taken from the article database engine: Emerald Insight, Semantic Scholar, Proquest, Science Direct, Elsevier, Springer Link with the keyword "Job Description". The intention of this review is to address the following questions related to (i) in what aspect the research related to job descriptions are conducted; (ii) what the contribution of job descriptions document to the decision-making in the company.
Changing Things

Digital networked technologies are currently at the forefront of contemporary innovation, driving changes in sociotechnical practices across industrial sectors and in everyday life. Yet technical innovation has been outpacing our capacity to make sense of these technologies and the fundamental changes associated with them. This sense-making enterprise is the focus of our current research in developing a design philosophy for changing things. We describe a conceptual framework developed around the concept of things as fluid assemblages to investigate and articulate what is going on with things, and how their development might be (re)directed toward preferable futures. Specifically, we here examine the important role of design philosophy in innovation, using the conceptual framework developed as a way to point toward potential sites for innovation in the current sociotechnical landscape. The line of investigation we pursue suggests that doing philosophy should become a central part of innovative design practices.
Combined Audio Steganography and AES Encryption to Hide the Text and Image into Audio using DCT

Recently, security has become the prime concern for any organization and other civil and military applications. In this field of security, the data security during communication over an insecure wireless channel is the most important task which can be done by performing cryptography, watermarking and steganography. However, cryptography and watermarking schemes can be identified easily because of change in the data structure hence attackers can focus on that particular part to hack the secret information whereas steganography is a hiding mechanism in which secret message can be concealed into the cover and it can be retrieved at the receiver end. Several techniques have been introduced during last decade which are focused on image-image steganography and audio steganography. In the proposed work, we concentrate on audio steganography and develop a novel approach where secret message can be in the form of plain text or image, whereas cover message is in the form of audio. In order to provide additional security to this model we incorporate AES encryption scheme where secret message is encrypted and hidden in the cover audio. The proposed approach uses DCT coefficient computation and AES encryption scheme. An extensive experimental study is carried based on different test cases and evaluated against state-of-art techniques. The experimental study shows that the proposed approach achieves better performance for audio steganography.
Investigating factors that affect the adoption of Covid-19 contact-tracing apps. A privacy calculus perspective

On June 16, 2020 the German government issued a Covid-19 contact-tracing app whose privacy settings were fiercely debated in advance. Contrary, potential benefits regarding the acceleration of contact-tracing were advertised. This study investigated the relative importance of privacy concerns and perceived benefits in relation to the decision to use the app adopting a privacy calculus perspective extended by trust in app privacy and technological knowledge about the app. Results of a representative German sample (N = 952) revealed that privacy concerns were negatively related to app-usage, and perceived benefits and knowledge were positively related to actual app-adoption. Additionally, perceiving app benefits was the most important factor for (continued) usage intention while privacy concerns did not play a role anymore. Trust was strongly negatively associated with privacy concerns and strongly positively with perceived benefits. Results are discussed concerning consequences for communication strategies when launching technology whose adoption should help in mitigating dangers of pandemics.
Technology and Innovation in the Development of Cognitive Activity

Modern education is intensively developing, manifesting new approaches to learning, and progressive concepts in the system of knowledge acquisition. In recent years, global changes have undergone in the Russian education system from pre-school education through higher education. First of all, they affected the higher education system. On the one hand, there was a significant reform of external features: the merger of several universities into one, and the adjustment of the names of universities and institutions. On the other hand, the changes affected the structure of higher education, the levels of education were defined from bachelor’s to master’s course. Competence and practice-oriented approaches began to occupy a dominant position in the learning process, as well as independent work of students was given special attention. On the basis of the Federal State Standards of Higher Education adopted in 2016 in the Law Enforcement major and Jurisprudence training area, for high-quality and effective implementation of their professional activities, graduates of the universities and colleges sponsored by agencies of the Russian Federal Penitentiary Service must acquire a set of cultural, general vocational and specific professional competencies. To achieve the necessary competence (educational) level in the course of learning, the cadets are faced with the task of solving ideological, political, moral, and cognitive problems. Cognitive activity is key and conscious in the learning process, because it permeates the entire educational path of a person, starting with the initial cognitive efforts and ending with the satisfaction of the cognitive needs of trainees. Effective cognitive activity is impossible without positive motivation, sustainable cognitive interest, self-development and self-determination of the individual.
HEART RATE READING PATTERNS OF MOMENT OF INFORMATION OVERLOAD DURING ONLINE INFORMATION SEARCHING

Users are confronted with information overload when searching for information in virtual libraries.Studies claim that information overload leads to changes in the physiological signals of an individual which later results in decreased efficiency in information processing.There is a strong perception that when something changes, there is a moment in which the change occurs.The primary purpose of this research is to detect an indication at the moment of change occurring when searching in virtual libraries by focusing on the pattern reflected in physiological data that can potentially be used as a signal to convey information overload.This study adopted user test methods and methods from psychophysiology.The results from quantitative analysis were presented through graphs and tables.The results indicated that heart rate measurement was the best measure compared to other physiological measurements and the underlying pattern signalling information overload was presented in the form of a matrix.Recommendations for future work include using the pattern to design an application to monitor information load in individuals.
Phygital Banking – A Game Changer In Indian Banking Sector

Phygital banking is a mix of physical and digital entities at all critical interaction points is necessary to ensure a positive customer journey. ‘Phygital’ is about being inherently digital across all channels (physical and digital), operations and the very way banks are organized and manage their business. Banks that can craft a blend that effectively touches customer experience, operations, and delivery are most likely to thrive in a fast-changing world. Banking executives will need to rethink traditional branches and integrate digital into their service models. As banks continue to invest in technology, the value of human interactions remains a crucial success factor.The present depends on secondary data. The aim of the study is to know the importance of transformation of phygital banking and also understand the issues and challenges of digital banking. Therefore, a better model is needed – one that provides the best of both worlds by combining physical interaction with digital services. Realizing the opportunity, some banks today offer phygital services by transforming their branches and humanizing digital experiences.
Cliticization as Extraction: The Big DP Hypothesis Revisited

In this paper, it is argued that cliticization is extraction out of a big DP. Big DPs contain both clitic pronouns and associate DP arguments (Torrego, 1995; Uriagereka, 1995, 2005, among others). The DP can be pronounced, which gives rise to a clitic doubling configuration, or can be null, in which case a simple clitic configuration is obtained. This hypothesis allows us to explain not only the limited movement possibilities of clitic pronouns (cf. Belletti 1999), but also their restricted inventory: Not all complements can be cliticized. If clitic pronouns are reduced maximal projections (Cardinaletti and Starke, 1999), the big DP hypothesis needs to be revisited. Clitic pronouns cannot be assumed to be the heads of big DPs, as was suggested in the abovementioned proposals. The proposal is that clitic pronouns are merged as specifiers of the highest projection of big DPs, namely K(ase)P, which also captures the fact that they are the only elements which bear morphological case in languages like Italian.
Adoption of the Internet of Things in the Healthcare Services of Sri Lanka

The study explored the research issue of factors in the adoption of the Internet of Things in the health care services of Sri Lanka. To address this issue, the study examined Four research objectives respectively Evaluation of the impact of the proposed technological acceptance determinants on adoption towards IoT in the healthcare system evaluation of the moderating effect of Provincial area, Gender, Age in adoption towards IoT in the healthcare system. By extending UTAUT2 with other significant variables, such as perceived creditability and attitude, this study brought the novel insights into consideration for further research. UTAUT2 argued that the most influential antecedent to adoption intention was performance expectancy. This study gave a new perspective to identify trust as an influential factor driving intention to adopt IoT technology. This study provided insights for companies, to understand better what the determinants of adopting IoT products are. The research suggests that increasing physician’s adoption towards IoT and healthcare organizations should create awareness of IoT products. This could be done in two comprehensive stages; first increase awareness among healthcare staff, which should be the focus on the way to bring business benefits to the organization. Secondly, awareness should be created among the patients, the final customers of IoT technology, which should be focused on enhancing both novelty and quality of IoT enabled healthcare products.However, the IoT technology is still in its early premature stage of development and requires an intense evangelization. To a certain extent, this research had identified the critical factors that impact adoption towards IoT in the healthcare industry in the Sri Lankan context. Then results were supported by the empirical study of the research and can be implemented both theoretical as well as managerial context to impose a radical change in the field of technology adoption of the healthcare industry.
Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models

In the financial industrial sector the lightning growth and participation of internet-based transactional events give rise to malicious activities like a fraud that result in financial loss. The malicious activities have no continuous pattern their pattern, behavior, working always keep on changing with the increasing growth in technology. Every time a new technology comes in the market the hoaxer study about that technology and implement malicious activity through the learned technology and internet-based activities. The hoaxer analyzes the behavior patterns of consumers to execute the plan of fraud to cause loss to the consumer. So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions by employing machine learning techniques. This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine. A dataset of electronic payment card is taken from European electronic cardholders, the machine learning techniques are applied on the unstructured and process-free data.
A feature selection model for prediction of software defects

Software is a collection of computer programs written in a programming language. Software contains various modules which make it a complex entity and it can increase the defect probability at the time of development of the modules. In turn, cost and time to develop the software can be increased. Sometimes, these defects can lead to failure of entire software. It will lead to untimely delivery of the software to the customer. This untimely delivery can responsible for withdrawal or cancellation of project in future. Hence, in this research work, some machine learning algorithms are applied to ensure timely delivery and prediction of defects. Further, several feature selection techniques are also adopted to determine relevant features for defect prediction.
Enterprise Risk Management at the State University of New York: A Benchmark for Saudi Universities

Universities face constantly a variety of risks, including strategic, financial, operational, compliance, and reputational risks. To help ensure goals and objectives are met, universities must manage these risks. While some organizations manage risk using an informal process, others have a formalized structured approach. Enterprise Risk Management, ERM, is a formal and continuous process that is designed to identify, assess, prioritize, and manage all risks and opportunities for an institution. The study proposes the use of ERM processes as used in the State University of New York as a benchmark or a reference for Saudi Universities.
Vehicle Counting and Detection

This paper describes about the system to count the number of vehicles on roads and highways by using adaptive background subtraction and blob tracking technologies. Overall, system requires a video stream captured from static cameras installed on roads and highways .The proposed system consists of four stages: 1) Adaptive background subtraction 2) image segmentation 3) vehicle counting 4) vehicle tracking. The necessity of tracing and counting the vehicles is helpful for traffic surveillance. The primary key features of the system are 1) Ability to count the vehicles 2) efficiency, to show that system would give the results with high perfection.