Reviewerly/exHarmony huggingface dataset:
TERTIARY STUDY ON LANDSCAPING THE REVIEW IN CODE SMELLS

Code smells are a sign of bad design and poor implementation decisions, which increases fault proneness and code complexity and reduces software system maintainability. Digital innovations are increasingly transforming the world in which businesses operate. The infusion of digital technologies into services, processes and goods has significant implications for how companies achieve and retain competitive advantages. The timely detection of code smells in a company&apos;s systems and software that operates an internet-based business, such as an e-commerce company that sells products and services over the internet, aids the company&apos;s team to build strategies to maintain and gain competitive advantages in the digital economy. However, there has been no specific landscaping research on code smells in recent studies that combine all of the disparate and classified information into a single paper. We performed a manual search process to collect various systematic literature reviews (SLRs) and mapping studies from 2015 to 2020. The aim was to cover and integrate as much information as possible based on code smells, such as previous years&apos; methods and techniques, domains targeted thus far, and challenges faced between 2015 and 2020.
Deep relightable appearance models for animatable faces

We present a method for building high-fidelity animatable 3D face models that can be posed and rendered with novel lighting environments in real-time. Our main insight is that relightable models trained to produce an image lit from a single light direction can generalize to natural illumination conditions but are computationally expensive to render. On the other hand, efficient, high-fidelity face models trained with point-light data do not generalize to novel lighting conditions. We leverage the strengths of each of these two approaches. We first train an expensive but generalizable model on point-light illuminations, and use it to generate a training set of high-quality synthetic face images under natural illumination conditions. We then train an efficient model on this augmented dataset, reducing the generalization ability requirements. As the efficacy of this approach hinges on the quality of the synthetic data we can generate, we present a study of lighting pattern combinations for dynamic captures and evaluate their suitability for learning generalizable relightable models. Towards achieving the best possible quality, we present a novel approach for generating dynamic relightable faces that exceeds state-of-the-art performance. Our method is capable of capturing subtle lighting effects and can even generate compelling near-field relighting despite being trained exclusively with far-field lighting data. Finally, we motivate the utility of our model by animating it with images captured from VR-headset mounted cameras, demonstrating the first system for face-driven interactions in VR that uses a photorealistic relightable face model.
Collaborative Learning and Social Media Acceptance for Student Engagement

This study examines the influence of collaborative learning dimensions and social media usage on tertiary students’ engagement via e-learning mode in the time of COVID-19 pandemic situations. The dimensions of collaborative learning are interaction with peers, interaction with lecturers, and knowledge sharing; the social media or technology acceptance dimensions are mainly on the ease of use and the perceived usefulness of this new media platform. Collaborative learning and social media usage are predicted to have influenced students' learning engagement in the online and e-learning mode. 201 Malaysian tertiary students from public and private universities have completed the survey forms via purposive and snowball sampling methods. Descriptive analysis was executed in SPSS, whereas model validation and structural model were analyzed using Smart-PLS 3.3. The results revealed that collaborative learning positively influences the interaction with peers and lecturers and knowledge-sharing behavior. Technology acceptance elements positively affect social media usage. Interaction with peers, social media usage, and knowledge sharing increases tertiary student engagement in e-learning. The only insignificant predictor is interaction with lecturers. This study revealed positive findings that social media usage is the most dominant and high-performance factor impacting tertiary student engagement in e-learning.
Sistem Keamanan Gedung Menggunakan Kinect Xbox 360 Dengan Metode Skeletal Tracking

The incidence of fire and theft is very threatening and causes disruption to people's lifestyles, both due to natural and human factors resulting in loss of life, damage to the environment, loss of property and property, and psychological impacts. The purpose of this study is to create a building security system using Kinect Xbox 360 which can be used to detect fires and loss of valuable objects. The data transmission method uses the Internet of Things (IoT) and skeletal tracking. Skeletal detection uses Arduino Uno which is connected to a fire sensor and Kinect to detect suspicious movements connected to a PC. Kinect uses biometric authentication to automatically enter user data by recognizing objects and detecting skeletons including height, facial features and shoulder length. The ADC (Analog to Digital Converter) value of the fire sensor reading has a range between 200-300. The fire sensor detects the presence of fire through optical data analysis containing ultraviolet, infrared or visual images of fire. The data generated by Kinect by detecting the recognition of the skeleton of the main point of the human body known as the skeleton, where the reading point is authenticated by Kinect from a range of 1.5-3 meters which is declared the optimal measurement, and if a fire occurs, the pump motor will spray water randomly. to extinguish the fire that is connected to the internet via the wifi module. The data displayed is in the form of a graph on the Thingspeak cloud server service. Notification of fire and theft information using the delivery system from input to database
Smart Railway Track and Crossing Gate Security System Based on IoT

The railway system is one of the most widely used modes of transportation due to its low cost. To keep the railway system running smoothly, continuous track monitoring is needed. These days, the railway system is manually supervised. As a result, there is a greater risk of disasters, such as fatalities, occurring as a result of human error while monitoring. The main problem with manual system monitoring is that it takes a long time to process all of the necessary data. Since railway tracks are built over thousands of miles, it is virtually impossible to manually control the device over such a longdistance. At railway crossings, a lot of accidents happen. Crossing gates are usually opened and closed after receiving direct input from the station. If there is a delay in obtaining information from the station, there is a risk of swearing incidents. The main goal of this research is to simplify and protect the railway system. The proposed system employs Force Sensitive Resistor (FSR) detectors for automatic side road crossing protection. Any type of breakage, as well as vibration, can be efficiently detected with a higher degree of precision using Light Dependent Resistor (LRR) and laser detectors. In the event of an unexpected situation, such as an accident, the GSM module will begin communicating via message with the nearest control room for assistance. Sonar sensors are often used for obstacle avoidance when something unexpectedly appears in front of the train. The Internet of Things (IoT) has been added to the system to allow it to be monitored from anywhere in the sphere. The Arduino UNO is a microcontroller that serves as the system's backbone. The framework has the potential to be extremely beneficial to our country's railway economic growth.
Nonlinear Modeling With Scikit-Learn, PySpark, and H2O

This chapter executes and appraises a nonlinear method for binary classification (called logistic regression) using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, Spark MLlib, and H2O). To begin, it clarifies the underlying concept behind the sigmoid function.
STEAM Competence for Teachers: Features of Model Development

The paper considers approaches to the development of model of STEAM competence for teachers.The concept of STEM/STEAM competence and their components are analyzed.The existing models of STEM/STEAM competence of foreign and Ukrainian researchers are considered, their components are described.The factors affecting the development of STEM competence model for teachers are identified.Based on the analysis of the considered researches, the authors developed the model of STEAM competence for teachers, defined and described its components.To clarify the structure of the model of STEAM competence for teachers, the authors conducted a diagnostic survey of Ukrainian educators.Its results are analyzed.They confirmed the correctness and completeness of the structure of the proposed model.
Machine Learning-Based Predictive Model to Improve Cloud Application Performance in Cloud SaaS

The cloud is changing the method of working for private companies. Using the cloud is turning out to be a pattern for everybody as it gives better information, data storage, adaptability, flexibility, scalability and security in comparison to the conventional methodologies. It provides hardware and software resources on demand. The virtualization method is utilized to give the resources as per need and use. The virtual machines are created on cloud and can be overseen either physically or by robotized calculations. The procedure of expanding the resources is called scaling. The cloud services will charge only for resources that you have used, you just need to pay for what you use. This saves the expense of introducing the actual hardware or buying products which are not frequently utilized. It gives business and economic advantages to organizations. Cloud computing is becoming widely popular due to its advantages and ease of use. A vital element of distributed computing or cloud is versatility which is also known as scaling, which permits the provisioning and de-provisioning of processing resources on interest, through autoscaling. Autoscaling strategies vary, and include different segments at the framework, stage and programming levels. Autoscaling likewise is covered under quality of service parameters, in this way adding to support level arrangements, and control methods to make the autoscaling measure versatile. An investigation of autoscaling models, existing procedures and open issues gives a thorough chance to recognize future exploration of better solutions.
Application of CNN-based Face Recognition Technology in Smart Logistics System

Smart logistics combines the Internet of Things with big data, cloud computing and other information processing technologies to improve all aspects of logistics business and optimize the status quo of logistics services. This research aims to study the application of Convolutional Neural Network-based face recognition technology in intelligent logistics systems. First, briefly describe the key concepts of Faster Region with CNN feature, convolutional neural networks, the essence of smart logistics, and the background of the overall architecture design of CNN-based smart face recognition logistics, including data modules, data storage modules, and data presentation layers. Analysis of bottlenecks in other smart logistics systems, such as time-consuming large-capacity storage. By choosing WIDER Face as the sample training set and FDDB as the sample test set, the traditional face detection algorithms are compared. Experimental results show that CNN has the fastest detection speed. And the loss rate of CNN is low, and the accuracy can reach 91% under the condition of unlimited faces.
Socio-Psychological Factors Influencing The Formation Of Leadership Qualities In The Student Community

This article of training sessions is designed to educate responsible, proactive, creative, purposeful leaders. The article is focused on identifying and realizing the leadership potential of a teenager. Information will be able to help students more fully and objectively realize their leadership potential and ways of its development in the framework of personally and socially useful activities.
STRUCTURAL-CONTENT MODEL FOR FORMING THE DIGITAL COMPETENCE OF TEACHERS

The article is devoted to the actual problems of the education system. The educational potential of digital technologies, the accelerated development of distance technologies contribute to the change of teaching methods, forms of education implementation. It is obvious that digital competence is an integral part of the professional competence of teachers, since it is important that teachers master learning technologies based on modern digital technologies, which are the driving force for improving education. This study, funded by the Science Committee of the Ministry of Education and Science of the Republic of Kazakhstan (Grant №AP09259047), reveals the prerequisites for the digital transformation of education, the importance of changing the structure of education and the organization of the educational process. General theoretical research methods were used: the study and analysis of literary data of a theoretical orientation; system-structural analysis; concretization of the role of approaches to the formation of digital competence of teachers and the development of a structural and content model for the formation of digital competence of teachers. Based on the analysis of the relevant literature, the author of the article identifies the advantages of the selected approaches to the formation of quantitative competence of teachers, their main characteristics. Approaches (systemic, activity-based, axiological, personality-oriented approaches) to the formation of digital competence. Used as the methodological basis of the study, not only do not contradict each other, but also complement each other, served as the basis for building the concept and structural and content model of the formation of digital competence of the teacher. A structural and content model of the formation of digital competence of teachers is proposed. The roles of the components of the structural and content model of the formation of digital competence of teachers are determined.
2019 Election Campaign Model in Indonesia Using Social Media

The presence of social media platforms, especially Twitter, in Indonesia since the 2014 and 2019 elections have been used as a campaign media used by political parties to change conventional campaign strategies into virtual campaigns that are more informative, interactive, and participatory. This paper will analyze Twitter social media used by political parties, including @PDI_Perjuangan, @Gerindra, @DPP_PPP, @PKSejahtera during the campaign from January to April in the 2019 General Election, with analysis tools using Nvivo 12Plus. Using big data analysis from the Twitter of political parties, we found several things: first, the various news posted during the campaign with the amount and intensity of the news and becoming a trending topic will affect the number of followers owned by political parties, the more often political parties post news it will affect voter segmentation, the number of likes for the message content, the number of followers, and the number of retweets. Second, the popularity of public figures can also be a measure of the success of the campaign strategy made through the news on Twitter social media. Third, the 2019 election campaign produced an effective campaign model when compared to previous campaigns on social media.
Lattice-Based Integer Factorisation: An Introduction to Coppersmith’s Method

In Chapter 4, Lattice-Based Integer Factorisation: An Introduction to Coppersmith’s Method, Alexander May investigates the use of LLL to factor integers as pioneered by Coppersmith. Conceptually, Coppersmith’s method can be deceptively simple: given additional information about an integer to factor (e.g., the knowledge that an RSA key pair (N; e) has a small corresponding private exponent d), derive a system of equations with a small root that reveals the factorization and use LLL to find the small root. As a result, it becomes possible to explore exponentially sized search spaces, while preserving polynomial time by using the famous LLL lattice reduction algorithm. Yet, exploiting Coppersmith’s method in a cryptographic context optimally often involves a number of clever choices related to which system of equations to consider. At first, this is a tantalisingly annoying problem where the choice may appear obvious only in retrospect. May uses his extensive experience in improving the state of the art to explain the reasoning behind various applications in Chapter 4.
Image Anomaly Detection Based on Adaptive Iteration and Feature Extraction in Edge-Cloud IoT

The Internet of Things (IoT) has penetrated into various application fields. If the multimedia information obtained by the IoT device is tampered with, the subsequent information processing will be affected, resulting in an incorrect service and even security threat. Therefore, it is very necessary to study multimedia forensics technology for IoT security. In the edge-cloud IoT environment, an image anomaly detection technology for security service is proposed in this paper. First, preprocessing is performed before image anomaly detection. Then, we extracted sparse features from the image to roughly localize the region of anomaly detection. Feature extraction based on the polar cosine transform (PCT) is then performed only on the candidate region of anomaly detection. To further improve the detection accuracy, we use iterative updating. This method makes use of the feature that the edge node is closer to the multimedia source in physical location and migrates the complex computing task of image anomaly detection from the cloud computing center to the edge node. Provide a security service for abnormal data and deploy it to the edge-cloud server to reduce the pressure on the cloud. Overall, preprocessing improves the ability of feature extraction in smooth or small region of anomaly detections, and the iterative strategy enhances the security service. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods.
Consumers’ perception on the use of cognitive computing

Abstract In a world dominated by data, information is one of the most important resources. But simply collecting and storing data is not enough, as data has to be transformed into information. Researchers agree on the fact that both computing power and analytical methods allow this at an unprecedented level. Given the consistent and wide applicability of such systems and technologies today, the present work goes into possible uses of cognitive computing and sets its focus on the retail sector. Cognitive computing generally helps with consumer profiling, deriving their preferences, delivering suitable product recommendations, and contouring the consumer experience when interacting with companies. The main aim of the present work is to investigate consumer experiences with the help of cognitive computing and lay ground for further research on this topic. In the following sections of the present article, we define the concept of cognitive computing, with regard to its meaning for multiple, diverse data streams and types. We then empirically investigate the use of CC for shaping and enriching consumer experiences with the help of cognitive virtual assistants (CVAs). After conducting quantitative research, we discuss differences in perceptions between two age groups (under and over 40 years).
Mastering the Skills Gap at Systemic Level – The Education Management Information System: A Key Element for an Effective Digital Transformation in Education

Educational leaders must make informed predictions in order to design and adopt relevant educational strategies to bridge the skills gap, so they need reliable and trustworthy data.A well designed and properly rolled-out Education Management Information System (EMIS) will assure precise, fluid and timely datasets which can be easily interpreted, being able to extract the insights needed by using the adequate tools for data analysis, filtering out redundant or irrelevant metrics.Being not only an IT tool and a database, but rather a system within a larger education system, EMIS contributes to implement the policies and practices by which higher education institutions effectively collect, protect and use digital information assets to meet academic and business needs.This paper highlights the role and the importance of an effective EMIS and the biggest challenges in implementing it.
Reflecting on the Distance Learning Provision in Dubai Private Schools

The purpose of this article is to reflect on the online learning offered by Dubai private schools in response to the Covid-19 pandemic. This article considers both positive and negative features of provision, in order to suggest recommendations for future outbreaks. A comprehensive review of literature synthesizes with the results of an online survey. The period of March until July 2020 is referred to as Phase 1 of online learning. Phase 2 covers September and December 2020. The findings show that educators were generally positive toward online learning but have mixed opinions regarding the permanence, opportunities, and approaches of individual schools. Teachers discussed the progression of online learning within their settings and made multiple recommendations for the future including focusing on social–emotional development and well-being, limiting screen time where possible, ensuring Ed-Tech is appropriate for individual needs, and committing to an ongoing evaluation of curriculum adaptation. It was also suggested that schools review their approach to building technological skills, as well as the age in which this area of development is encouraged.
Analysis and Discussion of Long-Sentence Structure in Legal Translation

This paper analyzes the three elements of the structure of legal English (conditional sentence, logical connector and and or, modified limit components), and proposes how these three elements can be used in Chinese and English legal translation to avoid ambiguity or error in a long sentence.
Demo: An Experimental Environment Based On Mini-PCs For Federated Learning Research

There is a growing research interest in Federated Learning (FL), a promising approach for data privacy preservation and proximity of training to the network edge, where data is generated. Resource consumption for Machine Learning (ML) training and inference is important for edge nodes, but most of the proposed protocols and algorithms for FL are evaluated by simulations. In this demo paper, we present an environment based on distributed mini-PCs to enable experimental study of FL protocols and algorithms. We have installed low-capacity mini-PCs within a wireless city-level mesh network and deployed container-based FL components on these nodes. We show the deployed FL clients and server at different nodes in the city and demonstrate how an FL experiment can be set and run in a real environment.
Domain Adaptation

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.