Reviewerly/exHarmony huggingface dataset:
Access Control Design Based on User Role Type in Telemedicine System Using Ethereum Blockchain

To fulfill health as basic human needs, health care services technology is always improved but also must maintain security and privacy of the data due to huge amount of data is created and distributed. Telemedicine with telecommunication technology offers convenience and cheaper cost, but still is vulnerable to cyber attacks, making it a threat for patients’ data's privacy. One approach that can be used to secure the data is applying access control to the data. Immutable blockchain can helps to enforce access control so it cannot be violated. We propose a system which implements Ethereum for the blockchain and React web application for the interface of the system. Data management and the access control are provided through Ethereum smart contracts, and the access control requires different role-based permission to upload and access the patients’ data. The system works successfully with average time taken to verify the role is 1.8033 seconds per session. The privacy of the patients’ data is ensured because only the patients that are allowed to keep the ID of their own data.
Investigating Digital Literacy Skills in Examination-Oriented Education System for the Post-Pandemic Era

Digital Literacy has become a pressing capability because of the infusion of digital technology in education systems due to the changes brought by COVID-19. Whether a student possesses digital literacy skills that are essential for online learning can be especially significant in an examination-oriented education system. In this qualitative study, we aimed to use the Digital Literacy Global Framework as our theoretical reference to 1) identify the digital literacy skills that students demonstrate in online exams, and 2) investigate their consistency in applying the identified skills. We utilized convenience sampling strategy to collect our data from a Hong Kong secondary school and we used content analysis to analyze our data. The validity and reliability of our data analysis were ensured by high intercoder agreement rates and Cohen's Kappa statistics. We found ten digital literacy skills of basic operation of devices and software that students demonstrate. In addition, some identified digital literacy skills are applied more consistently by students than other identified digital literacy skills. We discussed the findings in the context of implications to help researchers and professionals identify and assess essential digital literacy skills for online assessments. Suggestions for future studies were also made in the conclusions.
Infrared face recognition based on Yolo network and its application

In the process of indoor face detection, the problem of uneven illumination angle and illumination is always inevitable, which will have a certain impact on face detection and recognition. Therefore, this paper proposes an infrared face recognition algorithm based on Yolo network. Due to the small indoor face data set, the trained model often over fits the samples in the training set, resulting in poor generalization ability and so on. Firstly, this paper uses data enhancement technology to amplify the limited data set. Then, labelme software is used to label the face of the enhanced data set, and the data set is trained through Yolo network to generate the training model corresponding to the data set. Finally, the training model is invoked to test the infrared face images. The experimental results show that the algorithm can recognize the face quickly and accurately.
AMBUAPP: Ambulance Response Application

The usage of android phones is rising exponentially. In real-world scenarios, contacting and getting an ambulance quickly during an emergency is a real challenge. Searching for an available ambulance nearby has been one of the hustling factors that are faced by the fast pace community. The researchers are proposing this system entitled “AMBUAPP: Ambulance Response Application,” the new idea to automate this process of requesting an ambulance faster using mobile phones. This project aimed to develop an Android application that lets its users find an available ambulance through GPS and send an emergency notification to a nearby hospital in case of an emergency. The user of this app will no longer call any nearby hospitals to check the ambulance availability because the app responds with just one tap on the bottom. It will send a notification of the user’s details and location via GPS to a nearby hospital. If the request is accepted, the ambulance driver can receive the GPS location, which will lead to the user’s location. This will be more efficient and reliable for the ambulance driver, hospital, emergency rescue respondents, and users, for they can view a map showing their location and the ambulance responding.
A Survey of Cooperative Path Planning for Multiple UAVs

Cooperative mission for Multi UAVs has become an important trend. Cooperative path planning is one of the most killing part for the issue. The algorithms can be divided into the following categories, including optimal algorithm, graph-theory-based planning method, heuristic-information-based planning algorithm, swarm intelligence algorithm and neural network algorithm and so on. This paper systematically presents the research status and trend of the application of path planning in the field of multi-UAV cooperative mission and selects a variety of classic and up-to-date algorithms and their variants to promote future research.
Exploiting Similarity in Evolutionary Product Design for Improved Design Space Exploration

The design of new products is often an evolutionary process, where product versions are built on one another. This form of (PGE) reuses some parts of previously developed systems, while others have to be designed from scratch. In consideration of subsequent design steps, i.e., verification, testing, and production, PGE may significantly reduce the time-to-market as these steps can be skipped for reused parts. Thus, deciding which components have to be replaced or added to meet the updated requirements while preserving as many legacy components as possible is one of the key problems in PGE. A further aspect of PGE is the potentially more efficient search for valid design candidates. An already optimized base system can be systematically extended by new functionality without the necessity to search the entire design space. To this end, in this work, we propose a systematic approach, based on Answer Set Programming, to exploit the ideas of PGE in electronic system-level design space exploration. The idea is to gather information on a previous design, analyze the changes to a new version, and utilize the information to steer the search towards potentially good regions in the design space. Extensive experiments show that the presented approach is capable of finding near-optimal design points up to 1,000 times faster than a conventional approach.
Question Classification Method in Disease Question Answering System Based on MCDPLSTM

In disease question answering system, question text has some characteristics of diverse expression forms, close semantic connection and uneven distribution of key features. These characteristics make it difficult to extract information and lead to low information utilization. In order to solve the problems, a question classification method based on Merge-Convolutional Neural Networks - Deep Pyramid Convolutional Neural Networks-Long-Short Term Memory (MCDPLSTM) model is proposed. Firstly, question text is vectorized by BERT language model. Secondly, Convolutional Neural Networks (CNN), Deep Pyramid Convolutional Neural Networks (DPCNN) and Bidirectional Long-Short Term Memory (BiLSTM) constitute a multi-channel model to process feature vectors concurrently. And a method based on attention mechanism is used to fuse processed vectors of three channels. Finally, Softmax classifier is used for classification. The experimental results show that MCDPLSTM model has less time cost. Compare with the other models, precision of MCDPLSTM rose by 0.18%-5.43%, recall of MCDPLSTM rose by 0.42%-7.42% and F1-Score of MCDPLSTM rose by 0.47%-7.23%.
Earthquake disaster collection system based on mobile terminal

In this article, an earthquake disaster collection system is established based on mobile terminals, and the post-earthquake disaster information is transmitted to the field headquarters or the rear processing platform in real time through mobile terminals. Through the screening, classification and management of the reported disaster information, which combined with the basic geographic data, the professional data such as earthquake influence range and earthquake disaster degree are obtained. It can quickly obtain earthquake disaster information, establish a multi-level and multi-directional earthquake disaster investigation and collection system, realize the rapid acquisition and processing of earthquake disaster area information and related information.
Development of Applications for Generating and Reading QR Codes for the Micro1 Model

The paper provides an overview of barcode technology, describing the milestones of the technology evolution, with brief history outlines, depicting main points of barcode systems and highlighting emerging technologies having affected the way towards development of modern access codes. The underlined principles of QR are presented via code operations performed by example of a relatively simple code model widely known as Micro QR Model 1. The practical result obtained in the research was a simple application developed, which encodes a short piece of information into images according to the principle of Micro 1 model QR coding. Python-based encoder and decoder securely generating and processing Micro-1 codes was created. An essential part of the application code, application interface and a security block request are presented in the paper.
A Cluster-Based Weighted Feature Similarity Moving Target Tracking Algorithm for Automotive FMCW Radar

We studied a target tracking algorithm based on millimeter-wave (MMW) radar in an autonomous driving environment. Aiming at the cluster matching in the target tracking stage, a new weighted feature similarity algorithm is proposed, which increases the matching rate of the same target in adjacent frames under strong environmental noise and multiple interference targets. For autonomous driving scenarios, we constructed a method that uses its motion parameters to extract and correct the trajectory of a moving target, which solves the problem of moving target detection and trajectory correction during vehicle movement. Finally, the feasibility of the proposed method was verified by a series of experiments in autonomous driving environments. The results verify the high recognition accuracy and low positional error of the method.
Multi-view Subspace Clustering with View Correlations via low-rank tensor learning

With the purpose of boosting clustering performance, the manner of excavating underlying view correlations is an important issue of multi-view subspace clustering (MSC). Nevertheless, regarding most MSC approaches are centered on the view correlations in multiple subspace representations and neglect the view correlations in multiple feature representations. To address this limitation, this paper introduces a method, dubbed Multi-view Subspace Clustering with View Correlations (MSCVC), which excavates underlying view correlations in both multiple feature representations and multiple subspace representations simultaneously. Specifically, the Multi-view Principal Component Analysis (MPCA) is designed to capture the view correlations contained in multiple feature representations by using the orthogonal mapping and low-rank tensor constraint. As a consequence, view correlations in multiple feature representations can be effectively investigated and simultaneously embedded in the learned new feature representations. For view correlations in multiple subspace representations, the new feature representations learned in MPCA are employed and the low-rank tensor constraint is used as well. Experiments are conducted on nine benchmark datasets to demonstrate the progressiveness of our MSCVC compared to several state-of-the-art algorithms.
Explanatory Multi-Scale Adversarial Semantic Embedding Space Learning for Zero-Shot Recognition

The goal of zero-shot recognition is to classify classes it has never seen before, which needs to build a bridge between seen and unseen classes through semantic embedding space.Therefore, semantic embedding space learning plays an important role in zero-shot recognition.Among existing works, semantic embedding space is mainly taken by user-defined attribute vectors.However, the discriminative information included in the user-defined attribute vector is limited.In this paper, we propose to learn an extra latent attribute space automatically to produce a more generalized and discriminative semantic embedded space.To prevent the bias problem, both user-defined attribute vector and latent attribute space are optimized by adversarial learning with auto-encoders.We also propose to reconstruct semantic patterns produced by explanatory graphs, which can make semantic embedding space more sensitive to usefully semantic information and less sensitive to useless information.The proposed method is evaluated on the AwA2 and CUB dataset.These results show that our proposed method achieves superior performance.
On the Existence of Global Minima and Convergence Analyses for Gradient Descent Methods in the Training of Deep Neural Networks

In this article we study fully-connected feedforward deep ReLU ANNs with an arbitrarily large number of hidden layers and we prove convergence of the risk of the GD optimization method with random initializations in the training of such ANNs under the assumption that the unnormalized probability density function of the probability distribution of the input data of the considered supervised learning problem is piecewise polynomial, under the assumption that the target function (describing the relationship between input data and the output data) is piecewise polynomial, and under the assumption that the risk function of the considered supervised learning problem admits at least one regular global minimum. In addition, in the special situation of shallow ANNs with just one hidden layer and one-dimensional input we also verify this assumption by proving in the training of such shallow ANNs that for every Lipschitz continuous target function there exists a global minimum in the risk landscape. Finally, in the training of deep ANNs with ReLU activation we also study solutions of gradient flow (GF) differential equations and we prove that every non-divergent GF trajectory converges with a polynomial rate of convergence to a critical point (in the sense of limiting Fr\'echet subdifferentiability). Our mathematical convergence analysis builds up on ideas from our previous article Eberle et al., on tools from real algebraic geometry such as the concept of semi-algebraic functions and generalized Kurdyka-Lojasiewicz inequalities, on tools from functional analysis such as the Arzel\`a-Ascoli theorem, on tools from nonsmooth analysis such as the concept of limiting Fr\'echet subgradients, as well as on the fact that the set of realization functions of shallow ReLU ANNs with fixed architecture forms a closed subset of the set of continuous functions revealed by Petersen et al.
Survey of Visual-Semantic Embedding Methods for Zero-Shot Image Retrieval

Visual-semantic embedding is an interesting research topic because it is useful for various tasks, such as visual question answering (VQA), image-text retrieval, image captioning, and scene graph generation. In this paper, we focus on zero-shot image retrieval using sentences as queries and present a survey of the technological trends in this area. First, we provide a comprehensive overview of the history of the technology, starting with a discussion of the early studies of image-to-text matching and how the technology has evolved over time. In addition, a description of the datasets commonly used in experiments and a comparison of the evaluation results of each method are presented. We also introduce the implementation available on github for use in confirming the accuracy of experiments and for further improvement. We hope that this survey paper will encourage researchers to further develop their research on bridging images and languages.
Attacking End-to-End Visual Navigation Model: How Weak Existing Learning-Based Approaches Can Be?

Learning-based self-driving techniques and vehicle navigation approaches are the most hot topics in recent years. However, existing approaches typically assume the attack-free sensor data, the safety issue under large disturbance or external attack have not been well-solved. In this article, we build a simple FGSM-based attack method designed by minimizing the maximum value of the extracted visual features in order to greatly decrease the performance of the popular learning model for vehicle navigation and even make it totally fail. The proposed min-max operation based feature space attack method solves the problems of branch activation uncertainties and the lack of labels. Furthermore, we also provide a general adversarial training framework which can be used to overcome the proposed feature space attack. Simulational experiments in CARLA platform demonstrate the effectiveness and practical applicability of our attacking approach and defence strategy.
A Quick-Witted Cobweb for Enternet-Business Data Extraction

Today we are living in the era where information is the key of getting success in the field of electronic business. Today’s business industry is ruled by information and internet. The developing areas of electronic business has its impact on the inflation and CPI(Consumer Price Index). Self-Driven or we can say that automatic quick-witted cobweb, data extractor or web scrappers are the software which we can use to wring out the information which is available on the online platform. In this paper we are going to proposed a scrapper which is based on a object oriented programming language which is very popular among us i.e. python. The main motive of our project is to make the information wrenching process very easy. In this process there are various steps which must be followed to get well cooked information.
Experience for Curriculum Design and Innovation for Technical Subjects with regard to Distance Learning

The COVID 19 pandemic has called for new innovative ways in which educators and students can organize their teaching and learning activities and communicate effectively with each other. The article describes the Curriculum Design and Innovation for technical subjects focused on the creation of technical documentation, taking into account the conditions of the distance form of education. A summary of experiences with the distance form of their teaching is given. These are the basic subjects of education in the field of mechanical engineering, where students acquire the basics of technical thinking. The aim of the innovations was to adapt the curriculum to the needs and possibilities of distance education, including current trends in engineering in teaching, to motivate students to study, to keep them interested in the subject throughout the semester and to prepare them for exams and further study.
How are neural machine-translated Chinese-to-English short stories constructed and cohered? An exploratory study based on theme-rheme structure

Using theme-rheme structure in Systemic Functional Linguistics as an analytical framework, this study explores how neural machine-translated Chinese-to-English literary texts are organized and developed in terms of discourse coherence. To this end, we built a comparable translational corpus containing English translations of two modes, viz. neural machine translation (NMT) and human translation, of modern and contemporary Chinese short stories as source texts. The translated texts were annotated with themes, rhemes and their progressions throughout the texts. By analyzing and contrasting their thematic structure and progression patterns, we found that, compared with human translations, 1) NMT texts feature "pseudo-coherence" characterized by ill-connected information fragments linked by overusing and; 2) NMT system fails to adapt the organization of its textual elements to a given purpose and is less competent in maintaining discourse coherence due to a lack of smooth connection to the preceding sentence; 3) NMT system produces static and poorly interconnected texts that read as disjointed as a list consisting of unrelated items by recurrently resorting to a constant progression pattern rather than a dynamic linear progression characteristic of human translation. Hence, we suggest that theme-rheme structure be employed to assess the construction and coherence of machine-translated texts and be incorporated into machine translation models to improve the coherence of their products.
Path planning of manipulator based on improved RRT algorithm

Abstract Aiming at the problem that the 6-DOF manipulator can quickly avoid obstacles in high-dimensional space, the method of path planning based on the improved rapid expansion random tree (RRT) algorithm is proposed. The improved RRT algorithm combines the advantages of the target bias strategy and proposes a method for setting the intermediate bias point. In random sampling, the sampling point is biased to the intermediate bias point,with a certain probability to reduce the blindness of sampling, and use at the same time the path shortening method reduces path nodes and turning points, and optimizes the motion path of the robotic arm. By comparing the simulation results with the RRT and P-RRT algorithms, the effectiveness of the improved RRT algorithm in the calculation time and the number of path nodes is proved.
Model-Agnostic Temporal Regularizer for Object Localization Using Motion Fields

Video analysis often requires locating and tracking target objects. In some applications, the localization system has access to the full video, which allows fine-grain motion information to be estimated. This paper proposes capturing this information through motion fields and using it to improve the localization results. The learned motion fields act as a model-agnostic temporal regularizer that can be used with any localization system based on keypoints. Unlike optical flow-based strategies, our motion fields are estimated from the model domain, based on the trajectories described by the object keypoints. Therefore, they are not affected by poor imaging conditions. The benefits of the proposed strategy are shown on three applications: 1) segmentation of cardiac magnetic resonance; 2) facial model alignment; and 3) vehicle tracking. In each case, combining popular localization methods with the proposed regularizer leads to improvement in overall accuracies and reduces gross errors.
Analyzing Structural Security Posture to Evaluate System Design Decisions

Software systems are increasing in complexity, with attendant increases in the number of vulnerabilities they contain. Remediating these vulnerabilities, ideally during the early requirements and design phases, has been highly resource-intensive, and is often omitted due to lack of knowledge, time, and/or funds. We propose an approach, applied in these early phases, to address the following issues: 1) to enhance the developer's security knowledge of the system, we introduce the notion of structural security posture, which uses a collection of metrics to assess a system's security based on its structural view, 2) to guide the identification of vulnerabilities, we leverage external security data sources, and 3) to address the issue of resource intensiveness, we offer a tool for evaluating and analyzing a system's structural security posture. We illustrate how our approach facilitates the evaluation of design decisions to improve security using an example.