Reviewerly/exHarmony huggingface dataset:
P72 21st century immersive simulation lab

<h3>Background</h3> ASSERT, UCC are designing and self-building an immersive simulation space; a 21st century learning environment. The immersive space is a flexible learning space, which facilitates training for restrictive educational settings, is engaging to those outside of healthcare training, appealing to generation Z learning styles, and is scalable to leverage the potential of future XR technologies. <h3>Summary of work</h3> A high quality paint finish infused with fine silver and diamond reflects either moving, or still images from 6 professional 8500 lumen projectors, illuminating three walls. The corners are curved to avoid sharp right angles, allowing seamless blending and wrapping of video. Installation of high quality speakers ensures optimum audio in the 70m² immersive space. In addition to high quality projection and audio; DMX controlled scenting machines, fogging machine, lighting and high turbine fan ensure a multi-sensory experience, to increase fidelity and simulation immersion. Four HD cameras record live activity in the space, which can be used for debriefing, assessment, and remote observation. In an adjacent control room 8m², trainers can manipulate the projected images/videos from a powerful Alienware PC, operate simulators, control radio-microphones, operate sensory effects by DMX command, and view the participants on two 65’ OLED screens. OLED screens perform better in darkness, with superior viewing angles, deeper black levels giving higher quality contrast and richer colour, and therefore more suitable to simulation control room ergonomics. <h3>Results of work</h3> It is a large undertaking, which requires considerable resources in floor space, time and expertise. It has generated much interest with healthcare sciences, allied sciences and the community. <h3>Discussion</h3> The projects second phase will see the installation of motion detection devices, so there is full interactivity with the projection displays, and advancement of the applications of XR technologies. The aim will be to maximise telepresence in the virtual world with full interactivity and deep immersion. The technology will be used to supplement carefully designed curriculum and learning objectives, as it is not a substitute for high quality instructional and educational methods. <h3>Conclusion</h3> Applications are broad from training pre-hospital personnel in austere conditions, to most hospital and community settings. The application of immersive simulation has shown benefit to people with phobias, anxieties and learning difficulties¹. The space will be used to foster collaboration between departments, and to engage with industry and community, as it is a learner-centred environment to which generation Z will relate, respond and interact with. <h3>References</h3> Maskey, M., Lowry, J., Rodgers, J., McConachie H., Parr, J. (2014). Reducing Specific Phobia/Fear in Young People with Autism Spectrum Disorders (ASDs) through Virtual Reality Environment Intervention. <i>PLOS ONE</i>. http://doi.org/10.1371/journal.pone.0100374
Reversible logic in pipelined low power vedic multiplier

&lt;span&gt;With an ever growing demand for low-power devices, it is a general trend to search for ways to reduce the power consumption of a system. Multipliers are an important requirement in applications linked to Digital Signal Processing, Communication Systems, Optical Computing, Nanotechnology, Low-Power Very Large Scale Integration and Quantum Computing. Conventional mathematics makes multiplication a very long and time consuming process. The use of Vedic mathematics has led to great reduction in the time required for such calculations. The excessive use of Urdhava Tiryakbhyam sutra in multiplication surely proves its effectiveness and simplicity in this domain. This sutra supports the process of pipelining, a method employed in reduction of the power used by a system. Reversible logic has been gaining demand due to its low-power capabilities and is currently being used in many computing applications. The paper proposes two multiplier systems: one design employs the Urdhava Tiryakbhyam sutra along with pipelining and the second uses reversible logic gates into the first design. These proposed systems provide very less delay for result computation and low hardware utilization when compared to non-pipelined Vedic multipliers.&lt;/span&gt;
Book Review: New Top Technologies Every Librarian Needs to Know

New Top Technologies Every Librarian Needs to Know is a compilation of chapters by various authors from technical and digital public services backgrounds edited by Kenneth J. Varnum. A follow up to Top Technologies Every Librarian Needs to Know, also edited by Varnum, the Library and Information Technology Association (LITA) has published this second volume to review the predictions from the first, and “take a gaze into 2018’s near-term future with a new set” (xi). Recognizing most libraries have embedded some technologies previously discussed in the earlier volume fully, such as text mining, digital libraries, and cloud-based systems, while others such as virtual reality (VR) are still in the nascent stages, this volume explores how some technologies have changed, as well as investigating new ones being developed and implemented now.
Gemini Network for Temporal Action Localization

Temporal Action Localization in untrimmed videos is an important yet difficult task. It’s important for accurate temporal action localization to model temporal structure. In this work, we propose a novel model, Gemini CNN, which can effectively model temporal structure via several separated CNNs. Our first contribution is the idea of modular design of temporal structure modeling. Temporal structure is divided into long-range temporal structure and short-range temporal structure. Two types of temporal structures are respectively modeled in two subnets. The other contribution is the idea of auxiliary supervision. We add classifiers at not only the end of our framework, but also intermediate layers. The loss of auxiliary classifiers increases the gradient signal that gets propagated back, and boosts our framework’s performance. We achieve state-of-the-art performance for temporal action localization on two challenging temporal action localization datasets, THUMOS14 and ActivityNet.
The Growth of Students Who Started Research before Belonging to Laboratories and its Effects to Other Students

This paper examines the effect of our early education for undergraduate students majoring in electronics and computer engineering. In our department in Hiroshima Institute of Technology, we prepare Techno-club program for students who have no idea on how to study but are eager to learn something in their school lives. In the process of development for technology, engineers need to attend meetings, discuss matters, make presentations, and negotiate with people from various countries. It is necessary for engineers to have not only professional knowledge and technical skills but also communication abilities, the ability to think for themselves, and the skills to exchange their ideas to create something new with their colleagues. This program can work as one of the supporting project against a decline in academic standards and also help students find out what they want to do with their friends. Considering the results from first generation students had belonged to Techno-club and entered graduate school, reports on the institutional design, its practical results and comparative analysis are described.
Penerapan Algoritma K-Nearest Neighbor untuk Prediksi Kelulusan Siswa pada SMK Anak Bangsa

Student graduation is very important for world education achievement, student graduation also influences the value of accreditation of an educational unit itself, therefor research on graduation prediction becomes a very interesting thing study, this study proposes the use of the K-Nearest Neighbor method to do predict student graduation at Anak Bangsa Private Vocational School. The result of the research is the value of k=5 with an accuracy rate of 93.55% which is determined as K-Optimal. The value of k=5 is applied to the K-NN algoritma to predict student graduation based on attendance, attitude, and value of knowledge.
Implementasi Aplikasi Modul Elektronik Maintenance PC Berbasis Algoritma Kriptografi Rivest Code 6 (Studi Kasus Di SMKN 1 Sidrap)

This study aims to produce a basic electronic application basic tutorial maintenance PC-based android. This research is a type of Research and Development (R & D) research using Prototype model. The application design is implemented in seven stages, namely: gathering needs, building prototype, prototype evaluation, coding, system test, system evaluation, and system implementation. Test method used is software testing ISO 9126 Standard, covering aspects: functionality, maintainability, portability, reliability, effeciency and usability. Field trials were conducted by students of Majoring in Software Engineering at SMKN 1 Sidrap Sul-Sel, as many 30 people. The result of this research is an electronic module application basic tutorial maintenance PC based on android that can be used to facilitate in accessing and learning how to care for PC good and true. Testing using ISO 9126 Standard shows excellent results in every aspect tested. Respondents' response to the application is in the acceptable category.
PENGARUH KOMPRES ALOE VERA TERHADAP FLEBITIS AKIBAT PEMASANGAN INFUS (IV LINE)

Phlebitis can cause thrombus and embolism which can cause permanent damage to the vein and can cause infection if not treated properly. The aim of the study was to determine the effect of giving Aloe Vera gel on the scale of phlebitis. The study design used one group pretest-posttest. The variables in this study are Aloe Vera compresses as independent variables and phlebitis as the dependent variable. The population in this study were all patients who experienced phlebitis at Sembiring General Hospital in 2019. Sampling using Consecutive sampling techniques as many as 31 respondents. Data were collected by observation sheet of phlebitis scale and tested with Wilcoxon statistical test. The results showed that there was a change in the scale of phlebitis experienced by respondents who experienced changes from respondents who had not previously found that there were no symptoms of phlebitis after being given aloe vera compresses. Wilcoxon statistical test result show data ρ-value 0.000 &lt; α 0.05 then Ho is rejected and Ha is accepted, which means that there is a significant effect between aloe vera compresses to changes in the degree of phlebitis. Any changes that occur in patients who have experienced phlebitis indicate that giving aloe vera compresses can indicate the results of changes that are quite effective. It is expected that health workers can apply non-pharmacological therapies such as aloe vera compresses for the treatment of phlebitis so that the services provided can be of higher quality and the services performed more precisely.
Book Review: Collection Development and Management for 21st Century Library Collections

Vicki L. Gregory is a well-known academic who has written seven other books describing librarianship and electronic and web resources. This review pertains to the second edition of her 2011 landmark textbook, Collection Development and Management for 21st Century Library Collections, which had been in good company with Peggy Johnson’s Fundamentals of Collection Development and Management and Maggie Fieldhouse and Audrey Marshall’s Collection Development in the Digital Age. Providing a thorough introduction on the management and future of library collections, this text offers practical tools and invaluable advice. The content is geared toward students of information science who are new to collection maintenance and collection development. However, this book would also be beneficial for all levels of practitioners. Gregory clearly describes the useful collection development and maintenance processes that all librarians, whether in the collection manager role or not, would find invaluable.
Compact and Robust Models for Japanese-English Character-level Machine Translation

Character-level translation has been proved to be able to achieve preferable translation quality without explicit segmentation, but training a character-level model needs a lot of hardware resources.In this paper, we introduced two character-level translation models which are mid-gated model and multi-attention model for Japanese-English translation.We showed that the mid-gated model achieved the better performance with respect to BLEU scores.We also showed that a relatively narrow beam of width 4 or 5 was sufficient for the midgated model.As for unknown words, we showed that the mid-gated model could somehow translate the one containing Katakana by coining out a close word.We also showed that the model managed to produce tolerable results for heavily noised sentences, even though the model was trained with the dataset without noise.
Some Preliminary Remarks on the Cenoscopic and Idioscopic aspects of Information Retrieval

Human-computer information retrieval (HCIR) or IR is discipline/discourse and perhaps amongst the most commonly used applications-search-enabling our ubiquitous interconnected and technology-mediated modern reality.Technical discourses of IR aim to develop algorithms to effectively join users to information (documents, images, or more generally, information experiences).It also aims to evaluate its algorithms and systems, and to understand what works and what doesn't.This focus on discovering and analysing new phenomena pertaining to search strategies (and recommendation and ranking functions), user-interaction strategies and so on refers to the idioscopic dimension of IR.However, this discourses presumes-i.e. is founded-upon-deeper ideas pertaining for example to human-communication and psychology, problems of knowledge and about the structure of social reality, these are its cenoscopic or more philosophical aspects.This paper analyses some of these deeper ideas and how they have appeared in their corresponding idioscopic form as mathematical and cybernetic models of agent communication.This paper indicates that it is necessary to separate the idioscopic and cenoscopic aspects of such discourses to allow the respective phenomena to be analyzed according to the methodology proper to it.
Incremental Associative Mining based Risk-Mapping System for Earthquake Analysis in Indonesia

Indonesia is one of the largest archipelagic countries in the world that has the highest risk of an earthquake. The major causes of earthquakes in this country are plate movements and volcanic activity. Earthquakes in Indonesia has a cause and effect relationship between each province. This disaster caused severe damage including a lot of people to get killed, injured and lose their money and property. We must minimize the impact of the earthquake by forming earthquake risk mapping. The risk of seismicity in Indonesia can vary each year, so it needs to be analyzed how the changes in risk are each addition of earthquake data. This paper proposes an earthquake risk mapping system with Associative Mining based on incremental earthquake data that have the highest values of confidence rates from the seismic association between provinces in Indonesia. The system uses the Incremental Association rule method to see the trend in the value of changes in confidence for each addition of earthquake data every 5 years. This system proposes 3 main features, which are (1) Data Retrieval and Preprocessing, (2) Association Rule Mining, (3) Incremental Associative Mining based risk mapping. For the experimental study, the system used data from 1963-2018. The results show that the provinces of Maluku, North Maluku, Nusa Tenggara Timur, North Sulawesi, and Papua have an incremental association risk of an earthquake.
Generating RDF resources from web open data portals

&lt;span&gt;Open data are available from various private and public institutions in different resource formats. There are already great number of open data that are published using open data portals, where datasets and resources are mainly presented in tabular or sheet formats. However, such formats have some barriers with application developments and web standards. One of the web recommenced standards for semantic web application is RDF. There are various research efforts have been focused on presenting open data in RDF formats. However, no framework has transformed tabular open data into RDFs considering the HTML tags and properties of the resources and datasets. Therefore, a methodology is required to generate RDF resources from this type of open data resources. This methodology applies data transformations of open data from a tabular format to RDF files for the Saudi Open Data Portal. The methodology successfully transforms open data resources in sheet format into RDF resources. Recommendations and future work are given to enhance the development of building open data.&lt;/span&gt;
Temporal phase unwrapping using multi-scale deep neural networks

The multi-frequency temporal phase unwrapping (MF-TPU) method, as a classical phase unwrapping algorithm for fringe projection profilometry (FPP), is capable of eliminating the phase ambiguities even in the presence of surface discontinuities or spatially isolated objects. For the simplest and most efficient case, two sets of 3-step phase-shifting fringe patterns are used: the high-frequency one is for 3D measurement and the unit-frequency one is for unwrapping the phase obtained from the high-frequency pattern set. The final measurement precision or sensitivity is determined by the number of fringes used within the high-frequency pattern, under the precondition that the phase can be successfully unwrapped without triggering the fringe order error. Consequently, in order to guarantee a reasonable unwrapping success rate, the fringe number (or period number) of the high-frequency fringe patterns is generally restricted to about 16, resulting in limited measurement accuracy. On the other hand, using additional intermediate sets of fringe patterns can unwrap the phase with higher frequency, but at the expense of a prolonged pattern sequence. Inspired by recent successes of deep learning techniques for computer vision and computational imaging, in this work, we report that the deep neural networks can learn to perform TPU after appropriate training, as called deep-learning based temporal phase unwrapping (DL-TPU), which can substantially improve the unwrapping reliability compared with MF-TPU. We further experimentally demonstrate for the first time, to our knowledge, that the high-frequency phase obtained from 64-period 3-step phase-shifting fringe patterns can be directly and reliably unwrapped from one unit-frequency phase using DLTPU. These results highlight that challenging issues in optical metrology can be potentially overcome through machine learning, opening new avenues to design powerful and extremely accurate high-speed 3D imaging systems ubiquitous in nowadays science, industry, and multimedia.
Y’all should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts

Distinguishing between singular and plural "you" in English is a challenging task which has potential for downstream applications, such as machine translation or coreference resolution.While formal written English does not distinguish between these cases, other languages (such as Spanish), as well as other dialects of English (via phrases such as "y'all"), do make this distinction.We make use of this to obtain distantly-supervised labels for the task on a large-scale in two domains.Following, we train a model to distinguish between the single/plural 'you', finding that although in-domain training achieves reasonable accuracy (≥ 77%), there is still a lot of room for improvement, especially in the domain-transfer scenario, which proves extremely challenging.Our code and data are publicly available.
Generation of Mottled Images Using Smoothing Filtering and Extension / Return Processing

We propose a non-photorealistic rendering method for generating mottled images from photographic images. Mottled patterns are mixed with different colors and shades of the same color, and are irregular in how to change colors and shapes of the patterns. Mottled images are expressed by overlapping the mottled patterns on photographic images. Our method is executed by an iterative calculation using smoothing filtering and extension / return processing. Our method has an advantage that mottled patterns can be automatically generated according to the luminance of photographic images. In addition, our method can change the size of mottled images by changing the value of the parameters. In order to verify the effectiveness of our method, we conducted experiments using various photographic images.As a result of the experiments, we clarified that the two advantages can be reproduced.
Can we PASS beyond the Field of View? Panoramic Annular Semantic Segmentation for Real-World Surrounding Perception.

Pixel-wise semantic segmentation unifies distinct scene perception tasks in a coherent way, and has catalyzed notable progress in autonomous and assisted navigation, where a whole surrounding perception is vital. However, current mainstream semantic segmenters are normally benchmarked against datasets with narrow Field of View (FoV), and most vision-based navigation systems use only a forward-view camera. In this paper, we propose a Panoramic Annular Semantic Segmentation (PASS) framework to perceive the entire surrounding based on a compact panoramic annular lens system and an online panorama unfolding process. To facilitate the training of PASS models, we leverage conventional FoV imaging datasets, bypassing the effort entailed to create dense panoramic annotations. To consistently exploit the rich contextual cues in the unfolded panorama, we adapt our real-time ERF-PSPNet to predict semantically meaningful feature maps in different segments and fuse them to fulfill smooth and seamless panoramic scene parsing. Beyond the enlarged FoV,we extend focal length-related and style transfer-based data augmentations, to robustify the semantic segmenter against distortions and blurs in panoramic imagery. A comprehensive variety of experiments demonstrates the qualified robustness of our proposal for realworld surrounding understanding.
(Un)Structuring for the Next Generation: New Possibilities for Library Data with NoSQL

For many years, libraries have relied upon relational databases (RDBMS) to store, manipulate, and query various types of data, and this database model works extremely well when data are highly structured. As the data become more complex, however, the relational database model strains under the burden of maintaining complex joins, which can decrease a database's performance and limit its functionality. Furthermore, data are not always best represented in the RDBMS's flat, tabular format. Library data often require flexibility and extensibility to accommodate the increasing volume and variety of library resources and metadata. To address these issues, transforming the underlying structure of data will be as important as transforming the data itself. NoSQL offers an alternative model for efficiently managing dynamic data.
Evaluating linked data location based services using the example of Stolpersteine

Abstract. In this publication we introduce a linked data powered application which assists users to find so-called Stolpersteine, stones commemorating Jewish victims of the second world war. We show the feasibility of a dedicated location based service as an app using linked data resources and evaluate this approach against local data sources gathered by communities to find out if the current linked data environment can equally and/or sufficiently support an application in this knowledge domain.