Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of body mass index on inpatient rehabilitation outcome after stroke in an East-Asian cohort: a prospective study.

INTRODUCTION We examined the association between admission body mass index (BMI) and discharge rehabilitation functional outcome using the Functional Independence Measure (FIM) in an East-Asian cohort of stroke patients during inpatient rehabilitation. METHODS A prospective observational cohort study of stroke patients admitted to a single inpatient rehabilitation unit was conducted. Using the World Health Organisation Asian standards, BMI was classified as underweight (< 18.5 kg/m2), normal (18.5-22.9 kg/m2) and overweight (≥ 23 kg/m2). The primary outcome measure was discharge FIM, and secondary outcomes included FIM gain, FIM efficiency and FIM effectiveness. RESULTS 247 stroke subjects were enrolled (mean age 59.48 [SD 12.35] years, 64.4% [159] male, 52.6% [130] ischaemic stroke). The distributions of underweight, normal and overweight BMI were 10.9% (27), 33.2% (82), and 55.9% (138) respectively on admission and 11.7% (29), 38.1% (94), and 50.2% (124) respectively on discharge. Significant small decreases in BMI from admission (median [IQR]: 23.58 [23.40, 24.70]) to discharge (median [IQR]: 23.12 [22.99, 24.21]) (p < 0.001) were found. Similarly, clinically significant FIM gains (mean FIM 26.71 [95% CI: 24.73, 28.69], p < 0.001) were noted after 36 days of median length of stay. No significant relationships were found between BMI and discharge FIM (p = 0.600), FIM gain (p = 0.254), FIM efficiency (p = 0.412) nor FIM effectiveness (p = 0.796). CONCLUSION Findings from this study unequivocally support the benefits of acute inpatient stroke rehabilitation. Patients in the obese BMI range tended to normalise during rehabilitation. BMI, whether underweight, normal, or overweight was not correlated with discharge FIM.
Traffic Light Detection for Red Light Violation System

The goal of Red Light Violation Detection System (RLVDS) is to track down vehicles that violated traffic regulations using surveillance cameras and image processing techniques. A complete automated red light runner detection algorithm that satisfies real time requirement and gives higher accuracy have been developed. The system detects simultaneously a traffic light sequence, stop line and detection region to detect moving vehicles and capture the vehicles beyond the stop line while the light is red and finally generates the red light running vehicle images with date and time information. In this paper, we propose a traffic light detection algorithm which is one of the processes of automated red light runner detection system. The proposed traffic light detection system includes four steps: color space conversion based on RGB color space, some regions are extracted as candidates of a traffic light and morphological operation is applied to eliminate disturbance in the environment that can interfere with the traffic light states. Then, the types of traffic signals are judged by calculating image moments and the results of experiment show that the system is stable and reliable.
Segmentation of historical maps without annotated data

This paper presents the method which we submitted to the competition of Historical Map Segmentation, in ICDAR’21. The goal is to segment document images of Paris maps from the beginning of the 20th century: delineate the content of the map and locate the graticule line intersections, which give indications on the geographic coordinates of the map. Our contribution is entirely led by a rule-based method. Thus, it does not require a training phase, nor annotated data. We extract the line segments, in the image, at various resolutions. Then, a grammatical description combines those elements in a logical way to filter the relevant map contents. This is an original approach, at the era of deep learning techniques, but that is very convenient for old non-annotated documents. We validated this work during MapSeg competition. It obtained the third position for map segmentation and the second position for graduate line detection, with a localization score of 89.2%.
Parameter estimation of unknown properties using transfer learning from virtual to existing buildings

This study proposes a transfer learning (TL)-based inverse modelling to identify unknown building properties. This study examines the transfer from virtual buildings to existing buildings, especially for identifying wall U-value, HVAC efficiency and lighting power density (LPD). For this purpose, synthetic data were generated from simulation results of sampled EnergyPlus models, and then we developed artificial neural network (ANN) models using this data. By adopting TL, the ANN models were transferred to the domain of existing buildings and evaluated on 61 existing buildings. As a result, the relative improvements in CVRMSE achieved by the transferred models against the models trained only with existing buildings’ data were 8.85%, 10.34% and 15.73% for nominal cooling COP, wall U-value and LPD, respectively. Moreover, it is expected that the use of TL enables the developed model to be reusable for another group of buildings with improved performance and reduced training time.
Racial relations and life satisfaction among South Africans: Results from the 2017 South African Social Attitudes Survey (SASAS)

The current study examined differences in race relations as a predictor of life satisfaction among South African adults. We analysed data from the South African Social Attitudes Survey 2017 (n = 3 135; female = 61%; black = 61% , coloured/mixed-race = 16%, Indian South Africans = 11%, and white South Africans = 11%; mean age = 43 years, SD = 17.22 years). Linear regression models indicated that positive racial interaction predicted life satisfaction for black Africans, coloured/mixed-race, and the total sample in general. For the black Africans, education, household income, and living standard predicted life satisfaction, while age and household income predicted life satisfaction for the coloured/mixed-race group. Living standard predicted life satisfaction for South African Indians, and age and education predicted life satisfaction for white South Africans. These results support the importance of positive relations and diversity as salient sources of life satisfaction in a society transforming from a history of racial segregation.
Unsettling transdisciplinary perspectives on sustainability issues: A critical discourse (metadata) cross-case analysis

This study is a critical discourse (metadata) cross-case analysis of six manuscripts on psychology of sustainable development that comprise a special section on transdisciplinary approaches to sustainable development. These six manuscripts span a broad range of topics: the psychology of sustainability, pedagogical knowledge, early literacy, masculinity and gender equality, resilience and workplace requirements, and emotional labour. The analysis yielded the following cross-cutting themes: Socio-emotional well-being, praxis for development, future generations, resourcing for the future, gender and sustainability, and personal resources for sustainability. Evidence suggests that sustainability is not adequately researched and infused in psychology articles. However, a newly emerging paradigm of psychology of sustainable development is well positioned to generate psychology-based research on sustainable development, with implications for new fields of studies.
Does the personal characteristics of the public offering committee members affect the issuance quality? An empirical study based on gender characteristics

ABSTRACT The Public Offering Review Committee plays a significant role in China’s capital market, in which the individual determines whether the company can be listed or not, then affect the issuance quality. Most of the previous research take the ‘social connect’ as committee members’ personal characteristics, This paper tests the relationship between the committee members’ gender characteristics and the quality of the issuance. We found that the participation of female committee members significantly lowers the pass rate of applicant companies, and the accounting performance and market performance of listed companies approved by the female committee members are better than those of the listed companies approved by an all-male Committee. In theory, these findings may contribute to the understanding of the decision-making mechanism of the committee members and the role of women in organizational economic decision-making, and may provide certain policy reference in the reform of the offering system and the elimination of gender discrimination.
Traditional belief systems and trade in vulture parts are leading to the eradication of vultures in Nigeria: an ethno-ornithological study of north-central Nigeria

The population of various species of vultures are declining rapidly across Africa, with noticeable declines in most areas including protected areas. This sudden decline of vultures in African countries, especially Nigeria, is quite alarming. One of the most common species of vultures, the Hooded Vulture, is now rarely encountered. This study was carried out to determine the role of traditional belief systems and trade in vulture parts on the rapid decline in vulture populations in Nigeria. The study was conducted in Plateau State in the North-Central region in Nigeria. Ethno-ornithological data were collected using semi-structured questionnaires; supported by interviews and field observations. A stratified-random sampling technique was employed for the data collection, which was conducted within 34 districts at 68 points where vultures were observed. The 204 respondents were all selected by chance encounter across the survey sites to determine their perceptions about vultures. The key finding of this research revealed massive trade in vulture parts for belief-base use, which might be responsible for the decline of vulture populations in the area. There is evidence of the consumption of vulture meat according to some of the respondents. Stiff competition with vultures for food by human and dogs was found to be another contributory factor towards vulture population decline within the study area. Many people believe that vultures are evil; hence, they are often persecuted on sight. There is an urgent need to create awareness about the importance of vultures in our environment. It is also vital to review laws on endangered species and implement them with the support of law enforcement agencies. Public awareness and sensitization of people using mass and social media campaigns should be conducted regularly on the importance of vultures and how to co-exist with them. Vulture restaurants or artificial feeding points should be created to aid population recovery.
How Smart Services Affect Relevant Job Characteristics in Production Environments

Smart services and their associated technologies are more and more integrated in organizations. By using job design and different job characteristics as foundation, we show how the implementation of smart services affects employees. Thus, smart services are able to augment employees’ jobs by giving them insight into processes and tasks, support them in their daily work life, or providing new flexibility and requiring new skills and knowledge. Possible adverse effects are a loss of control or social interactions.
Foosball Table Goalkeeper Automation Using Reinforcement Learning

Reinforcement learning (RL) is used to learn strategies to autonomously control the goalkeeper rod on a physical Foosball table in an efficient manner. The training is performed in simulation to be transferred to the physical Foosball table for execution. In order to make the training in simulation more robust, we applied domain randomization. By performing experiments we could prove that the agent learned reasonable movement patterns as well as efficient strategies which are applied by professional Foosball players to maximize the chance of defending a ball. The agent learned these strategies solely by the use of a sparse reward in a trial and error manner and without any hand-crafted or domain-specific knowledge.
Visualization Methods for Periodic Time Series Data

This work presents visualization methods for the analysis of periodic or seasonal time series data sets in the univariate and multivariate case. Humans often find large data sets difficult to parse when they are plotted over a large time period. This poses a problem not only when labelling data for Machine Learning, but also when trying to explain an algorithm’s prediction based on an input: In the case of periodic time series, in order to judge the results, a human would have to visually compare several data points at the same position in a period over many pattern repetitions. For this reason, we propose to split time series along period borders and remove time shifts and trend differences while enabling filtering by on-click highlighting of line segments to make outliers visible at first glance. We demonstrate our method using examples from three different domains. This method is optimized towards periodic data sets and is robust with respect to changes in trend and minor irregularities in periodicity. The method can trivially be implemented in most visualization frameworks, and is adaptable towards the specific use case and domain of new problem statements.
Obtaining More Expressive Corpus Distributions for Standardized Ancient Languages

This paper introduces a latent variable model for ancient languages that aims at quantifying the influence that early authoritative works exert on their literary successors in terms of lexis. The model jointly estimates the amount of word reuse, based on uni-and bigrams of words, and the date of composition of each text. We apply the model to a corpus of pre-Renaissance Latin texts composed between the 3rd c. BCE and the 14th c. CE. Our evaluation focusses on the structures of word reuse detected by the model, its temporal predictions and the quality of the inferred diachronic distributions of words, which last aspect is assessed using a newly designed task from the field of computational etymology.
OCR-D & OCR4all: Two Complementary Approaches for Improved OCR of Historical Sources

In order to leverage recent advances within the field of computer science for tasks ranging from full text search to various forms of quantitative analysis, an important desideratum in many areas of historical research is the availability of sources in the form of machine-actionable text. Here we present two complementary and free-to-use approaches, OCR-D and OCR4all. They deal with the conversion of scanned historical documents into such machine-actionable text and allow to tackle a variety of different use cases with diverse user requirements regarding both quantity and quality.1
Constraint-Based Causal Structure Learning in Multi-GPU Environments

Learning causal structures from real-world high-dimensional data remains challenging due to algorithmic complexity and resulting long runtimes. We propose two multi-GPU approaches to discover causal relationships in multivariate normal distributed data, often assumed in genetic datasets, to address the long runtimes. In fact, on a high-dimensional dataset from The Cancer Genome Atlas (TCGA), we achieved a reduction in runtimes from over 100 hours on a multi-core CPU system to around 5 minutes on a system with multiple Graphics Processing Units (GPUs). Our two proposed multi-GPU approaches differ in their memory management. One version relies on the concept of Unified Memory (UM), while the other uses explicit memory management. Experiments on synthetic data show that explicit memory management is better suited for causal structure learning. On the one hand, it is faster than the version relying on UM by factors of up to 75 . On the other hand, the explicit memory-managed version is less prone to the system’s GPU interconnect topology effects.
More Data and New Tools. Advances in Parsing the Index Thomisticus Treebank

This paper investigates the recent advances in parsing the Index Thomisticus Treebank, which encompasses Medieval Latin texts by Thomas Aquinas. The research focuses on two types of variables. On the one hand, it examines the impact that a larger dataset has on the results of parsing; on the other hand, performances of new parsers are analysed with respect to less recent tools. Term of comparison to determine the effective parsing advances are the results in parsing the Index Thomisticus Treebank described in a previous work. First, the best performing parser among those concerned in that study is tested on a larger dataset than the one originally used. Then, some parser combinations that were developed in the same study are evaluated as well, assessing that more training data result in more accurate performances. Finally, to examine the impact that newly available tools have on parsing results, we train, test, and evaluate two neural parsers chosen among those best performing in the CoNLL 2018 Shared Task. Our experiments reach the highest accuracy rates achieved so far in automatic syntactic parsing of the Index Thomisticus Treebank and of Latin overall.
Detecting Narrativity Across Long Time Scales

Storytelling is a universal human practice that serves as a key site of education, collective memory, fostering social belief systems, and furthering human creativity. It can occur in different discursive domains for different social purposes with differing degrees of intensity. In this project, we develop computational methods for measuring the degree of narrativity in over 335,000 text passages distributed across two-to three-hundred years of history and four separate discursive domains (fiction, non-fiction, science, and poetry). We show how these domains are strongly differentiated according to their degree of narrative communication and, second, how truth-based discourse has declined considerably in its utilization of narrative communication. These findings suggest that there has been a long-term historical differentiation between the practices of knowing and telling, which raises important questions with respect to the social acceptance of both science and the arts.
Evaluating Heuristics for Audio-Visual Translation

Dubbing, i.e., the lip-synchronous translation and revoicing of audio-visual media into a target language from a different source language, is essential for the full-fledged reception of foreign audio-visual media, be it movies, instructional videos or short social media clips. In this paper, we objectify influences on the ‘dubbability’ of translations, i.e., how well a translation would be synchronously revoiceable to the lips on screen. We explore the value of traditional heuristics used in evaluating the qualitative aspects , in particular matching bilabial consonants and the jaw opening while producing vowels, and control for quantity , i.e., that translations are similar to the source in length. We perform an ablation study using an adversarial neural classifier which is trained to differentiate “true" dubbing translations from machine translations. While we are able to confirm the value of matching lip closure in dubbing, we find that the opening angle of the jaw as determined by the realized vowel may be less relevant than frequently considered in audio-visual translation.
Emojis in Lexicon-Based Sentiment Analysis: Creating Emoji Sentiment Lexicons from Unlabeled Corpora

Emojis represent an essential means of expressing sentiments such as opinions and attitudes in computermediated communication, especially in chats and social media. Knowledge about the sentiments expressed with emojis is therefore highly relevant for sentiment analysis. However, most previous approaches use labor-intensive manual annotation to determine the sentiments associated with emojis. Thus, for many emojis, especially platform-specific emojis, studies on expressed sentiments do not exist. Hence, the majority of emojis are not considered in sentiment analyses. In this paper, a method to effectively and efficiently determine the sentiments expressed by emojis without the need for manual annotation is presented. Sentiments expressed by emojis are statistically derived via occurrences in sentiment-bearing texts. Two emoji sentiment lexicons for UTF-8 emojis and platform-specific emojis (Twitch emotes) have been constructed using unlabeled corpora. A comparison with gold standard emoji sentiment lexicons has shown that the developed method achieves qualitatively similar results as a manual annotation.
The Challenge of Vernacular and Classical Chinese Cross-Register Authorship Attribution

Ming-Qing fiction is widely regarded as the pinnacle of classical Chinese literature, but over three-quarters of vernacular fictional works were anonymously or pseudonymously composed, frustrating literary-historical research. To begin to address the problem, we propose a cross-register authorship attribution task: recover the authorship of a vernacular Chinese text given classical Chinese writing samples of known authorship. A corpus of eight authors known to have written in both registers was assembled to serve as a testbed. We describe the performance of models using different sets of function character/word frequencies as input features. This standard approach to authorship attribution performs well in the same-register setting but poorly in the cross-register setting. We discuss the degree of vernacularization and the amount of dialog in texts as key factors contributing to the low cross-register accuracy.
Mapping AI Issues in Media Through NLP Methods

Using a variety of NLP methods on a corpus of press articles, we show that there are two dominant regimes of criticism of artificial intelligence that coexist within the media sphere. Combining text classification algorithms to detect critical articles and a topological analysis of the terms extracted from the corpus, we reveal two semantic spaces, involving different technological and human entities, but also distinct temporality and issues. On the one hand, the algorithms that shape our daily computing environments are associated with a critical discourse on bias, discrimination, surveillance, censorship and amplification phenomena in the spread of inappropriate content. On the other hand, robots and AI, which refer to autonomous and embodied technical entities, are associated with a prophetic discourse alerting us to our ability to control these agents that simulate or exceed our physical and cognitive capacities and threaten our physical security or our economic model.
The Role of the Global Value Chain in Improving Trade and the Sustainable Competitive Advantage: Evidence From China’s Manufacturing Industry

In the domain of globalization, the global value chain (GVC) is formed across diverse economies. Owing to the expansion of economic globalization and the resultant global trade liberalization, the geographical isolation of production and consumption has resulted in the transfer of polluting emissions from advanced economies to developing states. Developing economies, falling in the middle and low end of the GVC, have to strive hard to expand their production in the GVC, while simultaneously addressing the consequent environmental damage and attending to the core environmental concerns of sustainable growth, energy preservation and drastic cuts in CO2. This paper addresses how manufacturing subsidiaries can improve their low-carbon innovation ability and help to achieve a sustainable competitive advantage through the embedded GVC. Data were gathered employing interview and survey methods, 31 manufacturing companies and 56 enterprise groups across 16 Chinese provinces were selected as the case study. A partial least square structural equation modelling was adopted to analyse the responses. The results indicate that the embedded GVC significantly enhances low-carbon innovation capability and promotes a sustainable competitive advantage. This study proposes that companies should continuously identify high-quality resources from the GVC and discover ways of integrating internal and external carbon innovation resources to form innovation capabilities.