Reviewerly/exHarmony huggingface dataset:
General 3D Room Layout from a Single View by Render-and-Compare

We present a novel method to reconstruct the 3D layout of a room (walls, floors, ceilings) from a single perspective view in challenging conditions, by contrast with previous single-view methods restricted to cuboid-shaped layouts. This input view can consist of a color image only, but considering a depth map results in a more accurate reconstruction. Our approach is formalized as solving a constrained discrete optimization problem to find the set of 3D polygons that constitute the layout. In order to deal with occlusions between components of the layout, which is a problem ignored by previous works, we introduce an analysis-by-synthesis method to iteratively refine the 3D layout estimate. As no dataset was available to evaluate our method quantitatively, we created one together with several appropriate metrics. Our dataset consists of 293 images from ScanNet, which we annotated with precise 3D layouts. It offers three times more samples than the popular NYUv2 303 benchmark, and a much larger variety of layouts.
Meta-learning Amidst Heterogeneity and Ambiguity

Meta-learning aims to learn a model that can handle multiple tasks generated from an unknown but shared distribution. However, typical meta-learning algorithms have assumed the tasks to be similar such that a single meta-learner is sufficient to aggregate the variations in all aspects. In addition, there has been less consideration on uncertainty when limited information is given as context. In this paper, we devise a novel meta-learning framework, called Meta-learning Amidst Heterogeneity and Ambiguity (MAHA), that outperforms previous works in terms of prediction based on its ability on task identification. By extensively conducting several experiments in regression and classification, we demonstrate the validity of our model, which turns out to be robust to both task heterogeneity and ambiguity.
미군 참전자 공적기록을 통해 본 공주·대평리 전투 고찰

The purpose of this study is to improve the status of delayed warfare conducted by the US 24th Division. For this purpose, the exploits records of the veterans of the battle are checked. First, the current status of the Battle of Gongju Daepyeong-ri is confirmed through an analysis of the existing war history. For this, we refer to the public history of the US and North Korean forces. The exploits records of veterans who participated in the war can be checked through 『The Hall of Valor』. In addition, reference is made to domestic and foreign primary and secondary data.The assessment that the soldiers of the 24th Division had a weak will to fight was wrong. Many people sacrificed their lives for the troops and their comrades, ignoring their own safety.We re-examined the Battle of Gongju Daepyeong-ri through the achievements of the medal recipients. It was confirmed that most of them were similar to the existing war history. However, it is regrettable that the achievements of those of merit in combat are not properly evaluated. In the future, research on cross-validation and re-evaluation of meritorious achievements is needed.
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C^3 is available at https://dataset.org/c3/.
Smart reactive grading system based on genetic algorithms and behavioral psychology

In this paper, we propose an intelligent scoring system based on genetic algorithms to provide students with exams for their level of study. The system is reactive, adapting the exam to the student's level using genetic algorithms to generate exam questions, but it is also gamified using behavioral psychology to encourage students to get better results in exam. The reward model behind the generation of the exam is based on the behavioral psychology used in modern video games for a greater involvement of the student.
Lexical bundles academic articles by EAL authors.

With increasing numbers of scholars from around the world now engaged in international publishing to further their careers, many authors for whom English is not their first language worry about the acceptability of their language to journal editors. In this paper we explore this issue by focusing on a key component of fluent academic writing: the high frequency fixed-word collocations known as lexical bundles, strings which are ‘glued together’ and help characterize smooth production. Here we compare their use in the pre-submission drafts of authors with different first languages with published papers in leading international journals. Our results suggest that language background certainly contributes to the differences between our EAL texts and published papers, but that seniority and discipline also significantly impact these language choices.
일본어 ‘こと/の’ 절과 한국어 ‘것’ 절의 대조 연구

The purpose of this study is to contrast Japanese ‘koto/no’ and Korean ‘kes’ clauses in order to clarify their similarities and differences. ‘koto/no’ in Japanese ‘koto/no’ clause and ‘kes’ in Korean ‘kes’ clause, they serve as complementizers to form embedded noun clauses in Japanese and Korean. Since these are originally dependent nouns, we first analyze the features of the dependent nouns ‘koto/no’ and ‘kes’. Secondly, we contrast the ‘koto/no’ embedded noun clauses and the ‘kes’ embedded noun clauses, which are used as obligatory arguments for the verb of a matrix clause. In contrast, there are unusual ‘no’ and ‘kes’ clauses that seem to be embedded noun clause of the verb of a matrix clause in Japanese and Korean, therefore, we contrasted ‘no’ and ‘kes’ clauses. This study contrasts the similarities and differences between the Japanese ‘koto/no’ and the Korean ‘kes’ clauses, which are similar markers of grammatical functions in Korean and Japanese, namely, dependent nouns, which are originally grammaticalized and serve as complementizers to induce clauses. This study will be helpful both to teach Japanese as a foreign language and Korean as a foreign language.(Kyung Hee University)
Iterative attribute augmentation network for face image super resolution

Although recently a series of deep-learning based methods have promoted the performance of face super resolution (FSR), most of these methods cannot recover essential face attributes accurately, especially to super-resolve a very low-resolution (LR) face image ( 16 × 16 pixels) to its ( 8 × ) high-resolution (HR) version. To address this issue, a novel alternating optimisation algorithm to estimate facial attributes and restore facial images in a single network is presented . Specifically, two convolutional neural modules (denoted as Restorer and Corrector) are constructed and these two modules are alternated repeatedly to form an end-to-end trainable network. The Restorer module reconstructs face images based on estimated facial attributes, while the Corrector module corrects the estimated attributes with the help of restored face image. Since Corrector can utilise information from previous estimated attributes and the FSR image, the estimated attributes are iteratively corrected and gradually approach the ground truth. Moreover, a new attribute transformation scheme is designed to introduce attribution information into Restorer, in which the facial attribute vectors as the control conditions can guide the face image restoration explicitly. By conducting extensive experiments on the well-known CelebA dataset, it is demonstrated that the proposed method can provide superior FSR performance in both quantitative and qualitative measurements.
한국어, 중국어, 영어에서의 ‘물’과 관련된 관용표현 연구

This paper analyzes water-related idiomatic expressions in Korean, which were not well dealt with in prior research, by comparing them with Chinese and English, and identified the cultural background underlying the generation of idiomatic expressions. Among the idiomatic expressions related to water, idiomatic expressions created by the idea of water’s liquidity existed in both Korean, Chinese, and English. In addition, idiomatic expressions related to the negative effects of the idea that water can cause natural disasters exist in all three languages. However, there were differences in idiomatic expressions related to this between Asians who showed an attitude of acceptance towards the providence of nature and Westerners who considered nature an object of human exploration. This paper analyzes these differences as images of water appearing in the Tao-Te Ching (道德經) and the Bible that influenced Eastern and Western thought. Most of the water described in Tao-Te Ching is static water, such as in rivers and lakes. On the other hand, water in the Bible is mostly described as ocean water, and water is closely linked to an object and adventure that humans must conquer. This has created a difference in the view of water in Eastern and Western cultures, and this has been reflected in the idiomatic use of language. While analyzing these water-related idiomatic expressions, this paper was able to examine the differences between the common cognitive and cultural backgrounds of humans reflected in language.
Degradasi Metilen Biru Menggunakan Partikel Magnetik Pasir Pantai Padang Sumatera Barat Disintesis Menggunakan Metode Ball Milling 2 Tahap

Synthesis of magnetite nanoparticles (Fe3O4) of beach sand of Padang, WestSumatra using 2-stage ball milling method and its application for the degradingof methylene blue has been done. Identification of the elements in the sample wascarried out using X-Ray Fluorescence (XRF). The morphological properties ofmagnetic nanoparticles were studied using Scanning Electron Microscopy (SEM).The first stage of iron sand was milled using milling balls with a diameter of 1.5cm for 60 hours. Furthermore , ball milling stage 2 was carried out for another30 hours using milling balls with a diameter 1.5 cm and 0.7 cm. The magneticsuscepbility value of determined based on the total magnetic induction value ofthe solenoid which has 2500 turns, 10 cm long and 3 cm diameter. The resultsshowed that the shape of the resulting magnetite nanoparticles is irregular with asize of 1 μm – 300 μm. The particles size increases with decreases milling ballsize. The results also show that magnetite nanoparticles with synthesis using 2stage ball milling have the ability to degrade blue methylene as much as 99.61%within 14 hoursKey Words : natural sand, ball milling, magnetic susceptibility, degradation,(Fe3O4) and methylene blue.
Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial  Attacks

Despite the great successes achieved by deep neural networks (DNNs), recent studies show that they are vulnerable against adversarial examples, which aim to mislead DNNs by adding small adversarial perturbations. Several defenses have been proposed against such attacks, while many of them have been adaptively attacked. In this work, we aim to enhance the ML robustness from a different perspective by leveraging domain knowledge: We propose a Knowledge Enhanced Machine Learning Pipeline (KEMLP) to integrate domain knowledge (i.e., logic relationships among different predictions) into a probabilistic graphical model via first-order logic rules. In particular, we develop KEMLP by integrating a diverse set of weak auxiliary models based on their logical relationships to the main DNN model that performs the target task. Theoretically, we provide convergence results and prove that, under mild conditions, the prediction of KEMLP is more robust than that of the main DNN model. Empirically, we take road sign recognition as an example and leverage the relationships between road signs and their shapes and contents as domain knowledge. We show that compared with adversarial training and other baselines, KEMLP achieves higher robustness against physical attacks, $\mathcal{L}_p$ bounded attacks, unforeseen attacks, and natural corruptions under both whitebox and blackbox settings, while still maintaining high clean accuracy.
Integrating Gender Inclusion in Web Design Courses through Design Workshops

Web design courses are too often focused on developing programming competences but do not take into account that the final product has to be designed to promote inclusiveness, accessibility, and usability issues. Detaching these topics from the course and relegating them to seminars or lectures where students adopt a passive attitude might not help to highlight enough the importance, they have in the design process. In this paper, we present a series of workshops organized as part of a teaching innovation project to make students aware of gender bias in web design through their own analysis. Participants are enrolled in different degrees to ensure a variety both in the demographics and in the points of view. They worked in groups to identify the user personas of three websites about kitchen robots, beers, and toys. The results have shown a growing concern in finding stereotypes and gender bias in the images and texts used. The discussion within groups has helped them in building a common critical conscience and awareness about this problem. Students found the activity interesting not only for the topic and the content but also for the format since cooperating with others and practicing with real examples was beneficial for promoting critical thinking, decision-making, and problem-solving.
Applications of a Stochastic–Fuzzy Approach to Modeling and Optimal Control of Discrete Time Systems by Using Large Scale Data Processing: An advanced Approach

The main purpose of this chapter is to&nbsp; propose&nbsp; a mathematical method to fuzzy control of the stochastic-fuzzy object. The proposed hybrid method uses both the stochastic–fuzzy knowledge base describing the object under control, as well as the large scale data to calculate probabilities of fuzzy conditional statements in the knowledge base. This study includes: the&nbsp; presentation of multistage fuzzy optimization methods with reference to the dynamical discrete systems represented by deterministic, stochastic and fuzzy models (paragraph 2); the proposition of modeling stochastic-fuzzy object under control in the form of knowledge base (paragraph 3). Exemplary calculations illustrate the theoretical description.
북한 조선어교육 교재와 남한 한국어교육 교재 비교 연구 -김형직사범대학교와 세종학당 교재를 중심으로-

The purpose of this study is to confirm the characteristics and the appearance of KFL(Korean as Foreign Language) in North Korea through compare with KFL text books and KFL in North Korean textbooks. To this end, the KFL textbooks were selected for ‘Sejong Korean’, which was published by Sejong Hakdang, and the ‘Korean conversation: Ryuhaksang’ was selected for the KFL in North Korea, published by Kim Hyung-jik Pedagogical University. In the number of units analyzed, ‘Sejong Korean’ was more than ‘Korean conversation: Ryuhaksang’, but the number of dialogues and the pairs of dialogues in each unit were highly frequent in ‘Korean conversation: Ryuhaksang’. In addition, the lexical diversity can be deduced from the highly available ‘Korean conversation: Ryuhaksang’ to provide a more diverse vocabulary in KFL in North Korea than in KFL. However, in the frequency ranking of appearances by parts of speech, it was confirmed that the results of ‘Sejong Korean’ and ‘Korean conversation: Ryuhaksang’ were the same, so that the KFL and KFL in North Korea all formed a textbook focusing on the conversations used in real life. (Yonsei University)
An Ab Initio Local Principal Path Algorithm

We introduce an improved version of the principal path method, an algorithm conceived to find smooth paths between objects in space. Some key steps of the algorithm have been changed, making the solution intrinsically local and preventing it from being attracted by a global manifold. Judiciously performing the initialization step with the Dijkstra algorithm and a proper metric, the functional now only performs a final refinement of the initial solution. Hence the algorithm is stabler as the space of possible solutions has been considerably reduced with respect to the original method. We tested the proposed algorithm in 2D toy data sets (to understand the behaviour) and in high-dimensional data sets. Compared to the previous version of the algorithm, we obtained significantly stabler and more realistic generated samples.
Аналитик и кадр

The setting as a component of psychoanalysis is crucial for development this entire process and becomes a full participant therapeutic actions. It is customary to talk about the value of the setting for the patient. Let's start with the general scientific concepts of postmodernism, relevant to the current scientific paradigm. From the concepts of the anthropic principle, Boltzmann's brain, chaos theories, to move on to more specific ones for psychoanalysis development analyst condition and the effect of such a state on the psychoanalytic process as a whole.
Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts from this grey literature can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by creating the patterns of the security knowledge gained from different sources. Prior studies neither systematically analysed the wide range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Moreover, existing topic modelling methods are not capable of identifying the cybersecurity concepts completely and the generated topics considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories to analyse trending topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings as follows: (1) The impact reflected from cybersecurity texts strongly correlates with the monetary loss caused by cybercrimes, (2) security blogs have produced the context of cybersecurity most intensively, and (3) websites deliver security information without caring about timeliness much.
Cyber Crime during the COVID-19 Pandemic: Key Trends and Ways to Solve the Problem

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An Efficient Multi-Sensor Fusion Protocol in a Vehicle-Road Collaborative System

Nowadays, driving safety has become an important topic in the field of intelligent driving. The key step in the intelligent transportation system (ITS) is to detect and track the vehicles on the road through various sensors equipped on the vehicle. While putting the sensors aside the road can have benefits in the vehicle-road collaborative scenario. In this paper, we propose an efficient and applicable multisensor fusion protocol for a vehicle-road collaborative system where roadside units (RSUs) are equipped with cameras and radars. To fully use the history information and the multisource heterogeneous sensing data to improve the reliability of multi-sensor fusion, we further propose a Bayesian-based two-layer fusion scheme. Moreover, we evaluate the efficiency and robustness of our proposed scheme in a real highway environment in Changsha, China.
Video-Based Inpatient Fall Risk Assessment: A Case Study

Inpatient falls are a serious safety issue in hospitals and healthcare facilities. Recent advances in video analytics for patient monitoring provide a non-intrusive avenue to reduce this risk through continuous activity monitoring. However, in-bed fall risk assessment systems have received less attention in the literature. The majority of prior studies have focused on fall event detection, and do not consider the circumstances that may indicate an imminent inpatient fall. Here, we propose a video-based system that can monitor the risk of a patient falling, and alert staff of unsafe behaviour to help prevent falls before they occur. We propose an approach that leverages recent advances in human localisation and skeleton pose estimation to extract spatial features from video frames recorded in a simulated environment. We demonstrate that body positions can be effectively recognised and provide useful evidence for fall risk assessment. This work highlights the benefits of video-based models for analysing behaviours of interest, and demonstrates how such a system could enable sufficient lead time for healthcare professionals to respond and address patient needs, which is necessary for the development of fall intervention programs.
An Intelligent Management Functionality for In-Vehicle Driving Style Reconfigurations Leveraging on Bayesian Networking Principles

The ceaseless evolution of Information and Communication Technologies (ICT) is reflected on their migration towards the Future Internet (FI) era, which is characterized, among others, by powerful and complex network infrastructures, and innovative applications, services and content. An area of applications that finds prosperous ground in the FI era lies in the world of road transportation and intelligent vehicles. In particular, recent and future ICT findings are envisaged to contribute to the enhancement of transportation efficiency at various levels, such as traffic, parking, safety and emergency management. In this context, the goal of this paper is to introduce an Intelligent Management Functionality (IMF) that enables vehicles to operate each time in the best available Driving Style (DS) by responding quickly to changing driving environment situations and driver’s preferences. Such a functionality aims to operate on the basis of collecting information from various sources, intelligently processing it, integrating knowledge and experience and, finally, selecting the optimal DS. Knowledge is obtained through the exploitation of Bayesian networking principles and Naive-Bayes modeling. Indicative simulation results showcase the effectiveness of the proposed system.