Reviewerly/exHarmony huggingface dataset:
A Computational Perception of Locating Multiple Longest Common Subsequence in DNA Sequences

Bioinformatics is an active research area which combines biological matter as well as computer science research. The longest common subsequence (LCS) is one of the indispensable issue to be unravelled viably in computational science. Discovering LCS is fundamental undertaking in deoxyribonucleic acid (DNA) arrangement investigation and other molecular biology. In this paper, new calculation for discovering LCS of two DNA successions and its area is proposed. The objective of this created framework is to discover the area and length of all subsequences which introduces in the two arrangements. To achieve this, DNA sequences are stored in an array and the comparison of DNA sequences are performed using matching algorithm. At the end of matching process, group of subsequence are obtained. Then the length and location of the matched subsequence are computed. After completing the matching process, longest common subsequence(s) is located. In this proposed work, maximally obtained length of LCS is 8. Finally, the computation time is calculated for locating LCS in DNA sequences. In addition to this, computation time is analysed by gradually increasing the length (in characters count) of DNA sequences from 100, 200, 300, 400 and 500. It concludes that computation time for locating LCS in various lengths of DNA sequences took few seconds difference only.
Hypothesis Tests and Corroboration

According to Popper and other influential philosophers and scientists, scientific knowledge grows by repeatedly testing our best hypotheses. However, the interpretation of non-significant results—those that do not lead to a “rejection” of the tested hypothesis—poses a major philosophical challenge. To what extent do they corroborate the tested hypothesis or provide a reason to accept it? In this chapter, we prove two impossibility results for measures of corroboration that follow Popper’s criterion of measuring both predictive success and the testability of a hypothesis. Then we provide an axiomatic characterization of a more promising and scientifically useful concept of corroboration and discuss implications for the practice of hypothesis testing and the concept of statistical significance.
Cryptoasset Valuation

This chapter reviews practitioner and academic work on the topic of cryptoasset valuation, introduces a value-driven taxonomy of cryptoassets, and investigates several assets directly to demonstrate how they might be valued. The ease of creating a token and an upswing of global retail enthusiasm inaugurated a token sale boom in 2017 that eclipsed venture funding for start-ups within the industry. A maturing data environment and the innate transparency of blockchains has led to more sophistication in the analysis of these economic systems; but widely agreed-upon valuation methodologies still do not exist. It has been suggested that cryptoassets such as bitcoin cannot be valued, only priced. Practitioners would do well to cross the aisle and work with their academic counterparts on deriving meaningful models for these assets; equally, academia should continue to grant cryptoassets the attention they deserve.
Breaking down silos between business continuity and cyber security.

Every year, most businesses experience a cyber attack of some sort. Despite the fact that these attacks can interrupt business operations, many organisations lack an effective business continuity response. While some organisations do have cyber security and incident response functions in place, they focus more on mitigating the attack itself than on ensuring business operations can continue in the interim. To understand why it is important to integrate cyber security into the business continuity plan, business continuity planners must first be familiar with the common cyber threats organisations face as well as the far-reaching impact of data breaches. Then, they must address the root causes of the breakdown between business continuity and cyber security: the lack of a security culture, boardroom support and a coordinated response. Practical steps for integrating cyber security into the business continuity response include starting a conversation with those responsible for cyber security, determining the appropriate response to cyber incidents, assessing the organisation's recovery needs and testing the response strategy. Ideally, however, organisations should prevent attacks altogether. As employees are often the primary point of failure in cyber security preparedness, organisations should improve their cyber security posture by investing in education and awareness from the top down.
A Location Recommendation Based On User Reviews Using Cart

Right now, location recommendation plays a vital role in searching attractive places. Such recommendation places are identified by social network. The social networks are FourSquare, yelp, Jiepang, Uber etc., Ongoing analysis, based on user feedback finding a best restaurant, hotels etc., Users regularly leave reviews about the site on (LBSN) after visiting. This reviews differs from low level to high level. In this paper, recommending hotels to a user based on user inputs such as kind of outing Leisure or business, sort of movement Solo or family, sort of room, number of long periods of remain. Prescribe inns to a user based on client surveys. Hotels which are most similar in terms of reviews to the particular hotel specified by the user and recommend it to them. The main aim of this paper is to suggest the voyagers the tag of the inn dependent on their wish, by inspecting the further voyagers comments/feedbacks jointly with the rating an incentive to upgrade the recommendation. The new user cold start problem is a major concern in recommender system, because of the absence of accuracy in the recommendation. To fix this complication the resulting commitment will be made in this paper, 1. Regression model 2. Correlation 3. Classification tree analysis. Finally, we evaluate user reviews and also enhancing the accuracy of recommendation.
Adoption Of Pets In Distributed Network Using Blockchain Technology

As the whole world is moving towards digital payments, ethers and the transaction methods with quick payment features are stored in a blockchain in a distributed network. To adopt pet animals, this technology provides security and flexibility to users. The distributed network is a network system through which data, software and computer programming are spread across on more than one node(computers) and these nodes are dependent on each other. It is nothing but a peer-to-peer network which eliminates a single point of failure. The blockchain is a growing list of records called blocks, that are linked using cryptography. It is a decentralised, distributed and an immutable ledger to store digital transactions. Its databases are managed using a peer-to-peer network where all the nodes in a network are equal and are the major concern in the types of network architecture. The consensus protocol is used for transacting and communicating between the nodes. In this paper, a unique way of adopting pets is proposed where the transaction details are stored in a blockchain whereby using hash value returns further details of the transaction. It provides users more convenient access in adopting pets and provides a better approach to the existing centralised system.
CONCERNING THE ISSUE OF GENERATIVE ART CLASSIFICATION: DEFINITION OF CONCEPTS

The article refers to the problem of generative art taxonomy as a complex interdisciplinary phenomenon and analysis of its key features. The generative art is a relevant cultural and artistic phenomenon in Russian and foreign art that emerged in the 1910s within Russian and West European avant-garde painters and received a new boost for development thanks to contemporary computer technologies. Based on the classification model proposed by foreign researchers, M. Boden and E. Edmonds, the author of this article identifies concepts like Generative Art (G-art), Computer Art (C-art), Interactive Art (I-art), Interactive Computer Art (CI-art), and Computer Generative Art (CG-art) and introduces new scientific terms: Interactive Generative Art and Interactive Computer- Generated Art. The first one refers to a series of works of art created with the active participation of the audience and the use of traditional (non- computer) autonomous systems that provide a certain degree of randomness without the implementation of multimedia technologies. The second one refers to a series of artworks created by autonomous computer systems and involving active interaction with the audience. These phenomena are considered based on the art object’s possessing the features of generative (G), interactive (I) or computer (C) vectors that underlie the classification method of Gen Art and adjacent contextual phenomena developed by the author and visually modeled by him in the Cartesian coordinate system. Emphasis was made on Interactive Computer-Generated Art. A media installation by Stain art group called Mobile Interactive Multi-Parametric Image (MIMPI) is provided as an example; it is an interactive audio-visual environment first introduced in MEL Space gallery (Moscow) in May 2012 Finally, the author comes to the conclusion that granting the generative art the status of a natural transitional phase in the art history of the 20th — early 21st centuries, as proposed by him, is justified, since this art combines “dry” and “wet” technologies and the corresponding types of cultures.
A Model for evaluating popularity and semantic information variations in radio listening sessions

Comunicacio presentada a: 1st Workshop on the Impact of Recommender Systems dins el 13th ACM Conference on Recommender Systems (RecSys 2019) celebrat el 19 de setembre de 2019 a Copenhagen, Dinamarca.
TEACHING ENGLISH TO STUDENTS OF DENA UPAKARA WONOSOSOBO

Learning English as a foreign language is one of the mandatory subjects for junior high schools students in Indonesia. This subject is also among the subjects tested in national examination. This means that English is being taught in every high school, be it schools for normal students or schools for disabled students. Teaching English to students with hearing impairment is a challenge. This study aims to describe how English is taught for students in Dena Upakara School for the deaf in Wonosobo. It means the researcher explores what teaching methods are employed in the learning-teaching process. An observation and interviews were conducted to gather information on the question. The students involved in this study are students of the 7th grade. The result reveals that the teacher combines several methods to deliver the material. The methods employed by the teacher include the grammar-translation method, natural method, and lexical approach.
Evaluating Ways of Adapting Word Similarity.

People judge pairwise similarity by deciding which aspects of the words’ meanings are relevant for the comparison of the given pair. However, computational representations of meaning rely on dimensions of the vector representation for similarity comparisons, without considering the specific pairing at hand. Prior work has adapted computational similarity judgments by using the softmax function in order to address this limitation by capturing asymmetry in human judgments. We extend this analysis by showing that a simple modification of cosine similarity offers a better correlation with human judgments over a comprehensive dataset. The modification performs best when the similarity between two words is calculated with reference to other words that are most similar and dissimilar to the pair.
Penggunaan Alat Peraga untuk Meningkatkan Hasil Belajar Siswa Kelas V SD N 2 Gunung Katun Kecamatan Baradatu

The problem in this study is the still low learning outcomes of fifth grade students of Gunung Katun 2 Elementary School in District Baradatu. The aim is to improve the learning outcomes of students in the fifth grade mathematics lesson. The method used in this study is Classroom Action Research. The subjects of this study were the fifth grade totaling 19 students. The data analysis technique used is the quantitative method and then translated into qualitative descriptive. The results of this study indicate that the use of teaching aids in learning mathematics can improve student learning outcomes. This can be seen the learning outcomes of students in the first cycle the average value of 69 and the second cycle experienced an increase in the average value of 74 with completeness in the first cycle 68% and in the second cycle increased to 79%.
Automated Detection of Malaria Infected Red Blood Cells using Spatial Coherence Microscope via Ensemble Model

Malaria Detection is important at the early stage, hence it will induce death. We implement an ensemble method for the early diagnosis of malaria infected cells with extracted morphological parameters using spatial coherence microscope.
credit card fraud detection using machine learning

Credit card frauds are easy and friendly targets.E-commerce and many other online sites have increased the online payment modes, increasing the risk for online frauds.Increase in fraud rates, researchers started using different machine learning methods to detect and analyse frauds in online transactions.The main aim of the paper is to design and develop a novel fraud detection method for Streaming Transaction Data, with an objective, to analyse the past transaction details of the customers and extract the behavioural patterns.Where cardholders are clustered into different groups based on their transaction amount.Then using sliding window strategy [1], to aggregate the transaction made by the cardholders from different groups so that the behavioural pattern of the groups can be extracted respectively. Later different classifiers [3],[5],[6],[8] are trained over the groups separately.And then the classifier with better rating score can be chosen to be one of the best methods to predict frauds.Thus, followed by a feedback mechanism to solve the problem of concept drift [1].In this paper, we worked with European credit card fraud dataset.
Semantic Analysis of Soccer News for Automatic Game Event Classification

We are today overwhelmed with information, of which an important part is news. Sports news, in particular, has become very popular, where soccer makes up a big part of this coverage. For sports fans, it can be a time consuming and tedious to keep up with the news that they really care about. In this paper, we present different machine learning methods applied to soccer news from a Norwegian newspaper and a TV station's news site to summarize the content in a short and digestible manner. We present a system to collect, index, label, analyze, and present the collected news articles based on the content. We perform a thorough comparison between deep learning and traditional machine learning algorithms on text classification. Furthermore, we present a dataset of soccer news which was collected from two different Norwegian news sites and shared online.
Entropy-Based Selection of Cluster Representatives for Document Image Compression

In this work, we introduce an efficient method for lossy compression of digitalized documents. The method uses a dictionary which consists of class representatives defined using a minimum entropy criterion. The algorithm initially identifies the different symbols contained in a document image, and then the symbols are grouped in classes by means of a hierarchic clustering algorithm. For each class, a representative is selected using the principle of minimum entropy and suitable similarity distances. The technique creates a file in which every object belonging to a class is replaced by its class representative. Finally, the resulting file is compressed. The performance of the proposed algorithm is assessed using digitized files from a standard database for document compression along with different resolutions. Comparisons against other state-of-the-art algorithms are performed in this manuscript. The results establish quantitatively that the present methodology is a more efficient technique.
From Skokie to Charlottesville

This chapter argues that the events in Charlottesville, Virginia, in summer 2017, present a watershed moment when the general public realized that free speech can become weaponized to undercut discourse and destroy the social order. At a widely publicized event in 1977, a small group of neo-Nazis won the right, in a court decision, to march in a small town in Illinois. That legal decision set the cultural and legal precedent for the mainstream attitude toward hate speech for several decades. Critically, that legal decision was matched by public condemnations of anti-Semitism and racism by political figures all the way up to the US president. When a group of white supremacists and neo-Nazis marched in Charlottesville in the summer of 2017 and murdered or come to demonstrate a, the US president failed to unequivocally condemn these events. The chapter examines the assumption that tolerating hate speech does not mean condoning it in light of these two events.
Action Research on Problem-Based Learning (PBL) Lessons at a University: Focusing on Vocabulary Skills and Learning Attitudes

The present study aimed to investigate how problem-based learning (PBL) English classes affected vocabulary skills and how the students perceived the PBL lessons. Twenty-seven first-year medical university students took part in four PBL lessons over the course of one semester. The measure included two vocabulary tests; one in the middle of semester and the other in the end of semester comparing PBL-week vocabulary and non-PBL week vocabulary, a survey about the students’ perception of PBL activities, and learners’ reflection journals. The results showed three main findings. First, significant improvements were found between PBL-week and non-PBL week in vocabulary ‘meaning’ and vocabulary ‘use,’ but no significant change was found in vocabulary ‘form.’ Second, most participants strongly agreed that PBL classes were more interesting than normal classes, and students enjoyed participating in the problem-solving process due to the authentic PBL tasks related to their major. Third, the students’ reflective journals also showed a satisfaction of collaborative opinion exchange, and their learning attitudes were highly positive.
Crowd Estimation via Thermal Images Analyzed by Convolutional Neural Network

Summary form only given. This research focuses on improving the method of crowd estimation by using a single thermal picture as input. To be more specific, the purpose of this research is to combine state-of-the-art computer vision techniques and convolutional neural network on thermal image inputs to tackle the problem of estimating crowd density. Current crowd estimation methods are either too computationally expensive and privacy-invading like Switch Convolutional Neural Network or too complicated, needing multiple cameras or sensors. Different from preexisting methods, our approach focuses on developing different methods to estimate crowd density that utilized a single thermal image and the knowledge of the environment setting as the only input. Dataset used to train are collected during school events and are accurately labeled. The result shows that our method of using a single thermal camera in a given space to estimate crowd density is a promising approach. This approach offers high accuracy, low computational cost, and privacy protection. Additionally, using thermal images as input can easily avoid errors like counting pictures of faces on clothes or posters. Future improvements for this research include extending the data collection and building an online public database. The neural network will also be fine-tuned, and other state-of-the-art methods will be implemented. Future applications can include a low-cost approach in event reporting and security services in public places like subways and airports.
Learning Conditional Evidence

Learning indicative conditionals and learning relative frequencies have one thing in common: they are examples of conditional evidence, that is, evidence that includes a suppositional element. Standard Bayesian theory does not describe how such evidence affects rational degrees of belief, and natural solutions run into major problems. We propose that conditional evidence is best modeled by a combination of two strategies: First, by generalizing Bayesian Conditionalization to minimizing an appropriate divergence between prior and posterior probability distribution. Second, by representing the relevant causal relations and the implied conditional independence relations in a Bayesian network that constrains both prior and posterior. We show that this approach solves several well-known puzzles about learning conditional evidence (e.g., the notorious Judy Benjamin problem) and that learning an indicative conditional can often be described adequately by conditionalizing on the associated material conditional.
Context Effects on Human Judgments of Similarity.

The semantic similarity of words forms the basis of many natural language processing methods. These computational similarity measures are often based on a mathematical comparison of vector representations of word meanings, while human judgments of similarity differ in lacking geometrical properties, e.g., symmetric similarity and triangular similarity. In this study, we propose a novel task design to further explore human behavior by asking whether a pair of words is deemed more similar depending on an immediately preceding judgment. Results from a crowdsourcing experiment show that people consistently judge words as more similar when primed by a judgment that evokes a relevant relationship. Our analysis further shows that word2vec similarity correlated significantly better with the out-of-context judgments, thus confirming the methodological differences in human-computer judgments, and offering a new testbed for probing the differences.
El mundo editorial y las TICS

Now a days, it has been implanted technology and media resources that have set a standard in education, many of these documented electronic media, virtual and printed dissemination, these resources have supported for the teacher to incorporate in its class multimedia presentations, simulators, blogs, social networks, among other elements of Web 2.0 looking to add to the technology information and communication within the teaching-learning process using the ICTS. Fermin Toro University (UFT) through its virtual campus journal administered by the SAIA (Interactive Distance Learning System) provides these pedagogical and technical principles for the use of telematics in distance teaching-learning in their semiannual publications, accessible by undergraduate, graduate and extension programs. In this sense, the purpose of this research was the creation of a Tecno-Pedagogical training Plan to develop skills in teachers to use ICTS and document their experiences in the journal. This research was developed with a quantitative approach, framed in an experimental field design, between the findings it have trained more than 1500 teachers from different academic units as testimonies the developing of their technological skills after completing the training plan designed thus achieving the 4 digital publishing editions and guaranteed receipt of articles for 3 more editions.