Reviewerly/exHarmony huggingface dataset:
SDP: Scalable Real-time Dynamic Graph Partitioner

The time-evolving large graph has received attention due to it's participation in real-world applications such as social networks and PageRank calculation. It is necessary to partition a large-scale dynamic graph in a streaming manner in order to overcome the memory bottleneck while partitioning the computational load. Reducing network communication and balancing the load between the partitions are the criteria for achieving effective run-time performance in graph partitioning. Moreover, an optimal resource allocation is needed to utilise the resources while storing the graph streams into the partitions. A number of existing partitioning algorithms have been proposed to address the above problem. However, these partitioning methods are incapable of scaling the resources and handling the stream of data in real-time. In this study, we propose a dynamic graph partitioning method called Scalable Dynamic Graph Partitioner(SDP) using the streaming partitioning technique. The SDP contributes a novel vertex assigning method, communication-aware balancing method, and a scaling technique in order to produce an efficient dynamic graph partitioner. Experiment results show that the proposed method achieves up to 90% reduction of communication cost and 60%-70% balancing the load dynamically, compared with previous algorithms. Moreover, the proposed algorithm significantly reduces the execution time during partitioning.
Examining Korean EFL learners’ reactions using audience response systems in vocabulary learning

Developing technology provides more learning opportunities than ever before to support a game training environment for language education. It can enhance language experience for learners in constructing knowledge to acquire language through gamified audience response systems. Considering vocabulary is a critical component of English language learning, it is vital to find effective ways of incorporating technology into learning. More importantly, understanding language learners’ reactions are a crucial factor for positive learning effects as they lead to effective teaching strategies. Thus, this study examined 244 Korean EFL university learners’ reactions towards three gamified response programs―Socrative, Kahoot, and Quizlet―in vocabulary learning. Participants joined eight game activities, consisting of five activities for individual play and three for group play. This study also introduced how an instructor can create game activities and how language learners interact through three audio response systems (ARS) with free-to-play web browser games. The results showed that most participants responded positively to using gamified ARS programs, and the participants who were of a higher English level preferred to use game learning slightly more. In addition, all participants showed different attitudes for their preferred ARS program and the game activities for both individual and group play.
A Recurrent Neural Network Based on Multi Feature Fusion Module and Enhanced Bi-LSTM for Text Matching

In the field of Chinese text matching, it is difficult to matching similarity text because of the complex grammatical structure, the difficulty of word segmentation and the polysemy phenomenon. At present, neural network has been widely used in various fields, especially convolutional neural network and recurrent neural network. The method based on neural network can automatically learn text feature from multi-dimensional information of a large-scale text data. It's good at capturing text semantic, solving the problem of independent between words for traditional word vector model. But training neural network model is prone to gradient explosion and gradient disappearance leading to poor training results. In this paper, a recurrent neural network based on multi feature fusion module and enhanced Bi-LSTM is proposed to fully extract the correlation of sentences and improve the accuracy of text matching. In our method, a multi feature fusion module transforms the sentence into a matrix form of word vector and an enhanced Bi-LSTM module which is shared parameters extracts the features of each sentence independently. Moreover, in order to measure the similarity of the two sentences, manhattan similarity measure is chosen to help our neural network achieve faster convergence and higher accuracy. Experimental results show that our proposed method is superior in terms of accuracy to Siamese LSTM method and Siamese BI-LSTM method.
Data Mining Approach in Personnel Selection: The Case of the IT Department

Data mining studies have been frequently included in the literature recently. Data mining can be applied in every field, especially in banking, marketing, customer relationship management, investment and portfolio management. In the literature, the problem of personnel selection has been examined with the help of multi-criteria decision-making techniques. In this study, it has been aimed to apply data mining techniques in the field of human resources where relatively little has been used. The features of a large-scale construction company have been determined according to the competencies specified in the information technologies department announcement. The candidates were ranked according to these attributes. While ranking, accuracy values have been compared by using basic algorithms of data mining techniques. While applying the process steps, the necessary data pre-processing techniques have been applied to candidates who entered incomplete or incorrect information during the application process. Basically, the decision trees algorithm gave the highest accuracy. Also, random forest, adaboost, gradient boosting, and xgboost algorithms have been tried. In addition, it has found the attributes that should be looked at first in the application features. The high number of data enabled machine learning to learn information more easily and to weigh the existing criteria easily. With this study, it has been aimed to obtain a more objective result by weighting with machine learning algorithms instead of weighting the personnel selection problem with multi-criteria decision-making methodology. In addition, it is an extremely difficult process to interview candidates for recruitment under the current Covid-19 pandemic conditions that the whole world and our country are struggling with. Online conversations take a lot of time. With this study, it has been aimed to provide optimization by automating the process by weighting the features related to the existing data in the process. The study has been done in the WEKA and Python program.
Refining User Ratings Using User Emotions for Recommender Systems

In recommender systems, an item is recommended to a new user by analyzing purchase history of existing users along with the items’ information. In this area, collaborative filtering is a popular approach to predict user’s preferences from the existing users’ preferences. The user’s preferences are identified from the user rating and/or review data. It is observed in the real-world data that user is not expressing the same feelings in user rating and review text. As a result, the accuracy of the recommender system is affected. In this paper, we address this inconsistency and propose an approach called Emotion-Specific Prediction to refine the user ratings by applying proposed emotion detection algorithm on review text. The emotion detection algorithm extracts the user feelings as emotional features by exploiting the multi-polarity from review text. The proposed approach transforms these features into refined ratings and are used to predict the user ratings using collaborative filtering. The experimental evaluations conducted on real-world Amazon and Yelp data sets, and results show that the proposed approach reduces root mean square error (RMSE) and Normalized RMSE (NRMSE) significantly.
Efficient VLSI Architecture for Edge Sensing Anti-Aliasing Demosaicing

The majority of digital cameras reconstruct full-color images from raw data captured by image sensors through a process called demosaicing. In this paper, we propose an edge sensing anti-aliasing demosaicing (ESAAD) algorithm for the Bayer pattern, which is the most widely used color filter array (CFA) layout, and an efficient very-large-scale integration (VLSI) architecture for hardware implementation of the ESAAD. Traditional demosaicing methods recover the RGB channels of pixels on and not on edges in the same way while ESAAD detects edges from the RAW images and recovers pixels on and not on edges by different strategies. In this way, we can improve the visuality of recovered edges greatly compared with previous demosaicing methods. In addition, the simplicity of the ESAAD enables a hardware-friendly VLSI architecture which only requires 2 lines of line buffers. Under the same conditions of the process technology with 180nm CMOS, the image resolution with 1280×720 (HD), and the working frequency with 200MHz, the performance of this work significantly outperforms all the other methods on resources usage including both gate counts and core area, and power consumption.
A Novel Method to Denoise CEUS Image Combining Bidirectional ConvLSTM with 3D DnCNN

Due to the limitation of imaging technology, there is a lot of speckle noise in contrast-enhanced ultrasound (CEUS) images, which affects the improvement of resolution and the correct diagnosis made by doctors. It is difficult for existing methods to balance better image denoising and more detailed preservation. This paper presents a network combining bidirectional ConvLSTM (Convolutional Long Short-Term Memory) with 3D DnCNN to generate clearer images which we name BCDCNN. This makes full use of the correlation between CEUS video frame sequences. Firstly, we design an abnormal frame detection module to filter out abnormal frames from the video and combine several CEUS videos with synthetic CEUS videos to avoid over-fitting and improve the generalization ability of the model. Secondly, a three-dimensional convolution operator is imported to improve the two-dimensional DnCNN denoising model, so as to process image sequences on the time dimension. Finally, the bidirectional ConvLSTM is imported to learn the time characteristics of video, which makes full use of the inter-frame information. By observing the visual quality and various quantification indicators, our method performs better in denoising and detail preservation than the current mainstream methods, which is conducive to future medical analysis and diagnosis.
ASIST: Automated Strategic Partners Ranking and Recommendation System Using NLP and Security Intelligence

Strategic and Technological environment is changing rapidly in cyberspace. A robust cybersecurity ecosystem is driven by identifying an ideal corporate partnership. Performing research and analysis on a potential cybersecurity partnering company involves manual and labor-intensive processes without a standardized evaluation framework. Adding on, the task of performing security evaluation on the company's products is often overlooked and neglected in the research and analysis process. The proposed solution - ASIST - fills this gap in the overall comprehensiveness of the analysis made on a company. Firstly, the News Crawler Module was developed to automatically extract relevant cybersecurity news which is then passed to the Natural Language Processing Module to perform analysis and Strategy Roadmap Score tabulation. Concurrently, the Common Vulnerability Scoring System (CVSS) Crawler Module extracts the product security information of the company and performs tabulation of the Product Security Score. At the final stage of program runtime, the companies are ranked by the Reporting Module, utilizing the two scores that were tabulated by the prior modules. The Strategy roadmap score and Product Security score evaluated by ASIST are on an average within 5% of the manually evaluated scores from professional industry analysts.
Video Summarization by DiffPointer-GAN

Recent years have witnessed the explosive growth of video and clips with the popularization of mobile devices that are deeply shaping people’s life. Video summarization is an important approach to highlight and label the videos. Since the requirement of video summarization in practice is diverse due to the consumer majority and videos themselves, it is difficult for a neural network that extracts specific features to be adapted according to the changing requirements. In this paper, we propose a novel method of video summarization that employs the characteristics of the bullet-screen comments as a reference and train a novel neural network to generate a short clip as the video summary. First, we propose a method to select candidate clips for video summarization based on the distribution of the bullet-screen comments and these candidates are annotated manually to construct our labelled dataset from the Bilibili video library. Second, we propose and train a novel network combined with the pointer network and the General Adversarial Networks, named by diffPointer-GAN, to directly identify the start and end of the summary clip, other than the previous process composed of scoring, segmentation and selecting. Finally, we demonstrate that the proposed method has a definite priority to two baseline methods that are implemented on our dataset.
Research on Dimensional Model Design and Application Method in State Grid

This article aims to quickly, accurately, and flexibly construct a dimensional model of the state grid. An OLAP modeling scheme based on relational database and XML is proposed.
Ensuring robustness of solar tracker control systems under wind disturbances

The problem of increasing the energy efficiency of solar power plants through the use of solar trackers is considered. It is noted that the tracking accuracy is influenced by a large number of external factors, among which the wind load takes the leading place. In this regard, it is of great importance to develop an algorithm for the operation of a solar tracking controller and a control unit for electromechanical actuators, which allows maintaining the required tracking accuracy in a given range of wind loads. A method of robust stabilization of solar tracker control systems based on the concept of superstability of dynamic systems is proposed. It is shown that the use of the Ostrovsky's theorem for localizing the characteristic numbers of a closed-loop system allows one to reduce the synthesis problem to solving a system of algebraic inequalities with respect to unknown parameters of the control part of the system.
A Spectrum Monitoring and Analyzing Algorithm Based on Sparse Fourier Transform

Nowadays, cutting down on Fast Fourier Transform (FFT) calculating amount remains challenging in spectrum monitoring and analyzing field. The principle of Fast Fourier Transform determines that it consumes equal amount of calculation no matter whether the signal is sparse or dense. Considering this fact, this paper studies on spectrum analyzing algorithm based on Sparse Fourier Transform (SFT). The main theory of this algorithm is locating large value points of signal through Hash Mapping and frequency domain down-sample. After finding large values, Fourier Transform will be conducted only on these points, reducing mathematic operations. This method uses short point FFTs instead of large point ones, which is capable to be promoted even when lacking in hardware resource.
General Didactic Principles About University Education in Music in the Midst of a Pandemic

This article discusses some of the most important prerequisites for building and organizing the didactic structure of music education at the university level in the midst of a pandemic. The emphasis is on the methodology in designing online learning. The principles of carrying out the preliminary preparation for the transition to the new synchronous1 and asynchronous2 training are described. The basic rules for building and organizing online lectures in the conditions of distance learning are systematized. Models for asynchronous lectures according to already established principles are presented. An analysis of the difficulties and dangers of constant home office work has been performed. The authors of this article share their pedagogical experience regarding the specifics of conducting individual and group music disciplines in the context of distance learning, giving basic recommendations on the structuring of the learning process, which can be used not only in a pandemic situation, but also in the future for more flexible, modern and effective education related to constantly improving technologies.
Does Depth Estimation Help Object Detection?

When ground-truth depth information (as obtained by RGB-D cameras, for example) is used in addition to the RGB image, baseline object detection performance improves. However, the same result is not always valid for estimated depth. Many factors affect the performance of object detection when estimated depth is used. In this paper, we comprehensively investigate these factors with detailed experiments, such as using ground-truth vs. estimated depth, effects of different state-of-the-art depth estimation networks, effects of using different indoor and outdoor RGB-D datasets as training data for depth estimation, and different architectural choices for integrating depth to the base object detector network. We propose an early concatenation strategy of depth, which yields higher mAP than previous works’ while using significantly fewer parameters.
Development of a Diagnostic Tool for Balance Disorders Based on Machine Learning Techniques

A diagnostic tool is developed for balance disorders based on machine learning techniques. This tool is addressed at experts, in order to support the diagnosis of 5 categories of balance disorders and ultimately 11 specific disorders. Unlike previous works, for each case one general classification model and only one additional specialized classification model are used to provide the recommended diagnosis, while obtaining satisfactory results and overall performance. Certain features are also extracted and identified as determinant for the correct prediction of the diagnostic categories (general classifier) and the diagnoses of each diagnostic category (specialized classifiers).
Decoupling Server and Client Code Through Cloud-Native Domain-Specific Functions

Simple domain-specific graphical languages and libraries can empower a variety of users to create application behavior and logic. However, it remains challenging to produce and maintain a heterogeneous set of client applications based on these descriptions, as each client typically requires the developers to both understand and embed the domain-specific logic. This is because application logic must be encoded to some extent in both the server and client sides.In this paper, we propose an alternative approach, which allows the specification of application logic to reside solely on the cloud. In our system, reusable application components are assembled on the cloud in different logical chains and the client is solely concerned with how data is displayed and gathered from users. In this way, the chaining of requests and responses is done by the cloud and the client side has no knowledge of the application logic. This means that the experts in the domain build modular cloud components, arrange them in logical chains, generate a simple user interface, and later leave it to client-side developers to customize the presentation.
Behavior-Driven Development of Software Product Lines

Software product lines (SPLs) develop families of similar software products, which share a standard set of features, and they build in variety via optional features. That means customers can select features according to their needs and come up with a product configuration. Then the SPL is expected automatically to generate and test the software product for the chosen configuration. There are various SPL solutions for the automatic generation of software products, but those SPLs lack automatic testing of the generated product. To overcome this shortcoming, the SPL should automatically compose a test suite according to the selected features, automatically execute the test suite on the product, and automatically generate a test report delivered to the customer with the product. This paper proposes such an approach through behavior driven development. The proposed method is evaluated with a smart home SPL.
Random Sampling Weights Allocation Update for Deep Reinforcement Learning

Deep Reinforcement Learning (RL) has achieved great success in many tasks, and the key challenge of Reinforcement Learning now is the inefficient exploration and the unstable training problems brought by high-dimensional input state. Recently, some ensemble works have utilized multiple critics to provide a more specific Q-value and explore more by increasing the diversity of critics. However, these works can not ensure both robust training with effective exploration and thus get limited performance on high-dimensional continuous control tasks. To address this challenge, in this work, we propose Random Sampling Weights Allocation (RSWA), a new critic ensemble framework. Our method introduces the random sampling weights mechanism to increase training robustness and re-allocate the weights according to the Temporal-Difference in every training step to encourage efficient exploration. Our method is compatible with various actor-critic algorithms and can effectively improve the performance of them. We conduct experiments that couple RSWA with various current actor-critic RL algorithms on different OpenAI Gym and DM-Control tasks to verify the effectiveness of this method.
PRIMARY SCHOOL EXPERIENTIAL EDUCATION IN WORLD AROUND US LESSONS

Experiential education excites and involves primary school students. By participating in such activities, a teacher initiates and encourages freedom of creativity, spontaneous decision making, artistic expression, logical thinking and physical activeness. Students being involved in this active experiential process learn from each other, cooperate, develop their skills of making agreements. In order to have the most benefits from such education and be able to make conclusions or learn from it there is a crucial need for reflection. Students think over their roles in groups, level of their involvement, taking part in decision making which on the whole assists developing social competence and helps them apply gained skills in future life. Keywords: experiential education, logical thinking, social competence
Face Parts Recognition Using Deep Neural Networks

This paper has expressed overall procedure of the facial recognition with its importance and essential beneficial factors. CNN and ML methods are used to find out the accuracy of the model for which data test train and features extraction has been processed. The output accuracy is observed to be 91.8%. Involvement of optimizers, batch normalization and dropout functionalities reported advantages in proposed CNN model.
Monitor Social Distance Using Convolutional Neural Network and Image Transformation

The COVID-19 pandemic has claimed millions of lives worldwide. In these times, the only sure-shot way to stay safe is to avoid social contact and to follow social distancing regulations. These regulations define the minimum distance people should keep from each other so as to avoid the propagation of the Coronavirus. Hence, monitoring the social distance between people becomes a real-world problem so as to ensure safety for everyone. This is especially difficult to do manually in public places. Our proposed system aims to allow an easy and effective way to measure social distance and identify the people at risk using Convolutional Neural Networks and Image Transformation techniques.