Miracle-dz/UniFAR-FTUs huggingface dataset:
Swipe a Screen or Say the Word: Older Adults’ Preferences for Information-seeking with Touchscreen and Voice-User Interfaces

Over a decade ago, the introduction of touchscreens greatly enhanced older adults’ access to information online. Today, voice-user interfaces (VUIs) promise a similar revolution. However, relatively little is known about older adults’ preferences for using touchscreens versus VUIs to access different kinds of information. In this lab and interview study, older adults used both a touchscreen (tablet) and VUI (smart speaker) to search for various types of information requiring different levels of exploration and judgment. Participants found the VUI generally easy and efficient for obtaining simple information, but they critiqued its value for providing more in-depth information. The touchscreen seemed to offer greater control over accessing information that was of sufficient breadth and in preferred forms or presentations, and which participants felt they could trust. Further, the VUI raised interesting concerns about privacy. These findings suggest that older adults have clear preferences and want to be selective in how they access and use online information. When designing for older adults, focusing solely on efficiency and ease-of-use risks creating simplistic solutions that overlook people's preferences for control over information access and use.
Mid-Air Haptic Cues for Safety-Critical Communication: (Re-)Exploring Air-Vortex Rings in Human-Machine Interaction

In safety-critical human-machine interaction the allocation of users’ attention and the communication of key system information is crucial. Haptic feedback is a promising addition to visual and audio feedback as the latter communication channels are often heavily used in human-machine interaction. Mid-air haptic (MAH) feedback systems have shown to increase immersion, improve the usability of gesture-based interactions and thus might be beneficial for communicating system information in safety-critical environments. Ultrasound feedback systems are at the focus of current research as they deliver high resolution and instantaneous feedback. They come with two major drawbacks: limited interaction space and weak feedback intensity. Although having a lower resolution, feedback systems providing MAH feedback via air vortex rings promise to be better suited for interaction scenarios that require adaptive interaction spaces and high feedback intensities. In this paper, we explore air vortex rings as an alternative to ultrasound-based MAH feedback systems for communicating critical information. We present a vortex generator design that provides a wide interaction space and enables more complex feedback design. We evaluated MAH feedback within a user study (N = 21) using an integrated dual-task design within take-over requests in autonomous driving scenarios. Reaction time was measured to quantify objective performance. Participants further rated the subjective perceivability of the haptic feedback. We observed similar objective performance of vortex rings compared to ultrasound, visual and audio feedback. Qualitative data shows mixed results: feedback via vortex rings felt more intrusive and in part unpleasant to participants but was perceived to have a higher intensity. An expert workshop was conducted to gain insights on feedback design for vortex ring systems and to identify further application areas and research goals.
Social-Network-Assisted Task Recommendation Algorithm in Mobile Crowd Sensing

With the popularity of mobile smart devices, collecting data has become more convenient. Freeing from the constraints of professional equipment, Mobile Crowd Sensing (MCS) has gained wide attention. As the key component of MCS system, task recommendation directly affects the quantity and quality of task completion. However, most of previous task recommendation modules in MCS system only consider the situation where users complete tasks independently, without further consideration of the possibility that users can seek assistance through social networks. In this paper, we come up with a task recommendation algorithm combined social networks to maximize the number of completed tasks. We build up a user-task rating matrix based on the number of tasks performed by each user, and then we use the matrix factorization method to get the latent factor matrix. According to the latent factor matrix, we greedily select some tasks for each user. Next, we calculate the user extroversion and intimacy with others through social networks data to get the probability of users asking for help from their friends. We get the scoring matrix and task recommendation list, considering that users could complete the task together. Finally, we conduct lots of experiments based on a real-world dataset, and the experimental results show that our solution outperforms the existing algorithms.
”Do Metaphors Influence the Usability of Access Control?”: A Gamified Survey.

Metaphors are often considered to be a useful tool in user interface design. They teach a user new mechanics via transferring knowledge from one domain, where the user is experienced, to a new target domain. Specifically, this is helpful in areas with advanced technologies, such as security with its encryption and access control mechanisms. At the same time, some controversy exist on the efficacy of metaphors in user interface design. In this paper, we present our study results on the role of metaphors in a user interface for an access control decision mechanism. We performed our evaluation in a form of a web-based game, obtaining in total 143 responses. The study compared two interface metaphors along with a no-metaphor condition. Our main result suggests that metaphors in user interfaces of security applications can improve user performance, but the observed effect is limited.
Informed ShoppAR - Visualizing Privacy Information in Augmented Reality

Personal informatics devices, such as fitness trackers, become increasingly popular. When selecting a product in store, users do not only make a decision about a product’s functionality. Beyond what is written on the packaging, a purchase also decides about which consumer data will be collected, how it will be used and with whom it will be shared. Privacy policies and terms and conditions inform users before the initial product usage. Many do not read these texts and remain unaware of what they agreed to. With this paper we argue that privacy information should be shown before purchasing a device. We explore how Augmented Reality (AR) may be used to facilitate informed decision-making. We implemented and evaluated the user experience of an AR app in a mock-up electronics store. Our results highlight the potential of AR visualizations for displaying privacy information in a purchasing context.
Personalized Motion Analysis with Consideration of Body Segment Shapes

Our aim is the configuration and evaluation of individual exoskeletons. To do so, we decided to analyze individual biomechanical models of 3D scanned human bodies and subsequent motions in OpenSim (OSim). Other than most existing approaches, our biomechanical model is reduced to be well suited for the fundamental assessment of exoskeletons. To create the model, parameters are derived from body segment shapes. Further, motion trajectories from various tracking systems can be integrated due to the utilization of Mirevi Motion Hub (MMH). Tools are provided in order to cut the scanned body into segments, to automatically calculate mass as well as inertia properties and to integrate them in an OSim model. In addition, we propose a conversion from the MMH file format into an OSim readable file format. Overall, we consider our solution as flexible, as we only assume a mesh as output of the body scan and as different tracking systems can be leveraged by using the MMH middleware. Future work will address the evaluation of our framework.
Digitisation of maintenance work management - a work design perspective: Lessons Learned in an Industry Study

Work design theory provides an understanding of the characteristics of a task or job that makes work meaningful and motivating for individuals. Currently, maintenance technicians in hands-on manual trades roles working in industrial organisations are dealing with the introduction of digital technology to replace or augment their work practices. This paper elicits technicians’ views on potential changes to the nature of their work as a result of this new technology. We report on learnings from two focus groups with qualified technicians prior to the introduction of a tablet-based digital tool to replace paper-based procedures. The potential impact of the technology is framed in terms of six job characteristics: skill variety, task identity, task significance, autonomy, feedback and community. We identify lessons for Human-Computer Interaction (HCI) practitioners relating to how to present these ideas to audiences whose work is grounded in manual skills.
Network anomaly detection with machine learning techniques for SDN networks

Security is a concern for traditional networks and those based on new technology such as Software Defined Networks (SDN) and Internet of Things (IoT). Machine learning techniques are typical to automatically identify and classify attacks in the form of intrusion detection systems. This paper presents machine learning algorithms for attack classification over the CES CIC IDS2018 dataset. The analysis includes an evaluation of the performance of traditional Machine Learning (ML) techniques such as Decision Trees (DT), Random Forest (RF), and a Neural Network architecture in two different samples of the dataset: one with the all the features and another with selected features for SDN. The details of the dataset, as well as the used methodology and evaluation results, are presented in this paper. After a comparison between the different ML algorithms, the conclusion is that DT and RF are both highly accurate for classification (97% for all the features and 87% for the SDN features) and also require less processing.
Incremental Causal Consistency Checking for Read-Write Memory Histories

Causal consistency is one of the strongest consistency models that can be implemented to ensure availability under network partition in distributed systems. The problem of causal consistency checking asks whether a given history of some system is causally consistent. Recently Bouajjani et al. showed that for read-write memory histories in which writes assign unique values to each variable, this can be solved in polynomial time. The algorithm searches for bad patterns of causal consistency, which are defined using various relations derived from history. However, the high time complexity of the algorithm makes it not so practical. In this paper, we show how to improve this checking algorithm by incrementally computing the relations underlying the bad patterns. We also demonstrate its efficiency by conducting experiments on both random histories and those generated by MongoDB.
Happy Text Entering: Promoting Subjective Well-Being Using an Input Method for Presenting Positive Words and Phrases

Research on and the development of technology that promotes human subjective well-being is crucial for the current global information society. Focusing on positive psychological intervention in social communication through mobile devices, this study proposes an input method to promote subjective well-being by recommending positive words and phrases for their negative counterparts. Accordingly, a design workshop was conducted to develop a reframing dictionary in Japanese. This dictionary constitutes a collection of negative words and their corresponding positive words and phrases that convey the same meaning. We also developed an input method that encourages users to select positive words and phrases when they enter a negative word. Preliminary evaluation results indicate a significant difference in positive affect ratings before and after communicating on social networking sites using the proposed input method. Thus, this input method contributes to promoting psychological well-being during daily information activities.
A Cautionary Tale About AI-Generated Goal Suggestions

Setting the right goals and prioritizing them might be the most crucial and the most challenging type of decisions people make for themselves, their teams, and their organizations. In this article, we explore whether it might be possible to leverage artificial intelligence (AI) to help people set better goals and which potential problems might arise from such applications. We devised the first prototype of an AI-powered digital goal-setting assistant and a rigorous empirical paradigm for assessing the quality of AI-generated goal suggestions. Our empirical paradigm compares the AI-generated goal suggestions against randomly-generated goal suggestions and unassisted goal-setting on a battery of self-report measures of important goal characteristics, motivation, and usability in a large-scale repeated-measures online experiment. The results of an online experiment with 259 participants revealed that our intuitively compelling goal suggestion algorithm was actively harmful to the quality of the people’s goals and their motivation to pursue them. These surprising findings highlight three crucial problems to be tackled by future work on leveraging AI to help people set better goals: i) aligning the objective function of the AI algorithms with the design goals, ii) helping people quantify how valuable different goals are to them, and iii) preserving the user’s sense of autonomy.
Accessible Mobile Map Application and Interaction for People with Visual or Mobility Impairments

Modern map applications provide their users with a multitude of functions that can be used through different forms of interactions. However, many map providers do not adequately meet the needs of users with blindness, low vision or mobility impairments. Therefore, we investigated interaction concepts for an accessible mobile map application. Theses concepts not only focus on the user interface, but also on the map content, which should be accessible to the target groups as well. We conducted a pilot study in which nine participants tested the usability and accessibility of the developed application. Our results show that we created a viable alternative for making map content accessible to people with different needs by providing an additional list view. Feedback from subjects indicated that in future, the listed items need to be meaningfully classified and ordered such that users with visual impairments or blindness can use them to form a mental picture of their surroundings.
Autonomous Driving - Analysing the Impact of Resilience Engineering Features in Smart Car Interfaces

The communication between driver and vehicle plays an important role in the development of car automation. Being able to perform non-driving related tasks in uncritical situations is one of the biggest advantages of autonomous driving in smart cars. Yet, critical situations often require the driver to take over control again, thereby making the design of takeover maneuvers a highly relevant task. Our study thus investigated the impact of basic vs. enhanced HMIs considering the principles of resilience engineering on supporting drivers in taking control over the vehicle. We investigated driving on the highway in two critical scenarios: a roadworks and a potential collision scenario. In a VR study in a driving simulator 45 participants tested three HMIs: basic, visual, and visual + speech-based. Even though the results indicate high usability scores and a positive user experience for all HMIs, no significant differences regarding takeover time could be measured between the HMIs. Reasons include the visual differences in the interface design with regards to resilience engineering being minor and the sample size being too small to detect small effects. Implications for future research and the design of takeover maneuvers are discussed.
Functional Scenario Classification for Android Applications using GNNs

Functional scenario comprehension of screens in Android applications paves the way for Android app development and Android UI testing, especially in automated UI testing and test reuse. On the one hand, the screens of diverse Android applications contain widgets with many combinations. On the other hand, the screens of different scenarios may leverage similar widgets to fulfill the functionalities. Due to the above reasons, scenario comprehension is still hard to be solved by current approaches. In this paper, to fully understand the functionality of each screen, we propose a novel approach that employs Graph Neural Networks (GNN) to classify scenarios leveraging the transitions between screens and other available information of screens altogether. According to the result evaluated on 30 popular applications in the file management category, our approach improves the classification accuracy by at least 6% compared to previous work, demonstrating that GNN can fully utilize the potential relations and dependencies between the transitioned screens.
“A Helping Hand”: Design and Evaluation of a Reading Assistant Application for Children with Dyslexia

We designed and evaluated Augmenta11y, a cross-platform application that aims to provide ubiquitous reading and learning companionship to children with dyslexia. We developed the application following the Reflective Agile Iterative Design (RAID) framework, a participatory approach involving dyslexic individuals and special educators to provide feedback throughout the prototyping processes. We then conducted a user study with ten children with dyslexia between 10-14 years of age. We employed the think-aloud, post-usability session interview, and questionnaire methods to evaluate the tool's usability and usefulness. Our study revealed the Augmenta11y application's effectiveness in assisting children with dyslexia by providing affordable and accessible reading practices. We offer comprehensive suggestions and guidelines for designing future assisted reading applications. We foresee the opportunity for Augmenta11y to be an accessible, low-cost assistive reading solution for dyslexic children with little to no access to educational specialists or after-school practices/programs. The Augmenta11y application is available on iOS and Android.
Higher Spin Theories from Finsler Geometry

We provide observations that Finsler geometry could be useful tools to construct higher-spin theories. We suggest that a Finsler metric of constant flag curvature can be regarded as a metric encoding higher-spin fields. We also show that the Fronsdal's equations for free higher-spin fields can be derived from equations of motion of constant curvature in Finsler geometry.
Structure determination from heterogeneous NMR data

The principal difficulty in using nuclear magnetic resonance (NMR) data for biomolecular structure determination is not so much experimental imperfections but approximate theories relating structure to measurands. Furthermore, these theories are incomplete as they involve auxiliary parameters which are not measurable. In order to give a reliable picture of a biomolecule, structure determination methods need to determine unknown parameters from definite rules and ought to provide the uncertainty of the derived coordinates. Conventional approaches neglect uncertainties of any kind and therefore by definition fail to give an estimate of structural reliability. In order to deal with auxiliary parameters, they resort to heuristics which renders an objective interpretation of the generated atom positions impossible. Recently, we have introduced a fully probabilistic approach to structure determination from NMR data. We describe here an extension of this approach which incorporates inconsistent nuclear Overhauser effect and J‐coupling measurements. Auxiliary parameters are estimated along with the atomic coordinates using Markov Chain Monte Carlo. We apply the method to data sets for two small proteins.
Molecular-beam epitaxy growth of high-quality active regions with strained InxGa1-xAs quantum wells and lattice-matched AlxGayIn(1-x-y)As barriers using submonolayer superlattices

Submonolayer superlattices (SMS) of Ga0.47In0. 53As/InAs/Ga0.47In0. 52As and Ga0.47In0. 53As/Al0.48In0. 52As were used for the growth of strained quantum wells (QWs) and lattice-matched barriers, respectively, in the 1.55 μm active region. QWs grown with different compressive strains show excellent room-temperature photoluminescence, demonstrating the versatility of this technique. State-of-the-art current thresholds are reported for the broad-area lasers fabricated using the SMS active region.
LETTER TO THE EDITOR: Double active magnetic screening of coils in NMR

The principles of active screening of time dependent magnetic fields, introduced by Mansfield and Chapman (1986), have been extended to include two screens. Magnetic screening is especially useful in NMR when rapidly switched gradients are required within the close confines of a superconductive magnet. However, single screens reduce and spatially distort the field from the primary coil. With two active screens it is possible to fully screen the primary coil far field without impairing its near field spatial characteristics.
Possible effects of clinoptilolite on small intestinal ischemia-reperfusion injury caused by experimental mesenteric artery occlusion

Abstract Objectives Mesenteric ischemia is a surgical emergency caused by poor blood supply to the intestines. In ischemia, the decrease in blood flow to the tissue causes acidosis and cell death through anaerobic metabolism. Clinoptilolite is one of the most abundant natural zeolites, and it is used for its ion exchange and adsorbent properties. Clinoptilolite has been reported to have an immune-enhancing, anti-carcinogenic, and antioxidant effect in-vitro/in-vivo studies. Clinoptilolite’s histological and biochemical effects on ischemic small intestines. Methods The experimental animals were randomly divided into sham, control, and clinoptilolite treatment group. Clinoptilolite was administered intraperitoneally after ischemia/reperfusion. Cardiac blood was stored for biochemical analysis. Total antioxidant levels and total oxidant levels were analyzed from the sera taken from groups. Tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) expressions in blood samples were determined by RT-qPCR. At the end of the reperfusion, terminal ileum tissues were taken for histological tests. Results The mean TNF-α expression level was 3.89 in the control group and 2.91 in the clinoptilolite treatment group. The mean IL-6 expression levels were 2.32 in the control group and 1.49 in the clinoptilolite treatment group. Conclusions clinoptilolite administration provided healing in the rat ischemia-reperfusion injury model.