Reviewerly/exHarmony huggingface dataset:
Decoupling the Impact of Financial Sector Development on Income Inequality in Africa: Further Insights from Geographical Regions and Governance Factors

Abstract The current literature on the finance-inequality nexus lacks evidence in decoupling the impact of banking and the stock market on income inequality in Africa. Besides, the analysis is limited to the continental level without accounting for geographic regions and governance differences. We offer an answer on how financial sector development affects income inequality in Africa by isolating the impact of the banking, stock market, and composite index. The paper identified the heterogeneous effect by geographical regions and incorporated the role of governance from 1980 to 2017 across 49 African countries. Using a panel data fixed effect regression and two-stage least squared estimation techniques (2sls), we show that the banking, stock market, and the composite development widens income inequality. The relationship corroborates an inverted U shape hypothesis. Besides, corruption and political instability are essential elements in exacerbating income inequality. The geographical region configuration also shows that the continental level effect is highly driven by financial development in the Northern and Southern African countries. The direct implication of our result is that the current financial development in Africa is not inclusive, and the credit is directed to people who already access financial services. We highlight that financial inclusion, digitalization, easing the cost of borrowing money, ensuring good governance, and pro-poor financial development reverses the relationship presented in this paper.
Selected papers from the 2021 European Conference on Object-Oriented Programming

Probabilistic game structures combine both nondeterminism and stochasticity, where players repeatedly take actions simultaneously to move to the next state of the concurrent game. Probabilistic alternating simulation and bisimulation are important tools to compare the behaviour of different probabilistic game structures. In this paper, we present a sound and complete modal characterisation of these two relations by proposing a new modal logic based on probability distributions. This logic enables a player to enforce a property in the next state or distribution. We further extend the logic with fixpoint operators, which also characterises the simulation relations. This logic can express a lot of interesting properties in practical applications.
Active Learning Algorithm Based on Fast Optimization of Support Vectors

For the solution to the problems of being difficult to gain mass class-labeled samples in the supervised learning process and of reducing the cost of data labeling, a fast optimization of support vectors based on convex hull vector is proposed with the learning mechanism of Support Vector Machine (SVM). By means of calculating the hull vectors of the sample set, label those chosen hull vectors that are largest possible to be support vectors, and also add the unlabeled samples with high confidence coefficient of classifiers to the training sample set. The very information beneficial to the learner in the unlabeled samples set will be exploited. Hence, the thick convex hull method and the modified weighted SVM are separately directed for the nonlinear separable problem and the unbalanced training sample set. Via experimental testing on the UCI data set, the results demonstrate that the algorithm harvests SVM classifiers of higher classification accuracy and better generalization performance with fewer labeled samples, so as to cut down the labeling cost of samples for SVM training and learning.
Predicting Actions of Users Using Heterogeneous Online Signals

Advertising platforms have a growing need for improving prediction quality, as missing out on ad opportunities can have a negative effect on their performance. To that end, prediction tasks such as conversion prediction need to be continuously advanced through the inclusion of data from new sources or through algorithmic development that tackles existing challenges. The introduction of different data sources naturally brings unwanted noise, whereas underexplored areas still exist in modeling approaches, such as temporal information of events in sequences. In this study, we propose extensions for modeling online user activity trails that address two very important aspects of activities—time and noise, through dedicated layers that can be used in existing deep sequence-learning approaches. Our proposed method exhibited area under the receiver operating characteristic curve improvement of up to 3% and 1.75% compared with production and best baseline approaches, respectively, across two major advertiser data sets and several predictive tasks.
Humanoid intelligent robot (HIR) situation: research on the formation and influence of social value level substitution cha-xu pattern

Purpose A humanoid intelligent robot (HIR) possessing a human-like appearance can undertake human jobs, interact, communicate and even transmit emotions to human beings. Such robots have gradually been integrated into people's daily life and production scenarios. However, it is unclear whether and by what mechanism HIRs can stimulate people’s risk perception and its impact on consumption attitudes. Based on the risk decision theory, this study aims to take the social value substitution attribute of a HIR as the incentive and analyzes the influence of social value substitution and risk perception on the customers’ consumption attitudes. Design/methodology/approach Three experiments were conducted to investigate the related questions about the social value substitution attribute of a HIR, its impact on risk perception and the customers’ consumption attitudes. Findings The results reveal that physical labor, intellectual labor, friendship, kinship and the ego constitute the hierarchical elements of social value substitution. Among them, physical labor and intellectual labor pertain to the dimension of social function value substitution, while friendship, kinship and ego pertain to the dimension of social presence value substitution; social function value substitution and social presence value substitution affect the subjects’ risk perception positively, but the latter arouses a stronger risk perception; the 2 (risk perception of social function value: security/danger) × 2 (risk perception of social presence value: security/danger) condition corresponds to diverse consumption attitudes. Originality/value The results enrich the theories of the “cha-xu pattern” and “uncanny valley” and provide reference for the healthy development of the HIR industry.
Augmented Reality ASL for <i>11:11</i> at Theatre Passe Muraille

In November 2020, Theatre Passe Muraille produced a workshop as a part of their Accessibility Labs (funded through the Toronto Arts Council’s Open Door Project). This workshop connected the development of Samson Bonkeabantu Brown’s play 11:11, directed by Tsholo Visions Khalema and choreographed by Mafa Makhubalo, with a collaboration between technology collectives Toasterlab and Cohort to experiment with the use of augmented reality to support the American Sign Language interpretation and live captioning in consultation with Courage Bacchus, Marcia Adolphe, and Carmelle Cachero, Jenelle Rouse, and Gaitrie Persaud. The collaborators spent a week building an interpretation distribution system that used affordable technology to create a proof-of-concept experiment to see what it would look like to move both text and sign interpretation from just offstage into the visual field of the performance. Using Cohort’s media distribution app developed for the synchronous delivery of media to mobile devices, this project explored live and pre-recorded versions of performance interpretation for a Deaf audience. These were displayed on the audience’s phone screens and in simple augmented reality headsets. The goal was to explore expanding the opportunities for an audience to access interpretation, making the experience more customizable through different forms of engagement, and seeking to provide more universal access across all performances by making a recorded alternative available. The workshop also explored the dramaturgical and scenographic implications of adding this information into the direct visual field of an audience member. This article, in the form of a compiled oral history of the workshop, documents the process, the findings, and the follow-up questions that the team identified over our short time together to provide a baseline for further exploration into the use of mixed-reality technologies in support of accessible performance spaces. It considers situated identity and Deaf culture in relationship to translating interpreted performance to a technological solution as it both outlines the practical steps that allowed this to happen and explores artist, interpreter, and technologist perspectives on what was learned.
The Price Prediction of Sneakers Based on Machine Learning

Abstract-Sneaker culture is a self-organizing culture spontaneously formed by teenagers. In the Internet and digital age, sports shoes are endowed with appreciation and financial attributes through manual speculation, which has triggered a wave of sports shoes speculation with the participation of the whole people. As a human group behavior, the detonating mechanism behind speculative sneakers is worth thinking about. There is a set of logical behavior chains and practical logic in the behavior of frying shoes. It is necessary to use threshold theory to reveal the dynamic process of convergence from micro behavior to macro behavior. Therefore, machine learning can be used to predict the movement of sneaker prices in the sneaker market. The data set of this study is from stockx. In the research, the machine learning method is used to study the price of sports shoes and analyze the data. Four linear regression models were used for prediction. It is found that the OLS regression model’s accuracy is high. So OLS model is the best model for this dataset.
A Comprehensive Study of Various Outlier Detection Approaches

Nowadays, Internet of Things (IoT) and Big Data are the most hyped technologies. Huge amount of real-time data is produced by various inter-linked IoT devices. The main uniqueness of IoT data is its huge data size, high velocity data production, variegated data models, and veracity. But the bulky, complex, and dynamic data produced is onerous for any conventional data tools to capture, store, manage, and analyze. Outlier detection is the major trouble in the unpredicted data being apprehended. The motivation behind this survey is to present the mechanism of outlier detection systems. Extensive literature survey has been done to find out multiple existing techniques for outlier and anomalies detection in numerous fields. This survey also provides taxonomy of various outliers’ detection techniques in detail. At last, the challenges related to adoption of outlier detection are examined.
Watts: Infrastructure for Open-Ended Learning

This paper proposes a framework called Watts for implementing, comparing, and recombining open-ended learning (OEL) algorithms. Motivated by modularity and algorithmic flexibility, Watts atomizes the components of OEL systems to promote the study of and direct comparisons between approaches. Examining implementations of three OEL algorithms, the paper introduces the modules of the framework. The hope is for Watts to enable benchmarking and to explore new types of OEL algorithms. The repo is available at \url{https://github.com/aadharna/watts}
Case Study: An Efficient Survey on Security Analysis of Social Networking

Online informal communication is developing quickly with a full scope of administration that is accessible for gigantic number of clients to impart insights and interests, express feelings, and stay in contact with companions. The people group is getting to expand associated with one another. Numerous online locales give various approaches to get to the webpage utilizing gadgets, for example, PCs and advanced cells. Consequently, clients can utilize their locales rapidly and effectively, whenever, and any place. In this paper, we zeroed in on the way toward ordering on the web informal organization customer of different security that emphasis on every customer of long reach casual correspondence objections and studies various methods to dismember customer direct in online casual networks. Besides, we autonomously focus on different threats that grow on account of the sharing of sight and sound substance inside an individual-to-individual correspondence site.
IoT Data Analysis Using Different Clustering Algorithms: A Survey

There has been a major change in the rise of data creation and consumption due to the quick development of contemporary technology and the advent of the Internet of Things (IoT). With this shift comes a significant challenge in data management, with the goal of extracting useful information and patterns from the vast amount of unstructured IoT data. However, due to some limitations, we are still encountering issues and challenges in obtaining patterns and meaning information, and thus formulating a research agenda to get a sense of the challenges that one might encounter while conducting research on clustering of this massive chunk of unstructured IoT data. In light of these considerations, this study looks at some clustering methods that may be utilized when working with unstructured IoT data.
A Study of the True Cost on Password Authentication Policies

It currently analyzes which redefines modern password policies and practices. Users generally focus on maintaining security, but existing security policies are not so complex as to adapt to their capabilities, roles, and work environment. Therefore, these password policies will impose requirements on users and adversely affect their performance and ultimately their business.
The Impact of Academic Technology on Sustainable Academic Operations - a Review and Conceptualization

Education is continuously transforming with expansion and intervention of technology in the teaching learning pedagogy of HEI’s and Universities. The purpose of this study to assess the impacts of Academic Technology on sustainable academic operations, enhancing the prior reviews of the literature extending the perception from what Academic Technology do in pursuit on academic operations to support faculty and students in higher education institutions and universities. The ‘new normal’ in the academic arena would be a judicious choice of blended, hybrid model that combines the power of technology with the right physical deliveries - a Phygital model (Digital and Face-to-Face), to serve the needs of all stakeholders. The conception of this paper is to support the HEIs and Universities on sustainable academic operations by using the current digital platforms with the enhanced and expanded on-going ICT-based educational initiatives to meet the current and future challenges in providing quality education for all. [5]
Editorial Innovation of Teaching Research Publications in the Digital Era

With the continuous development of information technology in China, the era of digital publishing has arrived, overthrowing the traditional publishing paradigm. Compared with mass publications and professional publications, teaching research publications are unique. In the field of education, they play a guiding role in the teaching work and in promoting the development of education in China. The editorial work of teaching research publications should be innovated in the digital era. In order to ensure the dissemination value and depth of content of teaching research publications, as well as to provide high-quality publications with leading value for money, editorial transformation and work innovation should be carried out in time, taking into account the characteristics of the digital era. This paper makes a detailed analysis of the existing problems in the editorial work of teaching research publications to propose innovative strategies for the editorial work of teaching research publications in the digital era.
An application analysis method based on Clustering

Abstract Depending upon the load intensity and time variation information, we established the indicator system to analyze the of commercial halls, so that the further optimization actions can be taken in practical application scenarios. The representative clustering algorithms including the k-means and AC algorithm are chosen for the comparative analysis, and on this basis, an enhanced k-means algorithm is put forward in this study because the traditional method is sensitive to the initial clustering centroids and lacks the stability. A series of experiments are performed in this work, and the experimental findings reveal the validity of the proposed method. It provides a new idea for the efficiency analysis of commercial halls.
Analyzing Different Optimizers for the Recognition of Gurmukhi Handwritten Text by Employing CNN

Automatic recognition of handwritten data is an important application area in various fields. Recognition of handwritten text, which is cursive in nature, is a cumbersome task. Moreover, handwritten text by different writers makes the recognition even more difficult due to the different writing styles of the individuals. In this paper, a CNN model employing different optimizers is proposed for the recognition of Gurmukhi handwritten dataset. For the purpose of classification and recognition, five different classes of Gurmukhi handwritten text have been created where each class has 1,000 handwritten samples. Results are obtained using four different optimizers: Adagrad, Adam, Adamax, and RMS prop. Maximum validation accuracy of 99.20% is achieved using Adam optimizer.
Transfer Learning: A Paradigm for Machine Assisted Knowledge Transfer

This paper surveys transfer learning as a sustainable knowledge transfer mechanism. Conventional machine learning algorithms require a huge amount of labeled data for supervised learning. In absence of such data, the models suffer from performance degradation. Transfer learning enables the prior knowledge gained in doing a particular task to be reused or transferred to another new task of similar nature. This can speed up and improve the learning curve of the tasks in the new domain. The paper gives an overview of the transfer learning process and highlights how this innovative artificial intelligence (AI) technique can help achieve the goals of sustainable development. The literature survey highlights widely used mechanisms of Transfer Learning like homogeneous, heterogeneous, as well as instance-based, parameter-based, and relational-based implementation of transfer learning. It discusses how these mechanisms are utilized to create efficient AI-based applications which aid sustainability in the long run.
A Survey on Segmentation Techniques for Breast Cancer Detection

Breast cancer is second most rank disease after lung cancer between ladies around the world. Early detection declines the cancer death rate among women. Computer aided detection (CAD) system have been emerged which help the radiologists by specifying tumor region and reducing error mistake. Segmentation plays an important role in finding tumor area, i.e. Region of Interest (ROI). This paper investigates various segmentation techniques for breast cancer detection. Also, two segmentation techniques, Fuzzy C-Mean (FCM) and K-means, have been applied on mammogram images taken from MIAS database. Results shows that K-means is capable of estimating tumor region boundary as compared to FCM.
ILabel: Interactive Neural Scene Labelling

Joint representation of geometry, colour and semantics using a 3D neural field enables accurate dense labelling from ultra-sparse interactions as a user reconstructs a scene in real-time using a handheld RGB-D sensor. Our iLabel system requires no training data, yet can densely label scenes more accurately than standard methods trained on large, expensively labelled image datasets. Furthermore, it works in an 'open set' manner, with semantic classes defined on the fly by the user. ILabel's underlying model is a multilayer perceptron (MLP) trained from scratch in real-time to learn a joint neural scene representation. The scene model is updated and visualised in real-time, allowing the user to focus interactions to achieve efficient labelling. A room or similar scene can be accurately labelled into 10+ semantic categories with only a few tens of clicks. Quantitative labelling accuracy scales powerfully with the number of clicks, and rapidly surpasses standard pre-trained semantic segmentation methods. We also demonstrate a hierarchical labelling variant.
DeepAL: Deep Active Learning in Python

We present DeepAL, a Python library that implements several common strategies for active learning, with a particular emphasis on deep active learning. DeepAL provides a simple and unified framework based on PyTorch that allows users to easily load custom datasets, build custom data handlers, and design custom strategies without much modification of codes. DeepAL is open-source on Github and welcome any contribution.
A method of composition evaluation for photographs

Recently, with the development of smartphones and other small cameras, there are more and more opportunities to take photos even if you are not a professional photographer. In general, the photos with good composition are more impressive. Although there have been many studies on composition in photos, none have focused on the accuracy of determination and evaluation of composition. In this study, we propose a method to determine and evaluate the composition of photos taken. In the proposed method, composition is determined and evaluated in terms of visual and structural features. We believe that our method will assist in the selection of photos.
Internet Archive Open Library Holding: What we see, why we worry, why we hope (2013 edition)

Published inBoise, IdahoClassificationsDewey Decimal Class 330.905 Library of Congress HC444 .N357 2013 The Physical ObjectPagination 124 pages Number of pages 124 ID Numbers
Study of automatic generation of motif tag in Nishiki-e

The motif drawn on Nishiki-e is needed to register in the database as a search tag. The accuracies of the motif tag that are currently manually registered is unstable because it depends on the knowledge and interests of the registrant. Therefore, this study proposes an automatic generation method of motif tags using deep learning to support cultural activities. Nishiki-e is more difficult to collect training images that include specific motifs than photographs. In this study, we propose three methods for preparing training images. First, we applied a similar image generation model from a single image to a small number of Nishiki-e containing motifs to create training images. Second, we applied a Nishiki-e style processing model to photographs containing motifs to create training images. Third, we combined a small number of photographs with motifs with some background images to create training images. In particular, the third method can detect from a small number of inputs like the first method with an accuracy close to that of the second method.
Analysis of human's skilled process of assembly task using time-sequence-based machine learning

To efficiently teach novices skilled tasks, it is necessary to analyze the difference between a novice and an expert worker. Accordingly, a method for extracting differences (on the basis of skill level) in motions of workers performing tasks is proposed. As for this method, a network (multi-stream LSTM) that estimates skill level from 3D positional information of the worker’s visual point and joints is trained, and the internal structure of the network is then analyzed. The results of an experiment indicate that a particular motion, namely, “grasping an object,” becomes different when the worker becomes skilled; in particular, the worker grasps the object without moving their visual point to the position of the part, namely, without looking at the object, and uses both hands efficiently.