Reviewerly/exHarmony huggingface dataset:
"A Study on the Chinese Correspondence Expression of ‘-Deoni’ –Focusing on the Analysis of Parallel Corpus in Korean–Chinese Drama–"

The purpose of this study is to analyze the usage patterns of ‘-Deoni’ and Chinese counterparts using a parallel corpus of Korean- Chinese dramas built around the Korean language, and to suggest an educational plan for ‘-Deoni’ for Chinese learners. According to the result of the corpus analysis, five semantic functions of ‘-Deoni’ were analyzed: ‘contrast, trigger, supplement, causal, and evidence’. Syntactically, only declarative sentences and interrogative sentences appeared for ‘-Deoni’, and we looked the restrictions on the use of personal pronouns of ‘-Deoni’, which are used with the first personal pronouns, but it was difficult to see as a generalized usage because it was not frequent and received many restrictions. Lastly, the Chinese expression corresponding to ‘-Deoni’ was specifically reviewed and presented with its meaning and usage restrictions. It was confirmed that there was no Chinese expression corresponding to ‘-Deoni’ or that it appeared in various ways depending on the situation. Therefore, in Korean grammar education, it is suggested that the semantic and syntactic characteristics of ‘-Deoni’ should be considered, and the context of the situation used in the conversation should also be reviewed and presented to learners. Korean grammar education for Chinese learners should change the method of mechanically presenting Chinese expressions and contrasting them with Korean to consider the characteristics of Chinese expressions corresponding to Korean and to discuss them in detail by combining semantic functions and applied context. (Korea University)
"Analysis of Teacher Speech in Prospective Korean Teachers’ Class –Through the Comparison between Teacher Speech in the Lesson Plan and Mock Class–"

This study attempted to examine the relationship between teacher speech realized in the lesson plan and the teacher speech realized in the actual class by analyzing the teacher speech shown in the mock class of prospective Korean teachers. This study is meaningful in that it attempted to collect and analyze teacher speech in actual classes, and in particular, to reveal how teacher speech in the lesson plan is adjusted in the actual class. The types of linguistic modification shown in teacher speech of prospective Korean teachers were analyzed as first, when they spoke content that was not in the lesson plan, second, when they did not speak content in the lesson plan, and third, when they modified the contents of the lesson plan. Based on the analysis results, education plans for prospective Korean teachers were proposed in three aspects: lesson plan education, teacher speech education, and instructional diary writing education. The analysis results of this study are expected to help prospective Korean teachers write the lesson plan as perfectly as possible and realize the contents of the lesson plan in the actual class, thereby helping to realize modified and appropriate teacher speech. (Kyungpook National University)
Effects of immersion and navigation agency in virtual environments on emotions and behavioral intentions

We present a study investigating the question whether and how people’s intention to change their environmental behavior depends on the degrees of immersion and freedom of navigation when they experience a deteriorating virtual coral reef. We built the virtual reef on top of a biologically sound model of the ecology of coral reefs, which allowed us to simulate the realistic decay of reefs under adverse environmental factors. During their experience, participants witnessed those changes while they also explored the virtual environment. In a two-factorial experiment ( N = 224), we investigated the effects of different degrees of immersion and different levels of navigation freedom on emotions, the feeling of presence, and participants’ intention to change their environmental behavior. The results of our analyses show that immersion and navigation have a significant effect on the participants’ emotions of sadness and the feeling of helplessness. In addition, we found a significant effect, mediated by the participants’ emotions, on the intention to change their behavior. The most striking result is, perhaps, that the highest level of immersion combined with the highest level of navigation did not lead to the highest intentions to change behavior. Overall, our results show that it is possible to raise awareness of environmental threats using virtual reality; it also seems possible to change people’s behavior regarding these threats. However, it seems that the VR experience must be carefully designed to achieve these effects: a simple combination of all affordances offered by VR technology might potentially decrease the desired effects.
Structural modelling algorithms with faults in RN-GEOSIM software

Defining distance is crucial for morir-ling geological properties with geostat.istics.However, geological structures are generally deformed, making the usual Euclidean distance inappropriate for applying geostatistics.Considering this, the GeoChron theory maps geological models into a regular chronostratigraphic space, where deformations (especially those due to both faults and folds) have been removed [Mallet, 2004].Three curvilinear coordinates are used for this mapping, among which a time parameter, inspired from H. E. Wheeler's work, and two paleogeographic coordinates corresponding to the location of each particle at deposition time.To-date, the Geochron theory has been implemented by Moyen and Mallet [2004], Jayr et al. [2008], as a global optimization method which computes the three coordinates from chronostratigraphic interpretations.In this work, we propose instead to use sequential geomechanical restoration to compute paleogeographic coordinates.Geomechanical restoration is a way to infer the original position of a horizon taking rock physics into account.Each layer is restored into depositional state, which provides the paleogeographic coordinates of its hanging wall.These parameters are then propagated within the layer.Several methods, depending on the assumed hypotheses for deformation mode during syntectonic deposition were investigated.These methods are compared on a synthetic case.Doing so, it is possible to capitalize on restoration efforts to build a geochron parameterization, not only dependent on geometric criteria but also on rock rheology and on the deformation path inferred from the sedimentary record.
Need for Cybersecurity Solutions in IoT Systems

People are living in the world where billions of IoT systems are connected to the Internet. They are sending and receiving data via network, and are controlled remotely. Many IoT systems do not have complex tasks to perform or to control. Because of that, they are built on cheap and weak hardware. Sometimes there are not enough resources to implement suitable, high-quality protection mechanism. The simplicity of the IoT system and its task lead the developer to leave the system unprotected. In this paper, the authors present how easy is to attack unprotected and protected systems. In three examples, they use free tools from Kali Linux to take control or disable systems. The know-how is also available through YouTube videos, thus any person with basic level of ICT skills can become a potential attacker or hacker. The goal of this article is to highlight the importance of protecting the IoT system, because by controlling even some simply process, the hackers can cause considerable damage.
Challenges for the Energy Sector in the Light of Climate Change

Due to the heatwave of past few summer that influenced almost the entire territory of Europe, some reactors of nuclear power stations had to be closed down temporarily in France. This case is not unique and according to the predictions of the specialists, in the future, the times when due to the rise of the air temperature, similar measures will have to be introduced at the time when the need of the population for energy is elevated. A possible stoppage can produce serious consequences. The process of the energy production and thus the whole energy sector is of critical importance that has a specific situation due to the fact that the energy dependence of all other critical infrastructure elements is important. This reliance on the energy can be extended to all areas of life. The production and the energy need of the population have been rising. We need to face the fact that while the energy hunger of the mankind is rising, it goes hand in hand with the narrowing of the possibilities of economic acquisition of energy and with the effect of some energy production methods on the climate. In the long run the energy production needs to be sustainable and at the same time, economical. At the same time the long time climate change will influence the above wishes.
Improving Image Pair Selection for Large Scale Structure from Motion by Introducing Modified Simpson Coefficient

Selecting visually overlapping image pairs without any prior information is an essential task of large-scale structure from motion (SfM) pipelines. To address this problem, many state-of-the-art image retrieval systems adopt the idea of bag of visual words (BoVW) for computing image-pair similarity. In this paper, we present a method for improving the image pair selection using BoVW. Our method combines a conventional vector-based approach and a set-based approach. For the set similarity, we introduce a modified version of the Simpson (m-Simpson) coefficient. We show the advantage of this measure over three typical set similarity measures and demonstrate that the combination of vector similarity and the m-Simpson coefficient effectively reduces false positives and increases accuracy. To discuss the choice of vocabulary construction, we prepared both a sampled vocabulary on an evaluation dataset and a basic pre-trained vocabulary on a training dataset. In addition, we tested our method on vocabularies of different sizes. Our experimental results show that the proposed method dramatically improves precision scores especially on the sampled vocabulary and performs better than the state-of-the-art methods that use pre-trained vocabularies. We further introduce a method to determine the k value of top-k relevant searches for each image and show that it obtains higher precision at the same recall.
Highly-Accurate and Real-Time Speech Measurement for Laser Doppler Vibrometers

Laser Doppler Vibrometers (LDVs) enable the acquisition of remote speech signals by measuring small-scale vibrations around a target. They are now widely used in the fields of information acquisition and national security. However, in remote speech detection, the coherent measurement signal is subject to environmental noise, making detecting and reconstructing speech signals challenging. To improve the detection distance and speech quality, this paper proposes a highly accurate real-time speech measurement method that can reconstruct speech from noisy coherent signals. First, the I/Q demodulation and arctangent phase discrimination are used to extract the phase transformation caused by the acoustic vibration from coherent signals. Then, an innovative smoothness criterion and a novel phase difference-based dynamic bilateral compensation phase unwrapping algorithm are used to remove any ambiguity caused by the arctangent phase discrimination in the previous step. This important innovation results in the highly accurate detection of phase jumps. After this, a further innovation is used to enhance the reconstructed speech by applying an improved waveform-based linear prediction coding method, together with adaptive spectral subtraction. This removes any impulsive or background noise. The accuracy and performance of the proposed method were validated by conducting extensive simulations and comparisons with existing techniques. The results show that the proposed algorithm can significantly improve the measurement of speech and the quality of reconstructed speech signals. The viability of the method was further assessed by undertaking a physical experiment, where LDV equipment was used to measure speech at a distance of 310m in an outdoor environment. The intelligibility rate for the reconstructed speech exceeded 95%, confirming the effectiveness and superiority of the method for long-distance laser speech measurement.
BCGL: Binary Classification-Based Graph Layout

Graph layouts reveal global or local structures of graph data. However, there are few studies on assisting readers in better reconstructing a graph from a layout. This paper attempts to generate a layout whose edges can be reestablished. We reformulate the graph layout problem as an edge classification problem. The inputs are the vertex pairs, and the outputs are the edge existences. The trainable parameters are the laid-out coordinates of the vertices. We propose a binary classification-based graph layout (BCGL) framework in this paper. This layout aims to preserve the local structure of the graph and does not require the total similarity relationships of the vertices. We implement two concrete algorithms under the BCGL framework, evaluate our approach on a wide variety of datasets, and draw comparisons with several other methods. The evaluations verify the ability of the BCGL in local neighborhood preservation and its visual quality with some classic metrics.
On the intermediate asymptotic efficiency of goodness-of-fit tests in multinomial distributions

We consider goodness-of-fit tests for uniformity of a multinomial distribution by means of tests based on a class of symmetric statistics, defined as the sum of some function of cell-frequencies. We are dealing with an asymptotic regime, where the number of cells grows with the sample size. Most attention is focused on the class of power divergence statistics. The aim of this article is to study the intermediate asymptotic relative efficiency of two tests, where the powers of the tests are asymptotically non-degenerate and the sequences of alternatives converge to the hypothesis, but not too fast. The intermediate asymptotic relative efficiency of the χ 2 test wrt an arbitrary symmetric test is considered in details.
A Personalized Recommendation Method for Ethnic Music Teaching Resources Based on Video Tags

In order to improve the effect of ethnic music teaching and improve the recommendation accuracy of teaching resources, this paper proposes a personalized recommendation method for ethnic music teaching resources based on video tags. A national music teaching resource library is constructed as the resource source, and the K-nearest neighbor (KNN) algorithm is used to classify the teaching resources in the resource library. According to the results of resource classification, describe the characteristics of teaching resources, so as to recommend suitable teaching resources to corresponding students to meet individual needs. According to the main characteristics of tags, a tagging system is established, and at the same time, the decay of user interest over time is considered, and weights are assigned to user video interest tags in descending order of time, and the total user interest tag vector is calculated. In this paper, the obtained user total interest label vector is normalized. The vector space model is used to represent teaching resources and user interests, so as to realize personalized recommendation of teaching resources. The experimental results show that the accuracy rate of the proposed method is higher than 90%, the recall rate is more than 97%, and the F1 value is larger, and the convergence is better, reflecting the better recommendation effect of the method.
Neural Options Pricing

This research investigates pricing financial options based on the traditional martingale theory of arbitrage pricing applied to neural SDEs. We treat neural SDEs as universal It\^o process approximators. In this way we can lift all assumptions on the form of the underlying price process, and compute theoretical option prices numerically. We propose a variation of the SDE-GAN approach by implementing the Wasserstein distance metric as a loss function for training. Furthermore, it is conjectured that the error of the option price implied by the learnt model can be bounded by the very Wasserstein distance metric that was used to fit the empirical data.
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements

Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in today's software ecosystem, where vulnerable code can flow easily and unwittingly within and across software repositories like GitHub. Across such millions of lines of code, traditional static and dynamic approaches struggle to scale. Although existing machine-learning-based approaches look promising in such a setting, most work detects vulnerable code at a higher granularity -- at the method or file level. Thus, developers still need to inspect a significant amount of code to locate the vulnerable statement(s) that need to be fixed. This paper presents VELVET, a novel ensemble learning approach to locate vulnerable statements. Our model combines graph-based and sequence-based neural networks to successfully capture the local and global context of a program graph and effectively understand code semantics and vulnerable patterns. To study VELVET's effectiveness, we use an off-the-shelf synthetic dataset and a recently published real-world dataset. In the static analysis setting, where vulnerable functions are not detected in advance, VELVET achieves 4.5x better performance than the baseline static analyzers on the real-world data. For the isolated vulnerability localization task, where we assume the vulnerability of a function is known while the specific vulnerable statement is unknown, we compare VELVET with several neural networks that also attend to local and global context of code. VELVET achieves 99.6% and 43.6% top-1 accuracy over synthetic data and real-world data, respectively, outperforming the baseline deep-learning models by 5.3-29.0%.
Deep Correlation based Concept Recommendation for MOOCs

The current course recommendation in massive open online courses (MOOCs) usually ignores students' interests in some certain type of knowledge concepts, resulting in low completion of most courses.Therefore, it requires a concept recommendation to help students accurately choose courses in MOOCs.In this paper, we propose Deep Correlation based Concept Recommendation (DCCR) for MOOCs.It gathers the interactive information obtained by different entities through meta-paths in MOOCs and extracts the semantic information of concepts.To deeply capture the correlation information among users, a multi-relation graph is built to generate the correlation features which aggregates the abundant information under different meta-paths.Then through the graph convolutional neural networks, entity embeddings of users and knowledge concepts are generated.Additionally, a concatenation-based fusion function is designed to get the final joint representations reasonably.By verifying on two public datasets, experiments show that DCCR outperforms the state-of-the-art methods.
DEVELOPMENT OF MOODLE-BASED E-MODULES ON MOTION SYSTEMS FOR ELEVENTH GRADE STUDENTS OF SENIOR HIGH SCHOOL

This study aims to develop e-module teaching materials with the Moodle application on motion system materials. This e-module with the Moodle application has several advantages, namely learning is more interesting and can be done remotely where educators can still control learning and assessment activities, there are discussion forums and quizzes and has security features limiting access rights where unregistered users cannot open e- module. This research is a type of research and development (R&amp;D). The procedure for developing this research uses the ADDIE development model, where this development model consists of 5 stages, namely Analysis (analysis), Design (design), Development (development), Implementation (implementation) and Evaluation (evaluation). The research instrument was a validation sheet and a respondent's questionnaire. The media validation stage involved 5 subjects (1 media expert, 1 material expert, 1 linguist, 1 pedagogic expert, and 1 teacher). Feasibility assessment by experts using validation sheets. The practical test phase involved 5 people, the simulation test involved 5 master students at the University of Riau majoring in biology and teachers and the limited trial involved 40 students. The data analysis technique is to calculate the percentage of validation assessment scores and user responses. The results showed that the Moodle- based e-module developed met the very valid criteria with an average of 3.54, the practical aspect showed very practical with an average of 94.54, and the results of the student response test obtained a score of 96.4 with a very good category good. Based on the description above, it is concluded that the Moodle-based e- module that was developed can be a source of learning/teaching materials that can be used during learning because with the Moodle-based e-module the material presented is more interesting, educators can include videos, animations, pictures and learning can also be done remotely where educators can still control and evaluate students.
Dual Contrastive Learning for Unsupervised Knowledge Selection

Although dialogue systems based on the Seq2Seq model have achieved success, they suffer from tending to generate general responses.Recent works have shown that selecting external knowledge is helpful for dialogue systems to generate informative and diverse responses.However, selecting appropriate knowledge from an unlabeled knowledge set, which is referred to as unsupervised knowledge selection, remains a tricky challenge.Therefore, we propose a dual contrastive method, which utilizes two source-target pairs which are based on the same knowledge set to construct dual contrasts.Specifically, for a source utterance, we consider its paired and unpaired target response as a positive and negative sample, then obtain the positive and negative posterior distribution over the knowledge candidates set, respectively.Then we lead the prior distribution to be close to the positive posterior distribution and distant from the negative one.Similarly, the posterior distribution is treated with the same criterion.Experimental results show that our method improves generated responses in terms of BLUE, DISTINCT, and knowledge utilization.Our codes are available at https://github.com/CaoXiang1997/DualCL4UKS.
Enhancing Pre-Trained Language Representations Based on Contrastive Learning for Unsupervised Keyphrase Extraction

Keyphrase extraction (KPE) aims to obtain a set of phrases from a document that can summarize the main content of the document.Recently, pre-trained language models (LMs), especially BERT and ELMo, have achieved remarkable success, presenting new state-of-the-art results in unsupervised KPE.However, current pre-trained LMs focus on building language modeling objectives to learn a general representation, ignoring the keyphrase-related knowledge.Intuitively, the joint embedding of the keyphrase set should tend to be close to that of the extracted document, and far from those of other documents.In this work, we propose a contrastive learning-based semantic representation task to further improve BERT for unsupervised KPE.Particularly, we design a doc-phrase attention module to generate joint semantic embedding of the keyphrase set as a positive sample and select other semantically similar documents as hard negative samples.In the prediction layer, we further add an accumulated self-attention module to calculate the final scores of candidate phrases.We compare with eight strong baselines, and evaluate our model on three publicly available datasets.Experimental results show that our model is effective and robust on both long and short documents.