Reviewerly/exHarmony huggingface dataset:
A BIG DATA AND IT’S SECURITY CHALLENGE IN DIGITAL MEDIA

In this research work discuss about the big data and related problem. In the current generation big dataplay’s an important role in various data mining application. This research work discuss the various field in whichapply big data application. There are many critical issues and challenges face in the big data. Big DataDuplication is one of the challenging problem between researchers. In the last decade the growth of big dataexponentiation, Terabytes, Petabyte, Exabyte, Zettabyte bytes data generated in every day in a single stockmarket research application. In this process huge amount of decapitate data also generated in Terabytes as wellas Petabytes. Similar that social media application generated large amount of duplicate information. In thisresearch work focus on different problems and hurdles of big data and its related issues.However, encryptedinformation introduce new challenges for cloud information de-duplication that becomes crucial for giantinformation storage and process in cloud. De-duplication schemes cannot work on encrypted information.Existing solutions of encrypted information de-duplication suffer from security weakness. They cannot flexiblysupport information access management [13] and revocation. Therefore, few of them is pronto deployed inobserve. During this paper, we propose a theme to de-duplicate encrypted information hold on in cloudsupported possession challenge and proxy re-encryption. It integrates cloud information de-duplication withaccess management.
Guides: Technical Direction + Scenic Tech + Stage Automation: Video of Productions

Research resources for the study of technical direction, scenic technology, and stage automation.
Implementation of Parallel Simplified Swarm Optimization in CUDA

As the acquisition cost of the graphics processing unit (GPU) has decreased, personal computers (PC) can handle optimization problems nowadays. In optimization computing, intelligent swarm algorithms (SIAs) method is suitable for parallelization. However, a GPU-based Simplified Swarm Optimization Algorithm has never been proposed. Accordingly, this paper proposed Parallel Simplified Swarm Optimization (PSSO) based on the CUDA platform considering computational ability and versatility. In PSSO, the theoretical value of time complexity of fitness function is O (tNm). There are t iterations and N fitness functions, each of which required pair comparisons m times. pBests and gBest have the resource preemption when updating in previous studies. As the experiment results showed, the time complexity has successfully reduced by an order of magnitude of N, and the problem of resource preemption was avoided entirely.
Learning to Ask for Data-Efficient Event Argument Extraction

Event argument extraction (EAE) is an important task for information extraction to discover specific argument roles. In this study, we cast EAE as a question-based cloze task and empirically analyze fixed discrete token template performance. As generating human-annotated question templates is often time-consuming and labor-intensive, we further propose a novel approach called "Learning to Ask," which can learn optimized question templates for EAE without human annotations. Experiments using the ACE-2005 dataset demonstrate that our method based on optimized questions achieves state-of-the-art performance in both the few-shot and supervised settings.
The Trend of Blockchain in Vietnam and Its Implications for ROK

Bitcoin and blockchain are often making headlines not only on TV or media but also among the public in today’s society. These technologies have been developed after the risk of the centralized financial system came to the fore during the 2007 global financial crisis. Since then, an anonymous inventor called Satoshi Nakamoto penned the bitcoin white paper where a blockchain-based reference implementation was introduced. Bitcoin was able to achieve unprecedented growth by positioning itself as one of the top global currencies in terms of market capitalization after five years since its development. The pace of Vietnam’s economic development is notably fast among Asian nations, while the nation was expected to be a Southeast Asian blockchain hub but they have banned virtual currency trading recently. However, they’ve also designated the State Bank of Vietnam (SBV) as a responsible agency for the research of blockchain-based cryptocurrencies, the construction of a service ecosystem, and their test operations. The fast-growing economy, increasing number of smartphone users, and the Vietnam government’s support policies for startups substantiate these efforts. Therefore, this paper attempts to study the current status of Vietnam’s blockchain technology that has been considered to be the center of blockchain systems right behind Singapore, and its implications for Korean companies.
Structurally-Enhanced Approach for Automatic Code Transformation

In software development, developers often need to change or update lots of similar codes.How to perform code transformation automatically has become a research hotspot in software engineering.An effective way is extracting the modification pattern from a set of similar code changes and applying it to automatic code transformation.In the related work, deep-learning-based approaches have achieved much progress, but they suffer from the problem of significant long-term dependency between the codes.To address this challenge, an automatic code transformation method is proposed, namely ExpTrans.Based on the graphbased representations of code changes, ExpTrans is enhanced with the structural information of code.It labels the dependency between variables in code parsing and adopts the graph convolutional network and Transformer structure to capture the long-term dependency between the code.ExpTrans is first compared with existing learning-based approaches to evaluate its effectiveness; the results show that ExpTrans gains 11.8%-30.8%precision increment.Then, it is compared with rule-based approaches and the results demonstrate that ExpTrans significantly improves the correct rate of the modified instances.
Inability to Trust People in The Novel The Leavers by Lisa Ko

The purpose of this analysis is to expose the issue of inability to trust people which is done by an immigrant child who was separated multiple times from his family. The purpose of this study is to know the contribution of character, plot (conflict), and setting in revealing the inability to trust people. This analysis is related to the concept of neurosis by Sigmund Freud and Psychosocial Development by Erik Erikson that is supported by the text and context based interpretation. The result of this analysis shows the protagonist suffers from social relationship and mental problem that can be seen from his attitude and behavior.
Frequency Disentangled Residual Network

Residual networks (ResNets) have been utilized for various computer vision and image processing applications. The residual connection improves the training of the network with better gradient flow. A residual block consists of few convolutional layers having trainable parameters, which leads to overfitting. Moreover, the present residual networks are not able to utilize the high and low frequency information suitably, which also challenges the generalization capability of the network. In this paper, a frequency disentangled residual network (FDResNet) is proposed to tackle these issues. Specifically, FDResNet includes separate connections in the residual block for low and high frequency components, respectively. Basically, the proposed model disentangles the low and high frequency components to increase the generalization ability. Moreover, the computation of low and high frequency components using fixed filters further avoids the overfitting. The proposed model is tested on benchmark CIFAR10/100, Caltech and TinyImageNet datasets for image classification. The performance of the proposed model is also tested in image retrieval framework. It is noticed that the proposed model outperforms its counterpart residual model. The effect of kernel size and standard deviation is also evaluated. The impact of the frequency disentangling is also analyzed using saliency map.
Tracking Non-Visual Eye Movements Non-Invasively: Comparing Manual and Automatic Annotation Styles

Non-visual eye-movements (NVEMs) during episodic and semantic memory retrieval may reveal mental foraging, similar to bodily movements during physical foraging.However, measuring them is challenging as common eye-tracking methods might interfere with the natural eye gaze habits of participants.Our aim was to compare two annotation approaches to track NVEMs from raw video footage: First, manual annotation using a coding grid dividing the visual field into nine sections, and second, automated annotation using the neural-network driven face recognition software OpenFace representing eye gazes as Cartesian vectors.We found that both approaches showed moderate to excellent reliability, quantitatively.k-mean clusterings of the automatic annotations revealed high resemblance to the manual coding approach, qualitatively.We conclude that mapping between manual and automatic coding of NVEMs is feasible, and that the selection of the appropriate method should depend on the research question and available resources.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation

Predicting the next interaction of a short-term sequence is a challenging task in session-based recommendation (SBR).Multi-behavior session recommendation considers session sequence with multiple interaction types, such as click and purchase, to capture more effective user intention representation sufficiently.Despite the superior performance of existing multi-behavior based methods for SBR, there are still several severe limitations:(i) Almost all existing works concentrate on single target type of next behavior and fail to model multiplex behavior sessions uniformly.(ii) Previous methods also ignore the semantic relations between various next behavior and historical behavior sequence, which are significant signals to obtain current latent intention for SBR.(iii) The global cross-session item-item graph established by some existing models may incorporate semantics and context level noise for multi-behavior session-based recommendation. To overcome the limitations (i) and (ii), we propose two novel tasks for SBR, which require the incorporation of both historical behaviors and next behaviors into unified multi-behavior recommendation modeling. To this end, we design a Multi-behavior Graph Contextual Aware Network (MGCNet) for multi-behavior session-based recommendation for the two proposed tasks. Specifically, we build a multi-behavior global item transition graph based on all sessions involving all interaction types. Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii). In the end, we utilize the next behavior information explicitly to guide the learning of general interest and current intention for SBR. Experiments on three public benchmark datasets show that MGCNet can outperform state-of-the-art models for multi-behavior session-based recommendation.
Research on Building Intelligent Technology Based on Modern Information Artificial Intelligence Building Concept

This article selects artificial intelligence technology as the research object, and specifically analyzes the application of this technology in intelligent buildings. On the basis of clearly defining the concept of artificial intelligence technology, an analysis of the way to realize the intelligentization of intelligent buildings is carried out. Select specific examples to conduct simulation experiments on the application of artificial intelligence technology in intelligent buildings, and provide practical reference for the construction industry.
The Impact of Students` Needs and Interests on Vocabulary Acquisition in a Private University in Georgia (A Case of Georgian Higher Education Institution)

The aim of this research was to measure the impact of students` interest-based materials on vocabulary skills development through a twotailed quasi-experiment. Contemporary classrooms with digital natives seeking 21st-century skills require many significant changes in the instructional process. Being put in a democratic, student-centered environment, students have to be granted the opportunity to personalize their learning process. The issue of adjusting and tailoring materials to learners` needs, demands, and interests is widely considered daunting for teachers all over the world. The presented article highlights the importance of designing contextualized materials and adjusting materials to learners` interests and needs to achieve desired learning outcomes. The statistical data were gathered using a two-tailed quasi-experimental design in two control and two experimental groups through pre, while, and post-tests, and analyzed by paired samples tests. The study revealed that vocabulary acquisition is influenced significantly when materials are designed according to students` interests and needs.
Retrieval of GF-4 Satellite Image Data Surface Albedo Based on Angular Bin Algorithm

Albedo has important significance to the research of the surface energy balance and climate. GF-4 satellite is the earth synchronous orbit satellite, whose spatial resolution is 50 m, much better than other same orbit satellites. Because GF-4 only has one observation angle on a site, it's difficult to use bidirectional reflectance distribution function (BRDF) inversion method to retrieve the albedo. So, we use angular bin (AB1) algorithm to retrieve the albedo. AB1 is based on priori knowledge. Firstly, we use the data of Polarization and Directionality of the Earth's Reflectances (POLDER) to found the empirical explanation between albedo and the reflectivity of one angle, and then convert POLDER's empirical explanation to GF-4 by the spectral response functions of POLDER and GF-4.Next Plug in one GF-4's image and calculate the albedo using the empirical explanation. Last we use MODIS and Landsat 8 albedo products to verify the calculation results. The results show that the root mean square error (RMSE) of MODIS albedo and GF-4 albedo is 0.027 3, the RMSE of Landsat 8 albedo and GF-4 albedo is 0.017 6, verify that the accuracy of albedo inversion based on AB1 is reliable, proving that using the AB1to retrieve the GF-4's albedo has good precision.
A2CM: a new multi-agent algorithm

&lt;p class="Abstract"&gt;Reinforcement learning is currently one of the most researched fields of artificial intelligence. New algorithms are being developed that use neural networks to compute the selected action, especially for deep reinforcement learning. One subcategory of reinforcement learning is multi-agent reinforcement learning, in which multiple agents are present in the world. As it involves the simulation of an environment, it can be applied to robotics as well. In our paper, we use our modified version of the advantage actor–critic (A2C) algorithm, which is suitable for multi-agent scenarios. We test this modified algorithm on our testbed, a cooperative–competitive pursuit–evasion environment, and later we address the problem of collision avoidance.&lt;/p&gt;
ANALYSIS OF THE APPLICABILITY CRITERION FOR K MEANS CLUSTERING ALGORITHM RUN TEN NUMBER OF TIMES ON THE FIRST 25 NUMBERS OF THE FIBONACCI SERIES

In this research investigation Analysis Of The Applicability Criterion For K Means Clustering Algorithm Run Ten Number Of Times On The First 25 Numbers Of The Fibonacci Series is performed. For this analysis RCB Model Of Applicability Criterion For K Means Clustering Algorithm is used. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. K- Means clustering algorithm is a scheme for clustering continuous and numeric data. As K-Means algorithm consists of scheme of random initialization of centroids, every time it is run, it gives different or slightly different results because it may reach some local optima. Quantification of such aforementioned variation is of some importance as this sheds light on the nature of the Discrete K-Means Objective function with regards its maxima and minima. The K-Means Clustering algorithm aims at minimizing the aforementioned Objective function. The RCB Model Of Applicability Criterion for K-Means Clustering aims at telling us if we can use the K-Means Clustering Algorithm on a given set of data within acceptable variation limits of the results of the K-Means Clustering Algorithm when it is run several times. KEY WORDS: K-means clustering algorithm, RCB model and Cluster evaluation.
Constructing Locally Leakage-resilient Linear Secret-sharing Schemes

Innovative side-channel attacks have repeatedly falsified the assumption that cryptographic implementations are opaque black-boxes. Therefore, it is essential to ensure cryptographic constructions’ security even when information leaks via unforeseen avenues. One such fundamental cryptographic primitive is the secret-sharing schemes, which underlies nearly all threshold cryptography. Our understanding of the leakage-resilience of secret-sharing schemes is still in its preliminary stage.
Scalable Rule-Based Representation Learning for Interpretable Classification

Rule-based models, e.g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity. However, rule-based models are hard to optimize, especially on large data sets, due to their discrete parameters and structures. Ensemble methods and fuzzy/soft rules are commonly used to improve performance, but they sacrifice the model interpretability. To obtain both good scalability and interpretability, we propose a new classifier, named Rule-based Representation Learner (RRL), that automatically learns interpretable non-fuzzy rules for data representation and classification. To train the non-differentiable RRL effectively, we project it to a continuous space and propose a novel training method, called Gradient Grafting, that can directly optimize the discrete model using gradient descent. An improved design of logical activation functions is also devised to increase the scalability of RRL and enable it to discretize the continuous features end-to-end. Exhaustive experiments on nine small and four large data sets show that RRL outperforms the competitive interpretable approaches and can be easily adjusted to obtain a trade-off between classification accuracy and model complexity for different scenarios. Our code is available at: https://github.com/12wang3/rrl.
A study on the analysis of voluntary questioning of children in the exploration of educational robots

The purpose of this study is to develop a STEAM education program for children using educational robots, and to analyze questions asked by children as a prior basic study. To this end, 29 children (16 boys and 13 girls) aged 3, 4 and 5 who had never experienced unplugged educational robots play were surveyed and observed, and 184 questions were extracted from video data containing a total of 300 minutes of play time. We divided the voluntary questions of children by age and analyzed them according to STEAM elements. T(Technology) was the most common STEAM element for all ages. Research results is meaningful in presenting the direction for convergent experiences and the development of computational thinking to meet the demand of the present childhood education to provide integrated education.
Analysis on Spatial Composition and Design of University Libraries - Focused on Kunsan National University Library -

University libraries are changing as the teaching and learning environment changes. In the case of a new recently built or remodeled university library, an open private study space, a group study room, and a break room are being expanded, and a space to support various group activities is being planned. In this study, changes in the spatial composition of university libraries, characteristics of spatial planning, and evaluation indicators were investigated through literature review. A tool for evaluating the spatial composition of university libraries was developed. Then the library space composition was analyzed with the evaluation tool, and the design characteristics were evaluated. Spaces were divided into individual learning spaces, group learning spaces, complex spaces, and public spaces. Six characteristics of a university library, flexibility, accessibility, safety, functionality, convenience, and creativity were evaluated. As a result of the study, a university library needs a design plan that increases flexibility and creativity, combined with study spaces and rest facilities. A library basically requires functionality, convenience, and accessibility.
Reliably Fast Adversarial Training via Latent Adversarial Perturbation

While multi-step adversarial training is widely popular as an effective defense method against strong adversarial attacks, its computational cost is notoriously expensive, compared to standard training. Several single-step adversarial training methods have been proposed to mitigate the above-mentioned overhead cost; however, their performance is not sufficiently reliable depending on the optimization setting. To overcome such limitations, we deviate from the existing input-space-based adversarial training regime and propose a single-step latent adversarial training method (SLAT), which leverages the gradients of latent representation as the latent adversarial perturbation. We demonstrate that the L1 norm of feature gradients is implicitly regularized through the adopted latent perturbation, thereby recovering local linearity and ensuring reliable performance, compared to the existing single-step adversarial training methods. Because latent perturbation is based on the gradients of the latent representations which can be obtained for free in the process of input gradients computation, the proposed method costs roughly the same time as the fast gradient sign method. Experiment results demonstrate that the proposed method, despite its structural simplicity, outperforms state-of-the-art accelerated adversarial training methods.
Remote sensing, geological and geophysical integration – Study case in Valinho de Fátima, Portugal

Summary The work intends to establish a methodology for the characterization, evaluation of the potential and choice of sites in rock masses, for the implantation of ornamental rock explorations. A first phase was carried out with lithological and structural assessment at a regional scale, identifying in the Geological Map of Portugal (1: 50k). The next phase consisted of thematic geological mapping, with lithological and structural assessment at the local level. In parallel, geophysical exploration was carried out using the Electromagnetic Method in the Time Domain [TDEM], to map the variation and distribution of resistivity, underground, and its correlation with the existing and modern cartography. This document presents the results of remote sensing, using Copernicus Sentinel-2 composite images, in which the use of automatic assisted classification allows a quick thematic mapping of the surface, created from field knowledge and bibliographic knowledge of the region, for evaluation radiometric signatures for the objects of interest in the designated area.