Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges for Diagnostic Clarity for Post-stroke Cognitive Impairment and Behavioural Issues in Middle-Income Countries: Case Studies From Malaysia

Following stroke, individuals require ongoing screening, diagnosis and monitoring for cognitive impairment. Services and policies around these vary widely between settings, and reports from many countries highlight persistent under-diagnosis of cognitive impairment in the months and years after stroke. Missed and delayed diagnosis of post-stroke cognitive impairment, including dementia, are important factors in shaping the experiences of people so affected and their family members, especially in low- and middle-income countries. Drawing upon ethnographic research conducted in Malaysia, this article draws upon three case studies to examine the continued health-seeking behaviour after the appearance of salient cognitive and behavioural symptoms that occurred after stroke. Findings highlight the challenges in getting formal diagnostic clarity for cognitive and behavioural symptoms in a rural setting within a middle-income country. No study participants sought help for memory or cognitive problems, partly due to limited lay awareness of cognitive impairment but more significantly due to health service factors. Despite their elevated risk for dementia, participants were not monitored for cognitive impairment during any follow-up care in various health facilities. Furthermore, caregivers' attempts to seek help when behavioural issues became untenable were met with multiple health system barriers. The journey was complicated by the meanings attached to the reactions towards cognitive symptoms at the community level. We suggest that strategies seek to increase the awareness of post-stroke cognitive and behavioural symptoms, and incorporate clear treatment pathways into the long-term care plans of community-dwelling stroke survivors.
Thinking about the Past? Examining the Effects of Temporal Comparisons and Reminiscing on Cognitive Processing of Music and Radio Advertisements

ABSTRACT Exposure to music has been found to evoke temporal comparisons and reminiscing processes. However, little yet is known about how temporal comparisons and reminiscing influence cognitive processing of music and subsequent radio advertisements. The current study expands this research by examining how young adults cognitively process familiar and unfamiliar songs and subsequently placed radio advertisements. Results indicated that familiar songs generated greater self-reported temporal comparisons and reminiscing compared to unfamiliar songs. Moreover, temporal comparisons and reminiscing processes reduced cognitive resources allocated to encoding familiar songs and subsequently placed radio advertisements. The theoretical and practical implications of the results are discussed.
Peer assessment in MOOCs: Systematic literature review

ABSTRACT We report on a systematic review of the landscape of peer assessment in massive open online courses (MOOCs) with papers from 2014 to 2020 in 20 leading education technology publication venues across four databases containing education technology–related papers, addressing three research issues: the evolution of peer assessment in MOOCs during the period 2014 to 2020, the methods used in MOOCs to assess peers, and the challenges of and future directions in MOOC peer assessment. We provide summary statistics and a review of methods across the corpus and highlight three directions for improving the use of peer assessment in MOOCs: the need for focusing on scaling learning through peer evaluations, the need for scaling and optimizing team submissions in team peer assessments, and the need for embedding a social process for peer assessment.
Improving Item Searching On Trading Platform Based On Reinforcement Learning Approach

Item searching on trading platforms is a real challenge nowadays. The number of product offers on the trading platforms is significantly more than real goods. It increases the searching space for a customer and complicates the procedure of a product choosing. Often customers don't know for sure which particular sample of the product they need. They compare specific features among similar products, chose the item, and then compare pricing and shipping. For simplifying the buying process in the e-commerce market we propose to combine similar product offers from different sellers into groups and provide customers with groups of similar items. We propose an approach, which allows grouping product offers based on the pre-trained core of tags and reinforcement learning technique. The core of tags is built for each group of similar items by processing text descriptions of similar items. The suggested model builds a search query by combining words from the core of tags in order to receive the relevant list of similar items and propose a reference item of the group. As experiments have shown similar products from the e-commerce platform can be easily found if the core of tags for a group is known. The successful results significantly depend on the e-commerce platform, where the core of tags was obtained. It can significantly reduce the search space and alleviate the process of choosing a commodity.
Investigation of the Deep Learning Approaches to Classify Emotions in Texts

The article is devoted to the study of the most popular uses of deep learning - natural language processing, in particular, the extraction of emotions from the text. We compared several types of embeddings and different neural network architectures in solving the problem of classifying emotions from the text. For this, various corpora of text data were collected; they contain markup for the emotional components. Various approaches were analyzed such as Word Embeddings, Bidirectional LSTM, Bidirectional Gated Recurrent Unit, Convolution Neural Network. For comparative analysis, the models were trained and tested on the collected datasets. As a result, we were found approaches for neural networks creation that provide better results on the test samples.
Identification of the Author's Idea Based on the Modified TextRank Method

Taking into account the significant rate of new information volume formation, including scientific, semantic analysis of the text continues to be a relevant area of research. The results of this paper on extraction the author's idea can be applied to identify features of intellectual plagiarism to promote academic integrity. To identify the author's idea as the main content of the text, the authors use semantic and graph-based methods. The paper proposes a method for identifying the author's idea based on the modified TextRank algorithm. This method takes into account the pronominal anaphoric connections between sentences, allows to form a more complete description of the semantic relationships between sentences in the text. An experiment was carried out on scientific texts in the Ukrainian language, which confirmed an increase in the number of semantic links between sentences in comparison with the simple TextRank algorithm, which affects the weight of sentences and their order in the abstract.
Subsurface Topographic Modeling Using Geospatial and Data Driven Algorithm

Infrastructures play an important role in urbanization and economic activities but are vulnerable. Due to unavailability of accurate subsurface infrastructure maps, ensuring the sustainability and resilience often are poorly recognized. In the current paper a 3D topographical predictive model using distributed geospatial data incorporated with evolutionary gene expression programming (GEP) was developed and applied on a concrete-face rockfill dam (CFRD) in Guilan province- northern to generate spatial variation of the subsurface bedrock topography. The compared proficiency of the GEP model with geostatistical ordinary kriging (OK) using different analytical indexes showed 82.53% accuracy performance and 9.61% improvement in precisely labeled data. The achievements imply that the retrieved GEP model efficiently can provide accurate enough prediction and consequently meliorate the visualization insights linking the natural and engineering concerns. Accordingly, the generated subsurface bedrock model dedicates great information on stability of structures and hydrogeological properties, thus adopting appropriate foundations.
Natural and Political Determinants of Ecological Vulnerability in the Qinghai-Tibet Plateau: A Case Study of Shannan, China

: Changing land-use patterns in the Qinghai–Tibet Plateau (QTP) due to natural factors and human interference have led to higher ecological vulnerability and even more underlying issues related to time and space in this alpine area. Ecological vulnerability assessment provides not only a solution to surface-feature-related problems but also insight into sustainable eco-environmental planning and resource management as a response to potential climate changes if driving factors are known. In this study, the ecological vulnerability index (EVI) of Shannan City in the core area of the QTP was assessed using a selected set of ecological, social, and economic indicators and spatial principal component analysis (SPCA) to calculate their weights. The data included Landsat images and socio-economic data from 1990 to 2015, at ﬁve-year intervals. The results showed that the total EVI remains at a medium vulnerability level, with minor ﬂuctuations over 25 years (peaks in 2000, when there was a sudden increase in slight vulnerability, which switched to extreme vulnerability), and gradually increases from east to west. In addition, spatial analysis showed a distinct positive correlation between the EVI and land-use degree, livestock husbandry output, desertiﬁcation area, and grassland area. The artiﬁcial afforestation program (AAP) has a positive effect by preventing the environment from becoming more vulnerable. The results provide practical information and suggestions for planners to take measures to improve the land-use degree in urban and pastoral areas in the QTP based on spatial-temporal heterogeneity patterns of the EVI of Shannan City. The results showed that livestock husbandry output, land-use degree, and desertification area have been prominent since 1995. The grassland area and land-use degree contribute significantly to each of the principal components every year as well. Therefore, based on these results, land-use degree, livestock husbandry output, desertification area, and grassland area are the main factors influencing the EVI.
A Comparative Study on TF-IDF Feature Weighting Method and Its Analysis Using Unstructured Dataset

Text Classification is the process of categorizing text into the relevant categories and its algorithms are at the core of many Natural Language Processing (NLP). Term Frequency-Inverse Document Frequency (TF-IDF) and NLP are the most highly used information retrieval methods in text classification. We have investigated and analyzed the feature weighting method for text classification on unstructured data. The proposed model considered two features N-Grams and TF-IDF on the IMDB movie reviews and Amazon Alexa reviews dataset for sentiment analysis. Then we have used the state-of-the-art classifier to validate the method i.e., Support Vector Machine (SVM), Logistic Regression, Multinomial Naive Bayes (Multinomial NB), Random Forest, Decision Tree, and k-nearest neighbors (KNN). From those two feature extractions, a significant increase in feature extraction with TF-IDF features rather than based on N-Gram. TF-IDF got the maximum accuracy (93.81%), precision (94.20%), recall (93.81%), and F1-score (91.99%) value in Random Forest classifier.
Punctuation Restoration for Ukrainian Broadcast Speech Recognition System based on Bidirectional Recurrent Neural Network and Word Embeddings

The output of a speech-to-text conversion system is a sequence of words accomplished, optionally, with the speaker turn information. Lack of punctuation complicates reading for humans and degrades the performance of many downstream machine processing tasks. We investigated various punctuation restoration models based on Deep Learning for Ukrainian. The chosen tool uses Bidirectional Recurrent Neural Network to generate probabilities for hypothetically placed punctuation marks. The selected text input is based on publicly available text data. Experimentally was chosen an efficient way of text processing applied to the dataset. Significant improvement for punctuation generation accuracy was achieved with word embedding application. The broadcast transcribing system with supported punctuation restoration is presented.
Research of Antispam Bot Algorithms for Social Networks

There are many social media and messengers in use today, because of the situation with the corona virus pandemic the social media have become an integral part of our daily lives, including work activities. However, there is a lot of unnecessary information that comes to users in large quantities, so the problem of dealing with spam messages on social networks and messengers is now very relevant. By spam we mean any messages that a particular user (person, company, etc.) considers unnecessary in a particular text stream. The project is dedicated to solving the scientific problem of detecting spam messages in the text context of any social network or messenger using anti-spam bot that is based on various spam detection algorithms. Four algorithms were implemented and investigated: an algorithm using naive Bayesian classifier, support vector method, multilayer perceptron neural network and convolutional neural network. The main idea is to develop a complex spam detection algorithm for anti-spam bot, which is fast and easy to implement in a messenger (social network). We propose to use the application of the obtained solutions for IT companies. The developed complex algorithm can be used not only to remove spam, but also, for example, to monitor chats for messages that are important to a particular user.
Exploring Spatial Patterns of Virginia Tornadoes Using Kernel Density and Space-Time Cube Analysis (1960-2019)

This study evaluates the spatial-temporal patterns in Virginia tornadoes using the National Weather Service Storm Prediction Center’s Severe Weather GIS (SVRGIS) database. In addition to descriptive statistics, the analysis employs Kernel Density Estimation for spatial pattern analysis and space-time cubes to visualize the spatiotemporal frequency of tornadoes and potential trends. Most of the 726 tornadoes between 1960–2019 occurred in Eastern Virginia, along the Piedmont and Coastal Plain. Consistent with other literature, both the number of tornadoes and the tornado days have increased in Virginia. While 80% of the tornadoes occurred during the warm season, tornadoes did occur during each month including two deadly tornadoes in January and February. Over the 60-year period, a total of 28 people were killed in the Commonwealth. Most tornado activity took place in the afternoon and early evening hours drawing attention to the temporal variability of risk and vulnerability. Spatial analysis results identify significant, non-random clusters of tornado activity and increasing temporal frequency. While this study improves weather-related literacy and addresses a need in the Commonwealth, more research is necessary to further evaluate the synoptic and mesoscale mechanisms of Virginia tornadoes.
Cooperation and relationship in the triple helix model of innovation

During the last decades the cooperation between universities, companies and government has helped generate new knowledge, transfer technology and create incubators of small and medium enterprises (SME), and spin-offs. The triple helix model, which is the focus of this research, represents a paradigm for the university within this three-way relationship, with the development of the entrepreneurial university concept. In fact, business-minded universities have grown significantly in socio-economic terms, with the development and registering of patents, as well as in activities related to spin-offs. Spin-off companies created from universities help transfer knowledge and scientific research to the business sector, and facilitate communication between universities, the market and society. In recent years, technological change and cooperation between business and universities have become increasingly important. Competition in business, and the need for companies to collaborate more closely, is reshaping universities in terms of the research they undertake and their relationship with the state.
Model-based Engineering of Feedforward Usability Function for GUI Widgets

Feedback and feedforward are two fundamental mechanisms that support users’ activities while interacting with computing devices. While feedback can be easily solved by providing information to the users following the triggering of an action, feedforward is much more complex as it must provide information before an action is performed. For interactive applications where making a mistake has more impact than just reduced user comfort, correct feedforward is an essential step toward correctly informed, and thus safe, usage. Our approach, Fortunettes, is a generic mechanism providing a systematic way of designing feedforward addressing both action and presentation problems. Including a feedforward mechanism significantly increases the complexity of the interactive application hardening developers’ tasks to detect and correct defects. We build upon an existing formal notation based on Petri Nets for describing the behavior of interactive applications and present an approach that allows for adding correct and consistent feedforward.
Disambiguierung in Suchtrefferlisten aus großen Textkorpora: Anwendungsfelder und Perspektiven

Digitale Textkorpora bieten in vielen geisteswissenschaftlichen Arbeitsbereichen neuartige Möglichkeiten, Forschungsfragen an authentischen Sprachverwendungen zu untersuchen. Infrastrukturprojekte wie CLARIN bieten flexible Werkzeuge zur Datengewinnung und zur quantitativen Analyse an, mit denen große, linguistisch strukturierte Textkorpora ausgewertet werden können. Allerdings müssen die automatisch gewonnenen Daten oft noch manuell nachbearbeitet werden. Dies ist insbesondere der Fall, wenn nicht Wortformen, sondern sprachliche Zeichen – also Verbindungen von Form und Inhalt – quantitativ ausgewertet werden sollen, denn homonyme und polyseme Textwörter sind in aktuell verfügbaren Korpora nicht disambiguiert. Wenn die Unterscheidung von Homonymen und polysemen Lesarten für eine Forschungsfrage relevant ist, müssen die Daten bislang manuell disambiguiert werden. Der damit verbundene Aufwand ist oft erheblich; unter den zeitlichen Restriktionen eines Forschungsprojekts, einer Dissertation, einer studentischen Abschlussarbeit etc. können so bestimmte Fragestellungen gar nicht bearbeitet werden.
Die DARIAH-DE Architektur zur forschungsorientierten Föderation von Kollektionen in den Digital Humanities

Für die kulturund geisteswissenschaftliche Forschung relevante Ressourcen finden sich zu großen Teilen in den Sammlungen von Museen, Archiven, Bibliotheken, Universitäten und außeruniversitären Forschungseinrichtungen. Mit der Erweiterung des Anwendungsbereiches der Digital Humanities von den Sprachwissenschaften hin zu einer ganzheitlichen Sicht auf die Kulturund Geisteswissenschaften seit den 1990ern [1] wurden vermehrt Methoden, Anwendungen und Standards für die Digitalisierung, Analyse und Beschreibung von Ressourcen geschaffen. Die Menge der heute durch öffentliche Netzwerke verfügbaren und für die kulturund geisteswissenschaftliche Forschung relevanten Kollektionen steigt nicht zuletzt aufgrund der Verwendung von Zugriffsund Beschreibungsstandards stetig an und bietet Forscherinnen und Forschern einen potenziellen Zugang zu einer Vielzahl heterogener Ressourcen. In diesem Vortrag stellen wir eine neuartige Föderationsarchitektur vor, die auf eine Erfassung und Fall-basierte Zusammenführung von Forschungsdaten nach den individuellen Bedürfnissen von Forschungsprojekten abzielt. Digitale Sammlungen werden zentral verzeichnet, zur Vermeidung von Informationsverlusten jedoch nicht harmonisiert, sondern in Form von Beziehungen auf Schemaebene assoziiert, wodurch die Verwendung einer dynamisch föderierten Datenbasis in breiten und interdisziplinären, wie auch in fachspezifischen Anwendungskontexten ermöglicht werden kann [2]. Ein übergeordnetes Ziel besteht insbesondere in der Nutzbarmachung des durch Experten hinterlegten Wissens zu Kollektionen und Daten sowie deren Beziehungen für einen weiten Anwenderkreis.
Spatial Optimization of Mega-City Fire Stations Based on Multi-Source Geospatial Data: A Case Study in Beijing

The spatial distribution of fire stations is an important component of both urban development and urban safety. For expanding mega-cities, land-use and building function are subject to frequent changes, hence a complete picture of risk profiles is likely to be lacking. Challenges for prevention can be overwhelming for city managers and emergency responders. In this context, we use points of interest (POI) data and multi-time traffic situation (MTS) data to investigate the actual coverage of fire stations in central Beijing under different traffic situations. A method for identifying fire risks of mega cities and optimizing the spatial distribution of fire stations was proposed. First, fire risks associated with distinctive building and land-use functions and their spatial distribution were evaluated using POI data and kernel density analysis. Furthermore, based on the MTS data, a multi-scenario road network was constructed. The “location-allocation” (L-A) model and network analysis were used to map the spatial coverage of the fire stations in the study area, optimized by combining different targets (e.g., coverage of high fire risk areas, important fire risk types). Results show that the top 10% of Beijing’s fire risk areas are concentrated in “Sanlitun-Guomao”, “Ditan-Nanluogu-Wangfujing”, and “Shuangjing-Panjiayuan”, as well as at Beijing Railway Station. Under a quarterly average traffic situation, existing fire stations within the study area exhibit good overall POI coverage (96.51%) within a five-minute response time. However, the coverage in the northwest and southwest, etc. (e.g., Shijicheng and Minzhuang) remain insufficient. On weekdays and weekends, the coverage of fire stations in the morning and evening rush hours fluctuates. Considering the factors of high fire risk areas, major fire risk types, etc. the results of optimization show that 15 additional fire stations are needed to provide sufficient coverage. The methods and results of this research have positive significance for future urban safety planning of mega-cities.