Reviewerly/exHarmony huggingface dataset:
Design and Implementation of an Embedded system

This article presents the design and implementation of an embedded system called TES ROv2.0 non-invasive, capable of capturing and transmitting relevant biomedical signals such as: electrocardiography signals, heart rate, oxygen saturation in the blood and arterial pressure with the Support from the Clinical Information System called "SARURO", in which a detailed process of these biomedical signals is visualized and carried out by a doctor or specialist, without direct contact with the patient. Therefore, researchers have been able to affirm that integrated systems are tools that offer great versatility in the medical information market, allowing acquisition, adaptation, transformation in remote places, without linking operation to a single communication alternative, making the Telemonitoring system more versatile in its functionality to transmit over WPAN, WLAN, LAN and CELULARE networks.
Students’ Perceptions Toward The Use of Kahoot! Online Game for Learning English

This study aims to determine students' perceptions of using Kahoot! in learning English, the problems on the use of Kahoot! In learning English faced by students. This study used a qualitative research approach, and the subjects of this study were 10 students of the Ahmad Dahlan University of English Language Education. Data were collected using the open-ended interview as the instrument. Data analysis in this study used a qualitative descriptive. In analyzing data, this study refers to coding analysis consisting of data reduction, data presentation, and conclusion drawing. This research revealed that the benefits of using Kahoot! as follow: (1) motivating students to learn, (2) building a good atmosphere in class, (3) helping students to get focus, and (5) providing positive competition. The problem of using Kahoot! includes (1) unstable internet connection, and (2) the absence of space for students and teachers to discuss.
Image Classification with Classic and Deep Learning Techniques

To classify images based on their content is one of the most studied topics in the field of computer vision. Nowadays, this problem can be addressed using modern techniques such as Convolutional Neural Networks (CNN), but over the years different classical methods have been developed. In this report, we implement an image classifier using both classic computer vision and deep learning techniques. Specifically, we study the performance of a Bag of Visual Words classifier using Support Vector Machines, a Multilayer Perceptron, an existing architecture named InceptionV3 and our own CNN, TinyNet, designed from scratch. We evaluate each of the cases in terms of accuracy and loss, and we obtain results that vary between 0.6 and 0.96 depending on the model and configuration used.
Sketching in Bayesian High Dimensional Regression With Big Data Using Gaussian Scale Mixture Priors

Bayesian computation of high dimensional linear regression models with a popular Gaussian scale mixture prior distribution using Markov Chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive due to the heavy computational cost that grows in the cubic order of p, with p as the number of features. Although a few recently developed algorithms make the computation efficient in presence of a small to moderately large sample size (with the complexity growing in the cubic order of n), the computation becomes intractable when sample size n is also large. In this article we adopt the data sketching approach to compress the n original samples by a random linear transformation to m&lt;
Reflective Decoding: Beyond Unidirectional Generation with Off-the-Shelf Language Models

Publicly available, large pretrained LanguageModels (LMs) generate text with remarkable quality, but only sequentially from left to right. As a result, they are not immediately applicable to generation tasks that break the unidirectional assumption, such as paraphrasing or text-infilling, necessitating task-specific supervision. In this paper, we present Reflective Decoding, a novel unsupervised algorithm that allows for direct application of unidirectional LMs to non-sequential tasks. Our 2-step approach requires no supervision or even parallel corpora, only two off-the-shelf pretrained LMs in opposite directions: forward and backward. First, in the contextualization step, we use LMs to generate ensembles of past and future contexts which collectively capture the input (e.g. the source sentence for paraphrasing). Second, in the reflection step, we condition on these context ensembles, generating outputs that are compatible with them. Comprehensive empirical results demonstrate that Reflective Decoding outperforms strong unsupervised baselines on both paraphrasing and abductive text infilling, significantly narrowing the gap between unsupervised and supervised methods. Reflective Decoding surpasses multiple supervised baselines on various metrics including human evaluation.
Cultivation of Applied Foreign Language Talents in the Context of Hubei Free Trade Zone

This paper explains that the construction and development of Hubei Free Trade Zone has brought opportunities for the employment of applied foreign language talents. It analyzes the problems existing in the training of foreign language majors in Hubei local undergraduate universities: the professional positioning is vague, the curriculum system is unreasonable; the teaching mode is relatively outdated and cannot stimulate students’ interest in learning; the teaching skills are limited, and the teachers’ practical ability is insufficient; Improper methods, insufficient attention to quality training, etc. In response to the above problems, the article puts forward ideas and countermeasures for training applied foreign language talents.
A Deep Learning Mobile Application based Sign Language Recognition for Aphasic Person

Pervasive sign language is used relatively within our society, deaf and other verbally challenged communication of people with hearing impairments is of prime importance in our society. This paper presents a solution for this purpose. We developed an android application which integrates both computer vision techniques involving Histogram of Oriented Gradients (HOG) as well as Machine Learning techniques such as Convolutional Neural Network (CNN) and Multiclass Support Vector Machine (SVM) to detect and recognize gestures automatically. People tend to face difficulty in communicating on daily basis. Because of that development of assistive, cost-effective, as well as non-invasive technologies to aid the people in need is necessity.
Faster and Simpler Siamese Network for Single Object Tracking

Single object tracking (SOT) is currently one of the most important tasks in computer vision. With the development of the deep network and the release for a series of large scale datasets for single object tracking, siamese networks have been proposed and perform better than most of the traditional methods. However, recent siamese networks get deeper and slower to obtain better performance. Most of these methods could only meet the needs of real-time object tracking in ideal environments. In order to achieve a better balance between efficiency and accuracy, we propose a simpler siamese network for single object tracking, which runs fast in poor hardware configurations while remaining an excellent accuracy. We use a more efficient regression method to compute the location of the tracked object in a shorter time without losing much precision. For improving the accuracy and speeding up the training progress, we introduce the Squeeze-and-excitation (SE) network into the feature extractor. In this paper, we compare the proposed method with some state-of-the-art trackers and analysis their performances. Using our method, a siamese network could be trained with shorter time and less data. The fast processing speed enables combining object tracking with object detection or other tasks in real time.
CASIA-Face-Africa: A Large-scale African Face Image Database

Face recognition is a popular and well-studied area with wide applications in our society. However, racial bias had been proven to be inherent in most State Of The Art (SOTA) face recognition systems. Many investigative studies on face recognition algorithms have reported higher false positive rates of African subjects cohorts than the other cohorts. Lack of large-scale African face image databases in public domain is one of the main restrictions in studying the racial bias problem of face recognition. To this end, we collect a face image database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The performances of SOTA face recognition algorithms without re-training are reported as baselines. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc. The database can be downloaded from our http://www.cripacsir.cn/dataset/
Distributed algorithms for the least square solution of linear equations

This paper proposes distributed algorithms for solving linear equations to seek a least square solution via multi-agent networks. We consider that each agent has only access to a small and imcomplete block of linear equations rather than the complete row or column in the existing literatures. Firstly, we focus on the case of a homogeneous partition of linear equations. A distributed algorithm is proposed via a single-layered grid network, in which each agent only needs to control three scalar states. Secondly, we consider the case of heterogeneous partitions of linear equations. Two distributed algorithms with doubled-layered network are developed, which allows each agent's states to have different dimensions and can be applied to heterogeneous agents with different storage and computation capability. Rigorous proofs show that the proposed distributed algorithms collaboratively obtain a least square solution with exponential convergence, and also own a solvability verification property, i.e., a criterion to verify whether the obtained solution is an exact solution. Finally, some simulation examples are provided to demonstrate the effectiveness of the proposed algorithms.
Use of Information and Communication Technologies in Preschool Education

In this article it is written about the importance of using information communication technologies in preschool education system.
Incremental Training and Group Convolution Pruning for Runtime DNN Performance Scaling on Heterogeneous Embedded Platforms

Inference for Deep Neural Networks is increasingly being executed locally on mobile and embedded platforms due to its advantages in latency, privacy and connectivity. Since modern System on Chips typically execute a combination of different and dynamic workloads concurrently, it is challenging to consistently meet inference time/energy budget at runtime because of the local computing resources available to the DNNs vary considerably. To address this challenge, a variety of dynamic DNNs were proposed. However, these works have significant memory overhead, limited runtime recoverable compression rate and narrow dynamic ranges of performance scaling. In this paper, we present a dynamic DNN using incremental training and group convolution pruning. The channels of the DNN convolution layer are divided into groups, which are then trained incrementally. At runtime, following groups can be pruned for inference time/energy reduction or added back for accuracy recovery without model retraining. In addition, we combine task mapping and Dynamic Voltage Frequency Scaling (DVFS) with our dynamic DNN to deliver finer trade-off between accuracy and time/power/energy over a wider dynamic range. We illustrate the approach by modifying AlexNet for the CIFAR10 image dataset and evaluate our work on two heterogeneous hardware platforms: Odroid XU3 (ARM big.LITTLE CPUs) and Nvidia Jetson Nano (CPU and GPU). Compared to the existing works, our approach can provide up to 2.36x (energy) and 2.73x (time) wider dynamic range with a 2.4x smaller memory footprint at the same compression rate. It achieved 10.6x (energy) and 41.6x (time) wider dynamic range by combining with task mapping and DVFS.
Educational and methodological support for development of emotional intelligence of future teachers of musical art in professional training process

The article presents the author’s understanding of the concept of “emotional intelligence of a music art teacher” as an integral, personal and professional quality of a future specialist. It contains determination of the content and structure of emotional intelligence of a music art teacher, the peculiarities of its manifestation due to the specifics of professional activity. The content of educational and methodical support for the formation of emotional intelligence of future bachelors and masters of music art in the process of professional training has been revealed. It includes normative and selective professional disciplines; various types of pedagogical practice (educational, psychological and pedagogical, summer pedagogical practice in health camps, teacher training practice in general secondary education institutions, assistant practice in higher education institutions); different types of research work; selective course "Theory and Methods of Emotional Intelligence Development". It is noted that the curricula of all normative and selective disciplines, different types of pedagogical practice as well as the topics of research work of students are enriched with emotional and intellectual component. The developed content of educational and methodological support is implemented in various organisational forms and methods of development of future teachers’ emotional intelligence, which were classified into three groups: general didactic, special, conditioned by the peculiar nature of music and specific, related to the phenomenon of emotional intelligence.
See, Hear, Read: Leveraging Multimodality with Guided Attention for Abstractive Text Summarization

In recent years, abstractive text summarization with multimodal inputs has started drawing attention due to its ability to accumulate information from different source modalities and generate a fluent textual summary. However, existing methods use short videos as the visual modality and short summary as the ground-truth, therefore, perform poorly on lengthy videos and long ground-truth summary. Additionally, there exists no benchmark dataset to generalize this task on videos of varying lengths. In this paper, we introduce AVIATE, the first large-scale dataset for abstractive text summarization with videos of diverse duration, compiled from presentations in well-known academic conferences like NDSS, ICML, NeurIPS, etc. We use the abstract of corresponding research papers as the reference summaries, which ensure adequate quality and uniformity of the ground-truth. We then propose {\name}, a factorized multi-modal Transformer based decoder-only language model, which inherently captures the intra-modal and inter-modal dynamics within various input modalities for the text summarization task. {\name} utilizes an increasing number of self-attentions to capture multimodality and performs significantly better than traditional encoder-decoder based networks. Extensive experiments illustrate that {\name} achieves significant improvement over the baselines in both qualitative and quantitative evaluations on the existing How2 dataset for short videos and newly introduced AVIATE dataset for videos with diverse duration, beating the best baseline on the two datasets by $1.39$ and $2.74$ ROUGE-L points respectively.
Scalable Projection-Free Optimization

As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable attention in the machine learning community. In this dissertation, we study several topics on the FW variants for scalable projection-free optimization. We first propose 1-SFW, the first projection-free method that requires only one sample per iteration to update the optimization variable and yet achieves the best known complexity bounds for convex, non-convex, and monotone DR-submodular settings. Then we move forward to the distributed setting, and develop Quantized Frank-Wolfe (QFW), a general communication-efficient distributed FW framework for both convex and non-convex objective functions. We study the performance of QFW in two widely recognized settings: 1) stochastic optimization and 2) finite-sum optimization. Finally, we propose Black-Box Continuous Greedy, a derivative-free and projection-free algorithm, that maximizes a monotone continuous DR-submodular function over a bounded convex body in Euclidean space.
Automatic Legal Judgment Summarization Using Graph

Summarization is the process of generating summaries out of corpus. If the corpus is a legal document, the process is called legal summarization. Headnotes are concise notes prepared by lawyers for quick reference of a past Judgment. Judgments are always lengthy, and it is essential to make headnotes for legal practitioners for quick reference analysis. Legal experts generate headnotes, and it is a tedious task to prepare them by reading the whole judgment. With the advancements in technologies, a lot of methods are available to generate a summary of a given document. But for legal documents, the usual summarization technique have the impediment to precisely concise as judgments follow a legal parlance. The proposed method picks relevant paragraphs from a judgment at first, and then it is used to create an extractive summary.
From Website Redesign to Design Thinking: Putting Our Users First Without Putting Ourselves Last

A library website redesign can be an intensely complex and encompassing problem. But does it have to be? How might we change our practices to meet user needs without overburdening the organization?
A smoothed and probabilistic PARAFAC model with covariates

Analysis and clustering of multivariate time-series data attract growing interest in immunological and clinical studies. In such applications, researchers are interested in clustering subjects based on potentially high-dimensional longitudinal features, and in investigating how clinical covariates may affect the clustering results. These studies are often challenging due to high dimensionality, as well as the sparse and irregular nature of sample collection along the time dimension. We propose a smoothed probabilistic PARAFAC model with covariates (SPACO) to tackle these two problems while utilizing auxiliary covariates of interest. We provide intensive simulations to test different aspects of SPACO and demonstrate its use on immunological data sets from two recent cohorts of SARs-CoV-2 patients.
USE OF PEDAGOGICAL TECHNOLOGIES IN TEACHING AND LEARNING IN HIGHER EDUCATION

Technology is a Greek word, technos means skill, art, logos means science, doctrine. The concept of ‘technology' is defined as a set of methods and techniques used in production processes to obtain a finished product and as a science that produces and improves such methods and techniques.
Historical And Pedagogical Analysis Of The Problem Of Forming Professional-Value Orientations

The axiological orientation of modern social thought is implemented in educational policy, considering a modern university graduate as a person, not just possessing specific competences and knowledge, but also capable of cooperating and constructive multicultural dialogue, interacting with people of different cultures and knowing the system of socially significant world values.In modern pedagogical science, considerable experience has been accumulated in preparing students of different specialities for valueoriented professional interaction, forming and developing the tolerance, intercultural communicative competences, educating the younger generations in a multicultural world.The article is devoted to the concept of professional-value orientations, which is based on the analysis of the emergence and development of the problem of forming professional-value orientations in the would-be specialists of restaurant and hotel services, the study of the state of the problem in the philosophical, psychological, pedagogical, economic literature, regulatory documents.Based on the research of A.V. Kiryakova (2000), we come to the conclusion that education is the main channel for introducing the future Manager of restaurant and hotel services to the values of culture, education and profession.
딥러닝 언어모델과 중국어 문법 ―BERT를 활용한 방향보어의 예측 모형을 중심으로

In this study, we investigated how accurately the BERT model can predict Chinese directional complement. In addition, we analyzed which words the BERT model uses as an important clue in the Chinese directional complement inference process. According to the results of this study, it can be seen that the BERT model shows excellent performance in inferring distributional features and grammatical relationships based on transfer learning. Results of experiments with five Chinese directional complements show that the accuracy rate of predictions is quite high. In addition, as a result of analysis using the masked language model, it was found that the BERT model appropriately uses important clues to determine Chinese directional complement in context.We believe that this study is not only meaningful in the field of NLP, but also provides insight into Chinese grammar research or language education. If this methodology is properly utilized, it will be possible to establish an application system for Chinese grammar research and education. In Neural network models, sufficient language data learning allows us to predict which language expressions are more natural to use. Proper use of these advantages will give us insight into Chinese grammatical functions. This Chinese grammar prediction system will also help Chinese learners improve their skills by showing them what expressions are grammatically correct.
SauvolaNet: Learning Adaptive Sauvola Network for Degraded Document Binarization

Inspired by the classic Sauvola local image thresholding approach, we systematically study it from the deep neural network (DNN) perspective and propose a new solution called SauvolaNet for degraded document binarization (DDB). It is composed of three explainable modules, namely, Multi-Window Sauvola (MWS), Pixelwise Window Attention (PWA), and Adaptive Sauolva Threshold (AST). The MWS module honestly reflects the classic Sauvola but with trainable parameters and multi-window settings. The PWA module estimates the preferred window sizes for each pixel location. The AST module further consolidates the outputs from MWS and PWA and predicts the final adaptive threshold for each pixel location. As a result, SauvolaNet becomes end-to-end trainable and significantly reduces the number of required network parameters to 40K -- it is only 1\% of MobileNetV2. In the meantime, it achieves the State-of-The-Art (SoTA) performance for the DDB task -- SauvolaNet is at least comparable to, if not better than, SoTA binarization solutions in our extensive studies on the 13 public document binarization datasets. Our source code is available at https://github.com/Leedeng/SauvolaNet.