Reviewerly/exHarmony huggingface dataset:
Accurate Object Recognition for Unmanned Aerial Vehicle Electric Power Inspection using an Improved YOLOv2 Algorithm

With the growing application of Unmanned Aerial Vehicle (UAV) in the electric power inspection, there is an increasing demand for accurate recognition of power tower components from a large number of pictures and videos. Traditional machine learning methods require feature extraction of, such as edge, color, texture, etc., are not suitable for the complex scene of the UAV electric power inspection. The end-toend object detection algorithm YOLOv2, offers overall high recognition accuracy and speed, but is less so in recognizing small targets . In this study, the YOLOv2 was improved by: 1)adding an additional passthrough layer to the original network to fuse more fine-grained features to improve the recognition capability for small targets, and 2) removing the redundant convolutional layers and reducing the number of the filters in the middle convolutional layers to simplify the network structure. The datasets are from Multi-rotor UAV inspection of high voltage transmission lines. Three types of power tower components including the tower, tower base, and signs are selected as recognition targets. Results showed that the mean average precision (mAP) of the improved model for three types of target recognition was increased to 98.19%, higher than that of the original one, and the recognition speed was increased by 35%.
Recovering Capitalization for Automatic Speech Recognition of Vietnamese using Transformer and Chunk Merging

In the last few years, Automatic Speech Recognition (ASR) systems for Vietnamese are utilized in various applications with exceptional results. Nevertheless, such ASR output still contains limitations such as the absence of punctuation, capitalization and standardize numeric data. These shortcomings cause difficulties for readers to understand context efficiently and for Natural Language Processing (NLP) tasks to be well-performed. Capitalization is one of the most critical factors to enhance human readability, parsing, and Named Entity Recognition (NER). Additionally, Vietnamese ASR output has its own features comparing to English such as lisp words, local words, compound words, and homophone. In this paper, we propose a method to Recover Capitalization for long-speech ASR transcription of Vietnamese using Transformer models and chunk merging. Furthermore, we perform decoding in parallel while improving the prediction accuracy.
Instance Hardness as a Decision Criterion on Dynamic Ensemble Structure

Dynamic Feature Selection selects the best feature subset for each individual instance. In other words, each instance will be classified using its own feature subset. Recently, this idea has been combined with other dynamic approaches to create dynamic ensembles, in which an ensemble structure is selected for each testing instance. Nevertheless, dynamic selection approaches (feature and classifiers) are time consuming, once they aim to select a different setting for each testing instance. The integration of these two dynamic approaches in ensembles have obtained good accuracy results, but at the same time, the computational complexity increases when these approaches are used together. Due to this fact, this paper introduces the idea of using a decision criterion to reduce the complexity of the dynamic approaches in ensembles. The main aim is to use a complex dynamic ensemble for difficult instances, while the other instances will be classified by single classifiers. In order to validate the proposed approach, an empirical analysis will be conducted. The results of the empirical analysis indicate that is possible to reduce the processing time using a simple decision criteria, Instance Hardness, without deteriorating the accuracy of these systems.
Buzzard Optimization Algorithm: A Nature-Inspired Metaheuristic Algorithm

Various algorithms have proposed during the last decade for solving different complex optimization problems. The meta-heuristic algorithms have been highly noted among researchers. In this paper, a new algorithm, known as the Buzzards Optimization Algorithm (BUZOA), is introduced. Marvelous and special lifestyle of buzzards and their competition characteristics for prey has been the basic motivation for this new optimization algorithm. The algorithm performance has been compared with newest and well-known meta-heuristics on some benchmark problems and test functions. Results have shown the high performance of the proposed BUZOA compared to the other well known algorithms.
Childhood in the digital age: a socio-cultural and legal analysis of the UK’s proposed virtual legal duty of care

Abstract In 2019, the UK government issued an ambitious White Paper as a precursor to the regulation of ‘online harms’. This article adopts a socio-cultural and legal approach to analysing the proposed law in the context of children. How childhood is conceptualized influences public policy and legal interventions, including on the digital space. This remains a contested terrain with different conclusion on the effects of the cyberspace. The biggest challenge with legal interventions on the digital realm is the need to achieve a balance between protection and participation rights of children. The dominant conception of childhood as a period of vulnerability has meant ‘protection’ often overrides participation rights. However, such focus is the subject of challenge, with some suggesting that regulation is the product of moral panic. A further strand is the potential of disproportionate punitive measures against Internet companies against the backdrop of human rights obligations. The UK proposition is discussed within this socio-cultural and legal contexts with the objective of highlighting challenges and legal pitfalls. This article argues that Internet governance ought to give serious consideration to the new sociology of childhood.
ONLINE PUBLIC ACCESS CATALOGS IN BULGARIAN UNIVERSITY LIBRARIES AN EMPIRICAL STUDY OF SEVEN-YEAR EVOLUTION

ONLINE PUBLIC ACCESS CATALOGS IN BULGARIAN UNIVERSITY LIBRARIES AN EMPIRICAL STUDY OF SEVEN-YEAR EVOLUTION
Training Convolutional Neural Network for Sketch Recognition on Large-Scale Dataset

With the rapid development of computer vision technology, increasingly more focus has been put on image recognition. More specifically, a sketch is an important hand-drawn image that is garnering increased attention. Moreover, as handheld devices such as tablets, smartphones, etc. have become more popular, it has become increasingly more convenient for people to hand-draw sketches using this equipment. Hence, sketch recognition is a necessary task to improve the performance of intelligent equipment. In this paper, a sketch recognition learning approach is proposed that is based on the Visual Geometry Group16 Convolutional Neural Network (VGG16 CNN). In particular, in order to diminish the effect of the number of sketches on the learning method, we adopt a strategy of increasing the quantity to improve the diversity and scale of sketches. Initially, sketch features are extracted via the pretrained VGG16 CNN. Additionally, we obtain contextual features based on the traverse stroke scheme. Then, the VGG16 CNN is trained using a joint Bayesian method to update the related network parameters. Moreover, this network has been applied to predict the labels of input sketches in order to automatically recognize the label of a sketch. Last but not least, related experiments are conducted, and the comparison of our method with the state-of-the-art methods is performed, which shows that our approach is superior and feasible
Research on the shared control technology for robotic wheelchairs based on topological map

Purpose A robotic wheelchair system was designed to assist disabled people with disabilities to walk. Design/methodology/approach An anticipated sharing control strategy based on topological map is proposed in this paper, which is used to assist robotic wheelchairs to realize interactive navigation. Then, a robotic wheelchair navigation control system based on the brain-computer interface and topological map was designed and implemented. Findings In the field of robotic wheelchairs, the problems of poor use, narrow application range and low humanization are still not improved. Originality/value In the system, the topological map construction is not restricted by the environment structure, which helps to expand the scope of application; the shared control system can predict the users’ intention and replace the users’ decision to realize human-machine interactive navigation, which has higher security, robustness and comfort.
Selector-Actor-Critic and Tuner-Actor-Critic Algorithms for Reinforcement Learning

This work presents two reinforcement learning (RL) architectures, which mimic rational humans in the way of analyzing the available information and making decisions. The proposed algorithms are called selector-actor-critic (SAC) and tuner-actor-critic (TAC). They are obtained by modifying the well known actor-critic (AC) algorithm. SAC is equipped with an actor, a critic, and a selector. The role of the selector is to determine the most promising action at the current state based on the last estimate from the critic. TAC is model based, and consists of a tuner, a model-learner, an actor, and a critic. After receiving the approximated value of the current state-action pair from the critic and the learned model from the model-learner, the tuner uses the Bellman equation to tune the value of the current state-action pair. Then, this tuned value is used by the actor to optimize the policy. We investigate the performance of the proposed algorithms, and compare with AC algorithm to show the advantages of the proposed algorithms using numerical simulations.
PEMANFAATAN MEDIA BERBASIS ICT “KAHOOT” DALAM PEMBELAJARAN PPKN UNTUK MENINGKATKAN MOTIVASI BELAJAR SISWA

Abstract: The effective learning is learning centered to the students. This is because students are the centered of learning. The involvement of students in learning is important to provide student’s experience. Some effort to involve students in learning is to provide a learning media. The appropriate learning media must certainly be adapted to the needs of the students in the class. The learning which was conducted at MAN 3 Palembang is based on the result of the analysis of the needs and the materials that the students showed are more enthusiasm if it is use a media. One of the appropriate media is ICT-based which can be easily accessed by students is ICT-based. Kahoot is one of ICT-based media, because it depends network and device. Kahoot can increase students’ motivation because it is multimedia and give challenges to the students. The purpose of this study is to determine the use of kahoot media in learning PPKn to increase student motivation in MAN 3 Palembang. The research method of this study is library research. The discussion of this study are the important of accuracy in the selection of instructional media, kahoot as ICT-based media, efforts to increase students’ motivation and utilization of kahoot media in PPKn learning to increase students’ motivation. Key words: ICT-Based Media, Kahoot, Students’ Motivation
Improving Deep Neural Networks

We’re still on the subject of gradient descent. But let’s now talk about gradient optimization, because of its importance to gradient descent. It is an optimization method for finding the minimum of a function, and it’s important in deep learning. It works to update the weights of the neural network through backpropagation.
How Indian libraries tweet? Word frequency and sentiment analysis of library tweets

The study attempts to map Twitter activity of selected Indian libraries using word frequency and sentiment analysis.Tweets of 18 libraries’ (5 academic libraries, 5 government libraries, 5 school libraries and 3 public libraries) were downloaded during June and July 2019. ‘R’ software was used for the analysis. The study finds that Indian libraries are less active on Twitter. The word cloud based on the most frequently occurring words from the Tweets observed variations in Tweets depend upon the type of libraries. Sentiment analysis of Tweets showed that most of the libraries’ Tweets are positive in nature. The study recommends that Indian libraries should use twitter to promote their collections and services.
Improving ultrasonic medical image quality by attenuation of the secondary lobes

In ultrasound imaging, the delay-and-sum beamforming constitutes a solid measure of the acoustic reflectivity of the scene. If it is applied to synthetic images following the Total Focus Method, it can generate an image with high resolution and contrast. The present work analyzes the data set that composes an image point from a statistical point of view and proposes a new beam formation technique capable of improving the contrast and resolution of the image.
Feature Selection and Feature Engineering

Feature selection and engineering are important steps in a machine learning pipeline and involves all the techniques adopted to reduce their dimensionality. Most of the time, these steps come after cleaning the dataset.
Prototype for a permissioned blockchain in aircraft MRO

Purpose: The paper aims to show strengths, weaknesses and challenges in the application of Blockchain technology for aircraft maintenance, repair and overhaul (MRO). The analysis is based on a prototype of a permissioned Blockchain for the maintenance documentation of aircraft and their components. Methodology: A prototyping approach is used to gain a deeper understanding of the underlying problems and proposed solutions of applying Blockchain technology to aircraft MRO. The open source platform 'Hyperledger Fabric' and the toolset 'Hyperledger Composer' are used to develop the permissioned Blockchain application. Based on devised use cases the prototype is validated and evaluated. Findings: Examining the prototype presents opportunities to increase data credibility and efficiency within a decentralized business network through increased transparency, traceability and the use of Smart Contracts. The Blockchain technology could help address challenges in the aircraft MRO industry such as the use of counterfeit spare parts or costly documentation verifications. Originality: In-depth descriptions of Blockchain use-cases can rarely be found. These are necessary in order to understand the requirements, strengths and weaknesses of using Blockchain in certain business contexts. The development of a prototype furthermore shows a deep insight in the conceptual design, development and usage of Blockchain applications.
Performance Evaluation of Autoencoder for Coding and Modulation in Wireless Communications

The end-to-end autoencoder is a novel and attracting concept to innovate communication system architecture. In its training stage, the end-to-end autoencoder needs differentiable channel models to execute back-propagation algorithm. This shortage impedes the further development of the end-to-end autoencoder. Estimating the channel model by reinforcement learning is a good way to alleviate this problem. However, these training methods increase the difficulty in the training stage. In this paper, we set up a general channel model and employ the measured channel data as input data to alleviate this problem. We compare the performance of the end-to-end autoencoder and Hamming code modulated by Binary Phase Shift Keying under additive white Gaussian noise channel and Rayleigh channel and explore the performance of the end-to-end encoder under the real scenarios. Furthermore, we investigate the performance of the end-to-end autoencoder under mismatched channels. The results demonstrate that the performance of the end-to-end autoencoder is similar with Hamming code and has a significant performance improvement under mismatched channels.
Convolutional Neural Network

In the previous chapters, we studied fully connected multilayer neural networks and their training, using backpropagation. In a typical multilayer neural network layer, with n input nodes and m neurons, we need to learn n × m parameters or weights. While a multilayer neural network may perform well in some cases—in particular, for those where the features of different dimensions are independent—there are some additional properties in the connection architecture that we might desire. For example, if it is known that the dimensions of the input data are strongly correlated or that the size of multilayer neural networks (both the number of layers and the number of neurons in each layer) must be limited for computational considerations, should there be any architectural changes introduced to a standard multilayer neural network to accommodate this additional constraint about the data or the network complexity?
NMT’s wonderland where people turn into rabbits. A study on the comprehensibility of newly invented words in NMT output

Machine translation (MT) quality has improved enormously since the arrival of neural machine translation (NMT). The most noticeable improvement compared to statistical MT systems is the increased grammaticality and fluency of the produced MT output. At the lexical level, the quality of NMT systems is less promising. New types of lexical mistakes appear in NMT output, such as the occurrence of non existing words, i.e. words that are not part of the vocabulary of the target language and were thus invented by the NMT system. For MT use cases in which readers only have access to the MT output without the source text, such non-existing words can affect comprehension as the intended source meaning may not be recovered. To investigate if and to what extent non-existing words in English-to-Dutch NMT output impair comprehension, an experiment was set up in SurveyMonkey. Eighty-six participants were given 15 non-existing words (5 single words and 10 noun compounds) and were either asked to describe the meaning of these words or to select the correct meaning from a predefined list. The words were presented either in isolation or in sentence context. Participants were asked to indicate how confident they were about their answer. Results show that non existing words indeed impair comprehension as in 60% of the cases the participants gave a wrong answer. Sentence context had a positive impact and made it easier for the participants to determine the meaning of the non-existing word. Participants were also more confident about their answer when the words were presented in sentence context.
Deterministic and stochastic models of labor forces dynamics in the industry with pure competition

This article discusses two classes of models that describe the development dynamics of the industry labor force with pure competition. In the first case, it is assumed that the factors influencing the production process are strictly deterministic. The second class of models is based on the assumption of stochastic dynamics of external (exogenous) parameters. In the first case, a deterministic model of the development dynamics of the industry labor forces is formulated. In the second case, its generalization to the stochastic case is considered.
TOPIK MODELING PENELITIAN DOSEN JPTEI UNY PADA GOOGLE SCHOLAR MENGGUNAKAN LATENT DIRICHLET ALLOCATION

The mapping of research topics for lecturers is necessary to determine the research tendencies in a department or study program. This study aims to implement topic modeling in the publication titles of the Department of Electronics and Informatics Education Engineering of Universitas Negeri Yogyakarta (JPTEI UNY) lecturers taken from Google Scholar. The method used for topic modeling is the Latent Dirichlet Allocation (LDA). LDA is a generative probabilistic model for finding the semantic structure of a corpus collection based on the hierarchical bayesian analysis. After the topic modeling process, the results showed that JPTEI UNY lecturers tend to have four research clusters consisting of vocational education, system development, learning media, and vocational learning systems.
Neural Boxer at the IWCS Shared Task on DRS Parsing

This paper describes our participation in the shared task of Discourse Representation Structure parsing.It follows the work of Van Noord et al. (2019), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders.We provide a detailed look in the performance of this model and show that (i) the benefit of the linguistic features is evident across a number of experiments which vary the amount of training data and (ii) the model can be improved by applying a number of postprocessing methods to fix ill-formed output.Our model ended up in second place in the competition, with an F-score of 84.5.
A New Method for Sentence Vector Normalization Using Word2vec

Word embeddings (WE) have received much attention recently as word to numeric vectors architecture for all text processing approaches and has been a great asset for a large variety of NLP tasks. Most of text processing task tried to convert text components like sentences to numeric matrix to apply their processing algorithms. But the most important problems in all word vector-based text processing approaches are different sentences size and as a result, different dimension of sentences matrices. In this paper, we suggest an efficient but simple statistical method to convert text sentences into equal dimension and normalized matrices Proposed method aims to combines three most efficient methods (averaging based, most likely n-grams, and word’s mover distance) to use their advantages and reduce their constraints. The unique size resulting matrix does not depend on language, Subject and scope of the text and words semantic concepts. Our results demonstrate that normalized matrices capture complementary aspects of most text processing tasks such as coherence evaluation, text summarization, text classification, automatic essay scoring, and question answering.
Dynamics of Existential Personality Fulfillment in the Course of Psychotherapy

The purpose of the study is to explore the clients' perceptions of therapeutic changes due to their existential fulfillment experience and authenticity in their relationships with the world. The content of the study reveals the subjective perceptions and experiences of clients about the changes in the understanding of themselves and the world in the course of existential psychotherapy. Consideration of the qualitative changes in a person's life as a result of psychotherapy was based on the concept of existential fulfillment and de-sedimentation of "I-structure". An opening up of opportunities for experiencing the fullness of human existence, as well as exploring ways of avoiding existential fulfillment, present themselves as key aspects in existential psychotherapy. Research methods are Existence Scale (A. Längle and C. Orgler); semantic differential for measuring therapeutic changes of clients in the course of existential psychotherapy; and factor analysis. Going through a psychotherapeutic experience has a positive effect on the dynamics of self-distancing indices, self-transcendence, freedom, and responsibility. The experience of existential personal fulfillment in psychotherapy leads to changes in human contact with oneself and the ability to successfully interact with the external environment. Experiencing the true existential level of living helps a person to be aware of their needs and to stay in contact with their feelings.