Reviewerly/exHarmony huggingface dataset:
Question Analysis towards a Vietnamese Question Answering System in the Education Domain

Abstract Building a computer system, which can automatically answer questions in the human language, speech or text, is a long-standing goal of the Artificial Intelligence (AI) field. Question analysis, the task of extracting important information from the input question, is the first and crucial step towards a question answering system. In this paper, we focus on the task of Vietnamese question analysis in the education domain. Our goal is to extract important information expressed by named entities in an input question, such as university names, campus names, major names, and teacher names. We present several extraction models that utilize the advantages of both traditional statistical methods with handcrafted features and more recent advanced deep neural networks with automatically learned features. Our best model achieves 88.11% in the F 1 score on a corpus consisting of 3,600 Vietnamese questions collected from the fan page of the International School, Vietnam National University, Hanoi.
Autoencoder-based image processing framework for object appearance modifications

Abstract The presented paper introduces a novel method for enabling appearance modifications for complex image objects. Qualitative visual object properties, quantified using appropriately derived visual attribute descriptors, are subject to alterations. We adopt a basic convolutional autoencoder as a framework for the proposed attribute modification algorithm, which is composed of the following three steps. The algorithm begins with the extraction of attribute-related information from autoencoder’s latent representation of an input image, by means of supervised principal component analysis. Next, appearance alteration is performed in the derived feature space (referred to as ‘attribute-space’), based on appropriately identified mappings between quantitative descriptors of image attributes and attribute-space features. Finally, modified attribute vectors are transformed back to latent representation, and output image is reconstructed in the decoding part of an autoencoder. The method has been evaluated using two datasets: images of simple objects—digits from MNIST handwritten-digit dataset and images of complex objects—faces from CelebA dataset. In the former case, two qualitative visual attributes of digit images have been selected for modifications: slant and aspect ratio, whereas in the latter case, aspect ratio of face oval was subject to alterations. Evaluation results prove, both in qualitative and quantitative terms, that the proposed framework offers a promising tool for visual object editing.
Blended Learning Technology Realization Using a Basic Online Course

This paper analyzes an approach to the realization of the blended learning technology with the use of a basic online course to integrate modern smart pedagogy in higher schools. A basic online course on discrete mathematics has been designed for bachelors of the intramural form of study at ITMO University. The Remote Laboratory Control Protocol (RLCP)-compatible virtual laboratory technology for MOOC’s “Methods and algorithms of graph theory” has been modified and improved. It allows using virtual stands for practical tasks in the training mode. The article outlines the results of experimental research that proves the effectiveness of the blended learning technology with the use of a basic online course. The average number of attempts to solve problems has reduced by 45% in comparison with that of the students who completed the MOOC; the progress assessment results have increased by 18.8% in comparison with the traditional learning format. Tolls among students show their satisfaction with the blended learning technology using a basic online course (almost 80%).
Efficient Replication for Straggler Mitigation in Distributed Computing

Master-worker distributed computing systems use task replication in order to mitigate the effect of slow workers, known as stragglers. Tasks are grouped into batches and assigned to one or more workers for execution. We first consider the case when the batches do not overlap and, using the results from majorization theory, show that, for a general class of workers' service time distributions, a balanced assignment of batches to workers minimizes the average job compute time. We next show that this balanced assignment of non-overlapping batches achieves lower average job compute time compared to the overlapping schemes proposed in the literature. Furthermore, we derive the optimum redundancy level as a function of the service time distribution at workers. We show that the redundancy level that minimizes average job compute time is not necessarily the same as the redundancy level that maximizes the predictability of job compute time, and thus there exists a trade-off between optimizing the two metrics. Finally, by running experiments on Google cluster traces, we observe that redundancy can reduce the compute time of the jobs in Google clusters by an order of magnitude, and that the optimum level of redundancy depends on the distribution of tasks' service time.
Information Extraction and Graph Representation for the Design of Formulated Products

Formulated products like cosmetics, personal and household care, and pharmaceutical products are ubiquitous in everyday life. The multi-billion-dollar formulated products industry depends primarily on experiential knowledge for the design of new products. Vast knowledge of formulation ingredients and recipes exists in offline and online resources. Experts often use rudimentary searches over this data to find ingredients and construct recipes. This state of the art leads to considerable time to market and cost. We present an approach for formulated product design that enables extraction, storage, and non-trivial search of details required for product variant generation. Our contributions are threefold. First, we show how various information extraction techniques can be used to extract ingredients and recipe actions from textual sources. Second, we describe how to store this highly connected information as a graph database with an extensible domain model. And third, we demonstrate an aid to experts in putting together a new product based on non-trivial search. In an ongoing proof of concept, we use 410 formulations of various cosmetic creams to demonstrate these capabilities with promising results.
Human Resource Management System Development at Smart University

An important development trend of a modern smart organization is effective personnel management. This is due to the need of increasing competitiveness, attracting applicants, expanding the market for educational services and areas of research work, the emergence of new developments, and improving key indicators of the organization. The main goal of the personnel management system is to ensure the quality, rational formation, and development of intellectual resources to achieve the economic efficiency and competitiveness of the smart university. Currently, the role of man in the activities of each organization is of great importance, as it forms the main strategic resource in the competition. Personnel management at the smart university raises a number of controversial questions regarding personnel selection methods, criteria for evaluating them, methods of certification, stimulation and motivation, and formation of career growth which determines the relevance of a scientific article. The article clarifies the main elements of the personnel management system of the smart university. The result of the study is the proposed model for the development of a personnel management system that takes into account the requirements for meeting the performance indicators of a smart university, as well as achieving the goals and objectives of its development strategy. The results were tested during the personnel management of the smart unit of the university on the example of the department.
Reducing Threats by Using Bayesian Networks to Prioritize and Combine Defense in Depth Security Measures

Studied in this article is whether the Bayesian Network Model (BNM) can be effectively applied to the prioritization of defense in-depth security tools and procedures and to the combining of those measures to reduce cyber threats.The methods used in this study consisted of scanning 24 peer reviewed Cybersecurity Articles from prominent Cybersecurity Journals using the Likert Scale Model for the article's list of defense in depth measures (tools and procedures) and the threats that those measures were designed to reduce.The defense in depth tools and procedures are then compared to see whether the Likert scale and the Bayesian Network Model could be effectively applied to prioritize and combine the measures to reduce cyber threats attacks against organizational and private computing systems.The findings of the research reject the H 0 null hypothesis that BNM does not affect the relationship between the prioritization and combining of 24 Cybersecurity Article's defense in depth tools and procedures (independent variables) and cyber threats (dependent variables).
Modeling of Residual Knowledge Estimation in Smart University

Smart university is the element of educational environment of smart education. Smart university forms an intellectual infrastructure that ensures the introduction, use, and development of smart technologies in education. The aim of the study is to develop and test models for estimation scale of residual knowledge level in a smart university. The research indicated that the source data is used to build a model formed of a mathematical apparatus and approaches to its development are based on the identification parameters. Estimation models are a part of analytics learning needed to study, monitor, and predict the dynamics of remaining knowledge. The models, aimed to estimate students’ residual knowledge, can be used by smart universities to (a) build individual educational trajectories, while correcting curricula in preparation for an independent assessment of knowledge, (b) for preparation for the accreditation examination, or (c) in preparation for professional and public examination. The received data can be collected and processed by educational data mining tools.
Some Theoretical Insights into Wasserstein GANs

Generative Adversarial Networks (GANs) have been successful in producing outstanding results in areas as diverse as image, video, and text generation. Building on these successes, a large number of empirical studies have validated the benefits of the cousin approach called Wasserstein GANs (WGANs), which brings stabilization in the training process. In the present paper, we add a new stone to the edifice by proposing some theoretical advances in the properties of WGANs. First, we properly define the architecture of WGANs in the context of integral probability metrics parameterized by neural networks and highlight some of their basic mathematical features. We stress in particular interesting optimization properties arising from the use of a parametric 1-Lipschitz discriminator. Then, in a statistically-driven approach, we study the convergence of empirical WGANs as the sample size tends to infinity, and clarify the adversarial effects of the generator and the discriminator by underlining some trade-off properties. These features are finally illustrated with experiments using both synthetic and real-world datasets.
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report Generation

Beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the image could be considered as irrelevant noise in the training procedure. Secondly, there are many similar sentences used in each medical report to describe the normal regions of the image, which causes serious data bias. This deviation is likely to teach models to generate these inessential sentences on a regular basis. To address these problems, we propose an Auxiliary Signal-Guided Knowledge Encoder-Decoder (ASGK) to mimic radiologists' working patterns. In more detail, ASGK integrates internal visual feature fusion and external medical linguistic information to guide medical knowledge transfer and learning. The core structure of ASGK consists of a medical graph encoder and a natural language decoder, inspired by advanced Generative Pre-Training (GPT). Experiments on the CX-CHR dataset and our COVID-19 CT Report dataset demonstrate that our proposed ASGK is able to generate a robust and accurate report, and moreover outperforms state-of-the-art methods on both medical terminology classification and paragraph generation metrics.
MOBILE-ASSISTED ON EXTENSIVE READING: STUDENTS’ VOICES

Abstract&#x0D; As reading starts to lose its popularity for most students, digital reading can serve as a solution to overcome this problem. The integration of digital reading and extensive reading practice can be beneficial for language learners. A number of researchers have conducted the studies on this issue through different perspectives. The current study mainly focuses on the students’ perception of digital reading in extensive reading practice. The qualitative approach was employed to obtain the research data. Therefore, series of questions were addressed to achieve the research objective through an open-ended interview. The result showed that the students have positive attitudes towards digital reading when they have to read extensively. The practicality, portability, flexibility, accessability, and affordances of e-books helped them to apply extensive reading successfully. Moreover, extensive reading benefits the students to develop their reading habit, discover pleasure in reading, as well as improve other skills in English. &#x0D; Keywords: digital reading, extensive reading, students’ perception&#x0D;
An Automated Tube Labeler for High-Throughput Purification Laboratories

Centralized high-throughput purification laboratories routinely produce large numbers of test tubes with fractions containing the purified compounds of interest interspersed with test tubes containing fractions collected from undesired peaks. Because the next step after purification entails the removal of the solvent in a centrifugal evaporator with multiple sample positions per rotor, select test tubes must be labeled prior to dry-down to track the identity of each compound. The diversity of test tube sizes and tray configurations from different chromatography system vendors complicates this labeling task. Therefore, the development of an automated tube labeler that can accommodate a multitude of test tube and tray sizes can reduce the chances of error as well as reduce the hands-on labor required to complete this tedious but essential task. Custom hardware and software have been implemented to inform and to enable the Pick-n-Place arm of a commercially available Tecan EVO robotic system to pick up and present select tubes, filled with purified chromatography fractions from a multitude of vendor trays, to a custom label application station integrated with a commercially available Zebra label printer. Particular challenges existed with accurately positioning tubes in Agilent G1364-84544 trays onto the deck of the instrument. The resulting instrument reduces hands-on time for labeling fractions by approximately 60%.
Network of Fuzzy Comparators for Ovulation Window Prediction

This paper presents the problem and the solution of ovulation date prediction based on simple data acquired by a woman in home environment. It describes a method of processing collected data as a multivariate time series. The novelty of this algorithm lies in its ability to predict the ovulation date and not only to retrospectively detect it. This is achieved by applying the fuzzy network of comparators (NoC) to compare the menstrual cycle being analyzed with the reference set of historical cycles.
Efficient Poverty Mapping using Deep Reinforcement Learning

The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure measurement, and forest monitoring. However, the accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. This creates a substantial hurdle to the efficient scaling and widespread adoption of high-resolution-based approaches. To reduce acquisition costs while maintaining accuracy, we propose a reinforcement learning approach in which free low-resolution imagery is used to dynamically identify where to acquire costly high-resolution images, prior to performing a deep learning task on the high-resolution images. We apply this approach to the task of poverty prediction in Uganda, building on an earlier approach that used object detection to count objects and use these counts to predict poverty. Our approach exceeds previous performance benchmarks on this task while using 80% fewer high-resolution images. Our approach could have application in many sustainability domains that require high-resolution imagery.
3D Face Reconstruction using Color Photometric Stereo with Uncalibrated Near Point Lights

We present a new color photometric stereo (CPS) method that recovers high quality, detailed 3D face geometry in a single shot. Our system uses three uncalibrated near point lights of different colors and a single camera. For robust self-calibration of the light sources, we use 3D morphable model (3DMM) [1] and semantic segmentation of facial parts. For reconstruction, we address the inherent spectral ambiguity in color photometric stereo by incorporating albedo consensus, albedo similarity, and proxy prior into a unified framework. In this way, we jointly exploit multiple cues to resolve under-determinedness, without the need for spatial constancy of albedo. Experiments show that our new approach produces state-of-the-art results from single image with high-fidelity geometry that includes details such as wrinkles.
Analysis of Rabin-P and RSA-OAEP Encryption Scheme on Microprocessor Platform

This paper presents an analysis of Rabin-P encryption scheme on microprocessor platform in term of runtime and energy consumption. A microprocessor is one of the devices utilized in the Internet of Things (IoT) structure. Therefore, in this work, the microprocessor selected is the Raspberry Pi that is powered with a smaller version of the Linux operating system for embedded devices, the Raspbian OS. A comparative analysis is then conducted for Rabin-p and RSA-OAEP cryptosystem in the Raspberry Pi setup. A conclusion can be made that Rabin-p performs faster in comparison to the RSA-OAEP cryptosystem in the microprocessor platform. Rabin-p can improve decryption efficiency by using only one modular exponentiation while produces a unique message after the decryption process.
Comparison of Classification Algorithms for Detecting Patient Posture in Expandable Tumor Prostheses

Autonomous tumor prostheses are extended without the need of a clinic and of a medical supervision.It is necessary to make sure that the patient is not standing before extending these prostheses.This study aims to determine the posture of the patient for expandable tumor prostheses by employing oft-used three machine learning-based classification methods through comparing them all with each other.Patient posture is determined by using accelerometer and gyroscope data from inertial control unit placed in autonomous expandable tumor prosthesis.By using the created dataset, 48 features are extracted.Then, for optimization, with feature selection, the number of features is reduced to 10.The selected features are processed using the decision tree, the k-nearest neighborhood and support vector machine algorithms.These algorithms were compared with each other using machine learning performance parameters.Accuracy, recall, precision and F-score values are calculated and compared.Consequently, support vector machine is determined as the most successful technique.Then, the model is tested on the experimental setup developed within the scope of the study, and the posture is determined.It is found that with this system, in the presence of a load on the prosthesis, it can be accurately detected at a rate of 97.1% (the recall parameter).
An FPGA Based Tracking Implementation for Parkinson’s Patients

This paper presents a study on the optimization of the tracking system designed for patients with Parkinson’s disease tested at a day hospital center. The work performed significantly improves the efficiency of the computer vision based system in terms of energy consumption and hardware requirements. More specifically, it optimizes the performances of the background subtraction by segmenting every frame previously characterized by a Gaussian mixture model (GMM). This module is the most demanding part in terms of computation resources, and therefore, this paper proposes a method for its implementation by means of a low-cost development board based on Zynq XC7Z020 SoC (system on chip). The platform used is the ZedBoard, which combines an ARM Processor unit and a FPGA. It achieves real-time performance and low power consumption while performing the target request accurately. The results and achievements of this study, validated in real medical settings, are discussed and analyzed within.
Integration of Agile Methodology and PMBOK Standards for Educational Activities at Higher School

Nowadays, business environment requires “agile” employees who are capable dealing with customers in violative and uncertain society. The research relevance of the study is justified by the necessity of higher educational institutions to design and develop an innovative approach to their activity at the educational market environment featured with strict competition. The aim of the scientific research is to analyze the standardization issues for educational processes. The research methods are based on a systems approach including system analysis and synthesis for the managerial model. The article deals with the innovative processes taking place in the conditions of the economy “4.0” applied for the educational system. The University Marketing and Management Model based on the project management requirements known as the PMBOK standards and agile design technologies is represented. The proposed management model is considered as being useful for strategic planning of smart university development. The model has been implemented and tested at three higher institutions of the Russian Federation.