Miracle-dz/UniFAR-FTUs huggingface dataset:
Inclusive Gamified Participation: Who are we inviting and who becomes engaged?

Recent evaluations have shown that up until now, e-participation platforms have not been very effective in involving citizens in public decision-making. This is partly due to these novel forms of engagement not seeming to reach citizens beyond the ”usual suspects” of public participation. A trending approach to make these platforms more attractive, especially for less involved social groups, is to incorporate game-like elements. This research investigates the influence of demographical variables on participation and motivation when using a mobile gamified participation application. Our results show that participation was affected by age. The data further suggests that youth is both interested in urban planning and welcomes mobile participation forms, whereas older individuals feel less invited by novel technologies and engage less. Yet, older individuals and those less enthusiastic about games were not entirely put off by the included game elements.
A Framework for understanding & classifying Urban Data Business Models

Governments’ objective to transition to ‘Smart Cities’ heralds new possibilities for urban data business models to address pressing city challenges and digital transformation imperatives. Urban data business models are not well understood due to such factors as the maturity of the market and limited available research within this domain. Understanding the barriers and challenges in urban data business model development as well as the types of opportunities in the ecosystem is essential for incumbents and new entrants. Therefore, the aim of this paper is to develop a framework for understanding and classifying Urban Data Business Models (UDBM). This paper uses an embedded case study method to derive the framework by analyzing 40 publicly funded and supported business model experiments that address pressing city challenges under one initiative. This research contributes to the scholarly discourse on business model innovation in the context of smart cities.
Personalised Nudging for more Data Disclosure? On the Adaption of Data Usage Policies Format to Cognitive Styles

While highly sensitive data like personal health information (PHI) is valuable to digital health service providers, users often remain reluctant to disclose such personal data. Research has shown that personalised nudging, i.e. nudging that adopts content to user characteristics to nudge specific actions, can successfully increase purchase intention. However, its effect on consumers’ handling of sensitive data is unclear. We apply personalised nudging in the context of personalising data usage policies and investigate whether personalised nudges that match users’ cognitive styles (i.e. the way users process information), affects individuals’ level of trust, privacy concerns, risk, and PHI disclosure. Using an online experiment in the context of mobile apps for health bonus programmes, we find that, when presentation format matches the users’ cognitive style individuals’ PHI disclosure and trust are not affected, but that individuals’ privacy concerns and risk perceptions are significantly lower
New Solar Metallicity Measurements

In the past years, a systematic downward revision of the metallicity of the Sun has led to the “solar modeling problem”, namely the disagreement between predictions of standard solar models and inferences from helioseismology. Recent solar wind measurements of the metallicity of the Sun, however, provide once more an indication of a high-metallicity Sun. Because of the effects of possible residual fractionation, the derived value of the metallicity Z ⊙ = 0 . 0196 ± 0 . 0014 actually represents a lower limit to the true metallicity of the Sun. However, when compared with helioseismological measurements, solar models computed using these new abundances fail to restore agreement, owing to the implausibly high abundance of refractory (Mg, Si, S, Fe) elements, which correlates with a higher core temperature and hence an overproduction of solar neutrinos. Moreover, the robustness of these measurements is challenged by possible first ionization potential fractionation processes. I will discuss these solar wind measurements, which leave the “solar modeling problem” unsolved.
Measuring scheduling efficiency of RNNs for NLP applications

Recurrent neural networks (RNNs) have shown state of the art results for speech recognition, natural language processing, image captioning and video summarizing applications. Many of these applications run on low-power platforms, so their energy efficiency is extremely important. We observed that cache-oblivious RNN scheduling during inference typically results in 30-50x more data transferred on and off the CPU than the application's working set size. This can potentially impact its energy efficiency. This paper presents a new metric called Data Reuse Efficiency to gauge the RNN scheduling efficiency of a platform and shows the factors that influence the DRE value. Additionally, this paper discusses an optimization to improve reuse in RNNs and highlights the positive impact of this optimization on the total amount of memory read from or written to the memory controller (and, hence, the DRE value) during the execution of an RNN application for a mobile SoC.
Progressive Ensemble Networks for Zero-Shot Recognition

Despite the advancement of supervised image recognition algorithms, their dependence on the availability of labeled data and the rapid expansion of image categories raise the significant challenge of zero-shot learning. Zero-shot learning (ZSL) aims to transfer knowledge from labeled classes into unlabeled classes to reduce human labeling effort. In this paper, we propose a novel progressive ensemble network model with multiple projected label embeddings to address zero-shot image recognition. The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes. A progressive training framework is then deployed to gradually label the most confident images in each unlabeled class with predicted pseudo-labels and update the ensemble network with the training data augmented by the pseudo-labels. The proposed model performs training on both labeled and unlabeled data. It can naturally bridge the domain shift problem in visual appearances and be extended to the generalized zero-shot learning scenario. We conduct experiments on multiple ZSL datasets and the empirical results demonstrate the efficacy of the proposed model.
Variability in high‐mass X‐ray binaries

Strongly magnetized, accreting neutron stars show periodic and aperiodic variability over a wide range of time scales. By obtaining spectral and timing information on these different time scales, we can have a closer look into the physics of accretion close to the neutron star and the properties of the accreted material. One of the most prominent time scales is the strong pulsation, i.e., the rotation period of the neutron star itself. Over one rotation, our view of the accretion column and the X-ray producing region changes significantly. This allows us to sample different physical conditions within the column but at the same time requires that we have viewing-angle-resolved models to properly describe them. In wind-fed high-mass X-ray binaries, the main source of aperiodic variability is the clumpy stellar wind, which leads to changes in the accretion rate (i.e., luminosity) as well as absorption column. This variability allows us to study the behavior of the accretion column as a function of luminosity, as well as to investigate the structure and physical properties of the wind, which we can compare to winds in isolated stars.
Natural-Setting PHR Usability Evaluation using the NASA TLX to Measure Cognitive Load of Patients

While personal health records (PHRs) carry an array of potential benefits such as increased patient engagement, poor usability remains a significant barrier to patients’ adoption of PHRs. In this mixed methods study, we evaluate the usability of one PHR feature, an intake form called the pre-visit summary, from the perspective of cognitive load using real cardiovascular patients in a natural setting. A validated measure for cognitive load, the NASA Task Load Index, was used along with retrospective interviews to identify tasks within the pre-visit summary that increased participants’ cognitive load. We found that the medications, immunizations, active health concerns, and family history pages induced a higher cognitive load because participants struggled to recall personal health information and also due to user interface design issues. This research is significant in that it uses validated measures of cognitive load to study real patients interacting with their PHR in a natural environment.
Relational Action Forecasting

This paper focuses on multi-person action forecasting in videos. More precisely, given a history of H previous frames, the goal is to detect actors and to predict their future actions for the next T frames. Our approach jointly models temporal and spatial interactions among different actors by constructing a recurrent graph, using actor proposals obtained with Faster R-CNN as nodes. Our method learns to select a subset of discriminative relations without requiring explicit supervision, thus enabling us to tackle challenging visual data. We refer to our model as Discriminative Relational Recurrent Network (DRRN). Evaluation of action prediction on AVA demonstrates the effectiveness of our proposed method compared to simpler baselines. Furthermore, we significantly improve performance on the task of early action classification on J-HMDB, from the previous SOTA of 48% to 60%.
Task Assignment for Multiplayer Reach–Avoid Games in Convex Domains via Analytical Barriers

This article considers a multiplayer reach–avoid game between two adversarial teams in a general convex domain which consists of a target region and a play region. The evasion team, initially lying in the play region, aims to send as many team members into the target region as possible, while the pursuit team with its team members initially distributed in both play region and target region, strives to prevent that by capturing the evaders. We aim at investigating a task assignment about the pursuer–evader matching, which can maximize the number of the evaders who can be captured before reaching the target region safely when both teams play optimally. To address this, two winning regions for a group of pursuers to intercept an evader are determined by constructing an analytical barrier which divides these two parts. Then, a task assignment to guarantee the most evaders intercepted is provided by solving a simplified 0-1 integer programming instead of a nondeterministic polynomial problem, easing the computation burden dramatically. It is worth noting that except the task assignment, the whole analysis is analytical. Finally, simulation results are also presented.
The asymptotics of the partition of the cube into Weyl simplices, and an encoding of a Bernoulli scheme

We suggest a combinatorial method of encoding continuous symbolic dynamical systems. A continuous phase space, the infinite-dimensional cube, turns into the path space of a tree, and the shift is mapped to a transformation which was called a “transfer. ” The central problem is that of distinguishability: does the encoding separate almost all points of the space? The main result says that the partition of the cube into Weyl simplices satisfies this property.
Understanding the Effects of Temporal Differences in User-Centered Workflows on Solution Satisfaction

Technology-induced changes to workflows may not necessarily lead to user satisfaction with solutions expected at the end of each workflow. This eventually affects project success. The study investigates user expectation and confirmation regarding cognitive agreement with the changes from old paper workflows to new electronic workflows. In particular, a research model is developed with two mediated routes between user expectation and solution satisfaction: one route is via perceived workflow agreements and the other via perceived performances. Based on longitudinal survey data of 118 participants in an institution that recently underwent a major transition in workflows, it was found that old workflow agreement influenced solution expectation more than old perceived performance, but new perceived performance impacted solution satisfaction more than new workflow agreement.
Live Streaming Commerce: Uses and Gratifications Approach to Understanding Consumers' Motivations

In this paper, we introduce live streaming commerce – e-commerce integrated with real-time social interaction via live streams. Using a uses and gratifications framework, we identified four motivations (enjoyment of interaction, substitutability of personal examination, need for community, and trend setting) related to live streaming commerce, and explored relationships between motivations and behavioral intentions in three different scenarios: general watching scenario, product search scenario, and internet celebrity scenario. Results showed that substitutability of personal examination was associated with the general watching scenario and product search scenario, while enjoyment of interaction was associated with the internet celebrity scenario. Trend setting was associated with all scenarios but need for community was insignificant with all scenarios. Design implications based on results are discussed for future live streaming commerce system development.
Software Engineering in Civic Tech A Case Study about Code for Ireland

Civic grassroots have proven their ability to create useful and scalable software that addresses pressing social needs. Although software engineering plays a fundamental role in the process of creating civic technology, academic literature that analyses the software development processes of civic tech grassroots is scarce. This paper aims to advance the understanding of how civic grassroots tackle the different activities in their software development processes. In this study, we followed the formation of two projects in a civic tech group (Code for Ireland) seeking to understand how their development processes evolved over time, and how the group carried out their work in creating new technology. Our preliminary findings show that such groups are capable of setting up systematic software engineering processes that address software specification, development, validation, and evolution. While they were able to deliver software according to self-specified quality standards, the group has challenges in requirements specification, stakeholder engagement, and reorienting from development to product delivery. Software engineering methods and tools can effectively support the future of civic technologies and potentially improve their management, quality, and durability.
The Value of Patient-Reported Outcome (PRO) Data in Digital Healthcare: Using the How-R-you App as a PRO Instrument

This paper evaluates the value of using a mobile digital technology for the purpose of collecting PatientReported Outcome (PRO) data. We compare the use of a paper-based questionnaire and a mobile application as two different PRO instruments. Based on analysis, we conclude that use of the mobile application allows for a more nuanced picture of patients' health to be established. Implications for diagnosis and medical treatment are discussed along with the need for future
Unsupervised Feature Learning for Environmental Sound Classification Using Cycle Consistent Generative Adversarial Network

In this paper we propose a novel environmental sound classification approach incorporating unsupervised feature learning from codebook via spherical K-Means++ algorithm and a new architecture for high-level data augmentation. The audio signal is transformed into a 2D representation using a discrete wavelet transform (DWT). The DWT spectrograms are then augmented by a novel architecture for cycle-consistent generative adversarial network. This high-level augmentation bootstraps generated spectrograms in both intra and inter class manners by translating structural features from sample to sample. A codebook is built by coding the DWT spectrograms with the speeded-up robust feature detector (SURF) and the K-Means++ algorithm. The Random Forest is our final learning algorithm which learns the environmental sound classification task from the clustered codewords in the codebook. Experimental results in four benchmarking environmental sound datasets (ESC-10, ESC-50, UrbanSound8k, and DCASE-2017) have shown that the proposed classification approach outperforms the state-of-the-art classifiers in the scope, including advanced and dense convolutional neural networks such as AlexNet and GoogLeNet, improving the classification rate between 3.51% and 14.34%, depending on the dataset. Email addresses: mohammad.esmaeilpour. 1@ens.etsmtl.ca (Mohammad Esmaeilpour), patrick.cardinal@etsmtl.ca (Patrick Cardinal), alessandro.koerich@etsmtl.ca (Alessandro L. Koerich) Preprint submitted to Applied Soft Computing April 9, 2019 ar X iv :1 90 4. 04 22 1v 1 [ cs . L G ] 8 A pr 2 01 9
Significance of microRNA-330/TYMS expression axis through colorectal tumorigenesis

Colorectal cancer is one of the most common types of cancer worldwide. MiR-330 has been reported as a cell proliferation inhibitor by suppressing thymidylate synthase (TYMS) in FOLFOX, one of the major chemotherapy regimens used to treat colorectal cancers. A number of dysregulated miRNAs have been linked to CRC progression and treatment response and are thought to be promising prognostic biomarkers for this cancer. In the current study, miR-330, TYMS, and their interactions have been investigated in the absence of this chemotherapy to evaluate their therapeutic and diagnostic value for other treatment methods.
Criteria and Recommendations for IS Research that Bridges the Academic-Practitioner Gap

Industry practitioners deal with complex and fastmoving information system (IS) related challenges. IS academic researchers generate substantive theoretical outputs dealing with many of these challenges. Unfortunately, practitioners rarely make use of this academic research. This represents a serious gap that has negative consequences for academics and practitioners alike. This paper identifies aspects of the academic-practitioner gap and describes five criteria (value, velocity, visibility, voice, and verifiability) that researchers may use throughout the research process to increase the likelihood of creating research outputs that will be used by practitioners. These criteria are linked to three approaches for conducting research most likely to impact practitioners – direct creation of practitioner content, translation of scholarly research and co-production. We close with specific recommendations for IS academics to overcome the gap.
A Novel Privacy Preserving Search Technique for Stego Data in Untrusted Cloud

We propose the first privacy preserving search technique for stego health data in untrusted cloud in this paper. The Cloud computing is a popular technology to the healthcare providers for outsourcing health data due to flexibility and cost effectiveness. However, outsourcing health data to the cloud introduces serious privacy issues to the patient. For example, dishonest personnel of the cloud provider may disclose patient sensitive information to business organizations for some financial benefits. Using steganography, patient sensitive information is hidden within health data for privacy preservation. As a result, stego health data is generated. To the best of our knowledge, no method exists for searching a particular stego data without disclosing any information to the cloud. We propose a framework for privacy preserving search over stego health data. We systematically describe each component of the proposed framework. We conduct several experiments to evaluate the performance of the framework.
The Role of Pseudonymity in Mobile e-Participation

In addition to lack of knowledge and trust in official authorities, privacy concerns are among the reasons for non-participation in public discourse. Mitigating these barriers is important when aiming to promote and ultimately increase public participation. Through the lens of a long-term field study with a mobile participation prototype, this research investigated citizens’ participation patterns in relation to their choice in username (real name vs. pseudonym). Our data suggests that while engagement served less socializing purposes, social appreciation was not affected by pseudonymity. Interestingly, those participating with their real-name lost trust in the local government. Overall, we found no evidence that pseudonymity impacts the level of participation, but participants indicated to favor using a pseudonym in the future.
Impact of Interactivity on Guanxi Network Building in the Wechat Moments: A Social Capital Perspective

Mobile social platform such as Wechat Moments has gained great popularity in China in the past few years. However, there are still a lack of studies that focus on Guanxi network building in the virtual social community. Drawing upon interactivity and social capital theory, this study develops a research model to examine the influence of perceived interactivity on users' social capital and Guanxi network in the Wechat Moments. An empirical survey was conducted in China and 287 valid data were collected from Wechat users. Structural equation modelling analysis was used to test the research model. The empirical results suggest that interactivity has a strong influence on social interaction and shared understanding, which in turn promote users' Guanxi network in the Wechat Moments. A post-hoc analysis further suggests that the influence of interactivity on Guanxi network is contingent upon network size. Theoretical and practical implications are illustrated in the final section.
Three-Dimensional Laser Printing of Macro-Scale Glass Objects at a Micro-Scale Resolution

Three-dimensional (3D) printing has allowed for the production of geometrically complex 3D objects with extreme flexibility, which is currently undergoing rapid expansion in terms of materials, functionalities, as well as areas of application. When attempting to print 3D microstructures in glass, femtosecond laser-induced chemical etching (FLICE)—which is a subtractive 3D printing technique—has proved itself a powerful approach. Here, we demonstrate the fabrication of macro-scale 3D glass objects of large heights up to ~3.8 cm with an identical lateral and longitudinal feature size of ~20 μm. The remarkable accomplishment is achieved by revealing an unexplored regime in the interaction of ultrafast laser pulses with fused silica, which results in depth-insensitive focusing of the laser pulses inside fused silica.