Reviewerly/exHarmony huggingface dataset:
Multiplex network infomax: Multiplex network embedding via information fusion

For networking of big data applications, an essential issue is how to represent networks in vector space for further mining and analysis tasks, e.g., node classification, clustering, link prediction, and visualization. Most existing studies on this subject mainly concentrate on monoplex networks considering a single type of relation among nodes. However, numerous real-world networks are naturally composed of multiple layers with different relation types; such a network is called a multiplex network. The majority of existing multiplex network embedding methods either overlook node attributes, resort to node labels for training, or underutilize underlying information shared across multiple layers. In this paper, we propose Multiplex Network Infomax (MNI), an unsupervised embedding framework to represent information of multiple layers into a unified embedding space. To be more specific, we aim to maximize the mutual information between the unified embedding and node embeddings of each layer. On the basis of this framework, we present an unsupervised network embedding method for attributed multiplex networks. Experimental results show that our method achieves competitive performance on not only node-related tasks, such as node classification, clustering, and similarity search, but also a typical edge-related task, i.e., link prediction, at times even outperforming relevant supervised methods, despite that MNI is fully unsupervised.
Learning management system based on virtual reality technology in elementary school

The main problem faced by teachers both during the pandemic and post-pandemic is the interest and learning performance of elementary school students who are still not in line with expectations. This study aims to develop a learning management system based on virtual reality technology to increase the effectiveness of learning in elementary schools. This research is a design and development study. The subjects of this study were teachers and elementary school students. Data collection in this study was carried out through focus group discussions, interviews, observations and questionnaires. The data analysis technique used is the descriptive quantitative analysis and qualitative analysis through interactive analysis. The research findings indicate that the product developed can have a positive impact on both students and teachers. Students feel more motivated to participate in learning activities and learning feels more interesting. Students find it easier to understand the subject matter so it has an impact on mastery of the material and better learning outcomes. In addition, through this product, teachers can present learning efficiently, confidence in teaching will be better and the effectiveness of teaching activities will be better. Keywords: Learning management system, virtual reality, elementary school.
Intelligent Optimization Method of the Higher Vocational Education System for Labor Market Demand in Guangdong-Hong Kong-Macao Greater Bay Area

The current educational development in the Guangdong-Hong Kong-Macao Greater Bay Area is not enough to support the construction of a world-class bay area, so the education reform and innovation in the Greater Bay Area face many challenges. This study systematically sorts out the challenges faced by education in the Guangdong-Hong Kong-Macao Greater Bay Area, starting from the interpretation of the talent training standards in the new era, and proposes that the education innovation in the Guangdong-Hong Kong-Macao Greater Bay Area should re-understand and implement the national education right of the SAR, improve the standards and raining quality of talents, and strengthen innovation-driven development, and an education system with the characteristics of the Greater Bay Area should be built to give full play to the role of education in regional economic development and to cultivate talents in the new era.
On controllability and observability of multichannel automatic control systems in the synthesis by the modal method using the polynomial matrix decomposition of the object and controller

When considering the issue of synthesis of an automatic control system by a modular method using a polynomial decomposition of the transfer functions of an object and a controller, synthesis algorithms for fully controlled systems are proposed. However, the question arises about the possibilities of using this algorithm if this condition is not met. The consideration of this is-sue turned out to be especially relevant for multichannel models of objects with a non-square transfer function (having an unequal number of input and output channels). It is shown that for some fundamental terms of the theory of automatic control, such as controllability, reachability, observability, stability and some others, there are special definitions of them in the case of considering this type of objects. The term non‒square object is proposed for use, which is used mainly in foreign literature. Some restrictions on the modal synthesis of regulators by a method using a polynomial matrix separation of the object and the regulator are considered. Examples of internally and asymptotically unstable systems are given. A hypothesis is put forward about the stability of the controlled system. An example of a multichannel system "inverted pendulum on a cart" is considered, which is an object with a non-square matrix transfer function (in this example, the number of input actions is less than the number of output parameters). Using the static characteristics of this object, it is demonstrated that not always controlled systems can be stabilized in a given position. For example, in the case of setting the desired angle of an inverted pendulum other than zero, it is impossible to hold the position of the cart in a given coordinate. At the same time, if you set the angle at the equilibrium point as the desired angle of the inverted pendulum, then stabilization of the cart at a given coordinate becomes possible.
A fine-grained mixed precision DNN accelerator using a two-stage big–little core RISC-V MCU

Deep neural networks (DNNs) are widely used in modern AI systems, and their dedicated accelerators have become a promising option for edge scenarios due to the energy efficiency and high performance. Since the DNN model requires significant storage and computation resources, various energy efficient DNN accelerators or algorithms have been proposed for edge devices. Many quantization algorithms for efficient DNN training have been proposed, where the weights in the DNN layers are quantified to small/zero values, therefore requiring much fewer effective bits, i.e., low-precision bits in both arithmetic and storage units. Such sparsity can be leveraged to remove non-effective bits and reduce the design cost, however at the cost of accuracy degradation, as some key operations still demand a higher precision. Therefore, in this paper, we propose a universal mixed-precision DNN accelerator architecture that can simultaneously support mixed-precision DNN arithmetic operations. A big–little core controller based on RISC-V is implemented to effectively control the datapath, and assign the arithmetic operations to full precision and low precision process units, respectively. Experimental results show that, with the proposed designs, we can save 16% chip area and 45.2% DRAM access compared with the state-of-the-art design.
The Quality Evaluation and Knowledge System Construction of Hierarchical Teaching in the Curriculum Are Based on the Grey Association Method

Based on the theory of grey correlation, the hierarchical teaching quality evaluation and knowledge system structure of the curriculum are established. Using the optimal clustering method, the teaching quality assessment and knowledge system construction at the curriculum level were obtained, and the grey correlation method was used to study the teaching at the course level. Through the evaluation of the discipline level and the discipline level and the construction of the knowledge system, the evaluation of the discipline level and the construction of the discipline knowledge system are achieved. The study found that the correlation between the curriculum grading teaching and the establishment of the knowledge system using the grey correlation method was higher than 0.900, the evaluation and knowledge system of the curriculum grading teaching were better evaluated, and the selection and retesting of evaluation indicators exceeded 0.700.
ClusTorus: An R Package for Prediction and Clustering on the Torus by Conformal Prediction

Protein structure data consist of several dihedral angles, lying on a multidimensional torus. Analyzing such data has been and continues to be key in understanding functional properties of proteins. However, most of the existing statistical methods assume that data are on Euclidean spaces, and thus they are improper to deal with angular data. In this paper, we introduce the package ClusTorus specialized to analyzing multivariate angular data. The package collects some tools and routines to perform algorithmic clustering and model-based clustering for data on the torus. In particular, the package enables the construction of conformal prediction sets and predictive clustering, based on kernel density estimates and mixture model estimates. A novel hyperparameter selection strategy for predictive clustering is also implemented, with improved stability and computational efficiency. We demonstrate the use of the package in clustering protein dihedral angles from two real data sets.
Intelligent Decision-Support System for Epidemiological Diagnostics. II. Information Technologies Development*, **

The article projects the components of the intelligent decision support system for epidemiological diagnostics and investigates their interaction with the user. The system includes a bank of models and machine learning methods, a bank of population dynamics models, visualization and reporting tools, and management decision-making unit. The concept of information technology to ensure biosafety of the population is provided. A model of specified information technology use cases is developed and a sequence diagram is constructed. A model of information technology components and ways of their deployment on a server are proposed.
Landscape design of garden plants based on green and low-carbon energy under the background of big data

Data collection, which is characterized by large capacity, many types, fast access speed and high application value, was first used in the IT industry, and is now rapidly developing into the collection, storage and correlation of data with huge quantities, scattered sources and various formats. Analysis to discover new knowledge, create new value, and enhance new capabilities of a new generation of information technology and service formats. With the continuous advancement of development and construction, the problem of carbon dioxide emissions caused by large-scale development and rapid construction has become more and more prominent, resulting in global warming and the greenhouse effect, triggering the demand for low-carbon garden landscape construction. How to apply the concept of low-carbon construction to the planning and design of garden landscape is a problem that should be considered at present. Therefore, this paper proposes a research on landscape design of garden plants based on green and low-carbon energy in the context of big data. It first analyzes the energy efficiency problem in the era of big data, and then introduces the data envelopment analysis method in detail. After that, the green and low-carbon garden construction was planned, and the construction land, plant selection, materials and other aspects were analyzed. From the final experimental results, it can be known that through the planning and design of low-carbon urban landscaping, its net carbon emissions and carbon emissions have decreased significantly. Green space carbon emissions increased from 303,089.26 to 335,757.06, rising to 32,667.8, which proves the actual effect of low-carbon design and protects the local ecological environment.
Security and Privacy Challenges of Big Data Adoption: A Qualitative Study in Telecommunication Industry

The telecommunication industry is the leading industry in big data trends as this industry has the most capable infrastructure for big data. However, the adoption of big data in telecommunication services also raises important security and privacy challenges due to the high volume, velocity, and variety of big data characteristics. To address the issue, this study shed light on the security and privacy challenges of big data adoption in the telecommunication industry. This study focuses on investigating the security and privacy challenges for data users in telecommunication services from the lens of the TOE framework and examines the mitigation strategies to address the privacy and security challenges. This study is conducted using qualitative methodology. From the perspectives of data users (telecommunication providers), it could be concluded that data management, data privacy, data compliance, and regulatory orchestration challenges are the most pressing concerns in big data adoption. This study offers contributions in presenting a thematic classification of security and privacy challenges and their mitigation strategies for big data adoption in the telecommunication industry. The thematic classification highlights potential gaps for future research in the big data security domain. This study is significant in that it provides empirical evidence for the perspectives of telecommunication data users in addressing privacy and security issues that are related to big data adoption.
Collusion Resistant Federated Learning with Oblivious Distributed Differential Privacy

Federated learning enables a population of distributed clients to jointly train a shared machine learning model with the assistance of a central server. The finance community has shown interest in its potential to allow inter-firm and cross-silo collaborative models for problems of common interest (e.g. fraud detection), even when customer data use is heavily regulated. Prior works on federated learning have employed cryptographic techniques to keep individual client model parameters private even when the central server is not trusted. However, there is an important gap in the literature: efficient protection against attacks in which other parties collude to expose an honest client's model parameters, and therefore potentially protected customer data.
Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering

Abstract We consider the problem of diversity enhancing clustering , i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the context of fair clustering , diversity plays a major role when fairness is understood as demographic parity. To promote diversity, we introduce perturbations to the distance in the unprotected attributes that account for protected attributes in a way that resembles attraction-repulsion of charged particles in Physics. These perturbations are defined through dissimilarities with a tractable interpretation. Cluster analysis based on attraction-repulsion dissimilarities penalizes homogeneity of the clusters with respect to the protected attributes and leads to an improvement in diversity. An advantage of our approach, which falls into a pre-processing set-up, is its compatibility with a wide variety of clustering methods and whit non-Euclidean data. We illustrate the use of our procedures with both synthetic and real data and provide discussion about the relation between diversity, fairness, and cluster structure.
Comparison of machine learning algorithms to predict optimal dwelling time for package tour

This paper shows the comparison between several well-known classification algorithms in Machine Learning with the purpose of finding the most suitable algorithm to predict the dwelling time, that is, how long a certain tourist should stay in a particular tourist spot. This dwelling time prediction can be adopted by tour and travel agents to provide optimal scheduling for their package tours. The algorithm in question is strictly for classification because in this case, the dwelling time does not require a very specific number of minutes; thus, the time can be classified and restricted into several time frames. The origin and features of the dataset are described in this paper as well as the comparison methodology to show the procedure of how the comparison was made. Lastly, the performance results will be used to determine which algorithm to use for this specific case and it will be shown in a form of a graph.
The Relative Importance of Digital Competences for Predicting Student Learning Performance: An Importance-Performance Map Analysis

Today’s higher education learning environment expects students to have such digital skills as navigating a learning management system, using word processing or presentation software, and searching for online information. The widespread use of digital technology in teaching and learning has necessitated a need for students to be digitally competent in order to perform well in their studies. However, different types of digital competence may be necessary at different stages of the students’ learning journey, and some digital competences may be more essential to students’ academic performance than others. To identify the digital competences that students perceive to be most important for their learning performance, and to determine students’ perception of their level of performance in these digital competences, this study adopted the Digital Competence Framework for Citizens 2.2 (DigComp) to design a survey questionnaire and collect responses from university students. An importance-performance map analysis (IPMA) was conducted to examine the relative importance of each of the five digital competence areas and the 21 digital competences identified in DigComp, as well as how each performs in relation to the others, in predicting student learning performance. Study findings revealed that the problem solving competence area was perceived to be the most important for student learning performance, followed by information and data literacy, and communication and collaboration. The respondents did not perceive the digital content creation and safety competence areas as important for their learning performance. The study findings also found that the respondents differed in their perception of how well they were performing in these competence areas. Using DigComp as a point of departure, this study makes a novel attempt to determine the relative importance of each of the five digital competence areas and 21 digital competences identified in the framework, as well as how each one compares to the others in performance when it comes to predicting how well students will perform in their learning.
Backchannels: Covert Digital Backchannels in the Overt Classroom

Abstract: A study of backchannel communication during lessons in higher education.&#x0D; This paper presents a study of how students utilise social media as backchannels for ‘reflection in action’, ‘reflections on action’, social banter and critique of the ongoing physical presence lesson with digital technologies. The research question was sparked by the overt ways in which the students communicated in the chat in videoconferences in online lessons during the first lock-down (March-June 2020). The students appeared to help each other and sort out practical and academic issues without involving the lecturer in the chat. This initial discovery led to an actual investigation of how the students communicate, digitally during physical presence lessons after the lock-down lifted. It became evident that most students engage in covert, digital communication during lessons. So, while the backchannel was overt and inclusive in online lessons, it turned into a covert exclusive practice in the physical, presence lessons. The paper introduces the notion of a ‘backchannel’ that carries undisclosed strands of student-initiated communication during a lesson. The study also identifies different types of backchannel communication that take place amongst students during lessons. Furthermore, the paper suggests a typological organisation of the different types of reflection the backchannels support. A backchannel is defined as a secondary, informal, unmanaged communication channel that happens simultaneously with a physical, presence lesson. The backchannel seems to be a process that either, covertly, support the students or, overtly, supports the lecturer and the students. The identified backchannels are divided into three different kinds of backchannels in the paper. The types found in the study include: ‘Rhizomatic, covert backchannels’, ‘Lecturer initiated backchannels’ and ‘hybrid joint contribution backchannels’. The paper investigates the pedagogical circumstances under which the backchannels emerge and how they are utilized. Furthermore, the article discusses the implications that covered, digital meta-communication during the lessons that may have on the relationship between lecturer and students. Lastly, the paper presents suggestions for how to use the covert backchannels as a constructive element in the lessons. Finally, the article suggests pedagogical practices that offer the students other possibilities to engage and express insecurities and general questions during lessons and presentations.
Video joint denoising and demosaicing with recurrent CNNs

Denoising and demosaicing are two critical components of the image/video processing pipeline. While historically these two tasks have mainly been considered separately, current neural network approaches allow to obtain state-of-the-art results by treating them jointly. However, most existing research focuses in single image or burst joint denoising and demosaicing (JDD). Although related to burst JDD, video JDD deserves its own treatment. In this work we present an empirical exploration of different design aspects of video joint denoising and demosaicing using neural networks. We compare recurrent and non-recurrent approaches and explore aspects such as type of propagated information in recurrent networks, motion compensation, video stabilization, and network architecture. We found that recurrent networks with motion compensation achieve best results. Our work should serve as a strong baseline for future research in video JDD.
Evaluation of Automatic Speech Recognition Approaches

Automatic Speech Recognition (ASR) is essential for many applications like automatic caption generation for videos, voice search, voice commands for smart homes, and chatbots. Due to the increasing popularity of these applications and the advances in deep learning models for transcribing speech into text, this work aims to evaluate the performance of commercial solutions for ASR that use deep learning models, such as Facebook Wit.ai, Microsoft Azure Speech, Google Cloud Speech-to-Text, Wav2Vec, and AWS Transcribe. We performed the experiments with two real and public datasets, the Mozilla Common Voice and the Voxforge. The results demonstrate that the evaluated solutions slightly differ. However, Facebook Wit.ai outperforms the other analyzed approaches for the quality metrics collected like WER, BLEU, and METEOR. We also experiment to fine-tune Jasper Neural Network for ASR with four datasets different with no intersection to the ones we collect the quality metrics. We study the performance of the Jasper model for the two public datasets, comparing its results with the other pre-trained models.