Reviewerly/exHarmony huggingface dataset:
USING VIRTUAL REALITY IN AGED CARE SETTINGS: A SCOPING REVIEW

Abstract Recent advancement of virtual reality (VR) technology has led to growing interest in using VR in aged-care settings. VR can help ameliorate experiences of loneliness and social isolation, which is especially important during the COVID-19 pandemic. As a result, increasingly more studies are being published on this topic, and a comprehensive review of studies examining the facilitators and barriers of adopting VR in these settings is needed. This scoping review reports the facilitators and barriers to implementing VR in care settings among older adults, as well as the impact on social engagement and/or loneliness. We followed the Joanna Briggs Institute scoping review methodology, and searched the following databases: CINHAL, Embase, Medline, PsycInfo, Scopus, and Web of Science. Inclusion criteria includes articles published in the last five years that focus on older adults using VR in aged-care settings. 199 articles were retrieved and 21 articles were included in our review. Most of the articles (38%) originated from Australia. Key facilitators for using VR in aged care settings are the technology being user-friendly, comfortable, and easy to clean. Barriers included: technology issues (e.g., internet connectivity), staff attitude/ worries, and impact on residents, and structural considerations (e.g., lack of staff and time to assist with the VR program). VR technology can decrease loneliness and feelings of isolation, and provide opportunities to engage with others. Our review of the current evidence offers insights and recommendations for health care professionals to use VR technology in aged care settings, maximizing benefits and minimizing risks among users.
Discourse on Gayo through Historical Narration: Linge Kingdom, Loyang Mendale, and Loyang Ujung Karang

The construction of an understanding of Gayo is greatly influenced by how the discourse on Gayo is produced. Discourse about Gayo can only be traced through historical texts that document various narrations about Gayo. This study aims to describe how the historical discourse contained in the text is used as legitimacy in building constructions about Gayo. To analyze the various narratives about Gayo in the text, this research uses qualitative research methods with the DHA (Discourse Historical Approach). Through DHA, the construction of an understanding of Gayo in the present which was built based on historical discourse found the context. The results of this study indicate that: (1) the texts about Gayo which are grouped into three sets “Linge Kingdom Texts 1”, “Linge Kingdom Texts 2”, and “Loyang Mendale and Loyang Ujung Karang Texts” are narratively contradictory to each other; (2) the three sets of texts become a discourse that legitimizes its narrative based on historical events; (3) The “Text of the Kingdom of Linge 1” becomes the understanding of the dominant group in reproducing the understanding of the dichotomy between the Acehnese as the dominant group and the Gayo tribe as a minority through discursive practices.
Analisi dei turchismi utilizzati nel paragone nel romanzo di Meša Selimović Derviš i smrt e la loro traduzione italiana / On Cultural Differences in The Italian Translation of The Novel Death And The Dervish By Meša Selimović

This paper deals with the comparison based on Turcisms in the Italian translation of the novel Derviš i smrt by Meša Selimović. The novel was published in 1966 and Lionello Costantini did the translation from the then Serbo-Croatian to Italian (Il derviscio e la morte) in 1983. This paper focuses on the issue of translation equivalents, as some of the most important elements in the translation process, and on the process of translation itself, as well. A comparative analysis of the culturally specific elements in the original text and their translation in the target language was made, primarily of the examples of comparison based on Turcisms. We attempted to explain the motives behind these translation choices, and then tried to establish how close these choices are t Italian readers and whether they affect the process of understanding the translated text as much as the original text affects the readers of Serbo-Croatian. This paper aims to show which of the two processes suggested by the German theologist and philosopher Friedrich Schleiermacher (and then adopted by Venuti, Berman, and others) prevails between the original and the translated text. In other words, this paper is aimed at establishing whether the Italian translation of the novel Death and the Dervish enables the reader to fully grasp the linguistic and cultural universe of the period in which Selimović placed his work, or whether the translator had to reshape the original text so to fit the scope of understanding and cultural peculiarities of Italian readers.
THE RESPONSIBLE DESIGNER: USING A HUMAN-CENTERED DESIGN APPROACH TO CREATE AN ASSISTIVE TECHNOLOGY FOR AD

Abstract Our interdisciplinary team of designers, computer scientists, and engineers have begun the development of a low-cost social robot called FRED (the Friendly Robot to Ease Dementia). FRED has a conversational capability which will allow the device to be used as assistive companions for people with AD, engaging conversation, assisting with reminders, playing simple games and music, and relieving caregivers of some of their responsibilities. FRED was created using 3-d printing technology and a single board computer based on raspberry Pi. Interaction between human and the system was implemented using the Mycroft open-source voice assistant. The overall cost of the robot is estimated to be less than $300, rendering it possible for wide distribution among the public. Our presentation will share results from our participatory focus group which helped assist in the design and interactions of FRED. This human-centered design approach included neurologists, healthy older adults, as well as ADRD patients and their caregivers. A common language of important terms (e.g., feedback, interface, training, cognitive assessment) to aid in initial conversations were implemented. A thinking-aloud protocol was adopted once we had a prototype of FRED, where users were able to express freely any problems and/or concerns during interaction. Insights from the thinking-aloud results were used to improve the user interface design to enable the users and robot to interact and collaborate in an effective, natural way. Feedback from the focus group were carefully documented and the results will be shared during our presentation.
EXPLORING THE POTENTIAL OF WEARABLE DEVICES FOR HEALTH MONITORING: A FOCUS ON BLACK OLDER ADULTS

Abstract Chronic diseases are some of the top conditions leading to death in the United States. Management of these chronic diseases could benefit from monitoring risk factors, such as physical activity. Wearable devices (e.g., Fitbit) have the capability to track and give information to allow the individual to take control of their health. However, wearables are typically advertised to young, physically active, and individuals who belong to racial groups with the highest population. As such, the purpose of this study was to understand Black adults’ opinions and attitudes towards wearable design to support usage, focusing on both users and non-users. Participants were interviewed to explore their wants and needs towards wearables to support usage (e.g., aesthetics) and to overcome barriers (e.g., usability concerns). The findings from the thematic analysis will be presented to illustrate these facilitators and barriers identified by Black adults. These insights can guide future inclusive design.
A METHODOLOGICAL APPROACH TO IDENTIFY EPISODES OF LUCIDITY AMONG PEOPLE WITH ALZHEIMER'S DISEASE

Abstract People with late-stage Alzheimer’s disease and related dementias (ADRD) who are assumed to have lost coherent cognitive capacity may exhibit unexpected episodes of lucidity (EL). Given the transient nature and lack of scientific explanation of the phenomenon, EL is under-investigated and poorly understood. To better understand this phenomenon, we set out to develop an operational definition of EL. Based on survey data from former and current family caregivers participating in UsAgainstAlzheimer’s A-LIST® (N = 480), we defined four EL typologies. We then interviewed 25 caregiver respondents about their experiences and used analyzed qualitative data to refine the preliminary typologies. Finally, we conducted a Delphi consensus panel with clinicians, researchers, and health care providers with medical, pharmacological, and clinical expertise to describe potential explanations for EL to help further refine the typologies. Next we will test the validity of these typologies in a prospective, demographically diverse sample of current family caregivers.
Penerapan Prosedur, Metode, dan Analisis Terjemahan pada Teks Naratif “The Endless Tale”

This research aims to describe the process of translating the narrative text, i.e., “The Endless Tale”, by using Newmark’s model of translation methods and procedures and to analyze its translation. The translation was analyzed by providing annotation on the equivalence selected. The author employed an empirical-based qualitative method in the form of linguistic constituents (e.g., idioms and culture-related words or terms) through three stages, i.e., compiling, collecting, and analyzing the translation. For annotating or explaining idioms or terms in this narrative text, the author used both offline and online dictionaries to produce the most appropriate translation. The problems encountered in the translation process include matching words and sorting phrases or transferring the meaning of idioms in the text into the target language accurately.
Reservoir Effect on the Hybrid Solar-Hydroelectric (SHE) System

The solar-hydroelectric (SHE) energy system is a renewable hybrid energy system consisting of solar and hydroelectric energy. An optimization algorithm has been designed to work out the installed power size of the SHE hybrid system, which is planned to be integrated into the existing hydroelectric power systems. This designed algorithm provides the optimum installed power with the benefit/cost approach. The value of the hydro cost and also the energy generation is taken from the actual values since it's an existing facility, and also the electricity production and price of the solar power are obtained from the algorithm that works iteratively. This study aims to indicate that more electricity will be produced by regulating water flows due to the reservoir of hydroelectric power plants. Hydro energy enables energy management to be administrated more effectively with the reservoir, which could be a natural enclosure, without using the other energy storage equipment/method. As a result of the study, it's been shown that with the regulation of the hydro facility flows with a reservoir, 180% more solar power capacity installation with 20.9 MW installed power and 12% more electricity production with 75.3 GWh electricity production is provided compared to the unregulated situation.
SPECIES OF AQUATIC ANIMALS ASSOCIATED WITH THE DEVELOPMENT OF HAFF DISEASE IN HUMANS: A LITERATURE REVIEW

Haff 's disease is a syndrome of myalgia and rhabdomyolysis in humans caused by a thermostable toxin of unknown origin present in the muscles and viscera of certain fish and crustaceans and may be associated with the process of aquatic bioaccumulation.The objective of this work is to gather all the vectors associated with Haff disease since its first report until the present moment, in order to better understand its occurrence, distribution and pathogenicity.In 1924, in the Baltic region, the first cases of the disease were recorded after consumption of the fish species Burbot (Lota lota), Eel (Anguilla anguilla) and Pike (Esox lucius).In 1984 in the United States of America, the disease was reported for the first time after consumption of fish of the species Buffalo fish (Ictiobus cyprinellus), Red crayfish (P rocambarus clark ii), Salmon (Salmo spp.), Grass carp (Ctenopharyngodon idella).In 1990, in Japan, cases of Haff Disease were reported after consumption of marine fish Boxfish (Ostracion immaculatus) and Roundbelly Cowfish (Lactoria diaphana).In China, in 2000, the first record of the syndrome associated with the consumption of red crayfish (Procambarus clarkii) was made, this species being the main pathogenic factor of Haff disease in China.In the country there are also reports of the occurrence of the disease through the ingestion of "Pomfret" (Colossoma brachypomum).In Brazil, the first cases were registered from 2007 after consumption of Tambaqui (Colossoma macropomum), Pacu (Mylossoma ssp.), Pirapitinga (Piaractus brachypomus), Arabaiana (Seriola spp.) and Whiting (Mycteroperca spp.).There is no antitoxin for poisoning by Haff disease, treatment is carried out by relieving symptoms and treating complications, which further demonstrates the importance of studies that seek to deepen knowledge about the toxin and its mechanism of action both in fish as in humans.
Memory Compression with Quantum Random-Access Gates

In the classical RAM, we have the following useful property. If we have an algorithm that uses $M$ memory cells throughout its execution, and in addition is sparse, in the sense that, at any point in time, only $m$ out of $M$ cells will be non-zero, then we may "compress" it into another algorithm which uses only $m \log M$ memory and runs in almost the same time. We may do so by simulating the memory using either a hash table, or a self-balancing tree. We show an analogous result for quantum algorithms equipped with quantum random-access gates. If we have a quantum algorithm that runs in time $T$ and uses $M$ qubits, such that the state of the memory, at any time step, is supported on computational-basis vectors of Hamming weight at most $m$, then it can be simulated by another algorithm which uses only $O(m \log M)$ memory, and runs in time $\tilde O(T)$. We show how this theorem can be used, in a black-box way, to simplify the presentation in several papers. Broadly speaking, when there exists a need for a space-efficient history-independent quantum data-structure, it is often possible to construct a space-inefficient, yet sparse, quantum data structure, and then appeal to our main theorem. This results in simpler and shorter arguments.
Vexing Vexillological Logic

We define a new impartial combinatorial game, Flag Coloring, based on flood filling. We then generalize to a graph game, and find values for many positions on two colors. We demonstrate that the generalized game is PSPACE-complete for two colors or more via a reduction from Avoid True, determine the outcome classes of games based on real-world flags, and discuss remaining open problems.
Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing

Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics. These findings prompted large efforts aiming to understand and measure such effects, with the goal of providing benchmarks that can guide the development of techniques mitigating these stereotypical associations. However, as recent research has pointed out, the current benchmarks lack a robust experimental setup, consequently hindering the inference of meaningful conclusions from their evaluation metrics. In this paper, we extend these arguments and demonstrate that existing techniques and benchmarks aiming to measure stereotypes tend to be inaccurate and consist of a high degree of experimental noise that severely limits the knowledge we can gain from benchmarking language models based on them. Accordingly, we propose a new framework for robustly measuring and quantifying biases exhibited by generative language models. Finally, we use this framework to investigate GPT-3's occupational gender bias and propose prompting techniques for mitigating these biases without the need for fine-tuning.
PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition

The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually entailed by another, i.e. whether the entirety of its meaning can be inferred from the other. In current NLI datasets and models, textual entailment relations are typically defined on the sentence- or paragraph-level. However, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence. As these propositions can carry different truth values in the context of a given premise, we argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document, i.e. documents describing the same event or entity. We establish strong baselines for the segmentation and entailment tasks. Through case studies on summary hallucination detection and document-level NLI, we demonstrate that our conceptual framework is potentially useful for understanding and explaining the compositionality of NLI labels.
ORCA: A Challenging Benchmark for Arabic Language Understanding

Due to their crucial role in all NLP, several benchmarks have been proposed to evaluate pretrained language models. In spite of these efforts, no public benchmark of diverse nature currently exists for evaluation of Arabic. This makes it challenging to measure progress for both Arabic and multilingual language models. This challenge is compounded by the fact that any benchmark targeting Arabic needs to take into account the fact that Arabic is not a single language but rather a collection of languages and varieties. In this work, we introduce ORCA, a publicly available benchmark for Arabic language understanding evaluation. ORCA is carefully constructed to cover diverse Arabic varieties and a wide range of challenging Arabic understanding tasks exploiting 60 different datasets across seven NLU task clusters. To measure current progress in Arabic NLU, we use ORCA to offer a comprehensive comparison between 18 multilingual and Arabic language models. We also provide a public leaderboard with a unified single-number evaluation metric (ORCA score) to facilitate future research.
Contrastive Error Attribution for Finetuned Language Models

Recent work has identified noisy and misannotated data as a core cause of hallucinations and unfaithful outputs in Natural Language Generation (NLG) tasks. Consequently, identifying and removing these examples is a key open challenge in creating reliable NLG systems. In this work, we introduce a framework to identify and remove low-quality training instances that lead to undesirable outputs, such as faithfulness errors in text summarization. We show that existing approaches for error tracing, such as gradient-based influence measures, do not perform reliably for detecting faithfulness errors in NLG datasets. We overcome the drawbacks of existing error tracing methods through a new, contrast-based estimate that compares undesired generations to human-corrected outputs. Our proposed method can achieve a mean average precision of 0.93 at detecting known data errors across synthetic tasks with known ground truth, substantially outperforming existing approaches. Using this approach and re-training models on cleaned data leads to a 70% reduction in entity hallucinations on the NYT dataset and a 55% reduction in semantic errors on the E2E dataset.
A Dirichlet Process Mixture Model for Directional-Linear Data

Directional data require specialized probability models because of the non-Euclidean and periodic nature of their domain. When a directional variable is observed jointly with linear variables, modeling their dependence adds an additional layer of complexity. This paper introduces a novel Bayesian nonparametric approach for directional-linear data based on the Dirichlet process. We first extend the projected normal distribution to model the joint distribution of linear variables and a directional variable with arbitrary dimension as a projection of a higher-dimensional augmented multivariate normal distribution (MVN). We call the new distribution the semi-projected normal distribution (SPN); it possesses properties similar to the MVN. The SPN is then used as the mixture distribution in a Dirichlet process model to obtain a more flexible class of models for directional-linear data. We propose a normal conditional inverse-Wishart distribution as part of the prior distribution to address an identifiability issue inherited from the projected normal and preserve conjugacy with the SPN distribution. A Gibbs sampling algorithm is provided for posterior inference. Experiments on synthetic data and the Berkeley image database show superior performance of the Dirichlet process SPN mixture model (DPSPN) in clustering compared to other directional-linear models. We also build a hierarchical Dirichlet process model with the SPN to develop a likelihood ratio approach to bloodstain pattern analysis using the DPSPN model for density estimation to estimate the likelihood of a given pattern from a set of training data.
Every Bit Counts: A New Version of Non-binary VT Codes with More Efficient Encoder

In this work, we present a new version of non-binary VT codes that are capable of correcting a single deletion or single insertion. Moreover, we provide the first known linear time algorithms that encode user messages into these codes of length n over the $q$-ary alphabet for $q\ge 2$ with at most $\ceil{\log_q n} + 1$ redundant symbols, while the optimal redundancy required is at least $\log_q n + \log_q (q - 1)$ symbols. Our designed encoder reduces the redundancy of the best-known encoder of Tenengolts (1984) by at least $2+\log_q(3)$ redundant symbols, or equivalently $2\log_2 q+3$ redundant bits.
Task Ambiguity in Humans and Language Models

Language models have recently achieved strong performance across a wide range of NLP benchmarks. However, unlike benchmarks, real world tasks are often poorly specified, and agents must deduce the user's intended behavior from a combination of context, instructions, and examples. We investigate how both humans and models behave in the face of such task ambiguity by proposing AmbiBench, a new benchmark of six ambiguously-specified classification tasks. We evaluate humans and models on AmbiBench by seeing how well they identify the intended task using 1) instructions with varying degrees of ambiguity, and 2) different numbers of labeled examples. We find that the combination of model scaling (to 175B parameters) and training with human feedback data enables models to approach or exceed the accuracy of human participants across tasks, but that either one alone is not sufficient. In addition, we show how to dramatically improve the accuracy of language models trained without large-scale human feedback training by finetuning on a small number of ambiguous in-context examples, providing a promising direction for teaching models to generalize well in the face of ambiguity.