Reviewerly/exHarmony huggingface dataset:
Video Camera in the Ambient Assisted Living System. Health Versus Privacy

Significant growth of the ageing population segment brings the necessity of additional healthcare investment. Besides somatic disorders, part of the older patient group suffers from Mild Cognitive Impairment and dementia. According to the World Health Organization, currently 55 million people worldwide suffer from dementia only. The projection is 75 million in 2030 and 132 million by 2050 (WHO, 2021). Mild Cognitive Impairment is often the first stage of dementia. Most of the patients with MCI and dementia are home-based. Ambient Assisted Living can improve the wellbeing of patients and their relatives without considerably raising the price tag for healthcare. In the current work, the questionnaire was created for healthcare stakeholders in order to conceptualize potential AAL for MCI patients. In this paper, the role of a video observation in AAL is analyzed with help of non-parametric respondents’ group comparison.
Zero-shot Code-Mixed Offensive Span Identification through Rationale Extraction

This paper investigates the effectiveness of sentence-level transformers for zero-shot offensive span identification on a code-mixed Tamil dataset.More specifically, we evaluate rationale extraction methods of Local Interpretable Model Agnostic Explanations (LIME) (Ribeiro et al., 2016a) and Integrated Gradients (IG) (Sundararajan et al., 2017) for adapting transformer based offensive language classification models for zero-shot offensive span identification.To this end, we find that LIME and IG show baseline F 1 of 26.35% and 44.83%, respectively.Besides, we study the effect of data set size and training process on the overall accuracy of span identification.As a result, we find both LIME and IG to show significant improvement with Masked Data Augmentation and Multilabel Training, with F 1 of 50.23% and 47.38% respectively.Disclaimer : This paper contains examples that may be considered profane, vulgar, or offensive.The examples do not represent the views of the authors or their employers/graduate schools towards any person(s), group(s), practice(s), or entity/entities.Instead they are used to emphasize only the linguistic research challenges.
Personnel Management in a Preschool Educational Organization in Ulyanovsk: Competency-based Approach

The article analyzes the features of personnel management in preschool educational organizations in Ulyanovsk, they include the processes and practice of introducing a competency-based approach. The authors consider the features of applying the competence-based approach to support personnel management. They proposed a workshop for employees, which will allow heads of educational organizations to form skills in creating a competency model, designing activities for assessing competencies, as well as gaining knowledge and skills in implementing and using a competency model in practice.
Moodle and Whatsapp: Solutions for Learning Speaking Skills in The Covid 19 Pandemic

This research aimed to observe learning-speaking skills using Learning Management System (LMS) Moodle and Whatsapp application at Secretarial Academy of Budi Luhur Jakarta.This research was a part of instructional material development for a speaking course at ASTRI Budi Luhur Jakarta.The approach of this research was qualitative, while the data was collected through observation.This research was conducted from March 2020 to April 2021.The results indicated that both LMS Moodle and WhatsApp could be maximally utilized in learning speaking skills.Moodle's modules for learning speaking were: Attendance, Book, File, Folder, Label, Page, Assignment, Quiz, Chat, Forum, and Glossary.The attendance module could be used to check student attendance.Book, File, Folder, Label, and Page module can be used to deliver learning materials and examples in the form of text, audio, and video.Meanwhile, the assignment and quiz modules could be used to provide assignments, exercises, and learning evaluations to students.Finally, chat and forum modules were used to ensure the students' continuity of interaction and the lecturer or among students.The glossary module could be used to provide vocabulary that had to be studied.Meanwhile, the WhatsApp application could be used to interact, record audio or video, and collect tasks.So it could be concluded that LMS Moodle and WhatsApp applications could be used and were beneficial in learning during the Covid 19 pandemic.
Cooperative movement of an inverted two-wheeled vehicle by human force control

Robots designed to support the transportation of goods are expected to play an active role in addressing social problems caused by the declining birthrate and aging population. Conventional mobile robots that support cargo transportation are generally three- or four-wheeled. As a result, most of them are large and difficult to maneuver, and are primarily used in factories and warehouses where the environment is stable. However, they have not been put to practical use in situations where the environment changes rapidly, such as in human living environments. In this study, we demonstrate that an inverted two-wheeled vehicle, which has the advantages of being relatively lightweight, highly maneuverable, and having a small footprint, can move agilely in a complex and confined space by applying human manipulation force, and that it can move in cooperation with humans through force control. A force sensor was mounted on top of an inverted two-wheeled vehicle, and the operator's manipulation force was applied to the vehicle. In response to the applied manipulation force, force control is performed using admittance control with a variable viscosity coefficient, and the inverted two-wheeled vehicle moves in a coordinated manner to minimize the manipulation force. With the introduction of this system, the operator can transport a load with a minimum force, even in a complex environment.
ConDef: Automated Context-Aware Lexicography Using Large Online Encyclopedias

Current automated lexicography (term definition) techniques cannot include contextual or new term information as a part of their synthesis. Our work proposes a novel data-harvesting scheme that leverages lead paragraphs in Wikipedia to create a dataset used to train automated, context-aware lexicographical models. Furthermore, in order to validate the harvested dataset, we present ConDef, a fine-tuned BART model trained on the harvested data which defines vocabulary terms from a short context. ConDef is shown to be highly accurate in context-dependent lexicography as validated on ROUGE-1 and ROUGE-L measures in an 1000-item withheld test set, achieving scores of $$46.40\%$$ and $$43.26\%$$ respectively. Furthermore, we demonstrate that ConDef’s synthesis serves as a good proxy for term definitions by achieving a ROUGE-1 measure of $$27.79\%$$ directly against gold-standard WordNet definitions.
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task

Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall. Although one can collect data from these adverse conditions using cars equipped with sensors, it is quite tedious to annotate the data for training. In this work, we address this limitation and propose a CNN-based method that can leverage the steering wheel angle information to improve the road area semantic segmentation. As the steering wheel angle data can be easily acquired with the associated images, one could improve the accuracy of road area semantic segmentation by collecting data in new road environments without manual data annotation. We demonstrate the effectiveness of the proposed approach on two challenging data sets for autonomous driving and show that when the steering task is used in our segmentation model training, it leads to a 0.1–2.9% gain in the road area mIoU (mean Intersection over Union) compared to the corresponding reference transfer learning model.
Sparks: Inspiration for Science Writing using Language Models

Large-scale language models are rapidly improving, performing well on a variety of tasks with little to no customization.In this work we investigate how language models can support science writing, a challenging writing task that is both open-ended and highly constrained.We present a system for generating "sparks", sentences related to a scientific concept intended to inspire writers.We run a user study with 13 STEM graduate students and find three main use cases of sparks-inspiration, translation, and perspective-each of which correlates with a unique interaction pattern.We also find that while participants were more likely to select higher quality sparks, the overall quality of sparks seen by a given participant did not correlate with their satisfaction with the tool. 1
Seeing the advantage: visually grounding word embeddings to better capture human semantic knowledge

Distributional semantic models capture wordlevel meaning that is useful in many natural language processing tasks and have even been shown to capture cognitive aspects of word meaning.The majority of these models are purely text based, even though the human sensory experience is much richer.In this paper we create visually grounded word embeddings by combining English text and images and compare them to popular text-based methods, to see if visual information allows our model to better capture cognitive aspects of word meaning.Our analysis shows that visually grounded embedding similarities are more predictive of the human reaction times in a large priming experiment than the purely text-based embeddings.The visually grounded embeddings also correlate well with human word similarity ratings.Importantly, in both experiments we show that the grounded embeddings account for a unique portion of explained variance, even when we include text-based embeddings trained on huge corpora.This shows that visual grounding allows our model to capture information that cannot be extracted using text as the only source of information.
Visualization method of seismic profile data based on adaptive mosaic

Three-dimensional visualization of seismic profile data can better display seismic data information than traditional profile display, but there are mosaic errors in grid mosaic. In order to solve the problem of false mosaics, an adaptive mosaic method is proposed, first in the pixel shader Calculate the sample point most relevant to the current sample point on the next track, and then map its relative offset in the time direction to the tilt mode and render it to the texture. When filling the vertex index, fully consider the tilt of the adjacent sample points on the same track In order to realize the adaptive tessellation of vertices; finally, the effectiveness of the proposed method is verified through experiments.
Development of the <i>Umbul</i> Encyclopedia in Klaten as Social Studies Learning Media for SMP/MTs Students

The study and development was based on one of the Klaten Regency's schools, MTs N 5 Klaten.The school has never exploited the surrounding environment as a learning resource, and there is no learning media in the form of an encyclopedia in the classroom for social studies learning activities.The goal of the research and development is to build and understand the level of feasibility of the spring water encyclopedia in Klaten Regency as a social studies learning medium for students of MTs N 5 Klaten.The research comprises both research and development utilizing the ADDIE development approach.A questionnaire as a validation sheet was employed as the data collection instrument.The validation sheet is meant for material experts, media experts, the validator team of two social studies educators, and product testing for 14 students in class VIII A MTs N 5 Klaten.The validation sheet is important for determining the level of practicality of the spring water encyclopedia in Klaten Regency.The collected data was then evaluated using a multilevel scale for product evaluation and a Guttman scale for product testing.Material specialists rate the feasibility of encyclopedia products at 82.22 percent, media experts at 85 percent, social studies educators at 97.18 percent, and product trials at 98.01 percent.Based on this assessment, the result of the Klaten Regency spring water encyclopedia is characterized as very feasible for usage as a social studies learning medium for SMP/MTs students.
TwittIrish: A Universal Dependencies Treebank of Tweets in Modern Irish

Modern Irish is a minority language lacking sufficient computational resources for the task of accurate automatic syntactic parsing of usergenerated content such as tweets.Although language technology for the Irish language has been developing in recent years, these tools tend to perform poorly on user-generated content.As with other languages, the linguistic style observed in Irish tweets differs, in terms of orthography, lexicon, and syntax, from that of standard texts more commonly used for the development of language models and parsers.We release the first Universal Dependencies treebank of Irish tweets, facilitating natural language processing of user-generated content in Irish.In this paper, we explore the differences between Irish tweets and standard Irish text, and the challenges associated with dependency parsing of Irish tweets.We describe our bootstrapping method of treebank development and report on preliminary parsing experiments.
Hand gesture based X-ray image controlling using Convolutional Neural Network

This paper proposes a novel computer vision based system that allows doctors, surgeons and other physicians to control X-Ray images just by using simple gestures thus eliminating the need of traditional devices like mouse and keyboard. This will help reduce the risk of contamination in sterile environments like those found in the hospitals and it will also help in preventing the spread of covid by not allowing contact with contaminated surfaces. It is implemented using CNN model. CNN is specially used for image recognition as well as processing. The system detects gestures through in-built webcam and converts it into corresponding computer commands to perform its associated tasks.
The use of peer feedback via Instagram in Blended learning to improve students' Recount text writing

Writing is one of language skills which is considered as a complicated skill to be mastered because it requires variety of abilities such as generating ideas, determining purposes, developing arguments, organizing and managing the text effectively, and revising draft. Due to that matter, this research proposed instagram as one of the effective tools used in blended learning to improve student’s writing. The aims of this study are to investigate whether there is any difference in student’s writing achievement after the students are taught by using Peer feedback via instagram , to find out which aspect of writing that improves the most after being taught by using Peer feedback via instagram and to find out what aspect of writing that students correct the most. This research used the one-group pretest-posttest design that involved 30 students of X IPS 4 class at MAN 1 Bandar Lampung in the 2019/2020 academic year. The writing tests were also used as the instruments to collect the data. The data obtained were analyzed by using paired sample t-test in which the significance level was determined by sign &lt;0.05. With regard to the data analysis, the use of instagram can improve the Student’s writing achievement in all aspects of writing: content, organization, vocabulary, language use and mechanics. It is also found that content was the aspect of writing which improved the most. However, the aspect that the students corrected the most was language use.
SELF-HEALING TIME ESTIMATION OF ABANDONED MINE AREAS USING REMOTE SENSING

Lately, remote sensing has been an effective tool for monitoring the Earth and environmental changes.Landsat with its legacy is providing available data for time-based research.The aim of this paper is to estimate the time of the self-healing progress of an abandoned mine area using Landsat satellite images.In this study, five Landsat satellite images were used.As a study area, the abandoned Damjan mine in the southeastern part of the Republic of N. Macedonia has been chosen.Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values were used to classify Landsat images into barren land, bared soil, mixture of vegetation, vegetation, and water bodies.The NDVI values lower than zero are considered to be the area affected by the mine.The healing of the mine has been established using the annual growth rate analysis.It was found that the area has been healing approximately 10 % every year and a prediction was made for the study area till the year 2030.
Program Synthesis Through Learning the Input-Output Behavior of Commands

Program synthesis writes programs on behalf of humans, increasing software development productivity. Existing systems select a proper sequence of commands by assembling them in various ways and analyzing their meanings, which is accurate and fast when a detailed specification is given for each available command, including the syntax and semantics. However, preparing this specification is burdensome and must be repeated to add or modify available commands. We propose a synthesis system that requires moderate groundwork to achieve sufficient accuracy and speed. The proposed system receives the syntax of the available commands and learns their meanings independently by writing programs and observing their input-output behavior. Using the learned knowledge, the system selects a likely sequence of commands and gradually revises them to converge to a target program. We validated the system by synthesizing 1,000 integer-manipulation programs out of 245,410 possible programs. The system synthesized most of the programs within 1,000 revisions, which is 80% faster than the state-of-the-art system based on machine-learned input-output patterns. In addition, greater than 10% of the programs were synthesized within several revisions. We believe that the proposed system provides a basis for synthesis systems based on learning input-output behavior.
Bullying em profissionais de enfermagem no atendimento à COVID-19

The aim was to evaluate the studies that address the occurrence of bullying in the nursing practice environment during the care of COVID-19 patients. This is an integrative review, covering the period from January 2020 to December 2021, through the combination of the descriptors bullying/bullying “AND” nursing/nursing “AND” COVID-19/COVID -19. Sixteen studies were identified, four of which met the inclusion criteria. Studies show the exposure of professionals to acts of bullying related to COVID-19 care protocols and acts performed by other health professionals and patients affected by COVID-19. The studies selected in the review present acts of bullying such as censorship, reprimand and violence experienced by nursing professionals during the care of patients affected by COVID-19.
Social Media as a Learning Media to Improve Digital Literacy and Creation

The purpose of this study was to determine the literacy level of member of Komunitas Home Recording Indonesia (Indonesian Home Recording Community) and the form of creativity of its members.The method used in this study is a qualitative description.Data is collected through observations on social media such as Facebook, YouTube, Instagram, and blogs belonging to community members.The results of the study show that as internet users on social media and judging from the availability of internet networks, members of the Indonesian Home Recording Community (IHRC) already have good internet literacy, namely at level 4. When viewed from the use of information technology that supports activities in recording technology, then most members of IHRC in carrying out their activities on social media are still at level 2. In addition to interacting, sharing experiences and knowledge with those who have the same interest, namely the recording field, those who have experience and a high level of skill use social media to actualize themselves and benefit from it both socially and financially.
News from the Field

ACRL 2019-20, 2020-21 fiscal year financial reports now available 2022 EDUCAUSE Horizon Report Teaching and Learning Edition released ARL, CARL, Ithaka S+R release final report on aligning the research library to organizational strategy ACRL releases Interim Leadership in Libraries Peer Review: A Critical Primer and Practical Course now available New from ACRL—Your Craft as a Teaching Librarian CUPA-HR releases annual workforce surveys ASERL expands oral history collection
WHISTLE: CPU Abstractions for Hardware and Software Memory Safety Invariants

Memory safety invariants extracted from a program can help defend and detect against both software and hardware memory violations. For instance, by allowing only specific instructions to access certain memory locations, system can detect out-of-bound or illegal pointer dereferences that lead to correctness and security issues. In this paper, we propose CPU abstractions, called, to specify and check program invariants to provide defense mechanism against both software and hardware memory violations at runtime. ensures that the invariants must be satisfied at every memory accesses. We present a fast invariant address translation and retrieval scheme using a specialized cache. It stores and checks invariants related to global, stack and heap objects. The invariant checks can be performed synchronously or asynchronously. uses synchronous checking for high security-critical programs, while others are protected by asynchronous checking. A fast exception is proposed to alert any violations as soon as possible in order to close the gap for transient attacks. Our evaluation shows that can detect both software and hardware, spatial and temporal memory violations. incurs 53&#x0025; overhead when checking synchronously, or 15&#x0025; overhead when checking asynchronously.
&lt;i&gt;In-situ&lt;/i&gt; Measurement of Bainitic Transformation Process using Digital Holographic Microscope

A novel 3D in-situ measurement method was developed to clarify the bainitic transformation process of medium carbon steel. To achieve enhanced accuracy in 3D in-situ measurement by a digital holographic microscopy, the Gabor Wavelet transformation is introduced instead of the conventional reconstruction method based on the fast Fourier transformation. The effectiveness of the proposed method is first verified by comparison with the conventional reconstruction method as well as the other 3D measurement methods, such as atomic force microscopy and confocal laser microscopy. 3D in-situ observations were conducted to reveal the surface relief effect induced by the bainitic transformation behavior during continuous cooling. The results demonstrated that the measured shape deformation induced by the bainitic transformation corresponds well with the prediction by the phenomenological theory of martensite crystallography.