Reviewerly/exHarmony huggingface dataset:
SP-RAN: Self-Paced Residual Aggregated Network for Solar Panel Mapping in Weakly Labeled Aerial Images

With the rapid development of the solar distribution, solar panel mapping is becoming increasingly valuable to decision-makers. Weakly supervised methods have been developed to reduce the cost in training sample collection, and the most successful ones follow the alternative training scheme, which first generates coarse object localizations as pseudo labels (PLs) and then utilizes these PLs to train an end-to-end network for object extraction. As remote sensing images are typically characterized by multiple occurrences of objects and complicated backgrounds, the alternative training scheme suffers from low mapping accuracy and deficient boundary maintenance due to the varying quality of PLs. In this article, we focus on addressing these problems by adaptively adjusting the contributions of quality-varying PLs and propose a novel self-paced residual aggregated network (SP-RAN) for solar panel mapping. Specifically, with the initial PLs generated by gradient-weighted class activation mapping, a residual aggregated network is designed for target mapping with special consideration for the capability in producing complete and well-shaped mapping results. Considering the inconsistent quality of PLs, an effective confidence-aware (CA) loss is developed to emphasize the contribution of high-quality PLs and alleviate the negative impacts brought by the bad-quality ones in the training phase. Moreover, to concentrate on boundary maintenance, a novel self-paced label correction (SP-LC) strategy is proposed to selectively update PLs by considering their reliability. Extensive experimental comparisons with state-of-the-art methods and ablation study on two aerial datasets and a remote sensing dataset demonstrate the superiority of the proposed method.
The Effect of Minecraft Video Game on Students’ English Vocabulary Mastery

Vocabulary mastery is important to acquire a language. Learning vocabulary has several barriers that slow down the progress. Minecraft application is used as a new method of learning English vocabulary. The goal of this research was to investigate whether there is a significance positive effect after used Minecraft application on students’ vocabulary mastery for 5th grade elementary students. A quasi-experimental method was used in this research with 63 students of SDN Poris Plawad 1 Tangerang, which separated into an experimental group (n=31) and a control group (n=32). The results revealed that Minecraft has better score result than the conventional method. Besides, the students’ behavior also resulted to be better than the conventional method. The survey also revealed that the students agreed that Minecraft was making the learning easier, less boring, less stressful and happier. However, the results could not be generalized due to the small sample taken in this research.
Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge.

This competition concerns educational diagnostic questions, which are pedagogically effective, multiple-choice questions (MCQs) whose distractors embody misconceptions. With a large and ever-increasing number of such questions, it becomes overwhelming for teachers to know which questions are the best ones to use for their students. We thus seek to answer the following question: how can we use data on hundreds of millions of answers to MCQs to drive automatic personalized learning in large-scale learning scenarios where manual personalization is infeasible? Success in using MCQ data at scale helps build more intelligent, personalized learning platforms that ultimately improve the quality of education en masse. To this end, we introduce a new, large-scale, real-world dataset and formulate 4 data mining tasks on MCQs that mimic real learning scenarios and target various aspects of the above question in a competition setting at NeurIPS 2020. We report on our NeurIPS competition in which nearly 400 teams submitted approximately 4000 submissions, with encouragingly diverse and effective approaches to each of our tasks.
Attract–Repel Encoder: Learning Anomaly Representation Away From Landmarks

Anomaly detection (AD) has attracted great interest in the data mining community. With the development of deep learning, various deep autoencoders have been used and modified to solve AD problems due to their efficient data coding and reconstruction mechanisms. However, such methods still suffer challenges when solving some practical AD tasks. On the one hand, an AD dataset may contain diverse normal patterns rather than a universal pattern. Specifically, the normal data usually distribute in multiple clusters; meanwhile, the exact number of clusters is hard to know in practice. On the other hand, most existing autoencoder-based methods focus on encoding normal features but have not considered exploring the characteristics of abnormal data. To tackle these challenges, this article proposes a novel autoencoder-based AD model, the attract-repel encoder (ARE). ARE selects some landmarks in the encoding space to represent the diverse normal patterns. Besides, ARE can adaptively update the landmarks and their quantity during training. Then this article proposes the attract-repel loss (AR loss) function to train ARE. AR loss attracts normal samples to landmarks and repels anomalies away from landmarks so that it can learn both normal and abnormal features. Finally, ARE computes a sample's anomaly score by summing up its reconstruction error and its distance to the landmarks. Moreover, ARE can be trained either semisupervised or unsupervised. This article presents comprehensive experiments to evaluate the effectiveness of our approach.
RR-Net: Injecting Interactive Semantics in Human-Object Interaction Detection

Human-Object Interaction (HOI) detection devotes to learn how humans interact with surrounding objects. Latest end-to-end HOI detectors are short of relation reasoning, which leads to inability to learn HOI-specific interactive semantics for predictions. In this paper, we therefore propose novel relation reasoning for HOI detection. We first present a progressive Relation-aware Frame, which brings a new structure and parameter sharing pattern for interaction inference. Upon the frame, an Interaction Intensifier Module and a Correlation Parsing Module are carefully designed, where: a) interactive semantics from humans can be exploited and passed to objects to intensify interactions, b) interactive correlations among humans, objects and interactions are integrated to promote predictions. Based on modules above, we construct an end-to-end trainable framework named Relation Reasoning Network (abbr. RR-Net). Extensive experiments show that our proposed RR-Net sets a new state-of-the-art on both V-COCO and HICO-DET benchmarks and improves the baseline about 5.5% and 9.8% relatively, validating that this first effort in exploring relation reasoning and integrating interactive semantics has brought obvious improvement for end-to-end HOI detection.
Dynamic Artificial Bee Colony Algorithm with Hybrid Initialization Method

An improved basic artificial bee colony (ABC) algorithm with a self-adaptive technique, called dynamic ABC, is proposed. The dynamic ABC algorithm first uses a hybrid method combining the good-point-set method with the chaotic maps method to generate the initial population. Then, it applies self-adaptive population size at each generation, meaning that the population increases or decreases depending on some criteria, to enhance global convergence and avoiding local solutions. Experiments are carried out on a range of 10 popular benchmark functions. The results indicate that the dynamic ABC algorithm is superior to the basic ABC algorithm when considering both the speed and quality of the solution obtained.
Integration and analysis of geological, geochemical and remote sensing data of south of Neyshabur using principal component analysis

Lack of the existence of known mineral prospects in the preliminary stages of mineral exploration is the main problem of data-driven mineral potential modeling methods. On the other hand, applying the expert’s knowledge and judgment in different stages of mineral potential modeling, is the main difficulty of knowledge-driven mineral potential modeling methods. In addition, other difficulties in these methods can be mentioned such as determination of important variables, weighting to various classes of maps or information layers, and so on. Hence, the accuracy of the results of the knowledge-driven modeling methods is highly dependent on the amount of knowledge and experience of the expert. In this study, the principal component analysis (PCA) has been introduced as a knowledge-driven method with the least reliance on the expert’s knowledge for mineral potential modeling. In this method, the expert’s knowledge is only used in the interpretation of the results obtained from the modeling, and is not considered in the first stages of mineral potential modeling and definition of the conceptual model. In the introduced method, the interpretation of the results is conducted based on the positive and negative coefficients of variables in the eigenvalues table. Using these coefficients, it is determined that each principal component (PC) is associated with what type of mineralization. An advantage of this introduced method is to identify various types of mineralization in the area of interest using just one modeling effort. In order to evaluate the efficiency of this method, a region including two geological maps of Kadkan and Shamkan in the south of Neyshabur, northeast of Iran was selected. Two mineralization types including podiform chromite and epithermal gold-antimony mineralization types have been identified using the proposed method that presents more precise results than those of conventional univariate and multivariate geochemical studies.
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task

Despite their success, modern language models are fragile.Even small changes in their training pipeline can lead to unexpected results.We study this phenomenon by examining the robustness of ALBERT (Lan et al., 2020) in combination with Stochastic Weight Averaging (SWA)-a cheap way of ensembling-on a sentiment analysis task (SST-2).In particular, we analyze SWA's stability via CheckList criteria (Ribeiro et al., 2020), examining the agreement on errors made by models differing only in their random seed.We hypothesize that SWA is more stable because it ensembles model snapshots taken along the gradient descent trajectory.We quantify stability by comparing the models' mistakes with Fleiss' Kappa (Fleiss, 1971) and overlap ratio scores.We find that SWA reduces error rates in general; yet the models still suffer from their own distinct biases (according to CheckList).
Exploring the Effect of Randomness on Transferability of Adversarial Samples Against Deep Neural Networks

We investigate the transferability of adversarial attacks against deep neural networks (DNNs)—the contagion effect of adversarial attacks that, once deceiving one DNN model, can easily deceive other DNN models built on similar data. We demonstrate that introducing randomness to DNN models can break the curse of the transferability of adversarial attacks, given that the adversary does not have an unlimited attack budget. Two randomization schemes are explored: 1.) a random selection—single or ensemble—from a set of DNNs is surprisingly more robust against the strongest form of complete-knowledge attacks (a.k.a, white box attacks); 2.) after a small Gaussian random noise is added to its learned weights, a DNN model can potentially increase its resilience to adversarial attacks by as much as 74.2%. We compare the two randomization techniques to the Ensemble Adversarial Training technique and show that our randomization techniques are superior under different attack budget constraints. Furthermore, we explore the relationship between attack severity and decision boundary robustness in the version space. Finally, we connect the dots between the effectiveness of randomization to prevent attack transferability and the variability of DNN models through analyzing the differential entropy of sample hypotheses in the hypothesis space.
Analysis of Psychological and Emotional Tendency Based on Brain Functional Imaging and Deep Learning

When facing various pressures, human beings will have different degrees of bad psychological emotions, especially depression and anxiety. How to effectively obtain psychological data signals and use advanced intelligent technology to identify and make decisions is a research hotspot in psychology and computer science. Therefore, a personal emotional tendency analysis method based on brain functional imaging and deep learning is proposed. Firstly, the EEG forward model is established according to functional magnetic resonance imaging (fMRI), and the transfer matrix from the signal source at the cerebral cortex to the head surface electrode is obtained. Therefore, the activation results of fMRI emotional experiment can be mapped to the three-layer head model to obtain the EEG topographic map reflecting the degree of emotional correlation. Then, combining data enhancement (Mixup) with three-dimensional convolutional neural network (3D-CNN), an emotion-related EEG topographic map classification method based on M-3DCNN is proposed. Mixup is used to generate virtual data, the original data and virtual data are used to train the network together, the number of training samples is expanded, the overfitting phenomenon of 3D-CNN is alleviated, and 3D-CNN is used for feature extraction and classification. Experimental data analysis shows that, compared with traditional methods, the proposed method can retain emotion related EEG signals to a greater extent and obtain a higher accuracy of emotion five classifications under the same feature dimension.
Robustness and Adversarial Examples in Natural Language Processing

Recent studies show that many NLP systems are sensitive and vulnerable to a small perturbation of inputs and do not generalize well across different datasets.This lack of robustness derails the use of NLP systems in realworld applications.This tutorial aims at bringing awareness of practical concerns about NLP robustness.It targets NLP researchers and practitioners who are interested in building reliable NLP systems.In particular, we will review recent studies on analyzing the weakness of NLP systems when facing adversarial inputs and data with a distribution shift.We will provide the audience with a holistic view of 1) how to use adversarial examples to examine the weakness of NLP models and facilitate debugging; 2) how to enhance the robustness of existing NLP models and defense against adversarial inputs; and 3) how the consideration of robustness affects the real-world NLP applications used in our daily lives.We will conclude the tutorial by outlining future research directions in this area.
NDH-Full: Learning and Evaluating Navigational Agents on Full-Length Dialogue

Communication between human and mobile agents is getting increasingly important as such agents are widely deployed in our daily lives.Vision-and-Dialogue Navigation is one of the tasks that evaluate the agent's ability to interact with humans for assistance and navigate based on natural language responses.In this paper, we explore the Navigation from Dialogue History (NDH) task, which is based on the Cooperative Vision-and-Dialogue Navigation (CVDN) dataset, and present a stateof-the-art model which is built upon Vision-Language transformers.However, despite achieving competitive performance, we find that the agent in the NDH task is not evaluated appropriately by the primary metric -Goal Progress.By analyzing the performance mismatch between Goal Progress and other metrics (e.g., normalized Dynamic Time Warping) from our state-of-the-art model, we show that NDH's sub-path based task setup (i.e., navigating partial trajectory based on its correspondent subset of the full dialogue) does not provide the agent with enough supervision signal towards the goal region.Therefore, we propose a new task setup called NDH-FULL which takes the full dialogue and the whole navigation path as one instance.We present a strong baseline model and show initial results on this new task.We further describe several approaches that we try, in order to improve the model performance (based on curriculum learning, pre-training, and data-augmentation), suggesting potential useful training methods on this new NDH-FULL task. 1
Impacts of Remote Working on Employees During the COVID-19 Pandemic

This study investigates the impacts and influences of remote working on employees during the coronavirus pandemic, in terms of communication, job effectiveness, and employee engagement and productivity. Data were collected from participants from different sectors and industries in three regions in Saudi Arabia. The study's findings revealed that remote working significantly impacted communication, job effectiveness, and employee productivity. However, the impacts of remote working on employee engagement were insignificant which could be a result of neglecting activities that encourage employee engagement by the organization because of the nature of working remotely. This research presents two contributions. First, this article enhances the literature on remote working and employees during crises or pandemics. Second, it reveals that remote working could be applied to run daily operations when implementing the appropriate strategies and infrastructure. Disciplinary: Management Science (HRM). (C) 2021 INT TRANS J ENG MANAG SCI TECH.
Compressible gas density measurement by means of Fourier analysis of interferograms

This paper describes a method for nonintrusive compressible gas density measurement by means of automated analysis of interferograms using FFT (Fast Fourier Transform), and its implementation using DFT (Discrete Fourier Transform), that does make this measurement technique a fairly valuable and accessible experimental method. The presented approach makes it possible to use the finite fringe setting of the interferometer, thus reducing adjustment time. In the process of analysis, the errors originating from the imperfections of optical elements are reduced, and a digital image of a virtual infinite setting of the instrument is generated. As described here, the method is extremely beneficial, but not limited to, flows with shock wave – boundary layer interactions. A result of density measured for a flow with shock wave is presented.
Use of Virtual Resources as a Tool for Teaching Language Skills at the Colombian Caribbean Region Primary Basic Level

In recent times, education in general faces great challenges due the constant changes in the economic, social, and political spheres, as a product of globalization; therefore, it is essential to improve education and interaction during the teaching and learning processes. However, as a consequence of technological and scientifical development, every aspect of human life has been transformed, immersed, and influenced by it; the individual’s learning must be attached to the revolutionary changes brought by new technologies, Constant updating is essential, for the best use of technological applications and tools in educational institutions. In order to analyze JueduLand software, for teaching language skills in elementary educational institutions in the Colombian Caribbean, the results of a study with descriptive statistical analysis of variables are shown. The results allowed respondents to recognize each of the components that make up the variable, having a high presence in the use of JueduLand as a tool for teaching linguistics skills.
Design of a 5G Multimedia Broadcast Application Function Supporting Adaptive Error Recovery

The demand for mobile multimedia streaming services has been steadily growing in recent years. Mobile multimedia broadcasting addresses the shortage of radio resources but introduces a network error recovery problem. Retransmitting multimedia segments that are not correctly broadcast can cause service disruptions and increased service latency, affecting the quality of experience perceived by end users. With the advent of networking paradigms based on virtualization technologies, mobile networks have been enabled with more flexibility and agility to deploy innovative services that improve the utilization of available network resources. This paper discusses how mobile multimedia broadcast services can be designed to prevent service degradation by using the computing capabilities provided by multiaccess edge computing (MEC) platforms in the context of a 5G network architecture. An experimental platform has been developed to evaluate the feasibility of a MEC application to provide adaptive error recovery for multimedia broadcast services. The results of the experiments carried out show that the proposal provides a flexible mechanism that can be deployed at the network edge to lower the impact of transmission errors on latency and service disruptions.
Empirical analysis of the content of psychological competencies of active sales managers

Introduction. The subject of this article is the analysis of the requirements for the psychological properties of active sales managers from the point of view of the competence approach. It is assumed that the interaction of working conditions in the organization and the psychological properties of a professional, as well as the requirements for them from the tasks performed, generate a certain set of psychological competencies necessary for the productive activity of an active sales manager. The complex nature of human-to-human activities, as well as the need to perform organizational, intellectual, communicative and coordination tasks, forms a system of requirements for the psychological competencies of representatives of this profession, compliance with which increases the productivity of a professional. The article presents a theoretical and empirical analysis of the requirements for an active sales manager from the point of view of psychological competencies. Materials and Methods. Empirical research method - standardized self-reports with Likert scales, correlation and factor analysis. Results. Six main groups of required competencies are identified: value competence, regulatory competence, motivational competence, special competence, communication competence, strategic competence. It is assumed that the balanced development of these groups of competencies in accordance with the requirements of the profession of an active sales manager ensures high productivity of a specialist and his psychological well-being. Discussion and Conclusions. At the end of the article, the results obtained are evaluated from the point of view of their applicability in the training of specialists in key positions of sales departments, new research directions are outlined that allow for a more systematic study of the psychological competence of active sales managers, and it is also concluded that the content of psychological competencies should be taken into account when building educational programs for future sales managers.