Reviewerly/exHarmony huggingface dataset:
vRAS: An Efficient Virtualized Resource Allocation Scheme

In this paper, we present a virtualized resource allocation algorithm (vRAS), which is capable of dynamically predicting VM resource demands based on the real-time snapshot of server resource states. Furthermore, VMs experiencing the heavy resource utilization in the previous time period can be incentivized by obtaining more resources in the next time period, while trying to not aggravate other VMs' application bandwidth running on the same host. We compared our method to other schemes to verify its effectiveness.
Spiral Processual Approach in Organizing Lessons on Language Teaching Methodology for Linguistic Students

The paper aims to justify relevance of the spiral processual approach to compiling a lecture and seminar course by the example of the course “Methodology of Foreign Language Teaching” for linguistic students. The authors present a model of a textbook, provide examples of the spiral processual approach realization in the structure of an electronic course, and consider students’ motivation to study the discipline. Scientific originality of the research lies in the fact that the authors propose spiral processual methodology for classes of language teaching techniques. The research findings are as follows: using the data analysis method, the authors reveal students’ perception of an electronic textbook; it is proved that students are highly motivated to study the course due to its spiral processual structure.
Low Resolution and Multi-pose Face Recognition based on Residual Network

The purpose of low resolution face recognition (LRFR) is to recognize faces from small or low-quality images with different poses, illumination and expressions. However, the recognition effect of LRFR is not ideal under uncontrolled conditions. In this paper, residual network of resnet18 is used to test the recognition rate of six groups of UMIST face datasets with the resolution from 112×92 to 3×3. The results of recognition rate under different resolution states are compared and analyzed, and the dynamic curve of recognition rate with the change of resolution is obtained. In order to solve the problem that the recognition performance declines sharply when the resolution is lower than the minimum resolution, The minimum resolution image is reconstructed by super-resolution, considering the influence of the small scale of UMIST face datasets on the recognition rate, the data enhancement is used for training. The experimental results show that the method can effectively improve the recognition rate of the test image.
The effects of different environments on older adults’ ability to successfully cross a closing gap in virtual reality

To navigate through dynamically changing environments and to avoid collisions with stationary and moving obstacles, older adults tend to over rely on their visual system because it is a more reliable source of information. Aging affects both visuomotor integration and visual perception, often resulting in the inability to produce appropriate adaptive locomotor actions in a timely manner. Does peripheral visual information in the environment affected older adults’ ability to complete a gap-crossing task with a set of closing doors at different rates (0.6–1.2 m/s)? Fifteen older adults (65–74 years) completed the study inside a virtual environment with three different levels of peripheral visual information: 1) empty; 2) stationary avatars; and 3) moving avatars. Kinematic data was collected using an Optotrak camera system to track the older adults’ body movements during the task. The results demonstrated that regardless of the environment or closing door speed, older adults maintained consistent approach speeds. However, older adults collided with the fastest moving doors a significant number of times at the fastest door closing rates for the empty and moving avatar conditions. Although it appears that older adults are able to attend on a central task (i.e., passing through closing doors) and maintain constant behaviours regardless of the visual information from peripheral environment, richness of the peripheral environment provides accurate feedback about self-motion affects success rates.
Impacto del uso de las herramientas tecnológicas en los docentes de educación primaria en época del COVID-19

espanolEl proposito de este articulo es presentar el impacto que han experimentado los docentes con la incorporacion de los recursos educativos tecnologicos durante el confinamiento de la pandemia del COVID-19 en el binomio de ensenanza y aprendizaje. El trabajo se enmarco en una investigacion de campo y se aplico un cuestionario para el proceso de recoleccion de datos. Los resultados obtenidos demostraron que se hace inminente una formacion del docente en Tecnologias de Informacion y Comunicacion (TIC) que atienda y responda a estas nuevas practicas educativas, debido a que las mismas son una excelente herramienta pedagogica para la apropiacion de los conocimientos de los estudiantes EnglishThe purpose of this article is to present the impact that teachers have experienced with the incorporation of technological educational resources during the confinement of the COVID-19 pandemic in the teaching and learning binomial. The work was framed in a field investigation and a questionnaire was applied for the data collection process. The results obtained demonstrated that a teacher training in Information and Communication Technologies (ICT) that attends and responds to these new educational practices is imminent, since they are an excellent pedagogical tool for the appropriation of student knowledge
DAS-Autism: A Rule-Based System to Diagnose Autism Within Multi-valued Logic

In front of the continued growth of autistics number in the world, intelligent systems can be used by non-specialists such as educators or general physicians in autism screening. Moreover, it can assist psychiatrists in the diagnosis of autism to detect it as early as possible for early intervention. We propose in this chapter a tool for the diagnosis of autism: DAS-Autism. It is a knowledge-based system that handles qualitative knowledge in the multi-valued context. For this, we use our knowledge-based system shell RAMOLI, and its inference engine executes an approximate reasoning based on linguistic modifiers that we have introduced in a previous work. We have built a knowledge base that represents the domain expertise, in collaboration with a child psychiatry department of Razi hospital, the public psychiatric hospital in Tunisia. We have then conducted an experimental study in which we compared the system results to expert’s diagnoses. The results of this study were very satisfactory and promising.
Wayang Wong Bali performance in era pandemic Covid 19

Abstract This article aims to discuss the art of Wayang Wong Bali during the Covid 19 pandemic era. In line with the influence of the Covid 19 pandemic amid climate change and the tendency for many people to choose to stay at home, it has implications for the Balinese Wayang Wong performance’s marginalization. However, there appear to be differences in the presentation of the Wayang Wong Bali performance during the Covid 19 pandemic. The questions are : (1) What is the form of the Wayang Wong Bali performing arts in the Covid 19 pandemic era?; (2) What are the implications for their environment and the Wayang Wong performance art itself?. The research, which is located in Bali, was conducted using qualitative methods to study the Balinese Wayang Wong in the era of the Covid 19 pandemic. In this study, the data sources were the Wayang Wong Performing Arts performed by the millennial generation themselves, dancers, figures, and observers of the Balinese Wayang Wong performing arts. All data collected through observation, interviews, and literature study techniques were analyzed using aesthetic theory and reception theory. Based on the results of this study it can be concluded that : (1). The Millennials presented the Wayang Wong Bali Performing Arts during the pandemic era in the form of a technology-based mini oratorium. This can be seen from the way of presentation, fashion make-up, narration, and musical accompaniment from the show. (2) The development of Wayang Wong performing arts during the pandemic seems to have indirect implications for reducing audience concerns about disasters due to climate change and cultural enrichment, namely the emergence of new performing arts, cultural preservation, and character strengthening for those involved.
A Big Data Based Technology Study on College Publicity Monitoring System

Through the analysis and summary analysis of the actual needs of university network publicity monitoring, a college publicity monitoring and analysis system, which is based on Python crawler technology, is accordingly constructed, followed by the system being analyzed and designed in detail according to actual needs. University administrators obtain a large amount of publicity data via the network discussion platform for comprehensive analysis. The study modules mainly include: publicity keyword management module, publicity analysis module, publicity result visualization module, publicity report module, etc. From the establishment of publicity keywords to the display of publicity reports, the university publicity monitoring and analysis system thus provide university administrators with accurate and efficient publicity development trends, assist universities in understanding the inner motivation of students, and develop publicity in a timely manner, aiming to put forward opinions, adjust the management policies of universities, promote the healthy growth of students and the stable development of universities.
Introduction to DBMS, RDBMS, and NoSQL Database: NoSQL Database Challenges

In today’s Web world, we see that the data production has been enormous, and organizations also face tough challenge in terms of processing, analyzing, and storing big data. The issue faced by organizations was how to manage the data which was not in a fixed schema which is also called unstructured data. For example, data being collected from social media network. Because of the data format, Relational Database has some limitations where NoSQL comes-into the picture which offers highly flexible and horizontally scalable solution to store structured, semi-structured and unstructured data. The process of data storage in NoSQL is in form of key-value pairs which offer better availability and high throughput performance in terms of processing queries. Since it does not require to have a fixed Schema, we can customize it as per user requirements. The unstructured database has existed since late 1960’s but the need of such database never arose until the Web 2.0 companies needed it. It was when companies like Facebook, Google and Amazon.com wanted a database which could store the data produced by their web-based applications. NoSQL are also known as Not only SQL or No SQL database because it does not store data into a form of relational tables. The increasing trend of different type of applications and data has caused the developers to reevaluate how data is stored and managed. The behavior of today’s data needs a technology which provides scalable, flexible solution to manage their huge amount of unstructured data which are difficult, and sometimes impossible to be managed by relational database models (such as Oracle, MySQL, SQL Server, and IBM DB2). This problem caused many to divert their interest towards NoSQL Database for flexibility. NoSQL (not only SQL) were introduced to handle unstructured and continuous growth of unstructured data. In this paper, we will go through Introduction to DBMS, RDBMS and NoSQL database, NoSQL Database categories, and will be covering an example of NoSQL Database (MongoDB) briefly.
“Lenses on COVID-19”

This chapter presents a suite of five provocations from leading scholars who look at the impact of COVID-19 on digital disruption in schooling and explore the possibilities and challenges of digital education in the aftermath of the pandemic. Neil Selwyn, in his provocation, makes two points. The first is that the pandemic is being used as a cover for accelerating an EdTech framed future of schooling, and that consequently, there is an urgent need for critical education scholars to provide an alternative policy narrative for socially just schooling. Nick Couldry argues that digital disruption will possibly result in more intense governing of teachers and students, and that the pandemic is speeding up trends already underway. Anna Hogan and Ben Williamson suggest that the EdTech companies in the context of the pandemic have characterized themselves as emergency responders and that they will use this opportunity for further the privatization of schooling for their long-term benefit and profit. Sotiria Grek documents the ways in which teachers in England during the pandemic have been constituted in government discourses as anti-heroes, continuing the long-term demonizing of teachers in that context. Sam Sellar, drawing on the work of Zuboff (2019), suggests three strategies to resist the further privatization of schooling accelerated by the pandemic. These strategies include: “taming” achieved through legislation to protect privacy; “hiding,” where we mask our identities on digital platforms; and “indignation” concerning the negative digital effects of the pandemic. From the vantage point of indignation, Sellar suggests, there are hopeful political possibilities for repairing the infrastructure of public schooling and importance of collective experiences.
Measure of quality of finite-dimensional linear systems: A frame-theoretic view

A measure of quality of a control system is a quantitative extension of the classical binary notion of controllability. In this article we study the quality of linear control systems from a frame-theoretic perspective. We demonstrate that all LTI systems naturally generate a frame on their state space, and that three standard measures of quality involving the trace, minimum eigenvalue, and the determinant of the controllability Gramian achieve their optimum values when this generated frame is tight. Motivated by this, and in view of some recent developments in frame-theoretic signal processing, we propose a natural measure of quality for continuous time LTI systems based on a measure of tightness of the frame generated by it and then discuss some properties of this frame-theoretic measure of quality.
Foreign Language Learning via Mobile Devices during a Language Immersion Program

Abstract The progress of technology and the complete integration of its products in our daily lives is now a reality. In addition to this, more and more young children now own their personal mobile devices, most of which have a connection to the internet. These new portable devices offer unlimited possibilities to their users, yet they also create more and more challenges in the field of learning. Inevitably, Mobile-assisted language learning (MALL) as a subcategory of Mlearning is also affected. The purpose of this paper is to present students’ perceptions on the use of an application in the process of learning French as a foreign language. To this end, we developed an application for mobile devices in real teaching conditions, which respects the principles of the Common European Framework of Reference for Languages (CEFR). Experimental quantitative research was carried out in order to explore the students’ experience concerning the application as a portable learning tool, completely integrated into the course. The target group we addressed was international students from various countries in Europe, Asia and America, who were taught French during a language immersion program, as part of a language stay in Saint Raphaël in France.
JomAugmented Reality Effect Towards Online Shopping Platforms

The research aims to introduce Augmented Reality features into online shopping platforms to enhance consumer purchase behaviour in online shopping platforms. Online shopping has become a common activity done by young people aged between 18-50 nowadays. More and more people choose to purchase online instead of buying at retail shops. This project has proposed to make online shopping item comparison more convenient and less time-consuming. By providing this feature into online shopping platforms, the consumer can have a clear comparison between similar items, including price, product details, customer feedback and review. This project is carried out in two private universities for data collection.
Improve Visual Question Answering Based On Text Feature Extraction

Abstract VQA requires the machine to be able to answer questions related to the image based on the image. VQA is mainly divided into four modules: text extraction module, image extraction module, text-image feature fusion module and answer prediction module. In this paper, the most mainstream VQA2.0[1] data set is selected for experiment, and the text extraction module is improved on the basis of a baseline model. The Glove vector is used to preprocess the problem text data, and the GRU network replaces the traditional LSTM network encoding and processing text vector. Experimental results show that the improvement of the text extraction module improves by 2.42% on the basis of the original model, and the GRU network can also accelerate the training speed.
NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner

In recent years, several systems have been developed to regulate the spread of negativity and eliminate aggressive, offensive or abusive contents from the online platforms. Nevertheless, a limited number of researches carried out to identify positive, encouraging and supportive contents. In this work, our goal is to identify whether a social media post/comment contains hope speech or not. We propose three distinct models to identify hope speech in English, Tamil and Malayalam language to serve this purpose. To attain this goal, we employed various machine learning (SVM, LR, ensemble), deep learning (CNN+BiLSTM) and transformer (m-BERT, Indic-BERT, XLNet, XLM-R) based methods. Results indicate that XLM-R outdoes all other techniques by gaining a weighted f_1-score of 0.93, 0.60 and 0.85 respectively for English, Tamil and Malayalam language. Our team has achieved 1st, 2nd and 1st rank in these three tasks respectively.
BERTologiCoMix: How does Code-Mixing interact with Multilingual BERT?

Models such as mBERT and XLMR have shown success in solving Code-Mixed NLP tasks even though they were not exposed to such text during pretraining. Code-Mixed NLP models have relied on using synthetically generated data along with naturally occurring data to improve their performance. Finetuning mBERT on such data improves it’s code-mixed performance, but the benefits of using the different types of Code-Mixed data aren’t clear. In this paper, we study the impact of finetuning with different types of code-mixed data and outline the changes that occur to the model during such finetuning. Our findings suggest that using naturally occurring code-mixed data brings in the best performance improvement after finetuning and that finetuning with any type of code-mixed text improves the responsivity of it’s attention heads to code-mixed text inputs.
Particle Swarm Optimization for Query Items Re-rating

Nowadays, Swarm Intelligence (SI) is considered as one of the most promising class of soft computing and bio-inspired algorithms for solving complex optimization problems. Particle Swarm Optimization (PSO) is by far the most popular and widely used swarm intelligence algorithms because of its efficient simplicity, high flexibility, and fast convergence speed. On the other hand, Information Retrieval (IR) systems are still suffering from shortcomings related to query items rating and thus returning irrelevant answers. Therefore, in this paper, we adopt PSO algorithm to re-rate query items and enhance the retrieval effectiveness of IR systems. The key idea is to reduce the noise effect of widespread items by minimizing the number of answers that contain these frequent items, and increase the discriminative effect of rare items by maximizing the number of answers that contain these infrequent items. Extensive experiments have been conducted using real data from an online medical dataset. The results show that the proposed algorithm improves the retrieval effectiveness and demonstrates a substantial enhancement over the state-of-the-art.
Delving into Feature Maps: An Explanatory Analysis to Evaluate Weight Initialization

Convolutional neural networks have delivered exceptional performance in various areas of computer vision. There has been growing research to develop deeper architectures with the availability of large datasets. Training such deep networks on large datasets is a tedious process as it involves optimizing a loss function by updating the parameters of the network. Weight initialization is a vital step before training neural networks as the correct choice of network weights ensures that the optimization converges to global minima in the least time. The weight initialization strategies in the literature can be categorized as (1) Initialization without pre-training, and (2) Initialization with pre-training. This paper presents a comparative analysis of the convergence performance of some widely used weight initialization techniques in these categories. This analysis is based on the diversity insights measured in terms of mean standard deviation captured from the feature maps. The experimentation has been carried out by training the AlexNet and VGG16 network on CIFAR-10 and CIFAR-100 datasets. The experimentation results demonstrate that the He initialization technique, which shows the best convergence performance among the others considered for the study, leads the training process such that the diversity of feature maps increases with epochs for both AlexNet and VGG16 network.
Compression of Person Re-identification Model Based on Depthwise Separable Convolutional Network

Abstract In practical application, the network depth and the number of parameters of person re-identification(re-ID) model bring great challenges to its deployment on devices with weak computing capabilities such as cloud desktop, mobile terminal and embedded terminal. To solve the above problem, this paper proposed a model compression method based on a depthwise separable convolutional network. Knowledge distillation is used to the core idea of model compression. Knowledge distillation is to transfer knowledge from a complex model to a simple model. The complex model is called the teacher model. The simple model is called the student model. This paper proposed ResNet18 based on depthwise separable convolution (ResNet18-DSC). ResNet50 is used as a teacher network and ResNet18-DSC is used as a student network. To narrow the performance gap between student and teacher networks. KL divergence loss function is used to approximate the soft label distribution of student network to that of teacher network. With a slight decrease in recognition rate, this method reduces the number of parameters by about 20 times and improves the calculation speed by about 20 times.