Reviewerly/exHarmony huggingface dataset:
Auxlab—An integrative open-source computing environment software for audio and speech signal processing

A scripting language AUX (AUdio syntaX) allows auditory researchers and students with minimal training in programming to generate waveforms and manipulate them with easy and intuitive grammar [Kwon, Behave. Res. 44, 361–373 (2012)]. Akin to the MATLAB, R, or Python environment, AUXLAB is an integrative audio computing environment based on AUX, in which users play and record audio samples, visualize them, write the code for signal analysis and processing, and debug their code. A signal processing task can run on the fly with the microphone, line input to the PC sound card, or streaming audio input. Most notably, AUXLAB is free of charge with open C + + source and does not require special hardware. In this presentation, in addition to introducing newly added AUX language features with object-oriented styles, a hands-on demo of real-time operation emulating a hearing aid’s speech processing will be given. Further, the discussion will focus on how this could be used as a low-cost research tool for better hearing aid fitting.
Pelatihan dan Pendampingan Penulisan Artikel Ilmiah pada Guru Sekolah Dasar di Kecamatan Air Periukan Kabupaten Seluma Provinsi Bengkulu

Workshop and Mentoring of Scientific Writing on Elementary School Teacher in Air Periukan Subdistrict, Seluma Regency and Bengkulu Province&#x0D; This community service was aimed to help teachers understand the concepts and processes of publicizing scientific articles in scholarly journals. The target of the program was the teachers are able to compile a manuscript and be accompanied until it is ready to be sent to the scholarly journal. The participants were members of the Air Periukan Subdistrict Teacher Working Group in Seluma District, Bengkulu Province. The training was carried out with participatory lecturing and mentoring methods. The results of observations on the implementation of the program through evaluation showed that the teachers had a high interest in being trained to publish scientific articles. However, high interest has not been followed by the number of manuscripts produced. The unfamiliarity of the publication of scientific articles has led to scientific publications becoming a "scourge" and reducing the teacher’s self-confidence to publish their scholarly article in peer-reviewed scientific journals. This program produced 12% of article drafts from 52 participants. This concluded that the program has to be continued so that the scientific work of teachers published in the scholarly journals will increase.
Radial greed algorithm with rectified chromaticity for anchorless region proposal applied in aerial surveillance

In aerial images, human figures are often rendered at low resolution and in relatively small sizes compared to other objects in the scene, or resemble likelihood to other non-human objects. The localization of trust regions for possible containment of the human figure becomes difficult and computationally exhaustive. The objective of this work is to develop an anchorless region proposal which can emphasize potential persons from other objects and the vegetative background in aerial images. Samples are taken from different angles, altitudes and environmental factors such as illumination. The original image is rendered in rectified color space to create a pseudo-segmented version where objects of close chromaticity are combined. The geometric features of segments formed are then calculated and subjected to Radial-Greed Algorithm where segments resembling human figures are selected as the proposed regions for classification. The proposed method achieved 96.76% less computational cost against brute sliding window method and hit rate of 95.96%. In addition, the proposed method achieved 98.32 % confidence level that it can hit target proposals at least 92% every time.
Relation Prediction for Unseen-Entities Using Entity-Word Graphs

Yuki Tagawa, Motoki Taniguchi, Yasuhide Miura, Tomoki Taniguchi, Tomoko Ohkuma, Takayuki Yamamoto, Keiichi Nemoto. Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13). 2019.
Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition

Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic.Since an automatic detection of abusive language can support human moderators of online discussion platforms, detecting abusiveness has recently received increased attention.However, the existing approaches simply train one classifier for the whole variety of abusiveness.In contrast, our approach is to distinguish explicitly abusive cases from the more "shadowed" ones.By dynamically extending a lexicon of abusive terms (e.g., including new obfuscations of abusive terms), our approach can support a moderator with explicit unraveled explanations for why something was flagged as abusive: due to known explicitly abusive terms, due to newly detected (obfuscated) terms, or due to shadowed cases.
Unconstrained Road Marking Recognition with Generative Adversarial Networks.

Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often over-dependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates high-quality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a class-conditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.
Laws of Large Numbers

In the setting of high-frequency statistics for stochastic processes, the information contained in the observed data.
Aplikasi Pencatatan Data Dasar (PEDDAS) pada Pembinaan Kesejahteraan Keluarga Berbasis Website di Kelurahan Tamanbaru Banyuwangi

Family Prosperity Founding is an institution that is very closely related to the system ofgovernment and society. Various types of community data in an environment must bemanaged by the Family Prosperity Founding, both at the village / kelurahan and district levels.In managing community data, many parties are involved, from the managers of each of theguest houses for each RT to the Village / Village Office. Of course this requires a lot of energyand time so that it is not fast in presenting information needed by certain parties. What is donefor data collection on the Family Prosperity Founding of a village / kelurahan has thefollowing stages: 1. The initial data is made by each charisma in each RT, 2. RT records thedata obtained from the dasawisma group and then the data is given to RW, 3. RW receivesdata from each RT and recapitulation, then the RW provides data to the Working Group, 4.The working group recaps data from the assisted RW and provides recapitulation data to thevillage / kelurahan, 5. The village / kelurahan reconciles the overall data. This method tendsto cause errors in filling and the slow submission of data to the kelurahan. For this reason, awebsite-based PEDDAS application was created in the hope that it could overcome problemsand be able to present Family Prosperity Founding information in Tamanbaru Village. Theconclusion of this reserach is the basic data registration application on Family ProsperityFounding facilitates the Dasa Wisma group to input group’s data and activities on 10 mainprograms of the Family Prosperity Founding at Tamanbaru Banyuwangi Urban Village. Thisapplication is also able to give information about all Family Prosperity Founding activitiesdone by its members to the Family Prosperity Founding board in Urban Village Office
Философско-эстетические основания визуальных практик в журналистике

The author focuses on the visualization of journalistic content as one of the phenomena of the modern world. Having formulated the goal of the study – to consider the philosophical and aesthetic foundations of visual practices in journalism (using the example of infographics), the author consistently implements it. This is facilitated by a clearly structured article. The introduction notes that in modern conditions, texts in general and visual, in particular, combine the formation of humanistic traditions (attention to the Man) and posthumanist traditions (technologization). A literature review is given on the research topic – the author identifies three thematic groups: works devoted to the history of and visual culture; studies related to the concepts of media and media language; scientific works devoted to such a phenomenon as infographics. The article analyzes the origins of visualization, describes the main characteristics of visuality (post-modern culture as a generator of visibility) and visual language (visual image – a universal unit of modern visual language). The infographic content of journalistic materials is considered as an example. Moreover, the analysis of infographics is made from two positions: infographics – illustration of journalistic material and infographics – language.
Coming to the Cookout: Cultural Fluency and Black Twitter ; Decoding the Language of Emojis and Animated Gifs on Black Twitter

Abstract This poster focuses on how the Black Twitter community, its youth, in particular, exhibit their prowess in their fluency of African American culture as a means of displaying their ethnicity online. It analyzes how prosody and paralanguage, an integral aspect of person to person communication in the Black Community, is made manifest on Black Twitter utilizing emojis, memes, and animated gifs. Also, how pop culture and current events are transposed onto African American Vernacular English syntax revolutionizing the long-standing cultural heritage of narrating the African American experience on Black Twitter to celebrate their cultural heritage, to advocate and show solidarity toward their community.
A DNN-based Civil Structure Semantic Segmentation and Motion Information Identification Algorithm

Camera can capture large amount of visual information of target within its field of view theoretically. This capability allows a camera to act as a sensor for obtaining displacement or distortion of targets at a large range of distance. Considering that there are so many cameras in public society (including the camera in smartphone) and the cameras will record the vibration of structures in an earthquake event, which will be very helpful for structural seismic damage assessment post-earthquake. With the development of AI, people begin to use DNN (deep neural network) to refine more useful information from images and videos. Significant progress in deep learning began in 2012 as computer computing efficiency increased significantly. Since then, deep learning gradually replaced many traditional methods and has become a new direction for many algorithms, including computer vision algorithms. DNN is one of the earliest algorithms to play its role. It has been used widely in the fields of image classification, semantic segmentation, target tracking and so on. In this paper, a DNN is designed to identify the displacement field of a frame structure model subjected to earthquake ground motion on a shaking table. An efficient training sample generation method is also proposed to help train the network. The network is based on traditional network U-Net, which can tell the semantic segmentation information from images. The network consists of an hourglass-type structure with loose sides in the middle. This structure provides sufficient feature extraction capability for the network and allows it to output an image of the same size as the input, which is semantic segmentation information. Another branch of the network focuses on comparing the motional connections between two adjacent frames of video. The displacement field as well as semantic segmentation results of the frame structure model is finally obtained through this network
Teaching Exceptional Children With Mobile Technologies in a General Education Classroom

Meeting the needs of all students is of crucial concern for all teachers. As technology continues to change our culture and the ways students learn, it is important for teachers to embrace new pedagogies that meet the needs of both gifted students and students with special needs. This chapter proposes a framework that supports the integration of differentiation, inquiry learning processes, and mobile technologies. The goal of presenting this framework is to provide a research foundation for a conceptual ideal geared toward practical implementation that benefits these two marginalized populations of students.
SOFTWARE AND ALGORITHMIC SUPPORT OF GEOMAGNETIC TOMOGRAPHY TECHNOLOGY USING DATA FROM UAV MEASUREMENTS

A data inversion high-performance algorithm is proposed for an unmanned magnetometer complex. We give the testing results of the numerical algorithm for tomographic inversion of data of different-height magnetic survey. We show the results of calculations for the test mode.
Physical sensemaking : crafting for an invisible world of data

The potential of a ‘data-driven life’, together with the realization of Weiser’s vision of ‘the disappearing computer’ have been embraced by many. However, the increasing invisibility, virtuality and complexity of data systems also come with a variety of concerns, such as issues of sensemaking, ownership, representation and control. Although data and technology is all around us, its virtual and invisible nature, thereby its lack of material and tangible forms has implications on the way data systems are (mis) used, understood, experienced and perceived. This paper presents craft-based approaches for physical sense making –widely ranging from physical artefacts to show and hide from monitoring, to crafting data physicalizations for critical thinking, communication and creativity. In doing so, this paper discusses how crafting physical forms can be used as a way to grasp and understand ‘invisible’ data systems.
Data Collections and Files

There are several data stuctures in Python. We'll cover dictionaries, sets, tuples, and frozensets this week to add to our knowledge of collections. Each one has a specific purpose as we'll see the differences between each.
Super Size Hero

Supersizehero is an immerse VR game for HTC Vive which puts the player in the role of an overweight hero trying to save the day. A special crafted, tracked fat suit allowing the player to actively use his belly serves as the main gameplay mechanic. The game is highscore based - each round the player needs to prevent a prison breakout or bank robbing by bouncing fleeing prisoners back into the prison, interrupt bank robbers and bring money back to the bank in order to gain as much points as possible in the given round. At the start of every level the player can choose one of three suits - each grants special abilities and a unique playstyle.
Круглый стол «Роль научно-методических журналов в подготовке современного учителя»

Round Table «The Role of Scientific and Methodological Journals in the Training of a Modern Teacher».
RESPON GURU TERHADAP PENGGUNAAN BUKU PANDUAN PENYUSUNAN LKS (LEMBAR KEGIATAN SISWA) BERBASIS MULTIPLE INTELLIGENCES

This research was conducted with the aim to determine the extent of teacher responses related to the use of manuals for compiling LKS based on Multiple Intelligences. In this study using descriptive qualitative research. Data collection procedures carried out by observation, documentation, and questionnaires. While this study uses data analysis of the Miles and Huberman models. The results obtained from this study are the teacher gives a positive response related to the use of manuals for compiling LKS based on Multiple Intelligences. The teacher is more helpful or easier to understand LKS and types of intelligence. The teacher does not feel difficulties in making worksheets, because in the guide can provide inspiration to innovate in making worksheets.
The OSU/Facebook Realizer for SRST 2019: Seq2Seq Inflection and Serialized Tree2Tree Linearization

We describe our exploratory system for the shallow surface realization task, which combines morphological inflection using character sequence-to-sequence models with a baseline linearizer that implements a tree-to-tree model using sequenceto-sequence models on serialized trees.Results for morphological inflection were competitive across languages.Due to time constraints, we could only submit complete results (including linearization) for English.Preliminary linearization results were decent, with a small benefit from reranking to prefer valid output trees, but inadequate control over the words in the output led to poor quality on longer sentences.
ВОПРОСЫ ПОСТРОЕНИЯ НЕЙРОСЕТЕВОГО КЛАСТЕРИЗАТОРА ДЛЯ АВТОМАТИЧЕСКОГО РАСПОЗНАВАНИЯ ПОЧТОВОГО ИНДЕКСА

The article discusses topical issues of a neural network clusterizer development designed to recognize handwritten ZIP code for correspondence, specified using a simplified code stamp. A structured convolutional neural network is used as a neural network basis for clustering. It pro-vides the formation and clustering of classifying features set that characterize the index code characters. A software model of a neural network clusterizer has been developed that provides character-by-character recognition of the scanned and appropriately preprocessed zip code im-age. In developing the clusterizer we use the MatLab version 18b Deep Learning ToolBox pack-age. A digital set of MNIST handwritten Arabic digits was used to train the neural network clusterizer. The article presents the experiments results, from which it follows that the trained software model of a neural network clusterizer provides an error recognition of an arbitrary handwritten digit from the validation set at the level of 0.72 %. The advantages of the proposedapproach are: the rejection of the currently used code stamp by replacing it with a simplified rep-resentation and a formally unlimited length; the possibility of using as part of an index handwrit-ten symbol adopted by the national postal systems, subject to appropriate prior training neural network clusters; the possibility of applying the proposed approach at various stages of processing correspondence, ranging from its reception from citizens and ending with sorting machines as part of large sorting centers.
Knowledge Inference Through Analysis of Human Activities

Monitoring human activities provides context data to be used by computational systems, aiming a better understanding of users and their surroundings. Uncertainty still is an obstacle to overcome when dealing with context-aware systems. The origin of it may be related to incomplete or outdated data. Attribute Grammars emerge as a consistent approach to deal with this problem due to their formal nature, allowing the definition of rules to validate context. In this paper, a model to validate human daily activities based on an Attribute Grammar is presented. Context data is analysed through the execution of rules that implement semantic statements. This processing, called semantic analysis, will highlight problems that can be raised up by uncertain situations. The main contribution of this paper is the proposal of a rigorous approach to deal with context-aware decisions (decisions that depend on the data collected from the sensors in the environment) in such a way that uncertainty can be detected and its harmful effects can be minimized.
Changing Attitudes Toward Technology: A Multi-Institutional Study on Student Perceptions of Mobile Apps Replacing Textbooks Before and After App Use

Abstract Date Presented 04/04/19 This report will discuss multi-institutional data regarding student perceptions on the use of a mobile app as a required textbook before and after use. Outcomes reveal that student preference for using a mobile app over a traditional textbook improve significantly after exposure in a lab setting. When introducing new technologies, it is important to acknowledge how lack of experience can affect student preconceived notions and how attitudes can be altered after exposure within a classroom. Primary Author and Speaker: Amanda Giles Additional Authors and Speakers: Orli Weisser-Pike
Overview of Speech Communication Research

Although speech communication is a fundamental part of our daily behavior, it is not yet well understood. Speech communication research examines how spoken language is produced, transmitted and perceived. This involves a number of different disciplines, from linguistics and experimental psychology to speech and hearing sciences, and electrical engineering. The field covers a wide range of physiological, psychological, acoustic, and linguistic phenomena. In this talk, I will provide a broad overview of the types of research that happen within our TC. I will highlight the advantages of participating in the ASA through examples that show how work in Speech Communication may inform or be informed by work in other technical areas within our society.
Research Guides: Refugees and Displaced Persons: Information Resources for a Global Crisis: Electronic Books

Scholarly sources, data and data tools, news sources and in-depth background information addressing issues of human displacement and immigration.
Обеспечение коммуникации между научным сообществом и СМИ: проблемы и пути их решения медиаслужбой федерального университета

The article discusses mediatization in higher education and its impact on the relationship between the public and the academia, more specifically, the growing global trend towards more openness and transparency in scholarly communication. While some academics are willing to publicize their research and engage actively with the publics, others are wary of doing so, to a large extent, due to the perceived ‘pitfalls’ involved in communication with the press. A few ‘box office academics’, who enjoy being in the spotlight, are more of an exception than a rule and the majority of researchers at best have a ‘one-off relationship’ with the press. This situation increases the significance of university media- and PR-services, whose key functions include communication facilitation and mediation, on the one hand, and ‘gatekeeping’, on the other. In other words, these services seek to encourage positive publicity about their universities and to minimize the risk of negative publicity by controlling the outflow of information. The article focuses on the case of the media-centre of the Ural Federal University (Ekaterinburg, Russia) and the challenges it faces while trying to increase the university’s visibility in the media space.
Amicus Brief of Cybersecurity Law Professors in Enigma Software v. Malwarebytes

In Enigma Software v. Malwarebytes, the Ninth Circuit ruled that 47 U.S.C. 230(c)(2)(B) does not protect anti-threat classification decisions that are allegedly based on anti-competitive animus. This amicus brief, joined by 7 professors, explains how the Ninth Circuit's ruling makes the Internet less safe. Many anti-threat software programs are, in fact, rogue software that should be classified as threats. Those classification decisions will create greater legal risk, and that will discourage legitimate anti-threat vendors from labeling rogue software as threats. To ensure that anti-threat software vendors can continue to vigorously protect consumers from rogue software, the amicus brief urges the Ninth Circuit to rehear the case.