Reviewerly/exHarmony huggingface dataset:
Contextualised dictionary literacy, information literacy and information behaviour in the e-environment

Purpose Needs for information literacy, disparities in society, bridging digital divides, richness of information sources in electronic (e-)environments and the value of dictionaries have often been propagated. To improve information sources and information literacy training, information behaviour must be understood (i.e. all information activities). This paper conceptualises new opportunities for information sources (e.g. electronic dictionaries) to all society sectors, dictionary literacy and research lenses such as lexicography to supplement information literacy and behaviour research. Design/methodology/approach A scoping review of information literacy and behaviour, lexicography and dictionary literature grounds the conceptualisation of dictionary literacy, its alignment with information literacy, information activities and information behaviour and lexicography as additional research lens. Findings Research lenses must acknowledge dictionary use in e-environments, information activities and skills, meanings of information and dictionary literacy, the value of e-dictionaries, alignment with information behaviour research that guides the development of information sources and interdisciplinary research from, e.g. lexicography – thus contextualisation. Research limitations/implications Research implications – information behaviour and information literacy research can be enriched by lexicography as research lens. Further conceptualisation could align information behaviour, information literacy and dictionary literacy. Practical implications Dictionary training, aligned with information literacy training, can be informed by this paper. Social implications The value of dictionary literacy for all sectors of societies can be improved. Originality/value Large bodies of literature on information behaviour and lexicography individually do not cover combined insights from both.
Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

A bstract Effective String Theory (EST) represents a powerful non-perturbative approach to describe confinement in Yang-Mills theory that models the confining flux tube as a thin vibrating string. EST calculations are usually performed using the zeta-function regularization: however there are situations (for instance the study of the shape of the flux tube or of the higher order corrections beyond the Nambu-Goto EST) which involve observables that are too complex to be addressed in this way. In this paper we propose a numerical approach based on recent advances in machine learning methods to circumvent this problem. Using as a laboratory the Nambu-Goto string, we show that by using a new class of deep generative models called Continuous Normalizing Flows it is possible to obtain reliable numerical estimates of EST predictions.
More Than Meets the Eye: Misconduct and Decoupling Against Blockchain for Supply Chain Transparency

The fashion industry has consistently ranked high in terms of its association with modern slavery. While scandals like the Rana Plaza incident have focused the world's attention on Asia, in Brazil, over 35,000 people have been rescued from conditions analogous to slavery in the past 15 years. Outsourcing and the underlying social structures that blur the implementation of outsourcing are central to this problem. Fashion supply chains are highly fragmented and labour intensive, exhibiting power asymmetry and informality. Although supply chain research has focused on how focal firms can control or improve supplier practices, this study examines focal firm misconduct and decoupling. Our research presents an intervention aimed at developing a blockchain solution to create a census of fashion working conditions in Brazil. The project included two non-governmental organisations, two fashion brands and their suppliers, the fashion retail association, a blockchain start-up and the research team (18 organisations). We contribute to both theory and practice, revealing that the blockchain's potential to ease transaction costs is outweighed by governance costs related to third-party supervision. We show that focal firms justify unfair purchasing practices via victimisation and diverting attention from themselves to consumers (‘unwilling to pay’) and suppliers (‘not behaving as expected’), unveiling how these drivers of organisational misconduct lead to supplier decoupling (means–end) and focal firm decoupling (policy–practice). Such underlying social structures sustain inaction or, at best, advance focal firms’ visibility of their supply chains while offering no true transparency to the broader society.
Evaluating completion rates of COVID-19 contact tracing surveys in New York City

Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs.To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021.We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used.The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%.These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities.
Real-time anomaly detection of short-term traffic disruptions in urban areas through adaptive isolation forest

The escalating congestion impacts of short-term traffic disruptions, such as double parking or short-duration work zones, are gaining increased attention. This study introduces an enhanced isolation forest-based unsupervised anomaly detection algorithm to detect short-term traffic disruptions in urban areas. To enable the detection in real-time, a sliding window approach is introduced to allow the streaming data to be read and processed according to the window size. An adaptive threshold that automatically adjusts itself to overcome the limitations of the miss rate on local anomalies is shown to further enhance the model predictions. The proposed algorithm is empirically validated on four study sites in Manhattan, New York City. The experimental results demonstrate that the proposed unsupervised algorithm can effectively detect different types of traffic anomalies including accidents, work zones/road closures, traffic jams, double parking events and police activities. On all sites, the average detection rate is 81.1% for traffic jams and 89.6% for police activities, respectively. For three out of the four sites, the detection rate ranges from 71.4% to 100% for accidents, work zones and double parking. An optimizer using high-pass filter is also presented to further improve off-line detection. The primary advantages of this proposed computationally-efficient method are that its only required data input is travel time information and that it does not need labeled data for training, which make it highly deployable for real-time operational applications and can be easily adopted by other cities.
A control scheme for interval type‐2 T‐S fuzzy systems at constrained bit rates: A coding and decoding scheme

Summary This study focuses on a class of interval type‐2 T‐S fuzzy systems under bit rate constraint(BRC), which can adapt to complex systems more flexibly than the traditional interval type‐1 T‐S fuzzy model. In order to guarantee the security of data transmission, and considering that the channel is affected by the bit rate during transmission, this study introduces a codec scheme in the interval type‐2 T‐S fuzzy system and applies it to the channel from the sensor to the controller. With the support of the codec scheme, this study explores the sufficient conditions to satisfy the data recoverability, and based on the satisfaction of these conditions, the system is proposed to satisfy the sufficient conditions for input‐to‐state stability (ISS) and the controller gain. Through the in‐depth analysis in this paper, this study proposes a control scheme that can fit complex systems more accurately and also confirms the importance of considering bit rate constraints in communication systems. Finally, the paper is validated by simulation with two specific examples, which fully demonstrate the superiority and effectiveness of the proposed control scheme.
ASD-Net: a novel U-Net based asymmetric spatial-channel convolution network for precise kidney and kidney tumor image segmentation

Early intervention in tumors can greatly improve human survival rates. With the development of deep learning technology, automatic image segmentation has taken a prominent role in the field of medical image analysis. Manually segmenting kidneys on CT images is a tedious task, and due to the diversity of these images and varying technical skills of professionals, segmentation results can be inconsistent. To address this problem, a novel ASD-Net network is proposed in this paper for kidney and kidney tumor segmentation tasks. First, the proposed network employs newly designed Adaptive Spatial-channel Convolution Optimization (ASCO) blocks to capture anisotropic information in the images. Then, other newly designed blocks, i.e., Dense Dilated Enhancement Convolution (DDEC) blocks, are utilized to enhance feature propagation and reuse it across the network, thereby improving its segmentation accuracy. To allow the network to segment complex and small kidney tumors more effectively, the Atrous Spatial Pyramid Pooling (ASPP) module is incorporated in its middle layer. With its generalized pyramid feature, this module enables the network to better capture and understand context information at various scales within the images. In addition to this, the concurrent spatial and channel squeeze & excitation (scSE) attention mechanism is adopted to better comprehend and manage context information in the images. Additional encoding layers are also added to the base (U-Net) and connected to the original encoding layer through skip connections. The resultant enhanced U-Net structure allows for better extraction and merging of high-level and low-level features, further boosting the network's ability to restore segmentation details. In addition, the combined Binary Cross Entropy (BCE)-Dice loss is utilized as the network's loss function. Experiments, conducted on the KiTS19 dataset, demonstrate that the proposed ASD-Net network outperforms the existing segmentation networks according to all evaluation metrics used, except for recall in the case of kidney tumor segmentation, where it takes the second place after Attention-UNet.
NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour

Abstract Background Recent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status. Results &amp; discussion NiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting. Conclusion The NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health .
Artificial intelligence in voice assistants: User benefits explored

Abstract This study demonstrates that consumers' perceptions of voice assistant device (VAD) elements promote psychological ownership and interface flow experience, resulting in satisfaction with, and loyalty toward VADs. To that end, this study collected VAD users in the United States and conducted structural equation modeling to empirically examine the relationships among the variables. The empirical findings indicated that perceived artificiality and intelligence had significant influences on psychological ownership and flow experience. In addition, the empirical results demonstrated that psychological ownership resulted in flow experience with VADs, significantly affecting customer satisfaction and loyalty toward VADs. Finally, customer satisfaction had a significant impact on loyalty. Based on the empirical findings, this study provided meaningful implications for marketing scholars and practitioners.
Into the ether or the state? Legibility theory and the cryptocurrency markets

Abstract There is substantial cross-national variation in the level of regulatory clarity surrounding cryptocurrencies. What explains these differences? And, more broadly, what drives the divergent historical development of market regulation in different jurisdictions? To answer these questions, we present a new conceptual framework centered on the concept of market legibility. This term, inspired by the sociological literature, refers to the extent to which markets are made legible to the state through standardization. We contend that state supply of, and market demand for, legibility drives the primary political-economic dynamics of market regulation. Specifically, these factors combine to produce ideal type states of legibility that correspond to both distinct stages of market development and the relative level of regulatory clarity in any one jurisdiction. This framework is utilized to conduct a comparative historical analysis of cryptocurrency regulation in the EU, US, UK, and Japan. By performing these tasks, this article corrects the common assumption that states are constantly striving to impose their authority on unwilling markets. It demonstrates instead that state and private actor preferences to make markets legible vary, conditioning, in turn, the political economy of regulatory governance.
Women’s cellphone access and ownership in rural Uganda: implications for self-care interventions

Abstract Background The World Health Organization (WHO) call for cervical cancer elimination includes increasing global cervical screening coverage. HPV-based self-collection (HPV-SC) is a promising screening model for low- and middle-income countries (LMICs), and while digital technology, such as cellphones, can be used to streamline HPV-SC, there is limited data on digital technology penetration in LMICs. Determining women’s cellphone access is critical to understanding the feasibility of using cellphones to support HPV-SC. Methods This study is a secondary analysis of a larger clinical trial. Participants of a cluster-randomized trial comparing HPV-SC models in Uganda completed a survey, including questions about demographics, cellphone access/ownership, prior cervical cancer screening (CCS), and willingness to receive CCS information by text. A logistic regression model was used to determine adjusted rates of cellphone ownership using survey variables as factors. Results Of 2019 participants, 76.1% owned a cellphone. In non-cellphone owners ( n = 483), 82.4% had daily cellphone access and 7.3% had no access. Compared to non-cellphone owners, cellphone owners were significantly older, more educated, closer to major health centers, more likely to have prior CCS, and more willing to receive a CCS text. In the logistic regression model, the aforementioned variables were all significantly associated with the odds of owning a cellphone. Conclusions As health care systems consider adopting HPV-SC, it is imperative to understand digital technology penetration. The majority of participants were cellphone owners and were willing to receive CCS information by text; however, significant socioeconomic and demographic differences remain between cellphone owners and non-owners. Further investigation is needed to understand whether HPV-SC using cellphones is feasible in similar settings. Trial registration ISRCTN, 12767014 . ClinicalTrials.gov, NCT04000503 .
The impact of personality traits and cultural values on coordination effectiveness: A study of software development teams effectiveness

Abstract Software development projects depend on collaborative teams. In the past 50 years of research, various studies have explored the effect of software engineer's personality traits and cultural values on team performance. These studies have led to better understand these relationships; however, how the personality traits and cultural values influence the team effectiveness is still far away in the literature. This research aims to investigate the relationships between social and psychological complexities (including personality traits and cultural values), team coordination, team motivation, and team success (which comprises team effectiveness and team climate) to explore the impact of social and psychological issues on software team success. An online survey targeting software development professionals and unstructured interviews were followed for data collection. We received 112 responses from software developers working in different countries. Findings indicate that personality traits and cultural values, that is, consciousness, openness, harmony, and autonomy, have positive relationship with team coordination effectiveness, while other factors such as neuroticism, embeddedness, hierarchy, and mastery were found to be related negatively with it. These negative relationships can be mitigated by motivating team members appropriately. Based on our research findings, we conclude that the negative impact caused by different personality and cultural traits could be reduced by improving team coordination effectiveness using effective motivation.
A performance analysis of Basin hopping compared to established metaheuristics for global optimization

During the last decades many metaheuristics for global numerical optimization have been proposed. Among them, Basin Hopping is very simple and straightforward to implement, although rarely used outside its original Physical Chemistry community. In this work, our aim is to compare Basin Hopping, and two population variants of it, with readily available implementations of the well known metaheuristics Differential Evolution, Particle Swarm Optimization, and Covariance Matrix Adaptation Evolution Strategy. We perform numerical experiments using the IOH profiler environment with the BBOB test function set and two difficult real-world problems. The experiments were carried out in two different but complementary ways: by measuring the performance under a fixed budget of function evaluations and by considering a fixed target value. The general conclusion is that Basin Hopping and its newly introduced population variant are almost as good as Covariance Matrix Adaptation on the synthetic benchmark functions and better than it on the two hard cluster energy minimization problems. Thus, the proposed analyses show that Basin Hopping can be considered a good candidate for global numerical optimization problems along with the more established metaheuristics, especially if one wants to obtain quick and reliable results on an unknown problem.