Reviewerly/exHarmony huggingface dataset:
Situational Factors for Modern Code Review to Support Software Engineers’ Sustainability

Software engineers working in Modern Code Review (MCR) are confronted with the issue of lack of competency in the identification of situational factors.MCR is a software engineering activity for the identification and fixation of defects before the delivery of the software product.This issue can be a threat to the individual sustainability of software engineers and it can be addressed by situational awareness.Therefore, the objective of the study is to identify situational factors concerning the MCR process.Systematic Literature Review (SLR) has been used to identify situational factors.Data coding along with continuous comparison and memoing procedures of grounded theory and expert review has been used to produce an exclusive and validated list of situational factors grouped under categories.The study results conveyed 23 situational factors that are grouped into 5 broad categories i.e.People, Organization, Technology, Source Code and Project.The study is valuable for researchers to extend the research and for software engineers to identify situations and sustain for longer.
Position-based intensity change model for illumination variation in digital image correlation

The illumination variation exerts a significant influence on the accuracy and efficiency of digital image correlation (DIC) technology. However, the existing intensity change models assume that all the pixels in the reference subset have the same gray-level intensity change parameters, which cannot accurately reflect the effect of illumination variations. We pay attention to the position effect on intensity variations and propose a position-based intensity change model to study the illumination variation in DIC. The proposed model combines the gray-level intensities, the illumination variation, and the position parameters of a reference subset. The forward additive Gauss–Newton algorithm with a first-order shape function is applied to evaluate the feasibility and effectiveness. Simulation and experimental results verify that the proposed model has higher accuracy in different illumination conditions as compared with the existing second-order intensity change model.
An Efficient Personalized Video Recommendation Algorithm Based on Mixed Mode

The collaborative filtering recommendation algorithm based on k-means clustering is more accurate in the case of large amount of users' ratings, and it is not suitable for the situations that users' ratings are relatively small. The video gene recommendation algorithm based on linear regression uses the opposite scenario. In order to solve the problem of general application of the single recommendation algorithm, this paper proposes a hybrid recommendation algorithm based on collaborative filtering and video gene. This algorithm first constructs user project matrix, calculates user similarity, and then cluster to get a recommendation list by k-means clustering. Then the genetic structure of the video is analysed, the gene preference is combined by style preference and regional preference, and the weight of the gene is determined by linear regression, at last, a recommendation list is obtained by selecting the object with high gene preference. Finally, the final recommendation results are obtained by weighting the recommended results in two recommendation lists. The experimental results show that the proposed algorithm has higher accuracy no matter when the number of users' ratings is large or small.
Imbalanced Big Data Classification using Feature Selection Under-Sampling

Imbalanced learning is the classification problem where the number of observations of one class, far surpasses the number of observations of another class.Different sampling approaches are proposed for paired and Multi-Class imbalanced classification.Paired Imbalanced classification encompasses two classes: one of them is majority, while the other one is a minority class.Multi-Class imbalanced classification contains more than two classes for classification.Under-sampling technique is the better sampling technique among conventional approaches.However, existing approaches may not work in the Big Data environment, as considering all the features might compromise the performance of the system.In this work, a novel method is presented which takes into account only the essential features, as well as, deals with massive data as in Big Data environment.In the proposed system, Feature Selection Under-Sampling technique is used for resampling the data.Feature selection is the vital step because it not only decreases the dimensionality of data but also helps classifier to run faster, and accuracy can also be improved.Over that, SVM learning classifier is adopted to construct the model and test the data.The proposed system is implemented using MapReduce framework by integrating statistical analytical tool R.
Non-Markovian effect on quantum optical metrology under a dissipative environment

Quantum metrology utilizes quantum effects to reach higher-precision measurements of physical quantities compared with their classical counterparts. However, the ubiquitous decoherence obstructs its application. Recently, non-Markovian effects are shown to be effective in performing quantum optical metrology under locally dissipative environments [Phys. Rev. Lett. 123, 040402 (2019)]. However, the mechanism is still rather hazy. Here, we uncover the reason why forming a bound state can protect the quantumness against a dissipative ambient via the quantum Fisher information of entangled coherent states. An exact analytical expression of the quantum Fisher information in the long-encoding-time condition is derived, which reveals that the dynamics of precision can asymptotically reach the ideal-case-promised one easily when the average photon number is small. Meanwhile, the scaling exhibits a transition from the weak Heisenberg limit to the subclassical limit with increasing average photon number. Our work provides a recipe to realize ultrasensitive measurements in the presence of noise by utilizing non-Markovian effects.
Review of Secure File Storage on Cloud using Hybrid Cryptography

The Digital Revolution has brought with it an exponential growth in the usage of digital computation and along with it, the start of the Information Era.Furthermore, companies are expanding globally and opening offices at various locations across the globe.This has brought the need to make access to data from any location possible and feasible.This is where Cloud Computing and Storage comes into the picture.But with cloud storage comes security risks and data leak possibilities.Hence data security is a very important component of cloud storage.This paper presents a review of a system which stores data on the cloud after encrypting it.Hence even if a security breach were to take place, the attacker would get access to encrypted data, which would still ensure data confidentiality.In this system, the user uploads a file to the portal, it gets encrypted and then uploaded onto the cloud.The user can then download their files from the cloud through the portal, which results in the decrypted (or original) file getting downloaded to their local computer.The system also uses two different hybrid approaches for encryption and decryption, namely AES and RSA algorithms, and AES and Blowfish algorithms, and shows a comparative study on the difference between the two approaches.
An Improved Framework for Content-based Spamdexing Detection

To the modern Search Engines (SEs), one of the biggest threats to be considered is spamdexing.Nowadays spammers are using a wide range of techniques for content generation, they are using content spam to fill the Search Engine Result Pages (SERPs) with low-quality web pages.Generally, spam web pages are insufficient, irrelevant and improper results for users.Many researchers from academia and industry are working on spamdexing to identify the spam web pages.However, so far not even a single universally efficient method is developed for identification of all spam web pages.We believe that for tackling the content spam there must be improved methods.This article is an attempt in that direction, where a framework has been proposed for spam web pages identification. The framework usesStop words, Keywords Density, Spam Keywords Database, Part of Speech (POS) ratio, and Copied Content algorithms.For conducting the experiments and obtaining threshold values WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets have been used.An excellent and promising F-measure of 77.38% illustrates the effectiveness and applicability of proposed method.
LEA-SIoT: Hardware Architecture of Lightweight Encryption Algorithm for Secure IoT on FPGA Platform

The Internet of Things (IoT) is one of the emerging technology in today's world to connect billions of electronic devices and providing the data security to these electronic devices while transmission from the attacks is a big challenging task.These electronic devices are smaller and consume less power.The conventional security algorithms are complex with its computations and not suitable for IoT environments.In this article, the hardware architecture of the new Lightweight encryption algorithm (LEA) for the secured Internet of things (SIoT) is designed, which includes Encryption, decryption along with key generation process.The New LEA-SIoT is a hybrid combination of the Feistel networks and Substitutionpermutation Network (SPN).The encryption/decryption architecture is the composition of Logical operations, substitution transformations, and swapping.The encryption/decryption process is designed for 64-bit data input and 64-bit key inputs.The key generation process is designed with the help of KHAZAD block cipher algorithm.The encryption and key generation process are executing in parallel with pipelined architecture with five rounds to improve the hardware and computational complexity in IoT systems.TheLEA-SIoT is designed on the Xilinx platform and implemented on Artix-7 FPGA.The hardware constraints like area, power, and timing utilization are summarized.The Comparison of the LEA-SIoT with similar security algorithms is tabulated with improvements.
Mobile Assisted Language Learning: Evidence of an Emerging Paradigm

Mobile-Assisted Language Learning (MALL) may not have reached the highest levels of maturity; still, it manages to forge an identity of its own in the educational sphere.The intent of this paper is to substantiate the legitimacy of MALL.In particular, this study adopts a content-design approach as a means of researching the reciprocity between the offerings of mobile technology and the imperatives of language learning.The content-design estimation of the different extensions of MALL revealed the multiple affordances of mobile technology in the language learning discourse.Short Message Service (SMS)-based language learning, educational podcasting, mobile social networking, and game-based learning emphasise the potential of mobile technology in a wide range of language learning contexts.Still, for MALL to reach its full potential, it must belong dependently of a valid theoretical and pedagogical orientation in language learning.
Reversible Data Hiding Based Key Region Protection Method in Medical Images

The transmission of medical image data in an open network environment is subject to privacy issues including patient privacy and data leakage. In the past, image encryption and information-hiding technology have been used to solve such security problems. But these methodologies, in general, suffered from difficulties in retrieving original images. We present in this paper an algorithm to protect key regions in medical images. First, coefficient of variation is used to locate the key regions, a.k.a. the lesion areas, of an image; other areas are then processed in blocks and analyzed for texture complexity. Next, our reversible data-hiding algorithm is used to embed the contents from the lesion areas into a high-texture area, and the Arnold transformation is performed to protect the original lesion information. In addition to this, we use the ciphertext of the basic information about the image and the decryption parameter to generate the Quick Response (QR) Code to replace the original key regions. Consequently, only authorized customers can obtain the encryption key to extract information from encrypted images. Experimental results show that our algorithm can not only restore the original image without information loss, but also safely transfer the medical image copyright and patient-sensitive information.
Image Enhancement Techniques for Images at Blur Motion and Different Noises

Image restoration is an art to improve image quality by disposing of reducing the amount of noise and blurring present in the image.So it is one of the research titles of interest to the researchers.The images get degraded due to environmental conditions and atmospheric difference, it is therefore important to retrieve original images using different algorithms of image processing.There are widespread applications to restore images in our world today.In this research we present a new proposed method to manipulate image using median filter and has also applied inverse filter to image restoration, then combining the proposed method with inverse filter.The proposed method has been evaluated and compared with inverse Filter and with the merger by using different performance with parameters to verify efficiency and performances of these methods.The experimental results on the test image will explain the capabilities of the proposed method to restored image, were the value of the image quality and PSNR (peak Signal to Noise ratio) is better compared with inverse filter, where values of PSNR for Lena Image is 70.3536 and for Girl Image is 71.5410 when using the proposed method of median filter, while values of PSNR when using inverse filter are 62.3225 and 62.6008 respectively when speckle noise.
AEDmts: An Attention-Based Encoder-Decoder Framework for Multi-Sensory Time Series Analytic

Numerous IoT applications have emerged in human healthcare area with advances in wearable electronics. Multiple physical and physiological data bearing strong spatial-temporal characteristic collected by the wearable sensors is sent to the smartphones where it is aggregated and transferred to back-end applications for further processing. Analyzing the multivariate time series is of great importance yet very challenging as it is affected by many complex factors, i.e., dynamic spatial-temporal correlations and external factors. In this paper, we propose an attention-based encoder-decoder framework for multi-sensory time-series analytic. It consists of four parts: data collection, data mining, time-series analytic part and user interaction. A temporal-attention based encoder-decoder model is proposed to make a long-term prediction of multiple time series to realize the real-time user interaction. The proposed model uses the LSTM model to learn the long-term dependence of the time series related to certain motion sequence. The attention mechanism connects the encoder and the decoder to make long-term predictions for future time series. Through extensive experiments, the proposed model has achieved better results in short-term and long-term predictions compared with the state of art methods. An activity recognition algorithm based on LSTM is also proposed in this framework to identify daily human activities and sports activities accurately. Through five-fold and ten-fold cross-validation strategies and comparison with six baseline machine learning models, the activity recognition algorithm has a recognition rate of 98.89% and 99.28% for human activity.
Aid, Microfinance and income inequality: A global view

This article studies the relationship between foreign aid and microfinance flows and income inequality. Using a broad theoretical framework on inequality, and panel data for 87 countries from 1995 to 2012, we study the dynamics of aid and microfinance and how these relate to income inequality. Our results highlight important differences across world regions and the need to consider specificities of the context when assessing the impact of both aid and microfinance on inequality. In this line, we provide an analysis for three different countries: Bolivia, Bangladesh, and Indonesia.
THE ROLE OF INSURANCE IN CYBER RISK MANAGEMENT IN ENTERPRISES

Modern enterprises run their activities in an innovative environment.The use of modern technologies is, therefore, becoming a prerequisite for maintaining the current market position and ensuring competitiveness with other entities.However, the use of innovative IT solutions creates the risk that certain disturbances in the functioning of computer programs or the occurrence of cyber attacks will bring losses to the company, thus negatively affecting financial results.Materialisation of cyber risk may also have a negative impact on the company's image and, consequently, a potential decrease in sales of goods.Therefore, a proper assessment and management of cyber risk in modern enterprises is important.One of the methods to manage this risk is cyber insurance.A large number of cyber attacks and the development of new technologies has resulted in enterprises using cyber insurance, as evidenced by the significant increase in gross written premium in recent years.The research conducted by various institutions forecasts a dynamic growth of cybernetic insurance premiums in the coming years, suggesting that cyber insurance will grow in popularity.The purpose of the article is to present the necessary conditions and risks for the functioning of innovative enterprises in the current economic environment.In addition, the article analyses the occurrence of individual risks in business operations and their financial effects.The research also presents a scale in the use of cyber security in managing cybernetic risk of enterprises and provides future development directions of cyber security products.The research utilised methods of analysis and synthesis of secondary data in formulating conditions.
Toward FAIR Knowledge Turns in Bioinformatics

Sharing of bioinformatics data within research communities holds the promise of facilitating more rapid discovery, yet the volume of data is growing at a pace exponentially greater than what traditional biocuration can support. We present here an approach that we have used to empower data producing researchers to curate high quality shared data that is ready for reuse and re-analysis.
A Scalable IoT Video Data Analytics for Smart Cities

The smart city is a comprehensive application of information resources and a high degree of information technology integration.With the technical support from IoT (Internet of things), smart city need to have three features of being instrumented, interconnected and intelligent.IoT provides the ability to manage, remotely monitor and control devices from massive streams of real-time data.Our model offers a scalable IoT video data analytics applications for Smart cities to end users, who can exploit scalability in both data storage and processing power to execute analysis on large or complex datasets.This model provides data analytics programming suites and environments in which developers and researchers can design scalable analytics services and applications.A cloud/edge-based automated video analysis system to process large numbers of video streams, where the underlying infrastructure is able to scale based on the number of camera devices and easy to integrate analytic application.The system automates the video analysis process and reduces manual intervention.The design of our model is developed to be easily extended for new kinds of IoT devices, message routing and queueing, and data analytics, to permit specific application to be programmed via the paradigm to be flexible yet simple.
Ramath: Mobile Application for Math Learning using Augmented Reality

This paper presents a mobile application (app) using Augmented Reality (AR) with the support of game play mechanics in a non-game application, oriented to help 12 to 15 year's old students to learn several math subjects.We associate concepts such as gamification and Problem-Based Learning (PBL) within the app, making the subjects much more interesting to the students and encouraging them to be fearless of math by taking advantage of the human's psychological predisposition to engage in gaming.With the collected information from the research and the help of a predictive algorithm, we try to show how much the technology can help in education.
DESIGN AND IMPLEMENTATION OF VIRTUAL SIMULATION TEACHING SYSTEM FOR UAV BASED ON WEBGL

Abstract. With the advent of the 5G era of digital smart city, “UAV + Application” is booming, and there is more and more demand for UAV remote sensing technology. How to cultivate high-tech application talents of UAV has become the primary problem to be solved in the development process of UAV. Based on WebGL technology, this paper combines virtual reality and panoramic technology, and adopts lightweight B/S architecture to develop an open platform for virtual simulation teaching that integrates online display, teaching, practical training, assessment, communication and management of UAV. The platform realizes the “three-dimensional teaching material, teaching platform, virtual exhibition hall” of the trinity teaching mode. Students can log in through the account to carry out theoretical learning, flight control operation learning and other interfaces, which can meet the training needs of high-tech application talents of UAV and strengthen students' understanding of theory and practice, simulation training has the advantages of repeated practice, without being limited by the space and time, and solve the problems of low interest, poor scalability and high cost of traditional UAV training.
Technical Debt Aware Estimations in Software Engineering: A Systematic Mapping Study

Context:The Technical Debt metaphor has grown in popularity.More software is being created and has to be maintained.Agile methodologies, in particular Scrum, are widely used by development teams around the world.Estimation is an often practised step in sprint planning.The subject matter of this paper is the impact technical debt has on estimations.Objective: The goal of this research is to identify estimation problems and their solutions due to previously introduced technical debt in software projects. Method:The Systematic mapping study (SMS) method was applied in the research.Papers were selected from the popular digital databases (IEEE, ACM, Scopus, etc.) using defined search criteria.Afterwards, a snowballing procedure was performed and the final publication set was filtered using inclusion/exclusion criteria.Results: 42 studies were selected and evaluated.Five categories of problems and seven proposed solutions to the problems have been extracted from the papers.Problems include items related to business perspective (delivery pressure or lack of technical debt understanding by business decision-makers) and technical perspective (difficulties in forecasting architectural technical debt impact or limits of source code analysis).Solutions were categorized in: more sophisticated decision-making tools for business managers, better tools for estimation support and technical debt management tools on an architectural-level, portfolio approach to technical debt, code audit and technical debt reduction routine conducted every sprint. Conclusion:The results of this mapping study can help taking the appropriate approach in technical debt mitigation in organizations.However, the outcome of the conducted research shows that the problem of measuring technical debt impact on estimations has not yet been solved.We propose several directions for further investigation.In particular, we would focus on more sophisticated decision-making tools.