Reviewerly/exHarmony huggingface dataset:
Credit Risk Modeling Using Transfer Learning and Domain Adaptation

In the domain of credit risk assessment lenders may have limited or no data on the historical lending outcomes of credit applicants. Typically this disproportionately affects Micro, Small, and Medium Enterprises (MSMEs), for which credit may be restricted or too costly, due to the difficulty of predicting the Probability of Default (PD). However, if data from other related credit risk domains is available Transfer Learning may be applied to successfully train models, e.g., from the credit card lending and debt consolidation (CD) domains to predict in the small business lending domain. In this article, we report successful results from an approach using transfer learning to predict the probability of default based on the novel concept of Progressive Shift Contribution (PSC) from source to target domain. Toward real-world application by lenders of this approach, we further address two key questions. The first is to explain transfer learning models, and the second is to adjust features when the source and target domains differ. To address the first question, we apply Shapley values to investigate how and why transfer learning improves model accuracy, and also propose and test a domain adaptation approach to address the second. These results show that adaptation improves model accuracy in addition to the improvement from transfer learning. We extend this by proposing and testing a combined strategy of feature selection and adaptation to convert values of source domain features to better approximate values of target domain features. Our approach includes a strategy to choose features for adaptation and an algorithm to adapt the values of these features. In this setting, transfer learning appears to improve model accuracy by increasing the contribution of less predictive features. Although the percentage improvements are small, such improvements in real world lending could be of significant economic importance.
Information Technology for Librarians and Information Professionals

"Information Technology for Librarians and Information Professionals." Journal of Hospital Librarianship, 22(1), pp. 75–76
Adversarial Learning Enhancement for 3D Human Pose and Shape Estimation

Adversarial learning plays an important role in recovering 3D human pose and shape from monocular videos. However, the effectiveness of this process is not often considered. Hence we aim to improve the performance of adversarial learning in 3D human pose and shape estimation. The performance of adversarial learning is mainly influenced by two parts: generator and discriminator. For the generator, we utilize temporal information on a deeper level by adding an attention-based temporal encoder in generator to model the time series of features, which contributes to a more appropriate data representation for pose and shape regression. For the discriminator, we innovatively make use of human skeleton topology information when extracting features from the estimation results. To realize this, we base the discriminator’s design on the graph convolution network. In addition, to eliminate the jitter in the estimation results, we design a rotation disentangled smoothing module to process the estimated rotation parameters. We did adequate experiments on public in-the-wild datasets 3DPW and MPI-INF-3DHP. On both datasets, our method achieves higher accuracy and lower acceleration error compared with previous methods.
Text recognition method of electrical equipment nameplate based on improved similarity

In the text recognition on the electrical equipment nameplate, Chinese characters are very special. As pictographic characters, there are a lot of typographic characters in Chinese characters, which have a certain difficulty in recognition. Therefore, this paper proposed a CRNN and string text similarity calculation of electrical equipment nameplates identification method. In addition, the similarity algorithm is improved according to the characteristics of Chinese characters. Finally, the electrical equipment nameplates under different illumination are collected as experimental samples, and the electrical nameplate recognition system designed in this paper is used for recognition. The experimental results show that this method has strong practicability.
E-Book Reading Genre Preferences of Teachers Teaching in Foreign Languages in Private Schools (Case Study / Iraq)

This study examines the electronic book reading preferences of teachers who teach their subjects in a foreign language in secondary school, high school, and university in terms of different variables.This study examines the teachers' electronic book reading interests, the types of books, and book reading pages regarding gender variables.In the research, a descriptive survey model was used.Descriptive analysis was carried out on large groups.The opinions and attitudes of the individuals in the group were taken, and the facts and events were tried to be described (Karakaya, 2012).The study was carried out with 23 teachers working in 3 different private institutions in the Kurdistan region of Iraq in the 2021-2022 academic year.According to the findings, teachers' e-book reading genres increased in subjects that appealed to their careers.The researchers found that the difference between male and female teachers' book preferences is worth examining.This study explored what types of fiction and nonfiction genres teachers prefer to read digitally when digital reading is popular worldwide.This study is expected to contribute to the subsequent studies in related literature.
Text-Driven Video Acceleration: A Weakly-Supervised Reinforcement Learning Method

The growth of videos in our digital age and the users' limited time raise the demand for processing untrimmed videos to produce shorter versions conveying the same information. Despite the remarkable progress that summarization methods have made, most of them can only select a few frames or skims, creating visual gaps and breaking the video context. This paper presents a novel weakly-supervised methodology based on a reinforcement learning formulation to accelerate instructional videos using text. A novel joint reward function guides our agent to select which frames to remove and reduce the input video to a target length without creating gaps in the final video. We also propose the Extended Visually-guided Document Attention Network (VDAN+), which can generate a highly discriminative embedding space to represent both textual and visual data. Our experiments show that our method achieves the best performance in Precision, Recall, and F1 Score against the baselines while effectively controlling the video's output length.
Features and Applications of QR Codes

QR code refers to the expansion of another one-dimensional readable bar code based on one-dimensional bar code, which uses black-and-white rectangular pattern to represent binary data, and the information contained in it can be obtained after being scanned by the equipment. Like the previous one-dimensional bar code, two-dimensional bar code is widely used in commercial activities, especially in high-tech industry, storage and transportation industry, wholesale and retail industry and other industries that need cheap and fast labeling information for goods.
Task Allocation in Distributed Agile Software Development Environment Using Unsupervised Learning

In the paper, a novel approach for task allocation in DASD environment has been proposed. In the approach, new tasks (in the form of user – stories), are allocated to an employee, who is found to be ‘best’, on the basis of classification and rank ordering. For applying classification and rank ordering on data set of employees, Meta - Classifier Based Prediction Model (MCBPM) has been used that applied unsupervised learning. Results show that MCBPM – based task allocations provides accurate suggestions for the activity.
Gradient-Based Novelty Detection Boosted by Self-Supervised Binary Classification

Novelty detection aims to automatically identify out-of-distribution (OOD) data, without any prior knowledge of them. It is a critical step in data monitoring, behavior analysis and other applications, helping enable continual learning in the field. Conventional methods of OOD detection perform multi-variate analysis on an ensemble of data or features, and usually resort to the supervision with OOD data to improve the accuracy. In reality, such supervision is impractical as one cannot anticipate the anomalous data. In this paper, we propose a novel, self-supervised approach that does not rely on any pre-defined OOD data: (1) The new method evaluates the Mahalanobis distance of the gradients between the in-distribution and OOD data. (2) It is assisted by a self-supervised binary classifier to guide the label selection to generate the gradients, and maximize the Mahalanobis distance. In the evaluation with multiple datasets, such as CIFAR-10, CIFAR-100, SVHN and TinyImageNet, the proposed approach consistently outperforms state-of-the-art supervised and unsupervised methods in the area under the receiver operating characteristic (AUROC) and area under the precision-recall curve (AUPR) metrics. We further demonstrate that this detector is able to accurately learn one OOD class in continual learning.
Clusters of COVID-19 Indicators in India: Characterization, Correspondence and Change Analysis

We conduct a long-term epidemiology study of COVID-19 in India from Mar 2020 to May 2021 using a number of indicators such as active cases, daily new cases, and deaths, on a micro (district level, per capita) and macro level (state level). Our automated shape-based cluster discovery of the per capita daily new cases (case rate) during the first wave in India (between Mar 2020 and Jan 2021) revealed four distinct shape patterns: sharp-rise and decline, steady-rise and decline, plateau and multiple relatively high peaks. These clusters exhibit a strong geographical correlation. To determine the correspondence between clusters obtained by different indicators, we design a novel metric for determining edge-weights in their intersection graph. This is used for comparative analysis and to develop informative hierarchical cartographic visualizations. We then perform dynamic cluster analysis for different time windows to answer some pertinent questions. Is the second wave similar to or different from the first wave? How has the relative ranking (on micro- and macro-level indicators) of the states varied over the last one year? How much medical resources have been stressed during the peak? We demonstrate that using multiple indicators, we can assess the impact of the epidemic holistically in a particular geography. Our analysis techniques and insights obtained can help the local and state governments in monitoring and managing COVID-19 situation and fine-tuning the ongoing vaccination drive in India.
Is “My Favorite New Movie” My Favorite Movie? Probing the Understanding of Recursive Noun Phrases

Recursive noun phrases (NPs) have interesting semantic properties.For example, my favorite new movie is not necessarily my favorite movie, whereas my new favorite movie is.This is common sense to humans, yet it is unknown whether language models have such knowledge.We introduce the Recursive Noun Phrase Challenge (RNPC), a dataset of three textual inference tasks involving textual entailment and event plausibility comparison, precisely targeting the understanding of recursive NPs.When evaluated on RNPC, state-of-theart Transformer models only perform around chance.Still, we show that such knowledge is learnable with appropriate data.We further probe the models for relevant linguistic features that can be learned from our tasks, including modifier semantic category and modifier scope.Finally, models trained on RNPC achieve strong zero-shot performance on an extrinsic Harm Detection evaluation task, showing the usefulness of the understanding of recursive NPs in downstream applications. 1Task
Inductive Reasoning and its Underlying Structure: Support for Difficulty and Item Position Effects

automatization inductive reasoning difficulty effect item-position effect rule learning working memory updatingThis paper reports an investigation of the influence of method effects on the measurement of reasoning and of the relationships of these effects to basic cognitive processes.For this purpose, the variation due to the item-position and difficulty effects was separated from the variation due to the measured latent source of inductive reasoning.Data were collected by means of inductive reasoning items and cognitive tasks measuring working memory (WM) updating, rule learning, and automatization.Confirmatory factor analysis models served the decomposition of the variation of inductive reasoning data into a purified version of inductive reasoning, item-position, and difficulty components.The investigation of the relationships of corresponding latent variables and basic cognitive processes revealed two major associations: (a) the purified version of reasoning correlated with WM updating and (b) the item-position effect correlated with variants of learning.These results could be interpreted as signifying a two-dimensional structure of reasoning associated with executive functioning and learning processes.
Are working memory models WEIRD? Testing models of working memory in a non-WEIRD sample.

There are many competing working memory models, generally formulated from young adult, typically student, samples from Western, educated, industrialized, rich, and developed (WEIRD) contexts. Whether such models are adequate conceptualizations of working memory (WM) in young adults from non-WEIRD contexts is unclear.This study tested five possible models of the structure of working memory in young South African adults (n = 162; Mage = 20.41, SDage = 1.82, 38% female) who are multilingual, from predominantly low socioeconomic, largely rural, contexts.Confirmatory factor analyses showed that a four-component structure composed of domain-specific (verbal, visuospatial) storage and processing provided the best fit for the data.Neuropsychological tests and rehabilitation approaches are based on leading models of working memory, and so their cross-cultural appropriateness is important. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
The Improving Students’ Vocabularies Using Newspapers as an Authentic Materials

Authentic material is the material that the contents and the language used is close to the students’ real life. Authentic materials have not been specifically produced for the purpose of language teaching and this easily differentiates it from textbook materials. In addition, the use of authentic materials is expected to bring students in direct contact with reality so that students are interested in the vocabulary learning process. This research aims to find out the use of the authentic materials; English newspapers in teaching vocabulary. The participants of this research are the eleventh grader of Muhammadiyah 2 Genteng Senior High School which consists of 31 students. The design of this research is classroom action research. There are two kinds of data collected by the researcher; primary and secondary data. The primary data were collected from the students’ observation, and vocabulary test, while the secondary data were collected from the result of interview, and documentation. In this research, the researcher found that in the first meeting, the students’ participation was 65% and in the second meeting was 84%. Moreover, the students’ vocabulary scores have improved from 61.29% to 87.097%. It can be concluded that the use of the authentic materials; English newspapers can improve the eleventh grade students’ vocabulary achievement. &#x0D; Keywords: Authentic Materials, Vocabulary, Newspapers
Human Action Analysis at Nighttime under Different Temperature Conditions in Infrared Images Using CNN

Compared with daytime, more pedestrian-vehicle traffic accidents occur late at night, particularly involving the aged. To reduce the number of accidents, our study focuses on analyzing specific human actions using thermal infrared night image data, considering the aging society. This contributes to the development of advanced driver assistance systems (ADASs). To analyze human actions during walking in detail, our previous research recognized pedestrians at nighttime, indicating high quality of recognition performance, and with this foundation, we proceeded to analyze the simple actions of humans at night using a convolutional neural network (CNN), which comprises seven convolutional, three pooling, and two affine layers. To consider the infrared images that were influenced by the outdoor temperature and improve the accuracy of human action-analysis, we adopted the CNN model, evaluated the accuracy performance for 3 types of actions from 2 subjects in various environments: whether streetlamps were on or off; temperatures were high or low. The evaluation result achieved a high recognition accuracy of 84.4% on average in high temperature and 96.1% on average in low temperature. The results revealed that the proposed approach could be used to recognize human actions from infrared images in different temperature conditions at night with a high accuracy.
Density-Based Distance Preserving Graph: Theoretical and Practical Analyses

This brief aims to provide theoretical guarantee and practical guidance on constructing a type of graphs from input data via distance preserving criterion. Unlike the graphs constructed by other methods, the targeted graphs are hidden through estimating a density function of latent variables such that the pairwise distances in both the input space and the latent space are retained, and they have been successfully applied to various learning scenarios. However, previous work heuristically treated the multipliers in the dual as the graph weights, so the interpretation of this graph from a theoretical perspective is still missing. In this brief, we fill up this gap by presenting a detailed interpretation based on optimality conditions and their connections to neighborhood graphs. We further provide a systematic way to set up proper hyperparameters to prevent trivial graphs and achieve varied levels of sparsity. Three extensions are explored to leverage different measure functions, refine/reweigh an initial graph, and reduce computation cost for medium-sized graph. Extensive experiments on both synthetic and real datasets were conducted and experimental results verify our theoretical findings and the showcase of the studied graph in semisupervised learning provides competitive results to those of compared methods with their best graph.
Grounding Language to Natural Human-Robot Interaction in Robot Navigation Tasks

With the development of service robot technology, the natural interaction between humans and service robots has attracted increasing attention. People are no longer satisfied with using buttons or controllers to issue instructions and using natural language to guide the navigation of service robots has become a hot research topic. At present, most methods rely on corpora or labeled datasets with a large amount of data, which usually require huge labor costs. To address this problem, this paper proposes a robot navigation method based on natural language interaction in known environments. In the robot navigation instructions, the tasks are usually determined by the landmarks and the temporal words. In this work, the landmarks of a known environment is firstly extracted and a retrieval table of these landmarks is constructed, which is utilized to further landmark extraction from the natural language instruction. Then the landmarks and the temporal words are extracted from the instruction, sort the temporal words and their corresponding landmarks by their weights. Finally, the sorted landmarks are then published as navigation goals one by one and guide the robot in navigation. The experimental results show that our method is capable of guiding the robot navigation with natural language instructions in a known environment.
A Chat Recommender System for COVID-19 Support based in Textual Sentence Embeddings

With the emergence of the COVID-19 pandemic, the demand for health services has exponentially increased, which caused the saturation of hospital beds and a high death toll. Motivated by the need to provide more agility in patients’ attendance and unburden the health services, this work proposes a solution for automatic attendance via a Recommender System that uses sentence embeddings of text messages to train an LSTM classifier. This classifier can provide recommendations of a course of action for patients, instructing them to stay at home or seek medical support. Our numerical results validate the proposed solution and corroborate its reasonable accuracy rate.
Management of the Literacy Movement Program (LMP) to Improve Reading Culture in Elementary Schools

This study aims to analyze the planning, implementation, and evaluation of the elementary school literacy program in the city of Binjai. The method in this research is descriptive with a qualitative approach. The subjects of this study were principals, teachers, students, and parents of students. The results of the study indicate that planning is done by compiling a program by a team formed by the school. The implementation of the program is carried out with mandatory school library visits, reading books in class, developing literacy creativity, and student literacy work. While the evaluation is carried out to measure the achievement of the results of the literacy program run by the school. The conclusion from the results of this study is that the management of literacy program planning is carried out through a subject teacher discussion forum, planned annually based on the school's annual work program plan. The literacy program activity is an integration of listening, speaking, writing, reading, and critical thinking skills. In addition, the literacy program implemented has improved the reading culture among teachers and students, especially in superior and independent schools. The literacy program at Binjai Elementary School is carried out through three stages, namely the habituation, development, and learning stages. At the evaluation level, it is carried out in a programmed, measurable manner and is held every semester. In general, the literacy program is very good and effective because it can increase students' literacy interests and competencies, also increase school library learning resources, improve literacy culture and student character.
Linguistic justice as a framework for designing, developing, and managing natural language processing tools

As natural language processing tools powered by big data become increasingly ubiquitous, questions of how to design, develop, and manage these tools and their impacts on diverse populations are pressing. We propose utilizing the concept of linguistic justice—the realization of equitable access to social and political life regardless of language—to provide a framework for examining natural language processing tools that learn from and use human language data. To support linguistic justice, we argue that natural language processing tools (along with the datasets that are used to train and evaluate them) must be examined not only from the perspective of a privileged, majority language user, but also from the perspectives of minoritized language users. Considering such perspectives can help to surface areas in which the data used within natural language processing tools may be (often inadvertently) working against linguistic justice by failing to provide access to information, services, or opportunities in users’ language of choice, underperforming for certain linguistic groups, or advancing harmful stereotypes that can lead to negative life outcomes for members of marginalized groups. At the same time, this framework can help to illuminate ways that these shortcomings can be addressed and allow us to use inclusive language data and approaches to leverage natural language processing technologies that advance linguistic justice.