Reviewerly/exHarmony huggingface dataset:
Versioning Data During Migration Processes in Cloud Environment

Nowadays, big data influences many aspects of human life. They help in medicine with diagnosing different illnesses, in traffic with watching of traffic accidents, and of course, they have a crucial role in supporting decisions. It is appropriate to test another database, respectively, another database type, in every operation's unsatisfactory performance by using a set database. A transformation process is needed in this case. Big Data entering this database has a different structure and size, which influences the set transformation process's time difficulty. The transformation process changes the data structure, from relational to nonrelational, respectively nonrelational to a relational database, making it possible to stop the process or an error that can end up with an incomplete change of a data structure and the data this process must have been repeated. “Version” system we created in this paper is, in the case of incomplete data change, respectively failure of transformation process during the transformation of a relational database to a nonrelational or nonrelational database to relational, capable of continuing from the error point of the previous approach, and so it can erase necessity to perform whole transformation process from the very first beginning.
Comparative Analysis Of Chaotic Cat MAP And Fibonacci Image Scrambling Methods To Secure Digital Image: Comparative Analysis Of Chaotic Cat MAP And Fibonacci Image Scrambling Methods To Secure Digital Image

In the process of communication through the internet network, security is an important issue that needs serious attention. This is because the process of sending and communicating data through the internet has the possibility to be intercepted by other parties. This also applies to image data. Therefore, data to be sent via the internet must be secured first. However, the application of several cryptographic methods and several methods of randomization of images requires a relatively long time. To solve the problems encountered, the Image Scrambling Generalized Fibonacci And Chaotic Cat Map algorithm can be applied. The work process of the algorithm will start from the process of selecting the input image and charging the key value that will be used. After that, the process continues with the process of randomizing the image, so that it will produce a randomized image. The resulting image can be reconstructed again by applying an anti-scrambling algorithm. This process requires the same key to be used at the scrambling stage. The resulting application can randomize the original image by filling in the randomization key value. The resulting image can be reconstructed again using the same key. In addition, the application will also produce detailed reports of calculations performed during the scrambling and anti-scrambling processes for each of these methods.
Recommendations of Compatible Accessories in e-Commerce

We address the problem of learning how compatible two products are. Assessing compatibility is challenging because the meaning of compatibility changes depending on product categories. In this study, we leverage domain experts' knowledge to generate labels and datasets. Next, we engineer 58 different features from product titles and product descriptions. We experiment with both tree-based and deep learning classifiers using different sets of features to capture compatibility patterns across four product categories. Although the performance does not show consistent pattern across all categories, the precision and recall of the best algorithm from most categories are above 90%. In addition, we find that the performance of classifiers are in general satisfactory. Based on human validation, few best-performing classifiers demonstrate better performance than labels generated from domain experts.
Online Crime Reporting System—A Model

Crime which is an unlawful act is increasing in our society day by day. With the advancement of technology, criminals are also getting new ways of doing crimes. Crime takes place due to a few common reasons that are money, imbalance mentality, and emotions. After the crime takes place victims need to go through a very complicated and lengthy process for reporting the crime at police station. It’s also a very hectic process for the crime branch to do it manually and maintain the records. So the crime reporting system is the solution for all the victims and for the crime department. This will not only make the work easy but also it will help the users to access many features like news feed and all the updates regarding crime taking place in your locality. It will bring the police and the victims closer and hence increasing the security. This makes the FIR registration simple and easy hence making it time-efficient. The system will help the crime department to take action as quickly as possible and maintain the database efficiently. The police can even update an alert to the citizens regarding the most wanted persons, lost belongings, and any kind of emergency through this system. As a result, this system will give a sustainable solution to the users, police, and victims for managing the crime in a better and a structured way.
Colour-Code Combination for Secured Login

In earlier days there was no much use of computers. As developments took place in the field of computer science, every little and large industries or even every person started using computers. Computers are now in every field and in every little thing. Then there comes a part of internet for sharing information with each other. Internet is mainly used for social networking, online marketing, net banking, and online money transactions. With all this there comes the darker side of the internet that is: internet frauds, hackings, cracking etc. Whoever is using the internet first and foremost they wish their safety and security and this is done by using authentication. In most of the cases, the user is provided with USERNAMES and PASSWORDS for authentication. But hackers are now attacking authentication systems for doing online frauds and we need stronger solutions to avoid attacks, that will be possible using “colour-code combination for secured login” technique.
Efficient Group-Based Cohesion Prediction in Images Using Facial Descriptors

In this paper we study the problem of predicting the cohesiveness and emotion of a group of people in photo. We proposed a fast approach, consisting of face detection by using MTCNN, aggregation of facial features (age, gender and embeddings) extracted by multi-task MobileNet, prediction of group cohesion and classification of emotional background using multi-output convolution neural network. Experimental study on the Group Affect Dataset from EmotiW 2019 challenge demonstrated that our approach allows to achieve an improvement of quality and even to reduce the running time of an algorithm’s work when compared to known solutions. As a result, we obtained mean squared error 0.63 for cohesion prediction, which is 0.21 lower when compared to baseline CapsNet.
Discussion on the teaching application of VR technology in bronze ware making process

Under the background of new era, the Internet technology development, and is widely applied in the field of education, for the combination of virtual reality technology and bronze craftsmanship provides a possible, because of the virtual reality technology has the characteristics of immersion, interactivity and idea, to build a platform for interaction between learners and teaching, let learners in virtual 3 d interactive virtual experience, thus giving absorbed new identity makes it not restricted by space-time capture learning skills quickly. Based on the course teaching of bronze ware making process, this paper applies cutting-edge VR virtual technology to the teaching platform to solve the problems of traditional bronze ware making process complexity and high risk coefficient, and expounds its advantages, so as to improve the success rate of bronze ware making in the actual operation process.
Gaming, VR, and Immersive Technologies for Education/Training

Future digital lives are predicted to extend beyond mobile smart phones with devices appearing as standard eyeglasses having settings for Extended Reality (XR). This will be so that what one really experiences and what is computer-generated will be so tightly mixed together that a person will not be able to distinguish between what is real and what is an illusion. Rather than sliding a finger across a touch screen on a smart phone, it will be possible to make things happen by moving our eyes or by brainwaves. Talking with someone or playing an online game will involve seeing that person in the same room and being able to touch and feel him/her via tactile technology. XR will be used in a variety of education situations with head mounted displays (HMDs) in classrooms for all children and also in home environments where those being educated have their own headset and system; medical students and surgeons will be educated in practical skills by using virtual humans rather than cadavers; oilrig and wind-farm workers will understand how to handle maintenance, repairs, and emergencies, before they ever leave the home office (Abridged from original call for chapters for this book). This chapter reviews texts selected for this volume on Gaming, VR, and immersive technologies for education and in training. It begins with a brief introduction text speculating impact related to well-being.
Generation of Testing Metrics by Using Cluster Analysis of Bug Reports

One of the most significant challenges of defect reporting is how to compute and predict the testing metrics. Any software development project needs certain suitable testing metrics. Cluster analysis can be used to generate them. The interpretation of the received clusters helps to determine explicit and implicit characteristics of software testing and development. This paper describes several software solutions for clustering bug reports. We have extracted bug reports related to three open-source JBOSS projects and experimented using that data. Our experiments demonstrate that effective results can be achieved in the area of defect clustering. We provide the results achieved by using two clustering algorithms: k-means and EM. Our research shows that the usage of the EM algorithm generates more detailed information about the specifics of the project than the usage of the k-means algorithm. So, EM gives a possibility to create more diverse testing metrics suitable for project needs.
Towards Ontology-Guided Learning for Shepherding

Shepherding offers an exciting application for machine learning research. Shepherding tasks are scalable in terms of both complexity and dimension. This scalability supports investigations into the generality of learned multi-agent solutions. Shepherding is also valuable for the study of how multi-agent learning systems transition from simulation to physical systems. This chapter reviews previous learning strategies for shepherding and highlights the advantages of applying prior knowledge to the design of learning systems for shepherding. It presents ontology guided learning, a hybrid learning approach to learning. Ontology guided learning will enable the application of symbolic prior knowledge to non-symbolic learning systems. This will allow a non-symbolic system to reason on abstract concepts, reduce dimensionality by partitioning the state and action space, increase transparency and allow learning to focus on the parametric rather than semantic parts of the problem, where it will likely be most effective. This chapter concludes by describing how ontology guided learning could be applied to the shepherding problem.
Risk Level Calculation for IoT System

Due to the complexity and heterogeneity of technology, devices, data and computation, Internet of Things (IoT) systems are vulnerable to the cyber-attacks. Many cyber security risk assessment frameworks dedicated to IoT systems are under study. This study introduces the unique risk ranking method by calculating risk impact and risk likelihood by quantifying them. This unique computational approach is applied in the context of medial domain to calculate risk ranking of two medical devices used in medical IoT-based system.
Re-ranking Biomedical Literature for Precision Medicine with Pre-trained Neural Models

We propose a biomedical literature retrieval approach that incorporates a domain-specific BERT model as an auxiliary re-ranker. Experiments on TREC Precision Medicine dataset show its effectiveness in improving retrieval performance by 6.2% in inferred NDCG and 6.8% in R-precision over the best-published results. The contribution of this study is to provide evidence of incorporating BERT in a biomedical literature retrieval system, which serves the overall goal to improve the information retrieval for precision medicine.
Towards Formal Modeling of Quality-driven Systems under uncertainty

Modern software systems tend to become increasingly complex and dynamic where unpredictable changes in user needs, in platforms and in execution contexts are unavoidable. Therefore, self-adaptation presents a promising approach for managing the complexity of current systems. One major objective of software engineering for self-adaptive systems is to deal with such complexity, changing contexts and mainly uncertainty, but still maintaining the quality requirements of the system, namely security, reliability, robustness, and so on. Uncertainty can be of various sources as the changeable environment and variability in resource availability. In this paper, we introduce a formal model for self-adaptive systems operating under uncertainty, in which we make use of both high-level petri nets and plausible petri nets, in order to help the designer to model systems that are capable of monitoring their operation and adapt their behavior when their contexts or environment change.
A Semantic Framework for Chinese Historical Events Based on Linked Data and Knowledge Graph

In the era of big data, data redundancy has become an obstacle to deep reading. The objective of linked data as a new data organization model is to transform data into structured data following unified standards. The lack of Chinese conceptual terms has seriously hindered the semantization and standardization of Chinese domain ontology. Taking Chinese historical events as an example, ontology technology is used in this paper to standardize the definition of concepts and semantic relations in domain knowledge. Moreover, concepts from text resources are extracted through a deep learning algorithm Bi-LSTM-CRFs and combined with an external knowledge base to realize the fusion of related data within various data sets. Ultimately, the knowledge ontology of historical events is displayed in the way of a knowledge graph to further explore the practical application. The results show that the accuracy of the terms extraction of historical events is about 80% indicating the good recognition performance and portability of the model.
Video Object Detection Based on Deformable Convolution

Video object detection is one of the important research directions in the field of computer vision with applications in various domains, e.g. public security, traffic management, etc. Nevertheless, it is very challenging to extend the image-based object detection method to video object detection. In the existing methods, the feature information of multiple adjacent frames is aggregated into the current frame to improve the detection result. However, these methods only sample the information of adjacent frames on the sampling points obtained by prior knowledge or by calculating explicit motion references. Therefore, the feature information obtained by this sampling method is often insufficient. Furthermore, if the selected sampling points are inaccurate, the original feature information may be lost. In this paper, we use modulation-based deformable convolution for feature sampling, and the sampling positions and weight are generated through additional convolutional layers. This not only enriches the sampling information but also minimizes the intervention of prior knowledge. According to experiments on the Image VID dataset, the proposed method significantly improve detection accuracy compared with existing methods.
Region-Specific Opinion Mining from Tweets in a Mixed Political Scenario

Twitter sentiment analysis is used for many areas of market research and opinion mining in various sectors like poll campaign, crime detection and health care. Opinion mining in poll campaign is gaining its prominence since 2012. India as the largest democracy has different factors for different types of elections happening across the length and breadth of the country. The Twitter-based opinion mining for a particular regional election cannot be implied with the normal way of predicting the general elections. A region-specific weighted decision tree-based framework is proposed in this paper. The results provided by the proposed methodology obtained numbers to the existing AI-based pre-poll analysis methods. The actual numbers were able to predict the seats with high confidence factors. The data analysis done on the actual result margins shows that the proposed methodology is comparatively efficient enough in predicting a regional election.
Blockchain Track and Trace System (BTTS) for Pharmaceutical Supply Chain

Blockchain technology supports highly sensitive data sharing with the improvement of security and privacy using cryptography. It is highly preferred in scenarios where the difficulty of tampering the data is critical. To manage supply chain in industrial distribution effectively, privacy, transparency, security, provenance and correctness of sensitive data need to be controlled between different participants. In particular, it has crucial importance in pharmacology that affects finance, investment and health of society. Thus in this study, we propose a blockchain track and trace system (BTTS) for improving management of pharmaceutical supply chain. We have offered state-of-the-art Hyperledger Fabric BTTS that distributed participants can control correctness of the medicine and protect highly sensitive data provenance for pharmaceutical supply chain. Proposed Hyperledger Fabric permissioned blockchain-based BTTS presents smart contract structure and protection for highly sensitive manufacturer’s ownership data with less energy consuming than proof-of-work-based approaches.
PENGARUH GEOGEBRA VERSI ANDROID TERHADAP HASIL BELAJAR SISWA PADA MATERI INTEGRAL

Integral is a material that is often a difficulty for students in solving problems related to integral to it need a new learning innovation so that students can easily learn the integrals. One way to use the learning media is fun and in accordance with technological developments, namely by using GeoGebra in Mobile Android. The purpose of this study is to find out how much influence of the use of GeoGebra Android version of student learning outcomes. The method used in conducting this research is the experimental method. While the population in this study is all the class XI SMA Negeri 4 Unggulan Pagaralam. For the sampling technique is done by random technique. Obtained class XI MIPA 3 as experiment class and class XI MIPA 1 as control class. The data were collected using a test in essay form. Then the test results analyzed data obtained average experimental class 76.67, standard deviation experimental class 139.62 and average control class 66.25, standard deviation of control class 161.16 and t count 3.63> t tabel 1.69. So t count > t tabel , so hypothesis Ho rejected Ha accepted. This proves that there is influence of application of GeoGebra version of Android application to student's mathematics learning result on integral material of class XI SMA Negeri 4 Unggulan Pagaralam academic year 2017/2018. Keywords: GeoGebra, Android, Learning Outcomes, Integral.
Who and Where: Affordances of Personalization and Locativity

In investigating the theme of WORLDEDNESS, this chapter interrogates the ways in which our bodies and their technological extensions embed us in the shared world. Within a trend towards personalization of the devices and services of digital technologies, we use these devices and services in different ways to manifest ourselves in the world (the public sphere) before others and manage our public image and our web of connections to others. We become nodes in a broad distributed sentient cyborg sensorial network that overlays public space. Locative affordances of digital technologies link this virtual network to physical places, supporting both the commodification of location and the formation of “digital counterpublics.”
Analysis of Security Parameters and Network Infrastructure on Cloud

Abstract This paper pertains to an analytical approach towards establishing standards for network performance on the cloud. Cloud being the most promising domain, is yet hindered by the lack of standardization. To resolve this issue we propose a methodology suitable for all essential protocols in contrasting network architectures. In extension to this, security breaches also severe the threat to cloud data and its privacy. To bridge the two imperative aspects of a cloud service, we intend to deploy a deep learning classification model on Google collab, acting as an interface between the cloud database and client-server. Essential parameters including throughput(X), average delay(ζ), loss function(l) and accuracy. We extend to present our results as the optimized cloud standards.
Denoising of Multispectral Images: An Adaptive Approach

Spectral imaging enables detection of external details not processed by the human eye through its red, green, and blue receptors. Multispectral imaging aims to collect the range in a scene picture for each pixel and provide more accurate detail. Different noises eventually compromise Multispectral Image (MSI) due to hardware constraints and insufficiency in radiance. Hence, to enhance the quality of image, we proposed Kriging Interpolation-based Wiener Filtering (KIWF). This makes use of kriging interpolation algorithm to calculate the weights of wiener filter so that the best possible estimate is obtained for denoising the image. Initially, the pixels with noise are separated from clear pixels by global patch clustering, and the weight values are applied by estimating the semi-variance between the clear patches and noisy patches. Finally, the performance of the filter is tested and a comparative analysis is conducted with the existing denoising techniques to show its effectiveness.
Penalty Strategy in The Fitness Function of Grey Wolf Optimizer for Minimum Spanning Tree Problem

Abstract The Grey Wolf Optimizer (GWO) is a relatively new population-based optimizer. Various optimization problems have been solved using GWO. This paper presents an experiment on how to apply GWO to solve the minimum spanning tree (MST) problem. MST is normally solved using revision strategy when formulating the fitness in other population-based algorithms. Another strategy, called Penalty strategy, is used in the experiment. Existing dataset for MST problem is tested using GWO. The experiment showed that implementation of penalty strategy in the fitness function of GWO can find a solution with almost 96% accuracy.