Reviewerly/exHarmony huggingface dataset:
Young Foreign Language Learners’ Engagement and Motivation in Augmented Reality-based Vocabulary Learning

The potential of using augmented reality (AR) to create constructive learning opportunities in second language classrooms has been widely acknowledged. This understanding is based on the claim that ubiquitous learning paradigms, like AR-based instruction, motivate learners to engage in second language learning. To investigate the engagement and motivation of English as a foreign language learners in non-AR and AR-based vocabulary learning conditions, we performed a series of t-tests on a sample of third-, fourth-, fifth-, and sixth-grade students (n = 38, female = 18 students, mean age = 10.4 years old) whose engagement and motivation were assessed using the four scales of flow state (control, attention focus, curiosity, and internal interest). Moreover, a focus group interview was conducted on five students who were engaged in the AR-based instruction. The results of the flow questionnaire and focus group interview indicated that the AR-based instruction fostered higher interest and curiosity among young learners as compared to the non-AR-based instruction. These findings suggest that AR-based interventions designed to enhance young learners’ motivation to learn English as a foreign language can be beneficial.
Linked Open Data Framework for Ethnic Groups in Thailand Learning

The key significant worldview of the Semantic Web is Linked Open Data, another period of the World Wide Web that capacities to carry suggestions to information. An enormous number of both public and private foundations have dis-tributed their information following the Linked Open Data philosophies, or have done as such with information from different associations. To this degree, since the generation and production of Linked Open Data are thorough designing procedures that require high consideration so as to achieve high caliber, and since experience has uncovered that current general guidance is not constantly adequate to be applied to each area, this paper presents a lot of guidance system for creating and distributing Linked Open Data with regards to ethnic groups in Thailand to outside (TEG-LOD Framework). This framework offers an exhaustive depiction of the undertakings to perform, including a rundown of steps, tools that help in accomplishing the errand, different alternatives for achievement of the assignment, and best practices and proposals. Also, this paper exhibits a pilot model on the generation and distribution of Linked Open Data about ethnic groups in Thai-land, adhering to the available guidance, where the ethnic groups in Thailand are the property of the Princess Maha Chakri Sirindhorn Anthropology Center (SAC) have been made and distributed as Linked Open Data.
Notes on Ridge Functions and Neural Networks

These notes are about ridge functions. Recent years have witnessed a flurry of interest in these functions. Ridge functions appear in various fields and under various guises. They appear in fields as diverse as partial differential equations (where they are called plane waves), computerized tomography and statistics. These functions are also the underpinnings of many central models in neural networks. We are interested in ridge functions from the point of view of approximation theory. The basic goal in approximation theory is to approximate complicated objects by simpler objects. Among many classes of multivariate functions, linear combinations of ridge functions are a class of simpler functions. These notes study some problems of approximation of multivariate functions by linear combinations of ridge functions. We present here various properties of these functions. The questions we ask are as follows. When can a multivariate function be expressed as a linear combination of ridge functions from a certain class? When do such linear combinations represent each multivariate function? If a precise representation is not possible, can one approximate arbitrarily well? If well approximation fails, how can one compute/estimate the error of approximation, know that a best approximation exists? How can one characterize and construct best approximations? If a smooth function is a sum of arbitrarily behaved ridge functions, can it be expressed as a sum of smooth ridge functions? We also study properties of generalized ridge functions, which are very much related to linear superpositions and Kolmogorov's famous superposition theorem. These notes end with a few applications of ridge functions to the problem of approximation by single and two hidden layer neural networks with a restricted set of weights. We hope that these notes will be useful and interesting to both researchers and students.
Development and testing of an FPT.AI-based voicebot

In recent years, voicebot has become a popular communication tool between humans and machines. In this paper, we will introduce our voicebot integrating text-to-speech (TTS) and speech-to-text (STT) modules provided by FPT.AI. This voicebot can be considered as a critical improvement of a typical chatbot because it can respond to human’s queries by both text and speech. FPT Open Speech, LibriSpeech datasets, and music files were used to test the accuracy and performance of the STT module. For the TTS module, it was tested by using text on news pages in both Vietnamese and English. To test the voicebot, Homestay Service topic questions and off-topic messages were input to the system. The TTS module achieved 100% accuracy in the Vietnamese text test and 72.66% accuracy in the English text test. In the STT module test, the accuracy for FPT open speech dataset (Vietnamese) is 90.51% and for LibriSpeech Dataset (English) is 0% while the accuracy in music files test is 0% for both. The voicebot achieved 100% accuracy in its test. Since the FPT.AI STT and TTS modules were developed to support only Vietnamese for dominating the Vietnam market, it is reasonable that the test with LibriSpeech Dataset resulted in 0% accuracy.
Improved Bounds for Induced Poset Saturation

Given a finite poset $\mathcal{P}$, a family $\mathcal{F}$ of elements in the Boolean lattice is induced-$\mathcal{P}$-saturated if $\mathcal{F}$ contains no copy of $\mathcal{P}$ as an induced subposet but every proper superset of $\mathcal{F}$ contains a copy of $\mathcal{P}$ as an induced subposet. The minimum size of an induced-$\mathcal{P}$-saturated family in the $n$-dimensional Boolean lattice, denoted $\mathrm{sat}^*(n,\mathcal{P})$, was first studied by Ferrara et al. (2017).&#x0D; Our work focuses on strengthening lower bounds. For the 4-point poset known as the diamond, we prove $\mathrm{sat}^*(n,\Diamond)\geq\sqrt{n}$, improving upon a logarithmic lower bound. For the antichain with $k+1$ elements, we prove $$\mathrm{sat}^*(n,\mathcal{A}_{k+1})\geq \left(1-\frac{1}{\log_2k}\right)\frac{kn}{\log_2 k}$$ for $n$ sufficiently large, improving upon a lower bound of $3n-1$ for $k\geq 3$.
Server-aided immediate and robust user revocation mechanism for SM9

Abstract As the only approved Identity-Based Encryption scheme in China that is also standardized by ISO, SM9-IBE has been widely adopted in many real-world applications. However, similar to other IBE standard algorithms, SM9-IBE currently lacks revocation mechanism, which is vital for a real system. Worse still, we find that existing revocable techniques may not be suitable and efficient when applying to SM9-IBE. Given the widespread use of SM9-IBE, an efficient and robust user revocation mechanism becomes an urgent issue.In this work, we propose a dedicated server-aided revocation mechanism, which for the first time achieves the secure, immediate and robust user revocation for SM9-IBE. Provided with a compact system model, the proposed method leverages an existing server to perform all heavy workloads during user revocation, thus leaving no communication and computation costs for the key generation center and users. Moreover, the mechanism supports key-exposure resistance, meaning the user revocation mechanism is robust even if the revocation key leaks. We then formally define and prove the security. At last, we present theoretical comparisons and an implementation in terms of computational latency and throughput. The results indicate the efficiency and practicability of the proposed mechanism.
Implementation of a bit permutation-based advanced encryption standard for securing text and image files

&lt;span&gt;The paper proposes a modification of the Advanced Encryption Standard (AES) to address its high computational requirement steaming from the complex mathematical operations in the MixColumns Transformation which makes the encryption process slow. Bit Permutation was used instead of the MixColumns Transformation since the use of bit permutation in an encryption algorithm achieves efficiency by providing minimum encryption time and memory requirement. Results of the study showed that the modified AES algorithm exhibited faster encryption by 18.47% and faster decryption by 18.77% for text files. The modified AES algorithm also resulted to 16.53% higher avalanche effect compared with the standard AES thus improving the security performance. Application of the modified AES in encrypting images in Cipher Block Chaining mode showed that the modified algorithm also exhibited 16.88% faster encryption and 11.96% decryption compared with the standard AES. Likewise, modifying the algorithm achieved the ideal result in the histogram analysis, information entropy, the correlation coefficient of adjacent pixels to resist statistical attack. The ideal value in number of pixels change rate and unified average change intensity were also achieved making the modified algorithm resistant to differential attack. These results show that modifying AES by using bit permutation to replace MixColumns Transformation was able to address the high computational requirement of the algorithm resulting in a faster and more secure encryption algorithm for text files and images&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;
Semantic Comprehension of Questions in Q&amp;A System for Chinese Language Based on Semantic Element Combination

Question understanding is an extremely important part of the question and answer (Q&A) system, which is the basis of subsequent information retrieval and answer extraction.In order to improve the semantic understanding of question, we propose a method of understanding the question based on the combination of semantic element for Chinese language.Firstly, the method uses lexical and rules recognition to extract the semantic elements of questions and recognizes the functions according to the preprocessing pattern.Secondly, combining the dependency analysis tree structure of the question and the function type, it can produce the semantic elements.Finally, a normalized semantic expression was generated.We extract the user questions in Baidu-Zhidao as the test set.The result shows that the accuracy of the proposed method is 97.8%, so the semantics of the Chinese language questions can be effectively understood by this method.
Reflections on Eight Years of Instrument Creation with Machine Learning

Machine learning (ML) has been used to create mappings for digital musical instruments for over twenty-five years, and numerous ML toolkits have been developed for the NIME community. However, little published work has studied how ML has been used in sustained instrument building and performance practices. This paper examines the experiences of instrument builder and performer Laetitia Sonami, who has been using ML to build and refine her Spring Spyre instrument since 2012. Using Sonami’s current practice as a case study, this paper explores the utility, opportunities, and challenges involved in using ML in practice over many years. This paper also reports the perspective of Rebecca Fiebrink, the creator of the Wekinator ML tool used by Sonami, revealing how her work with Sonami has led to changes to the software and to her teaching. This paper thus contributes a deeper understanding of the value of ML for NIME practitioners, and it can inform design considerations for future ML toolkits as well as NIME pedagogy. Further, it provides new perspectives on familiar NIME conversations about mapping strategies, expressivity, and control, informed by a dedicated practice over many years.
Strengthening steganoghraphy by using crow search algorithm of fingerprint image

In image steganography, secret communication is implemented to hide secret information into the cover image (used as the carrier to embed secret information) and generate a stego-image (generated image carrying hidden secret information).Nature provides many ideas for computer scientists. One of these ideas is the orderly way in which the organisms work in nature when they are in groups. If the group itself is treated as an individual (the swarm), the swarm is more intelligent than any individual in the group. Crow Search Algorithm (CSA) is a meta-heuristic optimizer where individuals emulate the intelligent behavior in a group of crows. It is based on simulating the intelligent behavior of crow flocks and attempts to imitate the social intelligence of a crow flock in their food gathering process. This paper presents a novel meta-heuristic approach based on the Crow Search Algorithm (CSA), where at the beginning the color cover image is converted into three channels (RGB) and then those channels are converted into three spaces, which are Y, Cb, Cr. After applying Discrete wavelet transform (DWT) on each space separately, the CSA algorithm is used on each space (YCbCr) to find the best location that will be used to hide secret information, the CSA is used to increase the security force by finding the best locations that have high frequency and are invulnerable to attacks, the DWT is used to increase robustness against noise. The proposed system is implemented on three fingerprint cover images for experiments, for the quality of stego image the histogram, Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Number of Pixel Change Rate Test (NPCR), Structural Similarity Index Metric (SSIM) and Correlation Coefficients (CC) are computed. The result demonstrated the strength of the CSA to hide data, also discovered that using CSA may lead to finding favorable results compared to the other algorithms
Simple Shading Correction Method for Brightfield Whole Slide Imaging

Whole slide imaging (WSI) refers to the process of creating a high-resolution digital image of a whole slide. Since digital images are typically produced by stitching image sequences acquired from different fields of view, the visual quality of the images can be degraded owing to shading distortion, which produces black plaid patterns on the images. A shading correction method for brightfield WSI is presented, which is simple but robust not only against typical image artifacts caused by specks of dust and bubbles, but also against fixed-pattern noise, or spatial variations in pixel values under uniform illumination. The proposed method comprises primarily of two steps. The first step constructs candidates of a shading distortion model from a stack of input image sequences. The second step selects the optimal model from the candidates. The proposed method was compared experimentally with two previous state-of-the-art methods, regularized energy minimization (CIDRE) and background and shading correction (BaSiC) and showed better correction scores, as smooth operations and constraints were not imposed when estimating the shading distortion. The correction scores, averaged over 40 image collections, were as follows: proposed method, 0.39 ± 0.099; CIDRE method, 0.67 ± 0.047; BaSiC method, 0.55 ± 0.038. Based on the quantitative evaluations, we can confirm that the proposed method can correct not only shading distortion, but also fixed-pattern noise, compared with the two previous state-of-the-art methods.
Path Tracing Denoising Based on SURE Adaptive Sampling and Neural Network

A novel reconstruction algorithm is presented to address the noise artifacts of path tracing. SURE (Stein's unbiased risk estimator) is adopted to estimate the noise level per pixel that guides adaptive sampling process. Modified MLPs (multilayer perceptron) network is used to predict the optimal reconstruction parameters. In sampling stage, coarse samples are firstly generated. Then each noise level is estimated with SURE. Additional samples are distributed to the pixels with high noise level. Next, we extract a few features from the results of adaptive sampling used for the subsequent reconstruction stage. In reconstruction stage, modified MLPs network is adopted to model a complex relationship between extracted features and optimal reconstruction parameters. An anisotropic filter is used to reconstruct the final images with the parameters predicted by neural networks. Compared to the state-of-the-art methods, experiment results demonstrate that our algorithm performs better than other methods in numerical error and visual image quality.
EFFECTIVENESS OF CRITICAL MULTILITERATION MODEL WITH RADEC MODEL ON THE ABILITY OF WRITING EXPLANATORY TEXT

Abstract: Developing students' skill in writing scientific explanation is one of the main goals in education. The purpose of this study is to determine the effectiveness of the critical multi-literation model and the RADEC model on the ability in writing explanatory text of elementary school students. This research is conducted on 5th grade students of SDN 122 Cijawura, Buah Batu, Bandung. The samples used in this study are 39 students in the experimental class 1 that used the critical multi-literation model and 38 students in the experimental class 2 that used the RADEC model. The research method used in this study is a quasi-experimental method with a non-equivalent pre-test and post-test control group design. Based on the results of the study, it is found that the pre-test score in the experimental class 1 is 36.6 and the post-test result is 78. Whereas in the experimental class 2 the pre-test score obtained is 48.1 and the post-test score is 68.9. Furthermore, from the results of the independent sample test, the results of the post-test mean difference test are sig = 0.018, so it can be concluded that there are significant differences in the ability to write explanatory text of students between the experimental class 1 and the experimental class 2. Based on these results, the multi-literation model critical is more effective than the RADEC model in developing the ability in writing explanatory text in elementary school students.
Investigating Word-Class Distributions in Word Vector Spaces

This paper presents an investigation on the distribution of word vectors belonging to a certain word class in a pre-trained word vector space.To this end, we made several assumptions about the distribution, modeled the distribution accordingly, and validated each assumption by comparing the goodness of each model.Specifically, we considered two types of word classes -the semantic class of direct objects of a verb and the semantic class in a thesaurus -and tried to build models that properly estimate how likely it is that a word in the vector space is a member of a given word class.Our results on selectional preference and WordNet datasets show that the centroid-based model will fail to achieve good enough performance, the geometry of the distribution and the existence of subgroups will have limited impact, and also the negative instances need to be considered for adequate modeling of the distribution.We further investigated the relationship between the scores calculated by each model and the degree of membership and found that discriminative learning-based models are best in finding the boundaries of a class, while models based on the offset between positive and negative instances perform best in determining the degree of membership.
ChartDialogs: Plotting from Natural Language Instructions

This paper presents the problem of conversational plotting agents that carry out plotting actions from natural language instructions.To facilitate the development of such agents, we introduce CHARTDIALOGS, a new multi-turn dialog dataset, covering a popular plotting library, matplotlib.The dataset contains over 15, 000 dialog turns from 3, 200 dialogs covering the majority of matplotlib plot types.Extensive experiments show the bestperforming method achieving 61% plotting accuracy, demonstrating that the dataset presents a non-trivial challenge for future research on this task.
Differentially Private Stochastic Coordinate Descent

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this vector suffices to hide critical information about individuals, stochastic coordinate descent crucially relies on keeping auxiliary information in memory during training. This auxiliary information provides an additional privacy leak and poses the major challenge addressed in this work. Driven by the insight that under independent noise addition, the consistency of the auxiliary information holds in expectation, we present DP-SCD, the first differentially private stochastic coordinate descent algorithm. We analyze our new method theoretically and argue that decoupling and parallelizing coordinate updates is essential for its utility. On the empirical side we demonstrate competitive performance against the popular stochastic gradient descent alternative (DP-SGD) while requiring significantly less tuning.
An Experience Aggregative Reinforcement Learning With Multi-Attribute Decision-Making for Obstacle Avoidance of Wheeled Mobile Robot

A variety of reinforcement learning (RL) methods are developed to achieve the motion control for the robotic systems, which has been a hot issue. However, the performance of the conventional RL methods often encounters a bottleneck, because the robots have difficulty in choosing an appropriate action in the control task due to the exploration-exploitation dilemma. To address this problem and improve the learning performance, this work introduces an experience aggregative reinforcement learning method with a Multi-Attribute Decision-Making (MADM) to achieve the real-time obstacle avoidance of wheeled mobile robot (WMR). The proposed method employs an experience aggregation method to cluster experiential samples and it can achieve more effective experience storage. Moreover, to achieve the effective action selection using the prior experience, an action selection policy based on a Multi-Attribute Decision-Making is proposed. Inspired by the hierarchical decision-making, this work decomposes the original obstacle avoidance task into two sub-tasks using a divide-and-conquer approach. Each sub-task is trained individually by a double Q-learning using a simple reward function. Each sub-task learns an action policy, which enables the sub-task to selects an appropriate action to achieve a single goal. The standardized rewards of sub-tasks are calculated when fusing these sub-tasks to eliminate differences in rewards for sub-tasks. Then, the proposed method integrates the prior experience of three trained sub-tasks via an action policy based on a MADM to complete the source task. Simulation results show that the proposed method outperforms competitors.