Reviewerly/exHarmony huggingface dataset:
Geodiagnostics of lithogydrogenic systems for forecasting exoggeodynamic processes

The landscape indication, based on the automated analysis of remote sensing data, is one of the key methods of research and mapping of lithohydrogene geosystems. The article describes a set of methods for effective detection of types of lithohydrogene systems, including a set of modules for identifying dynamic and invariant descriptors of the territory; assessment of geophysical diversity of landscapes; analysis of the geophysical shell through the calculation of the descriptors of the neighborhood; ensemble-analysis of remote sensing data for monitoring the state of geosystems and forecasting of natural processes. The system of methods for detecting types of landscapes made it possible to conduct geodiagnostics of lithohydrogene systems of the Privolzhskaya Upland and the marginal part of the Oka-Don lowland reservoir within the boundaries of the Republic of Mordovia in order to predict the development of exogeodynamic processes.
Proper 2-coloring game on some trees

Let Pn denote the path with exactly n vertices and let Tn,i denote the tree obtained from Pn−1 by adding a pendant vertex to the (i+1)-th vertex of Pn−1, where n≥4 and 1≤i≤n−3. In this paper we study proper 2-coloring game on Tn,i by using Sprague–Grundy function and give the optimal strategy of proper 2-coloring game on Tn,i.
Study on camera calibration method in plane mirror detection system

Camera calibration is vital in inverse Hartmann system for measuring surface shape of large aperture plane mirrors. It directly affects system accuracy. Through calibration, the internal and external parameters of camera can be acquired. In order to increase the accuracy of camera calibration, this paper selects dot matrix pattern suitable for our system as calibration plate and applies centroid detection algorithm to extract position of feature points. The impact of number and diameter size of dots on camera calibration has been analyzed. Through experiment, the repeatability of the centroid detection algorithm for dot matrix reaches 0.004 pixels, while corner extraction algorithm for checkboard is only 0.007 pixels. It indicates that using dot matrix pattern to calibrate camera is better than using a checkboard in inverse Hartmann system.
A Study of an Automated Software Effort Measurement Method

Software companies have adopted project management methodologies suitable for their organizations and have made significant efforts in successfully applying them to improve the quality of software. In particular, a technology that can measure and analyze software project data is essential for effective project management and productivity improvement. Of these software project data, software effort is the key metric to be measured, given its direct relation to process improvement and quality but also have general management interest. However, in practice, there have been many difficulties in actually measuring effort data because of problems in continuous and consistent measurement. Therefore, in this paper, we propose an automated software effort measurement method that can apply during the entire software development life cycle, to overcome these problems and to achieve improvement of effort measurement outcomes. Experiments are performed to evaluate the proposed method from the viewpoint of effort measurement accuracy. The results indicate that the proposed method shows a significant improvement compared to the existing methods.
Vernacular Participatory Designs: The Do-It-Yourself (DIY) Housing Scene in South Korea

This article analyzes cases of do-it-yourself (DIY) homes in South Korea in terms of the role of architects in today’s information society. Some South Koreans have started to seek residential options that reflect their personal tastes and particularized lifestyles. With the development of digital technologies and widening access to computer programming tools, technology-savvy South Koreans have started to design and construct their own homes. This article explores the emerging DIY housing movement to see whether its participatory nature enhances individual agency and how the movement may influence the professional field of architecture. Contrary to the commonly held notion, this article concludes that the role of architects is unlikely to diminish in the context of a rising number of DIY homes. Although the possibility of neoliberal co-optation exists, the rise of DIY homes presents an opportunity for architects to play an enhanced role in promoting community empowerment.
Design of SNS-Based English Word Learning System for Daily Study

It is possible to design an English vocabulary learning system using a text to speech (TTS) engine and a module which creates push alarms for new words or sentences at a specific time every day. The function of selecting new words and sentences and showing them to the user is a core technology in vocabulary learning systems, and it has a great effect on the systems’ learning effectiveness. This paper proposes a method for creating a system which selects words and sentences. The system collects English words and sentences via social networks, refines them so that they are in a form which is appropriate for user learning, and allows the user to learn words which are related to their current location and weather.
Cloud-Oriented Packaging and Delivery

Web Engineering concerns the software development of web applications. A particular type of web systems are those for providing cloud services. In these systems the packaging and delivery sub-process has complexities associated to multiple server configurations and continuing operation of existing services. In this paper we show a detailed procedure for packaging and delivery resulting from a three-phase qualitative approach: first, a case study for eliciting an existing process in a small software house, second an expert judgment approach for improving the existing procedure which its main recommendation was to include containers technology, and, third, a focus group for consolidating a unified view of the improved process.
Using Lucas-Kanade Algorithms to Measure Human Movement

As an important part of clinical studies, Motion estimation knowledge is widely used to gather useful movement information for medical professionals to find out the best treatment for chronic pain. The purpose of this project is to develop a program to analyze patients’ movements and therefore to improve the treatment of patients. Initially, the basic Luca-Kanade algorithm was implemented. And this program was primarily improved upon by setting a threshold to decrease the noise, and then by selecting feature points to process. Additionally, the resizing method was adopted to further improve the whole system. The solution successfully meets the project aims as the system performs much better than the original one with higher accuracy and speed, while the motion trail can be represented clearly by multiple optical flow fields and the useful information can be detected from the video through all the improved implementations.
Refining semantics for multi-stage programming

The multi-stage programming paradigm supports runtime code generation and execution. Though powerful, its potential is impeded by the lack of static analysis support. Van Horn and Might proposed a general-purpose approach to systematically develop static analyses by transforming an environmental abstract machine, which evolves a control string, an environment and a continuation as a program evaluates. To the best of our knowledge, no such semantics exists for a multi-stage language like MetaML. We develop and prove correct an environmental abstract machine semantics for MetaML by gradually refining the reference substitutional natural semantics. Highlights of our approach include (1) progressively refining a single-stage language ISWIM to a CEK-like machine and managing to apply transformations of the same nature to MetaML, (2) leveraging explicit substitutions to bridge the gap between substitutional and environmental semantics, and (3) devising environment chains to model the complexities of variable bindings in multi-stage environmental semantics.
Title Cardiovascular Big Data Analytics

The evolving pattern towards precision medicine is based on analytics at the—omics, physiology and eHealth domains. One of the main driving forces is the management and analytics of multi-level multi-scale and multi-source data containing different semantics and possibilities for the realization of personalized health through the precision medicine approach. In the case of cardiovascular big data analytics progress is evident especially at the levels of physiology and eHealth. Specifically there is progress in the ICU domain, where streaming data are the big data source comprising a paradigm for point-of-care (PoC) diagnostics helping the clinical decision support system (CDSS) for physiological assessment of the cardiorespiratory system, as well as in the eHealth domain through the uptake of advanced closed loop telemonitoring systems addressing connected health and coordinated care for multimorbid chronic disease patients. The quantification of specific data analytics, data management and computational needs for the realization of interfaces conveying from cloud to local level the necessary input to the medical users is a complex procedure, which calls for the optimal use of all the cloud, computing and machine learning and visualization tools, let alone the interoperability and data security standards and apps. In this chapter, we shall give an overview of the current state-of-the-art concerning cardiovascular big data analytics as a function of available information and communication technology, computational engineering, and user driven systems platforms for use in connected health and integrated care environments, as well as one basic example related to big data analytics as a result of streaming data at a PoC from a clinically controlled environment such as the Intensive Care Unit (ICU). The output from the application of advanced machine learning and multiparametric analytics methodology conveyed as a CDSS to the users is considered and viewed as perhaps the most important function for the success of the big data analytics and management platforms.
Assuring Privacy in Surfing the Internet

On-line security is a very important aspect of Internet browsing, so a lot of research has been targeting confidentiality, integrity, authentication, non-repudiation and availability of on-line services. But another aspect that is at least equally important has been rather neglected: Internet privacy. On-line tracking can be used for legitimate purposes (e.g. preventing illegitimate login attempts), but it also invades the privacy of the users. This paper focuses on browser fingerprinting, which is the least known threat to privacy among Internet users, presenting it in detail, from definition to the possible countermeasures. Some software models built inside the so called crypto-for-privacy are also described. The paper also presents a Chrome extension that has been developed in order to obfuscate third party tracking. The browser fingerprint of a device has been consistently changed, assuring a balanced trade-off between privacy and usability.
Weak-Perspective and Scaled-Orthographic Structure from Motion with Missing Data

Perspective n-Point (PnP) problem is in focus of 3D computer vision community since the late 80’s. Standard solutions deal with the pinhole camera model, the problem is challenging due to the perspectivity. The well-known PnP algorithms assume that the intrinsic camera parameters are known, therefore, only extrinsic ones are needed to estimate. It is carried out by a rough estimation, usually given in closed forms, then the accurate camera parameters are obtained via numerical optimization. In this paper, we show that both the weak-perspective and scaled orthographic camera models can be optimally calibrated including the intrinsic camera parameters. Moreover, the latter one is done without iteration if the $$L_2$$ norm is used. It is also shown that the calibration can be inserted into a structure from motion algorithm. We also show that the scaled orthographic version can be powered by GPUs, yielding real-time performance.
Research on the Reading Status of University Faculty and Staff — A Case Study of Binzhou University

Universities are cultural highlands, and the reading cultural life of faculty and staff should also be rich and colorful.The author investigates and analyzes the reading conditions of the faculty and staff of Binzhou University, draws the conclusions of the investigation, and puts forward constructive opinions on the labor union organizing reading activities of the faculty and staff.
Synthetic Training with Generative Adversarial Networks for Segmentation of Microscopies

Medical imaging is often burdened with small available annotated data. In case of supervised deep learning algorithms a large amount of data is needed. One common strategy is to augment the given dataset for increasing the amount of training data. Recent researches show that the generation of synthetic images is a possible strategy to expand datasets. Especially, generative adversarial networks (GAN)s are promising candidates for generating new annotated training images. This work combines recent architectures of Generative Adversarial Networks in one pipeline to generate medical original and segmented image pairs for semantic segmentation. Results of training a U-Net with incorporated synthetic images as addition to common data augmentation are showing a performance boost compared to training without synthetic images from 77.99% to 80.23% average Jaccard Index.
Case-Based Expert Systems

In this chapter, we will introduce case-based expert systems, an example of the Artificial Intelligence branch of our Autonomous Learning taxonomy. There are two broad classes of expert systems, rule-based and case-based. Rule-based systems have a set of rules that are applied to come to a decision; they are just a more organized way of writing decision statements in computer code. These systems provide a way of automating the process when decision-making involves hundreds or thousands of rules. Case-based systems decide by example, that is, a set of predefined cases.
Improving Clinical Subjects Clustering by Learning and Optimizing Feature Weights

Data analytics methods in the clinical domain are challenging to put into practice. Unsupervised learning provides opportunity for giving the level of personalization in evidence based decision-making that can otherwise only be achieved through the use of prediction models, by helping doctors gaining insights from data. In this context, grouping of clinical subjects, in terms of biomedical information of patients, is an important task for patient cohort identification for comparative effectiveness studies and clinical decision-support applications. It allows the decision-making process to leverage not only on data but also on doctors’ domain knowledge. However, one of the issues that needs to be addressed for a focused and realist unsupervised clustering of clinical subjects, is the fact that in the majority of the cases patients datasets are heterogeneous, i.e. their data features belong to several different feature spaces, e.g. nominal, ordinal, interval or rational, with completely different variation ranges and statistical distributions, affecting clustering quality and performance. In order to use these data measurements properly in an unsupervised manner, their corresponding weights need to be modeled. In this paper, we present a method for learning feature weights on clinical data. We show that learning feature weights is necessary in order to generate meaningful separation of data in high dimensional space. The method is based on silhouette score and principal component analysis, demonstrating its performance on a clinical test dataset.
4 × 2D Visual Servoing Approach for Advanced Robot Applications

Actually, vision information can be used as sensory input in open-loop as well as in the closed-loop. However, the visual servoing approach is performed only inside the closed-loop robot control, because in the open-loop the vision sensor will represent the initial extraction of the features to generate directly the robot motion sequence and these features and motion could be off-line generated. On the contrast, closed-loop robot system uses the vision as real time sensor and it consists of two phases: tracking and control. Tracking provides a continuous estimation and update of features during the robot/object motion. Based on this information, a real time control loop will be generated. The main contribution of this work is a proposed visual servoing approach which will benefit from the images which are obtained by Kinect camera (RGB-D camera). The proposed visual servoing approach is called 4 × 2D visual servoing which combines the correspondent color and depth images to build two new images. Using these 4 images the control error signals will be calculated in order to track the objects. Firstly, this chapter will present all types of visual servoing then it will introduce the 4 × 2D visual servoing approach and the visible side coordinate system, after that it will illustrate the concept of the proposed approach and how the error signal will be calculated. In addition to that, this approach proposes a coordinate system which is called visible side coordinate system.
Malay-Corpus-Enhanced Indonesian-Chinese Neural Machine Translation

Due to the lack of structured language resources, low-resource language machine translation often faces difficulties in cross-language semantic paraphrasing. In order to solve the problem of low-resource machine translation from Indonesian to Chinese, a cognate-parallel-corpus-based expanding method of language resources is proposed, and an improved neural machine translation model is trained by the Malay-corpus-enhanced corpus. The improved model can achieve a comparable result as that of Google in the experiment of Indonesian-Chinese machine translation. The experimental results also show that the morphological similarity and semantic equivalence between the languages are very effective computational features to improve the performance of neural machine translation for low-resource languages.
How to Start Doing SoTL

The National Teaching & Learning ForumVolume 28, Issue 2 p. 10-11 SoTL in Action How to Start Doing SoTL Nancy Chick, Corresponding Author Nancy Chick NCHICK@rollins.edu Rollins CollegeContact: Nancy Chick at NCHICK@rollins.eduSearch for more papers by this author Nancy Chick, Corresponding Author Nancy Chick NCHICK@rollins.edu Rollins CollegeContact: Nancy Chick at NCHICK@rollins.eduSearch for more papers by this author First published: 13 February 2019 https://doi.org/10.1002/ntlf.30189Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume28, Issue2February 2019Pages 10-11 RelatedInformation
Replicated Computational Results (RCR) Report for “Fast Random Integer Generation in an Interval”

The article “Fast Random Integer Generation in an Interval” by Lemire (2018) addressed the problem of reducing the cost of machine instructions needed for the random generation of integer values in a generic interval [0, s ). The approach taken by the author is the one of exploiting the rejection method (Neumann 1951) to build an algorithm that almost eliminates the need for performing integer division operations—the algorithm still exploits divisions by powers of two, implemented in the form of cheap shift operations. In more details, the likelihood of not requiring an integer division in the proposed algorithm is 2 L − s / 2 L , where L denotes the number of bits used to represent integer values. The author also presents a comparative experimental study where the new algorithm, and its implementation for x86 processors, are compared with solutions offered by common software libraries for different programming languages.
Automatic Query Translation Disambiguation Using Bilingual Proximity-Based Approach

In this paper, we propose a new automatic query translation disambiguation using bilingual proximity-based approach. This approach combines a traditional bilingual dictionary and parallel bilingual corpus to build a bilingual semantic dictionary of contexts (BSDC) and identify the suitable translation of a word using a proximity matching model. Besides, it uses and extends an existing probabilistic semantic distance to compute similarities between words using a bilingual semantic graph of the traditional bilingual dictionary and the BSDC. We experiment and compare this approach using the French-English parallel text corpus Europarl and the CLEF-2003 French-English CLIR test collection. Our experiments highlighted the performance of our bilingual proximity-based approach compared to both the known efficient probabilistic and the possibilistic ones, for both long and short queries and using different assessment metrics.
Enhanced Requirement Traceability Link Using Developer's Updated Activity

In the software development cycle, the requirement traceability link is one of the important factors. Information retrieval techniques typically produce links with low precision and/or recall because, due to their very nature, they depend on the textual similarity between requirements and source code. The developers may not evolve requirements in synchronization with source code. But, they frequently update other sources of information including CVS/SVN repositories, bug-tracking systems, mailing lists, forums, and blogs. These sources of information will be used to build improved requirement traceability-recovery approaches. After the source code was developed, the developer compiles the source code and commits the source code into versions. Then admin needs to compare the templates and versions based on similarity measurement. Based on textual comparison, the similarity is calculated. The admin can do the same process for the forum requirements.
A scheme of on-line electric energy detection technology

Good quality power is the guarantee of normal operation of power equipment. This paper introduces a real-time electric energy detection system based on LabVIEW. On the premise of using LabVIEW to analyze, a virtual instrument detection device for signal processing and data analysis is designed. The voltage and current parameters collected in real-time can be accurately displayed by a special module. Establishment of Power Quality Analysis System Based on Virtual Instrument.
Foreground Enlargement of Spherical Images Using a Spring Model

In this paper, we propose a method for enhancing the visibility of omnidirectional spherical images by enlarging the foreground and compressing the background without provoking a sense of visual incompatibility by using a simplified spring model.
Characteristics and Principles to Apply Project-based Teaching Method in Life Ceramics Course

The application of project-based teaching method in life ceramics course is mainly divided into three major types: studio type, practice base type and people-oriented type, with distinctive features of clear objectives, remarkable innovation, strong practicality and diverse interaction. Principles of integrity, autonomy, moderation, cooperation, creativity and comprehensiveness are supposed to be adopted in the application.