Reviewerly/exHarmony huggingface dataset:
A Dataset for Action Recognition in the Wild

The development of autonomous robots for agriculture depends on a successful approach to recognize user needs as well as datasets reflecting the characteristics of the domain. Available datasets for 3D Action Recognition generally feature controlled lighting and framing while recording subjects from the front. They mostly reflect good recording conditions and therefore fail to account for the highly variable conditions the robot would have to work with in the field, e.g. when providing in-field logistic support for human fruit pickers as in our scenario. Existing work on Intention Recognition mostly labels plans or actions as intentions, but neither of those fully capture the extend of human intent. In this work, we argue for a holistic view on human Intention Recognition and propose a set of recording conditions, gestures and behaviors that better reflect the environment and conditions an agricultural robot might find itself in. We demonstrate the utility of the dataset by means of evaluating two human detection methods: bounding boxes and skeleton extraction.
Wireless Federated Distillation for Distributed Edge Learning with Heterogeneous Data

Cooperative training methods for distributed machine learning typically assume noiseless and ideal communication channels. This work studies some of the opportunities and challenges arising from the presence of wireless communication links. We specifically consider wireless implementations of Federated Learning (FL) and Federated Distillation (FD), as well as of a novel Hybrid Federated Distillation (HFD) scheme. Both digital implementations based on separate source-channel coding and over-the-air computing implementations based on joint source-channel coding are proposed and evaluated over Gaussian multiple-access channels.
The Experiences of Healthcare Professional Students about the Educational Impacts of Mobile Learning

The role of mobile devices in learning processes is growing rapidly and it is imperative to assess the effect of this technology. This paper explores the experience of healthcare professional students with regard to the educational impacts of mobile learning. We conducted a qualitative study using a conventional qualitative content analysis based on Graneheim and Lundman (2004) method to collect and analyze the experiences of 23 healthcare professional students. Two themes, each with subthemes, emerged from the findings: (1) perceived benefit in learning process, and (2) reflective self-assessment. The results revealed that mobile learning has a positive impact on both the process and the outcome of learning in healthcare professional students. Therefore, creating a supportive condition to promote mobile learning is recommended.
Knowledge Representation and Update in Hierarchies of Graphs

A mathematical theory is presented for the representation of knowledge in the form of a directed acyclic hierarchy of objects in a category where all paths between any given pair of objects are required to be equal. The conditions under which knowledge update, in the form of the sesqui-pushout rewriting of an object in a hierarchy, can be propagated to the rest of the hierarchy, in order to maintain all required path equalities, are analysed: some rewrites must be propagated forwards, in the direction of the arrows, while others must be propagated backwards, against the direction of the arrows, and, depending on the precise form of the hierarchy, certain composability conditions may also be necessary.
The Hermeneutic Circle of Data Visualization

In this article, we show how postphenomenology can be used to analyze the Affinity Map: a data visualization that reveals the hidden dynamics that exist between individuals within large organizations. We make use of the Affinity Map to expand the classic postphenomenology that privileges a ‘linear’ understanding of technological mediations and introduce the notions of ‘iterativity’ and ‘collectivity.’ In the first section of the paper, we discuss both classic and more recent descriptions of human-technology-world relations in order to transcendentally approach the discipline of data visualization. In the second section, we use the Affinity Map case study to consider three elements: 1) the collection of data and the design process; 2) the visual grammar of the data visualization, and 3) the process of self-recognition for the map ‘reader.’ In the third section, we introduce the hermeneutic circle of data visualization. Finally, we suggest that the Affinity Map, because of its ethical and political multistability, might be seen as a material encounter between postphenomenology, actor-network theory (ANT), and hermeneutics.
YNU_DYX at SemEval-2019 Task 9: A Stacked BiLSTM for Suggestion Mining Classification

In this paper we describe a deep-learning system that competed as SemEval 2019 Task 9-SubTask A: Suggestion Mining from Online Reviews and Forums.We use Word2Vec to learn the distributed representations from sentences.This system is composed of a Stacked Bidirectional Long-Short Memory Network (SBiLSTM) for enriching word representations before and after the sequence relationship with context.We perform an ensemble to improve the effectiveness of our model.Our official submission results achieve an F 1 -score 0.5659.
On the Weaknesses of Reinforcement Learning for Neural Machine Translation

Reinforcement learning (RL) is frequently used to increase performance in text generation tasks, including machine translation (MT), notably through the use of Minimum Risk Training (MRT) and Generative Adversarial Networks (GAN). However, little is known about what and how these methods learn in the context of MT. We prove that one of the most common RL methods for MT does not optimize the expected reward, as well as show that other methods take an infeasibly long time to converge. In fact, our results suggest that RL practices in MT are likely to improve performance only where the pre-trained parameters are already close to yielding the correct translation. Our findings further suggest that observed gains may be due to effects unrelated to the training signal, but rather from changes in the shape of the distribution curve.
Fitting the Style

It has been widely proven among several research disciplines that the information conveyed through the visual channel can be critical for communication. Altering either the motion or the appearance of our interlocutor can change the opinion we form about them or lead us to drastically different conclusions on what is being communicated. In this paper, we explore the influence of altering the visual appearance of a virtual 3D face model through several different stylization techniques on the perception of dynamic conversational facial expressions. We propose the use of a semantic-differential task to recover the underlying semantic/cognitive space for the expressions and their corresponding shifts due to the application of the different styles. Given the appropriate amount of stimuli, we consider this technique well fitted to provide a mapping between meaning of emotions and spectrum of rendering parameters. Our results show that the different techniques are capable to alter the perception of the expressions making them, for example, more intense or ephemeral in a rather consistent manner.
Learning a Domain-Invariant Embedding for Unsupervised Domain Adaptation Using Class-Conditioned Distribution Alignment

We address the problem of unsupervised domain adaptation (UDA) by learning a cross-domain agnostic embedding space, where the distance between the probability distributions of the two source and target visual domains is minimized. We use the output space of a shared cross-domain deep encoder to model the embedding space anduse the Sliced-Wasserstein Distance (SWD) to measure and minimize the distance between the embedded distributions of two source and target domains to enforce the embedding to be domain-agnostic.Additionally, we use the source domain labeled data to train a deep classifier from the embedding space to the label space to enforce the embedding space to be discriminative.As a result of this training scheme, we provide an effective solution to train the deep classification network on the source domain such that it will generalize well on the target domain, where only unlabeled training data is accessible. To mitigate the challenge of class matching, we also align corresponding classes in the embedding space by using high confidence pseudo-labels for the target domain, i.e. assigning the class for which the source classifier has a high prediction probability. We provide experimental results on UDA benchmark tasks to demonstrate that our method is effective and leads to state-of-the-art performance.
CoAStaL at SemEval-2019 Task 3: Affect Classification in Dialogue using Attentive BiLSTMs

This work describes the system presented by the CoAStaL Natural Language Processing group at University of Copenhagen.The main system we present uses the same attention mechanism presented in (Yang et al., 2016).Our overall model architecture is also inspired by their hierarchical classification model and adapted to deal with classification in dialogue by encoding information at the turn level.We use different encodings for each turn to create a more expressive representation of dialogue context which is then fed into our classifier.We also define a custom preprocessing step in order to deal with language commonly used in interactions across many social media outlets.Our proposed system achieves a micro F1 score of 0.7340 on the test set and shows significant gains in performance compared to a system using dialogue level encoding.
The Effects of Educational Context Variables on Achievement and Interest in Mathematics in High and Low Achieving Students

The purpose of this study is to investigate the effects of educational context variables on achievement and interest in mathematics in high and low achieving students in Korea. Students participated in TIMSS 2015 in Korea were divided into two groups according to their achievement in mathematics. And the effects of educational context variables on achievement and interest in mathematics were analyzed in each group using the Hierarchical Linear Model(HLM). Main findings of this study are as follows. First, variables which influence achievement also influence interest but any variables which influence interest don’t influence achievement in upper-group students. Number of Books and Students’ Perceived Mathematics Lessons have strong influence on achievement and interest in both forth and eighth grade students. Second, variables which influence achievement or interest in mathematics in lower-group also influence achievement or interest in mathematics in upper-group students. But any variables which influence achievement or interest in upper-group students don’t influence achievement or interest in lower-group students. For examples, Parents’ Education, Students’ Perceived Mathematics Lessons have effects on upper-group students’ achievement. Number of Books, Home Learning Environment, and Numeracy Activities Before School have significant effects on the achievement of forth grade students. Students’ Perceived Mathematics Lessons is the variable that influence on the interest of both forth grade and eighth grade students. This study suggests the ways to improve mathematics teaching and learning based on these results.
Effective coupling constant of plasmons

We study an ultrarelativistic QED plasma in thermal equilibrium. Plasmons---photon collective excitations---are postulated to correspond not to poles of the retarded photon propagator but to poles of the propagator multiplied by the fine-structure constant. This product is an invariant of the renormalization group that is independent of an arbitrarily chosen renormalization scale. In addition, our proposal is physically motivated since one needs to scatter a charged particle off a plasma system to probe its spectrum of collective excitations. We present a detailed calculation of the QED running coupling constant at finite temperature using the Keldysh-Schwinger representation of the real-time formalism. We discuss the issue of how to choose the renormalization scale and show that the temperature is a natural choice which prevents the breakdown of perturbation theory through the generation of potentially large logarithmic terms. Our method could be applied to anisotropic systems where the choice of the renormalization scale is less clear, and could have important consequences for the study of collective modes.
CLARK at SemEval-2019 Task 3: Exploring the Role of Context to Identify Emotion in a Short Conversation

With text lacking valuable information available in other modalities, context may provide useful information to better detect emotions.In this paper, we do a systematic exploration of the role of context in recognizing emotion in a conversation.We use a Naïve Bayes model to show that inferring the mood of the conversation before classifying individual utterances leads to better performance.Additionally, we find that using context while training the model significantly decreases performance.Our approach has the additional benefit that its performance rivals a baseline LSTM model while requiring fewer resources.
Identifying Knowledge from the Application of Natural Deduction Rules in Propositional Logic

Intelligent Tutoring Systems (ITS) are technological resources widely used in teaching-learning processes, and their studies are directed, mainly, at distance learning. In this sense, the purpose of this work is the redesign of a Student Model agent, in the context of an ITS applied to the teaching of Natural Deduction in Propositional Logic (NDPL) for computing. It is expected that the agent will be able to identify and represent the students’ knowledge states. In the modeling stage, we present the details of the knowledge representation, as well as about the inference mechanism, based on Bayesian networks. Regarding the results, students are satisfied with the Heráclito environment and that the agent achieves its main objective, evidencing the possibility of implementing personalized teaching strategies based on individual characteristics and knowledge from the students.
Collaboration FMADM And K-Means Clustering To Determine The Activity Proposal In Operational Management Activity

Indonesian government agencies under the Ministry of Energy and Mineral Resources still use manual methods in determining and selecting proposals for operational activities to be carried out. This study uses the Decision Support System (DSS) method, namely Fuzzy Multiple Attribute Decision Decision (Fmadm) and K-Means Clustering method in managing Operational Plan activities. Fmadm to select the best alternative from a number of alternatives, alternatives from this study proposed activity proposals, then ranking to determine the optimal alternative. The K-Means Clustering Method to obtain cluster values for alternatives on the criteria for activity dates, types of activities, and activity ceilings. The last iteration of the Euclidian distance calculation data on k-means shows that alternatives that have the smallest centroid value are important proposal criteria and the largest centroid value is an insignificant proposal criteria. The results of the collaboration of the Fmadm and K-Means Clustering methods show the optimal ranking of activities (proposal activities) and the centroid value of each alternative.
Banknote simulator for aging and soiling banknotes using Gaussian models and Perlin noise

In this paper, we propose a banknote aging simulator for generating aged banknotes from new banknotes. When new banknotes begin to be circulated, automatic teller machines (ATMs) are trained with these new banknotes. However, the ATM operations become more erroneous after several months as the banknotes start aging. Also, the process of updating ATM software for aged banknotes is costly and time-consuming. In this paper, we developed a simulator for banknote aging that uses the Gaussian and Perlin noise models. The proposed simulator allows ATMs to better handle aged and soiled banknotes without updating ATM software. The experimental results show that the classification and validation performance of aged and soiled banknotes improved noticeably when the classification and validation algorithms were designed using the proposed simulator for new banknotes.
Fringe Pattern Analysis in Wavelet Domain

We present a full-field technique for single fringe pattern analysis based on wavelet transform.Wavelets technique is a powerful method that quantifies at different scales how spatial energy is distributed.In the wavelets domain, fringe pattern analysis requires spatial modulation by a high-frequency carrier.We realize the modulation process numerically by combining the fringe pattern and its quadrature generated analytically by spiral phase transform.The first application concerns the speckle denoising by thresholding the two-dimensional stationary wavelet transform (2D-swt) coefficients of the detail sub-bands.In the second application, the phase derivatives are estimated from the 1D-continuous wavelet transform (1D-cwt) and 2D-cwt analysis of the modulated fringe pattern by extracting the extremum scales from the localized spatial frequencies.In the third application, the phase derivatives distribution is evaluated from the modulated fringe pattern by the maximum ridge of the 2D-cwt coefficients.The final application concerns the evaluation of the optical phase map using two-dimensional discrete wavelet transform (2D-dwt) decomposition of the modulated fringe pattern.The optical phase is computed as the arctangent function of the ratio between the detail components (high-frequency sub-bands) and the approximation components (low-frequency sub-bands).The performance of these methods is tested on numerical simulations and experimental fringes.
Learning Multi-Party Turn-Taking Models from Dialogue Logs

This paper investigates the application of machine learning (ML) techniques to enable intelligent systems to learn multi-party turn-taking models from dialogue logs. The specific ML task consists of determining who speaks next, after each utterance of a dialogue, given who has spoken and what was said in the previous utterances. With this goal, this paper presents comparisons of the accuracy of different ML techniques such as Maximum Likelihood Estimation (MLE), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN) architectures, with and without utterance data. We present three corpora: the first with dialogues from an American TV situated comedy (chit-chat), the second with logs from a financial advice multi-bot system and the third with a corpus created from the Multi-Domain Wizard-of-Oz dataset (both are topic-oriented). The results show: (i) the size of the corpus has a very positive impact on the accuracy for the content-based deep learning approaches and those models perform best in the larger datasets; and (ii) if the dialogue dataset is small and topic-oriented (but with few topics), it is sufficient to use an agent-only MLE or SVM models, although slightly higher accuracies can be achieved with the use of the content of the utterances with a CNN model.
The Effect of Fieldtrip on Geography of Student’s Ecological Intelligence

Abstract The occurrence of environmental degradation shows decreased ecological intelligence. Enterprises in evoking environmental awareness can be reflected in the learning process that should be oriented to the contextual problems that exist in the student environment. This study is aimed to find out the effect of using field trip method in enhanching students’ ecological intelligence. The pretest-posttest quasi-experimental design has been used in this study. The sample used in this study is class XI with a total of 62 students. Class XI IPS 1 has been selected as experimental group (N = 31) and class XI IPS 2 is selected as control group (N = 31) with visual media usage. The data collection method uses tests for ecological knowledge, questionnaires to measure attitudes, and performance assessments to measure ecological skills. Data analysis technique using paired sample t test. There is a statistically significant difference in experimental ecological intelligence compared to control groups using visual media. Field trip methods can increase knowledge as well as evoke awareness and awareness of the environment because it is faced directly with the real situation of local environmental problems that occur around it.
Career Path Management between the Theory and Application: Measuring its Relationship with Satisfying Employees’ Needs: A Study on the Jordanian Commercial Banks

Based on what has been proved in theory; human resources constitute the inimitable competitive advantage, and retaining them is crucial for sustainability. Hence it is necessary to satisfy employees&amp;rsquo; needs to reduce their intention to leave. Many researchers suggested career path management as a tool for satisfying employees&amp;rsquo; multi needs; accordingly this study aimed to examine the degree to which Jordanians Commercial banks are engaged in career path management in terms of career path planning, and career development (socialization, coaching and mentoring, and counseling), and if the Banks are able to satisfy employees needs based on Maslow&amp;rsquo;s five sets of needs. &#x0D; &#x0D; The study aimed also to assess the relationship between career path management and satisfying employees&amp;rsquo; needs. &#x0D; &#x0D; 7 banks participated in the study, 50 employees from each Bank represented the sampling unit, the questionnaire used for collecting the required data and information, 336 questionnaires were valid for statistical analysis using SPSS version 22. &#x0D; &#x0D; The main results of the study revealed that the surveyed banks are engaged in career path management, except for counseling sessions. &#x0D; &#x0D; Relied on the respondents&amp;rsquo; answers the Banks are able to satisfy three sets of needs &amp;ldquo;the security, self-esteem, and self-actualization&amp;rdquo;. &#x0D; &#x0D; The statistical results show also that there is a statistically significant relationship ranged between moderate to strong between career path management dimensions and the five sets of needs satisfaction&amp;nbsp; &amp;ldquo; physiological, security, social, self- esteem and self-actualization&amp;rdquo;. &#x0D; &#x0D; Based on the findings it is recommended to enhance the continuity of career path management process, in addition to search for means that can satisfy the physiological and social needs of employees.
Pengembangan Modul Pembelajaran Perakitan Komputer Berbasis Multimedia Interaktif

This study aims to determine the feasibility of interactive multimedia-based computer assembly learning modules for class X in SMKN 1 Sikur , to test the effectiveness of interactive multimedia-based computer assembly learning modules in students class X at SMKN 1 Sikur, and knowing the response of students to the interactive multimedia-based computer assembly learning module for class X in SMKN 1 Sikur. This research is Research and Development research with ADDIE development model, namely; Analysys, Design, Development, Implementation, and Evaluation. The assessment instrument of interactive multimedia-based learning modules is in the form of a 5-scale questionnaire. The sources of research data were 2 media experts, 2 material experts, and 30 class X majoring in SMKN 1 Sikur. The results of the feasibility test of media experts from 2 aspects are categorized as very feasible with a total percentage of feasibility of 84.5%. And the results of the feasibility test from material experts from 3 aspects are categorized as very feasible with a total percentage of feasibility of 87%. Whereas based on the responses of students in class X towards the interactive multimedia-based learning module developed was feasible with a total percentage of 78%.