Reviewerly/exHarmony huggingface dataset:
Efficient Plain Password Cryptanalysis Techniques

In this research work, some low complexity and efficient cryptanalysis approaches are proposed to decrypt password (encryption keys). Passwords are still one of the most common means of securing computer systems. Most organizations rely on password authentication systems, and therefore, it is very important for them to enforce their users to have strong passwords. They usually ignore the importance of usability of the password for the users. The more complex they are the more they frustrate users and they end up with some coping strategies such as adding “123” at the end of their passwords or repeating a word to make their passwords longer, which reduces the security of the password, and more importantly there is no scientific basis for these password creation policies to make sure that passwords that are created based on these rules are resistance against real attacks. The current research work describes different password creation policies and password checkers that try to help users create strong passwords and addresses their issues. Metrics for password strength are explored in this research and efficient approaches to calculate these metrics for password distributions are introduced. Furthermore, efficient technique to estimate password strength based on its likelihood of being cracked by an attacker is described. In addition, a tool called PAM has been developed and explained in details in this paper to help users have strong passwords using these metrics; PAM is a password analyzer and modifier.
Geoscience education in a Nepali school

&amp;lt;p&amp;gt;Nepal lies in the center of the Himalayas range and it is also in the active seismic zone. We have regularly experienced different kinds of geohazards like as earthquake, landslides, floods etc. Most of the students in Nepal badly felt the 2015 Gorkha earthquake but fortunately, it was during the weekend and schools were closed.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;For the purpose of earthquake education, we have installed seismometer inside the school under the framework of Seismology at School in Nepal program, which is recording earthquake continuously. We are teaching earth science in the classrooms using the seismometer. This seismometer is very good for the education purposes as each element is visible and easy to install in the classroom. In addition, we teach about seismic risk in the special lectures in classes range between 5 -12 using different educational materials provided by the Seismology at School in Nepal program. Sometimes, extra activities including the members from the communities are also performed to share the knowledge about natural hazards, its associated risk and the preparedness. The level of awareness and the motivation towards geoscience is found to be increased in the community after the installation of the seismometer. Students are interested to know the fact and real science even it is not the part of the official curriculum. It would be great if geoscience education in inserted to the high school curriculum. From the side of school, we are looking for the support and more materials to teach geoscience in the classroom in the future.&amp;lt;/p&amp;gt;
Visual Analysis of Actions Performed with Big Graphs

The basic concepts of using application development environments are presented in this paper. The way of using the GraphAnalyser application and its basic functions is also presented. All results of the experiments conducted are generated with this application. According to the experimental methodology, they fall into two groups: the first one includes actions related to the vertices of a graph, and the second one includes actions related to the edges and the dynamic allocation of memory to store the structure of a graph. The results show that when the number of vertices in a graph increases linearly, the time to add and remove these vertices also increases linearly. When the number of graph vertices increases linearly, the number of added vertices per millisecond remains relatively constant. However, the number of vertices removed for one millisecond for graphs containing between 10 and 70 million vertices varies. Similarly, when the number of graph edges increases linearly, the number of added edges per millisecond remains relatively constant. The summarized results of the two experiments show that the actions associated with adding, removing, and calculating the edge lengths are performed much more slowly than adding and removing the vertices.
Effective Analysis of an Iterative Median Filter-Hardware and Software Perspective

Image quality enhancement is a very predominant domain of discussion as the complexity of system increases, the software implementation becomes a key factor but it is not always reliable to use the approach for adaptable applications such as medical, military or real time purpose. In order to address such scenarios it is necessary to have a reconfigurable and an adaptable implementation. In this paper we have addressed the hardware modeling of a median filter using double iteration process. The iterative median filter is implemented on an Artix-7 FPGA.
Software Defect Prediction Mechanisms for Quality Enhancement

Software engineering is an imperative field that helps to provide a variety of software to the different industrial domains. In this regard, providing quality software is a challenging task. Now a lot of advancements are occurring in this field to enhance the software quality. But still software failures will occur at the development stage. To overcome these difficulties, various failure detection mechanisms were adopted. Among them, software defect prediction is one such mechanism to provide quality software. The efficacious of testing the software and guiding the distribution of resources can effectively enhanced by Software defect prediction (SDP). This research article will provide a complete knowledge about the defect prediction mechanism in the software domain. And also profound analyses were done with the existing literature to know how this mechanism will ameliorate the quality of the software product.
Semantic Search over Wikipedia Documents Meaning of Queries Based Pre-Trained Language Model

The previously trained on massive text corpora such as GPT-3 is powerful and has an open domain with more than 175 billion parameters. However, as our semantic search will make it possible to search with the keyword that will give you what it was searched for, it is still challenging for such models to train and get the accuracy level challenging model Coherently for prolonged passages of textual content, in particular at the same time as the models This focuses on the target area of the small figure. In the next few steps, the key formulas for domain precise content materials will become more and more complex. Wikipedia's semantic file search, to calculate the semantic relevance of language text, requires multiples of data set search. Which is, Important common sense and global knowledge on specific topics. By recommending Semantic Search Analysis (SSA), which is a fully specialized technique for representing text in the main superior domain obtained from Wikipedia. Using the previously trained strategy, we mainly construct the average value of the content of the text explicitly on the adaptive model from Wikipedia. Results display that our version outperforms different models.
The 3<sup>rd</sup> International conference on Statistics, Mathematics, Teaching, and Research 2019 (The 3<sup>rd</sup> ICSMTR 2019)

Forewords On behalf of the organizing committee, it is a great pleasure to extend a warm welcome to all readers for the 3 rd International Conference on Statistics, Mathematics, Teaching, and Research (ICSMTR) 2019 proceeding. This is a two-year conference on the field of statistics, mathematics, science, and education. The conference organized by the Faculty of Mathematics and Natural Sciences, Universitas Negeri Makassar, Indonesia. The conference conducted in two days 9 th to 10 th of October 2019 with one keynote speaker, nine invited speakers, 137 parallel speakers from different backgrounds, and 101 accepted paper submitted to IOP. I also would like to express my gratitude and sincere thanks to the keynote, invited and all parallel speakers for spending their valuable time and sharing their thoughts and works with us in the conference. I do hope that this conference would bring a great opportunity for all of us to gain new ideas, improve our knowledge, and enhance collaboration between researchers. Lastly, I do hope that this proceeding contribute for the knowledge especially in the field of Statistics, Mathematics, Science, and Education. I also would like you taking opportunity to meet again in the next ICSMTR 2021. Best regard, Dr. Ruliana - Chairman of ICSMTR 2019 - Department of Statistics, Universitas Negeri Makassar, Indonesia - ruliana@unm.ac.id Committee Member, Documentation, The Committee Members, the speakers, and audiences, The Dean of Faculty of Mathematics and Natural Sciences, UNM and the best presenters on the 3 rd ICSMTR 2019, photos are available in the pdf.
RDF—the basis of the Semantic Web

Enterprises have made amazing advances by taking advantage of data about their business to provide predictions and understanding of their customers, markets, and products. But as the world of business becomes more interconnected and global, enterprise data is no long a monolith; it is just a part of a vast web of data. Managing data on a world-wide scale is a key capability for any business today.
ECMC Rule based Software Module Defect Prediction using Support Vector Balanced Data

Software module defect prediction (SDP) is one of the promising research area due to its wide range of application across multiple cross domain areas. Early detection of the defect module in a software reduces lot of effort put up in developing that software and it also reduces the cost associated in developing that software. Hence, in a quest to provide a better solution for SDP, we propose a novel rule based software module defect prediction using ECMC method in combination with support vector balanced data. It has been observed from our experiments that the results of this novel method are promising and satisfactory.
Object Tracking using Supervised Level_Set Model (Slsm)

An item discovery framework discovers objects in this present reality in an advanced picture or video, in which the article can have a place with any of articles to be specific people, vehicles, and so on. So as to distinguish an article in a picture or video the framework needs couple of parts so as to finish the errand of recognizing an item, an element finder, a theorem and theorem checker.In this work survey of different strategies which are utilized to distinguish an article, limit an item, order an item, extricate highlights, appearance data in pictures and recordings. The remarks are dependent on the considered writing and major problems are likewise recognized significant to the item location. A thought regarding the conceivable answer for multiple class_object identification is likewise exhibited. This work is appropriate for specialists who are learners in this area.. We initially portray the proposed system of two-stage supervised level set model in target following, at that point give summed up multi-stage adaptation for managing multiple-target . Positive decline is utilized to modify the learning after some time, empowering following to proceed under fractional and add up to impediment. Test results in various testing arrangements approve the viability in proposed strategy.
Learning Patterns as Criterion for Forming Work Groups in 3D Simulation Learning Environments

This study analyzes the relationship between the use of learning patterns as a grouping criterion to develop learning activities in the 3D simulation environment at University. Participants included 72 Spanish students from the Education and Marketing disciplines. Descriptive statistics and non-parametric tests were conducted. The process was analyzed by means of teamwork measurements and the product was analyzed by assessing the final group performance. Results showed that learning patterns can be an effective criterion for forming work groups, especially when the students do not know each other.
The European Regulatory Conundrum to Face the Rise and Amplification of False Content Online

In the last couple of years, the dissemination of false content online has raised serious concerns worldwide. As a result, states have attempted to tackle disinformation in different ways. Regulating disinformation requires solving the following dilemma: How and to what extent can we regulate (false) speech? It is not by chance that democratic and authoritarian countries have followed different regulatory paths in this field. The social media landscape have contributed to increasing the complexity in the fight against disinformation. The pandemic has then amplified the challenges coming from the spread of false content. This work aims to outline anti-disinformation trends in Europe. By focusing on Europe as one of the most interesting areas of the world to analyse regulatory attempts concerning disinformation, the primary goal of this work is to provide a nuanced approach in this field, going beyond the mere description of supranational and legislative regulation and looking at the European regulatory framework under a multilevel constitutional perspective.
Only Portions of the Map Still Legibly Survive

The Yale ReviewVolume 108, Issue 3 p. 28-29 POETRY Only Portions of the Map Still Legibly Survive Carl Phillips, Carl Phillips Latest book of poems is Pale Colors in a Tall FieldSearch for more papers by this author Carl Phillips, Carl Phillips Latest book of poems is Pale Colors in a Tall FieldSearch for more papers by this author First published: 24 September 2020 https://doi.org/10.1111/yrev.13654AboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume108, Issue3Autumn 2020Pages 28-29 RelatedInformation
Our point-to-point answers in a clearer typing format

This study aims at proposing the hybrid Neural Network (NN) -variation-al data assimilation algorithm to estimate river discharge from simulated SWOT like data.Such methodological studies are very important and of the scope of the NPG.In addition, investigating the potential benefits of satellites prior to the launches is quite useful to improve satellite missions further.However, I think the present manuscript has some fatal issues that should be solved prior to publication.The authors seemed to investigate the method that would not be applicable to the real un-gauged river basins as elaborate below.I am compelled to suggest this manuscript be rejected.
Art hacks and mash-up play: introducing BitFlows

A new software tool called BitFlows has been developed to support creativity, collaboration, performance and innovation in New Media. New Media practitioners already have a diverse range of tools at their disposal. This range of tools is constantly growing fueled by hardware and software hacks, which allow individuals to creatively use and abuse consumer products in ways not intended by their original creators. Software such as Ableton Live, MaxiMSP and VVVV give creators the ability to perform and demonstrate works in a live setting. Influenced by Csikszentmihalyi's concept of Flow in creative work (Csikszentmihalyi, 1996) and Shneiderman's suggestion that creativity can be aided by smoother flow between applications (Shneiderman, 2000a), BitFlows provides a simple means for users to mash-up the data-flows from all of these diverse pieces of hardware and software, over the network, in single or collaborative settings.
Privacy preserving techniques for decision trees

As a representative classification model, decision tree has been extensively applied in data mining. It generates a series of if-then rules based on the homogeneity of class distribution. In a society where data spreads everywhere for knowledge discovery, the privacy of the data respondents is likely to be leaked and abused. Based on this concern, we propose an overview of the rapidly evolving research results focusing on privacy preserving decision tree induction. The research results are summarised according to the characteristics of related privacy preservation techniques, which include data perturbation, cryptography, and data anonymisation. In addition, we demonstrate the comparison between the merits and demerits of these methods considering the specific property of decision tree induction. At last, we conclude the future trend of privacy preserving techniques.
A control strategy of a mobile robot in a limited space using a laser rangefinder

Currently, one of the most promising areas of research in the field of robotics is related to the task of creating intelligent autonomous mobile robots able to move in a limited space, bypass stationary or moving obstacles, take by themselves optimal decisions based on the data obtained from measuring devices installed on the board of the robot, as well as build a map of the surrounding area, etc.This article deals with the development of a method for solving the problem of simultaneous localization and mapping (SLAM) by the use of a self-developed mobile platform in conditions of limited space, an algorithm of control for the mobile platform has been proposed, a visualization software to visualize the construction of the map by the robot in real time has been reached, an experiment to evaluate the accuracy of the system has been carried out.
Machine recognition of human gestures through principal joint variable analysis

Gestures are a fundamental part of human communication and are becoming a key component of human-computer interaction. Traditionally, to teach computers to recognize specific gestures, researchers have used a sensor, usually a camera, to collect large gesture datasets, which are then classified and structured using machine learning techniques. Yet finding a way to confidently differentiate between several gesture classes has proven to be rather difficult for those working in the gesture recognition field. To capture the samples of movements necessary to train gesture recognition systems, the first step is to provide research participants with appropriate instructions. As collecting gesture data is the crucial first step of creating a robust gesture dataset, this dissertation will examine the modalities of instruction used in gesture recognition research to examine whether appropriate directives are conveyed to research participants. These experiments will result in the creation of a new dataset, the PJVA-20 dataset, comprised of 50 samples of 20 gesture classes sampled from 6 participants. After collecting the gesture samples of the PJVA-20 dataset, this dissertation will establish the benchmark recognition system PJVA — chiefly comprised of AMFE, Polynomial Motion Approximation, and Principal Component Analysis (PCA)—to contribute to the gesture recognition literature in terms of novel gesture recognition algorithms that can achieve high speed and accuracy results. This also involves examining studies in the gesture recognition literature to determine which machine learning algorithms offer reliability, speed, and accuracy for solving complex gesture recognition problems, as well as experimenting and testing the PJVA approach against other researchers in the Computer Vision and Machine Learning fields. In particular, the MSRC-12 research provides a benchmark point of comparison for research in this field. To test the quality of samples on the PJVA-20 against the MSRC-12, a new method is established for extracting motion feature vectors through a novel gesture recognition approach, AMFE. This is tested by applying PJVA to extract and label gesture data from both the MSRC- 12 and PJVA-20 datasets.
A content-based image retrieval system for hat database

Content-based image retrieval (CBIR) is a technique for indexing and retrieving images based on the low-level features, middle-level features, and high-level features. Low-level feature is extracted from contents of the images such as color, texture and shape; middle-level feature is a region obtained as a result of image segmentation; high-level feature is semantic information about the meaning of image, its objects and their roles, and categories to which the image belongs. In this project, three low-level features texture-based retrieval, color-based retrieval and shape-based retrieval are implemented and compared on hat database. Texture features are obtained from parameters of a two-component Gaussian mixture model (GMM) in the wavelet domain. Color features are extracted from a two-component GMM on HLS color space. Shape features are extracted from the contour by using centroid-contour distance Fourier descriptor. A comprehensive experimental evaluation of the retrieval performance of different feature sets is performed. The experimental results indicate that the shape features based on the centroid-contour distance Fourier descriptor perform much better than the color and texture features for the hat database used in this project
Topological Insulators and Semimetals

Modern Condensed Matter Physics brings together the most important advances in the field of recent decades. It provides instructors teaching graduate-level condensed matter courses with a comprehensive and in-depth textbook that will prepare graduate students for research or further study as well as reading more advanced and specialized books and research literature in the field. This textbook covers the basics of crystalline solids as well as analogous optical lattices and photonic crystals, while discussing cutting-edge topics such as disordered systems, mesoscopic systems, many-body systems, quantum magnetism, Bose–Einstein condensates, quantum entanglement, and superconducting quantum bits. Students are provided with the appropriate mathematical background to understand the topological concepts that have been permeating the field, together with numerous physical examples ranging from the fractional quantum Hall effect to topological insulators, the toric code, and majorana fermions. Exercises, commentary boxes, and appendices afford guidance and feedback for beginners and experts alike.