Reviewerly/exHarmony huggingface dataset:
Hybrid Design using Counter Propagation Neural Network-Genetic Algorithm Model for the Anomaly Detection in Online Transaction

In e-commerce, credit card fraud is an evolving challenge. The increase in the number of credit card transactions provides more opportunity for fraudsters to steal credit card numbers and execute fraud. Fraud detection is a continuously evolving discipline to tackle ever changing tactics to commit fraud. Existing fraud detection systems have not been so much efficient to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. This research designed hybrid of Counter Propagation Neural Network and genetic algorithm (CPNN-GA) for the detection of anomaly in any online transactions.
On Source Study of Internet Materials: Approaches, Problems, and Prospects

The article considers some questions of studying of the Internet materials as a group of historical sources.For their collective designation the term "network text" is used.Many authors (N.G. Georgieva, V
Using Data Mining Algorithms to Discover Regular Sound Changes among Languages

This paper presents a method of using association rule data mining algorithms to discover regular sound changes among languages. The method presented has a great potential to facilitate linguistic studies aimed at identifying distantly related cognate languages. As an experimental example, this paper presents the application of the data mining method to the discovery of regular sound changes between the Hungarian and the Sumerian languages, which separated at least five thousand years ago when the Proto-Sumerian reached Mesopotamia. The data mining method discovered an important regular sound change between Hungarian word initial /f/ and Sumerian word initial /b/ phonemes.
Event Time Relationship in Natural Language Text

&lt;p&gt;Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing (NLP) perspective, "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP application like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure. As a result, a graph is derived to show event-time relationships in the given text. Events form the nodes in a graph, and edges represent the temporal relations among the nodes. Time of an event occurrence exists in two forms namely qualitative (like before, after, duringetc) and quantitative (exact time points/periods). To build the event-time-event structure quantitative time is normalized to qualitative form. Thus obtained temporal information is used to label the edges among the events. Data set released in the shared task EvTExtract of (Forum for Information Retrieval Extraction) FIRE 2018 conference is identified to evaluate the framework. Precision and recall are used as evaluation metrics to access the performance of the proposed framework with other methods mentioned in state of the art with 85% of accuracy and 90% of precision.&lt;/p&gt;
Review of Dimensionality Reduction Techniques in Data Mining from Big Data

Big Data is a large data index that increases in volume or size with very high velocity and whose variability or complexness is multidimensional. Capturing, managing, and processing or analyzing big data is really a complicated job. At present, big data analysis has reached a new stage. This new stage is known as rapid data where an enormous volume of data in gigabytes congregates with a targeted big data structure efficiently. The present structures of big data accumulate characteristically complex streams of data with respect to the big data offering to 6Vs such as velocity, variability, volume, veracity, value, and variety. After big data processing, a resultant database to be more helpful as compared to noisy, redundant, incompatible, and raw data. Another logical aspect of reducing big data is that it normally contains a large number of variables that makes it tough to discover various patterns as per the requirements. This research paper represents a comprehensive review of diverse methods that are applied for the process of big data reduction and conjointly presents a comprehensive discussion on big data dimension reduction processes, redundancy elimination, automatic learning process, data extraction, size or volume reduction, and big data compression.
Blockchain Powered Smart Cities

Typically, when a healthcare provider fails to give reasonable treatment to a patient which could causes injury, death or overcharges then its termed as medical malpractice.The medical negligence and cut practice are prevalent in developing and developed societies in one form or other.In the past there has been unfortunate case where doctors pretend to give treatment to an already expired patient to overcharge and even in some cases hospital authorities try to influence postmortem reports in their favor to avoid any medico-legal case.This paper attempts to provide a solution to restrict such medical malpractices in case of critically ill patients admitted to ICU having cancer, heart attack, coronary bypass surgery etc.The paper suggests building a permissioned private Blockchain solution with the help of Cloud, IoT, wearable devices and smart medical devices fitted with sensors.The Cloud based system will receive vital health parameters of patient with help of wearable and sensors devices on regular interval of time which is configurable and append new hash block to immutable Blockchain ledger using cryptographic hash function.It will provide role-based access to all the ecosystem partners for due diligence in the case of any unfortunate events.
Multi-adaptive Feature Extraction Method of Marine Plant Information in Internet of Things

Xing, J.; Wang, X., and Dong, J., 2019. Multi-adaptive feature extraction method of marine plant information in Internet of Things. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 495–500. Coconut Creek (Florida), ISSN 0749-0208.In ocean monitoring and development, underwater image processing and recognition has gradually become a hot topic in ocean research. Because of the complex shooting environment, the underwater image is often seriously distorted. The absorption of water will lead to the attenuation of light in the transmission process, and the scattering of water will make the whole image appear atomization effect. According to the characteristics of underwater light propagation and underwater imaging system, the characteristics of underwater noise are analyzed. Butterworth high pass filter is used to smooth the backscatter noise of low frequency underwater image. On the basis of the above, SIFT feature points are extracted from the improved gray image, and K-nearest neighbor algorithm is used to match the feature points extracted from the standard image data set to obtain the information features of marine plants. The experimental results show that this method can obtain more feature points of marine plant information, and at the same time guarantee the accuracy of feature extraction time.
Relation Classification in Scientific Papers Based on Convolutional Neural Network

Scientific papers are important for scholars to track trends in specific research areas. With the increase in the number of scientific papers, it is difficult for scholars to read all the papers to extract emerging or noteworthy knowledge. Paper modeling can help scholars master the key information in scientific papers, and relation classification (RC) between entity pairs is a major approach to paper modeling. To the best of our knowledge, most of the state-of-the-art RC methods are using entire sentence’s context information as input. However, long sentences have too much noise information, which is useless for classification. In this paper, a flexible context is selected as the input information for convolution neural network (CNN), which greatly reduces the noise. Moreover, we find that entity type is another important feature for RC. Based on these findings, we construct a typical CNN architecture to learn features from raw texts automatically, and use a softmax function to classify the entity pairs. Our experiment on SemEval-2018 task 7 dataset yields a macro-F1 value of 83.91%, ranking first among all participants.
DEVELOPMENT OF INFORMATIONAL SYSTEM FOR THE ELECTRONIC EDUCATIONAL AND METHODICAL COMPLEX OF THE DISCIPLINE

In this article is discussed the possibilities of students independent work of higher education institutions based on the use of electronic educational and methodological complexes as a component of the educational information environment of an educational institution, structure, advantages and disadvantages of its use in the educational process. The electronic educational and methodical complex structure as a full-fledged component of the modern educational process and criteria for its creation has been determined. The analysis was carried out and the existing educational information systems and electronic educational and methodical complexes were characterized. A list of training products that are included in the electronic educational-methodical complex structure of the discipline is given. The developed electronic educational and methodical complex, the scope of its application and use is presented. The diagrams of use cases were presented for individual users types. An introduction to the system of two types of users was provided. The functions and principles of the main service components, which allow organizing a planned, cognitive, organizational and methodological orientation of students' activities, which are targeting to achieve a result: the assimilation of a certain academic discipline, which students master independently without the information support and communication technologies. The developed database is presented, which enables you to operate educational complex.It was identified the main advantages and disadvantages of existing educational information systems. Attention was focused on the issue of assessing the quality of learning outcomes.
Social considerations to make data FAIR-er: Understanding researchers’ views on data “misuse” and credit

While community infrastructure initiatives work to make it easier for researchers to manage and share their data, two cultural and social factors require our attention. Just 9% of researchers feel they get sufficient credit for data sharing, according to the 2018 State of Data survey. In the same survey, researcher’s concern about “misuse” of their data was the leading reason given for not sharing data. The 2019 State of Open Data survey (forthcoming, 2019) digs into both topics in more detail. Using this new data, this poster will outline researcher attitudes and concerns, exploring what researchers mean by “misuse”, their views on credit, and how we can practically address them.
PENERAPAN METODE FONIK UNTUK GAME EDUKASI PENYANDANG DISLEKSIA MENGGUNAKAN VISUAL NOVEL

Multimedia that can help dyslexic children. Visual Novel is an interactive based game that displays stories in the form of static images, and is equipped with conversation boxes to convey narratives and sayings of each character, and sometimes each character has a sound effect so that every character in Visual Novel seems to be alive and able to speak. The method used in making this game is multimedia development life circle, and for the game itself, the phonics method will be used. As for the background of this writing, there is no Indonesian visual novel that has become a learning medium for children with dyslexia. And also visual novel is recommended to be used as a learning media, because it can help children enjoy the learning contained in it.
Ontological and epistemological assumptions of theories of understanding others

In the last few decades, an intensive debate concerning how we understand others has been conducted and, relating to this, different solutions were proposed. All these solutions are aimed at providing a complete explanation of the cases in which we typically hold that we do understand others or at least aim to explain why sometimes we make mistakes when we try to do that. In this paper, ontological and epistemological assumptions of the theory-theory and the simulation theory are assessed in order to clarify the whole debate. Special attention is paid to the notion of understanding embedded in both theories.
Digital Making in Educational Projects

Digital Making as an Educational Project is an innovative educational experience that has been carried out with students of the Primary Education and Social Education degrees for three consecutive years. The experience introduces digital making as an activity in which students create an object using digital technology. In the process, they not only gain an insight into how the technology works, but also learn the content and competences of the curriculum. This innovative teaching practice was carried out as action research in order to improve traditional higher education practices. In this sense, the proposal puts the student at the centre of the process as the author and protagonist of their own learning process. The experience is based on their own interests: they decide what to make based on a given context. The students work in groups and look for what they need to learn to overcome a particular challenge, while the teacher supports the process as a facilitator, offering guidance and resources when necessary. The evaluation of the whole process is regulated via a group diary (a shared online document) and an individual diary (a blog) that the students produce. The final evaluation is not only of the printed product; the students also produce a video in the form of storytelling, in which they explain how the process evolved from the initial idea to the final impression of the object. They also reflect on what they have learned, how teamwork has worked and what possibilities they believe digital making offers in the primary and non-formal educational contexts in which they will work. All of the processes are compiled in the students’ blogs, as well as in the teachers’ field notebooks. The experience was executed in collaboration with the Digital Fabrication Centres of Barcelona. The results were organised to highlight the strengths and weaknesses of using technologies to improve higher education offering an approach in which students are at the centre of the whole process. Strengths: strong student motivation, promotion of self-directed and collaborative learning and learning by doing, and familiarisation with a transforming integration of technology as protagonists. Weaknesses: hesitance and resistance to facing the challenge, management of scarce time, large time investment by the teachers, and the difficulty of achieving in-depth reflection on how digital fabrication could be introduced in educational contexts such as primary school and non-formal contexts.
Regular Super-resolution Restoration Algorithm Based on Fuzzy Similarity Fusion for a Single Haze Image over the Ocean

Xu, L. and Xu, X., 2019. Regular super-resolution restoration algorithm based on fuzzy similarity fusion for a single haze image over the ocean. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 489–494. Coconut Creek (Florida), ISSN 0749-0208.The current image restoration algorithms mainly use a single method to restore haze images over the ocean. It is impossible to perform super-resolution restoration, and at the same time, it is impossible to aggregate the advantages of various algorithms, which has some limitations. As a result, the image restoration performance is not good, and the effect is poor. To address this problem, a regular super resolution restoration algorithm for single fog haze images over the sea based on fuzzy similarity fusion is proposed in this paper. The non-local mean of the discrete cosine transform is used to denoise the haze images over the sea and keep the smooth part of the smog image and most of the details. The fuzzy similarity fusion algorithm is used to fuse the haze images over the sea after denoising, and the pixels of the restored image with a certain similarity is weighted and averaged. The weight is determined by the regional feature of the pixel, and finally the fusion image is obtained. Combining the haze images over the sea and the regular super resolution restoration algorithm, the priori information of the sequence image is used to effectively suppress the ringing effect of the restored image, and achieve the regular super resolution restoration of the single haze over a single sea. Experimental results show that the proposed algorithm can effectively improve the restoration effect of haze images over the sea.
Spatio of Lungs Tuberculosis (<i>Tb Lungs</i>) in East Java Using Geographically Weighted Poisson Regression (GWPR)

Pulmonary tuberculosis patients lose nearly 40% of their productive time. This results in reduced productivity if he is the head of the family or productive. In addition, pulmonary tuberculosis patients become highly dangerous transmitters if they do not pay attention to handling the correct cough and being in a location with a high sexual percentage. Likewise, people who are hurt, will have great information, contracting, if any, and very experienced. This study aims to map the grouping of tuberculosis patients and who are at risk of contracting by time rather than looking at the factors that influence it by using poisson regression that address the presence of GWPR over dispersion. The results obtained there are 5 grouping of areas, with the most significant factors of the district plus the ratio of Trenggalek basic health facilities (Z2) and the percentage of school (Z4) and poor population (X1), BTA + (X2), HIV/AIDS incidence rate (X3) incidence of diabetes mellitus (X4), population density (X5) and ratio of health personnel (X6).
A Comparative Analysis on Widely used Web Frameworks to Choose the Requirement based Development Technology

In this modern world of technology there are hundreds of programming languages that are being used in the IT industry for building website and software.It is not a standard practice to use raw programming codes, rather everyone prefers to use Framework which is nothing but a big collection of ready libraries, packages and modules for making the development quicker.For this reason, experts have developed different Frameworks based on different popular programming languages.Now, it becomes really a confusing and challenging matter for companies, developers and managers while they want to choose a framework for developing their software products.As there are variety of frameworks of different programming languages, the technology decision makers get confused while choosing a framework.Considering this as a big problem of the software industry our aim is to test, analyze, assess, and propose a set of guidelines for choosing a framework based on the requirement of a software product.
Corporations on Blockchain: Opportunities & Challenges

Blockchain technology has the potential to change the way corporations are managed and how they function. A system that offers greater decentralization and ability for shareholders to more actively and accurately engage in decision-making processes will be fundamental for modern corporate governance. We observe that shareholders in recent years have become more active and interested in corporate matters of the companies that they have invested. Decades after Berle & Means elemental publication, the division between the investment and control pertains. Often, because of the existing architecture of the system. Yet, the architecture is changing and blockchain represents a new component. Shareholders could thanks to the technological development strengthen their voice and be part of the long-term corporate decisions. Could technology be the answer to the long-lasting division between corporations and their investors as well as the division between investment shareholders and retail shareholder? If we organized a corporation and its information and decision-making mechanisms based on a new technology that promotes collaboration and can easily encompass incentive mechanisms, we might have a tool to provide more efficiency and transparency and even potentially address the values (or a lack of them) that today govern of our corporate system. This article provides an analysis on opportunities and potential challenges of using blockchain for the purposes of corporate governance in publicly traded corporation. Beside the inquiry into the technology behind blockchain, the article reflects the value, if any, the blockchain technology would have for the shareholders and suggests how the technology could be used in publicly traded companies.
USRRM: Pairwise Ranking and Scoring Images Using Its Aesthetic Quality

Image Aesthetics Analysis is a challenging research problem as aesthetics of an image is a subjective quality and it is quite difficult to formulate it into a mathematical or algorithmic problem. On the other hand, its applications are numerous, ranging from aesthetic based image retrieval to image editing. Earlier works on image aesthetics analysis relied upon handpicking the standard features from the image, based upon which, its aesthetics was quantified. This method was applied, in the belief that sufficient aesthetic features have been taken into consideration and no more features impact its aesthetic quality. This is not always the case, since, subjective quality as this, is defined individually and depends upon personal perspective. With the advent of deep learning, automatic feature learning became prevalent. The classification works on image aesthetics using deep learning have had some success. However, we are concerned with giving a tentative score to images and perform some sort of relative ranking among them. We formulate a unified loss objective accounting both of these factors and also devise a model named USRRM which learns from the global view and pixel-level finer details from an image. The external style and semantic information also aid in model learning. The scoring result of USRRM has the best correlation with the ground truth scores among the most similar previous models and comparable results with less similar models but evaluated with the same metrics. We quantify our results using different correlation coefficients and an accuracy metric.