Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Networking in a Virtual Environment

Facebook, the leading social network service (SNS), has been integrating its social networking experience into the Virtual Reality (VR) environment. It bought Oculus Rift for $2 billion in 2014 (Dredge 2014) and currently has a number of apps available in the Oculus store. VR is currently being used to transport individuals to collaborative spaces, allowing them to engage in social experiences. For instance, virtual project rooms have been introduced by apps such as Facebook Spaces (Neubauer et al. 2017). Facebook also allows live-streaming of 360 degree videos and has an app in the oculus store that enables one to experience 360 videos and images using immersive VR glasses. Market volume worldwide for these devices are expected to increase from 9.6 million units in 2016 to over 110 million units in 2020 (Hartl and Berger 2017). There is clearly an opportunity here for other social networking companies to use the virtual reality experience to grow their user base, increase user satisfaction and user retention and give their users an enriching social experience unlike the experience on a traditional website or mobile app. However, there are challenges which companies will have to face when migrating their services to the virtual realm. Research which looks at the use of social networking applications in an immersive virtual environment is lacking. The purpose of this study is to identify the factors which influence SNS members to continuously engage in social networking activities within a virtual environment. This research addresses the following research questions: (1) What factors influence the reuse of social networking applications via VR glasses? (2) What social networking features are suitable for an immersive virtual environment? Expected contributions of this research are as follows. SNS Companies will gain knowledge about the various characteristics related to the users, social networking apps and VR glasses which play a role in determining the continued use of social networking applications via VR headsets. SNS companies will gain knowledge about the features of social networking applications that users like and dislike. This should help give them insight into the SNS features worth investing into and developing within the virtual environment.
Influence of Light Availability and Prey Type on the Growth and Photo-Physiological Rates of the Mixotroph Noctiluca scintillans

A strain of the mixotrophic green Noctiluca scintillans (Noctiluca) isolated from the Arabian Sea afforded us an opportunity to investigate the photosynthetic and feeding characteristics of this organism which has recently replaced the once diatom dominated food chain of winter blooms in the Arabian Sea. Here we present the first in a series of experiments undertaken to study the interactive effects of irradiance and grazing response of this mixotroph to four phytoplankton species provided as food. Noctiluca showed a distinct preference for the dinoflagellate Peridinium foliciaeum and the diatom Phaeodactylum tricornutum but not the chlorophyte Pyramimonas nor the chain forming diatom Thalassiosira weissflogii. However, irrespective of food provided, adequate light was required for Noctiluca to grow as evidenced by its maximum growth rates of 1.44 cells day-1 when fed the preferred Peridinium and exposed to optimal irradiance of 250 E m-2 s-1 versus growth rates of 0.18 cells day-1 with the same food but at a low irradiance of 10 E m-2 s-1. Measurements of Noctiluca’s electron transport rates (ETR) per PSII Reaction Center as a function of irradiance also indicated severe light limitation of photosynthesis at 10 E m-2 s-1. The active fluorescence derived ETR versus Irradiance curves revealed an interesting finding in that there was no significant difference in photosynthetic parameters such the maximum photosynthetic capacity (ETRmax) nor α, the rate of increase of photosynthesis with light between fed and unfed cells under optimal light conditions. These results suggest that feeding does not enhance the photosynthetic activity of the endosymbionts when nutrients are not limiting as was the case in these experiments. Measurements of Noctiluca’s intracellular ammonium concentrations under optimal light conditions, the first for this strain, show significant accumulation of NH4+ (0.003-0.012 M NH4+ cell-1) after 14 days for fed and unfed Noctiluca which was undetectable 4 days later. A similar 14-day increase but of significantly higher concentrations (0.005-0.08 M NH4+ cell-1) was obtained under low light conditions. For Phaeodactylum and Thalassiosira fed cultures under light limitation, NH4+ continued to increase past the 14-day period suggesting Noctiluca's strong and efficient mechanism for regulation of intracellular nutrients.
Broadband Internet Access as a Localized Facilitator for Improving Security Knowledge

With an increasing number of threats to cybersecurity, research continues to focus on methods and behaviors by which individuals may better protect themselves. The availability of broadband infrastructure has been proposed to improve city and regional economic, educational, and health-related prospects, but its impact on facilitating security knowledge gathering has yet to be studied. This study assesses its influence using the data collected from 894 Internet users from across the United States with multiple analysis techniques supported by geographical information systems (GIS). The results indicate that broadband access, in addition to age and education level, is associated with higher levels of security knowledge. Moreover, geographical weighted regression analyses suggest that the significant variables vary in influence based on their locality.
CNNPred: CNN-based stock market prediction using several data sources

Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next day's direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in prediction's performance compared to the state of the art baseline algorithms.
A Stress-Strain-Outcome Model of Job Satisfaction: The Moderating Role of Professional Self-efficacy

In this study, we adopt a stress-strain-outcome framework and conceptualize work overload as a stress, work exhaustion as a strain, and job satisfaction as an outcome. We argue that professional self-efficacy is a coping mechanism, place it as a moderator, and hypothesize that it would attenuate the effects of 1) work overload on work exhaustion and 2) work exhaustion on job satisfaction. We test the model by using survey data collected from 144 IT professionals in Finland by means of the Partial Least Squares technique. The findings suggest that work overload has a significant positive effect on work exhaustion, work exhaustion has a significant negative effect on job satisfaction, and professional self-efficacy attenuates both of these relationships. These findings imply that managers need to keep in mind the possible risks from work overload and exhaustion. Furthermore, they should try to improve their employees’ professional self-efficacy to mitigate these risks.
An Empirical Study on the Factors Contributing to Disclosing Personal Information Online: Insecurity in the Digital Age

Individuals’ reluctance to provide personal information online could affect the US Governments’ ability to hire qualified personnel for sensitive positions. The aim of this study is to show perceived big data security, trust, perceived leadership competency, information sensitivity, privacy concern and reward of a job play a role in limiting the willingness of disclosing sensitive personal information online. As a quantitative study, data was collected through an online survey using a 7-Point Likert Scale. Partial Least Square Structural Equation Modeling was used for data analysis from a total of 206 responses received. Based on the results, it was found that leadership, website trust and job reward have a significant impact on an individual’s willingness to disclose, while perceived big data security and privacy concern did not. Of particular interest and contrary to previous studies on information disclosure, privacy concern did not show a significant influence on willingness to disclose.
Understanding Online Produce Cue Effects on Consumer Behavior: Evidence from EEG Data

The more information the linguistic symbol can convey, the better the effectiveness of communication will be. Thus, this study strived to explore the dissimilar use of product information cues in product evaluations of online shopping sites. We conducted different experiments with three product cues: country of origin, ranking, and sales. In addition, an electrophysiological monitoring method was used to measure neurophysiological states while customers assess the diagnosticity of cue usage in decision making. The valid data collected from 160 customers provide strong support that different extrinsic cues can stimulate different emotional responses at specific sites. The insights from the findings can benefit designers and marketers in implementing more effective marketing strategies.
IT for diabetes self-management - What are the patients' expectations?

Diabetes is a debilitating chronic condition that is touted to inflict 9% of adults. Despite the information technology (IT) solutions for the self-management of diabetes, there is a gap between user expectations, and what the IT solutions are offering. To understand these expectations, this research investigates the topics shared by a diabetic community on Twitter as an instant day-to-day micro-blogging platform and TuDiabetes as a social media forum for the community. We focus on studying personal experiences related to diabetes through adapt and apply text mining techniques to tweets of self-identified diabetic patients on Twitter and TuDiabetes. Through the analysis 75,864 tweets and 1,424 TuDiabetes discussions, we identify dominating talks on continuous glucose monitoring on both Twitter and TuDiabetes, we also spotted insightful discussions on diet, wearables, and technology for Type two diabetes patients on Twitter. The results could guide future IT interventions into the self-management of the condition.
Stress Effects on the Mechanisms Regulating Appetite in Teleost Fish

The homeostatic regulation of food intake relies on a complex network involving peripheral and central signals that are integrated in the hypothalamus which in turn responds with the release of orexigenic or anorexigenic neuropeptides that eventually promote or inhibit appetite. Under stress conditions, the mechanisms that control food intake in fish are deregulated and the appetite signals in the brain do not operate as in control conditions resulting in changes in the expression of the appetite-related neuropeptides and usually a decreased food intake. The effect of stress on the mechanisms that regulate food intake in fish seems to be mediated in part by the corticotropin-releasing factor (CRF), an anorexigenic neuropeptide involved in the activation of the HPI axis during the physiological stress response. Furthermore, the melanocortin system is also involved in the connection between the HPI axis and the central control of appetite. The dopaminergic and serotonergic systems are activated during the stress response and they have also been related to the control of food intake. In addition, the central and peripheral mechanisms that mediate nutrient sensing capacity and hence implicated in the metabolic control of appetite are inhibited in fish under stress conditions. Finally, stress also affects peripheral endocrine signals such as leptin. In the present minireview, we summarize the knowledge achieved in recent years regarding the interaction of stress with the different mechanisms that regulate food intake in fish.
Why Does Investment Experience Matter in Herding Rationality in P2P Lending?

Peer-to-peer (P2P) lending auction is the platform that offers online money lending by matching lenders with borrowers without intermediate. Comparing P2P model with traditional auction model, the administrative and service costs charged by the “middle-man” are saved. Although these explicit costs could be reduced, it deteriorates the problem of information asymmetry. P2P auction itself is difficult for borrowers to discern the credibility of lenders. However, the industry of P2P lending is astonishingly growing, especially in China. To explore this paradox, this study investigates how investor’s experience influences investors’ behavior and investment decision in PPDai. This research project aims to fill the gap to study the interaction between investment experience and herding rationality in IS study. Investment experience plays a critical role to influence herding decision. According to the theory of choice, the decision utility of an outcome could be different from the outcome from decision utility (Kahneman et al. 1997). The remembered utility measures the decision making based on the evaluation of past experiences. For the notions of herding behavior, Simon (1972) states the essential prerequisite to maximize gains is perfect information. These theories support our hypotheses of novice lenders to make perceived “best” but irrational behavior under information asymmetry. In the hypothesis testing, we argue that experienced lenders have accumulated rich experience and knowledge to recognize the quality and value of a listing with better information and to make faster decision. Novice lenders then observe this salient information and follow the “wisdom of crowd”. Rationality of herding during the decision making also possibly affects the quality and performance of the listing differently. We develop three concepts of irrational herding in P2P lending: Aggregated irrational herding (the ignorance of both credit and listing-based information), listingbased irrational herding (the ignorance of time-invariant credit information) and credit-based irrational herding (the ignorance of time-invariant listing attributes). We develop several econometric models to test the hypotheses. We adopt OLS and probit regression to examine the results of listing performance. Cox proportional hazard model (CPH) with weighted KaplanMeier method is used to study herding rationality. The significance difference of coefficients is tested by the interaction terms and paired difference tests to examine impact of experience on loan performance in the econometric models. The result of this study is expected to contribute to both academic and practice.
The Impact of Online Consumption Community on Crowdfunding Success

Crowdfunding is an emerging model of finance whereby backers gather online to fund projects, event, causes or events. Online consumption communities OCC play an important role in removing temporal and spatial boundaries by allowing consumers to convene online over a shared consumption interest. In this paper, we try to understand the impact of online consumption community membership on crowdfunding project success. We build on the online consumption community literature to structure our argumentation and develop our hypotheses. We then apply sophisticated statistical models on a rich dataset integrated from multiple data sources to test our hypotheses. Our analysis suggests that online consumption community membership is significantly associated with higher likelihood of crowdfunding project success. We argue that this impact is due to increasing the project exposure to the right set of potential backers who share the same funding interest. This can circumvent the need for pre-existing backers who are able to pledge to the project and thus lead to more success. Our study provides an important extension to the crowdfunding body of knowledge through introducing the concept of online consumption community and testing its impact on crowdfunding success. The study also provides insightful practical implications for all parties in the crowdfunding ecosystem, including project creators, backers, and platform designers.
Teaching Inverse Reinforcement Learners via Features and Demonstrations

Learning near-optimal behaviour from an expert's demonstrations typically relies on the assumption that the learner knows the features that the true reward function depends on. In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i.e., where there is a mismatch between the worldviews of the learner and the expert. We introduce a natural quantity, the teaching risk, which measures the potential suboptimality of policies that look optimal to the learner in this setting. We show that bounds on the teaching risk guarantee that the learner is able to find a near-optimal policy using standard algorithms based on inverse reinforcement learning. Based on these findings, we suggest a teaching scheme in which the expert can decrease the teaching risk by updating the learner's worldview, and thus ultimately enable her to find a near-optimal policy.
New Sufficient Conditions for Lower Bounding the Optimal Policy of a POMDP using Lehmann Precision

This paper provides new sufficient conditions so that the optimal policy of a partially observed Markov decision process (POMDP) can be lower bounded by a myopic policy. The two new proposed conditions, namely, Lehmann precision and copositive dominance, completely fix the problems with two crucial assumptions in the well known papers of Lovejoy 1987 and Rieder 1991. For controlled sensing POMDPs, Lehmann precision exploits both convexity and monotonicity of the value function, whereas the classical Blackwell dominance only exploits convexity. Numerical examples are presented where Lehmann precision holds but Blackwell dominance does not hold, thereby illustrating the usefulness of the main result in controlled sensing applications.
Kasvisruokaa kouluun – kokeileva tutkimus ruokavaliomurroksen tukena

Mitigation of climate change urges to reduce our meat consumption. This necessitates new eating practices and ways of thinking about food. In this paper we discuss how experimentative research can participate in making such changes. We introduce notions of pragmatic tinkering, speculative thinking and ethical discussion and evaluate their contribution to understanding of change in everyday practices. We investigate their prospects in relation to our experiments with sustainable school dining. In these experiments we introduced a free vegetarian choice to school meals and engaged pupils in their development. The results highlight the dynamic nature of change in everyday practices. The notion of tinkering highlights the small adjustments needed to reconcile the different goals in school dining. Our results stress that the practical adjustments easily uphold the conventional practices. Speculative thinking is needed in challenging them. In our experiments, the engagement of pupils opened places for hesitation, and eventually also to new ways of thinking about school food. The pupils also challenged our experiments. They reminded how eating is entangled to our shared cultural meanings on food, but also to our identity and understandings of gender. In future, experimentative research should build settings, which allow them becoming more multiple as well.
The Frontiers of Fairness in Machine Learning

The last few years have seen an explosion of academic and popular interest in algorithmic fairness. Despite this interest and the volume and velocity of work that has been produced recently, the fundamental science of fairness in machine learning is still in a nascent state. In March 2018, we convened a group of experts as part of a CCC visioning workshop to assess the state of the field, and distill the most promising research directions going forward. This report summarizes the findings of that workshop. Along the way, it surveys recent theoretical work in the field and points towards promising directions for research.
Agile Process Consultation - An Applied Psychology Approach to Agility

An agile change effort in an organization needs to be understood in relation to human processes. Such theory and accompanying tools already existed almost 50 years ago in applied psychology. The core ideas of Agile Process Consultation are that a client initiating a change toward more agility often does not know what is wrong and the consultant needs to diagnose the problem jointly with the client. It is also assumed that the agile consultant cannot know the organizational culture of the client's organization, which means that the client needs to be making the decisions based on the suggestions provided by the consultant. Since agile project management is spreading across the enterprise and outside of software development, we need situational approaches instead of prescribing low-level practices.
Requirements Analysis for an Open iPaaS: Exploring the CSP, ISP, and SME View

Small and Mid-sized Enterprises (SMEs), in particular, suffer from cost-inefficient projects for the integration of cloud applications due to conceptual differences from local applications and a variety of related technical obstacles. Open source projects have been around for 20 years and tackle highly complex and comprehensive problems in a cost-effective manner. Following design science research, we gather and identify requirements for the development of an open integration Platform as a Service (iPaaS). We conducted a total of 20 interviews and obtained insights from cloud service providers (CSPs), integration service providers (ISPs), and SMEs. In this work, 641 requirements are collected and 67 sub-themes are identified as a result of extensive analysis. Furthermore, we outline 10 themes that align with three layers (provider, integration, and user) forming the target architecture of the underlying open source project in this paper. Finally, multiple insights are gathered for further employment, development, and research.
Theoretical Support for Enhancing Data Quality: Application in Electronic Medical Records

This paper aims at reviewing the existing theoretical support to enhance data quality and utilizing the findings of the review in the context of electronic medical records (EMRs). For this to happen, we first conducted a survey of publications that have a focus on an empirical investigation of factors influencing data quality in the conceptual models. By using a well-established taxonomy development method from the discipline of information systems, we then proposed 3 dimensions for studying factors influencing data quality and constructing the conceptual model for enhancing data quality: breadth, depth, and interaction, within 9 characteristics under different dimensions. Last, we compared related studies using the proposed dimensions and utilized the findings of the review in enhancing EMRs quality to disclose the limitations and possibilities of new areas for further study.