Reviewerly/exHarmony huggingface dataset:
Detection of Frauds in Financial Reporting

Many researches have been done on annual reports to detect whether it is fraud or not by the analytical and empirical part of the report. Annual reports provide information on a company’s activities throughout a year. By analyzing the annual report, we can identify the condition of the company whether it is in crisis or operating perfectly. This research deals with the data that can be obtained from the reports’ text to determine the probability of being a fraudulent annual report. The verbal content of the report which determines the linguistic features are being analyzed using natural language processing tools to distinguish fraud financial reports from non-fraud financial reports. A set of 60 annual reports were taken for the study. Out of which 30 annual reports are labelled as fraud and the other 30 is labelled as non-fraud. The set of fraudulent companies were selected on the basis of a reporting case of fraudulency of another company or the same company in any other year of non-reporting of cases. The features are selected using a wrapper method search algorithm. A neural network model of MLP (Multi-Layer Perceptron) algorithm is used to classify the data with an accuracy of 85.1%. Classifiers like SVM (Support Vector Machines), Logistic Regression, Naïve Bayes and Random Forest algorithms were also used to identify the best classifier out of all the algorithms. Performance of all the techniques used in this paper are being analyzed and presented in terms of accuracy, precision, recall, F1 score, TN rate and FN rate.
Iterated Belief Revision

A discussion of the problem of extending the basic AGM belief revision theory to iterated belief revision: the problem of formulating rules, not only for revising a basic belief state in response to potential new evidence, but also for revising one’s revision rules in response to potential new evidence. The emphasis in the chapter is on foundational questions about the nature of and motivation for various constraints, and about the methodology of the evaluation of putative counterexamples to proposed constraints. Some specific constraints that have been proposed are criticized. The chapter emphasizes the importance of meta-information—information about one’s sources of information—and argues that little of substance can be said about constraints on iterated belief revision at a level of abstraction that lacks the resources for explicit representation of meta-information.
Novel Framework for Maximizing Security over Wireless Network

With the increasing adoption of application running over wireless networking system, there is also an increasing security concern in it. Review of existing security protocols in wireless networks shows that they are highly specific to adversaries and hence they cannot be applicable with the dynamic state of network vulnerabilities. Apart from this, it was also explored that public key encryption requires a drastic change in its design methodology in order to make it more resource friendly for increased network lifetime. Therefore, this manuscript presents a novel framework that develops an enhanced model of public key encryption using algebraic structure that can generate an elite secret key. The study also introduces a design of an efficient trapdoor function which renders maximum resiliency towards different forms of lethal attacks as well as adhere to maximum security standards in wireless network. The study outcome shows that proposed system out performs frequently used existing security standards in many aspects.
A Conceptual Model for Automated Attendance Marking System using Facial Recognition

The project represents an automated attendance system based on face recognition using discriminative local binary pattern and local directional pattern descriptors. The proposed system involves face detection, Features extraction and matching. The face detection is to detect faces based on Viola Jones algorithm . In feature extraction stage, the discriminative local binary pattern is used for different object texture feature extraction process. The proposed method with new features retain the contrast information of image patterns. The Facial recognition (or face recognition) is a type of biometric application that can identify a specific individual in a digital image by analyzing and comparing patterns based on the data stored in database. Smart attendance is a real time face recognition used for handling day to day activities of the employees and students. In manual attendance system there are several issues like fake attendance and mistakenly marked absent by carelessness of the faculty/teachers/lectures. In order to overcome these problems we are using this smart attendance system. Here multiple faces are detected and recognized with trained with various features. The automated face attendance marking system gives accurate performance.
Reading Skill Result Of Grade Ii Students By Using Tgt Model At Indonesian Language Lesson

The background of this research is the ability of students' reading skill carried out at Jarak III Elementary School of Wonosalam Jombang, which is less than perfect in learning. It is due to the limited knowledge of teachers in carrying out the learning system by using many kinds of learning models. Meanwhile, the learning model the teachers apply is only the lecturing and assignments model. Besides, its limitations is the lack of the existing media. This research is a qualitative research. It consists of several aspects of treatment and observations aimed at improving the eading skills through the Team Games Tournament (TGT) model for grade II at Jarak III Elementary School of Wonosalam Jombang. This research was conducted at Jarak III Elementary School of Wonosalam Jombang, which involved 17 students consisting of 8 boys and 9 girls who were registered in the 2018/2019 academic year. This study used a descriptive research design. The results showed that the first pre-test got 70.2% of the average results that did not meet the minimum completeness criteria (KKM). After the learning was carried out using the Team Games Tournament (TGT) model, the post test average score of students increased to 85.8%. This average has shown that it meets the minimum completeness criteria (KKM). Thus it can be concluded that learning reading skills with the Team Games Tournament (TGT) model can improve students' reading skills in grade II of Jarak III Elementary School of Wonosalam Jombang.
Double Security using Dynamic S-Box inside Aes Algorithm

Now a days internet and other electronic devices have become an non removable part of our society. Day by day we are increasing the usage of data for transmission and storage as well. But there is always a problem for our data to be in wrong hands or hacked by someone. In order to ensure our secrecy of data we use bunch of algorithms to secure it. These algorithms comes under the vast section of cryptology, which means an art of hiding the data to make it secure. But as we all know when there is a hacker he would find every possible way to bypass the security algorithms. Some of the attacks are very popular in cryptology like Brute Force Attacks which checks each possible key combination to hack the data message. Due to the arrival of quantum computers in upcoming future hacking time will be decreased about a factor of around 1000 times. Now the best possible solution for enciphering data is Advanced Encryption Standard .This algorithm consists of two basic things static Substitution Box (S-Box) and other register operation. In this paper we have introduced a new technique to ensure a secure communication by using a dynamic S-Box with avalanche value of 58.59% as well as we also improved the overall area, delay (1.227 ns) and optimized the power to possible extents. Our results also approached above the traditional AES security as our modification improves avalanche effect also.
Analyzing Physical Education Teachers' Behaviors on Supporting Learner's Autonomy and Leadership Styles

The general aim of the study is to examine the behaviors of secondary and high school physical education teachers in teacher leadership styles and behaviors to support learner autonomy according to gender, school type, professional working year and class sizes.The sample of the study consists of 132 physical education teachers working in secondary and high schools in Mersin with the easily accessible sampling method.41.7% of the physical education teachers participating in the study were female (n= 55) and 58.3% were male (n= 77).70.5% of the participants were working in secondary school (n= 93) and 29.5% in high school (n= 39).The duration of service of physical education teachers participating in the study ranged between 01-34 years.Class sizes of teachers were grouped as 20-25, 26-35 and 36."Teacher Leadership Styles Scale" and "Learner Autonomy Support Scale" were used as data collection tools in this study.Descriptive statistics, t-test, Kruskal Wallis H test and Pearson correlation coefficient were used to analyze the data.The margin of error was taken as 0.05.According to the analysis results; it was concluded that physical education teachers displayed semi-democratic leadership style (M= 69.79, SD = 3.32).Teachers' levels of perceiving learner autonomy support behaviors are necessary (M= 1.19, SD=0.55) lower than the display levels (M= 2.20, SD=0.55).Among the sub-dimensions of autonomy support, physical education teachers considered that evaluation process support was the most necessary (M= 1.94, SD = 0.72) and displayed (M= 2.34, SD = 0.73).In addition, teachers' gender [t (132) = 0.61; p> .05], to school levels [t (132) = 0.41; p>.05] and there was no
Institutionalized Quality

There is no silver bullet to quality in software development. Quality must be valued throughout an organization so that quality practices can exist at all levels and in all processes. In this chapter, we will focus on key practices that will help ensure quality throughout the entire process.
Making of the Ukrainian Scientometrics: History Journal Ranking

History as an Academic Subject is in crisis of a traditional paradigm, information technologies have transformed the world into the phenomenon of information society. The article is devoted to the problem of creating a specialized database of Ukrainian scientific journals (History studies). Author moderately criticizes the traditional methods of scientometrics. Generally, journal impact factor (Clarivate Analytics) and CiteScore (Elsevier). The specifics of inapplicability are based on main problem which flows to the fact that citation on the history of Ukraine (and number of local history problems) will be deliberately so low since it is less interesting for the scientific community of other countries. Since the creation of any Bibliometrics is based on the collection and addition of materials to the database, the main attention is focused on the definition of Content management systems for their further integration into History journal ranking frame. Our purpose is to define of CMS types and further writing of the corresponding software significantly optimizes the work of operators. For determination of CMS we analyze the HTML code of the web-pages. It is paid attention to consider the place of historical disciplines in the system of Ukrainian science according to Register of scientific journals of Ukraine.
Self-healing of web service compositions: a specification rewriting approach

We present an approach that improves the robustness of web service compositions enabling their recovery from failures that can happen at different execution times. We first present a taxonomy of failures as an overview of previous research works on the topic of fault recovery of service compositions. The resulting classification is used to propose our self-healing method for web service compositions. The proposed method, based on the refinement process of compositions, takes user preferences into account to generate the best possible recovering compositions. In order to validate our approach, we produced a prototype implementation capable of simulating and analysing different scenarios of faults. Our work introduces algorithms for generating synthetic compositions and web services. In this setting, the recovery time, the user preference degradation and the impact of different locations of failure are investigated under different strategies, namely local, partial or total recovery. These strategies represent different levels of intervention on the composition.
Border Image Generation Based on Residual Map Vote

In this paper, a new algorithm is proposed to automatically generate border image for paint color game.First of all, region grow algorithm is applied.And then morphological closing operation is used to smooth every separated region.All regions are then labeled and the un-labeled regions form residual map.We then use vote strategy to decide the label of residual pixels.The final border image will be generated until no residual pixel exists.The experiments show that the proposed algorithm is robust and effective to produce border image for paint color game.
Fuzz Neutrosophic Soft HX Subring

In this paper, we define the notion of fuzzy nutrosophic soft HX subring of a HX ring and some of their related properties investigated.We define the intersection, cartesian product of an fuzzy neutrosophic soft subset of an fuzzy nutrosophic soft HX ring and we show some results.
The Read Feed – Reviews

Bethany Blankemeyer reviews The ABC’s of ERM: Demystifying Electronic Resources Management for Public and Academic Librarians; Jan Mayo reviews Information Resource Description: Creating and Managing Metadata; Elizabeth Parang reviews Assessment Strategies in Technical Services.
Fake News Detection using Machine Learning Algorithm

In our modern society where internet is ubiquitous, everyone relies on various online resources for news.Along with the rise in use of social media platforms like Facebook, Twitter etc. news spread rapidly among various users within a really short span of your time.The spread of fake news has far reaching consequences like creation of biased opinions to swaying election outcomes for the benefit of certain candidates.Moreover, spammers use appealing news headlines to get revenue using advertisements via click-baits.During this project, we aim to perform a binary classification of varied news articles available online with the assistance of concepts per Decision tree algorithm and Naive Bayes Classification.Fake data detection is that the most vital problem to be addressed within recent years, there's lot of research happening during this field due to its serious impacts on the readers, researchers, government and personal agencies working together to resolve the problem.This project represents a hybrid approach for fake data detection using the multinomial voting algorithm.The list of algorithms that are used here is Decision Tree and Naïve Bayes algorithms.All these two algorithms use training data because the bag of words model which was created using Count vectorizer.Experimental data has collected from the kaggle data world.Python is used as a language to verify and validate the result.
Maximum Frequent Item Set based Clustering Algorithm for Big Text Data

Due to fast growth of internet and continuous expansion of World Wide Web like digital libraries, online news contributes to massive amount of electronic unstructured text documents on the web. Although lot traditional techniques are available to extract the knowledge from large collection of text documents, still to improve precision of the web search retrieval and to find most appropriate documents from huge text collections proficiently is a big challenge. Clustering techniques helps the search engine to retrieve the documents. The proposed system overcomes existing problems using bivariate n-gram frequent item clustering algorithm by concept of maximum frequent set which maintain the sequence and meaning of sentence in order to reduce huge dimension and and frequent item sets finds similarity. Then based on maximum document occurrence we cluster the documents. Thus our method obtains quality of clusters when compared with existing methodologies and improves the efficiency. The experiment is shown for sample Newsgroup dataset for existing K-Mean and FICMDO (Frequent item clustering method based on maximum document occurrence) and proved the f-measure is higher for our algorithm. Since the f-measure increases, obtains efficient clusters. Hence it is faster and efficient big data method which improves the performance when compared with vector space model like K-Means algorithm.
ACRL candidates for 2020: A look at who’s running

Lynn Silipigni Connaway is the director of library trends and user research at OCLC Research, a position she has held since 2018. Prior to this, Connaway served as senior research scientist and director of user research (2016-18), senior research scientist (2007-16), and consulting research scientist III (2003-07), all at OCLC Research. She was vice-president of research and library systems at NetLibrary (1999-2003), and director and associate clinical professor of the Library and Information Services Department at the University of Denver (1995-99). She served as assistant professor in the School of Library and Informational Science at the University of Missouri (1993-95), and as head of technical services and cataloging at Mesa State College Library (1984-89).Julie Garrison is dean of university libraries at Western Michigan University, a position she has held since 2016. Prior to this, Garrison served as associate dean, research and instructional services at Grand Valley State University Libraries (2009-16); director of off-campus library services at Central Michigan University (2003-07); and as assistant/associate director of public services at Duke University Medical Center Library (2000-02).
Novel Face Recognition Framework for Plastic Surgery and Unconstrained Facial Datasets

Since from the last decade, the importance of efficient and robust face recognition becomes interesting research problem due to its growing demand in user authentication process across different applications. Several face recognition method designed by considering the challenges of constrained and unconstrained face acquisition. The performance of face recognition systems becomes worst while working with unconstrained and plastic surgery datasets. The unconstrained dataset may contain the significant variations in illumination, expressions, and face image quality. This paper proposed the novel face recognition framework which is built on top of hybrid face descriptor and hybrid features extraction technique. The purpose of hybrid face descriptor is to achieve the bridge the gap between spatial information and histogram representations to address the challenges of unconstrained face conditions efficiently. The hybrid features extraction includes the histogram features (single and multi-level) and invariant features. Since the moment invariant features become effective shape descriptor to address the challenges of unconstrained face images, we proposed 11 different invariant moments from the hybrid face descriptor. To normalize and reduce the features, we applied lightweight features selection technique. The experimental result shows that proposed face recognition framework improves the overall accuracy different datasets.
Message from the Quantum Computing Workshop Chairs

It is with great pleasure that we welcome you to the 2nd International Workshop on Quantum Computing: Circuits Systems Automation and Applications (QC-CSAA) in conjunction with the ISVLSI 2021.The purpose of this workshop is to explore design paradigms of quantum computing including the development of full stack suite spanning from quantum algorithms to quantum hardware chips.The advances in quantum computing hardware, software, and algorithms as well as the contributions from transformative research needed to guide quantum computing performance toward practical and sustainable operations will be aligned with the goal of this workshop.Further, circuits and systems targeting novel and existing applications of quantum computing to fields such as quantum chemistry, linear algebra, scientific applications, aerospace, material science, cryptography, machine learning, etc., were of special interest.The QC-CSAA has received an excellent response to the call for papers with submissions received from all parts of the globe.After peer review by the technical program committee members, we have accepted 9 papers and 1 invited talk in topics such as cross talk analysis in Noisy Intermediate-Scale Quantum (NISQ) machines, quantum circuit simulation using hardware accelerated general purpose library, debugging combinational circuits using quantum computing, microwave quantum circuit, applying Grover's algorithm in maximum clique problem, tensor network simulations, quantum annealing in healthcare (stress detection), reproducibility in quantum computing and reliable quantum circuits.We wish to express our thanks to the authors for their submissions as well
Database Promotion at the Intersection of Electronic Resource Management and Outreach

During the electronic resource management cycle, library staff review, acquire, evaluate, and deactivate databases based on a number of factors that include usage statistics, collection development decisions, and budgetary factors. One aspect of electronic resource management that has increased in the Government Information and Library Services division is our promotion of new, updated, or existing databases to our state employee users.
Introduction of media educational technologies in the educational process in the disciplines of physical and mathematical cycle of higher educational institution

Бєльчев П. В., Бурцева О
Vagueness: A Variant Approach

Vagueness is a prime source of paradox. For vague terms have a more or less well-defined central core of application, surrounded by a large penumbra of indefiniteness and uncertainty. And since a term, T, that is vague will automatically have a complement, non-T, that is so as well, there will inevitably be a nebulous region of ambivalent overlap between T situations and non-T situations. Here, matters seem to stand both ways, so that a paradoxical inconsistency arises.
Text Normalization and Its Role in Speech Synthesis

As the technology is developing day-by-day and most of the human work is done by the machine or systems, it is the need of the today’s world to develop systems that can read informal text or words in a proper and standard way even though the format of writing these words or text does not match the standard English words. The informal texts types that exists are the dates, currencies, abbreviations and acronyms of standard words, measurements, URLs, phone numbers etc. This paper focuses on the normalization of such text that converts the informal text into their equivalent standard form which is called text normalization. To produce the equivalent speech form of these non-standard words is the necessity of the today’s system. Text normalization is pre-processing step of the natural language processing system. The paper discusses various techniques and methods for the conversion of the non-standard words into standard words. The methods used for classification of the token are regular expressions, used for simple patter match of the token. Naïve Bayes classification for number sense disambiguity and Stochastic Gradient Descent for resolving acronym and class ambiguity .The result and analysis are also mentioned in the form of error-rate of the system, which shows the area for the scope of more improvement in the system.