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In the last years the pervasive use of sensors, as they exist in smart devices, e.g., phones, watches, medical devices, has increased dramatically the availability of personal data. However, existing research on data collection primarily focuses on the objective view of reality, as provided, for instance, by sensors, often neglecting the integration of subjective human input, as provided, for instance, by user answers to questionnaires. This limits substantially the exploitability of the collected data. In this paper we present a methodology and a platform specifically designed for the collection of a combination of large-scale sensor data and qualitative human feedback. The methodology has been designed to be deployed on top, and enriches the functionalities of, an existing data collection APP, called iLog, which has been used in large scale, worldwide data collection experiments. The main goal is to put the key actors involved in an experiment, i.e., the researcher in charge, the participant, and iLog in better control of the experiment itself, thus enabling a much improved quality and richness of the data collected. The novel functionalities of the resulting platform are: (i) a time-wise representation of the situational context within which the data collection is performed, (ii) an explicit representation of the temporal context within which the data collection is performed, (iii) a calendar-based dashboard for the real-time monitoring of the data collection context(s), and, finally, (iv) a mechanism for the run-time revision of the data collection plan. The practicality and utility of the proposed functionalities are demonstrated by showing how they apply to a case study involving 350 University students.

Sensor data provide an objective view of reality but fail to capture the subjective motivations behind an individual's behavior. This latter information is crucial for learning about the various dimensions of the personal context, thus increasing predictability. The main limitation is the human input, which is often not of the quality that is needed. The work so far has focused on the usually high number of missing answers. The focus of this paper is on \textit{the number of mistakes} made when answering questions. Three are the main contributions of this paper. First, we show that the user's reaction time, i.e., the time before starting to respond, is the main cause of a low answer quality, where its effects are both direct and indirect, the latter relating to its impact on the completion time, i.e., the time taken to compile the response. Second, we identify the specific exogenous (e.g., the situational or temporal context) and endogenous (e.g., mood, personality traits) factors which have an influence on the reaction time, as well as on the completion time. Third, we show how reaction and completion time compose their effects on the answer quality. The paper concludes with a set of actionable recommendations., Comment: 25 pages, 16 figures, under review by Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Pervasive sensors have become essential in research for gathering real-world data. However, current studies often focus solely on objective data, neglecting subjective human contributions. We introduce an approach and system for collecting big-thick data, combining extensive sensor data (big data) with qualitative human feedback (thick data). This fusion enables effective collaboration between humans and machines, allowing machine learning to benefit from human behavior and interpretations. Emphasizing data quality, our system incorporates continuous monitoring and adaptive learning mechanisms to optimize data collection timing and context, ensuring relevance, accuracy, and reliability. The system comprises three key components: a) a tool for collecting sensor data and user feedback, b) components for experiment planning and execution monitoring, and c) a machine-learning component that enhances human-machine interaction., Comment: 8 pages, 3 figures, accepted by Aduous workshop

Datasets are essential for training and testing vehicle perception algorithms. However, the collection and annotation of real-world images is time-consuming and expensive. Driving simulators offer a solution by automatically generating various driving scenarios with corresponding annotations, but the simulation-to-reality (Sim2Real) domain gap remains a challenge. While most of the Generative Artificial Intelligence (AI) follows the de facto Generative Adversarial Nets (GANs)-based methods, the recent emerging diffusion probabilistic models have not been fully explored in mitigating Sim2Real challenges for driving data synthesis. To explore the performance, this paper applied three different generative AI methods to leverage semantic label maps from a driving simulator as a bridge for the creation of realistic datasets. A comparative analysis of these methods is presented from the perspective of image quality and perception. New synthetic datasets, which include driving images and auto-generated high-quality annotations, are produced with low costs and high scene variability. The experimental results show that although GAN-based methods are adept at generating high-quality images when provided with manually annotated labels, ControlNet produces synthetic datasets with fewer artefacts and more structural fidelity when using simulator-generated labels. This suggests that the diffusion-based approach may provide improved stability and an alternative method for addressing Sim2Real challenges.

Precise situational awareness is required for the safe decision-making of assisted and automated driving (AAD) functions. Panoptic segmentation is a promising perception technique to identify and categorise objects, impending hazards, and driveable space at a pixel level. While segmentation quality is generally associated with the quality of the camera data, a comprehensive understanding and modelling of this relationship are paramount for AAD system designers. Motivated by such a need, this work proposes a unifying pipeline to assess the robustness of panoptic segmentation models for AAD, correlating it with traditional image quality. The first step of the proposed pipeline involves generating degraded camera data that reflects real-world noise factors. To this end, 19 noise factors have been identified and implemented with 3 severity levels. Of these factors, this work proposes novel models for unfavourable light and snow. After applying the degradation models, three state-of-the-art CNN- and vision transformers (ViT)-based panoptic segmentation networks are used to analyse their robustness. The variations of the segmentation performance are then correlated to 8 selected image quality metrics. This research reveals that: 1) certain specific noise factors produce the highest impact on panoptic segmentation, i.e. droplets on lens and Gaussian noise; 2) the ViT-based panoptic segmentation backbones show better robustness to the considered noise factors; 3) some image quality metrics (i.e. LPIPS and CW-SSIM) correlate strongly with panoptic segmentation performance and therefore they can be used as predictive metrics for network performance.

Vapor sink is an effective strategy to suppress the formation of water vapor condensation around hygroscopic materials, and consequently reduce frosting and icing. However, traditionally used materials, such as salt solutions, fibers, exhibit insufficient condensation inhibition or pose safety concerns, such as corrosiveness. In this study, we highlight the remarkable anti-condensation properties of glycerol droplets, attributed to their strong hygroscopicity. We compared the anti-condensation capabilities of glycerol droplets with commonly used salt solutions and hygroscopic alcohols. The results indicated that glycerol droplets establish a relatively expansive dry zone where the condensation is effectively inhibited, while also offering safety compared to other materials. Furthermore, we conducted a systematic study of the anti-condensation properties of glycerol droplets with experiments and theoretical analysis. We explored the varying trends of the dry zone ratio concerning temperature, cooling time, humidity, and droplet volume on hydrophobic surfaces. To provide a comprehensive understanding, we propose a straightforward yet robust theoretical model that elucidates the relationship between this ratio and temperature, aligning well with the experimental data. Our study not only sheds light on the superior anti-condensation qualities of glycerol but also offers insights and guidelines for the development of effective anti-icing and anti-frost materials.

So far, most research investigating the predictability of human behavior, such as mobility and social interactions, has focused mainly on the exploitation of sensor data. However, sensor data can be difficult to capture the subjective motivations behind the individuals' behavior. Understanding personal context (e.g., where one is and what they are doing) can greatly increase predictability. The main limitation is that human input is often missing or inaccurate. The goal of this paper is to identify factors that influence the quality of responses when users are asked about their current context. We find that two key factors influence the quality of responses: user reaction time and completion time. These factors correlate with various exogenous causes (e.g., situational context, time of day) and endogenous causes (e.g., procrastination attitude, mood). In turn, we study how these two factors impact the quality of responses., Comment: 5 pages, 1 table

We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the personal situational context that the human is in. We outline the key challenges that this task brings forth, namely (i) handling the human-like and ego-centric nature of the the user's context, necessary for understanding and providing useful suggestions, (ii) performing lifelong context recognition using machine learning in a way that is robust to change, and (iii) maintaining alignment between the AI's and human's representations of the world through continual bidirectional interaction. In this short paper, we summarize our recent attempts at tackling these challenges, discuss the lessons learned, and highlight directions of future research. The main take-away message is that pursuing this project requires research which lies at the intersection of knowledge representation and machine learning. Neither technology can achieve this goal without the other., Comment: 8 pages

van der Waals (vdW) magnets are receiving ever-growing attention nowadays due to their significance in both fundamental research on low-dimensional magnetism and potential applications in spintronic devices. High crystalline quality of vdW magnets is key for maintaining intrinsic magnetic and electronic properties, especially when exfoliated down to the 2D limit. Here, ultrahigh-quality air-stable vdW CrSBr crystals are synthesized using the direct vapor-solid synthesis method. The high single crystallinity and spatial homogeneity have been thoroughly evidenced at length scales from sub-mm to atomic resolution by X-ray diffraction, second harmonic generation, and scanning transmission electron microscopy. More importantly, specific heat measurements of these ultrahigh quality CrSBr crystals show three thermodynamic anomalies at 185K, 156K, and 132K, revealing a stage-by-stage development of the magnetic order upon cooling, which is also corroborated with the magnetization and transport results. Our ultrahigh-quality CrSBr can further be exfoliated down to monolayers and bilayers easily, paving the way to integrate them into heterostructures for spintronic and magneto-optoelectronic applications., Comment: 13 pages, 5 figures

This paper presents a summary of the 2019 Unconstrained Ear Recognition Challenge (UERC), the second in a series of group benchmarking efforts centered around the problem of person recognition from ear images captured in uncontrolled settings. The goal of the challenge is to assess the performance of existing ear recognition techniques on a challenging large-scale ear dataset and to analyze performance of the technology from various viewpoints, such as generalization abilities to unseen data characteristics, sensitivity to rotations, occlusions and image resolution and performance bias on sub-groups of subjects, selected based on demographic criteria, i.e. gender and ethnicity. Research groups from 12 institutions entered the competition and submitted a total of 13 recognition approaches ranging from descriptor-based methods to deep-learning models. The majority of submissions focused on ensemble based methods combining either representations from multiple deep models or hand-crafted with learned image descriptors. Our analysis shows that methods incorporating deep learning models clearly outperform techniques relying solely on hand-crafted descriptors, even though both groups of techniques exhibit similar behaviour when it comes to robustness to various covariates, such presence of occlusions, changes in (head) pose, or variability in image resolution. The results of the challenge also show that there has been considerable progress since the first UERC in 2017, but that there is still ample room for further research in this area., Comment: The content of this paper was published in ICB, 2019. This ArXiv version is from before the peer review

Superconductivity is observed in a new nontoxic cuprate system Ga-Ba-Ca-Cu-O, with Tc = 82K for GaBa2Ca5Cu6O14+{\delta} (Ga-1256) and Tc = 116K probably for GaBa2Ca3Cu4O10+{\delta} (Ga-1234) or GaBa2Ca2Cu3O8+{\delta} (Ga-1223), respectively. All compounds are fabricated by solid state reaction method under high pressure and high temperature. Samples are characterized by resistivity, magnetization and X-ray diffraction (XRD) measurements. The temperature dependence of magnetization measured in both zero-field-cooled and field-cooled processes on one sample (S1) shows two superconducting transitions at about 82K and 113K. The estimated shielding fraction for the phase with Tc of 82K is about 67%, while the fraction for another phase with Tc of 113K is quite small. The XRD Rietveld refinement for S1 indicates two main phases existing in the sample, Ga-1256 with fraction of about 58% and non-superconducting Ca0.85CuO2 with fraction of about 42%, respectively. Thus, we can conclude the superconducting phase with transition temperature of 82K is due to Ga-1256. The resistivity measurement also confirms the superconductivity for S1, and the resistivity reaches zero at about 82K. The temperature dependence of magnetization for another sample (S2) shows much higher superconducting shielding fraction for the phase with Tc of 116K, which may be a promising prospective for the synthesis of Ga-1234 or Ga-1223 phase., Comment: 9 pages, 4 figures

One of the key factors limiting the high power applications for a type-II superconductor is the irreversibility line Hirr(T) which reflects the very boundary of resistive dissipation in the phase diagram of magnetic field versus temperature. In cuprate family, the Y-, Bi-, Hg- and Tl-based systems have superconducting transition temperatures exceeding the liquid nitrogen boiling temperature (~77K). However, the toxic elements Hg and Tl in the latter two systems strongly constrain the possible applications. The Bi-based (2223) system is nontoxic, but the irreversibility magnetic field is strongly suppressed in the liquid nitrogen temperature region. For this purpose, the best perspective so far is relying on the YBa2Cu3O7 (Tc~90 K) system which is nontoxic and has a relatively high irreversibility magnetic field. Here we report the study of a nontoxic superconductor (Cu,C)Ba2Ca3Cu4O11+{\delta} with Tc = 116K. It is found that the irreversibility magnetic field is unprecedentedly high among all superconductors and it thus provides a great potential of applications in the liquid nitrogen temperature region., Comment: 15 pages, 7 figures, including Supplementary Materials