Reviewerly/exHarmony huggingface dataset:
Practical Considerations for the Remediation of 3D CAD into Visual Technical Communication

A rapidly developing trend in the engineering community to exchange design information using 3D objects has the potential to empower technical communicators. 3D CAD software can often be exploited to remediate these design objects into a wide variety of diagrams, illustrations, photographic-like imagery, and interactive 3D compositions. This capability, combined with improvements in browser software, consumer level computers, and handheld devices raises important questions and considerations for the practice of technical communication. In the following paper the author describes avenues of approach that permit practitioners to take advantage of the increasing availability of 3D CAD and to engage this technology with proprietary and open-source software in a manner that increases the scope and domain of technical communication practice.
Improving the K-Means Clustering Algorithm Oriented to Big Data Environments

In recent years, the amount of texts in natural language, in digital format, has had an impressive increase. To obtain useful information from a large volume of data, new specialized techniques and efficient algorithms are required. Text mining consists of extracting meaningful patterns from texts; one of the basic approaches is clustering. The most used clustering algorithm is k-means. This chapter proposes an improvement of the k-means algorithm in the convergence step; the process stops whenever the number of objects that change their assigned cluster in the current iteration is bigger than the ones that changed in the previous iteration. Experimental results showed a reduction in execution time up to 93%. It is remarkable that, in general, better results are obtained when the volume of the text increase, particularly in those texts within big data environments.
Mining Hypernyms Semantic Relations from Stack Overflow

Communication between a software development team and business partners is often a challenging task due to the different context of terms used in the information exchange. The various contexts in which the concepts are defined or used create slightly different semantic fields that can evolve into information and communication silos. Due to the silo effect, the necessary information is often inadequately forwarded to developers resulting in poorly specified software requirements or misinterpreted user feedback. Communication difficulties can be reduced by introducing a mapping between the semantic fields of the parties involved in the communication based on the commonly used terminologies. Our research aims to obtain a suitable semantic database in the form of a semantic network built from the Stack Overflow corpus, which can be considered to encompass the common tacit knowledge of the software development community. Terminologies used in the business world can be assigned to our semantic network, so software developers do not miss features that are not specific to their world but relevant to their clients. We present an initial experiment of mining semantic network from Stack Overflow and provide insights of the newly captured relations compared to WordNet.
Producción y difusión de conocimientos durante la epidemia de COVID-19: entre la necesidad y el pragmatismo de contar con buenos factores pronósticos

ObjectiveTo review and appraise the validity and usefulness of published and preprint reports of prediction models for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital or dying with the disease. DesignLiving systematic review and critical appraisal by the covid-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sOurcesPubMed and Embase through Ovid, up to 17 February 2021, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. stuDy selectiOnStudies that developed or validated a multivariable covid-19 related prediction model. WhAt this study AddsOf models with a low risk of bias, four identify patients at risk in the general population; one assists in patient triage at the emergency department; and three estimate prognosis in patients admitted to hospital with covid-19 Calibration of these models is likely to vary over time and across settings There is an oversupply of models and external validations at high risk of bias, raising concern that predictions could be unreliable when these models are applied in dailly practice
P‐151: Content‐Adaptive RGBW Algorithm

The main advantage of the RGBW display is that its optical efficiency is at least 50% higher than the RGB display. However, the lack of an excellent RGB‐to‐RGBW algorithm was the biggest problem of its application. This paper discussed a phenomenon called “Simultaneous Contrast” which existed in RGBW displays and an Content‐Adaptive RGBW Algorithm was presented. The results indicated that the proposed method had superior performance upon conventional RGB in most scenarios.
Pattern classification of hyetographs and its application into flood control planning

Pattern classification methodology is applied into hydrological time sequences such as hyetograph or hydrograph to analyze their feature patterns. Classification objectives are proposed and optimal number of classified patterns is decided through AIC technology. Then the fluctuation of peak discharge against the different hyetograph under with same total volume is considered. Occurrence probability of hydrological event is evaluated from the viewpoint of feature pattern without the traditional probability approach. Moreover flood control planning matching the designed occurrence probability is discussed.
Performance Modeling of Publish/Subscribe Middleware Components

Internet of Things has changed the environment dramatically, it introduced the all-time connectivity at the level of individual user and enabled monitoring of all user activities by connecting various sensors and data sources into the IoT ecosystem. This was supported by publish/subscribe infrastructure that efficiently interconnects all data flows. In such, global, dynamic and heterogeneous environment satisfactory performance of the underling layer for information exchange is crucial in order to support IoT services in production deployment. Therefore, to plan and support all services it is necessary to investigate the performance of the publish/subscribe middleware in details, as opposed to the overall performance analysis where the publish/subscribe middleware is analyzed as black-box. Detailed analysis provides insights in the performance of the whole system, identifies possible bottlenecks and provides information about maximum throughputs that can be handled with given parameters. In this paper we extended the CUPUS middleware with the performance monitoring components that allowed detailed insight into processing time at internal components. This allowed us to model processing times at individual component with well-know distribution (such as exponential). Such data can be used to perform complex analysis of performance of the whole middleware.
A New Approach to Image Enhancement by Non-Linear Contrast Stretching

This paper looks at the challenge of creating new, effective ways to improve images that are intended for use in mobile applications. The purpose of this study is to improve the efficiency of enhancing images in a fully automatic mode by parameter-free image intensity transformation. This paper proposes a new approach to enhancing the image by adaptively transforming its intensity based on the analysis of brightness distribution at the boundaries of the objects of this image. To demonstrate the possibilities of this approach, new techniques of intensity transformation are proposed. The proposed techniques provide effective image enhancement without the appearance of distortion and artifacts and are focused on use in mobile applications.
An Intelligent Maritime Communication Signal Recognition Algorithm based on ACWGAN

In maritime communications systems, there are marine VHF communications systems that meet GMDSS standards in addition to AIS and VDES systems which use very high-frequency signals for information communications such as security rescue. But because its communication does not contain the identity information, the channel is easy to be occupied maliciously, thus interferes in normal maritime communication. This paper studies and analyzes the individual identification technology of the VHF signal based on the rf fingerprint technology of signal. Using the improved adversarial generation network ACWGAN(Auxiliary Classifier Wasserstein Generative Adversarial Networks) to train and identify, we obtain a better classification result. The recognition rate can reach 94% when the SNR is 5 dB for 10 different classes of VHF signal.
Model of University Education Informationization Maturity from the Perspective of Management

This paper combines the informationization maturity model to construct the education information maturity model and its evaluation system of colleges and universities, and then studies the evaluation system of college education informationization maturity model, and uses the combination weighting method to determine the index weight and give the score calculation. The method and the correspondence between the score interval and the maturity model level. Finally, an empirical study on the development level of college education informatization in China is carried out. The overall development status of education informatization in China’s deaf schools and the differences in the development of colleges at different stages and in the same stage are understood. The existing problems are identified and targeted opinions are proposed based on the maturity model.
Consensus Modeling: A Transfer Learning Approach for Small Health Systems

The adoption of predictive modeling for clinical decision support is accelerating in healthcare, however, the need for large sample sizes puts smaller health systems at a disadvantage. Small health systems have insufficient positive cases to build models are left with three choices. First, they can obtain already trained models, which are often too generic. Second, they can participate in research networks, building a model through a network-wide data set. Since small hospitals can only contribute small amounts of data influencing the resulting shared model minimally, this approach yields only minimal specialization. The third option is transfer learning, where a model previously trained on a large population is refined to the specific population, which carries the danger of over-specializing to the idiosyncrasies of the small data set. In this paper, we present a novel paradigm, consensus modeling, that allows a small health system to collaborate with a larger system to build a model specific to the smaller system without sharing any data instances. The method is similar to transfer learning in that it refines models from the larger system to be specific to the small system, but through iterative refinement, the larger system alleviates the risk of over-specializing to the small system. We evaluated the approach on predicting postoperative complications at two health systems with 9,044 and 38,545 patients. The model obtained from the proposed consensus modeling paradigm achieved a predictive performance on the small system that is as good as the transfer learning approach (AUC 0.71 vs 0.71) but significantly outperformed the transfer learning approach on the large dataset (AUC 0.80 vs 0.65) suggesting significantly reduced over-specializing.
An Agent Program in an IoT System to Recommend Plans of Activities to Minimize Childhood Obesity

Overweight and obesity in children is a recognized worldwide epidemic. They are associated with several current and future chronic diseases. OCARIoT is a joint EU-Brazil joint that aims to develop a sophisticated, noninvasive, unobtrusive, personalized IoT system to detect and normalize the behaviors that put a child at risk of developing obesity or eating disorders. In a recent written work, we proposed the design of an agent-based approach to recommend individual physical and food-related activities, based on data collected from wearable devices. In this paper, we present the design of an expanded approach that, in addition to recommendations for individual activities, should recommend activity plans, i.e., sequences of activities organized to minimize childhood obesity. The first results with the extended version were very promising. During the experiments, the selected individual activities and sequences of activities organized by the approach proved to be effective in conducting children, with different profiles and initial states, to the desired states of various attributes associated with childhood obesity.
Sign Language Interpreter Detection Method for Live TV Broadcast Content

Recent technological improvements in computing and embedded systems have enabled the development and improvement of systems and applications in a way that makes people’s lives simpler, safer and more accessible. The development of small and powerful hardware devices for signal processing, together with the use of techniques of artificial intelligence and machine learning can assist people with all kinds of limitations in the use of multimedia content on a TV broadcast video. Some broadcasters insert sign language interpreters on the transmitted content. The size and the position of the interpreter on the screen are not always the more appropriate for the best use of such tool. The use of artificial intelligence techniques make it possible to identify such elements on an image with high accuracy, reliability and repeatability, when compared to traditional systems. On this paper, a machine learning based method was developed with the aim of identify the presence and the position of a sign language interpreter on the screen, take an instance of its content and make it available for magnification, providing greater accessibility and experience improvement on watching TV for people with special needs. The developed system achieved final accuracy greater than 80% with inference time between 2 to 3 s, depending on the used hardware, running on a live broadcast environment.
Adaptive Method for Discovering Service Provider in Cloud Composite Services

Application service providers implement Software-as-a-Service applications through a large number of Cloud Computing infrastructures. It is an increasingly challenging demand to discover trusted service providers based on services' outputs. However, the quality of service output may descend due to: (a) internal application logic of a Cloud service and (b) competition of resources in the sharing-based Cloud systems. Therefore, we propose an efficient method of trusted service provider discovery, called TSD (Trusted Service Discovery), to ensure that each service instance of composite services in Cloud systems is trustworthy. TSD treats all services as black boxes, and evaluates the outputs of service providers in service classes to obtain their equivalent or nonequivalent relationships. According to the equivalent or nonequivalent relationships, trusted service providers can be found easily. TSD improves accuracy of processing results as shown in experiments.
Analysis of Imitating Behavior on Social Media

In the age of web 2.0, the concept of “the User Generated Content (UGC)” is put forward. Now millions of images posted on the photo-sharing service such as Instagram. In this paper, we choose the popular photos “follow me to” on Instagram as an example for analysis. By utilizing the comparative analysis of the “follow me to" photos of the photographer and the imitators, we explored the pattern of image propagation on Instagram and the characteristic of the popular images. In this paper, we also established a research method by content analysis and image analysis based on the image itself to study social media.
Transfer Learning from Planar to Spherical Images

In recent years, 360-degree cameras have been released, and making it easy to enjoy 360-degree (i.e., spherical) images. This new image format is accepted as a new personal recording medium. New businesses using it are also emerging. In such a background, 360-degree image search function increases its importance. In this paper, we propose a method for processing 360-degree images using a transfer learning framework. The reuse of network weights by transfer learning framework reduces the computing resources required for training period. The use of small image size reduces the computing resources required for test period. It also improves accuracy of image search function. This paper also discuss the relationship between this unexpected improvement and transfer learning method we used.
On Analytical Solutions to the Problems of Maintaining Local Consistency

One of the primary problems, arising in algebraic Bayesian networks, is the problem of checking and maintaining consistency of the knowledge pattern. It can be reduced to the linear programming problem, which methods of solving are well studied. However, acting as black box, this approach is ill-suited to solution of another important problem—research of the sensitivity of the probabilistic logical inference. In this work we prove the analytical representation of solutions of maintaining the local consistency problem for the knowledge pattern of small size and show the results of the experiment, comparing effectiveness of the solution using obtained formulae and simplex-method. The problem is being solved for the first time.
A Data Simulation Method of Bank Fraud Transaction Based on Flow-Based Generative Model

Abstract In order to solve the problem of category imbalance caused by the shortage of bank fraud transaction data, this paper proposes a bank fraud transaction data simulation method based on flow-based generative model. On the basis of the flow-based generative model and the real fraudulent transaction data of the bank, this method designs a generative model suitable for the bank data, and learns the distribution of the real data through the generators G and G -1 . The experimental results show that mixing the generated simulated data and real business data in a certain proportion to train the fraud detection model can improve the detection effect of the model to a certain extent.
Investigating the Perceived Strengths and Limitations of Free-Viewpoint Video

Free-viewpoint video (FVV) is a type of immersive content in which a character performance is filmed using an array of cameras and processed into a video-textured, animated 3D mesh. Although FVV content has a unique set of properties that differentiates it from other immersive media types, relatively little work explores the user experience of such content. As a preliminary investigation, we adopted an open-ended, qualitative approach to investigate these issues. Semi-structured interviews were conducted with six immersive content experts, exploring the perceived strengths and limitations of FVV as a content type. These interviews were analysed using inductive thematic analysis. We identified five themes during our analysis: they don’t look real, but that’s okay; they can really move; they don’t connect with me; encounter, legacy and truth; no technology is an island. Our analysis reveals a wide range of future research directions and provides insight into which areas may produce the most benefit in relation to the user experience. We discuss, for example, the potential impact of difficulties in supporting user engagement, aspects related to visual quality such as the importance of responding realistically to environment lighting, and tensions between visual and behavioural quality. The analysis also highlights the complex interplay of factors related to the content itself, such as performance style and the use of creative production techniques to reduce the impact of potential limitations.
Reform and Application of the Mixed Teaching Mode of Higher Mathematics Supported by Internet Courses

In order to adapt to the current trend of higher education popularization and the need of mathematics teaching in the Internet Age, this paper combines the mobile terminal of Internet technology with the traditional higher mathematics classroom teaching and explores the hybrid teaching mode based on the support of Internet class. The purpose is to fully give play to the teacher’s guiding role in guiding, inspiring, and monitoring the teaching process and mobilize the students’ enthusiasm and creativity in the learning process.
P‐127: Multi‐touch Tactile Display with Segmented Electrode Structure and Driving Method for Presenting Tactile Free‐shape Localized Textures

We propose an electrostatic multi‐touch tactile display with a segmented electrode structure and electrode‐driving method for presentation of free‐shaped localized textures. This display solves the tactile crosstalk issue of our previously proposed tactile display. We evaluated the proposed display in a user test.
Critical Analysis of International Shipments within Mainstream Blockchain Framework using Industrial Engineering Techniques

International trading is an amalgamation of confusion and misdirection when it comes to the security of documents. There are banks, insurers, carriers, freight forwarders and agents involved. Parties exchange data and documents in an isolated peer to peer manner. There is no consistent view of the process or document versions. Individual interfaces between partner systems are costly. Manual handling of paper-based documents is inefficient and delivering paper documents via expressing courier services adds another significant cost factor. Blockchain for International trading aims to establish a new level of security and transparency. This framework complements an updated IOT (Internet of Things) -based data feed structure to guarantee the reliability and security of information received and captured. Along with the replication of functions similar to that of a distributed ledger, it transforms shipping processes, enabling digital document sharing, signing and approval. In short, applying blockchain techniques result in cost savings, time efficiency, as well as the elimination of fraud and stolen freight. Through Blockchain and different Industrial Engineering techniques, we aspire on implementing the required protocols in order to optimize the process.
Comparing Neural Network Parsers for a Less-resourced and Morphologically-rich Language: Amharic Dependency Parser

In this paper, we compare four state-of-the-art neural network dependency parsers for the Semitic language Amharic. As Amharic is a morphologically-rich and less-resourced language, the out-of-vocabulary (OOV) problem will be higher when we develop data-driven models. This fact limits researchers to develop neural network parsers because the neural network requires large quantities of data to train a model. We empirically evaluate neural network parsers when a small Amharic treebank is used for training. Based on our experiment, we obtain an 83.79 LAS score using the UDPipe system. Better accuracy is achieved when the neural parsing system uses external resources like word embedding. Using such resources, the LAS score for UDPipe improves to 85.26. Our experiment shows that the neural networks can learn dependency relations better from limited data while segmentation and POS tagging require much data.