Miracle-dz/UniFAR-FTUs huggingface dataset:
Error Analysis of Nonconstant Admittivity for MR-Based Electric Property Imaging

Magnetic resonance electrical property tomography (MREPT) is a new imaging modality to visualize a distribution of admittivity γ = σ+iωε inside the human body where σ and ε denote electrical conductivity and permittivity, respectively. Using B1 maps acquired by an magnetic resonance imaging scanner, it produces cross-sectional images of σ and ε at the Larmor frequency. Since current MREPT methods rely on an assumption of a locally homogeneous admittivity, there occurs a reconstruction error where this assumption fails. Rigorously analyzing the reconstruction error in MREPT, we showed that the error is fundamental and may cause technical difficulties in interpreting MREPT images of a general inhomogeneous object. We performed numerical simulations and phantom experiments to quantitatively support the error analysis. We compared the MREPT image reconstruction problem with that of magnetic resonance electrical impedance tomography (MREIT) to highlight distinct features of both methods to probe the same object in terms of its high- and low-frequency conductivity distributions, respectively. MREPT images showed large errors along boundaries where admittivity values changed whereas MREIT images showed no such boundary effects. Noting that MREIT makes use of the term neglected in MREPT, a novel MREPT admittivity image reconstruction method is proposed to deal with the boundary effects, which requires further investigation on the complex directional derivative in the real Euclidian space \BBR3.
Designing for Digital Wellbeing: A Research & Practice Agenda

Traditionally, many consumer-focused technologies have been designed to maximize user engagement with their products and services. More recently, many technology companies have begun to introduce digital wellbeing features, such as for managing time spent and for encouraging breaks in use. These are in the context of, and likely in response to, renewed concerns in the media about technology dependency and even addiction. The promotion of technology abstinence is also increasingly widespread, e.g., via digital detoxes. Given that digital technologies are an important and valuable feature of many people's lives, digital wellbeing features are arguably preferable to abstinence. However, how these are defined and designed is something that needs to be explored further. In this one-day workshop we welcome both industry and academic participants to discuss what digital wellbeing means, who is responsible for it, and whether and how we should design for it going forward.
Risk-Sensitive Optimal Feedback Control for Haptic Assistance

While human behavior prediction can increase the capability of a robotic partner to generate anticipatory behavior during physical human robot interaction (pHRI), predictions in uncertain situations can lead to large disturbances for the human if they do not match the human intentions. In this paper we present a novel control concept in which the assistive control parameters are adapted to the uncertainty in the sense that a the robot takes a more or less active role depending on its confidence in the human behavior prediction. The approach is based on risk-sensitive optimal feedback control. The human behavior is modeled using probabilistic learning methods and any unexpected disturbance is considered as a source of noise. The proposed approach is validated in situations with different uncertainties, process noise and risk-sensitivities in a tow- Degree-of-Freedom virtual reality experiment.
Guess or Not?: A Brain-Computer Interface Using EEG Signals for Revealing the Secret behind Scores

Now examinations and scores serve as the main criterion for a student's academic performance. However, students use guessing strategies to improve the chances of choosing the right answer in a test. Therefore, scores do not reflect actual levels of the student's knowledge and skills. In this paper, we propose a brain-computer interface (BCI) to estimate whether a student guesses on a test question or masters it when s/he chooses the right answer in logic reasoning. To build this BCI, we first define the "Guessing'' and employ Raven's Progressive Matrices as logic tests in the experiment to collect EEG signals, then we propose a sliding time-window with quorum-based voting (STQV) approach to recognize the state of "Guessing'' or "Understanding'', together with FBCSP and end-to-end ConvNet classification algorithms. Results show that this BCI yields an accuracy of 83.71% and achieves a good performance in distinguishing "Guessing'' from "Understanding''.
Towards a Framework for Validating the Matching Between Notifications and Scents in Olfactory In-Car Interaction

Olfactory notifications have been proven to have a positive impact on drivers. This has motivated the use of scents to convey driving-relevant information. Research has proposed the use of such scents as lemon, peppermint, lavender and rose for in-car notifications. However, there is no framework to identify which scent is the most suitable for every application scenario. In this paper, we propose an approach for validating a matching between scents and driving-relevant notifications. We suggest a study in which the olfactory modality is compared with a puff of clean air, visual, auditory, and tactile stimuli while performing the same driving task. For the data analysis, we suggest recording the lane deviation, speed, time required to recover from the error, as well as the perceived liking and comfort ratings. Our approach aims to help automotive UI designers make better decisions about choosing the most suitable scent, as well as possible alternative modalities.
ClassBeacons: Enhancing Reflection-in-Action of Teachers through Spatially Distributed Ambient Information

Reflection-in-action (RiA) refers to teachers' reflections on their teaching performance during busy classroom routines. RiA is a demanding competence for teachers, but little has been known about how HCI systems could support teachers' RiA during their busy and intensive teaching. To bridge this gap, we design and evaluate an ambient information system named ClassBeacons. ClassBeacons aims to help teachers intuitively reflect-in-action on how to divide time and attention over pupils throughout a lesson. ClassBeacons subtly depicts teachers' division of time and attention over pupils through multiple light-objects distributed over students' desks. Each light-object indicates how long the teacher has been cumulatively around it (helping an adjacent student) by shifting color. A field evaluation with eleven teachers proved that ClassBeacons enhanced teachers' RiA by supporting their sensemaking of ongoing performance and modification of upcoming actions. Furthermore, ClassBeacons was experienced to unobtrusively fit into teachers' routines without overburdening teaching in progress.
Modeling Barrier Island Habitats Using Landscape Position Information

Barrier islands are dynamic environments because of their position along the marine–estuarine interface. Geomorphology influences habitat distribution on barrier islands by regulating exposure to harsh abiotic conditions. Researchers have identified linkages between habitat and landscape position, such as elevation and distance from shore, yet these linkages have not been fully leveraged to develop predictive models. Our aim was to evaluate the performance of commonly used machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, for predicting barrier island habitats using landscape position for Dauphin Island, Alabama, USA. Landscape position predictors were extracted from topobathymetric data. Models were developed for three tidal zones: subtidal, intertidal, and supratidal/upland. We used a contemporary habitat map to identify landscape position linkages for habitats, such as beach, dune, woody vegetation, and marsh. Deterministic accuracy, fuzzy accuracy, and hindcasting were used for validation. The random forest algorithm performed best for intertidal and supratidal/upland habitats, while the K-nearest neighbor algorithm performed best for subtidal habitats. A posteriori application of expert rules based on theoretical understanding of barrier island habitats enhanced model results. For the contemporary model, deterministic overall accuracy was nearly 70%, and fuzzy overall accuracy was over 80%. For the hindcast model, deterministic overall accuracy was nearly 80%, and fuzzy overall accuracy was over 90%. We found machine learning algorithms were well-suited for predicting barrier island habitats using landscape position. Our model framework could be coupled with hydrodynamic geomorphologic models for forecasting habitats with accelerated sea-level rise, simulated storms, and restoration actions.
Ethnographic Methods for Human Factors Researchers: Collecting and Interweaving Threads of HCI

This course offers an introduction to ethnography for Human Factors Research. It covers relevant topics along the research process from decision arguments for the method and study design, up to data collection, analysis and interpretation. Ethical questions will include the researcher's role(s) in the field and modes of data presentation. The collection of multi-dimensional sets of data - a trademark of high-quality ethnographic work - enables inter-weaving threads of HCI perspectives in complex human factors and user research contexts. To achieve this, a comprehensive toolbox of ethnographic methods is introduced along with practical hands-on sessions to familiarize with these methodological instruments.
NEAR OPTIMAL ROBUST ADAPTIVE BEAMFORMING APPROACH BASED ON EVOLUTIONARY ALGORITHM

The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the optimal performance. In this paper, we propose an evolutionary algorithm (EA) based robust adaptive beamforming that is able to achieve near optimal performance. The essence of the idea is to shape the array beam response such that it has maximum response in the desired signal’s angular range and minimum response in the interferences’ angular range. In addition, the approach introduces null-response constraints deduced from the array observation to achieve better interference cancelation performance. As a whole, the proposed optimization is solvable using an improved variant of the differential evolution (DE) algorithm. Numerical simulations are also presented to demonstrate the efficacy of the proposed algorithm.
Deep Player Behavior Models: Evaluating a Novel Take on Dynamic Difficulty Adjustment

Finding and maintaining the right level of challenge with respect to the individual abilities of players has long been in the focus of game user research (GUR) and game development (GD). The right difficulty balance is usually considered a prerequisite for motivation and a good player experience. Dynamic difficulty adjustment (DDA) aims to tailor difficulty balance to individual players, but most deployments are limited to heuristically adjusting a small number of high-level difficulty parameters and require manual tuning over iterative development steps. Informing both GUR and GD, we compare an approach based on deep player behavior models which are trained automatically to match a given player and can encode complex behaviors to more traditional strategies for determining non-player character actions. Our findings indicate that deep learning has great potential in DDA.
The Body as Starting Point: Applying Inside Body Knowledge for Inbodied Design

Inbodied design is an emerging area in HCI that focuses on using knowledge of the body's internal systems and processes to better inform em-bodied and circum-bodied design spaces. The current challenge in developing an inbodied approach to HCI research/design is domain expertise: accessing sufficient and appropriate information about how the body itself works and how the body's different systems interact dynamically. In this workshop, we review and build on last year's introduction to inbodied foundations, focusing on applying inbodied knowledge to design challenges to explore (1) the foundational pillars of the inbodied design approach, and (2) how inbodied knowledge can affect / alter our understanding of em-bodied and circum-bodied design challenges and better inform design decisions. Our aim with this hands-on and cross-domain workshop is for HCI researchers to create innovative designs taking the body as a starting point
Using Pirate Plunder to Develop Children's Abstraction Skills in Scratch

Scratch users often struggle to detect and correct 'code smells' (bad programming practices) such as duplicated blocks and large scripts, which can make programs difficult to understand and debug. These 'smells' can be caused by a lack of abstraction, a skill that plays a key role in computer science and computational thinking. We created Pirate Plunder, a novel educational block-based programming game, that aims to teach children to reduce smells by reusing code in Scratch. This work describes an experimental study designed to measure the efficacy of Pirate Plunder with children aged 10 and 11. The findings were that children who played the game were then able to use custom blocks (procedures) to reuse code in Scratch, compared to non-programming and programming control groups.
Assignment Problems of Different-Sized Inputs in MapReduce

A MapReduce algorithm can be described by a mapping schema, which assigns inputs to a set of reducers, such that for each required output there exists a reducer that receives all the inputs participating in the computation of this output. Reducers have a capacity that limits the sets of inputs they can be assigned. However, individual inputs may vary in terms of size. We consider, for the first time, mapping schemas where input sizes are part of the considerations and restrictions. One of the significant parameters to optimize in any MapReduce job is communication cost between the map and reduce phases. The communication cost can be optimized by minimizing the number of copies of inputs sent to the reducers. The communication cost is closely related to the number of reducers of constrained capacity that are used to accommodate appropriately the inputs, so that the requirement of how the inputs must meet in a reducer is satisfied. In this work, we consider a family of problems where it is required that each input meets with each other input in at least one reducer. We also consider a slightly different family of problems in which each input of a list, X, is required to meet each input of another list, Y, in at least one reducer. We prove that finding an optimal mapping schema for these families of problems is NP-hard, and present a bin-packing-based approximation algorithm for finding a near optimal mapping schema.
Expectation and Attention in Hierarchical Auditory Prediction

Hierarchical predictive coding suggests that attention in humans emerges from increased precision in probabilistic inference, whereas expectation biases attention in favor of contextually anticipated stimuli. We test these notions within auditory perception by independently manipulating top-down expectation and attentional precision alongside bottom-up stimulus predictability. Our findings support an integrative interpretation of commonly observed electrophysiological signatures of neurodynamics, namely mismatch negativity (MMN), P300, and contingent negative variation (CNV), as manifestations along successive levels of predictive complexity. Early first-level processing indexed by the MMN was sensitive to stimulus predictability: here, attentional precision enhanced early responses, but explicit top-down expectation diminished it. This pattern was in contrast to later, second-level processing indexed by the P300: although sensitive to the degree of predictability, responses at this level were contingent on attentional engagement and in fact sharpened by top-down expectation. At the highest level, the drift of the CNV was a fine-grained marker of top-down expectation itself. Source reconstruction of high-density EEG, supported by intracranial recordings, implicated temporal and frontal regions differentially active at early and late levels. The cortical generators of the CNV suggested that it might be involved in facilitating the consolidation of context-salient stimuli into conscious perception. These results provide convergent empirical support to promising recent accounts of attention and expectation in predictive coding.
What is going on in agile gamification?

The potential of gamification is based on the hypothesis that it improves user engagement, motivation, and performance. Gamification has already been used in software development with promising results. Our aim is to get a snapshot of the state of the practice of gamification and agile software development, and to discover what evidence there is about the benefits of this integration. A literature review revealed that current research in the field is at the very early stages, and there are very few studies, some of which do not provide empirical evidence of the impact of gamification on the agile process. Therefore, results will always be inconclusive. However, we found that the impact of gamification on agile team performance is perceived to be positive. Examples of other interesting issues that have arisen are that user stories are the most gamified agile practice or that the most frequently adopted gamification elements are points and badges.
BEYES: A Shopping Solution for Independent Clothing Experiences of the Visually Impaired

While clothing is one of the requisites of human life, the visually impaired have limitations in their shopping and need to rely on acquaintances or shop assistants to choose and purchase clothes. Given that fashion is a means of self-expression, their independency of expressing themselves has been limited. In this work, we sought to provide technological solutions for visually impaired people, to help them distinguish the color of clothes while shopping. After conducting exploratory user studies, we derived design requirements and developed a voice service mobile application which guides them through recognizing and choosing colors of clothing.
Investigating the Role of User's English Language Proficiency in Using a Voice User Interface: A Case of Google Home Smart Speaker

Amazon's Echo, and Apple's Siri have drawn attention from different user groups; however, these existing commercial VUIs support limited language options for users including native English speakers and non-native English speakers. Also, the existing literature about usability differences between these two distinct groups is limited. Thus, in this study, we conducted a usability study of the Google Home Smart Speaker with 20 participants including native English and non-native English speakers to understand their differences in using the Google Home Smart Speaker. The findings show that compared with their counterparts, the native English speakers had better and more positive user experiences in interacting with the device. It also shows that users' English language proficiency plays an important role in interacting with VUIs. The findings from this study can create insights for VUI designers and developers for implementing multiple language options and better voice recognition algorithms in VUIs for different user groups across the world.
Visual Quotes: Does Aesthetic Appeal Influence How Perceived Motivating Text Messages Impact Short-Term Exercise Motivation?

Visual Quotes, or the communication of motivational text messages in a visual format, are increasingly used across social media and online communities. While physical activity trackers could leverage visual quotes, empirical studies of activity tracking in HCI research have paid little attention to this phenomenon and their potential effects on motivation. In this work, we conducted an online experiment (129 participants) to evaluate the impact of aesthetic appeal in motivational text messages as it relates to extrinsic identified behavior regulation. This is the type of motivation linked to the initial adoption of exercise behavior. The results of our study demonstrate that a perceived motivating text message presented with different levels of aesthetic appeal - ugly, neutral, beautiful - has the same impact on the motivation linked to short-term exercise (extrinsic identified behavior regulation). In other words, the perceived aesthetic appeal did not influence the motivating capability of textual messages for encouraging physical activity.
Serum and dietary antioxidant status is associated with lower prevalence of the metabolic syndrome in a study in Shanghai, China.

OBJECTIVE The aim of our study was to examine the association between the metabolic syndrome (MS) and serum antioxidant status. METHODS A cross-sectional study was conducted with 221 cases and 329 controls aged 18 to 65 years. Weight, height, body mass index, waist circumference, blood pressure, fasting blood glucose and lipids, as well as serum superoxide dismutase, glutathione peroxidase, malondialdehide, vitamins A, E, β-carotene and lycopene were examined. Intakes of antioxidants were also estimated. RESULTS Mean serum superoxide dismutase activity, β-carotene concentrations were significantly lower, malondialdehide was higher (p<0.05) in persons with the MS (after adjusting for age, sex) than those without. Superoxide dismutase, glutathione peroxidase, and β- carotene also decreased significantly (p<0.05) with increased number of components of the MS. Low levels of serum superoxide dismutase activity and β-carotene concentration appeared to be associated with the MS status. Moreover, dietary energy, carbohydrate, vitamin C, zinc and copper intake in the MS patients were lower, but fat intake were higher. Vitamins E, C and manganese intake decreased with the elevated number of the MS components. For zinc and manganese, a lower risk was observed for other quartile of intake compared with the first one. Inverse links between dietary fat, energy intake and serum antioxidant status were found in MS patients, meanwhile dietary vitamin C was positively related with serum antioxidant level. CONCLUSIONS Serum antioxidant status was associated with a lower prevalence of the MS, and with lower dietary fat, energy intake and higher vitamin C intake.
Discovery of Web Usage Profiles Using Various Clustering Techniques

The explosive growth of World Wide Web (WWW) has necessitated the development of Web personalization systems in order to understand the user preferences to dynamically serve customized content to individual users. To reveal information about user preferences from Web usage data, Web Usage Mining (WUM) techniques are extensively being applied to the Web log data. Clustering techniques are widely used in WUM to capture similar interests and trends among users accessing a Web site. Clustering aims to divide a data set into groups or clusters where inter-cluster similarities are minimized while the intra cluster similarities are maximized. This paper reviews four of the popularly used clustering techniques: k-Means, k-Medoids, Leader and DBSCAN. These techniques are implemented and tested against the Web user navigational data. Performance and validity results of each technique are presented and compared. (Abstract) Keywords-component; web usage mining; k-means clustering; k- medoids clustering; leader clustering; DBSCAN