Reviewerly/exHarmony huggingface dataset:
Comparative study of Sampling Techniques for Software Defect Prediction

SDP has been an evolved and important step in the Software Development Life Cycle during the testing phase. Significant research has gone through the prediction of defects thereby enhancing the quality of software produced quickly and efficiently. SDP datasets are quite imbalanced, and this has caused the predictive models to suffer in terms of performance. In this research we have observed and studied the impact that Over Sampling Technique like Synthetic Minority Over Sampling Technique and Under Sampling Technique like Random Under Sampling Technique have on the prediction of software defects. Useful features were extracted from the datasets using Random Forest, then, we have implemented ensemble classification models such as AdaBoost and Bagging Classifier, in addition to that we have also observed the predictive nature of Support Vector Machine. Finally, we compared the results obtained between the various models, the different sampling techniques, using performance measures like El-Score, Accuracy, Area under the Receiver Operating Curve as well as G-Mean.
An Efficient Mouse Tracking System Using Facial Gestures

A large number of human beings influenced with neuro-locomotor handicaps or those incapacitated by injury can&#x0027;t utilize computers/laptops for fundamental assignments like sending or getting messages, perusing the web, watching beloved Television shows or films. Through a past research study, it was presumed that eyes are a fantastic possibility for universal registering since they move in any case during cooperation with processing hardware. Utilizing this hidden data from eye developments could permit taking the utilization of computers back to these type patients. For this reason, we propose an mouse tracking framework which is totally worked by natural eyes as it were. The motivation behind this work is to develop an mouse tracking system that can viably follow eye-developments and empower the client to perform activities planned to explicit eye developments/signals through computer/laptop webcam. The proposed model controls mouse movements through facial gestures.
Composite Movie Recommendation System

These days, recommendation systems have made discovering the things simple that we need. Movie recommendation systems target helping movies devotees by recommending what movies to observe without having to go through the long process of choosing from a large set of movies that go up to thousands and millions that is time-consuming and confusing. In this project, our point is to lessen human exertion by proposing motion pictures dependent on the client&#x0027;s advantages. to handle such problems, we introduced a model combining both content-based and collaborative approaches. It will give logically express results contrasted with various frameworks that depend on a content-based approach. Content-based proposal frameworks are compelled to individuals, these frameworks don&#x0027;t endorse things out of the container, along these lines restricting your decision to investigate more. In all what we are trying to say is that we have focused on a framework that is able to settle these issues.
Deep Learning Based Image Compression in DWT Domain

We live in a digital era where the generation and transmission of data increases almost exponentially with the advance of time. A major chunk of this data is in the form of images owing to the catchy nature of visual graphics and the affinity of human mind towards the same. This has led to the evolution as well as invention of numerous applications that are centered around the concept of socializing through images and has indeed brought to life the idea that an image speaks more than a thousand words. With such enormous amounts of data being generated in the form of images and the need for fast transmission, it becomes necessary to minimize the storage space and the transmission cost of these images which is where compression is needed. Over the years, a lot of research has been carried out with the same objective and lot of techniques have been adopted to design an efficient model for performing this compression. Here, we have introduced a model which works in the wavelet transform domain. It makes use of the single Low Frequency sub band (LF) to predict the three High Frequency sub bands (HF) into which the image is split. This helps in the reduction of redundancy in the HF bands post which they are passed through the Auto Encoders for encoding which makes use of conditional probability models to predict the encoded codes for better compression. The model is a neural network and the training performed is unsupervised. The proposed model performs better than the traditional compression models such as JPEG, JPEG2000 and BPG.
Machine Learning Based Network Wide Ad Blocking System

In this modern world, internet has become a very basic requirement for every human being. This attracted Corporates to show advertisements on the internet. Meanwhile there were lots of scams, annoying advertisements, trackers, spy-wares, key-loggers, viruses and worms on the internet. There exists many software to block those malicious URLs but they work only on web browsers. These software&#x0027;s has predefined list of URLs which are matched and removed while loading a webpage causing a delay every time. A lots of new phishing URLs are created everyday which are not blocked by the software. To overcome all these problems an access point is created using raspberry pi in which the malicious URLs are blocked using DNS-Masq. A Decision-Tree based machine learning model is created to predict the maliciousness of URLs and added to blacklist regularly. So that people can escape from tracking, advertisements and malicious threats permanently and work without any fear and distractions.
Performance Evaluation of GWO Algorithm Using Different Initialization Strategies

This paper compares the performance of grey wolf optimization (GWO) algorithm for two different initialization strategies. These initialization strategies are adopted when a dimension of any solution over whole iterative procedure violates the search space. In first strategy, the violating dimension is initialized at the boundary of search space. While, in second one, the violating dimension is initialized randomly within the search space. Thus modified GWO algorithm is applied on five different unimodel benchmarks functions for performance comparison. The performance comparison is done on the basis of results obtained for the value of objective function (i.e. figure of merit), standard deviation, mean, minimum and maximum values figure of merit.
Two-Way Sign Language Conversion for Assisting Deaf-Mutes Using Neural Network

In the human-machine interactive era gesture plays an important role in communication. Building a bridge between the deaf-mute community and society seems to be vital in the fast-moving world with increasing employment opportunities. The usage of sign language has reduced the gap but studying and understanding sign language seems to be difficult for the common people. Developing an assistive device that will translate the sign language to a readable format will help the deaf-mutes to communicate with ease to the common people. The proposed system uses Convolution Neural Network (CNN) for converting sign language to speech. The CNN model gives an accuracy of 95.5&#x0025;.
Gradient and Color Intensity based Dense Disparity Estimation Using Adaptive Weight Aggregation

Dense disparity estimation is an important step in reconstruction of 3D scene which provides disparity at each pixel of an image. Disparity estimation techniques employ various attributes of an image pixel such as gray level, color intensity and gradient value for accurate depth computation. Both the intensity as well as the gradient based cost measures have their shortcomings: the intensity based methods are noise prone whereas the gradient based cost measures fail in smooth regions. In this paper, a disparity estimation scheme has been proposed which applies the gradient cost measure on grayscale and sum of absolute difference cost measure on color level which is aggregated using guided filtering. The disparity map refinement is carried out using weighted median filtering and a filtering mask. This scheme is applied to the standard Middlebury data set and compared online with other existing methodologies. Simulation results prove the efficacy of the proposed technique and is found to perform better than most of the existing techniques.
Building information modeling on blockchain: basic principles, development tools, an application scenario, and future directions

During the 2010-2020 decade, blockchain technology, a technology in search of real-world use cases, has received attention from the construction sector. This chapter introduces basic blockchain concepts, presents development tools, and demonstrates an application scenario in the context of delivering a critical infrastructure project. The chapter also highlights the potential, limitations, and applicability of blockchain technology in the construction sector. The chapter outlines prospects and challenges in running BIM applications, as well as project delivery systems in general, on blockchain technology. Research into blockchain technology by construction engineering and management researchers are covered in the process. Development of a simple, naive smart contract-powered social network platform for project delivery is described to depict the dynamics between the front- and the back-end of a distributed application running on blockchain. Coordinated efforts are critical to avoid reproduction of a fragmented, uncoordinated, and inefficient legacy construction sector in virtual space.
Blocks

The claim that blockchain will disrupt life as we know it has been voiced in almost every corner of the world leaving few topics untouched. But how can we engage with such a claim? How can we try to understand the kinds of changes that are being fostered by this technology? The idea for this chapter is to try and sketch out ways of reading blockchain technology as narrative technology. Rather than inquiring into the stories about blockchain technology, I focus on the narrative capacity of the technology itself; treating the technology as a mode for mediating how we interact in and understand our social, economic and institutional engagements. I conceptualize the architecture of the technology as a site of politics, where normativity is constituted through the demands of representation of the narrative form of the technology. Rather than providing a final judgment on the potential and threats of the technology, the contribution aims at developing a vocabulary that foregrounds the ordering capacity of blockchain technology and inquiries into the ways it mediates our lives.
Machine learning prediction of 6-DOF motions of KVLCC2 ship based on RC model

This study uses a machine learning technique based on the Reservoir Computing (RC) model to predict the surge, sway, heave, roll, pitch, and yaw (6-DOF) motions of the KVLCC2 ship in an irregular wave environment. The trained RC model can predict the 6-DOF motions and give the predicted length of 2–5 wave cycles ahead with good accuracy. This work shows the strong ability of machine learning to predict vessel wave-excited motions. It implies that machine learning has important guiding significance in real-time forecasting for motions of both manned and unmanned ships.
Utilization of INAA Technique for Studying the Negative Impact of Human Activities on Coral Reefs in the Quseir harbour area

Abstract Coral reef samples collected from the Egyptian Red Sea coast’s Quseir harbour were analysed using the INAA technique for qualitative and quantitative analysis. The concentration of heavy metals, rare earth elements, and elemental contents were measured in coral reefs. The samples were assembled with their standards and irradiated in Egypt’s Second Research Reactor at the same time (ETRR-2). The gamma-ray spectra of an HPGe detector with a 100 percent relative efficiency were studied. The elements determined are: Al, Ti, Na, K, Mg, V, Cl, Ca, Mn, Cr, As, Co, Zn, Fe, Br, Tb, Zr, Hf, Sb, Ba, Sc, Nd, La, Sm, Ce, Eu, Yb, Ta, U, and Th. The present work is worried about the use of INAA to attain an accurate knowledge about the elemental content in coral reef samples in the Quseir harbour area and revealing potential environmental indicators for heavy metals contamination as well as pollution hot spot throughout this area to assess the public health for workers, tourists, and the surrounding population.
Effective Hybridization of Biogeography Based Optimization and Simulated Annealing

Abstract Since the optimization process constitutes a great step in solving complex real world problems, the development of novel optimization algorithms is one of the growing interest topics that attracted researchers in the recent decades. This paper presents the hybridization of bio-inspired Biogeography Based Optimization (BBO) algorithm and physics-inspired Simulated Annealing (SA) algorithm, into a new variant called BBO-SA. The proposed algorithm uses the concepts of the SA to enhance the diversity of BBO solutions which in turn improves the obtained solution. For validating the performance of BBO-SA, it is compared to that of BBO algorithm in solving a set of thirteen complex benchmark functions. Validation results prove the superior performance of the proposed BBO-SA algorithm over the BBO algorithm in solving complex function in terms of escaping from local optima and reaching near optimal solution in lower execution times. Besides, the proposed algorithm is applied to solve a very challenging problem denoted as the RFID Reader Deployment Problem (RRDP). Such problem can be solved by finding the optimal distribution of the RFID readers which fulfils the set of RFID planning objectives. A comparison is held between the BBO-SA algorithm and other optimization algorithms on a large RFID model. Simulation results verified the superiority of the algorithm over the compared ones for solving the RRDP with satisfying the deployment objectives.
Exploring clusters of rare events using unsupervised random forests

Abstract Given highly imbalanced data, most learning algorithms face the challenge of accurately predicting rare events, while such cases are the ones that carry importance and useful knowledge. In a binary class label dataset, the rare events are the ones in the minority class. This study used a stroke dataset with a binary class label and the class imbalance ratio was 54:1. In addition to that, the dataset contained missing values and mixed data types. To identify the intrinsic structures in the minority class (the stroke group), Random Forest Clustering was used to produce the proximity matrix and fed to Partition around Medoid (PAM) clustering method to identify the optimal number of clusters. The proximity plot seems to show there could be cluster tendency as Hopkins’s statistics test value was H = 0.8735 and k=2 was identified to be the optimal number of clusters. Based on the internal cluster validation, however, the silhouette coefficient width was small (0.1), indicating that many of the data objects were within the other boundary of the other class. We have suggested a further investigation plan in this paper for the next action.
New Marketing Communications to Promote Agricultural Products to Foreign Markets

Abstract The widespread development of the Internet has allowed the use of information and communication technologies and transformed the traditional methods of promoting products in the domestic and foreign markets. Against the background of the general trend towards individualization in working with consumers, information and communication technologies are becoming more widespread in order to expand communication and receive feedback from the end user. At the present stage of development, marketing is becoming one of the conditions for successful functioning in market conditions for enterprises in the agricultural sector. The advancement of communication technologies and their widespread adoption have prompted agricultural producers and retailers to explore various online resources and channels of communication with consumers and use them to promote their products. The paper examines the experience of using various types of marketing communications to promote agricultural products for export and analyzes their potential and prospects for use by domestic producers.
The Proposal of Adding a Society Value to the Software Process Improvement Manifesto

The use of computers has become ubiquitous and spread out to every part of our personal lives and businesses. Computer Ethics focuses on the questions of right and wrong that arise from the development and deployment of computers. Thus, it urges that the social impact of computers must be analysed. In software engineering, the Software Process Improvement (SPI) Manifesto was developed by groups of experts in the field, aimed to improve the software produced, through improving the process, the attitudes of software engineers, and the organisational culture and practices. The manifesto is centred on three basic values: people, business focus and organisational change, underpinning the philosophy of software process improvement; and ten corresponding principles, which serve as foundations for action. In this paper, we argue that SPI professionals, need to, in addition to fulfilling duties to the Organisation, the Business, Employees who participate in SPI, and People who will be most affected, broaden their obligations to include wider society. The impact of developed and deployed software systems is often beyond the organisation and affects the daily lives and activities of citizens in society. This paper argues for the inclusion, in the SPI Manifesto, of a fourth value titled Society, along with six corresponding principles. These half dozen principles are based on traditional moral and ethical concepts, sourced from the field of Computer Ethics. This proposed revision to the SPI manifesto would explicitly espouse the notion of serving the public interest. It will likely help SPI professionals to remember that working in the public interest is also important in Software Process Improvement, thus, making the duty to society clear and obvious or evident.
VRWalk – a Case Study Regarding Different Movement Options in Virtual Reality

The use of Virtual Reality (VR) and Augmented Reality (AR) for trainings in virtual settings has become increasingly popular in the last years. By using these technologies, trainees can learn how to behave and operate equipment in virtual environments before being trained in real-world settings. However, it has been reported that, especially in motion and dynamic settings, trainees tend to experience problems like nausea, headaches and other problems. In this study, we investigate the effect of different tasks in a controlled way by comparing two groups in a real and a virtual setting. A questionnaire is developed to assess the degree of sickness of the probands and insights about the effect of each type of tasks are assessed in a qualitative way. This investigation was done as part of the project VRWalk (2021–2022) and funded by the WST3 (Funding programs for Lower Austrian companies in the areas of business, tourism and technology).
“Let's talk about el catalan's 😉”

In this chapter, we explore code-switching choices in a multimodal mobile-supported exchange between future foreign language teachers, paying particular attention to the impact of the co-participants’ code-switching on the sequentiality of the interaction. Extending from the observation that emoji may have more communicative purposes than simply conveying emotions or pictorially representing facial expressions or gestures (Kelly and Watts, 2015), it is suggested that emojis may be deployed to orchestrate the interaction (e.g. mitigation through humour) or to elicit a next-turn interaction from other participants (e.g. orientation of an expected response). This study, which uses Conversation Analysis (CA), seeks to deepen our understanding of the impact of technology on interactional practices in intercultural settings by exploring a WhatsApp interaction during a Virtual Exchange (VE) in a language teacher education course.
Co-Creating History: The Case of WORTHY as a Virtual Collaborative Museum

Value co-creation unfolds as a system of interactions in which the user and service provider play an active role in collaboratively creating values that go beyond economical and financial worth. Value co-creation is a growing trend in museums; however, little is known about how it can enhance educational activities, especially concerning digital technologies. This study analyzes the role of digital technology in the value co-creation process by addressing the following research questions: (1) how can museums, and schools implement actions to enhance co-creation activities? and (2) how does digital technology contribute to the generation of a value co-creation approach, engaging a young target audience in history education? The research adopts a single case study methodology, which is based on WORld Wars Toward Heritage for Youth (WORTHY), an educational European project that involves cultural and educational institutions from Germany, Poland, Croatia, England, and Italy. WORTHY creates a virtual collaborative museum through a digital platform. The paper’s findings make clear that digital technologies play a role in value co-creation through two main experiences: learning and sharing. In the learning experience, digital co-creation is grounded in artifacts and contents, while during the sharing experience, digital technologies play a role relatively to people and content.
360-Degree Immersive Experience for Indoor Cycling

Indoor cycling is a concept that has emerged with technological advancements to serve multiple purposes such as fitness, virtual tourism, and leisure. Commercial solutions have been developed to serve different user groups, with emphasis on corresponding aspects of user experience. This paper proposes a novel approach to indoor cycling, combining key features including real-world 360-degree view, immersive environment, real-time interaction between the real and virtual worlds and dynamic resistance control to simulate changes in elevation. In the proposed solution, a mobile application provides an immersive cycling environment by playing 360-degree videos on a Virtual Reality headset. The environment responds to user movements such as head rotation and cycling speed changes, and in return replicates the actual resistance to the rider using automatic resistance control. These components are connected by a local Wi-Fi network and is fine-tuned for minimal latency based on international benchmarks. Our prototype system was tested for performance both qualitatively and quantitatively, particularly for the impact of the mobile device resolution and processing capabilities. Further improvements were identified to enhance the overall user experience
Solar Irradiance Forecasting using FFNN with MIG Feature Selection Technique

Forecasting Irradiance value well ahead may allow a Power System Engineer to decide the generating capacity of a thermal power plant to achieve Economic Load Dispatch (ELD) and indirectly this may reduce the usage of fossil fuels, which may contribute to reducing global warming. Also, one may know how the Irradiance is varying in that particular region before the establishment of a solar power plant so that he can estimate the revenue. To forecast the Irradiance value here Feed Forward Neural Network (FFNN) is used and for training the model, the Irradiance data set has been taken from NREL - NRDB website. The performance indices used here are correlation coefficient (R), Root Mean Square Error (RMSE), Mean Average Error (MAE), and Mean Absolute Percentage Error (MAPE). Overall, the R-value achieved is 0.99904, which indicates that the model is well trained.