Miracle-dz/UniFAR-FTUs huggingface dataset:
Customer Responses to Service Robots - Comparing Human-Robot Interaction with Human-Human Interaction

This paper investigates how service failures affect customers by comparing human-robot interactions with human-human interactions. More specifically, it compares customers’ satisfaction in a service robot interaction depending on a service failure with the customers’ satisfaction in a frontline service employee interaction. On a theoretical basis, extant literature on the uncanny valley paradigm proposed that service robots would create lower satisfaction than human frontline employees would. However, I find that service robots could keep up with human frontline employees. Based on an extensive literature research on service failures, I propose that customer satisfaction after a service failure declines far less for a human frontline employee compared with a service robot. Nevertheless, I find evidence that service robots create even higher customer satisfaction than human frontline employees after the exactly similar service failure. I base my findings on an experimental laboratory study with 120 student participants and the service robot “Pepper” from Softbank Corp.
The Influence of Design Updates on Users: the Case of Snapchat

Today’s smartphone apps are regularly updated and enhanced through software updates. The case at hand is the popular social multimedia messaging app Snapchat that released a design overhaul in February 2018. While the update neither changed any features nor caused any relevant bugs or crashes, it led to an uproar of Snapchat’s users and significantly decreased its app store ratings and consequently revenue. As a result, Snap Inc., the company behind Snapchat, was forced to reverse design changes to appease their users. The initial adverse effects of the update were surprising; however, after using difference-in-difference tests in combination with sentiment analysis, our results indicate that design updates can be perceived negatively by users. We contribute to IS literature by evaluating the effect of design changes and the role of perceived ease of use in the post-adoption stage.
Towards Understanding the Influence of Nature Imagery in User Interface Design: A Review of the Literature

Nature imagery is frequently employed as a design element to improve how users experience interactions with computerized artifacts such as websites and mobile apps. However, literature on the influence of such imagery on human perception and behavior is scant and highly fragmented. In this paper, we develop a theoretical framework that integrates the different pathways for how nature imagery embedded in user interface design may affect user perception and behavior. Building on this framework, we synthesize the results of existing literature on how humans perceive nature imagery and the potential cognitive, affective, and behavioral responses. By providing a concise overview of key theories and results of the extant literature, this study contributes to the knowledge base of (1) scholars who theorize on the impact of nature imagery on user perception and behavior and (2) systems designers who intend to utilize nature imagery in their user interfaces.
Towards Motion Invariant Authentication for On-Body IoT Devices

As the rapid proliferation of on-body Internet of Things (IoT) devices, their security vulnerabilities have raised serious privacy and safety issues. Traditional efforts to secure these devices against impersonation attacks mainly rely on either dedicated sensors or specified user motions, impeding their wide-scale adoption. This paper transcends these limitations with a general security solution by leveraging ubiquitous wireless chips available in IoT devices. Particularly, representative time and frequency features are first extracted from received signal strengths (RSSs) to characterize radio propagation profiles. Then, an adversarial multi-player network is developed to recognize underlying radio propagation patterns and facilitate on-body device authentication. We prove that at equilibrium, our adversarial model can extract all information about propagation patterns and eliminate any irrelevant information caused by motion variances. We build a prototype of our system using universal software radio peripheral (USRP) devices and conduct extensive experiments with both static and dynamic body motions in typical indoor and outdoor environments. The experimental results show that our system achieves an average authentication accuracy of 90.4%, with a high area under the receiver operating characteristic curve (AUROC) of 0.958 and better generalization performance in comparison with the conventional non-adversarial-based approach.
Supporting Cross-Organizational Assimilation of IoT Innovation Exemplified by the ChainPORT Initiative

The chainPORT community of port authorities (PAs) around the world gave their commitment to collaborate. Many PAs developed Internet of Things (IoT) based solutions to increase their operational efficiency. Within its IT solutions workgroup, the challenge of supporting the diffusion and assimilation of these IoT innovations was adressed by creating a centralized communication platform for IoT solutions to allow inter-organizational knowledge exchange. We draw upon the knowledge gained by analyzing 24 solutions from 8 port authorities and present concepts on how the specific challenges in this setting were adressed and what principles guided the creation of the emerging IT artifact.
Application of data compression techniques to time series forecasting

In this study we show that standard well-known file compression programs (zlib, bzip2, etc.) are able to forecast real-world time series data well. The strength of our approach is its ability to use a set of data compression algorithms and "automatically" choose the best one of them during the process of forecasting. Besides, modern data-compressors are able to find many kinds of latent regularities using some methods of artificial intelligence (for example, some data-compressors are based on finding the smallest formal grammar that describes the time series). Thus, our approach makes it possible to apply some particular methods of artificial intelligence for time-series forecasting. As examples of the application of the proposed method, we made forecasts for the monthly T-index and the Kp-index time series using standard compressors. In both cases, we used the Mean Absolute Error (MAE) as an accuracy measure. For the monthly T-index time series, we made 18 forecasts beyond the available data for each month since January 2011 to July 2017. We show that, in comparison with the forecasts made by the Australian Bureau of Meteorology, our method more accurately predicts one value ahead. The Kp-index time series consists of 3-hour values ranging from 0 to 9. For each day from February 4, 2018 to March 28, 2018, we made forecasts for 24 values ahead. We compared our forecasts with the forecasts made by the Space Weather Prediction Center (SWPC). The results showed that the accuracy of our method is similar to the accuracy of the SWPC's method. As in the previous case, we also obtained more accurate one-step forecasts.
BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation

Age estimation is an important yet very challenging problem in computer vision. Existing methods for age estimation usually apply a divide-and-conquer strategy to deal with heterogeneous data caused by the non-stationary aging process. However, the facial aging process is also a continuous process, and the continuity relationship between different components has not been effectively exploited. In this paper, we propose BridgeNet for age estimation, which aims to mine the continuous relation between age labels effectively. The proposed BridgeNet consists of local regressors and gating networks. Local regressors partition the data space into multiple overlapping subspaces to tackle heterogeneous data and gating networks learn continuity aware weights for the results of local regressors by employing the proposed bridge-tree structure, which introduces bridge connections into tree models to enforce the similarity between neighbor nodes. Moreover, these two components of BridgeNet can be jointly learned in an end-to-end way. We show experimental results on the MORPH II, FG-NET and Chalearn LAP 2015 datasets and find that BridgeNet outperforms the state-of-the-art methods.
A Latent Dirichlet Allocation Approach using Mixed Graph of Terms for Sentiment Analysis

The spread of generic (as Twitter, Facebook or Google+) or specialized (as LinkedIn or Viadeo) social networks allows to millions of users to share opinions on different aspects of life every day. Therefore this information is a rich source of data for opinion mining and sentiment analysis. This paper presents a novel approach to the sentiment analysis based on the Latent Dirichlet Allocation (LDA) approach. The proposed methodology aims to identify a word-based graphical model (we call it a mixed graph of terms) for depicting a positive or negative attitude towards a topic. By the use of this model it will be possible to automatically mine from documents positive and negative sentiments. Experimental evaluation, on standard and real datasets, shows that the proposed approach is effective and furnishes good and reliable results.
The Effect of Stress on Reliance Decisions

Appropriate reliance on automation is critical in high-risk/high-stress contexts such as military operations. The current study examines how stress affects the decision to rely on automation. Reliance will be examined using a decision making framework, taking into account the cognitive processes that are being affected by stress. Additionally, the role of feedback is essential in updating information to be able to make a more informed decision in the future. Reliability of automation will be manipulated so that actual reliability will be lower than expected reliability in one condition, and will be the same as expected in the other. Participants’ ability to incorporate feedback into subsequent reliance decisions will be assessed between stress conditions. Finally, since motivation can encourage more deliberate thinking, motivation will be manipulated between subjects. A better understanding of how reliance decisions are made under stress can inform the design of systems for better human-automation collaboration.
Electronic Health Records Communication among Team Members and Quality of Care and Costs for Patients with Cardiovascular Disease in Primary Care

This study determines how changes in electronic health record (EHR) communication patterns in primary care teams are related to quality of care and costs for patients with cardiovascular disease. Counts of EHR messages routed between any two team members were extracted from the EHR system, and flow betweenness, the proportion of information passed indirectly within the team, was calculated. The analysis related changes in team flow betweenness to changes in acute care visits and associated medical costs for the teams’ patients with cardiovascular disease. The results indicated that patient hospital visits increased by 7% (SE 3%) for every 1% increase in team EHR flow betweenness. Medical costs increased by $141 (SE $67) per patient for every 1% increase in team EHR flow betweenness. EHR team communication flow patterns may be an important avenue to explore for raising quality of care and lowering costs for primary care patients with cardiovascular disease.
Superfluids, Fluctuations and Disorder

We present a field-theory description of ultracold bosonic atoms in the presence of a disordered external potential. By means of functional integration techniques, we aim to investigate and review the interplay between disordered energy landscapes and fluctuations, both thermal and quantum ones. Within the broken-symmetry phase, up to the Gaussian level of approximation, the disorder contribution crucially modifies both the condensate depletion and the superfluid response. Remarkably, it is found that the ordered (i.e., superfluid) phase can be destroyed also in regimes where the random external potential is suitable for a perturbative analysis. We analyze the simplest case of quenched disorder and then we move to present the implementation of the replica trick for ultracold bosonic systems. In both cases, we discuss strengths and limitations of the reviewed approach, paying specific attention to possible extensions and the most recent experimental outputs.
To Calculate or To Follow Others: How Do Information Security Managers Make Investment Decisions?

Economic models of information security investment suggest estimating cost and benefit to make an information security investment decision. However, the intangible nature of information security investment prevents managers from applying cost-benefit analysis in practice. Instead, information security managers may follow experts’ recommendations or the practices of other organizations. The present paper examines factors that influence information security managers’ investment decisions from the reputational herding perspective. The study was conducted using survey questionnaire data collected from 106 organizations in Finland. The findings of the study reveal that the ability and reputation of the security manager and the strength of the information about the security investment significantly motivate the security manager to discount his or her own information. Herding, as a following strategy, together with mandatory requirements are significant motivations for information security investment.
Modeling Privacy Preservation in Smart Connected Toys by Petri-Nets

Children data privacy must be considered as integral and factored into the system design of Smart Connected Toy (SCT). The challenge is that SCTs are capable to gather significant amount volunteered and nonvolunteered data, which lacks privacy considerations. It is imperative to adopt a modeling technique that autonomously preserves privacy and secure children’s data in SCT transactions. This paper surveys the current data flow modeling techniques, which most of them do not have elements to address the privacy of Personal Identifiable Information (PII). This paper shows a PetriNet simulation which provides privacy assurance in order to minimize the risk of privacy violation of a child’s PII and related data.
Semidefinite Programming in Timetabling and Mutual-Exclusion Scheduling

In scheduling and timetabling applications, the mutual-exclusion constraint stipulates that certain pairs of tasks that cannot be executed at the same time. This corresponds to the vertex colouring problem in graph theory, for which there are well-known semidefinite programming (SDP) relaxations. In practice, however, the mutual-exclusion constraint is typically combined with many other constraints, whose SDP representability has not been studied. We present SDP relaxations for a variety of mutual-exclusion scheduling and timetabling problems, starting from a bound on the number of tasks executed within each period, which corresponds to graph colouring bounded in the number of uses of each colour. In theory, this provides the strongest known bounds for these problems that are computable to any precision in time polynomial in the dimensions. In practice, we report encouraging computational results on random graphs, Knesser graphs, ``forbidden intersection'' graphs, the Toronto benchmark, and the International Timetabling Competition.
Trends in Detection and Characterization of Propaganda Bots

Since the revelations of interference in the 2016 US Presidential elections, the UK’s Brexit referendum, the Catalan independence vote in 2017 and numerous other major political discussions by malicious online actors and propaganda bots, there has been increasing interest in understanding how to detect and characterize such threats. We focus on some of the recent research in algorithms for detection of propaganda botnets and metrics by which their impact can be measured.
Issue Framing in Online Discussion Fora

In online discussion fora, speakers often make arguments for or against something, say birth control, by highlighting certain aspects of the topic. In social science, this is referred to as issue framing. In this paper, we introduce a new issue frame annotated corpus of online discussions. We explore to what extent models trained to detect issue frames in newswire and social media can be transferred to the domain of discussion fora, using a combination of multi-task and adversarial training, assuming only unlabeled training data in the target domain.
Parametrization of Stochastic Inputs Using Generative Adversarial Networks With Application in Geology

We investigate artificial neural networks as a parametrization tool for stochastic inputs in numerical simulations. We address parametrization from the point of view of emulating the data generating process, instead of explicitly constructing a parametric form to preserve predefined statistics of the data. This is done by training a neural network to generate samples from the data distribution using a recent deep learning technique called generative adversarial networks. By emulating the data generating process, the relevant statistics of the data are replicated. The method is assessed in subsurface flow problems, where effective parametrization of underground properties such as permeability is important due to the high dimensionality and presence of high spatial correlations. We experiment with realizations of binary channelized subsurface permeability and perform uncertainty quantification and parameter estimation. Results show that the parametrization using generative adversarial networks is very effective in preserving visual realism as well as high order statistics of the flow responses, while achieving a dimensionality reduction of two orders of magnitude.
CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation

We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information. CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector. Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items. Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods.
IT-Based Self-Monitoring Interventions to Promote Physical Activity and Weight Loss: A Meta-Analysis of Change-from-Baseline Effects

IT-based self-monitoring (ITSM) has attracted increasing interest as a strategy for chronic disease self-management and day-to-day fitness promotion. Despite the increasing popularity of various selfmonitoring technologies such as fitness trackers and biosensors, their effectiveness is less certain. The objective of the current review is to determine the effectiveness of ITSM interventions on two types of key chronic care outcomes: weight management and physical activity. A systematic review employing a meta-analysis identified 42 ITSM studies that report change-from-baseline effects on weight and physical activity-related outcomes. Overall, a small effect size is found for body weight, BMI, waist circumferences, and step-based physical activity. The effect estimates on time-based physical activity are moderate. However, the effects on physical activity show variability and potential publication bias. A post-hoc analysis of the effects of ITSM on self-efficacy exhibit a small yet significant effect size, which shows the potential mediating role of patients’ psychological outcomes on the ultimate behavioral outcomes. In summary, ITSM is a potentially useful approach to manage weight and physical activity. Further study is needed to determine the source of heterogeneity as well as the types of ITSM interventions that are effective for weight, physical activity, but also other chronic care outcomes.
Optimal Operator Training Reference Models for Human-in-the-loop Systems

The human operator is an integral part of a stable and safe power system. While there is increasing attention paid to automation improvements, the importance of understanding and training human operators may be understated. This paper discusses a project to enhance operator training programs by evaluating human performance relative to a reference operator model identified using optimal control theory. Along with establishing a simple computer-based operator workstation for future training purpose, this paper describes the optimal control response design methodology for a human-in-the-loop power system experiment. The overall system model is presented. An optimal controller synthesis methodology is applied to the model system and the optimal controller is designed. The performance of the optimal controller is then compared to human subject performance.
Extracting Factual Min/Max Age Information from Clinical Trial Studies

Population age information is an essential characteristic of clinical trials. In this paper, we focus on extracting minimum and maximum (min/max) age values for the study samples from clinical research articles. Specifically, we investigate the use of a neural network model for question answering to address this information extraction task. The min/max age QA model is trained on the massive structured clinical study records from ClinicalTrials.gov. For each article, based on multiple min and max age values extracted from the QA model, we predict both actual min/max age values for the study samples and filter out non-factual age expressions. Our system improves the results over (i) a passage retrieval based IE system and (ii) a CRF-based system by a large margin when evaluated on an annotated dataset consisting of 50 research papers on smoking cessation.
Exploring Automated Leadership and Agent Interaction Modalities

Advances in computer technology and research in the field of artificial intelligence have enabled computers to take on roles traditionally held by humans. Insights from leadership research have identified behaviors that, when applied strategically and systematically, can improve individual and team performance. We propose that some aspects of leadership are candidates for automation. This paper briefly reviews relevant leadership literature and describes three leadership behaviors that may be possibly automated: goal setting, performance monitoring, and performance consequences. The paper also explores the relationship of different embodiments of the artificial leaders, the impact of these embodiments in conveying social presence and the impact of this presence on performance and satisfaction outcomes. We conducted an experiment to investigate the effect of automated leadership on follower attitudes and behavior. Initial results suggest that automated leadership may positively influence performance and accuracy for individuals engaged in a clerical task.