Reviewerly/exHarmony huggingface dataset:
An exploratory study on application of big science business ecosystem for K-DEMO project

The goal of this research is to study building of business ecosystem in order to drive K-DEMO R&D, design and construction activities. Fusion energy development in Korea proceeds largely in 4 phases: KSTAR, ITER, K-DEMO and commercial fusion power plant. Particularly, based on that the goal of the K-DEMO project is to demonstrate fusion-driven electricity production and build economic foundation for fusion energy, this study analyzed the expert interviews and industry perception surveys with the purpose of seeking the ways to build the business ecosystem for K-DEMO project. We defined the industrial technologies for K-DEMO core components and based on these, conducted surveys on the perception of industries associated with accelerators, nuclear power and space business as well as fusion industries. The results are applied to identify the current technology status of industry and the demands from industry on K-DEMO R&D, design, construction activities and develop the proposal to build the K-DEMO business ecosystem.
Correction Method of Visual Errors in Pole Vault Take-off Action Images

Pole Vault is one of the most difficult and complicated sport in athletic and tracks events; it is high jumping action with vaulting pole stick. In the study of Pole Vault technology, the rapid advancement of Augmented Reality (AR) technology has considerable significance for the visual error correction of the pole vault take-off action images in the simulated world. The key objective is to evaluate the correction algorithm of the visual errors of take-off action images of pole vault sport. This study analyzes the AR framework, features, and pole vault, as well as province methods for correcting visual defects. Pole vault take-off action images have been simulated with AR computation technique, and the AR images of continues pole vault made. This paper primarily provides an overview of augmented reality (AR) technology, including its properties and benefits, as well as a few visual error correcting methods. AR discusses how to choose an aerobics instructor who can help you maintain an aerobics regimen on a regular basis. Augmented reality has been used to model these pictures, and AR images of aerobics have been created. The visual error correction of the longitudinal direction, transverse direction, and height direction of AR simulated continue pole vault images with Scale Invariant Feature Transform (SIFT) algorithm, Mean Absolute Deviation (MAD) algorithm, Normalize Cross-Correlation (NCC) algorithm, and Sobel Edge Detection (SED) algorithm is proposed in this study. This revealed that poor performance in the pole vault sport is due in large part to a lack of a well-planned structure for implementing a corrective mechanism for visual mistake correction in take-off action images. The statistical result of perfect pole vault technique dissected and described, including the best manage, takeoff, drive and swing, and bar separation. Along with technical work, a pole vaulters training regimen should involve sprinting, weight lifting, and gymnastics as well.
A gradient deconvolutional network for side-channel attacks

Recently, deep learning has been introduced as an interesting alternative to perform side-channel attacks, which are nowadays well-known threats to secure systems in the electronic equipment. However, these end-to-end devices can limit their adoption in the complex power traces due to lack of transparency. In this work, we propose a new deconvolution architecture to evaluate the security of secure systems, which combines gradient calculation and deconvolutional operation. First, we use the U-Net-like structure to classify to verify the positive impact of the deconvolution on classification. Next, we propose a gradient deconvolution network (GDN), which combines gradient calculation and model training firstly. The result of gradient calculation locates the leakage and transfers the information to the model to get a better training effect. Finally, we evaluate our methodology with the public datasets and provide visualization of Points of Interest (POIs).
Intelligent Analysis System of Movie Reviews Using Deep Learning and Convolutional Neural Networks

Along with the rapid popularization of electronic products, various social media such as Twitter and Microblog provide people with more platforms to express their opinions. To effectively extract and classify the sentiment under such a massive number of opinions, usually expressed by plain text, has aroused people’s closed attention from the perspective of data analysis. With the development of deep learning algorithm, natural language processing model has been enhanced to achieved impressive success on opinion mining and text classification. In this paper, we retrieved 25,000 movie reviews with their corresponding sentiment labels and attempted to build a sentiment analysis system of movie reviews. We improved the traditional Bag of Words (BoW) model with TF-IDF algorithm to vectorize the data and constructed Convolutional Neural Network (CNN) training algorithm to detect people’s outlook on the movie whether they have positive or negative feeling. The final model is evaluated with average accuracy, average stability, and separate performance on detecting positive and negative sentiment. The best model is shown to have 80.62% average accuracy, with 1.33 standard deviation and 76.45%, 84.66% accuracy on detecting positive and negative sentiment, respectively.
Simulation of A Decision Support System Using Data Mining Method with C4.5 Algorithm: A Case Study

Every university has a goal to produce quality and highly competitive students. To achieve this goal, Telkom University Information Systems Study Program has three learning methods, namely, class lectures, practicum, and expertise groups. To implement this learning method, stakeholders are needed, commonly referred to as Laboratory Assistants and Expert Group Members. However, in the stakeholder recruitment process there is still subjectivity and difficulty in determining applicants who fit the criteria. In order to improve the quality and excellent quality, every university is required to be able to maximize the processes that can have an impact on the quality and quality. This research was conducted to study the C4.5 Algorithm method which was implemented in the recruitment process for Laboratory Assistants and Expert Group Members. Data were collected through file collection and interviews conducted at the Expert Group Trustees and laboratories. From the simulation, 49 patterns were obtained for the old laboratory assistant recruitment data, 9 patterns for the new laboratory assistant recruitment data and 8 patterns for the Expertise Group recruitment data. From each of these data, the decision-making factors that most influence recruitment graduation are obtained, namely, the Interview attribute for the Laboratory Assistant recruitment data and the Motivation Letter attribute for the recruitment data of Expert Group Members.
Scala Collections

The collections class in Scala is a high-performance and type-parameterized framework with support for mutable and immutable type hierarchies. These distinct and independent mutable and immutable type hierarchies make switching between mutable and immutable implementations much simpler. Scala’s object-oriented collections also support functional higher-order operations such as map, filter, and reduce, which let you use expression-oriented programming in collections. You can access and use the entire Java Collections library from your Scala code because Scala is a JVM language, but this is not recommended because higher-order operations are not available with the Java Collections library. Scala has a rich collection library. This chapter gives you a tour of the most used collection types and operations, showing just the types you will use most frequently. The goal of this chapter is to guide you through the myriad options to find the solutions you need.
Research and Application of Computer Artificial Intelligence Technology in Machine Pronunciation

Abstract The traditional oral English training system uses a layered training strategy, which cannot meet the system performance design requirements. For this reason, the thesis designs a computer artificial intelligence system for oral English training based on human-computer interaction. The system hardware structure is composed of C8051F021 single chip microcomputer, LM317 chip, LM337 chip, A/D chip and IN5822 diode, which is used to reliably control the sampling frequency of the chip. At the same time, the paper introduces Sugeno points to realize the correct evaluation of spoken pronunciation. First, the randomness of natural language pronunciation and the instability of the speech processing system are modeled by fuzzy measure and credibility, and then they are integrated into the Sugeno integration framework to evaluate the language score instead of the specific score. The experimental results prove the credibility and stability of the evaluation model in closed and open tests.
Research on Science Popularization Management of Community Sports Facilities Based on Artificial Inelligence Decision Tree Network

Artificial intelligence is a field of computer science. It is a new science and technology to study and develop the theory to simulate, extend and expand human intelligence. The construction of urban intelligent community sports facilities has been gradually carried out. This paper discusses C4.5 and ID3, two artificial intelligence decision tree network classification algorithms. After repeating 10 experiments, it is found that the calculation speed of the C4.5 algorithm is about 186% of that of ID3. Compared with ID3, the overfitting degree of C4.5 is about 34%. Decision tree C4.5 algorithm has certain advantages over ID3.
A Method for Reducing Number of Parameters of Octave Convolution in Convolutional Neural Networks

Computational machine learning has attracted a great deal of attention for its ability to analyze large-scale data. In particular, convolutional neural networks (CNNs) have been proposed in the fields of image recognition and object detection in efforts to develop models with improved accuracy as well as more lightweight models that require a smaller number of parameters and a lower computational cost. Octave Convolution (OctConv) is a method used to reduce the memory and computational cost of a model while also improving its accuracy by replacing the conventional convolutional layer with an OctConv layer. However, the number of parameters used in OctConv is almost the same as that in the case of conventional convolutional processing. In this paper, we propose the Pointwise Octave Convolution (Pointwise OctConv) method, which combines the Pointwise Convolution (Pointwise Conv) method with OctConv to reduce the number of parameters used in OctConv and thus create a lighter model. In the proposed method, the number of parameters is reduced by performing Pointwise Conv before and after the convolution process for each path in the OctConv layer. In an evaluation using ResNet-56, the proposed method reduces the number of parameters by about 63.8 $$\%$$ with a loss of classification accuracy of 3.04 $$\%$$ when $$\alpha = 0.75$$ .
Afro-Latina Minimal Computing

In this interview the authors speak with Sandra Abd’Allah-Álvarez Ramìrez, an Afro-Cuban scholar, activist and blogger. As the figure behind <italic>Negra Cubana</italic>, a renowned blog that discusses racism and feminism at the intersection of technology in Cuba, Sandra converse about topics such as internet access, infrastructure and the possibilities and flaws brought about by digital technology. Through the interview, Sandra shows minimal computing becomes a critical position from which her communities manage to get connected at the same time that evaluate their path forward.
A Validation Framework for ARP Similarity Measure

In the last years, a wide range of new similarity measures has been designed and applied to different contexts. Currently, there’s a deep lack in the validation and evaluation steps for novel similarities. In general, new measures are validated mostly through numerical experiments using different data sets. But this is not enough, as in order to gain relevance for a domain, a more complex validation process is necessary. To overcome this, a validation framework is proposed for the recently introduced ARP (Attractiveness-Relevance-Popularity) similarity measure. The validation process consists of five main steps: metrics conditions check, usefulness, expressivity, correlations to other measures, and noise robustness.
Automatic Text Summarization with Categorization on Online News About Indonesian Public Figures Using Fuzzy Logic Method

Mass media has a main purpose to provide actual information and lifestyle in society. For that purpose, mass media has been developed into many forms with online news as one of them. With so many news articles on the internet, it’s been difficult for readers to get information about an event or a person in a short time and efficiently. Text summarization is one solution to save time and improve efficiency by shortening the document into a shorter version but containing important information from the source document. This research’s goal is to create an automatic text summarization in several news articles in mass media that have been categorized regarding Indonesian public figures using Fuzzy Logic approach. Automatic text summarization can be used in a variety of news articles which are structured texts with neat grammar to help users find the desired information from the many news articles that are available. The result showed automatic text summarization with categorization could provide summarized news articles that are easy to understand, contain the main information of the article, grouped into matched categories and public figure, and worked efficiency.
Multi-Tenant RDBMS Allocation in the Cloud Environment

Tenants in a multi-tenant business share the same applications and databases for data storage. However, because of the widespread use of a multi-tenant system, service providers are faced with severe difficulties on a regular basis. These difficulties are reflected in how to ensure the quality of service given to tenants, which is documented in a legal contract known as a Service Level Agreement (SLA), while considering tenants' irregular workload patterns, which may affect the level of guarantee. A Multi-Tenant Database Management System (MT-DBMS) is presented in this research. In addition, a Multi-Tenant Allocation Algorithm (MT-A) is presented to decrease collisions and inconsistencies between migration decisions for a group of violated tenants based on the number of SLA violations and the variance rate. The experimental results demonstrate that the proposed MT-A algorithm is ideal for allocating the violated tenants since it decreases both the total number of SLA violations and the number of client sites SLA violations.
Human Skin Detection: An Unsupervised Machine Learning Way

Researchers have been involved for decades in search of an efficient skin detection method. However, current methods have not overcome the significant limitations. A clustering and region growing-based skin detection method is proposed to overcome these limitations. Together with significant insight, these methods result in a more effective algorithm. The insight concerns a capability to de- fine the number of clusters dynamically in a collection of pixels organized as an image. In Clustering for most problem domains, the number of clusters is fixed a priori and does not perform effectively over a wide variety of data contents.Therefore, this research paper proposed a skin detection method that validated the above findings. This method assigns the number of clusters based on image properties and ultimately allows freedom from manual thresholding or other manual operations.The dynamic determination of clustering outcomes allows for greater automation of skin detection when dealing with uncertain real-world conditions.
Construction and Application of Interactive Assessment Environment Based on Document Cloud

Using advanced information technology to develop assessment tools plays an important role in promoting the innovation of assessment and examination methods. Computer-assisted assessment has been proven useful in e-learning. However, it is time-consuming and laborious work for teachers in an unfamiliar software environment to implement an assessment. Based on the WPS Office, this study constructed an interactive assessment environment named Edu WPS, using the Edu WPS client and Document Cloud. Several key technologies were involved in the construction process, such as assessment plug-ins, automatic recognition of items, and collaborative document editing and sharing. The experimental results show that the Edu WPS can improve the learning effect of students.
DRAGON: Detection of Related Account Groups for Online services with uncertain graphs

With the rising influence of current online services, it is important for service providers to discover related accounts because it helps detect suspicious account groups. Existing research on this topic mostly focuses on a variety of account behaviors. Little attention has been paid to relations among account identity, which unveils the relationship between accounts and real-world people. In this paper, we propose DRAGON for modeling an account identity network and detecting suspicious account groups among this network. To this end, identifiers for tracking physical devices are collected and uncertain graph is used for modeling uncertainty in the network. Within this network, a strategy for detecting suspicious account groups is also investigated in DRAGON. We evaluate DRAGON using a real-world dataset. The results indicate that DRAGON achieves a 280% improvement in precision and 150% improvement in recall compared to a binary classifier.
Image-to-Image Bidirectional Generation Net for MR Images

For the medical images, the prediction is a hot topic in computer research. In this paper, we propose a novel bidirectional generation net that transforms MR images from one modality to the other. The proposed method operates in an end-to-end manner at the image level, and learns nonlinear mapping by bidirectional generation of paired images. An extended receptive field convolution module (ECM) is designed in the generation network to enhance the acquisition of neighborhood information. In order to reduce the generalization error, a regularizer is applied to the generator, and the image details are captured through a fine-tuned VGG16 network. The experimental results show that the network has good performance on the public dataset.
Comparative Research of Dynamic Target Detection Algorithms Based on Static Background

Dynamic target detection is a process of capturing moving objects in videos. Detection results are affected by various factors that limit the algorithms&#x2019; further development. For example, mutual occlusion between objects can disrupt the overall structure of the target, which can easily lead to missed and false detections. In addition, when the light intensity increases or decreases, the pixel values of the input image also change, causing the results inaccurate. Considering the above factors, we need to understand how external conditions affect the accuracy and real-time performance of the algorithms to help us select the appropriate method in different scenes. In this paper, we propose a method for selecting suitable algorithms for different static scenes. It evaluates the number of dynamic targets and the light variation in the detected static background, which can help us select a suitable detection algorithm. First, we use the inter-frame difference, background difference, and the optical flow method to detect a single human target with a simple motion state in a closing scene. Then we apply these three algorithms to detect multiple targets in an open scene. In this situation, the algorithms need to deal with more complex information, bring a significant challenge for real-time and accuracy. During the process, the running frame rate is adjusted by modifying parameters in the experiments, improving the algorithms&#x2019; real-time performance and accuracy. Finally, we compare the performance data of these algorithms in different scenarios and give an evaluation and analysis for each algorithm. In brief, we provide a basis and reference for selecting detection algorithms for different static scenes, which is of great significance.
Deep MinMax Networks

While much progress has been achieved over the last decades in neuro-inspired machine learning, there are still fundamental problems in gradient-based learning using combinations of neurons, especially away from the infinite width limit.These problems, such as saddle points and suboptimal plateaus of the cost function, can lead in theory and practice to failures of learning. In addition, because of their global geometry, Rectified Linear Units (ReLUs) do not allow local adjustment of a pre-learned global network, making the incorporation of new data inefficient.This paper describes an alternative local learning approach for arbitrary piece-wise linear continuous functions. Neurons are not superimposed, but rather are spatially separated. Also, contrary to classical approximations based e.g. on radial basis functions or finite elements, there is no pre-fixed set of basis functions, but location and support are all adaptable.The approach yields a new multi-layer algorithm based on learning a MinMax combination of linear neurons, which is applicable directly to the high-dimensional case. Global exponential convergence of the algorithm is established using Lyapunov analysis and contraction theory. It is shown that the algorithm corresponds to a set of separate exponentially stable linear regressions, whose combination is Lyapunov or contraction stable under observability conditions.Learning is fundamentally local, which allows simple progressive adjustment of a previously learned network as new data becomes available. Unused neurons are pruned, avoiding unnecessary over-parameterization. Conversely, new neurons are created in areas where better approximation of the true function is needed. Overall stability is shown for this combination of pruning and creation mechanisms.
HiTNet: Byte-to-BPE Hierarchical Transcription Network for End-to-End Speech Recognition

In this paper, we propose a new byte to byte-pair-encoding (BPE) Hierarchical Transcription Network (HiTNet) architecture for end-to-end (e2e) automatic speech recognition (ASR). The proposed HiTNet architecture simultaneously encodes as well as decodes information hierarchically at different levels of linguistic granularity such as bytes and BPE. In general this idea can be extended to any levels of granularity including phonemes or graphemes or bytes (character to sub-character in some languages), to sub-words or byte-pair encodings (BPE), to words, and so on. Existing hierarchical e2e ASR models primarily encode the acoustic information in an hierarchical manner governed by weaker linguistic constraints at each level. The language information at each level is neither embedded or used explicitly, nor is the information decoded at each level passed on to the next stage. The proposed architecture primarily decodes information in an hierarchical manner utilizing the linguistic information at each level explicitly, while at the same time utilizing the hierarchically encoded acoustic information at each level. Experiments with a two-level byte-to-BPE (b2B) hierarchical transcription show that the proposed architecture significantly reduces the word error rates of both the byte and BPE decoders compared to baseline byte and BPE based attention encoder-decoder models.