Reviewerly/exHarmony huggingface dataset:
Content Matters; An alternative view on Organisational Change

This case study shows how the content of a change initiative can be used to predict the extent to which this initiative will activate the learning process of employees and, consequently, cause them to change their working methods. The object of this case study is a school for secondary education in The Netherlands. Its management team wants to improve school results and has formulated the content of the change as an elaboration of the ideology of professionalism. Both the change content and the employees' cognitions are analysed by means of a mixed-method approach that is based on notions and techniques of cognitive anthropology. The results show that the envisioned change lacks cognitive relevance, as a result of which a change of working practice is not likely to occur, a conclusion that is claimed to be valid for organisational change processes more generally. This paper concludes that an analysis of the cognitive relevance of the content of a change enhances the quality of the predictions of success or failure of organisational change in concrete cases, and can be used alongside more conventional change approaches that focus on processual factors.
Foreword

This is the second of three issues of American Journal of Science in which the papers have been submitted by friends and colleagues as a mark of respect to Alfred Kroner, who passed away just a few months before his 80th birthday in 2019. Alfred has left an indelible mark on the geoscience community
MEDIA

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 56, Issue 3 p. 22243C-22245C Social and Cultural MEDIA First published: 11 April 2019 https://doi.org/10.1111/j.1467-825X.2019.08802.xRead 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. Volume56, Issue3April 2019Pages 22243C-22245C RelatedInformation
Approximation Measures

The focus of Chapter 3 is the central Bayesian tenet that a thinker’s unconditional credences should be probabilistically coherent. Plausibly, thinkers who don’t have coherent credences can be more or less incoherent, i.e. their credences can diverge from complying with the probability axioms only a little, or quite substantially. We can capture this intuitive idea by representing a thinker’s credence function as a vector, and measuring its distance to the closest probabilistically coherent credence function that is defined over the same set of propositions. The problem that arises in this context is that there are many possible measures we can use to determine the distance from coherence, and those measures can deliver incompatible rankings. I present a representative range of such measures and illustrate the ways in which they differ.
News and Notes: Conferences

Journal of the History of the Behavioral SciencesVolume 55, Issue 2 p. 176-177 NEWS AND NOTES News and Notes: Conferences First published: 14 April 2019 https://doi.org/10.1002/jhbs.21972Read 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. Volume55, Issue2SPRING 2019Pages 176-177 RelatedInformation
L’annotazione di testi storico-letterari al tempo dei social media

The annotation of historical and literary texts is approached differently by traditional philologists and digital philologists.The former are concentrated on the detailed study of a given text (close reading) while the latter are focused on the study of large quantities of texts (distant reading).A structured and collaborative annotation makes it possible both to add information to particular passages of individual texts, as in a traditional linear comment, and to connect data from entire textual collections through rigorous protocols.However, the standards developed by digital philologists are not highly appreciated by traditional academics, since the effort necessary to apply the proposed technologies allegedly diverts researchers' attention from the object of study.As opposed to this objection, we intend to highlight that it is indeed possible to maintain the precision requisite for the application of computational tools to digital resources without renouncing the annotation practices established in traditional contexts.In support of the method, we report a number of case studies of digital scientific editions whose goals include both reconstructing respective texts and encouraging the dissemination of contents and public participation in the academic debate.In particular, we will discuss the following projects: a) the stylistic annotation of three different editions of Giacomo Leopardi's translation of the Batracomiomachia; b) the scientific edition of Bellini's letters; c) the multi-level annotated edition of Bassani; and d) the comparison of Umberto Eco's variants of his Il nome della rosa.
Foundation

Compared with traditional strategies (e.g., information theoretic learning He et al, Robust recognition via information theoretic learning, 2014, [19]), deep learning techniques have recently revealed its superiority in various computer vision tasks. Deep face representation is a compact and discriminative description of raw face data (e.g., face images) extracted by deep networks. It is a crucial step in face recognition system which is one of the fundamental tasks in face analysis. This chapter gives a brief introduction of basic deep learning concepts in face analysis and synthesis. (1) A coherent study of the deep face representation is first presented. We start with a compact survey of CNN based face recognition, then followed with a discussion on unit activation functions in CNN. Moreover, a concrete instance (namely Light CNN) is described in details, serving as a representative of CNN architectures in deep face representation. (2) As the most prominent deep generative models, the classical versions of GAN and VAE are briefly introduced in particular. Additionally, we discuss their limitations as well as improved variants.
The Media Machine

This chapter examines one of the largest interventions in computer-based learning currently underway, the One Laptop per Child (OLPC) project. Started in 2005 by people from the MIT Media Lab, this project distributed more than 2.5 million laptops worldwide, over 80 percent of them in Latin America. Drawing on 2010 and 2013 fieldwork investigating a project in Paraguay with 10,000 of OLPC’s “XO” laptops, the chapter explores the ways in which the children who were meant to be the primary beneficiaries interpreted leisure laptop use as “learning.” It also shows that the most captivating uses of the laptops were not “productive” or programming-centric, as OLPC’s developers hoped, but “consumptive” and media-centric, focused on music, videos, and video games. It discusses the learning benefits and drawbacks of this use, as understood by participants and in light of education research, and in light of the broader context of transnational corporations interested in marketing to these children. In the process, it weighs OLPC’s utopian dreams against the interests of the child beneficiaries, concerns of media imperialism, and a potential shift in the meaning of computers.
Logistic Regression

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Hacking Relationships

In an effort to keep up with a world of too much, life hackers sometimes risk going too far.The open access edition of this book was made possible by generous funding from the MIT Libraries. Life hackers track and analyze the food they eat, the hours they sleep, the money they spend, and how they're feeling on any given day. They share tips on the most efficient ways to tie shoelaces and load the dishwasher; they employ a tomato-shaped kitchen timer as a time-management tool.They see everything as a system composed of parts that can be decomposed and recomposed, with algorithmic rules that can be understood, optimized, and subverted. In Hacking Life, Joseph Reagle examines these attempts to systematize living and finds that they are the latest in a long series of self-improvement methods. Life hacking, he writes, is self-help for the digital age's creative class.Reagle chronicles the history of life hacking, from Benjamin Franklin's Poor Richard's Almanack through Stephen Covey's 7 Habits of Highly Effective People and Timothy Ferriss's The 4-Hour Workweek. He describes personal outsourcing, polyphasic sleep, the quantified self movement, and hacks for pickup artists. Life hacks can be useful, useless, and sometimes harmful (for example, if you treat others as cogs in your machine). Life hacks have strengths and weaknesses, which are sometimes like two sides of a coin: being efficient is not the same thing as being effective; being precious about minimalism does not mean you are living life unfettered; and compulsively checking your vital signs is its own sort of illness. With Hacking Life, Reagle sheds light on a question even non-hackers ponder: what does it mean to live a good life in the new millennium?
On collaborative filtering model optimised with multi-item attribute information space for enhanced recommendation accuracy

Recommender system is a type of information filtering system that is designed to curtail the difficulties of information overload by automatically suggesting relevant items to users tailored to their preferences. Bayesian personalised smart linear methods (BPRSLIM) is a variant of item-based collaborative filtering technique used in information filtering system. Although, this algorithm has shown outstanding performance in a range of applications, nevertheless it suffers serious limitation of inability to provide accurate and reliable recommendations when the user-item matrix contains insufficient rating information, this always reduces its accuracy. In this paper, we propose a framework that integrates multi-item attribute information besides the classic information of users and items into BPRSLIM model in order to ease the sparsity problem associated with it and hence improves its performance accuracy. The enhanced model is expected to outperform the original BPRSLIM model.
An Improvement for Value-Based Reinforcement Learning Method Through Increasing Discount Factor Substitution

Discount factor is typically considered as a constant value in conventional Reinforcement Learning (RL) methods, and the exponential inhibition is used to evaluate the future rewards that can guarantee the theoretical convergence of Bellman Equation. However, exponential inhibition mode greatly underestimates future rewards, which is obviously unreasonable. Future rewards, especially those that are closer to the completion of the task, should be given greater importance. In this paper, we review the rationale of discount factor and propose an increasing discount factor to reduce the underestimation effect of exponential inhibition on future rewards. We test two value-based reinforcement learning methods in three scenarios to verify our method. The experimental results show that value-based reinforcement learning with increasing discount factor is more efficient than it with fixed discount factor under certain circumstances.
SUDAN: Cash Crisis Tops Economic Woes

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 56, Issue 4 p. 22539C-22541A Policy and Practice SUDAN: Cash Crisis Tops Economic Woes First published: 02 June 2019 https://doi.org/10.1111/j.1467-6346.2019.08895.xRead 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. Volume56, Issue4June 2019Pages 22539C-22541A RelatedInformation
Session details: Theme: Distributed systems: DADS - Dependable, adaptive, and secure distributed systems track

While computing is provided by the cloud and services increasingly pervade our daily lives, dependability, adaptiveness and security become a cornerstone of the information society. Unfortunately, most innovative systems and applications suffer from a lack of dependability and security, which is fueled by global scale, mobility and heterogeneity, as well as the demand for resource awareness, green computing, and increasing cost pressure.
Introduction

Since the advent of the internet, our world has become more and more connectedevery day. The International Telecommunications Union reports that the number of internet users has increased from 400 million in 2000 to 3.2 billionin 2015. This growth rate is expected to be faster in the future as a result ofinternet penetration in the developing nations. The Internet of Things (IoT)is a network of connected devices ranging from powerful personal computersand smart phones to low-cost passive RFID tags. These devices are capable of computing together and exchanging information with or without humanintervention and are present in many areas of our life such as smart homes, smart grids, intelligent transportation, smart cities.
Constant-size ring signature scheme using multilinear maps

Ring signature is a group-oriented digital signature with anonymity. Most of existing ring signature schemes use bilinear pairings, are provably secure in the random oracles, or are linear signature size to the number of ring member. In this paper, we use multilinear maps, which have been widely used to construct many novel cryptographic primitives recently, to present a ring signature scheme with constant signature size. The proposed scheme is proven to be anonymous against full key exposure and unforgeable against chosen-subring attacks based on the multilinear computational Diffie-Hellman assumption in the standard model. Furthermore, our scheme has the advantage of tighter security reduction by using an optimal security reduction technique.
Tracking Human Motion in Monocular Video Sequence with the De-Mc Particle Filter

Tracking human motion from monocular video sequences has attracted a great deal of interests in recent years. The difficulty in solving this problem is largely due to the nonlinear property of human dynamics and the high dimensionality of the state vector space required to model human motion. Traditional particle filtering methods usually fail in this situation because the distributions they sample from are ill-defined. In this thesis we propose a novel tracking algorithm, namely the Differential Evolution - Markov Chain (DE-MC) particle filtering. It is based on the particle filter framework but makes substantial changes to its core, i.e. the sampling strategy. In this new approach, the Differential Evolution algorithm and the Markov Chain Monte Carlo algorithm are integrated, aiming at improving both the accuracy and efficiency in approximating the posterior distribution. Global optimization and importance sampling are spirits of the proposed method. To apply the DE-MC particle filter to articulated model-based human motion tracking, we also integrate multiple image cues including the area of silhouettes, color histograms and boundaries to measure the image likelihoods. We find the Fourier Descriptor (FD) to be a new and effective image feature in human motion tracking applications. Our other contributions, such as a modified color cue-based measurement function and a simple adaptive strategy for sampling, also help to improve the performance of the human tracker. Experimental results including the comparison with the performance of other particle filtering methods demonstrate the power of the proposed approach.
Development of Thematic Multi Media-Based Learning Model to Increase Clean Life Skills

This study aims to develop multi-media based products on thematic learning sub themes "cleanliness at home". This research was applied in class II of elementary school. Multimedia products produced are short animations with the theme of hand washing. This research method uses Research and Development (R &amp; D) with the Borg and Goal model. Three stages of development research are: first, preliminary research into the two development models and third the model dissemination. The results of the preliminary study concluded that multi-media based applications are needed for thematic learning in elementary schools. The development of multi-media animation by giving a message of six steps of hand washing was developed through the stages of product development. The first stage of expert judgment is carried out by media, material and language experts. The second stage is one to one and small group. This study produced a valid multi-media assisted thematic learning model applied to elementary school students.
Buzz Group Technique to Increase Writing Skill at EFL Students of Junior High School in Indonesia

This research aimed to find out whether there was a significant difference of writing skill achievement between EFL students who were taught using Buzz Group technique and those who were taught using a conventional method by expecting the Buzz Group technique improved EFL students’ writing skill achievement at SMP Negeri 7 Baubau, Southeast Sulawesi. Design of this research was a quasi-experimental in which there were 22 students in both control and experimental groups. The findings of this research obtained: 1) the mean score of EFL students’ writing skill at pretest in experimental class was 51.09 in low category and the mean score of posttest was 72.18 in high category; 2) the mean score of EFL students’ writing skill at pretest in control class was 49.82 in low category and the mean score of posttest was 56.36 in moderate category; 3) the score of Asymp. Sig. (2-tailed) from hypothesis test was 0.000 in which it was less than 0.05. Therefore, this research concluded that there was a significant difference between of writing skill achievement between EFL students who were taught using Buzz Group technique and those who were taught using the conventional method at SMP Negeri 7 Baubau. In addition, the use of Buzz Group technique is better than the conventional method to improve EFL students’ writing skill achievement.
Application study on image recognition algorithm in visual service of deaf mute under complex environment

In order to benefit the disadvantaged deaf people and eliminate the communication disorder between the hearing and deaf mute to get harmony and symbiosis really, the visual identification and service research of deaf people has been paid more and more attention by experts and scholars. The paper presented an improved advanced local binary pattern (ALBP) algorithm for face feature recognition. Aimed at being not ideal in recognition effect resulted in ignoring the contrast between the local image in traditional face feature recognition algorithm and discarding some important texture feature information, it first made the illumination normalisation pre-processing of face images under different illumination conditions to control the illumination changes in a certain range, then the improved local binary pattern (LBP) algorithm is used to map the contrast value of the local area pixels into an interval value, so that it made the image own the illumination invariance, thereupon then it could better identify the face features. The experimental results show that the presented ALBP algorithm can be better applied to the visual service system of the deaf.
A Research on Breast Cancer Detection using Mammography

The digital mammogram has developed as the standard screening approach for breast cancer detection and further defects in human breast tissue problem. Early detection is an efficient manner to decrease mortality in worldwide. In the past decades, several researchers implemented many methods to consistently identify the breast cancer by mammogram images. Those methods were employed to produce systems to support radiologists and physicians attain more accurate diagnosis. Accurate segmentation and classification of various tumors in the mammography plays a complex role in the early diagnosis of breast cancer. This paper defines the research on Breast Cancer Detection (BCD) methods which includes two major steps such as segmentation and classification. This research presented the different types of BCD methods with their main contributions. Additionally, it assists the researchers in the area of breast cancer detection by providing the basic knowledge and common understanding of the newest BCD methods.
MEDIA

Africa Research Bulletin: Political, Social and Cultural SeriesVolume 56, Issue 6 p. 22352A-22353C Social and Cultural MEDIA First published: 14 July 2019 https://doi.org/10.1111/j.1467-825X.2019.08954.xRead 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. Volume56, Issue6July 2019Pages 22352A-22353C RelatedInformation