Reviewerly/exHarmony huggingface dataset:
Film Big Data Visualization Based on D3.js

In order to understand and mine the information behind the film more intuitively, D3.js is used to visualize the film big data. After obtaining the film data released in China in 2019 through the crawler, use D3.js to present the data in the form of histogram, doughnut chart, force-directed graph, map, Word Cloud, and add rich interactive functions to enable users to quickly obtain the information they need. The final analysis can provide some decision support for the Chinese film industry, as well as a reference for users to select films. Research shows that D3.js is adaptable and reliable for the visualization goals at high speed with low cost in big data processing activities.
An Object Detection Algorithm with Disparity Values

It is a technically difficult issue that the object segmentation under real world applications, because of the overlapping problem and the scattering disparities of the same object. This paper proposes an object detection algorithm with disparity values to solve the problem. The proposed algorithm uses the connected component algorithm and disparity values to connect pixels with similar disparities, and the recursive object merge uses to merge the similar neighboring contours for locating and bounding the contours of an object. Experimental results show that the proposed algorithm can resolve the problem of object detection in overlapping objects, and the detection results can be used for subsequent object recognition applications.
Triangular arbitrage across forex and cryptocurrency markets during the COVID-19 crisis: a MRS-AR approach

This article employs a Markovian regime-switching autoregressive approach to examine triangular arbitrage across forex and cryptocurrency markets during the coronavirus disease 2019 crisis. The findings suggest the following: (1) profitable triangular arbitrage tends to occur in the turbulent period during the crisis, significantly outperforming cryptocurrency investments; and (2) the persistent profitability of triangular arbitrage ensues from strong memory of high returns, low risk, and shock response to global quantitative monetary easing policy. Regulatory authorities should consolidate cryptocurrency supervision systems and establish cross-border coordination mechanisms to stabilize exchange rates and enhance market efficiency.
Deep Deblocker Driven Adaptive Iteration Scheme for Compressed Image Recovery

It is challenging to propose a flexible and effective framework for various JPEG compressed image recovery (CIR) tasks. In this paper, we propose a novel deep deblocker-driven adaptive iteration scheme, which can quickly and flexibly address various CIR tasks. First, a novel fidelity (NF) is introduced into CIR, and then the CIR problem is divided into inversion and deblocking subproblems by our improved split Bregman iteration (ISBI) algorithm. Next, we design a set of compact yet effective deep deblockers. These deblockers are used as implicit priors and also used for NF in the CIR problem. The convergence of our method is proved as well. To the best of our knowledge, our method is the first work to use deblockers as implicit priors. Extensive experiments demonstrate the effectiveness of our CIR method.
Digital empowerment for whom? An analysis of ‘Network sovereignty’ in low-income, rural communities in Mexico and Tanzania

When researchers invoke the term 'last billion' to refer to emerging ICT users, they often focus on network access as a 'solution' while neglecting important considerations such as local ownership or knowledge, both of which are essential to sustainable and empowering uses of these technologies in developing contexts. Research reveals that mere access to networks without active community involvement can fail to empower already marginalized and disenfranchized users. Building upon these findings, this article uses ethnographic methods to explore the meanings of 'network sovereignty' in rural, low-income communities in developing countries. It presents two case studies focused on local network initiatives in Oaxaca, Mexico and Bunda, Tanzania and then offers an assessment matrix to support future network sovereignty research based on five categories: community engagement; local cultures/ontologies; digital education and technological knowledge; economic ownership; and community empowerment. Our comparative research reveals that communities that are able to assert collective ownership over local infrastructure, embed network initiatives within local cultures, and prioritize digital education are much more likely to create and sustain local networks that support their economic, political, and cultural lives.
Analysis of terrain navigability in underwater terrain aided navigation

Abstract Aiming at the problem that the single terrain characteristic parameter cannot comprehensively evaluate the terrain navigability of the matching areas, the weighted vector correlation degree is introduced, and the terrain characteristic parameters such as the standard deviation of terrain elevation, roughness, correlation coefficient, terrain information entropy and terrain slope are comprehensively considered. A multi-index comprehensive decision-making evaluation method based on grey fuzzy theory is proposed. In addition, aiming at the problem that different matching routes have different navigability for the specific terrain, in order to select matching routes with better navigability, a navigation coefficient based on terrain statistical parameters is proposed to analyze the navigability of different routes. And the point mass filter (PMF) algorithm is used to simulate the navigability of the matching areas with different terrain features and matching routes. The simulation results show that the proposed evaluation method and navigation coefficient are feasible in the evaluation of terrain navigability. The stronger the navigability is, the smaller the matching error is and the better the navigation performance is. This research results can provide theoretical basis for analysis of terrain navigability, selection of matching areas and matching routes planning in terrain aided navigation.
Research on the Online Teaching of the Course Appreciation of Vocal Masterpieces

In the era of all-round development of network technology, people strive to expand new development situations with network. It is better for the network to take advantages of it especially when the human society cannot operate normally in the face of various uncertain factors. Exactly as the worldwide teaching field is striving to explore new paths for online teaching during the COVID-19 epidemic. Compared with offline classrooms, online teaching has both considerable advantages and certain drawbacks. So the new design for teaching to enable the course to be carried out smoothly online is urgent. At the same time, whether the online teaching design can carry forward the advantages of the network and avoid its disadvantages is a prerequisite for the future of online teaching. Taking the course Appreciation of Vocal Masterpieces as an example, a brand-new design for the online teaching of it is carried out from three aspects of pre-recorded courses, live streaming courses and assessment methods, of which the. Application of flipped classroom is the core technology of course designs, and the guarantee of teaching quality and the promotion of teaching effects are the lifeline of online classrooms. The teaching design aims to enable this offline course to rejuvenate in the network era.
Clinical Notes Mining for Post Discharge Mortality Prediction

Unstructured clinical data such as nursing notes are relatively less explored for building a predictive model for post-discharge mortality despite containing rich information about the health of the patients. Our work examines a simple bag of words (BOW) approach for 7/30/180/365 day mortality prediction of a patient. We have also explored syntactic sentiment dimensions from nursing notes as a predictor of mortality and report the survival analysis results. Our simple BOW model using logistic regression achieved 0.884 AUC for 30-day mortality. We also found out that the polarity may serve as a proxy for patient’s post-discharge survival.
Digital art as an integration tool general education and vocational training and student personality development

This article discusses the possibility of using elements of digital art in the educational process in institutions of secondary vocational education, the impact of digital art on the integration of general education and vocational training, as well as the importance of digital art for the development of students’ personalities. The authors prepared a brief overview of existing methods of applying the elements of digital art in education, including the example of higher education, and also conducted an experiment to introduce elements of digital art into the curriculum of the discipline «Computer Science» for college students. The results of the study showed that the introduction of elements of digital art in the educational process contributes to the development of a harmonious personality of the student, the development of creative skills and the ability to work independently with information, and increases the motivation of students to study. It has been shown that digital art can also be successfully used in teaching specialties not directly related to art.
Two-Stage Lean Startup Model for Subscription Business

Over the past decade or so, a customer-centric, iterative process methodology for growing startups, known as lean startup, has emerged. Because lean startup emphasizes its versatility in applying it in various industries, it mainly focuses on qualitative aspects. Therefore, it is difficult to quantitatively predict the future based on product and service improvements. This study deals with a business that can be defined as a subscription-type business by rethinking it from the traditional one-shot sales to continuous sales. We then propose a two-stage lean startup as a business model for fostering startup companies engaged in this type of business. In the upstream stage of this model, the policy of the business is discussed mainly through qualitative observations and experiments, and in the downstream stage, quantitative investigations and surveys are conducted to quantitatively understand the development potential of the startup. Finally, the Monte Carlo simulation is used to present numerical suggestions for achieving the target KPIs. As a practical example, we take up a venture that has started the business-to-consumer e-commerce (B2C-EC) of health food products and show the results of applying the proposed method in the company and verify its effectiveness.
Error Term Checking: Towards Chosen Ciphertext Security without Re-encryption

Chosen ciphertext security for lattice based encryption schemes is generally achieved through a generic transformation such as the Fujisaki-Okamoto transformation. This method requires full re-encryption of the plaintext during decapsulation, which typically dominates the cost of the latter procedure. In this work we show that it is possible to develop alternative transformations specifically designed for lattice based encryption schemes. We propose two novel chosen ciphertext transformations, ETC1 and ETC2, in which re-encryption is replaced by checking the error term of the input ciphertext. We show that our new ciphertext validity check can be securely applied to lattice based encryption schemes under specific conditions. For the NIST post-quantum standardization candidate Threebears we show a speed-up for decapsulation of up to 37.4%. Moreover, as our method only changes the validation check during decapsulation, it is fully backwards compatible with existing implementations of the Fujisaki-Okamoto transformation.
Framework for an agile, databased development

In today’s VUCA world, machinery and plant engineering companies face the major challenges of an advancing globalization and rapid technology changes. This forces them to react to changing requirements during product development and to shorten the time-to-market continuously due to competitive pressure. Especially companies from the wind turbine sector have to individualize their products for a variety of markets and customers, while at the same time taking into account complex dependencies between the various systems components. To address the stated challenges, this paper presents a framework for an agile and databased product development process that enables these companies to derive a development process that is best suited for their individual situation. The framework consists of a process model as well as an information model. The process model records the current development process with regard to the development network, the business process landscape and the actual development activities. The information model consists of an analysis of the currently used information systems, the description of the development’s information need and the identification of information carrier objects. The implementation of an agile, databased development process is the integrated application of both, process and information model. The recurring process modules, which are allocated to specific physical product modules, and the information and data model, which assures the information supply during product development, characterize the reconfigurable development process of this framework. The validation of this framework is carried out in cooperation with a company from the field of wind turbine development.
Bringing runtime verification home: a case study on the hierarchical monitoring of smart homes using decentralized specifications

We use runtime verification (RV) to check various specifications in a smart apartment. The specifications can be broken down into three types: behavioral correctness of the apartment sensors, detection of specific user activities (known as activities of daily living), and composition of specifications of the previous types. The context of the smart apartment provides us with a complex system with a large number of components with two different hierarchies to group specifications and sensors: geographically within the same room, floor or globally in the apartment, and logically following the different types of specifications. We leverage a recent approach to decentralized RV of decentralized specifications, where monitors have their own specifications and communicate together to verify more general specifications. We leverage the hierarchies, modularity and re-use afforded by decentralized specifications to: (1) scale beyond existing centralized RV techniques, and (2) greatly reduce computation and communication costs.
Determinants of grader agreement: an analysis of multiple short answer corpora

Abstract The ’short answer’ question format is a widely used tool in educational assessment, in which students write one to three sentences in response to an open question. The answers are subsequently rated by expert graders. The agreement between these graders is crucial for reliable analysis, both in terms of educational strategies and in terms of developing automatic models for short answer grading (SAG), an active research topic in NLP. This makes it important to understand the properties that influence grader agreement (such as question difficulty, answer length, and answer correctness). However, the twin challenges towards such an understanding are the wide range of SAG corpora in use (which differ along a number of dimensions) and the hierarchical structure of potentially relevant properties (which can be located at the corpus, answer, or question levels). This article uses generalized mixed effects models to analyze the effect of various such properties on grader agreement in six major SAG corpora for two main assessment tasks (language and content assessment). Overall, we find broad agreement among corpora, with a number of properties behaving similarly across corpora (e.g., shorter answers and correct answers are easier to grade). Some properties show more corpus-specific behavior (e.g., the question difficulty level), and some corpora are more in line with general tendencies than others. In sum, we obtain a nuanced picture of how the major short answer grading corpora are similar and dissimilar from which we derive suggestions for corpus development and analysis.
Modeling and Representation by Graphs of the Reasoning of an Emergency Doctor: Symptom Checker MedVir

This article deals with the symptom checker MedVir which is modeled on the reasoning of an emergency physician. His reasoning is very particular because he often has no knowledge of the patient and he doesn’t have much time to evaluate the situation. He needs to make decisions rapidly based on diagnostic hypotheses and an estimation of the severity of the patient’s condition. We present a ten step model of the reasoning of an emergency physician by a four layer network composed with what we call a “neuronal entity" and a question prioritization algorithm which checks the most important questions. This “neuronal entity" generalizes the neuron concept but differs from those usually used in machine learning. Visualization by graphs displays all the characteristics of each neuron and each synapse thickness corresponds to the argumentative strength of a question. Hence, these graphs could be very useful in the training of physicians and health professionals.
Speckle Noise Removal Using Spatial and Transform Domain Filters in Ultrasound Images

Noise in the Ultrasound (US) images creates difficulties in interpreting the actual information in the image. Various studies have been done in this direction to retain useful information from the images. Speckle noise is an important noise type present in ultrasound images in the area of Synthetic Aperture Radar (SAR), active Radar, Optical coherence tomography (OCT) and medicine. The granular pattern of speckle noise is formed due to the simultaneous processing of backscattered signals from dispersed targets. This noise limits the quality and contrast of the obtained images resulting in poor understanding of the underlying facts. This work focuses on the various denoising techniques in spatial and transform domain for speckle noise removal in ultrasound images. The performance evaluation of these techniques was done with metrics like Speckle suppression index (SSI), Peak signal-to-noise ratio (PSNR) and Structural similarity index measure (SSIM). From the experiments, it is evident that SRAD algorithm in spatial domain really out performed all other filters with better SSI, PSNR and SSIM values.
Analysis of Results of Some Techniques for the Recognition of Circular Shapes in the Steel Bar Counting System Using Image Processing

In this paper, a comparison of the performance of three methods of detection of circles in black and white images is tested. For this, we use a proprietary database previously preprocessed to compare the three methods with the same conditions. This work represents a contribution to the pattern detection (circle detection) of a benchmarking of images. Once defined, the new based algorithm and ratio detection in the figure, circular images were set to the edges detection (circles) with ratio selection dependents on the number of circles to be detected in the selected template. The project in question views the study and development of an algorithm that can detect circles to start many images. This identification is possible due to the circle detection algorithm developed in MATLAB used in this project. This work’s main contribution is related to the circle detection techniques using an algorithm developed in MATLAB to detect circular elements in mechanical parts. We believe that industries and mechanical processes, such as the milling process, need intelligent machinery to make inspections with precision and reliability. Therefore, with this vision in mind, an algorithm was developed to detect circumferences in various mechanical parts with precision.
Unwinding Environment in the Lecture Room Using Mobile Learning (M-Learning) Adoption among the Millennial Generation

Abstract Information and Communication Technology (ICT) has provided flexibility for teachers and students to engage in academic discourse irrespective of the location. With rapid technological advancements, Mobile learning offers incredible opportunities, especially in higher education especially during this Covid-19 pandemic. The advancement of mobile and wireless communication technologies has encouraged the mobile learning method where students are able to learn via mobile devices without being limited by space and time. The millennial generation can be situated in a real-world scenario associated with the learning content in higher education. This paper discovered how mobile learning approach can enhance engagement skills and communication skills among millennial generation and employed action research methodology. In this research, mobile learning application such as kahoot! E-exercises and e-quizzes adoption of mobile learning during lecture and outside lectures activities for conversations and consultation. The positive findings showed those students’ engagement skills and communication skills increased via the mobile learning adoption. Students had active learning opportunity where effective conversations and consultation between the lecturer and students. This paper heightens lecturer awareness in identifying the learning tasks via mobile learning that are well developed. This may further be a motivating factor to adopt measures and new strategies for the improvement of students’ skills. Statistics revealed a higher engagement rate and better communication skill when courses are delivered using the mobile learning approach.
Empirical Study on Urban Waterlogging Data Governance Model Based on DID

Abstract In this paper, 11 communities in Xiqing District of Tianjin are selected as the research objects. According to the data and information of drainage network during 2010-2020, the effect of urban waterlogging control after the application of data management model in 2016 is tested by using double check difference method. The empirical results show that the implementation of data governance model has a positive effect on reducing the vulnerability of urban drainage network and can improve the governance efficiency; It is advisable to take the fragility of urban drainage network as an important parameter in urban waterlogging control.
Fake News and Imbalanced Data Perspective

Fake news has grabbed attention lately. In this chapter, the issue is tackled from the point of view of collection of quality data (i.e., instances of fake and real news articles on a balanced distribution of subjects). It is predicted that in the near future, fake news will supersede true news. In the media ecosystem this will create a natural imbalance of data. Due to the unbounded scale and imbalance existence of data, detection of fake news is challenging. The class imbalance problem in fake news is yet to be explored. The problem of imbalance exists as fake news instances increase in some cases more than real news. The goal of this chapter is to demonstrate the effect of class imbalance of real and fake news instances on detection using classification models. This work aims to assist researchers to better resolve the problem by illustrating the precise existence of the relationship between the imbalance and the resulting impact on the output of the classifier. In particular, the authors determine that data imbalance and accuracy are inversely proportional to each other.