Reviewerly/exHarmony huggingface dataset:
3D imaging endoscopy using small-size prism-based stereo systems

We address the problem of non-contact geometrical measurements of hard-to-reach objects that is an important task in various industrial and medical applications. We have developed two small-size prism-based systems for simultaneous acquisition of stereoscopic images by a single sensor. For a correct mathematical description of these systems, we use a ray tracing camera model based on a vector form of Snell’s law. We demonstrate that using the chessboard calibration target allows simultaneous geometrical calibration and image quality assessment. We show that the appearance of chromatic aberrations in RGB images caused by the prism may be significantly reduced by applying separate rectification procedure to each color channel. Experiments confirm that the developed optical systems provide high image quality and the software provides high precision of three-dimensional (3D) geometrical measurements. Described systems may become the basis of small-diameter endoscopic probes for various applications.
Nonparametric Distribution Prior Model for Image Segmentation

In many real application scenarios of image segmentation problems involving limited and low-quality data, employing prior information can significantly improve the segmentation result. For example, the shape of the object is a kind of common prior information. In this paper, we introduced a new kind of prior information, which is named by prior distribution. On the basis of nonparametric statistical active contour model, we proposed a novel distribution prior model. Unlike traditional shape prior model, our model is not sensitive to the shapes of object boundary. Using the intensity distribution of objects and backgrounds as prior information can simplify the process of establishing and solving the model. The idea of constructing our energy function is as follows. During the contour curve convergence, while maximizing distribution difference between the inside and outside of the active contour, the distribution difference between the inside/outside of contour and the prior object/background is minimized. We present experimental results on a variety of synthetic and natural images. Experimental results demonstrate the potential of the proposed method that with the information of prior distribution, the segmentation effect and speed can be both improved efficaciously.
Clip-Level Feature Aggregation: A Key Factor for Video-Based Person Re-identification

In the task of video-based person re-identification, features of persons in the query and gallery sets are compared to search the best match. Generally, most existing methods aggregate the frame-level features together using a temporal method to generate the clip-level features, instead of the sequence-level representations. In this paper, we propose a new method that aggregates the clip-level features to obtain the sequence-level representations of persons, which consists of two parts, i.e., Average Aggregation Strategy (AAS) and Raw Feature Utilization (RFU). AAS makes use of all frames in a video sequence to generate a better representation of a person, while RFU investigates how batch normalization operation influences feature representations in person re-identification. The experimental results demonstrate that our method can boost the performance of existing models for better accuracy. In particular, we achieve 87.7% rank-1 and 82.3% mAP on MARS dataset without any post-processing procedure, which outperforms the existing state-of-the-art.
ParkingSticker: A Real-World Object Detection Dataset

We present a new and challenging object detection dataset, ParkingSticker, which mimics the type of data available in industry problems more closely than popular existing datasets like PASCAL VOC. ParkingSticker contains 1,871 images that come from a security camera's video footage. The objective is to identify parking stickers on cars approaching a gate that the security camera faces. Bounding boxes are drawn around parking stickers in the images. The parking stickers are much smaller on average than the objects in other popular object detection datasets; this makes ParkingSticker a challenging test for object detection methods. This dataset also very realistically represents the data available in many industry problems where a customer presents a few video frames and asks for a solution to a very difficult problem. Performance of various object detection pipelines using a YOLOv2 architecture are presented and indicate that identifying the parking stickers in ParkingSticker is challenging yet feasible. We believe that this dataset will challenge researchers to solve a real-world problem with real-world constraints such as non-ideal camera positioning and small object-size-to-image-size ratios.
Strategic Planning for Academic Libraries: A Step-by-Step Guide

"Strategic Planning for Academic Libraries: A Step-by-Step Guide." Cataloging & Classification Quarterly, 58(2), pp. 183–184
Hyperspectral Image Denoising via Subspace Low-rank Representation and Spatial&amp;#x2013;spectral Total Variation

Hyperspectral images (HSIs) acquired actually often contain various types of noise, such as Gaussian noise, impulse noise, and dead lines. On the basis of land covers, the spectral vectors in HSI can be separated into different classifications, which means the spectral space can be regarded as a union of several low-rank (LR) subspaces rather than a single LR subspace. Recently, LR constraint has been widely applied for denoising HSI. However, those LR-based methods do not constrain the intrinsic structure of spectral space. And these methods cannot make better use of the spatial or spectral features in an HSI cube. In this article, a framework named subspace low-rank representation combined with spatial&#x2010;spectral total variation regularization (SLRR-SSTV) is proposed for HSI denoising, where the SLRR is introduced to more precisely satisfy the low-rank property of spectral space, and the SSTV regularization is involved for the spatial and spectral smoothness enhancement. An inexact augmented Lagrange multiplier method by alternative iteration is employed for the SLRR-SSTV model solution. Both simulated and real HSI experiment results demonstrate that the proposed method can achieve a state-of-the-art performance in HSI denoising.
A Typical Remote Sensing Object Detection Method Based on YOLOv3

considering the problem of typical remote sensing object detection, this paper proposes an improved remote sensing object detection method based on YOLOv3 algorithm. A new activation function is designed automatically by genetic algorithm. The classification task training result of the convolutional neural network on remote sensing image is used as the evaluation function of genetic algorithm. The function enhances its nonlinear transformation ability to remote sensing features; then the activation function is embedded in the YOLOv3 algorithm, and the object detection task is trained on the self-made typical remote sensing target dataset. The experimental results show that the method can significantly improve the detection accuracy of various typical remote sensing targets.
О преимуществах соционики перед другими пост-юнговскими типологиями

A comparison of socionics as a science and technology with the Myers-Briggs typology and theory Keirsey is conducted. It is shown that, despite the general origin of these typologies from Jung's typology, socionics, in contrast, allows to give a complete picture of the structure of mental functions, description and forecast intertype relations in small and large groups. In the practical field socionics allows forming teams and collectives of any complexity and resolve conflicts. Using the information approach allows to apply the socionics methods in marketing, advertising, sociology and political science studies.
Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain

In this paper, we present a method of building strong, explainable classifiers in the form of Boolean search rules. We developed an interactive environment called CASE (Computer Assisted Semantic Exploration) which exploits word co-occurrence to guide human annotators in selection of relevant search terms. The system seamlessly facilitates iterative evaluation and improvement of the classification rules. The process enables the human annotators to leverage the benefits of statistical information while incorporating their expert intuition into the creation of such rules. We evaluate classifiers created with our CASE system on 4 datasets, and compare the results to machine learning methods, including SKOPE rules, Random forest, Support Vector Machine, and fastText classifiers. The results drive the discussion on trade-offs between superior compactness, simplicity, and intuitiveness of the Boolean search rules versus the better performance of state-of-the-art machine learning models for text classification.
METHOD FOR DETERMINING THE TYPE OF CONVOLUTIONAL NEURAL NETWORKS FOR ANALYSIS OF THE KEYBOARD HANDWRITING

Вчені записки ТНУ імені В.І.Вернадського.Серія: технічні науки and verifying the results obtained.Using the proposed method, it was determined that in conditions of limited access to the databases of keyboard handwriting parameters, the availability of available and tested tools for implementing neural network tools, acceptable resource consumption of hardware and software implementation, acceptable training and recognition periods, and sufficient recognition accuracy with the most effective type of convolution The neural network is SqueezeNet.As a result of computer experiments, it was calculated that the accuracy of personality recognition using SqueezeNet is approximately 10-11% higher than the accuracy of recognition using other types of convolutional neural networks, which confirms the effectiveness of the developed method.It is proposed to correlate the paths of further research with the adaptation of SqueezeNet parameters to the conditions of the task of analyzing keyboard writing.
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularised by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.
Global Virtualization as a Result of Society Informatization

The necessity to study the processes of the society informatization is conditioned by the fact that penetration of information technologies in all the spheres of social life leads to transition of the digital society to a new stage of its evolution - the global digital society. Informatization leads to inconvertible, ambiguous changes affecting all the spheres of the global community’s life. The author reveals the influence of the Internet on the process of forming the virtual global community, justifies the relevance of the studies devoted to analysing current global processes and identifying their influence on the society.
PENINGKATAN PERCAYA DIRI SISWA DALAM PEMBELAJARAN MELALUI MODEL TGT(TEAMS GAMES TOURNAMENT)BERBANTUAN MEDIA KARTUPADA TEMA BERBAGAI PEKERJAAN KELAS IV SD 2 BAKALAN KRAPYAK

This study aims to improve teachers’ skills, students’ confidence in learning, and students’ learning outcomes through the Teams Games Tournament learning model usingcards media on theme Berbagai Pekerjaan of class IV SD 2 Bakalan Krapyak. Students’ self-confidence in learning and learning outcomes are results of learning activities. Teams Games Tournament is a learning model that involves active and confident students in the learning process through tournament play activities and makes students group with enthusiasm through team activities.This classroom action research was conducted in class IV of SD 2 Bakalan Krapyak with 19 students as subjects of research. This research was carried out in two cycles, each cycle was consisting of four stages. Those are planning, implementation, observation, and reflection. The dependent variables in this study were the students' confidence in learning and learning outcomes, while the independent variables were the Teams Games Tournament learning model and cards media. This study used data collection techniques including interviews, observations, tests, and documentations. Data analysis in the study consisted of quantitative data analysis and qualitative data analysis.The results of the study showed an increase in learning. The teacher's skills in the cycle I obtained an average percentage of 79% (good), while in the cycle IIwas 89% (very good). The percentage of completeness of cognitive learning outcomes of students in the pre-cycle was 50% (less), after the cycle I of research conducted, there was an increase to 74% (good) and that percentage increased more to 84% (very good)in the cycle II. Students' self-confidence in learning realmof attitude at the pre-cycle level was 46% (not good), after doing the research in the cycle I it changed into 75% (good) and increased more in the cycle IIto be 87% (very good). Students' self-confidence in the learning realm of skills in the pre-cycle obtained a percentage of 40% (not good), after the research in the cycle Ithat obtained 76% (good) and increased more in the cycle IIto be 88% (very good).Based on the results of the research conducted on students of class IV in SD 2 Bakalan Krapyak, it can be concluded that the Teams Games Tournament learning model usingcards media can improve teachers’ skills, students’ learning outcomes, and students’ confidence in learning.
VERONICA: Expressive and Precise Concurrent Information Flow Security (Extended Version with Technical Appendices)

Methods for proving that concurrent software does not leak its secrets has remained an active topic of research for at least the past four decades. Despite an impressive array of work, the present situation remains highly unsatisfactory. With contemporary compositional proof methods one is forced to choose between expressiveness (the ability to reason about a wide variety of security policies), on the one hand, and precision (the ability to reason about complex thread interactions and program behaviours), on the other. Achieving both is essential and, we argue, requires a new style of compositional reasoning. We present VERONICA, the first program logic for proving concurrent programs information flow secure that supports compositional, high-precision reasoning about a wide range of security policies and program behaviours (e.g. expressive de-classification, value-dependent classification, secret-dependent branching). Just as importantly, VERONICA embodies a new approach for engineering such logics that can be re-used elsewhere, called decoupled functional correctness (DFC). DFC leads to a simple and clean logic, even while achieving this unprecedented combination of features. We demonstrate the virtues and versatility of VERONICA by verifying a range of example programs, beyond the reach of prior methods.
Exploiting Dependency-based Pre-ordering for English-Myanmar Statistical Machine Translation

Word reordering is one of the challenging problems for machine translation when translating between different word order languages, such as English and Myanmar. The grammatical forms of these two languages are totally dissimilar so it is very difficult to translate from English to Myanmar with proper reordering. In this paper, we employed a pre-ordering approach to learn reordering rules automatically that aims to adjust the word order of the source sentence to that of the target sentence prior to translation. The rules are learned with source-side dependency parse trees and bilingual word alignments to obtain a monotonic parallel corpus. Experiments on English-Myanmar translation show that the pre-ordering approach obtains statistical improvement by up to 1.8 BLEU points on Asian Language Tree-bank (ALT) data, compared to baseline state-of-the-art phrase-based statistical machine translation (PBSMT).
Magnetic Seed Localisation for Impalpable Breast Lesions is Preferred over Hooked-Wire

Background: In breast surgery, screening programs and high resolution imaging result in higher frequency of impalpable breast lesions, requiring preoperative localisation. Since September 2018, Magseed® localisation has become standard of care in UZ Leuven, providing an alternative for the hooked-wire localisation method. This prospective observational study of Magseed® localisation compared clinical safety, effectiveness and surgeon satisfaction with a historical series of cases using the hooked-wire localisation.
How to Fix Popped Screws/Nails on Drywall

Often at times, due to carelessness or wrong use of tools, installing drywall doesnt go as planned. Consequently, a lot of errors can occur and they ...
Knowledge and Information-Seeking Behaviour onNasopharyngeal Cancer in Urban and Rural Sarawak,Malaysia

The study examined knowledge and information-seeking behaviour on nasopharyngealcancer (NPC) in urban and rural Sarawak. A survey was conducted in Kuching andKampung Bengoh involving 121 respondents (44 Kuching; 77 Kampung Bengoh). Theresults showed that most respondents do not have family and friends who had NPC, andtheir work are not related to NPC. A majority of them had not looked for information onNPC and only four of them have had screening for NPC. As for intention to obtain moreinformation on NPC in the next six months, only 46.3% reported likelihood to do so but20.7% were unlikely to do so because they felt uncomfortable with the subject of cancerand they did not feel a need to find out more about it. There were no significantdifferences between the Kuching and Bengoh respondents on their attitudes towardsinformation-seeking on cancer and intention to seek information on NPC. However, thecommon sources of information on NPC differ for the urban and rural respondents: theurban respondents relied on the Internet, their doctors and leaflets for NPC informationbut the rural respondents depended on the television, radio and newspaper are the mainsources of health information. These results suggest that different channels of informationneed to be used to address the low level of awareness on NPC and ambivalent attitudestowards seeking information on NPC in rural and urban areas in Sarawak.
A.S. Barsov and Academic Book Printing in the 18th century

In 2018, we celebrated three hundred years birth anniversary of the prominent Russian scholar A.S. Barsov (1718—1763). While scientific legacy of other Lomonosov’s contemporaries has been hugely studied in dozens of articles and books, Barsov’s name has been rarely mentioned in the indexes of the extensive literature devoted to M.V. Lomonosov. Meanwhile, a vast array of documents have been preserved and partially published, indicating that Barsov played an important role in the development of book printing in Russia and elaboration of the Russian literary language norms. The purpose of the article is to determine the role of A.S. Barsov in the process of formation of new, secular culture in the post-Petrine time. The authors attempted to highlight the life and manners of academic students and employees in the middle of the 18th century, to show in what difficult conditions there were created the book masterpieces of the Academy, preserved in hundreds of libraries around the world. The article established the main milestones of A.S. Barsov’s life path. He was born in the family of priest; studied at the Slavic-Greek-Latin Academy. After five years at the Academy Gymnasium and University, he was examined by professors and appointed proofreader at the Academic printing house; later on — supervisor of the printing house and its Figure chamber; shortly before his death, he was subjected to severe administrative sanctions. The authors analyse the fate of A.S. Barsov as one of the typical representatives of the early development stage of the academic science in Russia. The article introduced into scientific circulation a number of sources that allow assessing from a new, unexpected angle the evolution of the Academy of Sciences in the 1730s — 1760s. The authors revealed the degree of influence of his activity on the development of publishing activity and librarianship in Russia. The results of this study can be used in preparation of fundamental works in the field of science studies, library science, history of publishing, historiography, as well as special courses on the history of culture.
ERROR ANALYSIS IN THE NEWS TEXTS MAIN PAGE OF JAMBI EKSPRES DAILY NEWSLETTER

Abstract&#x0D; Mass media is one of the media that greatly influences the understanding of human language and is one of the pathways for developing good and correct guidance and development of Indonesian. Errors are often found in the process of forming words or errors that have the potential to change the meaning and shape of words. This article is based on Mini Research results on the topic; Error Analysis in the News Texts Main Page Newspapers (Descriptive-Analytical Study of Jambi Ekspres Daily News Texts December 26, 2018). This study focuses on the errors of the standard Indonesian language, namely sentences that have ambiguity or mean and ambiguous meaning, words that are influenced by various conversational languages, words that do not have the correct spelling, words that are influenced by foreign languages, and words that do not match the context of the sentence. Data obtained in the form of words, speech or behaviour, are not expressed in the form of numbers or statistical numbers but rather in a qualitative form, by giving an image according to what it is. The aim is to describe the symptoms that exist in the data without giving any form of treatment to the subject to be studied.&#x0D; Keywords: language errors, written language, standard language, spelling, newspapers.
On the Interaction of Object-Oriented Design Patterns and Programming Languages

Design patterns are distilled from many real systems to catalog common programming practice. However, some object-oriented design patterns are distorted or overly complicated because of the lack of supporting programming language constructs or mechanisms. For this paper, we have analyzed several published design patterns looking for idiomatic ways of working around constraints of the implementation language. From this analysis, we lay a groundwork of general-purpose language constructs and mechanisms that, if provided by a statically typed, object-oriented language, would better support the implementation of design patterns and, transitively, benefit the construction of many real systems. In particular, our catalog of language constructs includes subtyping separate from inheritance, lexically scoped closure objects independent of classes, and multimethod dispatch. The proposed constructs and mechanisms are not radically new, but rather are adopted from a variety of languages and programming language research and combined in a new, orthogonal manner. We argue that by describing design patterns in terms of the proposed constructs and mechanisms, pattern descriptions become simpler and, therefore, accessible to a larger number of language communities. Constructs and mechanisms lacking in a particular language can be implemented using paradigmatic idioms.