Reviewerly/exHarmony huggingface dataset:
Iterative space-variant sphere-model deflectometry enabling designation-model-free measurement of the freeform surface

Freeform optics, offering high degrees of design freeform to control light propagation, have already been widely applied in various photoelectric equipment. The form quality of those optics is crucial to their opto-electronics functionalities, which requires to be measured accurately. The deflectometry is a promising technology to test the complex freeform surfaces. In general, there is a designed surface model for the monoscopic deflectometry to estimate the positions of whole measured points to solve the issue of height-slope ambiguity. However, the unknown or inaccurate surface model can induce errors into the measured normal, thereby decreasing the measurement precision. In this paper, without relying on the known surface model, the proposed method iteratively optimizes a sphere model to describe the measured surface by changing the spherical radius. In order to reduce the global error, the space-variant spheres are optimized, respectively, to estimate the whole-aperture surface coordinate. With the help of the iteration surface reconstruction process, the optimal number of the space-variant spheres is achieved to meantime obtain the final reconstructed surface. Compared to the measurements by using the plane model, the form accuracy can be improved by three times. Experiments demonstrate that the proposed method can successfully reconstruct the complex surfaces without the need of a known surface model, which can greatly improve the measuring flexibility and measurement accuracy.
Online shopping during the Covid-19 crisis: the impact of anthropomorphic virtual agents on consumers' psychological states

Because of the unpredictability and uncertainty of the Covid-19 pandemic, online shopping and the use of artificial intelligence featured in the e-commerce have helped companies in attracting clients through a better online shopping environment and experience. However, the e-commerce still lacks human warmth to pretend and equalise with in-store human interactions and experience. This paper addresses this issue by examining the anthropomorphic characteristics of the embodied virtual agent and by measuring their impact on the user's state of flow and telepresence experience. To identify the characteristics that could potentially make virtual agents more 'humanised', an exploratory approach was implemented. An experimentation survey with 660 internet users followed to test the relation between the anthropomorphic dimensions of embodied virtual agents and consumers' psychological states and online word of mouth. Our findings confirm the influence of the anthropomorphic characteristics investigated in improving consumers' psychological states.
UIGR: Unified Interactive Garment Retrieval

Interactive garment retrieval (IGR) aims to retrieve a target garment image based on a reference garment image along with user feedback on what to change on the reference garment. Two IGR tasks have been studied extensively: text-guided garment retrieval (TGR) and visually compatible garment retrieval (VCR). The user feedback for the former indicates what semantic attributes to change with the garment category preserved, while the category is the only thing to be changed explicitly for the latter, with an implicit requirement on style preservation. Despite the similarity between these two tasks and the practical need for an efficient system tackling both, they have never been unified and modeled jointly. In this paper, we propose a Unified Interactive Garment Retrieval (UIGR) framework to unify TGR and VCR. To this end, we first contribute a large-scale benchmark suited for both problems. We further propose a strong baseline architecture to integrate TGR and VCR in one model. Extensive experiments suggest that unifying two tasks in one framework is not only more efficient by requiring a single model only, it also leads to better performance. Code and datasets are available at GitHub.
Seed-Guided Topic Discovery with Out-of-Vocabulary Seeds

Discovering latent topics from text corpora has been studied for decades.Many existing topic models adopt a fully unsupervised setting, and their discovered topics may not cater to users' particular interests due to their inability of leveraging user guidance.Although there exist seed-guided topic discovery approaches that leverage user-provided seeds to discover topicrepresentative terms, they are less concerned with two factors: (1) the existence of out-ofvocabulary seeds and (2) the power of pretrained language models (PLMs).In this paper, we generalize the task of seed-guided topic discovery to allow out-of-vocabulary seeds.We propose a novel framework, named SEE-TOPIC, wherein the general knowledge of PLMs and the local semantics learned from the input corpus can mutually benefit each other.Experiments on three real datasets from different domains demonstrate the effectiveness of SEETOPIC in terms of topic coherence, accuracy, and diversity. 1
Learning From High-Cardinality Categorical Features in Deep Neural Networks

Some machine learning algorithms expect the input variables and the output variables to be numeric. Therefore, in an early stage of modelling, feature engineering is required when categorical variables present in the dataset. As a result, we must encode those attributes into an appropriate feature vector. However, categorical variables having more than 100 unique values are considered to be high-cardinality and there exists no straightforward methods to handle them. Besides, the majority of the work on categorical variable encoding in the literature assumes that the categories is limited, known beforehand, and made up of mutually-exclusive elements, inde-pendently from the data, which is not necessarily true for real-world applications. Feature engineering typically practices to tackle the high cardinality issues with data-cleaning techniques which they are time-consuming and often needs human intervention and domain expertise which are major costs in data science projects The most common methods of transform categorical variables is one-hot encoding and target encoding. To address the issue of encoding categorical variables in environments with a high cardinality, we also seek a general-purpose approach for statistical analysis of categorical entries that is capable of handling a very large number of catego-ries, while avoiding computational and statistical difficulties. Our proposed approach is low dimensional; thus, it is very efficient in processing time and memory, it can be computed in an online learning setting. Even though for this paper, we opt to utilize it in the input layer, dictionaries are typically architecture-independent and may be moved between different architectures or layers.
A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments

Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning.
A Underwater Sequence Image Dataset for Sharpness and Color Analysis

The complex underwater environment usually leads to the problem of quality degradation in underwater images, and the distortion of sharpness and color are the main factors to the quality of underwater images. The paper discloses an underwater sequence image dataset called TankImage-I with gradually changing sharpness and color distortion collected in a pool. TankImage-I contains two plane targets, a total of 78 images. It includes two lighting conditions and three different water transparency. The imaging distance is also changed during the photographing process. The paper introduces the relevant details of the photographing process, and provides the measurement results of the sharpness and color distortion of the sequence images. In addition, we verify the performance of 14 image quality assessment methods on TankImage-I, and analyze the results of 14 image quality assessment methods from the aspects of sharpness and color, which provides a reference for the design and improvement of underwater image quality assessment algorithm and underwater imaging system design.
On Attacking Out-Domain Uncertainty Estimation in Deep Neural Networks

In many applications with real-world consequences, it is crucial to develop reliable uncertainty estimation for the predictions made by the AI decision systems. Targeting at the goal of estimating uncertainty, various deep neural network (DNN) based uncertainty estimation algorithms have been proposed. However, the robustness of the uncertainty returned by these algorithms has not been systematically explored. In this work, to raise the awareness of the research community on robust uncertainty estimation, we show that state-of-the-art uncertainty estimation algorithms could fail catastrophically under our proposed adversarial attack despite their impressive performance on uncertainty estimation. In particular, we aim at attacking out-domain uncertainty estimation: under our attack, the uncertainty model would be fooled to make high-confident predictions for the out-domain data, which they originally would have rejected. Extensive experimental results on various benchmark image datasets show that the uncertainty estimated by state-of-the-art methods could be easily corrupted by our attack.
Facial Expression Recognition System using Convolutional Neural Networks

Facial expression recognition has been a functioning exploration territory in the previous ten years, with developing application regions including symbol activity, neuromarketing and amiable robots. The acknowledgment of outward appearances isn't a simple issue for AI techniques, since individuals can change altogether in the manner they demonstrate their looks. Indeed, even pictures of a similar individual in a similar outward appearance can shift in splendor, foundation and present, and these varieties are underscored if thinking about various subjects (due to varieties fit as a fiddle, ethnicity among others). Albeit outward appearance acknowledgment is contemplated in the writing, few works perform reasonable assessment abstaining from blending subjects while preparing and testing the proposed calculations. Thus, outward appearance acknowledgment is as yet a difficult issue in PC vision. In this work, we propose a straightforward answer for outward appearance acknowledgment that utilizes a blend of Convolutional Neural Network and explicit picture pre-handling steps. Convolutional Neural Networks accomplish better precision with huge information. Be that as it may, there are no openly accessible datasets with adequate information for outward appearance acknowledgment with profound structures. Subsequently, to handle the issue, we apply some pre-preparing systems to extricate just demeanour explicit highlights from a face picture and investigate the introduction request of the examples amid preparing. An investigation of the effect of each picture pre-preparing task in the precision rate is displayed. The proposed strategy: accomplishes aggressive outcomes when contrasted and other outward appearance acknowledgment techniques – going up to 92% precision - it is quick to prepare, and it takes into consideration ongoing outward appearance acknowledgment with standard PCs.
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding

Task-adaptive pre-training (TAPT) and Selftraining (ST) have emerged as the major semisupervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data.However, it's unclear whether they learn similar representations or they can be effectively combined.In this paper, we show that TAPT and ST can be complementary with simple TFS protocol by following TAPT → Finetuning → Selftraining (TFS) process.Experimental results show that TFS protocol can effectively utilize unlabeled data to achieve strong combined gains consistently across six datasets covering sentiment classification, paraphrase identification, natural language inference, named entity recognition and dialogue slot classification.We investigate various semi-supervised settings and consistently show that gains from TAPT and ST can be strongly additive by following TFS procedure.We hope that TFS could serve as an important semi-supervised baseline for future NLP studies.
Impact of Organizational Culture on Innovative Work Behavior

‘Organization’ is an important tool that helps us organize both goods and services we need to sustain our lives in better quality. Managers and employees in a company struggle to use that tool in a suitable way to increase their success in the market to be able to survive in the long term. This paper aimed to investigate the effectiveness of an organization in terms of training, co-workers, and supervisor support in this respect. The results revealed that ‘training’ was the strongest influencer on the innovative work behavior of employees at various firms in the region. Secondly, the impact of ‘supervisor support’ on innovative work behavior was significant. Lastly, the impact of ‘co-worker support’ was also significant but weaker compared to the other two variables.
Emotions in texts

Several studies have used different techniques to detect and identify emotions expressed in various sets of texts corpora. In this paper, we review different emotion models, emotion datasets and the corresponding techniques used for emotion analysis in past studies. We observe that researchers have been using a wide variety of techniques to detect emotions in texts and that there is currently no gold standard on which dataset or which emotion model to use. Consequently, although the field of emotion analysis has gained much momentum in previous years, there seems to be little progress into relevant research with findings that may be useful in real world applications. From our analysis and findings, we urge researchers to consider the development of datasets, evaluation benchmarks and a common platform for sharing achievements in emotion analysis to see further development in the field.
Automatic Vowel Recognition from Assamese Spoken words

Vowel plays the most important role in any speech processing work. In this research work, recognition of Assamese vowel from spoken Assamese words is explored. Assamese is a language which is spoken by major people in Brahmaputra Valley of Assam, Assam is a state which is situated in the North-East part of India. This automatic vowel recognition system is implemented by using three efficient techniques Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Random Forest (RF) classifier. The database used in the experiments is specially designed for this purpose. A list of phonetically vowel rich Assamese words is prepared for the experiment. As an initial effort, twenty different (20) words uttered by fifty-five (55) speakers are taken. Utterances from both male and female speakers are collected. Each utterance was repeated two times by every speaker. A database of the total of 2200 samples is prepared. After experimenting on different samples it is seen that Random Forest (RF) is giving the best performance compared to the other two classifiers. The performance of the system is shown with testing dataset and comparison is done. Outcome of this research work will enhance the Machine Translation from Assamese to any other language.
Academic English Proficiency Assessment Using a Computerized Adaptive Test

This paper describes the steps to convert a paper-and-pencil English proficiency test for academic purposes, consisting of multiple choice items administered following the Admissible Probability Measurement Procedure [24], adopted by the graduate program at the Institute of Mathematics and Computer Sciences at the University of São Paulo (ICMC-USP), Brazil, to a computerized adaptive test (CAT) based on an Item Response Theory Model (IRT). Despite the fact that the program accepts various internationally recognized tests that attest non-native speakers English proficiency, such as the Test of English as a Foreign Language (TOEFL), the International English Language Testing System (IELTS) and the Cambridge English: Proficiency (CPE), for instance, its requirement is incoherent in public universities in Brazil due to the cost, which ranges from US$ 200.00 to US$ 300.00 per exam. The TAI-PI software (Computerized Adaptive Test for English Proficiency), which was developed in Java language and SQLite, started to be used to assess the English pro?ciency of students on the program from October, 2013. The statistical methodology used was defined considering the history and aims of the test and adopted Samejima's Graded Response Model [21], the Kullback-Leibler information criterion for item selection, the a posteriori estimation method for latent trait [2] and the Shadow Test approach [29] to impose restrictions (content and test length) on the test composition of each individual. A description of the test design, the statistical methods used, and the results of a real application of TAI-PI for graduate students are presented in this paper, as well as the validation studies of the new methodology for pass or fail classification, showing the good quality of the new evaluation system and examination of improvement using the IRT and CAT methods.
Research Activities in the Organization of Professional Activities of Students of Service Studies

Relevance: В under the conditions of reforming the characteristics of the Russian system of higher education are gradually changing in accordance with the General directions of development of modern society. The role of professionalism, personal activity of the student, his interest in self-improvement and readiness for self-education increases. The understanding of education is formed not as a process focused on the transfer of ready-made knowledge from teacher to student, but as a comprehensive system aimed at individualizing the student's mastery of the skills necessary for successful employment in the labor market.This approach to education is based on the development of professional thinking of students by shifting the emphasis of educational work on independent activities of students and its motivation. Independent work of students is considered as a methodological basis for achieving strong and in-depth knowledge, professional competencies, formation of cognitive activity and commitment. Excel software environment, which makes it possible to run cluster analysis of large amounts of statistical data promptly and with minimal time expenses. Conclusion: The paper’s authors consider that the automation of clustering process in the Microsoft Excel software environment allows decreasing time significantly. The process of cluster analysis in the MS Excel program without the use of add-on is very labor-intensive and requires a lot of time to complete it. Also, in case of very large number of items, the time for running cluster analysis significantly increases and when the number of items is estimated in hundreds and thousands, the time of effective cluster analysis running can be estimated in days, which is very ineffective. The use of add-in doesn’t require special knowledge on the mechanism of running cluster analysis from the user and takes a few seconds to complete it.
The Impact of Educational Games on Enhancing Elementary Stage Students' Acquisition and Retention of English Vocabulary

Learning EFL vocabulary is a difficult task so that teachers try as much as possible to facilitate it for their learners. One of the best strategies that can be used is educational games strategy. The goal of this research is to check the effectiveness of using the educational games strategy in enhancing English vocabulary acquisition and retention at the First stage of basic education pupils. The study adopted the quasi-experimental pre-post test control / experimental group design. The study sample consisted of 40 pupils in sixth elementary grade and was equally divided into two groups: an experimental group and a control one. Students of the experimental group learnt vocabulary through the educational game strategy, while students in the control group received regular instruction. Tools of the study included a pre-post vocabulary achievement test and a text course for grade six named Get Ready 6. The students submitted to a pre-post vocabulary achievement test and the results indicates that there is a statistically significant difference between the mean scores of the experimental group and the control group in the pre/ post-test of English vocabulary achievement in favor of the experimental group.
PERSEPSI MAHASISWA TENTANG PERANAN MATA KULIAH MICRO TEACHING TERHADAP KESIAPAN MENGAJAR PADA MAHASISWA PENDIDIKAN EKONOMI UNP

The purpose of this article is to analyze the effect of student perceptions about the role of micro teaching courses on student teaching readiness. This type of research is quantitative descriptive. The population of this research is the 2014 UNP FE Economics Education students who have completed the micro teaching course and the Education Field Experience Program course totaling 84 people. The sampling technique is saturated sampling (total sampling) so that the entire population is sampled. Research data are primary data collected by questionnaire. Instrument testing uses validity and reliability tests. Data were analyzed by descriptive and inductive analysis using normality test, heterocedasticity test, hypothesis testing using simple regression, t test and coefficient of determination with α = 0.05. The results showed that there was a significant and positive influence between students' perceptions of the role of micro teaching courses on student teaching readiness, namely sig 0,000 &lt;0,05. Keywords: Perception, Role, Micro Teaching, Teaching Readiness