Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dataset and Machine Learning Approach to Classify and Augment Interface Elements of Household Appliances to Support People with Visual Impairment

Many modern household appliances are challenging to operate for people with visual impairment. Low-contrast designs and insufficient tactile feedback make it difficult to distinguish interface elements and to recognize their function. Augmented reality (AR) can be used to visually highlight such elements and provide assistance to people with residual vision. To realize this goal, we (1) created a dataset consisting of 13,702 images of interfaces from household appliances and manually labeled control elements; (2) trained a neural network to recognize control elements and to distinguish between PushButton, TouchButton, Knob, Slider, and Toggle; and (3) designed various contrast-rich and visually simple AR augmentations for these elements. The results were implemented as a screen-based assistive AR application, which we tested in a user study with six individuals with visual impairment. Participants were able to recognize control elements that were imperceptible without the assistive application. The approach was well received, especially for the potential of familiarizing oneself with novel devices. The automatic parsing and augmentation of interfaces provide an important step toward the independent interaction of people with visual impairments with their everyday environment.
CoColor: Interactive Exploration of Color Designs

Choosing colors is a pivotal but challenging component of graphic design. The paper presents an intelligent interaction technique supporting designers’ creativity in color design. It fills a gap in the literature by proposing an integrated technique for color exploration, assignment, and refinement: CoColor. Our design goals were 1) let designers focus on color choice by freeing them from pixel-level editing and 2) support rapid flow between low- and high-level decisions. Our interaction technique utilizes three steps – choice of focus, choice of suitable colors, and the colors’ application to designs – wherein the choices are interlinked and computer-assisted, thus supporting divergent and convergent thinking. It considers color harmony, visual saliency, and elementary accessibility requirements. The technique was incorporated into the popular design tool Figma and evaluated in a study with 16 designers. Participants explored the coloring options more easily with CoColor and considered it helpful.
Understanding Adoption Barriers to Dwell-Free Eye-Typing: Design Implications from a Qualitative Deployment Study and Computational Simulations

Eye-typing is a slow and cumbersome text entry method typically used by individuals with no other practical means of communication. As an alternative, prior HCI research has proposed dwell-free eye-typing as a potential improvement that eliminates time-consuming and distracting dwell-timeouts. However, it is rare that such research ideas are translated into working products. This paper reports on a qualitative deployment study of a product that was developed to allow users access to a dwell-free eye-typing research solution. This allowed us to understand how such a research solution would work in practice, as part of users’ current communication solutions in their own homes. Based on interviews and observations, we discuss a number of design issues that currently act as barriers preventing widespread adoption of dwell-free eye-typing. The study findings are complemented with computational simulations in a range of conditions that were inspired by the findings in the deployment study. These simulations serve to both contextualize the qualitative findings and to explore quantitative implications of possible interface redesigns. The combined analysis gives rise to a set of design implications for enabling wider adoption of dwell-free eye-typing in practice.
A Survey on Machine Learning in Hardware Security

Hardware security is currently a very influential domain, where each year countless works are published concerning attacks against hardware and countermeasures. A significant number of them use machine learning, which is proven to be very effective in other domains. This survey, as one of the early attempts, presents the usage of machine learning in hardware security in a full and organized manner. Our contributions include classification and introduction to the relevant fields of machine learning, a comprehensive and critical overview of machine learning usage in hardware security, and an investigation of the hardware attacks against machine learning (neural network) implementations.
Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems

Content recommender systems often rely on modeling users’ past behavioral data to provide personalized recommendations - a practice that works well for suggesting more of the same and for media that require little time investment from users, such as music tracks. However, this approach can be further optimized for media where the user investment is higher, such as podcasts, because there is a broader space of user goals that might not be captured by the implicit signals of their past behavior. Allowing users to directly specify their goals might help narrow the space of possible recommendations. Thus, in this paper, we explore how we can enable goal-focused exploration in recommender systems by leveraging explicit input from users about their personal goals. Using podcast consumption as an example use-case, and informed by a large-scale survey (N=68k), we developed GoalPods, an interactive prototype that allows users to set personal goals and build playlists of podcast episode recommendations to meet those goals. We evaluated GoalPods with 14 participants where participants set a goal and spent a week listening to the episode playlist created for that goal. From the study, we identified two types of user goals: low-involvement (e.g. “combat boredom”) and high-involvement (e.g. “learn something new”) goals. Users found it easy to identify relevant recommendations for low-involvement goals, but they needed more structure and support to set high-involvement goals. By anchoring users on their personal goals to explore recommendations, GoalPods (and goal-focused podcast consumption) led to insightful content discovery outside the users’ filter bubbles. Based on our findings, we discuss opportunities for designing recommender systems that guide exploration via interactive goal-setting as well as implications for providing better recommendations by accounting for users’ personal goals.
Generation of surface plasmons with compact devices

We review the properties of the generation of surface plasmons by subwavelength isolated slits in metal films and by small ensembles of slits. After an introduction, in Section 2, we recall the theoretical modal formalism that allows us to calculate the generation efficiency of SPP from the total field scattered by an indentation on a metal film. We also rapidly discuss the main results known of the SPP generation efficiency by subwavelength tiny slits or grooves. In Section 3, we consider the special case of wavelength-large slits that support two propagative modes and that allow us to dynamically control the direction of generated surface plasmons. In Section 4, we conclude by describing a compact and efficient device capable of launching SPPs in a single direction with a normally incident beam.
Resource Management in Mobile Edge Computing: A Comprehensive Survey

With the evolution of 5G and Internet of Things technologies, Mobile Edge Computing (MEC) has emerged as a major computing paradigm. Compared to cloud computing, MEC integrates network control, computing, and storage to customizable, fast, reliable, and secure distributed services that are closer to the user and data site. Although a popular research topic, MEC resource management comes in many forms due to its emerging nature and there exists little consensus in the community. In this survey, we present a comprehensive review of existing research problems and relevant solutions within MEC resource management. We first describe the major problems in MEC resource allocation when the user applications have diverse performance requirements. We discuss the unique challenges caused by the dynamic nature of the environments and use cases where MEC is adopted. We also explore and categorize existing solutions that address such challenges. We particularly explore traditional optimization-based methods and deep learning-based approaches. In addition, we take a deeper dive into the most popular applications and use cases that adopt MEC paradigm and how MEC provides customized solutions for each use cases, in particular, video analytics applications. Finally, we outline the open research challenges and future directions.1
Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI – rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users’ reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI’s prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user’s task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user’s decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data.
Gaze Speedup: Eye Gaze Assisted Gesture Typing in Virtual Reality

Mid-air text input in augmented or virtual reality (AR/VR) is an open problem. One proposed solution is gesture typing where the user performs a gesture trace over the keyboard. However, this requires the user to move their hands precisely and continuously, potentially causing arm fatigue. With eye tracking available on AR/VR devices, multiple works have proposed gaze-driven gesture typing techniques. However, such techniques require the explicit use of gaze which are prone to Midas touch problems, conflicting with other gaze activities in the same moment. In this work, the user is not made aware that their gaze is being used to improve the interaction, making the use of gaze completely implicit. We observed that a user’s implicit gaze fixation location during gesture typing is usually the gesture cursor’s target location if the gesture cursor is moving toward it. Based on this observation, we propose the Speedup method in which we speed up the gesture cursor toward the user’s gaze fixation location, the speedup rate depends on how well the gesture cursor’s moving direction aligns with the gaze fixation. To reduce the overshooting near the target in the Speedup method, we further proposed the Gaussian Speedup method in which the speedup rate is dynamically reduced with a Gaussian function when the gesture cursor gets nearer to the gaze fixation. Using a wrist IMU as input, a 12-person study demonstrated that the Speedup method and Gaussian Speedup method reduced users’ hand movement by and respectively without any loss of typing speed or accuracy.
Augmented Reality Supported Learning for Cultural Heritage of Taiwan in On-Site and Off-Site Environments: The Case of a Daxi Old Street

This study aims to explore whether the integration of Augmented Reality (AR) technology used in community-based art education can improve the participants’ learning effectiveness and to compare its effects in two different learning environments (on-site and off-site). A quasi-experimental research design has been conducted to check the effectiveness of integrating AR activities in terms of learners’ effectiveness. The sample for this study includes 40 and 64 participants in on-site and off-site groups, respectively. The on-site group visited the real environment and viewed AR-integrated materials such as posters and rhymes; the off-site group visited the school exhibition and viewed the same materials as the on-site group. The data was obtained through motivation, learning satisfaction, technology acceptance, and learning effectiveness scales designed by the researchers. Descriptive statistics and an independent t-test were utilized for the data analysis. There was a significant difference in learning motivation, learning effectiveness, and learning satisfaction between on-site and off-site groups; however, there was no significant difference in the mean of technology acceptance. This study finds that the use of AR digital materials facilitated the visitors’ experience of cultural heritage and improved participants learning effectiveness, and particularly learning satisfaction and learning motivation, more so in the on-site experience than in the off-site experience. Results also revealed that there was a significant difference in gender and age toward learning motivation and learning effectiveness. With respect to participants with different artistic and educational backgrounds, the results showed no significant differences in learning motivation, learning effectiveness, learning satisfaction, and technology acceptance. Finally, recommendations for future practices and research are discussed.
The Importance of Multimodal Emotion Conditioning and Affect Consistency for Embodied Conversational Agents

Previous studies regarding the perception of emotions for embodied virtual agents have shown the effectiveness of using virtual characters in conveying emotions through interactions with humans. However, creating an autonomous embodied conversational agent with expressive behaviors presents two major challenges. The first challenge is the difficulty of synthesizing the conversational behaviors for each modality that are as expressive as real human behaviors. The second challenge is that the affects are modeled independently, which makes it difficult to generate multimodal responses with consistent emotions across all modalities. In this work, we propose a conceptual framework, ACTOR (Affect-Consistent mulTimodal behaviOR generation), that aims to increase the perception of affects by generating multimodal behaviors conditioned on a consistent driving affect. We have conducted a user study with 199 participants to assess how the average person judges the affects perceived from multimodal behaviors that are consistent and inconsistent with respect to a driving affect. The result shows that among all model conditions, our affect-consistent framework receives the highest Likert scores for the perception of driving affects. Our statistical analysis suggests that making a modality affect-inconsistent significantly decreases the perception of driving affects. We also observe that multimodal behaviors conditioned on consistent affects are more expressive compared to behaviors with inconsistent affects. Therefore, we conclude that multimodal emotion conditioning and affect consistency are vital to enhancing the perception of affects for embodied conversational agents.
Conductivity of Multiwall Carbon Nanotubes: Role of Multiple Shells and Defects

This work reports on laterally resolved measurements of the current-induced gradient in the electrochemical potential of multiwall carbon nanotubes. Nanotubes with different classes of defects were studied at room temperature. The potential profile of the outermost shell along the tube was measured in a local as well as in a nonlocal geometry. The data have been used to separate the contributions of various shells to the total resistance of the whole tube. For this purpose, a classical resistivity model was used that describes the measured potential profiles well. Additionally, the influence of structural defects on the conductivity has been quantified. Particularly, defects such as an ending outermost shell, an intratube junction, and a plastically stretched tube with a kink were investigated. Additionally, measurements at low temperatures revealed some quantum conductance effects, such as weak localization and oscillations in the potential profile. The latter could be traced back to the same origin as as the universal conductance fluctuations.
Detection of Babesia Spp., Theileria Spp., and Anaplasma Ovis in Ornithodoros Lahorensis from Southern Xinjiang, China

Abstract Introduction Ticks are blood-sucking arthropods that have negative economic impacts and can spread a variety of diseases through their bites. There are few reports on soft ticks (Acari: Argasidae) and tick-borne pathogens in southern Xinjiang, China. This investigation supplements the available information for this region and is concerned with an argasid tick, apicomplexan parasites of the Babesia and Theileria genera and a bacterium of the Anaplasma genus. Material and Methods In this study, 330 soft ticks were collected from nine sampling sites in southern Xinjiang between 2020 and 2021. The ticks were identified according to their morphological characteristics and confirmed as Ornithodoros lahorensis using mitochondrial 16S rDNA sequences. Babesia and Theileria were identified at the species level based on two fragments of the 18S rRNA gene, and one set of primers targeting the 16S rRNA gene was used to identify the Anaplasma genus. Results Among the 330 samples, one Babesia species (Babesia sp.), two Theileria species (T. ovis and T. annulata), and one Anaplasma (A. ovis) species were detected. Conclusion This study provides fundamental evidence for the occurrence of Babesia, Theileria and Anaplasma spp. in soft ticks. To the best of our knowledge, this is the first report of the detection of Babesia sp. and T. annulata in O. lahorensis. Therefore, the potential threat of soft ticks to livestock and humans should not be ignored.
Representing Bidirectional Hydraulic Continuum Between the Stream and Hillslope in the National Water Model for Improved Streamflow Prediction

Although hydraulic groundwater (GW) theory has been recognized as a promising tool for understanding the role of the aquifer(s) in the surface‐subsurface hydrologic cycle, the integrated modeling community still lacks a proper hydrologic structure to apply the well‐studied theory to large‐scale hydrologic predictions. This study aims to present a novel hydrologic structure that enables the Boussinesq equation‐based depiction of the bidirectional stream‐hillslope processes for applying hydraulic GW theory to large‐scale model configurations. We integrated the BE3S's (Hong et al., 2020, https://doi.org/10.1029/2020wr027571) representation scheme of the catchment‐scale stream‐hillslope continuum into the National Water Model (NWM) and applied the modified NWM (i.e., the NWM‐BE3S) to three major basins in Texas (i.e., the Trinity, Brazos, and Colorado River basins). Since the NWM currently relies on a single reservoir model for baseflow simulation, we used the Boussinesq aquifer as an alternative subsurface hydrology routine and evaluated its predictive skill and efficacy. We identified that the implemented Boussinesq aquifer(s) in the NWM‐BE3S yielded noticeable improvements in predicting streamflow for aquifers that exhibited higher nonlinearities in the observed recessions. The varying degree of improvements in streamflow predictions per the recession nonlinearities demonstrated not only (a) the algorithmic enhancement of subsurface hydrology (physics) but also (b) the applicability of the Boussinesq theory‐based depiction of the stream‐hillslope two‐way continuum. We diagnosed each stream's state based on the bidirectional stream‐hillslope exchanges and identified the dominant processes (i.e., river infiltration or baseflow) that were represented spatially in the NWM‐BE3S.
An Empirical Account of Compositionality of Translation through Translation Data

Both in theoretical and applied research of machine translation it is often assumed that translation between natural languages can be treated in a compositional fashion, but it has proven far from trivial to develop a compositional translation system, or theoretically show it exists. In this thesis, an empirical investigation of compositionality of translation is presented, of which the main purpose is to find empirical evidence for the compositionality of actual translation data in the form of parallel corpora. All maximally compositional translation structures of sentences in parallel corpora aligned at the word level were studied, to gain information about the system that generated them. In particular, it was studied whether monolingual information from dependency parses could be the basis of this underlying system. Experiments showed that hardly over fifty percent of the dependency relations were preserved during translation if no modifications in the dependency relations were allowed. Considering deeper versions of dependency parses boosted this score with over thirty percentage points for all datasets. A manual analysis showed that most of the structure deviations were caused by errors in the data or systematic differences between the languages. The results are encouraging for pursuing development of compositional translation systems based on dependency parses. A proposal for doing so is presented in the discussion of this thesis. Tools to execute this proposal, as well as tools to conduct further empirical research, have been made available.
Interpretation of Predictions in Drug-Gut Bacteria Interactions Using Machine Learning

Gut bacteria play a crucial role in host’s metabolism. Both antibiotic and non-antibiotic drugs affect the gut bacteria ecosystem, which negatively affects the host’s health. Also, gut bacteria metabolize drugs, making them ineffective to the target. The structure-activity relationship studies of drugs have the scope to make them more effective, efficient, and specific to the target. Previous machine learning studies use the available data to predict the activity of drugs and gut bacteria on each other, but these models lack interpretability. Herein, we study the drug-gut bacteria interaction using interpretable machine learning models. In this study, we identify the most important physicochemical features of the drug, which decide the drug-gut bacteria interactions with each other. One of the key findings of this work is that the higher-positive charged drug molecules can inhibit the growth of gut bacteria more. In contrast, the higher-negative charged drug molecules have higher possibility to get metabolized by gut bacteria.