Reviewerly/exHarmony huggingface dataset:
Comprehensive Application of Big Data in Lean Management of Distribution Network Operation

In order to solve the problems of distribution network, equipment, management difficulty, low-real-time distribution network information, this paper systematically analyzes the current situation of distribution network operation and maintenance. As well as the application and research of data mining and analysis processing technology in distribution network, big data mining application is introduced into daily operation and maintenance of distribution network, and lean management is carried out to strengthen the effective control and control of power network from the source, so as to eliminate waste and create value according to ‘eliminate waste, create value, continuous improvement, excellence’ requirements, all.
ОСОБЛИВОСТІ ЕТНОКУЛЬТУРНОЇ ПІДГОТОВКИ МАЙБУТНІХ УЧИТЕЛІВ МУЗИЧНОГО МИСТЕЦТВА

The study presents various aspects of training future music teachers. The author focuses on the variability of preparing future teachers for the challenges of our time, but taking into account the age-old traditions of different peoples and ethnic groups
Robotic Welding Optimization Using A* Parallel Path Planning

The world of robotics is in constant evolution, trying to find new solutions to improve on top of the current technology and to overcome the current industrial pitfalls. To date, one of the key intelligent robotics components, path planning algorithms, lack flexibility when considering dynamic constraints on the surrounding work cell. This is mainly related to the large amount of time required to generate safe collision-free paths for high redundancy systems.Furthermore, and despite the already known benefits, the adoption of CPU/GPU parallel solutions is still lacking in the robotic field. This work presents a software solution able of connecting the path planning algorithms with parallel computing tools, reducing the time needed to generate a safe path. The output of this work is the validation for the introduction of intelligent parallel solutions in the robotic sector.
A Hybrid Thresholding Strategy combining RCut and PCut for Multi-label Classification

Multi-label classification is a variant of the classification problem where multiple labels may be assigned to each instance. Usually multi-label classification algorithms output a numerical score for each label, indicative of their relevance to a query instance. However, in many applications the desired output is a bipartition of the labels into relevant and irrelevant w.r.t the query instance. Bipartitions can be obtained from scores using various thresholding strategies, such as PCut strategy which selects relevant instances per label, and RCut strategy which selects relevant labels per instance. However, we suggest that a combination of both strategies would provide better classification performance. In this paper, we propose a fuzzy-based approach to combine PCut and RCut strategies, by converting the crisp relevance into fuzzy one, merging them linearly, and defuzzifying again. Our experiments shows that our hybrid approach indeed outperforms both strategies.
Aspect-Based Sentiment Analysis of User Created Game Reviews

There is an increase in the selling of games through an online market such as Steam and with it the opinions of customers on the products. Using Python, this study developed a method that gathers reviews written on the Steam website and classifies them to specific aspects such as Audio, Gameplay, and Graphics and sub-categorizes them into Positive, Neutral, and Negative sentiments. The study used Support Vector Machine (SVM) Classifiers for both the Aspect Classifier and the Polarity Classifiers. The Aspect Classifier which classifies any combination of the three aspects yielded the highest accuracy when classifying Gameplay with Graphics at 97% and the highest precision and recall when classifying Gameplay with 86% and 83% respectively. While the Polarity classifiers for Audio, Gameplay, and Graphics yielded an accuracy of 91 %, 72%, and 84% respectively.
Multispectral Image Enhancement Based on Weighted Principal Component Analysis and Improved Fractional Differential Mask

Compared with single-band images, multispectral images with multiple wavelengths contain more spectral information and can fully reflect the detailed features of ground objects in different bands. To synthesize the feature information, a method of multispectral image enhancement based on improved weighted principal component analysis (WPCA) and improved fractional differential (IFD) filtering is proposed. First, the optimum index factor (OIF) model is modified to select the bands for easy follow-up processing. Then an improved WPCA transform that uses the average gradient and texture roughness is applied to compensate for each band. This approach preserves the main information, compresses the amount of image data, and obtains uncorrelated principal components. According to the correlation of neighboring pixels, a new mask is introduced to enhance the first principal component. Finally, the brightness values of the image are adjusted after inverse WPCA transform to obtain the final enhanced image. The experimental results demonstrate the superiority of the proposed method over related methods. Its future practical applications are discussed in the conclusion.
Effects of an Augmented Reality-Based Educational Game on Student’s Learning Performance and Motivation in Nature Course

The application of Augmented Reality (AR) on teaching and learning has been regarded as an important development at educational fields. Numerous studies have pointed out that incorporating the new technology into the learning model may increase interesting interaction and no longer limit to traditional instruction. Therefore, this study applies AR technology and ARCS model to develop the course plan of Mystery of Mineral board game. The Mystery of Mineral board game was designed for elementary students at a science course to learn rocks and mineral knowledge. A quasi-experiment was implemented to explore how integration of AR technology, board game, and ARCS model influence students' learning performance. The experimental results show compared to traditional instruction, applying AR technology into elementary science course can increase students' learning motivation and improve learning performance more effectively.
Profiled Attacks Against the Elliptic Curve Scalar Point Multiplication Using Neural Networks

In recent years, machine learning techniques have been successfully applied to improve side-channel attacks against different cryptographic algorithms. In this work, we deal with the use of neural networks to attack elliptic curve-based cryptosystems. In particular, we propose a deep learning based strategy to retrieve the scalar from a double-and-add scalar-point multiplication. As a proof of concept, we conduct an effective attack against the scalar-point multiplication on NIST standard curve P-256 implemented in BearSSL, a timing side-channel hardened public library. The experimental results show that our attack strategy allows to recover the secret scalar value with a single trace from the attacked device and an exhaustive search over a set containing a few hundreds of the sought secret.
Transferable Features from 1D-Convolutional Network for Industrial Malware Classification

With the development of information technology, malware threats to the industrial system have become an emergent issue, since various industrial infrastructures have been deeply integrated into our modern works and lives. To identify and classify new malware variants, different types of deep learning models have been widely explored recently. Generally, sufficient data is usually required to achieve a well-trained deep learning classifier with satisfactory generalization ability. However, in current practical applications, an ample supply of data is absent in most specific industrial malware detection scenarios. Transfer learning as an effective approach can be used to alleviate the influence of the small sample size problem. In addition, it can also reuse the knowledge from pre-trained models, which is beneficial to the real-time requirement in industrial malware detection. In this paper, we investigate the transferable features learned by a 1D-convolutional network and evaluate our proposed methods on 6 transfer learning tasks. The experiment results show that 1D-convolutional architecture is effective to learn transferable features for malware classification, and indicate that transferring the first 2 layers of our proposed 1D-convolutional network is the most efficient way to reuse the learned features.
Research on SPARQL Semantic Query Technology Based on Knowledge Hybrid Storage

At present, knowledge graphs are usually expressed in RDF and given semantics. SPARQL is a semantic query language recommended by W3C. With the development of the Semantic Web technology, the current different storage modes for knowledge management mainly have the incompatibility of support for massive knowledge storage and semantic query standards. For example, Neo4j cannot use SPARQL for semantic query. Aiming at the low efficiency of technology fusion query conversion under the knowledge storage hybrid storage mode, this paper introduces the Apache TinkerPop graph calculation framework to build a bridge between RDF graphs and attribute graphs, and proposes a method for converting SPARQL queries to Gremlin attribute graph queries. Realize the semantic query ability under the massive knowledge storage, and make up for the shortcomings of different storage modes. In addition, TinkerPop must use a certain constant key value for binding query to expand to meet the completeness requirement of knowledge query. Finally, the experiment was verified by building a microservice API with Spring Boot.
Recognition of Emotions in User-Generated Videos through Frame-Level Adaptation and Emotion Intensity Learning

Recognition of emotions in user-generated videos has attracted considerable research attention. Most existing approaches focus on learning frame-level features and fail to consider frame-level emotion intensities which are critical for video representation. In this research, we aim to extract frame-level features and emotion intensities through transferring emotional information from an image emotion dataset. To achieve this goal, we propose an end-to-end network for joint emotion recognition and intensity learning with unsupervised adversarial adaptation. The proposed network consists of a classification stream, an intensity learning stream and an adversarial adaptation module. The classification stream is used to generate pseudo intensity maps with the class activation mapping method to train the intensity learning subnetwork. The intensity learning stream is built upon an improved feature pyramid network in which features from different scales are cross-connected. The adversarial adaptation module is employed to reduce the domain difference between the source dataset and target video frames. By aligning cross domain features, we enable our network to learn on the source data while generalizing to video frames. Finally, we apply a weighted sum pooling method to frame-level features and emotion intensities to generate video-level features. We evaluate the proposed method on two benchmark datasets, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> VideoEmotion-8 and Ekman-6. The experimental results show that the proposed method achieves improved performance compared to previous state-of-the-art methods.
DICO-2 : An Improvement of the Contextualized Lexical Resource DICO for Educational Opinion Mining Using Machine Learning Classifiers

Opinion mining is currently considered one of the most active research fields. It aims to analyze people's feelings, which makes it an important element in the decision-making process. It is introduced in different fields, especially in the education one. The objective of this work is to improve a lexical resource called DICO, which we have presented in our previous works for educational opinion mining, through a recalculation of polarities, in order to improve the opinion detection process. This improvement is done by training machine learning classifiers with EDUCA, a corpus built from educational communities, including a set of manually annotated subjective comments. This approach is implemented using the WEKA platform which allows the generation of classification models. These models classify the terms of the lexical resource DICO and, the polarities attributed by these models are used for their recalculation to get a better lexical resource called DICO-2. This last is evaluated by comparing the classification of the EDUCA corpus using DICO with that one using DICO-2. The results obtained show a better classification of opinions with increase of the performance, especially the accuracy.
Deep Learning-based Face Mask Usage Detection on Low Compute Resource Devices

The advent of the COVID-19 pandemic has brought several never-before-seen changes in the daily lives of people around the globe. As a way to curb the spreading of the disease, wearing face masks has become mandatory in the majority of public places. To solve the necessity of face mask detection in such situations, there have been only a handful of research endeavors up to this date. Computer vision has advanced multi-fold with the advent of AlexNet architecture. With a motivation to go deeper with the neural network architecture, the concept of Depthwise Separable Convolutions and projection layer was developed in MobileNetV1. In this work, a novel lightweight deep learning model based on Single Shot Detector (SSD) MobileNetV2 architecture is proposed for face mask detection using images and video streams of crowds aiming its utilization on low compute re-source environment. An open benchmark face mask dataset, with 4095 images including masked and no mask images, is utilized to train the model for detection. The model is initialized using transfer learning with the freezing of base layers. The proposed methodology can efficiently aid in tracking and enforcing social distancing rules in crowded places with the use of surveillance cameras. On the different benchmarks that we have tested, the model proved to be highly successful and has achieved an accuracy rate of 99.39% and an F1 score of 0.995.
KDA-Based WKNN-SVM Method for Activity Recognition System From Smartphone Data

This article describes a new scheme for a physical activity recognition process based on carried smartphone embedded sensors, such as accelerometer and gyroscope. For this purpose, the WKNN-SVM algorithm has been proposed to predict physical activities such as walking, standing, or sitting. It combines weighted k-nearest neighbours (WKNN) and support vector machines (SVM). The signals generated from the sensors are processed and then reduced using the kernel discriminant analysis (KDA) by selecting the best discriminating components of the data. The authors performed different tests on four public datasets where the participants performed different activities carrying a smartphone. They demonstrated through several experiments that KDA/WKNN-SVM algorithm can improve the overall recognition performances and has a higher recognition rate than the baseline methods using the machine learning and deep learning algorithms.
My encounters with free radical biology: a summing up

This paper studies the following approximate minimization problem: given a minimal weighted tree automaton A with n states recognizing a weighted tree language f, can we construct a smaller automaton Aˆ with nˆ<n states recognizing a language fˆ that is a good approximation of f? The corresponding problem for weighted automata on strings was recently studied by Balle et al. [16], [15], where the authors introduced a new canonical form for weighted automata called singular value automata inspired by spectral methods, and showed that truncating such canonical form yields a solution for the problem satisfying a certain approximation criteria. In this paper we take a similar approach and show that in the tree case one can obtain an analogous canonical form that we call singular value tree automata, and use it to study the approximate minimization problem for weighted tree automata. We first establish the existence of this canonical form for weighted tree automata and then provide bounds on the quality of the resulting approximation method based on truncation. We also study the problem of computing the canonical form given a minimal weighted tree automaton and show that in the tree case this task is considerably more complicated than in the string case. In particular, computing the canonical form reduces to solving a system of polynomial equations. By further reducing this problem to the computation of generalized partition functions for weighted tree automata, we propose and analyze two methods for computing the canonical form based on iterative methods: fixed point iteration and Newton's method. Our analysis of Newton's method unveils a connection between iterative methods and sequences of sets of trees satisfying a certain technical condition that might be of independent interest.
METHODOLOGY OF SYSTEM DEVELOPMENT TO FORM READINESS OF PEDAGOGICAL UNIVERSITY STUDENTS FOR MEDIA EDUCATIONAL ACTIVITIES AND TECHNOLOGY OF ITS IMPLEMENTATION

Modern requirements for future teachers are determined by the processes of informatization and digitalization of education and by the increasing use of modern media in the educational process. These trends actualize the preparation of future teachers for the implementation of effective media educational activities. This kind of activity can be effective only if future teachers have a corresponding readiness. The purpose of the article is to determine the essence of students' readiness for media educational activities, to describe the system of its formation, to identify relevant methodological approaches and principles for implementing this, and to develop a stepbystep technology to form the readiness for media educational activities in future teachers. The novelty of the research lies in the fact that it is conducted in line with the idea of a systemic and stage-by-stage formation of the studied readiness using innovative forms and methods of teaching students. The results of the research are the developed blocks of the system and technology of formation of readiness for media educational activity among students of a pedagogical university. The results can be used in training bachelor students.
Political inclusion and democracy in Africa: some empirical evidence

The objective of this study is to examine the effect of political inclusion on democracy in Africa. The results of the analyses through the OLS, system GMM, IV-Tobit and IV-2SLS show that political inclusion enhances democracy in Africa. This result is robust across alternative specifications of political inclusion and democracy. Besides, the results equally stood when controlled for colonisation and internal conflicts. As policy implications, policy makers in Africa should enhance their fight for political inclusion as one of the gateways to promoting democracy. In this respect, national laws could be put in place, which impose gender quotas in political positions in every country. Equally, the African Union could sign a convention on these quotas for respective countries to ratify.
Design Considerations within Cloud Based System-of-Systems Architecture Framework

System-of-Systems design has focused on integrated service offerings. The underlying expectation is that services have a reliance on each other to achieve a greater level of service and outcomes. Within this context, Cloud Computing provides a service consideration that extends system-of-systems design and thinking. Integration with cross provider services, operational and architectural design, systems operations, trust, and cost analysis must all be considered when addressing complex, cloud-based application design. This paper opens a discussion on these key attributes and provides a framework for future research whilst introducing key operational requirements to enable successful implementation of cross hyperscaler system-of-systems architectural models.
Temporal Ensemble SSDLite: Exploiting Temporal Correlation in Video for Accurate Object Detection

We propose “Temporal Ensemble SSDLite,” a new method for video object detection that boosts accuracy while maintaining detection speed and energy consumption. Object detection for video is becoming increasingly important as a core part of applications in robotics, autonomous driving and many other promising fields. Many of these applications require high accuracy and speed to be viable, but are used in compute and energy restricted environments. Therefore, new methods that increase the overall performance of video object detection i.e., accuracy and speed have to be developed. To increase accuracy we use ensemble, the machine learning method of combining predictions of multiple different models. The drawback of ensemble is the increased computational cost which is proportional to the number of models used. We overcome this deficit by deploying our ensemble temporally, meaning we inference with only a single model at each frame, cycling through our ensemble of models at each frame. Then, we combine the predictions for the last N frames where N is the number of models in our ensemble through non-max-suppression. This is possible because close frames in a video are extremely similar due to temporal correlation. As a result, we increase accuracy through the ensemble while only inferencing a single model at each frame and therefore keeping the detection speed. To evaluate the proposal, we measure the accuracy, detection speed and energy consumption on the Google Edge TPU, a machine learning inference accelerator, with the Imagenet VID dataset. Our results demonstrate an accuracy boost of up to 4.9% while maintaining real-time detection speed and an energy consumption of 181mJ per image.
From Graphematics to Phrasal, Sentential, and Textual Semantics Through Morphosyntax by Means of Corpus-Driven Grammar and Ontology: A Case Study on One Tibetan Text

Abstract This article presents the current results of an ongoing study of the possibilities of fine-tuning automatic morphosyntactic and semantic annotation by means of improving the underlying formal grammar and ontology on the example of one Tibetan text. The ultimate purpose of work at this stage was to improve linguistic software developed for natural-language processing and understanding in order to achieve complete annotation of a specific text and such state of the formal model, in which all linguistic phenomena observed in the text would be explained. This purpose includes the following tasks: analysis of error cases in annotation of the text from the corpus; eliminating these errors in automatic annotation; development of formal grammar and updating of dictionaries. Along with the morpho-syntactic analysis, the current approach involves simultaneous semantic analysis as well. The article describes semantic annotation of the corpus, required by grammar revision and development, which was made with the use of computer ontology. The work is carried out with one of the corpus texts – a grammatical poetic treatise Sum-cu-pa (VII c.).
Adaptive Biometric Cloud Facial Aging Template Policy - A Time Point Analysis

In this paper we use Facenet, an open-source machine learning cloud platform to verify biometric facial templates created with the ones already enrolled. Our efforts seek to determine if, for different sample images of different ages when evaluated against our pattern matching algorithm, the template policy changes with the aged images. Whilst our approach may hold true for small data set changes that the template policy does not see any anomalies, all other things being equal, as in the results in the current paper, we argue that the template policy could change if we explore the concerns over a large continuous data set as our next step and treated as independent work to this current paper.