Reviewerly/exHarmony huggingface dataset:
Edge-guided Adversarial Network Based on Contrastive Learning for Image-to-Image Translation

In recent years, generative adversarial networks have made great progress in image synthesis and image translation tasks in the field of image processing and computer vision. However, the quality of the generated image and the scalability over multiple datasets is still not satisfying. We briefly review some prior works and propose a method for image-to-image translation, which is learning a mapping between different visual domains. The network extracts edge feature from both domains of output and target, and minimizes the difference using a framework based on patchwise contrastive learning. We apply edge feature guidance in our method and select Sobel operator among several classical edge detection operators. We demonstrate that our method outperforms existing approaches in the task of unpaired image-to-image translation across datasets.
Using Neural Networks and Genetic Algorithms for Predicting Human Movement in Crowds

Safety is an important issue at large gatherings of people such as at religious gatherings or sporting events. Therefore, it is important to control crowds and identify in advance when dangerous situations may arise. Simulations play an important role in predicting how people in crowds will react to one another and their environment. Simulations often rely on a priori models of human behavior to predict crowd behavior. We combine genetic algorithms and neural networks to learn how people behave in crowds to avoid assumptions used in a priori models. We examine learning in two important regimes: structured crowds, where individuals are moving in a specified direction, as in the Islamic Hajj or Hindu Kumbh Mela, and unstructured crowds, such as town squares and train stations. In this preliminary work, we are most concerned with questions of trainability. In order to provide sufficient data and control qualitative features of our crowd data, we begin use generated data based on elaborations of wildlife flocking models in NetLogo. We compared performance on structured and unstructured crowds by predicting a series of next locations. The results showed that we are able to predict crowd motion, but error rates for individuals grow as time passes; however, the structured crowd gave more reliable results than the unstructured crowd.
The Use of Learning Strategies to Overcome Difficulties in Academic Listening Class

Of the four language skills in English, Listening seems to be the least frequently taught in classrooms in universities in Indonesia. The reasons for this vary, ranging from the lack of facilities to the importance of mastering the skill itself. It is undeniably true that teaching listening is quite complex. It requires not only special equipment, such as computers or CD players, and loud-speakers, but also skilled human resources. The absence of all these factors makes the teaching of listening neglected. Nevertheless, the need to master listening skills has increasingly become obvious as in the future universities across the country are encouraged to conduct Massive Open Online Courses which means students need to have some mastery of English as classes might be conducted in English. This paper tries to examine what difficulties students commonly have in classrooms and how teachers can help them deal with these difficulties by using some strategies. As a case study, the research involves years of teaching Academic Listening classes, interviews with the students and teachers. The results show that there are common mistakes that keep occurring from year to year. The results from the interviews with the students show that they made mistakes for some reasons, such as they did not know how the word was spelled, or they were not able to catch what the speakers had been saying. The students admitted that they had difficulties listening to lectures, talks or conversations in English. It is hoped if teachers are able to examine the causes of these listening difficulties, they can show students strategies which can help them cope with the difficulties and the students can eventually improve the skills on their own.
한국어교원 윤리강령 제정을 위한 예비적 고찰 - 사회복지사와 청소년상담사 윤리강령 사례 분석을 중심으로 -

In contrast to the fact that legal grounds for acquiring certificates for Korean as a Foreign Language (KFL) teachers are systematically set, code of ethics for Korean as a Foreign Language (KFL) Teachers have not been studied. This study began with acknowledging the needs for standards and establishing a code of ethics that may prove suitable after an individual is qualified as a KFL teachers. Subsequently, the guidelines for the establishment of code of ethics were proposed on the basis of analyzing cases of other national certifications such as those of social workers and youth counselors. This study has significance because it laid emphasis on the necessity of a code of ethics for KFL teachers and compiled primary sources for the enactment. The study is expected to bring more scholarships and investigations covering desirable attitudes for KFL teachers, the obligation and responsibility that KFL teachers must be well-acquainted and the sustaining efforts and commitments of KFL teachers to strengthen their expertise.
Frame Rate Up-Conversion Algorithm Based on Infrared Image

With the more and more common use of infrared video in life, people have higher and higher requirements for infrared imaging frame rates. Better quality infrared images provide a better basis for subsequent target recognition, video compression and decompression operations. Therefore, it is of great significance to effectively obtain infrared high frame rate images and improve the quality of infrared images. As an effective means of video conversion, frame rate enhancement technology has become a research hotspot in the direction of computer vision. However, the existing video interpolation methods based on infrared images are all implemented based on block matching, which cannot well approximate the complex moving real world. In order to solve these problems, an optical flow method based on pixel motion compensation is proposed for infrared video interpolation. The second interpolation method can make better use of the motion information in the video. In the end, an optical flow optimization network is used for optimization, which can better optimize the artifacts in the optical flow estimation and improve the final image quality. Experiments show that our method has a better effect than existing methods on models on various infrared video data sets, and has lower computational complexity.
Each Realistic Continuous Functional Dependence Implies a Relation Between Some Variables: A Theoretical Explanation of a Fuzzy-Related Empirical Phenomenon

In principle, one can have a continuous functional dependence $$y=f(x_1,\ldots ,x_n)$$ for which, for each proper subset of $$n+1$$ variable $$x_1,\ldots ,x_n,y$$ , there is no relation: i.e., for each selection of n variables out of these $$n+1$$ , all combinations of these n values are possible. However, for fuzzy operations, there is always some non-trivial relation between y and one of the inputs $$x_i$$ ; for example, for “and”-operations (t-norms) $$ y=f_ \& (x_1,x_2)$$ , we have $$y\le x_1$$ ; for “or”-operations (t-conorms) $$y=f_\vee (x_1,x_2)$$ we have $$x_1\le y$$ , etc. In this paper, we prove a general mathematical explanation for this empirical fact.
OPEn: An Open-ended Physics Environment for Learning Without a Task

Humans have mental models that allow them to plan, experiment, and reason in the physical world. How should an intelligent agent go about learning such models? In this paper, we will study if models of the world learned in an open-ended physics environment, without any specific tasks, can be reused for downstream physics reasoning tasks. To this end, we build a benchmark Open-ended Physics Environment (OPEn) and also design several tasks to test learning representations in this environment explicitly. This setting reflects the conditions in which real agents (i.e. rolling robots) find themselves, where they may be placed in a new kind of environment and must adapt without any teacher to tell them how this environment works. This setting is challenging because it requires solving an exploration problem in addition to a model building and representation learning problem. We test several existing RL-based exploration methods on this benchmark and find that an agent using unsupervised contrastive learning for representation learning, and impact-driven learning for exploration, achieved the best results. However, all models still fall short in sample efficiency when transferring to the downstream tasks. We expect that OPEn will encourage the development of novel rolling robot agents that can build reusable mental models of the world that facilitate many tasks.
Robust and Precise Matching Algorithm Combining Absent Color Indexing and Correlation Filter

This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.
BA2P : Bidirectional and Anonymous Auction Protocol with Dispute-Freeness

Electronic auction is a popular platform to sell goods, task assignment, and resources’ allocation due to reductions of transaction costs and has attracted a huge number of potential buyers. However, it is challenging to address the disputes between the buyer and the auctioneer. The main reason is, on the one hand, solving such problem leverages to the broad domain of research aspects, such as economic theory, engineering, and cryptography, and, on the other hand, it is difficult to arbitrate in a decentralized and anonymous setting. In this work, we consider a more general framework to solve the potential disputes by enforcing bidirectional confirmation and public verification. Hence, the bidding procedure is clear to inspect and potential disputes can be erased. To achieve this goal, we propose policy-driven chameleon hash and revised linkable-and-redactable ring signature as building blocks. We used these two tools to build a bidirectional and anonymous auction protocol called <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msup> <mrow> <mtext>BA</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> P. In our <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msup> <mrow> <mtext>BA</mtext> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </math> P protocol, the bidders can competitively and anonymously place their bids to outbid others. At the end of the auction protocol, everyone can verify the validity of the bidding proof and decide the winner. Thus, dispute-freeness feature is achieved. The analysis suggests that our proposal is provably secure and practically efficient, and it trades some efficiencies with dispute-freeness feature.
Heuristic Depth Estimation with Progressive Depth Reconstruction and Confidence-Aware Loss

Recently deep learning-based depth estimation has shown the promising result, especially with the help of sparse depth reference samples. Existing works focus on directly inferring the depth information from sparse samples with high confidence. In this paper, we propose a Heuristic Depth Estimation Network (HDEN) with progressive depth reconstruction and confidence-aware loss. The HDEN leverages the reference samples with low confidence to distill the spatial geometric and local semantic information for dense depth prediction. Specifically, we first train a U-NET network to generate a coarse-level dense reference map. Second, the progressive depth reconstruction module successively reconstructs the fine-level dense depth map from different scales, where a multi-level upsampling block is designed to recover the local structure of object. Finally, the confidence-aware loss is proposed to trigger the reference samples with low confidence, which enforces the model focusing on estimating the depth of the tiny structure. Extensive experiments on the NYU-Depth-v2 and KITTI-Odometry dataset show the effectiveness of our method. Visualization results demonstrate that the dense depth maps generated by HDEN have better consistency at the entity edge with RGB image.
Food-Oriented Art Activities Focused on Children's Emotional Awareness and Expression Effect on Prosocial Behavior

The purpose of this study was to develop an intervention program using food for the improvement of 5-year-olds’ emotional awareness and expression and to analyze the effects of this program on 5-year-olds’ prosocial behavior. The subjects of this study were sixty-nine 5-year-olds attending three day care centers located in D city. The experimental group (n=23) participated in food-oriented art activities focused on children’s emotional awareness and expression for a total of 16 times for eight weeks, while the comparative group (n=23) participated in art activities focused on children’s emotional awareness and expression. And the control group (n=23) participated in the daycare program based on the Nuri Curriculum. Their prosocial behavior was measured before and after the experiment. The data were analyzed using ANCOVA. As a result, this program had an effect on the improvement of 5-year-olds’ prosocial behavior. Art activities focused on children’s emotional awareness and expression utilizing food are recommended as necessary activities to develop in order to improve children’s prosocial behavior.
Semantic Information in Gating Patterns of Dynamic Convolutional Neural Networks

Dynamic Convolutional Neural Networks are an emerging class of models characterized by their ability to dynamically adjust inference complexity at run-time, by identifying parts of the model with minimal contribution to the result and skipping the corresponding computations. A prominent such category includes models that generate binary gating signals indicating whether specific convolutional kernels need to be computed or can be omitted based on the characteristics of each processed datum. These signals are usually generated by branches of the same model which are typically learned simultaneously to the main task, with their main objective being to enable good performance with parsimony of computations. We argue that such objective incentivizes the model to implicitly optimize and utilize kernels in class/concept –specific groups, hence ascribing semantic information to the gating signals. We demonstrate this behavior by studying the characteristics of such signals for popular CNN architectures in the ImageNet database. By comparing the relationship between gating signals from different visual categories in the ImageNet hierarchy, it is shown that the gating patterns’ dissimilarity correlates well with semantic span of the underlying classes. It is also demonstrated that through appropriate distance measures, gating patterns can be used for ranking classes’ similarity with comparable performance to that off standard CNN-generated image descriptors, but in a significantly more compact representation due to their binary nature. (Abstract)
Wideband and Entropy-Aware Deep Soft Bit Quantization

Deep learning has been recently applied to physical layer processing in digital communication systems in order to improve end-to-end performance. In this work, we introduce a novel deep learning solution for soft bit quantization across wideband channels. Our method is trained end-to-end with quantization-and entropy-aware augmentations to the loss function and is used at inference in conjunction with source coding to achieve near-optimal compression gains over wideband channels. We prove and verify that a proper weight initialization scheme leads to a reduced feature variance, which allows us to use a fixed latent feature quantization scheme. When tested on channel distributions never seen during training, the proposed method achieves a compression gain of up to 10% in the high SNR regime versus previous state-of-the-art methods.
An Approach to Interactive Analysis of StarCraft: BroodWar Replay Data

This paper presents an interactive approach for exploratory data analysis of StarCraft: BroodWar replay data. Our approach encodes the basic facts of StarCraft units into statements in first-order logic (FOL). The information from replay data is also encoded into FOL statements. This database of FOL facts, in conjunction with a logic programming language, enables a human user to easily query and augment the database with new facts. This approach encourages an interactive workflow to aid in the analysis of replay data. We present examples that demonstrate the utility of the approach.
An Area-Optional Regeneration RRT Algorithm for Dynamic Path Planning of Service Robots

At present, the walking routes of service robots are mostly formulated and fixed. Therefore, there are many places that the robots cannot reach and appear inflexible. When obstacles are on the walking route of the robot, collisions may occur. Thus, an area-optional regeneration Rapidly-Exploring Random Tree (RRT) algorithm for dynamic path planning is proposed to plan the walking route of the service robot in real-time in an environment with moving obstacles in this paper. First, sub-regions that need path planning can be determined according to the target points, and each sub-region corresponds to a root node, so that a feasible path that does not touch obstacles through a narrow area can be found faster, and the speed of path planning can be improved. Secondly, in real-time path planning, it is analyzed that whether tree nodes near moving obstacles need to be removed. Besides, how to remove, add, and reconnect are also analyzed, avoiding global planning and improving the efficiency of path planning in frequently changing environments. It is verified by simulation that the efficiency of path planning can be improved after dividing the area, and the effectiveness and superiority of the algorithm are verified in a real-time changing environment.
3D Asset for Web-based Virtual Engineering Platfrom as Implementation of Industry 4.0

The industrial revolution 4.0 has developed and become a trend by combining automation technology and cyber technology. The more industry 4.0 develops, the more developed the simulation modeling system needed in all scientific fields. The new concept that can bring simulation modeling to the digital realm is needed. Virtual Engineering is a new technological development concept in simulation modeling. 3D modeling using real-world objects is a form of Virtual Engineering. Manipulating a 3D model requires an asset and a system to integrate assets into the virtual world. However, currently there is no system that can integrate assets automatically with the cyberspace. In this research, asset development is made to assist users in providing and managing assets and integrating assets automatically into the virtual world. The system has three parts, namely an asset development application, an application to provide assets through 3D scanning, a turntable system to help users perform scanning, and a 3D library system to manage assets and integrate them into the Virtual Engineering platform. Based on the test result, it was found that 3D scanning with a turntable required the fastest time that could be obtained by 34 seconds for an object weighing 500 grams. The optimal distance for the camera to scan an object is 28 cm. The 3D Library system uses the MongoDB database to integrate assets into the Virtual Engineering platform, and the system has been successfully connected to the Virtual Engineering platform.
Approximately Valid and Model-Free Possibilistic Inference

Existing frameworks for probabilistic inference assume the inferential target is a feature the posited statistical model's parameters. In this paper, we develop a new version of the so-called generalized inferential model framework for possibilistic inference on unknowns that are well-defined independent of a statistical model. We provide a bootstrap-based implementation and establish approximate validity.
Subordination in Italian and English: Implications for Second Language Acquisition1

Our study focuses on morpho-syntactic complexity, trying to identify the specific subordinated modalities of organizing and packaging information in a narration. This purpose will be achieved by combining the morpho-syntactic analysis with the type of contents that subordinate clauses convey with respect to the informational flux of textual structure (foreground vs. background alternation). A functionalist and enunciative framework is adopted. The following questions will be addressed: (1) which types of morpho-syntactic structures – main or subordinated, finite or non finite – are exploited to convey the subordinated contents selected? (2) do the informants tend to hierarchize the expressed contents? (3) which types of semantic and/or logical components (temporality, causality, etc.) are selected to be narrated and highlighted through subordination? We shall demonstrate that only the interaction of several factors – core morphological facts, interactional and discourse habits – can exhaustively explain the textual perspectives observed in our L1 and L2 data, with interesting consequences for second language acquisition.
A Hybrid Scattering Transform for Signals with Isolated Singularities

The scattering transform is a wavelet-based model of Convolutional Neural Networks originally introduced by S. Mallat. Mallat's analysis shows that this network has desirable stability and invariance guarantees and therefore helps explain the observation that the filters learned by early layers of a Convolutional Neural Network typically resemble wavelets. Our aim is to understand what sort of filters should be used in the later layers of the network. Towards this end, we propose a two-layer hybrid scattering transform. In our first layer, we convolve the input signal with a wavelet filter transform to promote sparsity, and, in the second layer, we convolve with a Gabor filter to leverage the sparsity created by the first layer. We show that these measurements characterize information about signals with isolated singularities. We also show that the Gabor measurements used in the second layer can be used to synthesize sparse signals such as those produced by the first layer.