Reviewerly/exHarmony huggingface dataset:
AI and Blockchain: potential and challenge for building a smart E-Learning system in Vietnam

Abstract Smart E-Learning is an education form, in which information technology plays an important role. Smart E-Learning utilizes the achievements of modern technologies such as artificial intelligence (AI). So, it can help to gain the ability of connectivity, sharing, convenience, and flexibility for learners. All activities including teaching, learning, discussing, testing, and grading, are well done over the internet environment. However, some potential risks can cause participants, especially, about data privacy. The issue is a big challenge for developing applications for smart E-Learning. Therefore, the goal to combine E-Learning with Blockchain technology to improve security for smart E-Learning is necessary. In this paper, we discuss potential and challenge for applying AI and Blockchain for developing applications for smart E-Learning in Vietnam. We also propose to combine independent testing models integrated with AI and Blockchain into the same smart E-Learning system.
Develop an Android-Based Learning Media Integrated with a Scientific Approach to the Colligative Solution's Nature

This study aimed to develop an Android-based learning media integrated with a scientific approach to the colligative solution's nature. This study uses the Research and Development (R &amp; D) method with the ADDIE model development stages. The population of the study was all students of class XII SMA using the 2013 curriculum. The sample was class XII SMA Swasta Al - Azhar Medan. The validation of learning media based on Android integrated scientific approach by material experts and media experts obtained a feasibility score of 4.4 and 4.8, respectively. In contrast, the feasibility score by two chemistry teachers received an average value of 4.7. The validation of Android-based learning media integrated with the scientific approach shows that the average score is 4.6, which means that the learning media used has very feasible criteria. Android-based learning media products integrated with the scientific approach were tested on 26 students of class XII at Al-Azhar Medan Private High School. The results showed that the learning media that had been compiled could be used as learning media for the colligative solution's nature.
Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes

The WHO announced that more than 52 million people tested positive for Covid-19, and 1.2 million died in the second week of November 2020. Meanwhile, Indonesia recorded 463 thousand individuals with 15,148 deaths that were confirmed positive. Strategy against pandemics by incorporating socialization. However, learning that was initially bold as a technique became controversial due to the briefness of the adaptation process. a wide continuum of social reactions has resulted in the sudden transition from face-to-face learning to bold learning on a large scale. This research focuses on public opinion on online learning during the Indonesian COVID-19 pandemic in early November 2020. The analysis was carried out on Twitter by mining document-based text that was interpreted using the Naïve Bayes algorithm. The results show that online learning has a positive sentiment of 30 percent, a negative sentiment of 69 percent, and a neutral 1 percent over the period. Due to community dissatisfaction about online learning, a significant amount of negative sentiment is created. Some tweets indicate disappointment with the words' stress 'and' lazy 'in the conversation being high-frequency words.
University Students' Perception on the Verbal Interaction through WhatsApp Chat Group

Verbal interactions will occur when both the teacher/lecturer and learners could completely understand what each other means. It happens verbally among the teacher and learners during the teaching learning activity (Mardiana et al., 2019). This study was mainly conducted to examine the University students’ perception on verbal interaction through WhatsApp chat group. This study used qualitative descriptive method was used to achieve the objective of this study. Observation and a questionnaire were adopted as tools for data collection. A total number of samples was 14 English as a Foreign Language learners in university students. The analysis of the data revealed that university students’ perception on verbal interaction gave the positive feedback towards WhatsApp chat group. This proves that ordinarily learners selected agree with utilizing WhatsApp chat group in doing verbal interaction. Hence, utilizing of WhatsApp chat group can be recommended as an efficient technique in verbal interaction for teaching and learning process.
Window‐aware guided image filtering via local entropy

Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over-smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window-aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non-box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity-similar strategy, the local non-box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non-box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.
Image Semantic Segmentation Use Multiple-Threshold Probabilistic R-CNN with Feature Fusion

With continuous developments in deep learning, image semantic segmentation technology has also undergone great advancements and been widely used in many fields with higher segmentation accuracy. This paper proposes an image semantic segmentation algorithm based on a deep neural network. Based on the Mask Scoring R-CNN, this algorithm uses a symmetrical feature pyramid network and adds a multiple-threshold architecture to improve the sample screening precision. We employ a probability model to optimize the mask branch of the model further to improve the algorithm accuracy for the segmentation of image edges. In addition, we adjust the loss function so that the experimental effect can be optimized. The experiments reveal that the algorithm improves the results.
LLISP: Low-Light Image Signal Processing Net via Two-Stage Network

Images taken in extremely low light suffer from various problems such as heavy noise, blur, and color distortion. Assuming the low-light images contain a good representation of the scene content, current enhancement methods focus on finding a suitable illumination adjustment but often fail to deal with heavy noise and color distortion. Recently, some works try to suppress noise and reconstruct low-light images from raw data. But these works apply a network instead of an image signal processing pipeline (ISP) to map the raw data to enhanced results which leads to heavy learning burden for the network and get unsatisfactory results. In order to remove heavy noise, correct color bias and enhance details more effectively, we propose a two-stage Low Light Image Signal Processing Network named LLISP. The design of our network is inspired by the traditional ISP: processing the images in multiple stages according to the attributes of different tasks. In the first stage, a simple denoising module is introduced to reduce heavy noise. In the second stage, we propose a two-branch network to reconstruct the low-light images and enhance texture details. One branch aims at correcting color distortion and restoring image content, while another branch focuses on recovering realistic texture. Experimental results demonstrate that the proposed method can reconstruct high-quality images from low-light raw data and replace the traditional ISP.
Challenges experienced by teachers regarding access to digital instruments, resources, and competences in adapting the educational process to physical distancing measures at the onset of the COVID-19 pandemic in Romania

Emergency remote teaching replaced face-to-face education almost everywhere in the world at the onset of the Covid-19 pandemic.Romania switched to online teaching in March 2020 as a recommendation that became an obligation to teach online in late April 2020.Teachers saw themselves confronted with the need to adapt to a completely new infrastructure for teaching and learning in the digital sphere.Building on qualitative data (interviews and focus groups), collected as part of a participatory action research project aiming to support school teachers in adapting to online education, we explore the question: How did teachers understand and respond to the challenges brought about by using digital instruments and resources in the educational process?Our findings show that teachers faced challenges in accessing technological and digital infrastructures, as well as in ensuring that pupils had access to online teaching and learning activities.Moreover, teachers were faced with the need to rapidly develop the digital competences necessary for online teaching.This prompted exchanges between teachers, pupils and parents, as well as the setting up of support structures within and outside school structures.
An empirical assessment of deep learning approaches to task-oriented dialog management

Deep learning is providing very positive results in areas related to conversational interfaces, such as speech recognition, but its potential benefit for dialog management has still not been fully studied. In this paper, we perform an assessment of different configurations for deep-learned dialog management with three dialog corpora from different application domains and varying in size, dimensionality and possible system responses. Our results have allowed us to identify several aspects that can have an impact on accuracy, including the approaches used for feature extraction, input representation, context consideration and the hyper-parameters of the deep neural networks employed.
Cluster-Former: Clustering-based Sparse Transformer for Question Answering

Transformer has become ubiquitous in the deep learning field.One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected contextual encoding over input tokens.However, despite its effectiveness in modeling short sequences, self-attention suffers when handling inputs with extreme long-range dependencies, as its complexity grows quadratically w.r.t. the sequence length.Therefore, long sequences are often encoded by Transformer in chunks using a sliding window.In this paper, we propose Cluster-Former, a novel clusteringbased sparse Transformer to perform attention across chunked sequences.The proposed framework is pivoted on two unique types of Transformer layer: Sliding-Window Layer and Cluster-Former Layer, which encode local sequence information and global context jointly and iteratively.This new design allows information integration beyond local windows, which is especially beneficial for question answering (QA) tasks that rely on long-range dependencies.Experiments show that Cluster-Former achieves state-of-the-art performance on several major QA benchmarks.
Beyond fine-tuning: Classifying high resolution mammograms using function-preserving transformations

The task of classifying mammograms is very challenging because the lesion is usually small in the high resolution image. The current state-of-the-art approaches for medical image classification rely on using the de-facto method for convolutional neural networks-fine-tuning. However, there are fundamental differences between natural images and medical images, which based on existing evidence from the literature, limits the overall performance gain when designed with algorithmic approaches. In this paper, we propose to go beyond fine-tuning by introducing a novel framework called MorphHR, in which we highlight a new transfer learning scheme. The idea behind the proposed framework is to integrate function-preserving transformations, for any continuous non-linear activation neurons, to internally regularise the network for improving mammograms classification. The proposed solution offers two major advantages over the existing techniques. Firstly and unlike fine-tuning, the proposed approach allows for modifying not only the last few layers but also several of the first ones on a deep ConvNet. By doing this, we can design the network front to be suitable for learning domain specific features. Secondly, the proposed scheme is scalable to hardware. Therefore, one can fit high resolution images on standard GPU memory. We show that by using high resolution images, one prevents losing relevant information. We demonstrate, through numerical and visual experiments, that the proposed approach yields to a significant improvement in the classification performance over state-of-the-art techniques, and is indeed on a par with radiology experts. Moreover and for generalisation purposes, we show the effectiveness of the proposed learning scheme on another large dataset, the ChestX-ray14, surpassing current state-of-the-art techniques.
Designing the user experience of musical sonification in public and semi-public spaces

Sonification refers to sonic expression of data or information. It is often thought of as an auditory complement, providing additional information about data which can reveal patterns and facilitate interpretation and understanding of the data. Hence, the listening space created by a sonifi cation is always a hybrid where auditory augmentation complements other information modalities and, in some cases, also spatial qualities. In this work, we focus on sonifi cation in public and semi-public spaces, and specifi cally on musical sonifi cation – the use of musical sounds to create a sonic environment, augmenting or complementing a physical shared space. We draw upon established approaches in interaction design to focus our work on the user experience of musical sonifi cation in public and semi-public spaces. Specifi cally, we fi rst identify the experiential qualities of sonic atmosphere and performativity as important aspects of sonifi cation in public and semi-public spaces, then use those experiential qualities generatively in the speculative design of a musical sonifi cation sketch. The design sketch comprises a dynamic musical sonifi cation of air quality data, intending to give citizens an awareness and an enhanced individual and interpersonal understanding of air quality in their city.
A general framework for hypercomplex-valued extreme learning machines

This paper aims to establish a framework for extreme learning machines (ELMs) on general hypercomplex algebras. Hypercomplex neural networks are machine learning models that feature higher-dimension numbers as parameters, inputs, and outputs. Firstly, we review broad hypercomplex algebras and show a framework to operate in these algebras through real-valued linear algebra operations in a robust manner. We proceed to explore a handful of well-known four-dimensional examples. Then, we propose the hypercomplex-valued ELMs and derive their learning using a hypercomplex-valued least-squares problem. Finally, we compare real and hypercomplex-valued ELM models' performance in an experiment on time-series prediction and another on color image auto-encoding. The computational experiments highlight the excellent performance of hypercomplex-valued ELMs to treat high-dimensional data, including models based on unusual hypercomplex algebras.
Refining Network Alignment to Improve Matched Neighborhood Consistency

Network alignment, or the task of finding meaningful node correspondences between nodes in di↵erent graphs, is an important graph mining task with many scientific and industrial applications.An important principle for network alignment is matched neighborhood consistency (MNC): nodes that are close in one graph should be matched to nodes that are close in the other graph.We theoretically demonstrate a close relationship between MNC and alignment accuracy.As many existing network alignment methods struggle to preserve topological consistency in di cult scenarios, we show how to refine their solutions by improving their MNC.Our refinement method, RefiNA, is straightforward to implement, admits scalable sparse approximation, and can be paired post hoc with any network alignment method.Extensive experiments show that RefiNA increases the accuracy of diverse unsupervised network alignment methods by up to 90%, making them robust enough to align graphs that are 5⇥ more topologically di↵erent than were considered in prior work.
USING MOBILE-BASED FORMATIVE ASSESSMENT IN ESL/EFL SPEAKING

With the widespread application of smartphones in and outside the classroom, mobile-based teaching and learning is drawing much attention and hence being extensively practised nowadays across the globe. Recently, using smartphones for assessment purposes has been a new phenomenon and the researchers are still examining what processes the use of mobile-based assessment tools may include and what outcomes and challenges they can cause to teachers and students in terms of learning/teaching performance, motivation and attitudes. There have been a good number of research studies on the use of Mobile Assisted Language Learning (MALL) or Mobile Learning (ML) in EFL or ESL classroom but not much literature is known about the mobile-based language assessment, especially mobile-based formative assessment (MBFA). Hence, this study attempts to shed light on MBFA and review the recent literature available on it and its effective utilization in developing ESL/EFL speaking skill. This paper uses a qualitative research method that exclusively uses the relevant secondary references/works available on the topic. The literature revealed that MBFA practices in ESL/EFL speaking classes are effective to a certain extent and some tools and procedures seem to be more effective than others depending on the design principles and strategies used by teachers or app developers.
Entanglement of formation and monogamy of multi-party quantum entanglement

Abstract We provide a sufficient condition for the monogamy inequality of multi-party quantum entanglement of arbitrary dimensions in terms of entanglement of formation. Based on the classical–classical–quantum(ccq) states whose quantum parts are obtained from the two-party reduced density matrices of a three-party quantum state, we show the additivity of the mutual information of the ccq states guarantees the monogamy inequality of the three-party pure state in terms of EoF. After illustrating the result with some examples, we generalize our result of three-party systems into any multi-party systems of arbitrary dimensions.
Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference

Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.
Predicting the Depression of the South Korean Elderly using SMOTE and an Imbalanced Binary Dataset

Since the number of healthy people is much more than that of ill people, it is highly likely that the problem of imbalanced data will occur when predicting the depression of the elderly living in the community using big data.When raw data are directly analyzed without using supplementary techniques such as a sample algorithm for datasets, which have imbalanced class ratios, it can decrease the performance of machine learning by causing prediction errors in the analysis process.Therefore, it is necessary to use a data sampling technique for overcoming this imbalanced data issue.As a result, this study tried to identify an effective way for processing imbalanced data to develop ensemble-based machine learning by comparing the performance of sampling methods using the depression data of the elderly living in South Korean communities, which had quite imbalanced class ratios.This study developed a model for predicting the depression of the elderly living in the community using a logistic regression model, gradient boosting machine (GBM), and random forest, and compared the accuracy, sensitivity, and specificity of them to evaluate the prediction performance of them.This study analyzed 4,085 elderly people (≥60 years old) living in the community.The depression data of the elderly in the community used in this study had an unbalance issue: the result of the depression screening test showed that 87.5% of subjects did not have depression, while 12.5% of them had depression.This study used oversampling, undersampling, and SMOTE methods to overcome the unbalance problem of the binary dataset, and the prediction performance (accuracy, sensitivity, and specificity) of each sampling method was compared.The results of this study confirmed that the SMOTE-based random forest algorithm showing the highest accuracy (a sensitivity ≥ 0.6 and a specificity ≥ 0.6) was best prediction performance among random forest, GBM, and logistic regression analysis.Further studies are needed to compare the accuracy of SMOTE, undersampling, and oversampling for imbalanced data with high dimensional y-variables.
Learning Dynamics Models with Stable Invariant Sets

Invariance and stability are essential notions in dynamical systems study, and thus it is of great interest to learn a dynamics model with a stable invariant set. However, existing methods can only handle the stability of an equilibrium. In this paper, we propose a method to ensure that a dynamics model has a stable invariant set of general classes such as limit cycles and line attractors. We start with the approach by Manek and Kolter (2019), where they use a learnable Lyapunov function to make a model stable with regard to an equilibrium. We generalize it for general sets by introducing projection onto them. To resolve the difficulty of specifying a to-be stable invariant set analytically, we propose defining such a set as a primitive shape (e.g., sphere) in a latent space and learning the transformation between the original and latent spaces. It enables us to compute the projection easily, and at the same time, we can maintain the model's flexibility using various invertible neural networks for the transformation. We present experimental results that show the validity of the proposed method and the usefulness for long-term prediction.