Reviewerly/exHarmony huggingface dataset:
Automated Tool for Resume Classification Using Sementic Analysis

Recruitment in the IT sector has been on the rise in recent times. Software companies are on the hunt to recruit raw talent right from the colleges through job fairs. The process of allotment of projects to the new recruits is a manual affair, usually carried out by the Human Resources department of the organization.This process of project allotment to the new recruits is a costly affair for the organization as it relies mostly on human effort. In the recent times, software companies round the world are leveraging the advances in machine learning and Artificial intelligence in general to automate routine tasks in the enterprise to increase the productivity. In the paper, we discuss the design and implementation of a resume classifier application which employs an ensemble learning based voting classifier to classify a profile of a candidate into a suitable domain based on his interest, work-experience and expertise mentioned by the candidate in the profile. The model employs topic modelling techniques to introduce a new domain to the list of domains upon failing to achieve the threshold value of confidence for the classification of the candidate profile. The Stack-Overflow REST APIs are called for the profiles which fail on the confidence threshold test set in the application. The topics returned by the APIs are subjected to topic modelling to obtain a new domain, on which the voting classifier is retrained after a fixed interval to improve the accuracy of the model.Overall, emphasis is laid out on building a dynamic machine learning automation tool which is not solely dependent on the training data in allotment of projects to the new recruits. We extended our previous work with new learning model that has the ability to classify the resumes with better accuracy and support more new domains.
A Study on the Present Perfect used for Topic Communication in CNN and Yonhap News Reports

The purpose of this research is to figure out the features of the present perfect tense used in news reports especially from a perspective of topic flow. Based on two data sets, we analyzed topic types that the present perfect carried and reached following results. First, the present perfect was used for four topic types such as ‘topic introduction’ in the head, ‘topic shift’ and ‘topic elaboration’ in the middle, and ‘topic termination’ in the end texts. Second, the present perfect was found to convey more general information than specific information. The present perfect conveying general information includes the types used for ‘topic introduction’ delivering hot news in the lead, ‘topic shift’ providing other new information derived from the lead, and ‘topic termination’ expressing the final summary in the end. The present perfect carrying specific information includes ‘topic elaboration’. Third, the present perfect was also used for ‘topic emphasis’ as the present perfect, a more marked type than simple tenses, entered various locations amongst multiple past and present simples to highlight the gist of news topics in a variety of contexts.
Traffic Police Gesture Recognition by Pose Graph Convolutional Networks.

Gestures from traffic police give the authorized information, especially in some urgent situation. Thus, understanding of traffic police instruction accurately and promptly is particularly crucial for the automated driving system. However, this task is a great challenge not only because of the dynamic and diversity characteristics of the human gesture, but also the high requirement for real-time performance in each frame. We propose an online activity recognition method based on pose estimation and Graph Convolutional Networks (GCN) to recognize the traffic police gesture in frame level. The main contribution in this work is the development of an online framework based on graph convolutional networks for traffic police recognition. Our approach obtained the state-of-the-art results on Traffic Police Gesture Recognition (TPGR) dataset.
A smart-contract based blockchain for a healthcare IoT network

The demand for IoT systems in healthcare services is increasing widely and the data generated from these networks should be transferred and stored in a highly secure manner. Blockchain technology helps in maintaining the privacy and integrity of the electronic health records (EHR) data with the help of smart contracts that authenticate the users and maintain confidentiality in the network. Integrating blockchain into IoT systems improves the overall security of the network. In this paper, we propose an architecture based on Hyperledger Fabric which is a private blockchain platform that is used for storing the EHR data which are collected from various IoT sensors. The deployed smart-contract helps in performing some of the basic database functionalities onto the blockchain.
Large-Scale Ab Initio Calculation of Ultrafast Dynamics in Thin-Film Dielectrics

We have achieved a large-scale ab initio simulation of light-matter interaction over 10,000 atoms. Nonlinear optical phenomena in glass thin film including high harmonic generation and light-matter energy transfer are described with unprecedented details.
PBL적용수업이 특성화 고등학교 학생의 직업기초능력에 미치는 영향

Modern education is changing from one-sided, knowledge-oriented, instructor-led education to active learning by learners themselves. In this study, the contents of classes that should be learned in the up-style curriculum of hairdressing were developed and applied based on PBL, confirming the effects of PBL classes on job basic skills and self-learning evaluations. In this study, the Cronbach''s α coefficient was calculated to see the internal consistency of the questions in order to verify the reliability of the questionnaires and the questionnaires for self-study assessment sheets, measured on a multiple question scale. In this study, the problem was developed with the aim of identifying changes in students by combining existing lecture-style classes with PBL-applied classes. To develop the PBL class model, the design model was first developed, and feasibility and usefulness were reviewed through expert advice. The model of the PBL class was designed in eight stages: 1 class development, 2 problem preparation, 3 group discussion, 4 individual learning activities, 5 solution design, 6 presentation and arrangement, 7 direct training, 8 final course evaluation, and applied to a total of 6 classes. As a result of this study, the application of the upstream class model based on PBL to learners was generally more satisfactory than the existing lecture class, and the effectiveness of PBL classes was verified. In order to enhance the satisfaction level of beauty education, diverse and effective teaching methods will need to be developed and applied, not conventional lecture-style classes.
3D Printing Intelligent Soccer Robot Match System

A desktop intelligent soccer robot match system based on 3D printing technology is designed.we use 3D printing technology to make soccer robot body, and open-source hardware like Arduino to make soccer robot control system. The four-wheel soccer robot controlled by Bluetooth is designed by using 3D printing technology. In an internet of things smart arena, the robot is controlled by mobile phone via Bluetooth, catch the football and shoot into the opponent's goal. The children in the game scan the QR code and install the APP, choose the team to join, participate in the game, and the score more goals to win. We also designed an intelligent table football field based on the Internet of Things technology with automatic referring and scoring, which combines desktop game and the innovation of robot game. It has the advantages of low cost and strong interest.
The importance of pedagogical speech in society

The article provides information on the requirements for the educator, and the importance of these requirements for the educator. It also details how much the educator should pay attention to the culture of speech, and the role of speech culture in human society
Center of Gravity Method for Finding Center of Laser Beam Projection on Landslide Measurement

Landslides have caused many casualties and property. High rainfall is a major cause of soil slip. Landslides will usually be preceded by creeping soil movements. Several types of ground motion detectors including inclinometers have been used to detect this state. Proposed method is using laser beam and camera as detector of landslide. As for the benefits for science is the development of monitoring methods using laser light which is very rare or even done so far. Signals in the form of laser beam projection images captured from the camera are sent using the internet network. The image will be processed so that it can be compared with images taken previously. Changes in laser point indicate ground shift. The accuracy of determining the point of the laser beam that spreads when it is received on the target board is corrected using the cluster center method. In addition, the placement of the control plate will make the direction of the laser beam always towards the target plate. The results of the study have concluded that this system can work well and accurately.
Post Training Mixed-Precision Quantization Based on Key Layers Selection

Model quantization has been extensively used to compress and accelerate deep neural network inference. Because post-training quantization methods are simple to use, they have gained considerable attention. However, when the model is quantized below 8-bits, significant accuracy degradation will be involved. This paper seeks to address this problem by building mixed-precision inference networks based on key activation layers selection. In post training quantization process, key activation layers are quantized by 8-bit precision, and non-key activation layers are quantized by 4-bit precision. The experimental results indicate an impressive promotion with our method. Relative to ResNet-50(W8A8) and VGG-16(W8A8), our proposed method can accelerate inference with lower power consumption and a little accuracy loss.
Property Inference From Poisoning.

Property inference attacks consider an adversary who has access to the trained model and tries to extract some global statistics of the training data. In this work, we study property inference in scenarios where the adversary can maliciously control part of the training data (poisoning data) with the goal of increasing the leakage. Previous work on poisoning attacks focused on trying to decrease the accuracy of models either on the whole population or on specific sub-populations or instances. Here, for the first time, we study poisoning attacks where the goal of the adversary is to increase the information leakage of the model. Our findings suggest that poisoning attacks can boost the information leakage significantly and should be considered as a stronger threat model in sensitive applications where some of the data sources may be malicious. We describe our \emph{property inference poisoning attack} that allows the adversary to learn the prevalence in the training data of any property it chooses. We theoretically prove that our attack can always succeed as long as the learning algorithm used has good generalization properties. We then verify the effectiveness of our attack by experimentally evaluating it on two datasets: a Census dataset and the Enron email dataset. We were able to achieve above $90\%$ attack accuracy with $9-10\%$ poisoning in all of our experiments.
Implementation of Digital Educational Games to Improve Learning Outcomes for Senior High School Students

The development of Information and Communication Technology (ICT) currently requires teachers to make learning innovations, one of which is learning media, namely digital educational games. This study aims to determine the effectiveness of implementing digital educational games to improve high school students' learning outcomes. This research type is a pre-experimental design with a one shoot case study conducted in class X Mathematics and Natural Sciences in Biology at SMA N 1 Kutasari. The digital educational game used is the digital education game Adventure of Plant, developed using the ADDIE model (Analyze, Design, Development, Implementation, Evaluation). The data collection techniques used were test techniques and questionnaires. The instruments used were in the form of a test instrument (Pretest-Postest) and a questionnaire. Data were analyzed using quantitative descriptive and statistical analysis using the Mann-Whitney U test. The results showed that the pretest means the value was 57.37 (sufficient) with a standard deviation of 8.87, and the posttest means the value was 77.24. (good) with a standard deviation of 8.74. The Mann-Whitney U test results showed a very significant difference (Sig. 0,000) between learning outcomes before and after the implementation of digital educational games. The performance of educational games also gives positive responses from students and teachers. Students provide an assessment of three aspects of the game: the learning aspect, the material aspect, and the display aspect, with a score of 3.34 - 3.56 (very good). The teacher's assessment results show a score of 3.0-3.45 in three aspects, namely the content aspect, the objective aspect, and the instructional aspect. This study concludes that the implementation of digital educational games effectively improves the learning outcomes of class X students at SMA N 1 Kutasari, Purbalingga.
A Taxonomy of Defensive Deception

The game theory described in Chaps. 2 and 3 offers versatile possibilities for quantifying cyber deception. Yet specific game-theoretic models must be carefully chosen in order to model different types of deception. In this chapter, therefore, we propose a taxonomy that classifies deception into six categories. We then propose game-theoretic models that fittingly describe each category. Our analysis is based on both theoretical considerations and an empirical study of 25 recent articles in game theory for defensive cyber deception. The taxonomy provides a systematic foundation for understanding the three types of defensive deception that we study in Chaps. 5 – 7 .
Cascaded Tracking via Pyramid Dense Capsules

The tracking-by-detection is a two-stage framework including, collecting the candidates around the target object and classifying each candidate as the target object or as background. Despite Convolutional Neural Networks (CNNs) based methods have been successful in tracking-by-detection framework, the own set of flaws of CNNs will still affect the performance. The underlying mechanism of CNNs that are based on the positional invariance (i.e., lose the spatial relationships between features) cannot capture the small affine transformations. This would ultimately result in drift. To solve this problem, we dig into spatial relationships endowed by the Capsule Networks (CapsNets) for tracking-by-detection framework. To strengthen the encoded power of convolutional capsules, we generate the convolutional capsules through a pyramid dense capsules (PDCaps) architecture. Our pyramid dense capsule representation is useful in producing comprehensive spatial relationships within the input. Besides, the critical challenges in the tracking-by-detection framework are how to avoid overfitting and mismatch during training and inference, where a reasonable intersection over union (IoU) threshold that defines the true/false positives is hard to set. To address the issue of the IoU threshold setting, a cascaded PDCaps model is proposed to improve the quality of candidates, and it consists of a sequential PDCaps model trained with increasing IoU thresholds to improve the quality of candidates sequentially. Extensive experiments demonstrate that our tracker performs favorably against state-of-the-art approaches.
Medical image segmentation based on the minimum spanning tree

Due to the complex background of medical image, the diversity of target features and the multiple effects of noise, the medical image segmentation has always been a difficult and hot topic in this field. To tackle this challenge, the paper combines the minimum spanning tree (MST) image segmentation model and proposes a new weight calculation standard. The weight calculation formula effectively solves the influence of noise in the medical image, and preserves the details in the image during the segmentation process, which is convenient for subsequent medical processing. The formula fully considers the spatial distance and vector relationship between pixels, and has good stability and application effect. We have proved through experiments that the segmentation algorithm has certain advancement among similar methods.
The Factors Of Organization And Management Of Processes Of Development Of Social Pedagogical Activity In Students

The article reflects the factors that must be taken into account when organising and managing the development of students' social and pedagogical activities, the specific features of the subjects studied, and the social functions organised. It was also noted that although students have knowledge in their field, there is a need for research into independent self-governance, socialisation in social society and the development of social pedagogical activities in the education system.Prospective reforms aimed at improving the system of ensuring the rights and freedoms of students and supporting their aspirations have been described. Opinions on developing the introduction of effective forms and methods of the educational process to ensure the development of social and pedagogical activity of young people based on educational requirements.
The Agda Universal Algebra Library and Birkhoff's Theorem in Martin-Löf Dependent Type Theory.

The Agda Universal Algebra Library (UALib) is a library of types and programs (theorems and proofs) we developed to formalize the foundations of universal algebra in dependent type theory using the Agda programming language and proof assistant. This paper describes the UALib and demonstrates that Agda is accessible to working mathematicians (such as ourselves) as a tool for formally verifying nontrivial results in general algebra and related fields. The library includes a substantial collection of definitions, theorems, and proofs from universal algebra and equational logic and as such provides many examples that exhibit the power of inductive and dependent types for representing and reasoning about general algebraic and relational structures. The first major milestone of the UALib project is a complete proof of Birkhoff's HSP theorem. To the best of our knowledge, this is the first time Birkhoff's theorem has been formulated and proved in dependent type theory and verified with a proof assistant.
Gibbs Parameter Sampling Algorithm Based on Finite Mixture Model of Random Point Pattern

A parameter sampling algorithm for finite mixture model of random point pattern by using the Gibbs sampling is proposed. First, the random finite set theory is used to construct finite mixture model of random point pattern. Secondly, using Gibbs sampling algorithm to estimate the parameters of mixture model. Finally, using BIC criterion to judge the effect of algorithm model fitting real data. In the simulations Gaussian mixture distribution is used as the characteristic distribution of random point pattern, several groups of random point pattern distribution are considered and compared with the traditional finite mixed model algorithm. The results show that the RPPFMM algorithm proposed in this paper is more accurate than the traditional FMM algorithm.
Penerapan Sistem Pakar pada Diagnosa Penyakit Tanaman Karet dengan Metode Forward Chaining (FC)

The study aimed to analyze rubber plant diseases using expert system techniques. Sources of data obtained from PT. Bridgestone Jl. Dolok Merangir Satu, Simalungun Regency by conducting observations and interviews with PT. Bridgestone. The presence of several diseases in rubber plants can make PT. Bridgestone incurs losses, so it needs proper handling. The solution given is the Forward Chaining (FC) method, which is part of an expert system so that a more efficient solution is to detect diseases in rubber plants. The results of the study indicate that the analysis can be carried out using the FC method in the diagnosis of rubber plants by implementing it into a web-based programming language so that the company can detect rubber plant diseases based on the symptoms given and find out solutions to overcome diseases in rubber plants.
Gesture Recognition Algorithm Based on New EMG Representation and Convolutional Neural Network

Gesture recognition based on EMG is a promising method for human-computer interaction. And it is important to improve the accuracy of EMG signal analysis. Current EMG gesture recognition has problems of long training time and low recognition accuracy. To solve these problems, a new EMG representation for gesture recognition algorithm based on convolution neural network is proposed. In this method, the organization of input matrix containing EMG information is improved to reduce the loss of feature extraction. And to enhance the time correlation between adjacent elements in the input matrix, the adjacent data in time domain are put together. Then the LeNet-5 network is applied for signal classification. The classic NinaDB1 data set is adopted in the test. Simulation results show that the method proposed has the advantages of high accuracy and high stability.
Semantically-Adaptive Upsampling for Layout-to-Image Translation

We propose the Semantically-Adaptive UpSampling (SA-UpSample), a general and highly effective upsampling method for the layout-to-image translation task. SA-UpSample has three advantages: 1) Global view. Unlike traditional upsampling methods (e.g., Nearest-neighbor) that only exploit local neighborhoods, SA-UpSample can aggregate semantic information in a global view. 2) Semantically adaptive. Instead of using a fixed kernel for all locations (e.g., Deconvolution), SA-UpSample enables semantic class-specific upsampling via generating adaptive kernels for different locations. 3) Efficient. Unlike Spatial Attention which uses a fully-connected strategy to connect all the pixels, SA-UpSample only considers the most relevant pixels, introducing little computational overhead. We observe that SA-UpSample achieves consistent and substantial gains on six popular datasets. The source code will be made publicly available.
Classification of Nucleotides Using Memetic Algorithms and Computational Methods

This paper presents an approach to solve an optimization problem using clustering by genetic algorithm approach. The central idea is to form clusters of patients’ nucleotide data sets. The genetic algorithm is applied to this initial cluster population. The fitness function for the genetic algorithm is calculated using intra-cluster and inter-cluster distances. Later genetic crossover functions are applied. This procedure is iterated until the stopping condition is reached. The superiority of this algorithm lies in comparing the performance with Ant Colony Optimization and simulated annealing algorithms.
Raba izraza oči so gledale v delih Ivana Cankarja

Sentences containing the compact phrase oci so gledale 'the eyes were looking', as well as phrases in which these three words are separated by some others, were isolated in works by Ivan Cankar and included in a set of 329 examples that served as a basis for research. Such phrases were used by Cankar in describing both male and female characters in various circumstances. The occurrences of such phrases in his works, their distribution in novels, as well as their length were examined quantitatively. The classification of a set of isolated examples was performed with regard to the use of adverbs with the verb gledati 'to look'.