Reviewerly/exHarmony huggingface dataset:
A Study on Experiences on the Use of Comic Strips as EFL Reading Materials: Focused on University Students Majoring in Faculty of Health and Welfare

본 연구는 EFL 환경의 한국 대학생들에게 영어 읽기 자료로 연재만화를 활용해 영어 읽기 활동을 경험하게 한 후, 그 의미의 본질을 현상학적 연구 방법으로 탐색하는 것을 목적으로 하였다. 연구 대상은 충청남도 J 대학 보건 복지학 전공 재학생 8명이다. 자료수집은 심층 면담을 통해 이루어졌고, 수집된 자료는 현상학적 연구 방법에 의해 분석하였다. 도출된 내용은 28개의 중심내용과 7개의 대주제, 그리고 6개의 장점과 1개의 단점으로 범주화할 수 있었다. 연구 결과로 나타난 구체적 현상들은 다음과 같다. 첫째, 연재만화가 학습자들의 심리적 부담을 낮춰준다는 것이다. 둘째, 영어어휘 습득이 용이했다는 것이다. 셋째, 영어 읽기 학습에 높은 접근성이다. 넷째, 현지인이 사용하는 실제 삶의 언어를 습득했다는 것이다. 다섯째, 자발적 영어 학습이 가능하다는 것이다. 여섯째, 고차원적 사고능력의 향상이다. 일곱째, 영어 읽기 자료로 활용되기 어려운 측면이 있다는 것이다. 이러한 현상은 영어 읽기 자료로 연재만화가 효과적이라는 것을 보여주는 것이며, 영어 읽기 자료로 활용되기 위해 세부적인 제안이 필요하다.The purpose of this study aims to examine university students’ experiences on the use of comic strips as EFL reading materials using the phenomenological research method. For the research, 8 students majoring in faculty of health and welfare in J University of ChungcheongNam-Do province were chosen. Individual in-depth interviews were conducted for the data collection. From 28 significant statements, 7 themes, 6 advantages, and 1 disadvantage were emerged. The findings identified in the data analysis were as follows. First, psychological burdens of participants were reduced while reading. Second, they perceived that learning English vocabulary is easy. Third, they perceived that comic strips have powerful English reading materials. Forth, they acquired native speakers’ language in daily lives. Fifth, comic strips enables them to enhance self-learning ability Sixth, comic strips helped them to improve their higher-order thinking. Seventh, comic strips had difficulty on applying to English reading. Through this study, it can be understood that comic strips needed to improve EFL learners’ reading and at the same time detailed guidelines by English teachers will be suggested for the effective use of comic strips.
Application of cloud computing technology in computer network secure storage system

With the needs of computer network secure storage, cloud computing technology continues to optimize and design the security model, so as to improve the overall performance of computer network secure storage and the anti attack ability of network storage system. There are also many problems in the improvement of computer network secure storage. Cloud computing technology designs and restructures the key parts to further maintain the stability of computer network secure storage. Therefore, this paper makes a comprehensive analysis of the key applications of cloud computing technology in computer network secure storage, so as to provide a development direction for the future development of computer network secure storage.
SIFT image matching algorithm using fusion color information

In order to address the issue that (scale invariant feature transformation,SIFT) algorithm will lose the original image color information, resulting in poor matching ability after graying the color image, this study proposes a SIFT image matching algorithm that incorporates color information . To get the grayscale image to be matched, the algorithm employs a bimodal distribution function to minimize the grayscale difference between pixels and the color contrast between pixels; On this basis, the nearest neighbor distance matching method using Hellinger distance as similarity measure and vector space cosine similarity matching method are combined for feature matching, so as to effectively maintain the color contrast of the original image, improve the number of effective feature points of adjacent images, eliminate the wrong matching point pairs and reduce the wrong matching. The algorithm can effectively discriminate regions with similar gray levels but distinct hues, enhance the matching accuracy, and preserve real-time performance, according to experimental results.
Research on the Construction of Non-subject-object Argument Knowledge Base of Chinese Verbs

Whether in the field of linguistics or natural language processing (NLP), the study of Chinese verb arguments has always been a priority. The knowledge base of verb arguments is an essential resource for linguistic research and natural language processing research. However, previous knowledge bases have the following problems: (1) The method of constructing argument knowledge with verbs as items only lists few instances that can act as verb-argument roles, and cannot comprehensively present a large number of instances that act as argument roles. (2) Regarding determining the number of argument roles dominated by verbs, except for subject and object arguments, there is no clear operational standard for how many non-subject-object argument roles a verb dominates and how to determine them. Therefore, we propose an argument knowledge acquisition strategy based on big data, relying on a structured corpus and a structured retrieval system to acquire verb-argument instances from a large-scale corpus. At the same time, a calculation method for automatically determining the priority of verb non-subject-object argument is proposed, and preliminary experiments verify its effectiveness.
An Underwater Image Enhancement Fusion Network

To deal with the challenging underwater vision problem, we propose an underwater image enhancement fusion network, which uses a data-driven neural network to implement a fusion principle. Specifically, this network utilizes residual learning and multi-scale fusion modules to complete the fusion process. The numerical experiments over different underwater image datasets demonstrate that the proposed network consistently produces satisfied enhancement results compared to state-of-the-arts including both model-based and data-driven counterparts. Furthermore, the proposed network significantly reduces the neuron consumption that improves its practical applicability especially for the resource-limited underwater environment.
Design of 2D DMA Controller for Crypto-engine in Video Stream Data Encryption Card

In this paper, a direct memory access controller for multimedia data crypto-engine (MC-DMA) is proposed. In order to reduce the address calculation time, MC-DMA supports coordinate configuration based on 2-dimensional data, and converts the X-Y coordinate information configured by the user into a physical data address. By analyzing DDR command pattern and bus schedule, a scheme of DMA bank interleaving is proposed to improve the bus utilization. In addition, with the mechanism of data prefetch and data reorganization, unspecific address aligned with 2D data of multimedia is allowed to be transferred in word size, which greatly improves the communication efficiency. The experimental results show that throughput of the proposed MC-DMA is improved by 2-3 times compared with the traditional DMA supporting 2D transmission.
FPGA implementation of a Convolutional Neural Network for image classification

In this paper, a technique for implementing a Convolutional Neural Network (CNN) is presented. The implementation is performed on a reconfigurable Field Programmable Gate Array (FPGA) circuit of the PYNQ-Z1 platform. The objective is to improve the processing performance for embedded applications. The Theano Lasagne framework and the BNN-PYNQ library have been used to develop the CNN model, called Fruits_Classifier, using Python programming language. The performances of this technique were evaluated in terms of accuracy and cost. Simulation results showed the efficiency of this technique and validate the proposed design approach. The proposed technique is ready for a hardware FPGA implementation and it can be very useful for real-time embedded applications.
Connectivity Preserving Anonymization of Smart Grid Network Configurations

We describe a method where a utility can anonymize network device configurations and upload them to a remote service provider who analyzes connectivity (oblivious to the anonymization) and returns the results in anonymized coordinates to the utility, where they are de-anonymized. The approach has application for sharing problematic configurations with vendors, for cloud-based services that analyze connectivity and detect problems, and for connectivity of the sort regulated by NERC-CIP requirements.
Implementation of Classic McEliece key generation based on Goppa binary code

With the development of quantum computer, the security of traditional public key encryption algorithm cannot meet the demand, and post quantum cryptography that can resist the attack of quantum computer is needed. The Classic McEliece post quantum algorithm based on coding has maintained the strongest security record for more than 40 years. This paper designs a Classic McEliece key generation system based on Goppa binary code for PQCRYPTO recommended parameters: m=13, t=119, n=6960. Through the fixed circuit structure, the clock cycle consumption caused by multi parameter configuration can be reduced. The synthesis and simulation of the system based on the Ultrascale+ FPGA are completed, and the generation of the key can be completed in about 10 million cycles, which reduces the clock cycle by 1.7% compared with the previous work.
Oral Materials in the Legal Workplace Applied in the Automatic Push of Chinese Teaching Material

The automatic pushing of teaching materials is a new trend in the development of online education. This study investigates the material content suitable for Chinese textbooks based on the legal workplace. We summarize all the spoken communication scenes, including 18 primary and 56 secondary scenes, and analyzed the special vocabulary in the scenes concerning the word frequency and word vector-based clustering results. On this basis, we explored the communicative functions of all the conversations; 32 functions and typical functional sentences in the legal workplace are obtained. Moreover, this research constructed the pattern of “Scenedialogue- vocabulary-functional sentences”. This paper can provide resource support for the automatic pushing of legal workplace teaching materials.
Boundary Feature Analysis and Automatic Recognition of Indirect Speech in the Korean News Field

Indirect speech is an essential part of complex sentences in Korean. Reviewing the literature on Korean Indirect Speech (KIS) before, we found that most of them focus on the Right Boundary (RB) of indirect speech, and there's no consensus on a universal and clear definition of the left boundary (LB) of KIS yet. By tagging and analyzing corpus data, we summarize the types of indirect speeches’ Left Boundary Marks (LBMs) and the priority order of LB recognition, figure out the features of KIS at the syntactic level, design the LB recognition process according to the priority order, and put the automatic recognition process into practice.
Pandemic ethics: the intersection of technology, trust, and privacy, and implications for marginalized communities

To contribute to an ethical public health response to COVID-19, we should promote and develop technologies that are effective, protective, equally available to all people, and not unnecessarily privacy invasive. The lack of comprehensive data protection in the United States puts more pressure on ethical analyses of our pandemic responses, particularly manual and technological responses that are data driven. Assessing whether pandemic responses are ethical necessitates examining data practices in context. In the United States, the historical use, misuse, and abuse of data by the government and by corporations have eroded trust, and this erosion of trust combined with our lack of data protection legislation has compromised our ability as a nation to respond to the pandemic. Rebuilding trust and protecting data, while challenging, is necessary to promote participation in public health initiatives. This chapter will discuss ethical obligations raised by our pandemic responses, such as contact tracing; the relevance of trust and privacy; and the implications of our decisions for marginalized communities and vulnerable populations.
Blockchain for IoT Security and Privacy: Challenges, Application Areas and Implementation Issues

Blockchain and IoT are the most exciting technologies in the current world, combining these two together may resolve a lot of issues. In the current scenario, we are using IoT devices in nearly everything. By the end of this era, we can presume that all of our day-to-day use devices will be smart. But with this various issue may rise like safety, security, and performance concerns of smart devices. To resolve these issues, blockchain technology has emerged as a very powerful tool. In this chapter, the basics of blockchain along with its architecture and algorithms involved are discussed. IoT challenges and related literature are also discussed along with blockchain as an efficient technology to resolve these issues. The chapter also includes the challenges in using blockchain in IoT devices.
Implementation of Blockchain Technology for Big Data

The focus of this chapter is to provide brief knowledge about the concept and advantages of integrating Big Data and blockchain technology. As we are focusing on the blockchain and Big Data, it is suitable to introduce Big Data before exploring its interactions with blockchain. The blockchain technology is introduced and then the interaction between blockchain technology and Big Data is focused, in order to gain a clear understanding of how blockchain technology is used for Big Data. Thereafter, the different applications of blockchain and Big Data are explored.
Triplet Network for Topology Identification of Distribution Network

The distribution network has the problems of inaccurate topology and incomplete measurement configuration. In this paper, a method of distribution network topology identification based on the triplet network is proposed. In order to improve the generalization ability of the model, the Latin Hypercube Sampling (LHS) method considering the source and load correlation was used to generate PV and load data. A hybrid feature selection algorithm combining MLP and PSO is proposed to reduce the number of input measurements. Sequence-to-image conversion using Gramian Angular Field (GAF) is implemented to improve model training efficiency. We introduce a momentum encoder to select hard triplet samples, which solves the problem of easy gradient dissipation when triplet samples are selected randomly. The IEEE33 node system is used to verify the accuracy and superiority of the proposed algorithm, especially in small sample and weak loop network scenarios, the identification accuracy can reach 92% and 89%.
GH-Feat: Learning Versatile Generative Hierarchical Features From GANs

Recent years witness the tremendous success of generative adversarial networks (GANs) in synthesizing photo-realistic images. GAN generator learns to compose realistic images and reproduce the real data distribution. Through that, a hierarchical visual feature with multi-level semantics spontaneously emerges. In this work we investigate that such a generative feature learned from image synthesis exhibits great potentials in solving a wide range of computer vision tasks, including both generative ones and more importantly discriminative ones. We first train an encoder by considering the pre-trained StyleGAN generator as a learned loss function. The visual features produced by our encoder, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Generative Hierarchical Features (GH-Feat)</i> , highly align with the layer-wise GAN representations, and hence describe the input image adequately from the reconstruction perspective. Extensive experiments support the versatile transferability of GH-Feat across a range of applications, such as image editing, image processing, image harmonization, face verification, landmark detection, layout prediction, image retrieval, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc.</i> We further show that, through a proper spatial expansion, our developed GH-Feat can also facilitate fine-grained semantic segmentation using only a few annotations. Both qualitative and quantitative results demonstrate the appealing performance of GH-Feat. Code and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://genforce.github.io/ghfeat/</uri> .
ENSEMBLE OF MACHINE LEARNING CLASSIFIERS FOR SCHIZOPHRENIA DETECTION

Schizophenia disease is characterized by odd behavior, weird speech and decreased capacity to apprehend reality. The diagnosis of schizophrenia requires a complete and detailed medical examination. Machine learning has also helped computer scientists to classify and diagnose schizophrenia using neuroimaging data. This research implored the use of computer aided diagnosis to classify neuroimaging data of schizophrenia. The dataset of 86 instances which include 40 schizophenia patients, 46 healthy patients and 32 variable. They were obtained from Kaggle MLSF 2014 classification challenge and augmented due to small sized using synthetic minority oversampling technique (SMOTE). This yielded a larger data set of 1806 instances. The augmented data set were classified using machine learning algorithms support vector machine, K-neareast neighbours, logistic regression, Naïve bayes, artificial neural network. 350 instances was used for the training (70%) and 150 instances was used for testing (30%), KNN and SVM correctly classified 162 as Schizophrenia patients and classified 188 as healthy control, Tree correctly classified 159 as schizophrenia, mis-classified 3 as schizophrenia, correctly classified 185 as healthy and mis-classified 3 as healthy control, Logistic Regression correctly classified 139 as schizophrenia, mis-classified 23 as schizophrenia, correctly classified 170 as healthy and mis-classified 18 as healthy control, Naive Bayes correctly classified 139 as schizophrenia, mis- classified 23 as schizophrenia, correctly classified 166 as healthy and mis-classified 22 as healthy control. ANN used 549instances, 60% for training, 20% for testing and 20% for validation got an accuracy of 100%, this makes it the best classification method.
Introduction

The Going Digital Guide to Data Governance Policy Making (hereafter the Guide) aims to advance the development, revision and implementation of policies for data governance, by helping to overcome key related policy tensions. Addressing the complexities arising from the nature of data as an intangible infrastructural resource of global strategic importance, the Guide helps policy makers design effective, technology-neutral, forward-looking and coherent data governance policies across sectors, policy domains and jurisdictions. It proposes a set of questions and highlights promising policy approaches based on three policy tensions and objectives that characterise data governance policy making: balancing data openness and control while maximising trust; managing overlapping and potentially conflicting interests and regulations related to data governance; and incentivising investments in data and their effective re-use.
Privacy-preserving Healthcare Informatics using Federated Learning and Blockchain

In the Bigdata era, healthcare informatics need exploration of health records to identify hidden patterns. Machine learning and Deep learning techniques provide classification, clustering and prediction tasks. Healthcare data are processed in a centralized architecture pose a single point of failure and difficult to collaborate with different distributions of data to design a robust system. The sensitive data of the healthcare system are private and fragmented, difficult to collaborate for efficient learning models. Federated learning (FL) is a distributed preservation of privacy learning paradigm to address the data sensitiveness and silos. The model is trained with different distributions of data with distributed models to provide the global model. The sensitive data present in the local device model are not shared with the global model but only the gradients are transmitted till the convergence of the model. The privacy-preserving mechanism is essential to protect the model from privacy attacks. Differential privacy preserving is immune to privacy attacks on aggregated data of FL. The striking features of blockchain like decentralization, provenance, immutability, and finality enables a single shared ledger of the patient data and its distribution among the stakeholders with the mitigation of privacy threats. Blockchain provides the secure transaction between the local health model and the global health model during its gradient updation. Differential privacy mechanism with blockchain provides secure E-health data maintenance and data analytics in the distributed healthcare 4.0 industry. This article identifies the current challenges in healthcare informatics and addresses those issues with enabling technologies like FL, blockchain and differential privacy preserving security mechanisms. Communication efficient FL, and fusion learning is identified for E-health data management. The extraction of knowledge structure in healthcare data provides insight into recent trends in the domain and its opportunities in the associated industries. This article in wholesome explores healthcare informatics with the application of artificial learning and security mechanisms.
Automatic Age Estimation of Persons with Dark Skin tone using Deep Learning Approach

Age estimation has become very important for many organizations and human endeavour.Traditional machine learning methods have previously been deployed in previous times by many researchers to solve this problem automatically.However, the use of deep learning methods in recent times has shown superior performance in artificial intelligence tasks.This study employs the deep learning method gleaned from the ResNet50 convolutional neural network (CNN) to solve this problem; but on persons with dark skin tone, because pre-existing automatic age estimation models were trained on datasets with a limited population of persons with darker skin tones, as recent studies have shown that the aging features of dark skin tone persons cannot be learnt from persons with light skin pigmentations (white skin tone persons).A combination of persons with dark skin tones from the UTKFace, APPA-REAL and BlackFaces datasets was used to train the CNN.At the end of the experiment, the proposed approach attained a mean absolute error of 5.21 years on the validation set, and showed good performance on age estimation of dark skin tone persons.
Fast 3D measurement based on fringe projection profilometry and deep learning

Deep learning is currently gaining a lot of attention in the field of optical metrology and has shown great potential in solving various optical metrology tasks such as fringe analysis, phase unwrapping, and hologram reconstruction. For fringe analysis, current major works use U-Net and its derivatives as the backbone of the deep learning network, but suffer from a large number of model parameters and computational redundancy of the U-Net network, which outputs low-precision prediction results while taking up a lot of GPU memory. To solve these problems, compared with U-Net, a lightweight fringe analysis network with the size of only 1.7G is proposed to reduce the memory usage by over 70%, while improving the accuracy of phase retrieval by 10%, providing a new path for the widespread implementation in mobile devices of deep learning-based optical metrology.