Reviewerly/exHarmony huggingface dataset:
Analysis of Twitter Data for Identifying Trending Domains in Blockchain Technology

Opinion data collection is one of the most important forms of data analysis to understand and gain more insight about the trending information related to any domain or technology. The need for opinion data mining on the Twitter data is the demand of the indeed titled historical big data era. Blockchain is the technology which was introduced for cryptocurrency and later claimed to be embraced in most of the technologies because of its efficiency in ensuring privacy, security, and data management. With the increase in the popularity of blockchain technology, the opinion data collection for the blockchain technology is becoming compulsive to identify its substance in the practical application of different sectors. The utmost intention of this research is twitter data analysis centred on the domains that are believed to be domains that apply blockchain technology and hence ascertain that they are active and trending domains. The data analysis is performed on the tweets downloaded using tweepy API. This research engages different data visualizations, and Domain Identification by extracting features from tweets. The trend analysis for the opinion mining is accomplished by considering the re-tweets of the considered tweets. The proposed analysis is carried out on tweets which are carefully streamed using filter words related to the domain which claim to be active applicants of the blockchain technology. We will focus on techniques for the extraction of the Twitter data related to blockchain, processing, segregation, pattern visualization and trend identification by considering natural language processing paradigm.
Analogies Between Sentences: Theoretical Aspects - Preliminary Experiments

Analogical proportions hold between 4 items a, b, c, d insofar as we can consider that “a is to b as c is to d”. Such proportions are supposed to obey postulates, from which one can derive Boolean or numerical models that relate vector-based representations of items making a proportion. One basic postulate is the preservation of the proportion by permuting the central elements b and c. However this postulate becomes debatable in many cases when items are words or sentences. This paper proposes a weaker set of postulates based on internal reversal, from which new Boolean and numerical models are derived. The new system of postulates is used to extend a finite set of examples in a machine learning perspective. By embedding a whole sentence into a real-valued vector space, we tested the potential of these weaker postulates for classifying analogical sentences into valid and non-valid proportions. It is advocated that identifying analogical proportions between sentences may be of interest especially for checking discourse coherence, question-answering, argumentation and computational creativity. The proposed theoretical setting backed with promising preliminary experimental results also suggests the possibility of crossing a real-valued embedding with an ontology-based representation of words. This hybrid approach might provide some insights to automatically extract analogical proportions in natural language corpora.
Application of Data Analytics Techniques for Decision Making in the Retrospective Stage of the Agile Scrum Methodology

The Scrum retrospective is the last activity within the Scrum methodology, and it is the previous one to Scrum planning within the activities of the method. To propose improvements for future Sprints, the team performs frequent inspection and adaptation processes analyzing what has worked and what has not worked with respect to the current Sprint and the different related tasks such as the estimation of the Product Backlog, assignment of Story Points and prediction of team velocity. These practices are usually supported by empirical team approaches, implying taking risks that can cost development resources. The adoption of data analytics within the business world has gained relevance in recent years, since through this it is possible to process data for decision-making based on predictive models. Today these models are used based on the management and processing of historical data, using data analytics and artificial intelligence techniques. Taking advantage of the fact that software project managers publicly offer Datasets with information on their Sprints and their characteristics, these could be used with data analytics, in conjunction with supervised learning models, both prediction and classification to support a more efficient decision making, thereby achieving an improvement in the process with a more efficient use of resources.
A Large-scale Empirical Analysis of Browser Fingerprints Properties for Web Authentication

Modern browsers give access to several attributes that can be collected to form a browser fingerprint. Although browser fingerprints have primarily been studied as a web tracking tool, they can contribute to improve the current state of web security by augmenting web authentication mechanisms. In this article, we investigate the adequacy of browser fingerprints for web authentication. We make the link between the digital fingerprints that distinguish browsers, and the biological fingerprints that distinguish Humans, to evaluate browser fingerprints according to properties inspired by biometric authentication factors. These properties include their distinctiveness, their stability through time, their collection time, their size, and the accuracy of a simple verification mechanism. We assess these properties on a large-scale dataset of 4,145,408 fingerprints composed of 216 attributes and collected from 1,989,365 browsers. We show that, by time-partitioning our dataset, more than 81.3% of our fingerprints are shared by a single browser. Although browser fingerprints are known to evolve, an average of 91% of the attributes of our fingerprints stay identical between two observations, even when separated by nearly six months. About their performance, we show that our fingerprints weigh a dozen of kilobytes and take a few seconds to collect. Finally, by processing a simple verification mechanism, we show that it achieves an equal error rate of 0.61%. We enrich our results with the analysis of the correlation between the attributes and their contribution to the evaluated properties. We conclude that our browser fingerprints carry the promise to strengthen web authentication mechanisms.
Constrained Adjusted Maximum a Posteriori Estimation of Bayesian Network Parameters

Maximum a posteriori estimation (MAP) with Dirichlet prior has been shown to be effective in improving the parameter learning of Bayesian networks when the available data are insufficient. Given no extra domain knowledge, uniform prior is often considered for regularization. However, when the underlying parameter distribution is non-uniform or skewed, uniform prior does not work well, and a more informative prior is required. In reality, unless the domain experts are extremely unfamiliar with the network, they would be able to provide some reliable knowledge on the studied network. With that knowledge, we can automatically refine informative priors and select reasonable equivalent sample size (ESS). In this paper, considering the parameter constraints that are transformed from the domain knowledge, we propose a Constrained adjusted Maximum a Posteriori (CaMAP) estimation method, which is featured by two novel techniques. First, to draw an informative prior distribution (or prior shape), we present a novel sampling method that can construct the prior distribution from the constraints. Then, to find the optimal ESS (or prior strength), we derive constraints on the ESS from the parameter constraints and select the optimal ESS by cross-validation. Numerical experiments show that the proposed method is superior to other learning algorithms.
Analysis of methods for treatment of industrial wastewaters containing polycyclic aromatic hydrocarbons

Abstract Various methods and stages of industrial wastewater treatment from polycyclic aromatic hydrocarbons are estimated in the article. The development of technologies for the removal of toxic compounds from industrial wastewater is one of the key tasks for solving the problems of sustainable development. First of all, it is related to the fact that toxic compounds adversely affect the efficiency of wastewater treatment. They are not neutralized in natural processes, negatively affect the environment and, as a consequence the human health. Toxic compounds deplete nature’s ability to perform self purification and restoration of resources. One one of the most important resources is water. These toxic compounds include polycyclic aromatic hydrocarbons.
Use of the virtual teaching campus in the competencies of university teachers

The present research study aims to determine the virtual teaching campus's effect on the competencies of teachers of the Faculty of Industrial Engineering, Systems and Computer Science of the Universidad Nacional José Faustino Sánchez Carrión -2017.In addition, the research is applied, deductive method, quasi-experimental design of longitudinal cut and explanatory level.It was determined for the variable Virtual Teaching Campus dimensions such as technological infrastructure, quality of content, and web system, and the variable competencies of teachers the dimensions ICT and social competencies and methodological strategies.In every university, they are vital to improving the quality of teaching.On the other hand, an observation guide was applied as an instrument, validated by expert judgment reaching 91.4% and with acceptable reliability obtained through Cronbach's Alpha of 0.981.The population comprises 65 teachers of the Faculty of Industrial Engineering, Systems and Computer Science; we used a census sample because the population was small.Furthermore, the Student's T statistical method was applied.The results obtained show, with 95% confidence, that there is a high positive significant influence (p=0.000 and α=0.
Breast Cancer Classification Prediction Based on Ultrasonic Image Feature Recognition

Exploring an effective method to manage the complex breast cancer clinical information and selecting a suitable classifier for predictive modeling still require continuous research and verification in the actual clinical environment. This paper combines the ultrasound image feature algorithm to construct a breast cancer classification model. Furthermore, it combines the motion process of the ultrasound probe to accurately connect the ultrasound probe to the breast tumor. Moreover, this paper constructs a hardware and software system structure through machine vision algorithms and intelligent motion algorithms. Furthermore, it combines coordinate transformation and image recognition algorithms to expand the recognition process to realize automatic and intelligent real-time breast cancer diagnosis. In addition, this paper combines machine learning algorithms to process data and obtain an intelligent system model. Finally, this paper designs experiments to verify the intelligent system of this paper. Through experimental research, it can be seen that the breast cancer classification prediction system based on ultrasonic image feature recognition has certain effects.
What Kind of Learning Media do You Want? Need Analysis On Elementary School Online Learning

This preliminary research was conducted to find information on learning media that teachers and students want in online learning. The qualitative descriptive research method is applied in this study. The study was conducted in August 2021. The study was conducted in an elementary school in both urban and rural areas. A total of 105 people were interviewed at the school, including teachers and students. The data collection technique was carried out through a survey using a google questionnaire form which was divided into 2 forms, namely forms for teachers and students. The findings of the teacher survey indicate that learning media is needed to provide students with a realistic picture of phenomena in online learning. By providing interactive learning media, students feel it will be easier to understand the material than just being explained or given assignments. On the other hand, the results of student questionnaires also indicate that they want learning media in the online learning process. learning media that can convey the topic so that students can see the phenomenon.
Knowledge Graphs Representation for Event-Related E-News Articles

E-newspaper readers are overloaded with massive texts on e-news articles, and they usually mislead the reader who reads and understands information. Thus, there is an urgent need for a technology that can automatically represent the gist of these e-news articles more quickly. Currently, popular machine learning approaches have greatly improved presentation accuracy compared to traditional methods, but they cannot be accommodated with the contextual information to acquire higher-level abstraction. Recent research efforts in knowledge representation using graph approaches are neither user-driven nor flexible to deviations in the data. Thus, there is a striking concentration on constructing knowledge graphs by combining the background information related to the subjects in text documents. We propose an enhanced representation of a scalable knowledge graph by automatically extracting the information from the corpus of e-news articles and determine whether a knowledge graph can be used as an efficient application in analyzing and generating knowledge representation from the extracted e-news corpus. This knowledge graph consists of a knowledge base built using triples that automatically produce knowledge representation from e-news articles. Inclusively, it has been observed that the proposed knowledge graph generates a comprehensive and precise knowledge representation for the corpus of e-news articles.
Literature Review: The Effectiveness of Tabletop Disaster Exercise on Disaster Preparedness

Backgrounds: Disasters often come without warning and canresulted in a lot of losses. Preparedness is a form of activity carried out to anticipate disasters. One of the activities that can be done to improve preparedness is by simulating using the Tabletop Disaster Exercise (TDE) media. Therefore, it is important to identify the effectiveness of tabletop disaster exercise on disaster preparedness.Objective : To assess the effectiveness of tabletop disaster exercise on disaster preparedness.Methods: The study was conducted based on the study. The search is limited to articles published in 2017-2021, full text, using the tabletop disaster exercise method with the type of original research. The databases used are Pubmed, National Library of Indonesia, and Google Scholar with the keywords tabletop disaster exercise, disaster, preparedness.Results: Five articles were found on the implementation of TDE which aims to improve preparedness and knowledge of disasters. The article contains knowledge, self-confidence, understanding of the role of disaster preparedness. The results of the analysis carried out on the five articles taken were the effectiveness of the tabltop disaster exercise on disaster preparedness with a value of p = 0.001 or p 0.05.Conclusion: The Tabletop Disaster Exercise is effectively implemented to improve disaster preparedness.Keywords: tabletop disaster exercise, disaster, preparedness
Improving Synthetic to Realistic Semantic Segmentation With Parallel Generative Ensembles for Autonomous Urban Driving

Semantic segmentation is paramount for autonomous vehicles to have a deeper understanding of the surrounding traffic environment and enhance safety. Deep neural networks (DNNs) have achieved remarkable performances in semantic segmentation. However, training such a DNN requires a large amount of labeled data at the pixel level. In practice, it is a labor-intensive task to manually annotate dense pixel-level labels. To tackle the problem associated with a small amount of labeled data, deep domain adaptation (DDA) methods have recently been developed to examine the use of synthetic driving scenes so as to significantly reduce the manual annotation cost. Despite remarkable advances, these methods, unfortunately, suffer from the generalizability problem that fails to provide a holistic representation of the mapping from the source image domain to the target image domain. In this article, we, therefore, develop a novel ensembled DDA to train models with different upsampling strategies, discrepancy, and segmentation loss functions. The models are, therefore, complementary with each other to achieve better generalization in the target image domain. Such a design does not only improves the adapted semantic segmentation performance but also strengthens the model reliability and robustness. Extensive experimental results demonstrate the superiorities of our approach over several state-of-the-art methods.
BERT-based distractor generation for Swedish reading comprehension questions using a small-scale dataset

An important part when constructing multiple-choice questions (MCQs) for reading comprehension assessment are the distractors, the incorrect but preferably plausible answer options. In this paper, we present a new BERT-based method for automatically generating distractors using only a small-scale dataset. We also release a new such dataset of Swedish MCQs (used for training the model), and propose a methodology for assessing the generated distractors. Evaluation shows that from a student’s perspective, our method generated one or more plausible distractors for more than 50% of the MCQs in our test set. From a teacher’s perspective, about 50% of the generated distractors were deemed appropriate. We also do a thorough analysis of the results.
IFRAD: A Fast Feature Descriptor for Remote Sensing Images

Feature description is a necessary process for implementing feature-based remote sensing applications. Due to the limited resources in satellite platforms and the considerable amount of image data, feature description—which is a process before feature matching—has to be fast and reliable. Currently, the state-of-the-art feature description methods are time-consuming as they need to quantitatively describe the detected features according to the surrounding gradients or pixels. Here, we propose a novel feature descriptor called Inter-Feature Relative Azimuth and Distance (IFRAD), which will describe a feature according to its relation to other features in an image. The IFRAD will be utilized after detecting some FAST-alike features: it first selects some stable features according to criteria, then calculates their relationships, such as their relative distances and azimuths, followed by describing the relationships according to some regulations, making them distinguishable while keeping affine-invariance to some extent. Finally, a special feature-similarity evaluator is designed to match features in two images. Compared with other state-of-the-art algorithms, the proposed method has significant improvements in computational efficiency at the expense of reasonable reductions in scale invariance.
Hybrid Deep Learning Vision-based Models for Human Object Interaction Detection by Knowledge Distillation

People hope that computers can be in constant intelligence development. Just like humans, they can ”see” the world and ”recognize” a visual event. We propose an approach based on computer vision methods to recognize Human-Object interaction(HOI). The technique stands on aggregating significant contextual features Human-Object interactions and scene recognition. We design a branch architecture consisting of the main branch for HOI detection and a supplementary branch for scene recognition. We explore the deep learning models through the knowledge distillation method and the Cross Branch Integration mechanism for encoding models into graph neural network architecture. We construct a knowledge graph to merge between high-level context information. When trained collaboratively, those models allow computing efficiency, strong context knowledge.
Research Trend Analysis for Sustainable QR code use - Focus on Big Data Analysis

The purpose of the study is to examine the current study trend of 'QR code' and suggest a direction for the future study of big data analysis: (1) Background: study trend of 'QR code' and analysis of the text by subject field and year; (2) Methodology: data scraping and collection, EXCEL summary, and preprocess and big data analysis by R x 64 4.0.2 program package; (3) the findings: first, the trend showed a continuous increase in 'QR code' studies in general and the findings were applied in various fields.Second, the analysis of frequent keywords showed somewhat different results by subject field and year, but the overall results were similar.Third, the visualization of the frequent keywords also showed similar results as that of frequent keyword analysis; and (4) the conclusions: in general, 'QR code' studies are used in various fields, and the trend is likely to increase in the future as well.And the findings of this study are a reflection that 'QR code' is an aspect of our social and cultural phenomena, so that it is necessary to think that 'QR code' is a tool and an application of information.An expansion of the scope of the analysis is expected to show us more meaningful indications on 'QR code' study trends and development potential.
Fast Path Planning for Firefighting UAV Based on A-Star algorithm

Abstract When the long distance oil-gas pipeline accident occurs, the fire UAV can give priority to the place where the accident occurs. Emergency investigation and fire rescue, greatly reduces the harm of the accident. However, due to the limitation of the UAV navigation system, the UAV will accumulate the positioning errors over time in the flight process. If the positioning errors can not be corrected in time, it will make the UAV unable to reach the intended destination, thus leading to the failure of the rescue mission. In view of this phenomenon, we propose a UAV path planning scheme considering positioning errors. Taking the UAV’s total flight path as short as possible and errors correction points as few as possible, a multi-objective programming model is established considering errors correction constraints and path constraints. The Euler distance with coefficient is selected as the estimated cost function of A-Star algorithm, and the optimal flight path of UAV is quickly planned through heuristic search. Taking the data of a certain flight area as an example, we have simulation results showing that the A-Star algorithm can quickly and effectively solve the problem of UAV flight path planning considering positioning errors.
A Combinatorial Coordinate System for the Vertices in the Octagonal $C_{4}C_{8}(S)$ Grid

We define a new integer-valued combinatorial coordinate system for the vertices in the octagonal C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</inf> (S) grid. We review the existing coordinate systems proposed in the literature and provide formulas for the conversion between our and existing coordinate systems, as well as with Cartesian coordinates. The neighborhood relation between vertices can be obtained through arithmetic operations on vertex coordinates.