Reviewerly/exHarmony huggingface dataset:
A Patient-Initiated Digital COVID-19 Contact Notification Tool (TellYourContacts): Evaluation Study

Background Contact notification is a method used to control the spread of infectious disease. In this process, a patient who tests positive for an infectious disease and public health officials work to identify the patient’s close contacts, notify them of their risk of possible exposure to the disease, and provide resources to facilitate the decreased spreading of disease. Contact notification can be done physically in person, via phone call, or digitally through the use of media such as SMS text messages and email. When alerts are made through the latter, it is called digital contact notification. Objective For this study, we aim to perform a preliminary evaluation of the use of the TellYourContacts website, a digital contact notification tool for COVID-19 that can be used confidentially and anonymously. We will gather information about the number of website users and message senders, the types of messages sent, and the geographic distribution of senders. Methods Patients who chose to get tested for COVID-19 and subsequently tested positive for the disease were alerted of their positive results through Curative Inc (a COVID-19 testing laboratory) and Healthvana (a results disclosure app). Included in the notification was a link to the TellYourContacts website and a message encouraging the person who tested positive for COVID-19 to use the website to alert their close contacts of exposure risk. Over the course of three months, from May 18, 2020, to August 17, 2020, we used Google Analytics and Microsoft Excel to record data on the number of website users and message senders, types of messages sent, and geographic distribution of the senders. Results Over the course of three months, 9130 users accessed the website and 1474 unique senders sent a total of 1957 messages, which included 1820 (93%) SMS text messages and 137 (7%) emails. Users sent messages from 40 US states, with the majority of US senders residing in California (49%). Conclusions We set out to determine if individuals who test positive for COVID-19 will use the TellYourContacts website to notify their close contacts of COVID-19 exposure risk. Our findings reveal that, during the observation period, each unique sender sent an average of 1.33 messages. The TellYourContacts website offers an additional method that individuals can and will use to notify their close contacts about a recent COVID-19 diagnosis.
Null-Labelling: A Generic Approach for Learning in the Presence of Class Noise

Class noise in datasets presents a significant challenge to accurate classification, requiring classifiers that can refuse to classify noisy instances. We demonstrate the inability of the popular confidence-thresholding rejection method to learn from relationships between input features and not-at-random class noise. To take advantage of these relationships, we propose a novel null-labelling scheme based on iterative re-training with relabelled datasets that enables a classifier to learn to reject instances that are likely to be misclassified. We demonstrate the ability of null-labelling to achieve a significantly better tradeoff between classification error and coverage than confidence-thresholding. Models generated by the null-labelling scheme have the added advantage of interpretability, in that they are able to identify features correlated with class noise. We also unify prior theories for combining and evaluating sets of rejecting classifiers.
Phenomenology of Engineering Devices

The article examines the interaction between humans and complex digital technology; the relationship between humans and modern technologies. The peculiarity of the approach is that technical devices are taken not at the stage of their development, production or sale but as an element of the man-made environment surrounding a person (“technohabitus”, sharedspace of the “smart” environment of a modern person). Neutral technical characteristics of the device are manifested in the system of specific human needs, lifestyle, social and professional factors - in the user's existence. Optimal interaction of a technical device with a user presupposes the removal of a specific alienation between man and technology. Technical characteristics are reduced to specific ways of entering the device into the digital environment of human existence. On the other hand, the reduction of the living mind to countable, algorithmic schemes is inevitable. It is not about the degradation of reason, but about its hybrid connection with the logic of engineering devices and objects. The physical substrate (das Gestell) of consciousness expands beyond the human body, uniting with the dense “technohabitus” that surrounds the individual as his personal noosphere. This issue unfolds in the article towards the philosophical and methodological problems of the highest forms of digital devices - cobots, chatbots, service robots (Robo-Helfer), cyborgs, etc. The author's particular attention is drawn to the methodology of "field research" of such a technique. Gradually, the possibility of a single hybrid phenomenon arises - a localized rational sphere extending from the habitat of the individual and his social chronotope to the free movement of semantic flows on the Internet.
“Musical Informatics”. Program of the Discipline for Visually Impaired Students

Thereunder is a publication the program of the discipline of “Musical Informatics” devoted to the theory and methodology of teaching student musicians with strong visual impairments. The methodological and content-rich components of study of this discipline in the federal state-run professional educational institution “Kursk Music Residential College for the Blind” of the Ministry of Labor and Social Protection of the Russian Federation are presented. The author of the article describes the special conditions for realizing the adapted program of the discipline of “Musical Informatics,” the thematic plan of the discipline, the methodological recommendations for teachers who implement the educational process within the walls of the college, the hardware-software, educational methodological and informational support for the realization of the educational process in specialized institutions the activities of which are directed at education, upbringing, rehabilitation and social adaptation of visually impaired invalids.
Using Software Containers for Privileged Access Management in Cloud Environments: A Novel Approach to Handle Access Management for Cloud-based Networks

This paper presents a novel approach for privileged access and session management using containers. Current solutions are built using proxies, proxy suites or jump servers, but they do not cater for third party remote access security requirements, have additional vulnerabilities and have scalabilty limitations.&#x0D; The novelty of the solution proposed in this paper is a global orchestrator that instantiates a purpose-built container adapted to the virtual network functions’ system. Every container has a logging function, a pre-defined time-to-live and one-time-credentials. This approach is secure because the containers isolate different connections, privileges are restricted, permissions are always time-limited and the provider has full control over the sessions. The solution brings several other security enhancements, discussed in this paper.
Vehicle Speed and Dimensions Estimation from On-Road Cameras by Identifying Popular Vehicles

One of the major issues related to traffic monitoring systems is analyzing the behavior of vehicles and identifying their characteristics. In this paper, an automated algorithm is proposed for calibration of the road cameras. This calibration is used to estimate the speed and dimensions of the passing vehicles. In this method, a motion plane is obtained in the initial frames according to the direction of moving vehicles. After modeling the background, removing shadows, and identifying the exact boundaries of passing vehicles, a number of common vehicles are recognized by the Bag of Words method. Given the actual metric dimensions of these vehicles and the equivalent dimensions on the motion plane, the metric coefficients are calculated and the calibration process is completed. Each passing vehicle is projected on the motion plane, and speed and dimensions are calculated by tracking it along the road. To test the accuracy of the proposed method, we constructed a ground-truth video dataset, by simultaneous capturing the road vehicles using RGB and speed cameras. Furthermore, to identify common vehicles, a dataset of vehicle images was collected. The proposed method evaluated on our dataset and the mean error of 1.15 km/h is achieved. Accordingly, it outperforms previous methods.
Towards a Machine Learning-based Decision Support System for Dispatching Helicopters in New Zealand

Helicopters play an important role in emergency medical service systems worldwide.In sparsely populated countries like New Zealand with long distances between hospitals, helicopters are often the best way to help critically injured patients.As helicopters are extremely costly, they should only be dispatched when really necessary.In this paper, we use data from the South Island of New Zealand to test several Machine Learning approaches and show that they can be used to support dispatchers by identifying emergencies likely to require a helicopter response.We follow a non-static dataset, as the information is successively available during an emergency, and demonstrate that even a limited approach, based only on geographic incident information, can yield an Average Precision of 94% for highlighting critical emergencies.In the latter parts of this paper, we investigate different compositions of training data to assess the impact of a potential concept drift.
Geological Survey of South Australia: SARIG updated and a new AusLAMP model

While all geophysical surveys reported in open file company exploration reports are available via SARIG’s Georeference catalogue, they may not yet appear as a layer on SARIG. The South Australian g...
PENGEMBANGAN BAHAN AJAR BERBASIS MULTIMEDIA INTERAKTIF PADA MATA KULIAH PENDIDIKAN SENI MUSIK DI PROGRAM STUDI PENDIDIKAN GURU SEKOLAH DASAR STKIP PGRI LUBUKLINGGAU

The aimed of this research was to create a teaching materal product based on multimedia interactive at PGSD Study program of STKIP PGRI Lubuklinggau. The mothod of this research was research and development by Rowntree model. The result of language validation gotten score avarage 3,25 that have meaning valid. The Material validation gotten score avarage 4,62 that has meaning valid. The media validation gotten score avarage 4,24 that has meaning valid. In the small group test, researcher got the score avarage 81,11. It showed the result of developing material was valid and practice.Keyword : Development, Material, Multimedia Interactive
Interdisciplinary integration as a pedagogical condition for preparing students for professional activities

The strategic objectives of trilingual education in Kazakhstan have actualized the need to prepare students of pedagogical specialties to use English in their professional activities in the context of modernization of educa-tion in the Republic of Kazakhstan. On the basis of the research carried out, the authors of the article present their vision of the success of this process, which, in their opinion, lies in the careful selection of pedagogical conditions, one of which is interdisciplinary integration, as a tool that contributes to the integrated educational and methodological support of the process of teaching professionally oriented English with taking into ac-count the specifics of the specialty. A consistent transition between disciplines will contribute to the for-mation of a consistency of knowledge based on the development of general scientific ideas and concepts, the improvement of professionally oriented knowledge and skills of students, coordination in the work of teach-ers of various disciplines, their cooperation, a unified interpretation of general scientific concepts, consistency in the implementation of complex forms of organizing the educational process, integrated educational and methodological support of the process of teaching professionally oriented English, taking into account the specifics of the specialty.
A Public Opinion Analysis System Based on Emotion Analysis in Linyi

Abstract This article, the official news site of Linyi five services in Shanghai, Yiwu GanZhou Shenzhen national logistics hub of news as the data source, through Word2Vec and construction LSTM emotion classification model, with positive or negative emotion in general categories, calculating to analyze its emotional value, from the emotional category and time series analysis and word frequency vector to Linyi public opinion analysis of logistics hub
Emotion Analysis for Literary Studies

Most approaches to emotion analysis in fictional texts focus on detecting the emotion class expressed over the course of a text, either with machine learning-based classification or with dictionaries.These approaches do not consider who experiences the emotion and what triggers it and therefore, as a necessary simplicifaction, aggregate across different characters and events.This constitutes a research gap, as emotions play a crucial role in the interaction between characters and the events they are involved in.We fill this gap with the development of two corpora and associated computational models which represent individual events together with their experiencers and stimuli.The first resource, REMAN (Relational EMotion ANnotation), aims at a fine-grained annotation of all these aspects on the text level.The second corpus, FANFIC, contains complete stories, annotated on the experiencer-stimulus level, i. e., focuses on emotional relations among characters.FANFIC is therefore a character relation corpus while REMAN considers event descriptions in addition.Our experiments show that computational stimuli detection is particularly challenging.Furthermore, predicting roles in joint models has the potential to perform better than separate predictions.These resources provide a starting point for future research on the recognition of emotions and associated entities in text.They support qualitative literary studies and digital humanities research.The corpora are freely available at http://www.ims.
A robust fuzzy clustering algorithm using spatial information combined with local membership filtering for brain MR images

MRI brain segmentation plays an important part in computer-aided diagnosis, which visually reveals the changes in brain structure for doctors to quickly and accurately discover and treat diseases related to brain tissue morphology. The fuzzy C-means (FCM) algorithm performs well when the segmenting images with no noise and with intensity uniformity. However, the MRI brain images are always defective in noise and intensity nonuniformity and thus we propose a novel FCM algorithm named adaptive FCM with neighborhood membership (FCM_anm). We design a filtering process with neighborhood membership to reduce the negative influence of noise and a novel objective function which further considers the spatial membership information adaptively. Finally, to verify the performance of our method, several experiments comparing among the Experimental results demonstrate the proposed method consistently outperforms the state-of-the-art FCM-based algorithms in synthetic images, simulated and real brain MR images with effects of the noise and intensity non-uniformity.
Standarisasi Indeks Eksposur untuk Memenuhi Kriteria Anatomi dan Aspek Teknis pada Radiografi Thorax Pediatrik

Background: Computed Radiography has an exposure index that used as an exposure indicator. But on radiographic examination, exposure index value sometimes ignored, and in the preliminary survey of pediatric chest examination resulting a large exposure index range. The aim of this study is to know the profile of exposure index value and the setting of the exposure factors, the assessment of anatomy criteria and technical aspect, and the right exposure factors such as kV and mAs on pediatric chest examination.Methods: The type of this research is descriptive quantitative. The research was done by collecting data related to pediatric chest radiograph (≤ 2 years) the value of exposure index was recorded, then the radiograph was assessed using questionnaires that filled by one respondent who is a radiologist. The data was analyzed by displaying the data of exposure index and anatomy criteria from questionnaires into the table form, and then conducted a descriptive analysis to be drawn conclusions.Results: The results showed the profile of exposure index value and the setting of the exposure factor has a fairly large exposure index range of 1084 – 2175, using 40 kV and 10 mAs and the collimation still often exceeds the object. Then for the assessment of the thorax anatomical criteria and the technical aspect overall was “Good Enough”, and the right exposure factors, that is: at 6 and 7 cm chest thickness was using 60 kV and 1,6 mAs; at 11 cm chest thickness was using 60 kV and 2 mAs, FFD 100 cm, and the setting of collimation as wide as object, the exposure index generated in the normal range that is 1251 – 1382.Conclusion: The right exposure factors on pediatric chest examination, that is: at 6 and 7 cm chest thickness was using 60 kV and 1,6 mAs; at 11 cm chest thickness was using 60 kV and 2 mAs, FFD 100 cm, and the setting of collimation as wide as object.
Self-Supervised Rigid Registration for Small Images

For small image registration, feature-based approaches are likely to fail as feature detectors cannot detect enough feature points from low-resolution images.The classic FFT approach's prediction accuracy is high, but the registration time can be relatively long, about several seconds to register one image pair.To achieve real-time and high-precision rigid registration for small images, we apply deep neural networks for supervised rigid transformation prediction, which directly predicts the transformation parameters.We train deep registration models with rigidly transformed CIFAR-10 images and STL-10 images, and evaluate the generalization ability of deep registration models with transformed CIFAR-10 images, STL-10 images, and randomly generated images.Experimental results show that the deep registration models we propose can achieve comparable accuracy to the classic FFT approach for small CIFAR-10 images (32×32) and our LSTM registration model takes less than 1ms to register one pair of images.For moderate size STL-10 images (96×96), FFT significantly outperforms deep registration models in terms of accuracy but is also considerably slower.Our results suggest that deep registration models have competitive advantages over conventional approaches, at least for small images.
The Impact of Mobile-Assisted Language Learning (MALL) in Developing the Listening Skill: A Case of Students at Dubai Men's College, the United Arab Emirates

Mobile technologies are one of the fastest-growing technologies in education. They offer an appealing opportunity to access multiple sources of information anytime and anywhere. Students who join Dubai Men's College in the Foundations program suffer from accumulative weakness in the language skills in general and in the listening skill in particular. The study aimed to confirm the positive impact of Mobile-Assisted Language Learning (MALL) on English Listening Skills. It attempted to examine the effect of MALL on students' acquisition of English language Listening Skill at Dubai Men's College (DBM), the United Arab Emirates. The study also investigated teachers' perspectives on the effect of MALL on the students' listening skill. The research question was: To what extent students' listening skills have in the experimental group developed through the use of MALL compared to the students in the control group? A mixed-method approach composed of qualitative and quantitative data collection and analysis was used to answer this question. 66 students participated in a quasi-experiment of an experimental group, and a control group and 20 teachers participated. The findings indicated that there were statistically significant differences between both groups in favour of the experimental group. The research stressed the benefits of using MALL to improve the students' listening skills, especially those who seem to be demotivated. Finally, some teachers showed a lot of support for the use of MALL. Additional empirical research should be conducted to increase the generalizability of the findings.
A Multi-Module Based Method for Generating Natural Language Descriptions of Code Fragments

Code fragment natural language description generation, also known as code summarization, refers to obtaining a natural language sequence describing a given code fragment's functionality. It is broadly agreed that applying code summarization into production can significantly improve the efficiency of software development and maintenance. In recent years, syntactic analysis (SA) technology and Latent Dirichlet Allocation (LDA) has been widely used in code summarization and has achieved good results. However, most of the existing techniques focus on core code statements, and thus their generated code summarization lacks a logical description of the code fragment's holistic information. To this end, we propose a code summarization method based on multiple modules to generate natural language for each code statement by constructing a new type of natural language template. Meanwhile, to utilize the code fragment's holistic information, we adopt the code statement partition rules and cosine similarity measure to rank and optimize the weight of the overall information of the code fragment, and finally generate the holistic natural language description of the code fragment. The experimental results demonstrate that our method can generate more concise and logical natural language descriptions than existing models.
Application Management Practice of Investment Statistics Business Based on Blockchain Technology

As a new decentralized infrastructure and distributed computing paradigm, blockchain is of great significance to break through the “mutual trust problem” between statistical specialty and basic data providing departments, and between the upper and lower levels of statistical data reporting units, and further improve the quality of statistical work. This paper first briefly analyzes the problems existing in the current investment statistics business, and introduces the basic principles and characteristics of blockchain technology. This paper studies the idea and process optimization of the application of blockchain technology in investment statistics business, and expounds the specific application schemes of blockchain technology in different links, such as automatic data collection, automatic data verification, automatic report generation, and automatic index release. Finally, the expected effect of the application of blockchain technology in investment statistics business is analyzed.
A Quantum Time Coordinate

We discuss quantum time states formed with a finite number of energy eigenstates with the purpose of obtaining a time coordinate. These time states are eigenstates of the recently introduced discrete time operator. The coordinate and momentum representations of these time eigenstates resemble classical time curves and become classical at high energies. To illustrate this behavior, we consider the simple example of the particle-in-a-box model. We can follow the quantum-classical transition of the system. Among the many existing solutions for the particle in a box, we use a set which leads to time eigenstates for use as a coordinate system.