Smart textiles for improved quality of life and cognitive assessment

Smart textiles can be used as innovative solutions to amuse, meaningfully engage, comfort, entertain, stimulate, and to overall improve the quality of life for people living in care homes with dementia or its precursor mild cognitive impairment (MCI). This concept paper presents a smart textile prototype to both entertain and monitor/assess the behavior of the relevant clients. The prototype includes physical computing components for music playing and simple interaction, but additionally games and data logging systems, to determine baselines of activity and interaction. Using microelectronics, light-emitting diodes (LEDs) and capacitive touch sensors woven into a fabric, the study demonstrates the kinds of augmentations possible over the normal manipulation of the traditional non-smart activity apron by incorporating light and sound effects as feedback when patients interact with different regions of the textile. A data logging system will record the patient’s behavioral patterns. This would include the location, frequency, and time of the patient’s activities within the different textile areas. The textile will be placed across the laps of the resident, which they then play with, permitting the development of a behavioral profile through the gamification of cognitive tests. This concept paper outlines the development of a prototype sensor system and highlights the challenges related to its use in a care home setting. The research implements a wide range of functionality through a novel architecture involving loosely coupling and concentrating artifacts on the top layer and technology on the bottom layer. Components in a loosely coupled system can be replaced with alternative implementations that provide the same services, and so this gives the solution the best flexibility. The literature shows that existing architectures that are strongly coupled result in difficulties modeling different individuals without incurring significant costs. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

R U :-) or :-( ? Character- vs. Word-Gram Feature Selection for Sentiment Classification of OSN Corpora

Binary sentiment classification, or sentiment analysis, is the task of computing the sentiment of a document, i.e. whether it contains broadly positive or negative opinions. The topic is well-studied, and the intuitive approach of using words as classification features is the basis of most techniques documented in the literature. The alternative character n-gram language model has been applied successfully to a range of NLP tasks, but its effectiveness at sentiment classification seems to be under-investigated, and results are mixed. We present an investigation of the application of the character n-gram model to text classification of corpora from online social networks, the first such documented study, where text is known to be rich in so-called unnatural language, also introducing a novel corpus of Facebook photo comments. Despite hoping that the flexibility of the character n-gram approach would be well-suited to unnatural language phenomenon, we find little improvement over the baseline algorithms employing the word n-gram language model

Data-driven decision-making in COVID-19 response : a survey

COVID-19 has spread all over the world, having an enormous effect on our daily life and work. In response to the epidemic, a lot of important decisions need to be taken to save communities and economies worldwide. Data clearly play a vital role in effective decision-making. Data-driven decision-making uses data-related evidence and insights to guide the decision-making process and verify the plan of action before it is committed. To better handle the epidemic, governments and policy-making institutes have investigated abundant data originating from COVID-19. These data include those related to medicine, knowledge, media, and so on. Based on these data, many prevention and control policies are made. In this survey article, we summarize the progress of data-driven decision-making in the response to COVID-19, including COVID-19 prevention and control, psychological counseling, financial aid, work resumption, and school reopening. We also propose some current challenges and open issues in data-driven decision-making, including data collection and quality, complex data analysis, and fairness in decision-making. This survey article sheds light on current policy-making driven by data, which also provides a feasible direction for further scientific research. © 2014 IEEE

'The first day of summer': Parsing temporal expressions with distributed semantics

Detecting and understanding temporal expressions are key tasks in natural language processing (NLP), and are important for event detection and information retrieval. In the existing approaches, temporal semantics are typically represented as discrete ranges or specific dates, and the task is restricted to text that conforms to this representation. We propose an alternate paradigm: that of distributed temporal semantics - where a probability density function models relative probabilities of the various interpretations. We extend SUTime, a state-of-the-art NLP system to incorporate our approach, and build definitions of new and existing temporal expressions

CenGCN : centralized convolutional networks with vertex imbalance for scale-free graphs

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines. © 1989-2012 IEEE

The application of adaptive systems in condition monitoring

This paper provides an overview of four large collaborative projects, which are currently being undertaken by the Centre for Adaptive Systems at the University of Sunderland. The projects utilise adaptive system technology to solve condition monitoring problems, and are funded from a variety of sources including UK Government agencies and the European Union (EU). Each of the projects is very strongly problem and industry driven and aims to produce real results for the benefit of companies

On average, a professional rugby union player is more likely than not to sustain a concussion after 25 matches

Objectives To investigate concussion injury rates, the likelihood of sustaining concussion relative to the number of rugby union matches and the risk of subsequent injury following concussion. Methods A four-season (2012/2013–2015/2016) prospective cohort study of injuries in professional level (club and international) rugby union. Incidence (injuries/1000 player-match-hours), severity (days lost per injury) and number of professional matches conferring a large risk of concussion were determined. The risk of injury following concussion was assessed using a survival model. Results Concussion incidence increased from 7.9 (95% CI 5.1 to 11.7) to 21.5 injuries/1000 player-match-hours (95% CI 16.4 to 27.6) over the four seasons for combined club and international rugby union. Concussion severity was unchanged over time (median: 9 days). Players were at a greater risk of sustaining a concussion than not after an exposure of 25 matches (95% CI 19 to 32). Injury risk (any injury) was 38% greater (HR 1.38; 95% CI 1.21 to 1.56) following concussion than after a non-concussive injury. Injuries to the head and neck (HR 1.34; 95% CI 1.06 to 1.70), upper limb (HR 1.59; 95% CI 1.19 to 2.12), pelvic region (HR 2.07; 95% CI 1.18 to 3.65) and the lower limb (HR 1.60; 95% CI 1.21 to 2.10) were more likely following concussion than after a non-concussive injury. Conclusion Concussion incidence increased, while severity remained unchanged, during the 4 years of this study. Playing more than 25 matches in the 2015/2016 season meant that sustaining concussion was more likely than not sustaining concussion. The 38% greater injury risk after concussive injury (compared with non-concussive injury) suggests return to play protocols warrant investigation

Exploring the relationship between testosterone and diabetes within the UK Biobank data

The UK Biobank (UKB) cohort data aims to improve the prevention, diagnosis, and treatment of a wide range of serious diseases, including diabetes. Presented is a population-based retrospective cohort study to explore the relationship between steroid hormones and the prevalence of diabetes. In particular, free testosterone is calculated from available serum biochemical markers in the UKB data, prevalent diabetes is calculated across a range of UKB data fields and ICD10 codes are generalized to their top-level classifications. It is then possible to explore relationships between testosterone levels, diabetes presence, and associated morbidities. © 2023 ACM

Themes in data mining, big data, and crime analytics

This article examines the impact of new AI-related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI-powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools. © 2021 Wiley Periodicals LLC