Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing Efficacious Mobile Technologies for Anxiety Self-Regulation

This paper presents a step-by-step process that was developed primarily to extract design pre-requisites for personalized mobile technologies assisting anxiety self-regulation. This process, which is recognized as a preliminary framework, was developed, refined, and tested based on a multidisciplinary literature review and an exploratory study conducted with mental health professionals who treat anxiety disorders. The step-by-step nature of this framework draws from multiple disciplinary and stakeholder perspectives, integrates knowledge about efficacious psychological interventions, considers individual differences and specific challenges faced by patients, and realizes contextual needs. It also includes incremental and iterative refinements based on multidisciplinary sources to foster more evidence-based interface designs. Once reached its maturity, this framework can potentially be applied for designing efficacious technologies for a range of mental health conditions. The expected future contributions and limitations of the framework are also discussed.
Demonstrating VRBox: A Virtual Reality Augmented Sandbox

We present VRBox--an interactive sandbox for playful and immersive terraforming that combines the approach of augmented sandboxes with virtual reality technology and mid-air gestures. Our interactive demonstration offers a virtual reality (VR) environment containing a landscape, which the user designs via interacting with real sand while wearing a VR head-mounted display (HMD). Whereas real sandboxes have been used with augmented reality before, our approach using sand in VR offers novel and original interactive features such as exploring the sand landscape from a first person perspective. In this demo, users can experience our VR-sandbox system consisting of a box with sand, multiple Kinect depth sensing, an HMD, and hand tracking, as well as an interactive world simulation.
Microservice architecture and model-driven development: yet singles, soon married (?)

Microservice Architecture (MSA) is a comparatively novel service-based architectural style with a strong focus on high cohesion, loose coupling, and independence of services and their development teams. In this position paper we argue that Microservice Architecture (MSA) can benefit from the application of Model-driven Development (MDD). Therefore, we elucidate how typical MSA concerns may be addressed by means of MDD such as abstraction, model transformation, and modeling viewpoints. Because this contemplation is driven from a conceptual perspective on MSA, we conversely present an overview of existing methodologies and tools for applying MDD holistically in MSA development to further substantiate our position.
Tetracycline Rapidly Reaches All the Constituent Cells of Uropathogenic Escherichia coli Biofilms

ABSTRACT We have developed a method for visualizing Escherichia coli cells that are exposed to tetracycline in a biofilm, based on a previous report that liposomes containing the E. coli TetR(B) protein fluoresce when exposed to this antibiotic. By our method, cells devoid of TetR(B) also exhibited tetracycline-dependent fluorescence. At 50 μg of tetracycline ml−1, planktonic cells of a uropathogenic E. coli (UPEC) strain developed maximal fluorescence after 7.5 to 10 min of exposure. A similar behavior was exhibited by cells in a 24- or 48-h UPEC biofilm, as examined by confocal laser microscopy, regardless of whether they lined empty spaces or occupied densely packed regions. Further, a comparison of phase-contrast and fluorescent images of corresponding biofilm zones showed that all the cells fluoresced. Thus, all the biofilm cells were exposed to tetracycline and there were no pockets within the biofilm where the antibiotic failed to reach. It also appeared unlikely that niches of reduced exposure to the antibiotic existed within the biofilms.
Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.
Building Footprint Extraction from High-Resolution Images via Spatial Residual Inception Convolutional Neural Network

The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.
SoundGlance: Briefing the Glanceable Cues of Web Pages for Screen Reader Users

Screen readers have become a core assistive technology for blind web users to browse web pages. Although screen readers can convey the textual information or structural properties of web pages, they cannot deliver their overall impression. Such a limitation hinders blind web users from obtaining an overview of the website, which non-blind people can do in a short time. As such, we present SoundGlance, a novel application that briefly delivers an auditory summary of web pages. SoundGlance supports the screen reader users by converting the important glanceable cues of the pages into sound. The feasibility of prototype was examined in a pilot study with fourteen blind people. Several practical insights were derived from the experiment.
Chats with Bots: Balancing Imitation and Engagement

Advances in AI are paving the way towards more natural interactions, blurring the line between bot and human. We present findings from a two-week diary study exploring users' interactions with the chatbot Replika. In particular, we focus on how users anthropomorphize chatbots and how this influences their engagement. We find that failing to adhere to social norms and glaring signs of humanity leads to decreased engagement unless balanced appropriately.
VRIA - A Framework for Immersive Analytics on the Web

We report on the design, implementation and evaluation of , a framework for building immersive analytics (IA) solutions in Web-based Virtual Reality (VR), built upon WebVR, A-Frame, React and D3. The recent emergence of affordable VR interfaces have reignited the interest of researchers and developers in exploring new, immersive ways to visualize data. In particular, the use of open-standards web-based technologies for implementing VR in a browser facilitates the ubiquitous and platform-independent adoption of IA systems. Moreover, such technologies work in synergy with established visualization libraries, through the HTML document object model (DOM). We discuss high-level features of and present a preliminary user experience evaluation of one of our use cases.
μ FLU 12-102 VISUALISATION OF SINGLE SUBMICRON PARTICLES BY LIGHT SCATTERING IN A MICROFLOW

We describe the fabrication and operation of microfluidic chips for optical visualization of individual sub-micron and nanoscopic particles by light scattering in a continuous flow. Particles beyond the resolution limit of an optical microscope can be directly seen and recorded as they scatter the incident laser light, which permits label-free detection of single particles. Microchannels of 120 μm height structured in Ordyl dry film resist on the chips were traversed by a laser beam coupled in by a glass fiber so that particles in the flow cross the beam and scatter a portion of the incident light. At an angle of 90° to the laser beam the scattered light is observed via a microscope and recorded with a camera. 250 nm particles appear as clearly visible bright spots of several micrometer diameter on the video and can individually be counted. Hydrodynamic focusing of particles with a sheath flow was performed in two ways. Vertical focusing was done in order to confine the particle containing sample liquid to the bottom of the chip so that all particles were approximately in the focal plane of the microscope and hence circular fringes could be avoided. By using lateral focusing the sample stream was confined to the middle of the channel, which leads to a smaller detection region and allows the use of higher microscope magnification. With the proposed sensor setup and microfluidic chips it is possible to visualize and count suspended particles.
Robust uplink to downlink spatial covariance matrix transformation for downlink beamforming

We propose a new uplink to downlink spatial covariance matrix transformation technique that provides robustness against uplink steering vector mismatches for frequency duplex systems (FDD) in a frequency selective fading environment. Our approach leads to a matrix robust least squares (RLS) problem that can be formulated in a convex form as a second order cone (SOC) program and solved efficiently using the well-established interior point methods. The performance of the proposed method is compared with that of (K. Hugl et al., 1999) and (T. Aste et al., 1998) using a dynamic system level simulator developed for investigating the capacity improvement of a UMTS-FDD system with smart antennas at the base stations. Downlink capacity is used as a measure of system performance by finding the number of users for which a certain quality of service (QoS) requirement is fulfilled. System level simulations are carried out for 4-element uniform circular antenna (UCA) array. Simulation results show that our method outperforms both (K. Hugl et al., 1999) and (T. Aste et al., 1998) by supporting about 48% and 15% more average number of users than that of (K. Hugl et al., 1999) and (T. Aste et al., 1998) , respectively without any significant degradation in performance when compared to (T. Aste et al., 1998) with known uplink steering vector.
Towards Better Security Decisions: Applying Prospect Theory to Cybersecurity

Normal users are usually not good at making decisions about cybersecurity, being easily attacked by hackers. Quite a few tools have been devised and implemented to help, but they can not balance security and usability well. To solve the problem, this paper explores the application of prospect theory to security recommendations. We conducted online surveys (n=61) and a between-subjects experiment (n=106) in six conditions to investigate the issues. In the experiment, we provided different security recommendations about two-factor-authentication (2FA) to participants in different conditions and recorded their decisions about enabling it. Results show that participants in the condition "Disadvantage" were willing to adopt 2FA the most. The findings indicate that showing disadvantages can be useful to persuade users into better security decisions.
Privacy Therapy with Aretha: What If Your Firewall Could Talk?

The rapid adoption of smart home devices has brought with it a widespread lack of understanding amongst users over where their devices send data. Smart home ecosystems represent complex additions to existing wicked problems around network privacy and security in the home. This work presents the Aretha project, a device which combines the functionality of a firewall with the position of voice assistants as the hub of the smart home, and the sophistication of modern conversational voice interfaces. The result is a device which can engage users in conversation about network privacy and security, allowing for the forming and development of complex preferences that Aretha is then able to act upon.
Designing Hypercontextualized Games: A Case Study with LieksaMyst

Digital technology empowers one to access vast amounts of on-line data. From a learning perspective, however, it is difficult to access meaningful on-site information within a given context. The Hypercontextualized Game (HCG) design model interweaves on-site resources, translated as content, and the digital game. As a local game design process, HCG builds on the resources available on-site (context) and transfers them beyond their origin (hyper). A successful example is the HCG stories in LieksaMyst, an application developed for the Pielinen Museum, in which the player is guided through a series of activities by a virtual host from the historic past. It took three years of extensive work and research for the LieksaMyst HCG stories to mature from brainstorming concepts to a fully-fledged museum service. Curators, educational technology experts and a total of 476 visitors (from 6 to 77 years old) contributed to the design process. The analysis of the context and feedback from the visitors enabled us to choose media, content and activities suitable to the Pielinen Museum. Our findings indicate that quality time, sincere reflection and communication between local experts and potential players, are indispensable when designing a HCG based game. The analysis benefits researchers and practitioners who are interested in the ways in which a game can bridge the gap between people and relevant on-site information.
Change Analysis in Urban Areas Based on Statistical Features and Temporal Clustering Using TerraSAR-X Time-Series Images

The existing unsupervised multitemporal change detection approaches for synthetic aperture radar (SAR) images based on the pixel level usually suffer from the serious influence of speckle noise, and the classification accuracy of temporal change patterns is liable to be affected by the generation method of similarity matrices and the pre-specified cluster number. To address these issues, a novel time-series change detection method with high efficiency is proposed in this paper. Firstly, spatial feature extraction using local statistical information on patches is conducted to reduce the noise and for subsequent temporal grouping. Secondly, a density-based clustering method is adopted to categorize the pixel series in the temporal dimension, in view of its efficiency and robustness. Change detection and classification results are then obtained by a fast differential strategy in the final step. The experimental results and analysis of synthetic and realistic time-series SAR images acquired by TerraSAR-X in urban areas demonstrate the effectiveness of the proposed method, which outperforms other approaches in terms of both qualitative results and quantitative indices of macro F1-scores and micro F1-scores. Furthermore, we make the case that more temporal change information for buildings can be obtained, which includes when the first and last detected change occurred and the frequency of changes.
Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing

Mobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in this article, we introduce a framework enabling mobile crowdsensing in fog environments with a hierarchical scheduling strategy. We first introduce the crowdsensing framework that has a hierarchical structure to organize different resources. Since different positions and performance of fog nodes influence the quality of service (QoS) of IoT applications, we formulate a scheduling problem in the hierarchical fog structure and solve it by using a deep reinforcement learning–based strategy. From extensive simulation results, our solution outperforms other scheduling solutions for mobile crowdsensing in the given fog computing environment.
Computation of the exact bit-error rate of coherent M-ary PSK with Gray code bit mapping

The problem of calculating the average bit-error probability (BEP) of coherent M-ary phase-shift keying (PSK) over a Gaussian channel has been studied previously in the literature. A solution to the problem for systems using a binary reflected Gray code (BRGC) to map bits to symbols was first presented by P.J. Lee (see ibid., vol.COM-34, p.488-91, 1986). We show that the results obtained by Lee are incorrect for M/spl ges/16. We show that the reason for this is an invalid assumption that the bit-error rate (BER) is independent of the transmitted symbols, an assumption which has also propagated to textbooks. We give a new expression for the BER of M-PSK systems using the BRGC and compare this with Lee's results.
Web-Scale Data Integration: You can afford to Pay as You Go

The World Wide Web is witnessing an increase in the amount of structured content – vast heterogeneous collections of structured data are on the rise due to the Deep Web, annotation schemes like Flickr, and sites like Google Base. While this phenomenon is creating an opportunity for structured data management, dealing with heterogeneity on the web-scale presents many new challenges. In this paper, we highlight these challenges in two scenarios – the Deep Web and Google Base. We contend that traditional data integration techniques are no longer valid in the face of such heterogeneity and scale. We propose a new data integration architecture, PAYGO, which is inspired by the concept of dataspaces and emphasizes pay-as-you-go data management as means for achieving web-scale data integration.
New Directions for the IoT: Automate, Share, Build, and Care

As the IoT is taking hold in the home, in healthcare, factories, and industry, new challenges and approaches arise for HCI research and design. For example, HCI is exploring agency delegation and automation to support the user in managing the deluge of IoT data, make decisions, or even take actions on behalf of the user, while economic models are being proposed to drive sharing economy services. This creates new problems including how to design appropriate solutions for uncertain and dynamic human behaviour, how to ensure resources are distributed fairly, and how to ensure that the user can understand system actions and ultimately remains in control. These issues are becoming more pertinent as the IoT diversifies into safety-critical domains such as manufacturing and healthcare. This one-day workshop intends to bring together the CHI community to explore the interactional, socio-cultural, ethical, and practical challenges and approaches that these new domains raise for the IoT. With this, we want to consider how such approaches could be integrated to achieve more sustainable, inclusive, or effective interactions.
On the negative impact of team independence in microservices software development

Microservices allow teams to work in isolation, and reduce the need of communication among the teams. While this aspect could be considered a short-term benefit due to the reduction of the communication effort, the reduced amount of communication could create issues in the general synchronization of the whole project. In this work we start to depict the potential negative impact of the reduced need of communication in the development of microservices-based systems. We propose the design of an empirical study to understand if this reduction is beneficial, and when and how communication between teams should be enforced.
All the World (Wide Web)'s a Stage: A Workshop on Live Streaming

With the rise of live streaming and esports in recent years, it becomes increasingly important for the HCI community to understand this phenomenon. The organizers encourage people to submit papers with novel interfaces they wish to explore in a live streaming context. In this workshop, participants will discuss different facets of the live streaming experience and obtain a greater understanding of the culture that exists on streaming platforms like Twitch. They will then participate in a design exercise, forming groups and iterating on a design together. Discussion/design topics will include ideas encouraging audience participation, moderation of toxicity, and other such topics. Participants will leave the workshop with ideas about how they can better design games and other experiences taking the live streaming ecosystem into consideration.
Performance evaluation of a relational associative processor

A relational associative processor called RAP has been designed to provide hardware support for the use and manipulation of a relational data base. In this paper, we describe the relational operations provided by the RAP hardware, and devise a representative approach to providing the same relational operations with conventional software and hardware. Analytic models are constructed for both RAP and the conventional system. All simplifying assumptions are made to favor the conventional system. The execution times of several of the operations are shown to be improved with RAP by several orders of magnitude.