Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multi-view Classification Framework for Falls Prediction: Multiple-domain Assessments in Parkinson's Disease

Falls are one of the most common causes of injury and disability in people with Parkinson’s disease (PD). This study developed an augmented machine learning framework for screening the risk of falling in people with PD using multiple domain assessments. A sample of 109 people with PD (50 fallers and 59 non-fallers) undertook four domains of assessment: disease-specific rating scales, clinical examination measures, physiological assessments, and gait analysis. A multiview classifying framework was developed from a sequence of procedures and achieved 77.50% average predicting accuracy. The robustness of the multi-view framework was tested by comparing outcomes of three different view selection methods. The developed framework may have implications for clinical decision making, as some of the PD fall risk variables/features may be amenable to treatment. Our results showed that external reliability can be achieved by a simple voting mechanism from multiple, perhaps diverse, perspective
Information Disclosure in Mobile Device: Examining the Influence of Information Relevance and Recipient

Privacy enhancing technologies (PETs) in mobile platforms typically restrict undesired information flow based on its sensitivity. However, sensitivity is often regarded as dichotomous and inflexible to the ever-changing contexts. Improving the effectiveness of PETs requires a better understanding of these contexts. In this paper, we examine the influence of contextual factors in users’ mobil e usage based on Nissenbaum’s framework of contextual integrity. Specifically, we conducted a user study (n = 2889) to investigate the influence of relevance of information types on the willingness of disclosure towards typical groups of recipient. While the results suggest a significant relationship between information relevance (of different information) and willingness to disclose (to different recipients), closer examination reveals the relationship is not always clear-cut, and there is a potential influence of recipient. Therefore, incorporating the recipient factor can serve as a potential improvement to the existing approach in privacy management in the mobile device.
Machinelike or Humanlike? A Literature Review of Anthropomorphism in AI-Enabled Technology

Due to the recent proliferation of AI-enabled technology (AIET), the concept of anthropomorphism, human likeness in technology, has increasingly attracted researchers’ attention. Researchers have examined how anthropomorphism influences users’ perception, adoption, and continued use of AIET. However, researchers have yet to agree on how to conceptualize and operationalize anthropomorphism in AIET, which has resulted in inconsistent findings. A comprehensive understanding is thus needed of the current state of research on anthropomorphism in AIET contexts. To conduct an in-depth analysis of the literature on anthropomorphism, we reviewed 35 empirical studies focusing on conceptualizing and operationalizing AIET anthropomorphism, and its antecedents and consequences. Based on our analysis, we discuss potential research gaps and offer directions for future research.
Neural Machine Translation for Conditional Generation of Novel Procedures

Procedural knowledge is generally dispersed across many experts within or across organizations which might lead to inefficiencies and redundancy. Historically, computers have been well suited to store procedural knowledge but they have lacked the capability to produce natural language text. Nonetheless, recent advances in machine learning permit a higher linguistic coherence which benefits applications with longer text outputs such as procedures. This work closes the gap between human experts and computers by proposing a framework for automatic, computer generation of procedures based on neural machine translation and the BART model. Furthermore, we define two benchmark problems for procedure generation and establish a set of evaluation metrics that can be used as a reference in further work. We demonstrate the potential of this solution on the task of generating cooking recipes based on available ingredients. The evaluation results on the Recipe1M dataset showcase the method’s superiority over other, fairly novel, neural architectures.
Content and Social Network Analyses of Depression-related Tweets of African American College Students

The prevalence of depression is higher among African American college students compared to their White counterparts. They are also more likely to disclose feelings of depression on Twitter. The aim of this exploratory study was to answer the following questions: What are the most common themes of depression-related tweets among African American college students? Are there differences in the social network characteristics of college students that have posted a depression-related tweet or retweet and those who have not? Content and social network analyses were conducted. The study results showed the most common themes focused on feelings of depression, casual mentions, and supportive messages. In addition, we observed that the social networks of users posting depression-related tweets have more mutual connections with their friends than the users who did not post a depression-related tweet. These findings may help to inform the design of social media interventions for African American college students.
Highlighting High-quality Content as a Moderation Strategy: The Role of New York Times Picks in Comment Quality and Engagement

News commenting is a prevalent form of online interaction, but it is fraught with issues, such as a low quality of discussion that often takes place. While various moderation methods can be used to maintain online discussion quality, one moderation strategy that is underexplored is for professional moderators to mark high-quality posts that are further highlighted in the interface. In this work, we look at the impact of New York Times (NYT) Picks. We present an analysis of more than 13 million NYT comments, examining the quality and frequency of commenting on the site in response to NYT Picks. The findings offer evidence that NYT Picks are associated with an increase in the quality of first-time receivers’ next approved comment, as well as the commenting frequency during commenters’ early tenure on the site. The quality boost associated with receiving a Pick attenuates after subsequent picks and diminishes over time as the user continues commenting but is still higher than commenters who do not receive Picks. Visible comment quality has a relatively small but significant positive correlation with the quality of the next comment, and exposure to Pick badges is also positively correlated with subsequent higher-quality approved comments, albeit to a lesser extent. Our results underscore the potential for news organizations to adopt the moderation strategy of highlighting professionally selected high-quality comments to improve overall community quality. We discuss the implications of our findings and offer design opportunities for comment sections that could further enhance quality in online discourse.
A Polarization Approach for Understanding Online Conflicts in Times of Pandemic: A Brazilian Case Study

As society becomes digitalized, online social networks tend to be primary places for debate but can turn into a battlefield for imposing conflicting narratives. Automating the identification of online conflicts is a challenge due to difficulties in defining antagonist communities and controversial discussions. Here, we propose a polarization approach for understanding Twitter conflicts in Brazil during the COVID-19 pandemic, where a small group of polarizers influences a larger group of polarizees according to their ideological leaning. Polarizers are automatically identified by centrality metrics in following, retweet, and reply networks and manually labeled as leftists, rightists, or undefined. We collected and analyzed the polarization of 21 potentially conflicted political events in Brazil. Our results show that polarizers adequately represent the polarization of events, the traditional media is giving way to a new breed of tweeters, and retweet and reply play different roles within a conflict that reflects their polarization level. © 2021 IEEE Computer Society. All rights reserved.
Effectiveness of Online Consumer Product Review: The Role of Experiential Information

Product reviews as consumer-generated information have drawn great attention from researchers and practitioners. A substantial academic effort has been made to comprehend factors influencing the helpfulness of reviews, largely centering on a few quantitative factors (e.g., star rating, review length). However, research investigating qualitative aspects of product reviews still lags, though product reviews consist mainly of peer consumers’ experiences and opinions. In this study, we use the smartphone reviews to investigate consumers’ experiences and opinions in relation to review helpfulness. By statistical analysis, we demonstrate that consumers’ experiential information plays a significant role to make product reviews helpful. We furnish additional evidence of the statistical results by predictive analytics. Our findings suggest that consumers’ experiential information conveys meaningful implication to better understand the nature of product reviews. Therefore, this study contributes to the extant literature of e-commerce and to practitioners to utilize the consumer reviews of their products.
ALGA: Automatic Logic Gate Annotator for Building Financial News Events Detectors

We present a new automatic data labelling framework called ALGA - Automatic Logic Gate Annotator. The framework helps to create large amounts of annotated data for training domain-speciﬁc ﬁnancial news events detection classiﬁers quicker. ALGA framework implements a rules-based approach to annotate a training dataset. This method has following advantages: 1) unlike traditional data labelling methods, it helps to ﬁlter relevant news articles from noise; 2) allows easier transferability to other domains and better interpretability of models trained on automatically labelled data. To create this framework, we focus on the U.S.-based companies that operate in the Apparel and Footwear industry. We show that event detection classiﬁers trained on the data generated by our framework can achieve state-of-the-art performance in the domain-speciﬁc ﬁnancial events detection task. Besides, we create a domain-speciﬁc events synonyms dictionary.
Pressure extinction limits of non-premixed flames

Pressure extinction limits of non-premixed (diffusion) flames are studied theoretically by examining the two key non-dimensional parameters: Damkohler number and radiation-heat-loss parameter. It is found that there is a critical value of the reaction order, n cr , for a given flame and flow configuration. If the overall combustion reaction order n is less than n cr , a high-pressure flame extinction limit exists as a result of a small limiting Damkohler number. If the overall combustion reaction order n is greater than n cr , both heat loss and Damkohler number contribute to the existence of the low-pressure extinction limit. The contribution of the Damkohler number increases with flame intensity. Since the value of the critical reaction order n cr varies with flame configuration and flow type, three examples of diffusion flames adjacent to a condensed fuel are given in the paper: stagnation-point flame, side-stabilised flame and spherical flame. The reason for the different value of the critical n cr is due to the different pressure dependence of the flow residence time in the flame stabilisation zone of these flames.
Pathways of Data-driven Business Model Design and Realization: A Qualitative Research Study

Maximizing the value from data has become a key challenge for companies as it helps improve operations and decision making, enhances products and services, and ultimately, leads to new business models (BMs). Aiming to achieve the latter, companies take different pathways. Building on a grounded theory research approach, we identified four pathways for designing and realizing data-driven business models (DDBMs). To achieve this goal, we conducted 16 semi-structured interviews with experts from consulting and industry firms. The results fill the gap in the literature on the design and realization of DDBMs and act as a guide for companies.
Fighting the wicked problem of plastic pollution and its consequences for developing regions with expert and crowd solutions

The wicked problem of plastic pollution is one of the key global challenges. Finding adequate solutions to this complex problem requires cross-cultural and inter-organizational collaboration among diverse sets of stakeholders. In this context, the Ellen Mac Arthur Foundation approaches the problem of plastic pollution not only by involving experts into innovation processes but also by integrating the general public in form of an IT enabled crowdsourcing initiative. In this study, we analyze the outcomes of these actions with the help of automated text mining techniques. Our analysis demonstrates significant differences between the solutions given by experts and the crowd along various criteria. Further, this study provides guidance for practitioners on how to integrate diverse sets of individuals in problem solving processes with the help of information systems technologies. Especially for sustainability issues affecting both, developed and developing regions.
A Recommender System for Healthy Food Choices: Building a Hybrid Model for Recipe Recommendations using Big Data Sets

Advances in Big Data analytics and machine learning have offered intangible benefits across many areas of one’s health. One such area is a move towards healthier lifestyle choices such as one’s diet. Recommender systems apply techniques that can filter information and narrow that information down based on user preferences or user needs and help users choose what information is relevant. Commonly adopted across e-commerce sites, social networking and entertainment industries, recommender systems can also support nutrition-based health management, offering individuals more food options, not only based on one’s preferred tastes but also on one’s dietary needs and restrictions. This research presents the design, implementation and evaluation of three recommender systems using content-based, collaborative filtering and hybrid recommendation models within the nutrition domain.
Network Contagion vs. Spatial Contagion: The Diffusion of EHR Incentive Programs in Physician Networks

The present study supported the network contagion theory that healthcare providers are more likely to adopt the Medicare and Medicaid Electronic Health Record (EHR) incentive program when their direct relations have more prior adopters. Spatial contagion, however, exhibits an opposite finding that healthcare providers geographically surrounded by more prior adopters are less likely to adopt the EHR incentive program. When taking both network contagion and spatial contagion into account, healthcare providers connected with more prior adopters within 30 miles are more likely to adopt the EHR incentive program. The findings enrich our understanding of how network contagion influences the diffusion of EHR incentive programs and how spatial contagion moderates the effects of network contagion on the diffusion of the EHR incentive programs.
Revenue Cycle Management in the Wake of EMR Implementation: A Competing Logics Perspective

The health information technology (HIT) literature has focused on how healthcare organizations use electronic medical record (EMR) systems and other clinical IT for care delivery and coordination. However, few studies have examined how implementation of these technologies impact the organizations’ revenue cycle management (RCM) and consequent financial sustainability. Against that backdrop, we draw on institutional logics perspective to analyze experiences from two action research engagements that leveraged EMR implementations in medical clinics to improve RCM. As a result, we identify four coexisting yet competing logics—logic of care, logic of business, logic of management, and logic of technology—that shaped how the clinics addressed challenges in their revenue cycle. While IT transformed practices and information exchanges, the competing logics shaped the clinics’ RCM in the wake of their EMR implementation. We conclude with contributions to research and practice.
Why Phishing Works on Smartphones: A Preliminary Study

Phishing is a form of fraud where an attacker attempts to acquire sensitive information from a target by posing as trustworthy. One strategy to fool the target is spooﬁng of a legitimate website. But why do people fall for phishing, and what security indicators are utilized or not utilized when deciding the legitimacy of a website? Hitherto, two studies have been conducted in 2006 and 2015. As time has passed since then, we like to check if people are meanwhile more certain in identifying spoofed websites. Therefore, 20 participants were observed when they analyzed and classiﬁed websites as legitimate or spoofed. On average participants had a success rate of 69 %, like previous studies results. The URL was used as an indicator by most of the participants (80 %), indicating user behavior and ease of identifying spoofed and legitimate websites is not very different on a smartphone compared to a desktop. Almost all participants used the content of the website at least once when deciding if a website was spoofed or legitimate. These ﬁndings will be used to conduct a bigger study to create more resilient results.
A Probabilistic Location Prediction Approach to Optimize Dispatch Processes in the Ride-Hailing Industry

For peer-to-peer ride-hailing providers, it is a crucial competitive advantage to cost-efﬁciently dispatch passenger requests and to communicate accurate waiting times. To determine waiting times and dispatch decisions, transport network companies need precise information about the location of all available drivers. Due to technical limitations and outdated data (e.g., low sample rates, continuous movement of drivers), however, existing systems, which typically use the last observed locations of drivers, regularly suffer from dispatches with critical delays. In this paper, we present an approach to predict probability distributions for drivers’ future locations, which are calculated based on patterns observed in past trajectories. We evaluate the applicability and accuracy of the proposed algorithm on a large real-world trajectory dataset of a transportation network company. Our results allow quantifying the risk of critical delays and thus enable risk considerations in improved dispatching strategies.
How Boards of Directors Can Contribute to Governing IT

Digital transformation is becoming ubiquitous as organizations increasingly rely on IT to create business value. As a result, IT strategy is increasingly intertwined with organizational strategy, with risks as IT becomes progressively more important, requiring both management and governance. Given boards of directors are ultimately accountable for strategic decision-making and control, these changes charge boards with accountability for governing digital assets. Whilst board-level IT governance should enable better organizational performance, research suggests that the value created by the board in governing IT depends on the roles they play. In exploring these roles, we use the more mature research domain of corporate governance to develop improved understanding of the type of board roles and the importance assigned by a board to governing IT.
Subjectively Experienced Time and User Satisfaction: An Experimental Study of Progress Indicator Design in Mobile Application

I n the user’s interaction with systems, waiting and interruptions often constitute a source of negative experiences. However, system response time can be difficult or impossible to control. This study explores “subjective experienced time”, which refers to the users’ assessment of system respons e timeliness. The aim of this study is to gain increased knowledge of user satisfaction and subjectively experienced time in interaction with mobile applications. Thirty participants used and evaluated three mobile applications, containing unique stimuli in progress indicators. The results show correlation between progress indicators’ degree of feedback and the subjectively experienced time and user satisfaction. Contributions include increased insight into the somewhat complex connection between the degree of feedback, subjectively experienced time and user satisfaction, as well as design implications for user-centred design.
Automatically Mapping Ad Targeting Criteria between Online Ad Platforms

Targeting criteria in online advertising differ across platforms and frequently change. Because advertisers are increasingly taking a multi-channel approach to online marketing, there is a need to automatically map the targeting criteria between ad platforms. In this research, we test two algorithmic approaches − Word2Vec and WordNet − for mapping ad targeting criteria between Google Ads and Facebook Ads. The results show that Word2Vec outperforms WordNet in finding matches (97.5% vs. 63.6%), covering different criteria (20.0% vs. 13.5%), and having higher similarity scores. However, WordNet outperforms Word2Vec in expert evaluation (Mean Opinion Score = 3.05 vs. 2.46), implying that algorithmic performance metrics may not correlate with expert ratings. Overall, due to specific requirements for mapping ad targeting criteria, automatic means do not (at least yet) offer a satisfactory solution for replacing human judgment.
Gesture Recognition with non-contact sensor for Natural User Interface in the COVID-19 era

COBID-19, an infectious disease transmitted by droplet and contact, is prevalent. In order to reduce the risk of contact infection, various operations should be performed in silence and non-contact. A user interface using non-contact sensors is effective in such an environment. Among them, Natural User Interface based on Gesture Recognition using non-contact sensors are useful, we think. We have developed our NUI system in which the user instructs the computer in a full-body gesture. In this paper, we discuss several methods available for gesture recognition based on skeleton recognition. And, for some of the gesture recognition systems we have implemented with the combination of such methods, the design policy and experimental results of each are presented. © 2021 IEEE Computer Society. All rights reserved.
Social media processing in crisis response : an attempt to shift from data to information exploitation

Information about the ongoing events is of the utmost importance during emergencies. Previous work in crisis informatics found new ways to pull data from unexploited sources, such as social media. But while the volume of information is crucial, the way the information is reported and provided becomes increasingly important as the volume grows. Yet, little has been achieved on information management. This article proposes a way to automatically organize information from social media data up to decision-makers. This organization is enabled by a metamodel \cite{benaben_metamodel_2016} designed to model crucial information in crises. The article is organized as follows. First, the organization of current social media processing systems is presented. Then, the article presents the metamodel used and how it is relevant to organized information in crisis events through the lens of the 6W's \cite{kropczynski_identifying_2018}. Finally, it walks through the implementation of the proposal based on the two previous parts.