Reviewerly/exHarmony huggingface dataset:
Hitchcock and Humor: Modes of Comedy in Twelve Defining Films. Wes D.Gehring. McFarland &amp; Company, 2019.

The Journal of American CultureVolume 43, Issue 2 p. 166-167 Book Review Hitchcock and Humor: Modes of Comedy in Twelve Defining Films. Wes D. Gehring. McFarland & Company, 2019. James Altman, James Altman University of Nevada Las VegasSearch for more papers by this author James Altman, James Altman University of Nevada Las VegasSearch for more papers by this author First published: 30 June 2020 https://doi.org/10.1111/jacc.13172Read the full textAboutPDF ToolsRequest permissionExport 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. Volume43, Issue2June 2020Pages 166-167 RelatedInformation
Professional-Oriented Training of Military Personnel

The most common concept of military specialists training is the concept of improving the professional continuous training of officers. The essence of this concept is that throughout their service, the officers go through a multi-level system of training and re-training, which allows them not only to preserve the knowledge and skills they obtained at the initial stage, but also to develop and improve them. This article reveals both positive aspects and contradictions and shortcomings in the modern system of training military specialists.
Wecter, D - Biographical entry - Index to the AMA archive

Wecter, D - Biographical entry - Index to the AMA archive - NAME is a biographical, bibliographical and archival database of SUBJECT with links to related articles and images. ADD MORE DESCRIPTION AS REQUIRED
An Integrated Private Cloud Storage for Small and Medium Scale Business Industries

Cloud storages are becoming popular due to raising privacy concern regarding the available public cloud services. The main concern was to prevent data loss that occur due to various problems. Although it is challenging to embrace hybrid cloud solutions for personal use. People tend to use Network-Attached Storage (NAS) and hybrid cloud solutions which are available for enterprise usage. Currently, NAS is classified as two types, Storage NAS and Platform NAS. The research of this paper focuses on Storage NAS, which is connected to a network that allows storage and retrieval of data from a central location for authorised user and clients to effectively develop the system architecture for Small and Medium Enterprise (SME) businesses. This solution provides the users with expected privacy for content and features of remote file browsing, shareable link generation, robust synchronization among personal cloud devices, simple user management similar to cloud services and remote file uploading/downloading facilities. The results and evaluation of the paper demonstrate the real world and practical business cases under the NAS server, in which it significantly shows the contribution of the System architecture development and amortization of the affordable business costs, that leads to reasonable and flexible operational security for SME sectors in the commercial cities in developing countries such as India in Asia.
Your IP is unique: what about your name?

Your IP is unique: what about your name? was presented by Matthew Buys (ORCID) at the WIPO Standards Workshop on Name Standardization on May 3, 2019.
An Interoperable Repository of Clinical Data

This article shows an innovation project that aims contributing, from the ICT perspective, to necessities of health sector, specifically in interoperability and generation of information starting from distributed sources. For these purposes, the technological objective is to develop a standardized interoperable repository and intelligent applications, at prototype level, feasible to massify in order to contribute to the timely care of patients, through information generated by the processing of radiological images and reports associated with clinical records.
Performance Analysis of Different Kernel Functions for MRI Image Segmentation

Nowadays, Kernel-based clustering methods have gained popularity over the widely used Euclidean distance-based methods in the area of image segmentation. Kernel-based methods generally replace the inner product with the positive definite function and perform a non-linear mapping of input data into a high dimensional feature space. The use of these non-linear kernel methods makes the process of clustering more robust and accurate. Different types of kernel methods have been used for the process of clustering. In this paper, we are going to explore the performance of different kernel functions applied with fuzzy c-means for the process of image segmentation. We have incorporated Gaussian kernel function, hyper-tangent kernel function, log-based kernel function and Cauchy kernel function. Experiments are performed on various synthetic and real MRI brain images. The performance is measured on the basis of segmentation accuracy, false-negative ratio and false-positive ratio.
Learning Outcomes and Their Relatedness Under Curriculum Drift

A typical medical curriculum is organized as a hierarchy of learning outcomes (LOs), each LO is a short text that describes a medical concept. Machine learning models have been applied to predict relatedness between LOs. These models are trained on examples of LO-relationships annotated by experts. However, medical curricula are periodically reviewed and revised, resulting in changes to the structure and content of LOs. This work addresses the problem of model adaptation under curriculum drift. First, we propose heuristics to generate reliable annotations for the revised curriculum, thus eliminating dependence on expert annotations. Second, starting with a model pre-trained on the old curriculum, we inject a task-specific transformation layer to capture nuances of the revised curriculum. Our approach makes significant progress towards reaching human-level performance.
A Linguistic philosophical Study on the Practice of Preaching

Preaching proclaims the word of God in language. Is it possible to put God’s revelation in human language? What is the relationship between God’s Word and human language? What does language mean to humans? The preacher must ponder the language-related problems.BR This paper focuses on the relationship between language and preaching, focusing on Wittgenstein, which caused the linguistic exhibition of philosophy. In particular, he comprehensively looks at the biography and later language. Within his philosophical convention, both light and shadow for preaching are contained. Therefore, the possibility of preaching can be reconfirmed by reflecting on preaching. We also look at Austin’s theory of language behavior. Because the possibility of preaching always involves practice, preaching is not just a voice signal, it is a practical work that greatly affects the audience. Lastly, it aims to indirectly impress the importance of preaching the Word by briefly examining the Christian tradition.
Mathematical Perspective of Machine Learning

We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.
Analyze the Relationship between Public relations and Knowledge sharing and their Impact on achieving Excellence Performance

The research sought to test the relationship between the dimensions of public relations and represented (trust, commitment and satisfaction) as an independent variable and knowledge sharing as an intermediate variable through its dimensions (individual dimension, organizational dimension and technological dimension) and to know the impact of that relationship on achieving Excellence Performance as an approved variable, and within that framework Is it necessary to answer a basic question that is there a relationship and influence between public relations, knowledge sharing, and outstanding performance? For this purpose, private sector banks were chosen in the city of Erbil. The study sample included a group of managers of private sector banks in the city of Erbil, who numbered (92) managers.&#x0D; The research adopted the descriptive analytical method. The questionnaire promised the main tool for collecting the required data, which was analyzed by adopting the statistical software (pct v.25 SPSS).&#x0D; The research reached a set of conclusions, the most important of which is the existence of a relationship and an effect of public relations in knowledge sharing, which has proven its direct impact on Excellence Performance.&#x0D; Keywords: public relations, - knowledge sharing, Excellence Performance.
A Big Bang – Big Crunch Type-2 Fuzzy Logic Based System for Fraud-Detection in the Sudanese Financial Sector

ABSTRACT– Improved fraud detection systems are vital tools for the evolution of the Sudanese banking sector where the traditional fraud detection models are incapable of overcoming the emerging, innovative and new attacks that threaten large financial institutions. Hence, there is a need for accurate and transparent techniques which can automatically detect fraud with high speed and identify its causes and common patterns. Many of the Artificial Intelligence (AI) techniques are effective and provide good predictive models. Nevertheless, they are considered as black-box models. On the other hand, the white box models are easy to understand and analyze, but result in a large number of rules, besides having many parameters in each rule. In this paper, we present a novel system based on the Big Bang–Big Crunch optimization (BB–BC) approach, which is combined with type-2 Fuzzy Logic Systems to result in a small set of short IF-Then rules for the fraud detection within the Sudanese banking sector. The proposed system uses real-world dataset from Balad Bank – Sudan, which contains 803,386 transactions with 107 fraud transactions. Hence, the positive class (frauds) rate is 0.0133% of all transactions. The experimental results demonstrate that the performance of proposed system is effective in tuning the parameters of the rule base and membership functions of the Type-2 FLSs (T2FLSs) to improve the accuracy, where the proposed T2FLSs outperformed the Type-1 FLSs (T1FLSs) counterpart, as well as each rule can be simply explainable. Therefore, this can be very helpful for the Sudanese banks to start tracking the fraud cases.
Price dispersion in bitcoin exchanges

Bitcoin is traded in a number of exchanges, and there is a large and time-varying price dispersion among them. We identify the sources of price dispersion using a standard time-varying vector autoregression model with stochastic volatility, and we find that shocks to transaction fees and bitcoin price growth explain on average 20%, and sometimes more than 60%, of the variation of price dispersion.
Process Reliabilism, Prime Numbers and the Generality Problem

This paper aims to show that Selim Berker’s widely discussed prime number case is merely an instance of the well-known generality problem for process reliabilism and thus arguably not as interesting a case as one might have thought. Initially, Berker’s case is introduced and interpreted. Then the most recent response to the case from the literature is presented. Eventually, it is argued that Berker’s case is nothing but a straightforward consequence of the generality problem, i.e., the problematic aspect of the case for process reliabilism (if any) is already captured by the generality problem.
Building up Ontologies from the EnglishWikipedia and Comparing with YAGO

Large-scale ontologies are attracting attention in various fields such as information retrieval, data integration and question answering. Because it takes many costs for human experts to build and maintain ontologies, much attention has been come to the work on ontology learning from semi-structured information resources, such as Wikipedia. YAGO and DBpedia are famous examples of such researches aimed at (semi) automatic construction fromWikipedia. However, in Wikipedia, there are still many information that YAGO and DBpedia can not extract, such as texual definition, headings, list articles and list structures. Also, YAGO mainly extracts class hierarchies and class instance relations and does not extract property types such as symmetric, transitive, functional and inverse functional. DBpedia has property domains (rdfs:domain), property ranges (rdfs:range), and some property types. However, they are described manually, which causes problems in terms of maintenance and update. In this paper, we present construction methods of ontology which complement YAGO and DBpedia. We use Wikipedia categories, Infobox, Infobox templates, list articles, texual definition, headings and list structures as Wikipedia resources. The ontologies include Is-a relations (rdfs:subClassOf), class instance relations (rdf:type) and triple. We compare them with overseas Wikipedia ontologies such as YAGO and DBpedia. Also we show that we can extract relations which YAGO and DBpedia can not extract.
PERANCANGAN PEMETAAN MODEL KOMPETENSI DI TEMPAT KERJA BERBASIS SISTEM OLAP

Graduate profile matches with needed competency by the workplace to be very important learning target sought in the vocational education. This research worshe a On Line Analyical Processing (OLAP) System Design, it can solve to competency model mapping problem, that is done and developed in workplace. This design aim to be helped in formulation of the outcome’s profile, and to be used as source’s information to curriculum development and learning outcome on vocational education. This research is designed as engeneering research with laboratory-explanatory on quasi-experiment by incremental model, and data analytical technic is descriptive-evaluative. As research object is The Network Administrator of the Competency Ocupation Mapping on Communication and Imformation Technic.The conclutions obtained the competency model can be extracted to be multidimensional data so by implementation the OLAP system design can be founded the critical aspects of the competency model. Futher research recommendations are needed field research in OLAP system development for the mapping of competency model by Machine Learning and Deep Learning Technology.
Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information

Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associated with other variables present in the data, without losing interpretability. Moreover, the metrics for calculating dissimilarity between qualitative values often scale poorly for high dimensional mixed datasets. In this study, we propose a novel method for clustering qualitative values, based on Hierarchical Clustering (HQC), and using Maximum Mean Discrepancy. HQC maintains the original interpretability of the qualitative information present in the dataset. We apply HQC to two datasets. Using a mixed dataset provided by Spotify, we showcase how our method can be used for clustering music artists based on the quantitative features of thousands of songs. In addition, using financial features of companies, we cluster company industries, and discuss the implications in investment portfolios diversification.
Public Provocation to Commit a Terrorist Offence: Balancing between the Liberties and the Security

Modern terrorism is global and decentralized like cyberspace. While the Darknet is mostly used by terrorists for fundraising campaigns and illicit trading, publicly accessible social platforms such as Twitter, Facebook or YouTube are abused for terrorist propaganda. Combating terrorism remains one of the top priorities of the European union (hereinafter as “the EU”). The approach towards the online content possibly connected to terrorist propaganda has become stricter.This paper focuses on the development of the EU legislation on the offence related to terrorist activities: the public provocation to commit a terrorist offence, as well as on the obligations of hosting service providers. It also analyses the impact of the Directive (EU) 2017/541 of the European Parliament and of the Council of 15 March 2017 on combating terrorism and replacing Council Framework Decision 2002/475/JHA and amending Council Decision 2005/671/JHA. The article observes a changing attitude on private monitoring of online information in the development of the EU legislation. It analyses changes in the Proposal for a Regulation of the European Parliament and of the Council on preventing the dissemination of terrorist content online. These changes signify a shift in the perception of the necessary level of freedom to receive and impart information through the internet.
Satisfiability Solving Meets Evolutionary Optimisation in Designing Approximate Circuits

Approximate circuits that trade the chip area or power consumption for the precision of the computation play a key role in development of energy-aware systems. Designing complex approximate circuits is, however, very difficult, especially, when a given approximation error has to be guaranteed. Evolutionary search algorithms together with SAT-based error evaluation currently represent one of the most successful approaches for automated circuit approximation. In this paper, we apply satisfiability solving not only for circuit evaluation but also for its minimisation. We consider and evaluate several approaches to this task, both inspired by existing works as well as novel ones. Our experiments show that a combined strategy, integrating evolutionary search and SMT-based sub-circuit minimisation (using quantified theory of arrays) that we propose, is able to find complex approximate circuits (e.g. 16-bit multipliers) with considerably better trade-offs between the circuit precision and size than existing approaches.
ANALYSIS OF ALL-PASS FILTERS APPLICATION TO ELIMINATE NEGATIVE EFFECTS OF LOUDNESS WAR TREND

In this paper the influence of all-pass filters on musical material with applied hypercompression dynamics (loudness war trend) was analyzed. These filters are characterized by shifting phase in selected frequency band of signal, not by change of their amplitude levels. Because a lot of music information is present in music tracks, the dynamic range was tested together with influence of other sound parameters like selectivity or instruments’ arrangement on scene, by running subjective tests on a group of respondents.
Cyber Extortion and Threats: Analysis of the United States Case Law

This article presents an analysis of the cyber extortion and threats cases brought to the United States courts. The inquiry employed content analysis to identify important elements and attributes and answers research questions concerning essential attributes, legal elements, and how do the courts interprets these offenses. The article extends the understanding of this phenomenon by providing a thorough discussion of the conceptual issues and characteristics and an analysis of the most important litigation aspects, such as intent, true threats, sentencing, and the insanity defense. The article concludes with recommendations for stakeholders, to more effectively address the phenomenon.
Pan-Sharpening with Color-Aware Perceptual Loss and Guided Re-Colorization

We present a novel color-aware perceptual (CAP) loss for learning the task of pan-sharpening. Our CAP loss is designed to focus on the deep features of a pre-trained VGG network that are more sensitive to spatial details and ignore color information to allow the network to extract the structural information from the PAN image while keeping the color from the lower resolution MS image. Additionally, we propose "guided re-colorization", which generates a pan-sharpened image with real colors from the MS input by "picking" the closest MS pixel color for each pan-sharpened pixel, as a human operator would do in manual colorization. Such a re-colorized (RC) image is completely aligned with the pan-sharpened (PS) network output and can be used as a self-supervision signal during training, or to enhance the colors in the PS image during test. We present several experiments where our network trained with our CAP loss generates naturally looking pan-sharpened images with fewer artifacts and outperforms the state-of-the-arts on the WorldView3 dataset in terms of ERGAS, SCC, and QNR metrics.
STUDENTS’ PERCEPTION ON THE USE OF ROLE PLAY BY TUTOR IN CLASSROOM

The use of learning methods that are in accordance with the conditions of students is a must for teachers in order to maximize the results of the learning process. One method that can be used is role play. This study aims to determine student perceptions of learning activities followed by students who use the role play method by the teacher during the learning process in the classroom. Data is collected by using interviews with students who have carried out role play activities. Data were analyzed using thematic analysis techniques (Braun & Clarke, 2006). The results show that use of role play can: (1) increase students’ confidence to communicate with others, (2) increase students’ ability to build language competence, (3) increase students’ motivation, (4) help students  eager to learn (5) motivate students  to be active in learning process. Thus, students' perceptions of learning using role play show positive student perceptions.