Reviewerly/exHarmony huggingface dataset:
The Actuality-Time Continuum: Visualizing Interactions and Transitions Taking Place in Cross-Reality Systems

In the last decade, researchers contributed an increasing number of cross-reality systems and their evaluations. Going beyond individual technologies such as Virtual or Augmented Reality, these systems introduce novel approaches that help to solve relevant problems such as the integration of bystanders or physical objects. However, cross-reality systems are complex by nature, and describing the interactions and transitions taking place is a challenging task. Thus, in this paper, we propose the idea of the Actuality-Time Continuum that aims to enable researchers and designers alike to visualize complex cross-reality experiences. Moreover, we present four visualization examples that illustrate the potential of our proposal and conclude with an outlook on future perspectives.
Single User Cross Reality Workflow for Reservoir Engineering Work in Progress

We propose a prototype for a single-user cross reality (CR) workflow for reservoir engineering. Cross reality is the technology that links systems at various points of the Reality-Virtuality Continuum (RVC), in which CR applications, by definition, should enable the transition between or simultaneous usage of different systems along the RVC [21]. The proposed prototype connects commercial and VR reservoir analytic tools through CR, allowing seamless transitions of reservoir models between desktop monitors and augmented reality (AR) space. The paper discusses related research on commercial and VR reservoir analytic software and cross-reality interaction methods. The design objectives and an overview of the prototype implementation are presented.
Towards Seamless Egocentric Hand Action Recognition in Mixed Reality

Understanding user actions from egocentric videos is crucial in developing intelligent mixed reality (MR) systems. One key aspect is the recognition of hand actions and gestures, which enables user interaction and adaptation of the system to real-world user actions. In this paper, we present a comprehensive pipeline for egocentric hand action recognition for mixed reality applications. Our approach incorporates an MR-guided data collection method that eliminates the need for explicit manual annotation and guidance. We also propose a robust and efficient skeleton-based hand action recognition model specifically designed for real-time MR use cases. To validate our proposed framework and demonstrate its effectiveness, we conducted a case study involving industrial precision inspection tasks. Utilizing our MR-guided data collection system, we efficiently collected hand inspection action data and built a comprehensive dataset. We then trained our proposed model on this dataset, employing a feature refinement strategy. We conducted extensive evaluations, including standard offline analysis and real-time inference in an MR system, to thoroughly test the model. Our experimental results showcase the efficacy of our proposed pipeline and its potential for practical use in various scenarios. The supplementary materials including source codes, datasets, and demo videos are publicly available on www.sail-nu.com/ismar-har.
Deep Neural Network Assisted Quantum Chemistry Calculations on Quantum Computers

The variational quantum eigensolver (VQE) is a widely employed method to solve electronic structure problems in the current noisy intermediate-scale quantum (NISQ) devices. However, due to inherent noise in the NISQ devices, VQE results on NISQ devices often deviate significantly from the results obtained on noiseless statevector simulators or traditional classical computers. The iterative nature of VQE further amplifies the errors in each loop. Recent works have explored ways to integrate deep neural networks (DNN) with VQE to mitigate iterative errors, albeit primarily limited to the noiseless statevector simulators. In this work, we trained DNN models across various quantum circuits and examined the potential of two DNN-VQE approaches, DNN1 and DNNF, for predicting the ground state energies of small molecules in the presence of device noise. We carefully examined the accuracy of the DNN1, DNNF, and VQE methods on both noisy simulators and real quantum devices by considering different ansatzes of varying qubit counts and circuit depths. Our results illustrate the advantages and limitations of both VQE and DNN-VQE approaches. Notably, both DNN1 and DNNF methods consistently outperform the standard VQE method in providing more accurate ground state energies in noisy environments. However, despite being more accurate than VQE, the energies predicted using these methods on real quantum hardware remain meaningful only at reasonable circuit depths (depth = 15, gates = 21). At higher depths (depth = 83, gates = 112), they deviate significantly from the exact results. Additionally, we find that DNNF does not offer any notable advantage over VQE in terms of speed. Consequently, our study recommends DNN1 as the preferred method for obtaining quick and accurate ground state energies of molecules on current quantum hardware, particularly for quantum circuits with lower depth and fewer qubits.
Analyzing Elementary School Teachers’ Perception Types of Family Structure and Function

Objectives With nontraditional family types increasing, this study aims to derive elementary school teachers’ perception types of family structure and function and to analyze the characteristics of each type.&#x0D; Methods In order to achieve this purpose, Q methodology was applied to this study. Q-samples representing perception of family structure and function was selected from 481 statements obtained through open questionaires and literature review, and Q-sort was conducted with the Q-samples to derive P-samples’ perception types of family structure and function.&#x0D; Results The result of this study are as follows. There were four types of elementary school teachers’ perception in family structure: Open minded type-opens up various possibilities in both the scope of family members and the method of distributing power within the family, Mixed type-aims for a nuclear family based on marriage or blood ties in which equal power distribution is achieved, Conservative type-emphasizes family solidarity through blood and marriage, Extended type-accepts the family form that has varied by social changes and recognizes objects as family even not human. And three types of elementary school teachers’ perception in family function: Affection exchange type which is more interested in internal functions based on affection exchange within the family, Growth support type which values solving family problems, children’s education and their socialization. Last, Saving system type which pursues members to perform their respective roles for maintenance of family and society.&#x0D; Conclusions Through this study, the unique characteristics of each perception type in family structure and function were confirmed, and it was suggested that elementary school teachers need support to form their inclusive family concept.
Digital Competence and Disability: A Qualitative Approach from the Perspective of University Teachers in Andalusia (Spain)

AbstractDigital competence in teaching is a challenge in the professional practice of university teachers and a problem identified in the research that needs to be addressed. In particular, assessment of the level of technological skills is a key element for making decisions regarding teacher training and future lines of research, especially when it comes to improving the learning of students with disabilities. Taking this challenge as a focus, the aim of our study has been to understand the perception of university teachers in the Autonomous Community of Andalusia (Spain) about their level of digital competence for the care of students with disabilities. The methodology used was qualitative, based on an interpretative paradigmatic perspective. In-depth interviews were used as an instrument for collecting information. The sample was made up of 64 university teachers belonging to nine University Education Centres in the Autonomous Community of Andalusia (Spain). Among the conclusions, this study highlights the low level of training in digital skills to support students with disabilities, despite an awareness of the importance of technology to help these students learn.Keywords: Digital competencedisabilityteacher traininghigher education AcknowledgmentsThis study is part of a doctoral thesis developed within the framework of the Doctoral Programme in Education at the University of Seville (Spain). Support has also been received from the Predoctoral Training Programme - Predoctoral Contracts (PIF), for the development of the VII-PPITUS.Disclosure StatementNo potential conflict of interest was reported by the authors.Additional informationFundingThis publication is part of the Ministerio de Economía y Competitividad, PID2019-108230RB-I00, funded by MCIN/AEI/10.13039/501100011033.Notes on contributorsJosé Fernández-CereroJosé Fernández-Cerero is research staff of the Department of Didactics and Educational Organisation of the University of Seville (Spain).Marta Montenegro-RuedaMarta Montenegro-Rueda is research staff of the Department of Didactics and Educational Organisation of the University of Seville (Spain).
Reading recognition of pointer meters based on an improved UNet++ network

Abstract Pointer meters are widely used in modern industries, such as petrochemical applications, substations, and nuclear power plants. To overcome the reading errors and inaccurate measurements due to uneven or fluctuating illumination in practical applications, this paper proposes an improved UNet++ network for recognizing pointer meter readings. First, the scale invariant feature transform feature-matching algorithm is used to adjust the captured tilted meter images to a symmetrical and upright shape. Then, the UNet++ network is used to segment the scale and pointer regions in the dashboard to eliminate background interference. Furthermore, part of the convolution in the UNet++ network is replaced with dilated convolution with different expansion rates to expand the perceptual field during network training. In the UNet++ network jump connection, the attention mechanism module is also introduced in the path to enhance the region’s features to be segmented and suppress the parts of the non-segmented area. A hybrid loss function is used for the network model training to prevent the imbalance of the segmented region share. Finally, the distance method is used to read the gauge representation. Experiments were conducted to compare the performance of the proposed method with that of the original UNet++ network in terms of feasibility and precision. The experimental results showed that the recognition reading accuracy was significantly improved by the enhanced network, with the accuracy, sensitivity, and specificity reaching 98.65%, 84.33%, and 99.38%, respectively. Furthermore, when using the improved UNet++ network for numerical reading, the average relative error was only 0.122%, indicating its robustness in a natural environment.
Semi-supervised Model-Based Clustering for Ordinal Data

This paper introduces a semi-supervised learning technique for model-based clustering. Our research focus is on applying it to matrices of ordered categorical response data, such as those obtained from the surveys with Likert scale responses. We use the proportional odds model, which is popular and widely used for analyzing such data, as the model structure. Our proposed technique is designed for analyzing datasets that contain both labeled and unlabeled observations from multiple clusters. The model fitting is performed using the expectation-maximization (EM) algorithm, incorporating the labeled cluster memberships, to cluster the unlabeled observations. To evaluate the performance of our proposed model, we conducted a simulation study in which we tested the model from eight different scenarios, each with varying combinations and proportions of known and unknown cluster memberships. The fitted models accurately estimate the parameters in most of the designed scenarios, indicating that our technique is effective in clustering partially-labeled data with ordered categorical response variables.
BPMF: A Backprojection and Matched-Filtering Workflow for Automated Earthquake Detection and Location

Abstract We introduce BPMF (backprojection and matched filtering)—a complete and fully automated workflow designed for earthquake detection and location, and distributed in a Python package. This workflow enables the creation of comprehensive earthquake catalogs with low magnitudes of completeness using no or little prior knowledge of the study region. BPMF uses the seismic wavefield backprojection method to construct an initial earthquake catalog that is then densified with matched filtering. BPMF integrates recent machine learning tools to complement physics-based techniques, and improve the detection and location of earthquakes. In particular, BPMF offers a flexible framework in which machine learning detectors and backprojection can be harmoniously combined, effectively transforming single-station detectors into multistation detectors. The modularity of BPMF grants users the ability to control the contribution of machine learning tools within the workflow. The computation-intensive tasks (backprojection and matched filtering) are executed with C and CUDA-C routines wrapped in Python code. This leveraging of low-level, fast programming languages and graphic processing unit acceleration enables BPMF to efficiently handle large datasets. Here, we first summarize the methodology and describe the application programming interface. We then illustrate BPMF’s capabilities to characterize microseismicity with a 10 yr long application in the Ridgecrest, California area. Finally, we discuss the workflow’s runtime scaling with numerical resources and its versatility across various tectonic environments and different problems.
Belenios with Cast as Intended

We propose the BeleniosCaI protocol, a variant of Belenios which brings the cast-as-inended property, in addition to other existing security properties. Our approach is based on a 2-part checksum that the voting device commits to, before being challenged to reveal one of them chosen at random by the voter. It requires only one device on the voter’s side and does not rely on previously sent data like with return codes. Compared to the classical Benaloh audit-or-cast approach, we still have cast-as-intended with only some probability, but the voter’s journey is more linear, and the audited ballot is really the one that is cast. We formally prove the security of BeleniosCaI w.r.t. end-to-end verifiability and privacy in a symbolic model, using the ProVerif tool.
Overstatement-Net-Equivalent Risk-Limiting Audit: ONEAudit

A procedure is a risk-limiting audit (RLA) with risk limit $$\alpha $$ if it has probability at least $$1-\alpha $$ of correcting each wrong reported outcome and never alters correct outcomes. One efficient RLA method, card-level comparison (CLCA), compares human interpretation of individual ballot cards randomly selected from a trustworthy paper trail to the voting system’s interpretation of the same cards (cast vote records, CVRs). CLCAs heretofore required a CVR for each cast card and a “link” identifying which CVR is for which card—which many voting systems cannot provide. This paper shows that every set of CVRs that produces the same aggregate results overstates contest margins by the same amount: they are overstatement-net-equivalent (ONE). CLCA can therefore use CVRs from the voting system for any number of cards and ONE CVRs created ad lib for the rest. In particular: This works for every social choice function for which there is a known RLA method, including IRV. Sample sizes for BPA and CLCA using ONE CVRs based on contest totals are comparable. With ONE CVRs from batch subtotals, sample sizes are smaller than for BPA when batches are homogeneous, approaching those of CLCA using CVRs from the voting system, and much smaller than for BLCA: A CLCA of the 2022 presidential election in California at risk limit 5% using ONE CVRs for precinct-level results would sample approximately 70 ballots statewide, if the reported results are accurate, compared to about 26,700 for BLCA. The 2022 Georgia audit tabulated more than 231,000 cards (the expected BLCA sample size was $$\approx $$ 103,000 cards); ONEAudit would have audited $$\approx $$ 1,300 cards. For data from a pilot hybrid RLA in Kalamazoo, MI, in 2018, ONEAudit gives a risk of 2%, substantially lower than the 3.7% measured risk for SUITE, the “hybrid” method the pilot used.
Recovery With Incomplete Knowledge: Fundamental Bounds on Real-Time Quantum Memories

The recovery of fragile quantum states from decoherence is the basis of building a quantum memory, with applications ranging from quantum communications to quantum computing. Many recovery techniques, such as quantum error correction, rely on the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi></mml:math> knowledge of the environment noise parameters to achieve their best performance. However, such parameters are likely to drift in time in the context of implementing long-time quantum memories. This necessitates using a "spectator" system, which estimates the noise parameter in real-time, then feed-forwards the outcome to the recovery protocol as a classical side-information. The memory qubits and the spectator system hence comprise the building blocks for a real-time (i.e. drift-adapting) quantum memory. In this article, I consider spectator-based (incomplete knowledge) recovery protocols as a real-time parameter estimation problem (generally with nuisance parameters present), followed by the application of the "best-guess" recovery map to the memory qubits, as informed by the estimation outcome. I present information-theoretic and metrological bounds on the performance of this protocol, quantified by the diamond distance between the "best-guess" recovery and optimal recovery outcomes, thereby identifying the cost of adaptation in real-time quantum memories. Finally, I provide fundamental bounds for multi-cycle recovery in the form of recurrence inequalities. The latter suggests that incomplete knowledge of the noise could be an advantage, as errors from various cycles can cohere. These results are illustrated for the approximate [4,1] code of the amplitude-damping channel and relations to various fields are discussed.
Towards Benchmarking for Evaluating Machine Learning Methods in Detecting Outliers in Process Datasets

Within the integration and development of data-driven process models, the underlying process is digitally mapped in a model through sensory data acquisition and subsequent modelling. In this process, challenges of different types and degrees of severity arise in each modelling step, according to the Cross-Industry Standard Process for Data Mining (CRISP-DM). Particularly in the context of data acquisition and integration into the process model, it can be assumed with a sufficiently high degree of probability that the acquired data contain anomalies of various kinds. The outliers must be detected in the data preparation and processing phase and dealt with accordingly. If this is sufficiently implemented, it will positively impact the subsequent modelling in terms of accuracy and precision. Therefore, this paper shows how outliers can be identified using the unsupervised machine learning methods autoencoder, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest (iForest), and One-Class Support Vector Machine (OCSVM). Following implementing these methods, we compared them by applying the Numenta Anomaly Benchmark (NAB) and sufficiently presented the individual strengths and disadvantages. Evaluating the correctness, distinctiveness and robustness criteria described in the paper showed that the One-Class Support Vector Machine was outstanding among the methods considered. This is because the OCSVM achieved acceptable anomaly detections on the available process datasets with comparatively little effort.
From TARA to Test: Automated Automotive Cybersecurity Test Generation Out of Threat Modeling

The United Nations Economic Commission for Europe (UNECE) demands the management of cyber security risks in vehicle design and that the effectiveness of these measures is verified by testing. Generally, with rising complexity and openness of systems via software-defined vehicles, verification through testing becomes a very important for security assurance. This mandates the introduction of industrial-grade cybersecurity testing in automotive development processes. Currently, the automotive cybersecurity testing procedures are not specified or automated enough to be able to deliver tests in the amount and thoroughness needed to keep up with that regulation, let alone doing so in a cost-efficient manner. This paper presents a methodology to automatically generate technology-agnostic test scenarios from the results of threat analysis and risk assessment (TARA) process. Our approach is to transfer the resulting threat models into attack trees and label their edges using actions from a domain-specific language (DSL) for attack descriptions. This results in a labelled transitions system (LTS), in which every labelled path intrinsically forms a test scenario. In addition, we include the concept of Cybersecurity Assurance Levels (CALs) and Targeted Attack Feasibility (TAF) into testing by assigning them as costs to the attack path. This abstract test scenario can be compiled into a concrete test case by augmenting it with implementation details. Therefore, the efficacy of the measures taken because of the TARA can be verified and documented. As TARA is a de-facto mandatory step in the UNECE regulation and the relevant ISO standard, automatic test generation (also mandatory) out of it could mean a significant improvement in efficiency, as two steps could be done at once.
Detecting Asthma Presentations from Emergency Department Notes: An Active Learning Approach

Emergency department (ED) triage notes contain valuable information for near real-time syndromic surveillance of emergent public health events. With the increasing use of machine learning algorithms for classification of ED triage notes, active learning (AL) offers a way to automatically obtain high-quality data for labelling, resulting in a reduced requirement for annotators to search for suitable data. Active learning involves selecting the most valuable data to enhance the model's learning process, guided by a query strategy that identifies samples with most impact on the model's training. The objective of this study was to assess the effectiveness of active learning in developing a high-performing model for ED syndrome detection. The research aimed to explore pool-based active learning and investigated various query strategies to improve the model's performance in identifying asthma presentations from ED triage notes. Our results showed that AL can be highly effective for reducing annotation effort while building reliable models. Uncertainty sampling strategy outperformed all other methods, achieving an F1 score of 0.91 to improve the baseline score by 7.6 percentage points.
VALERIE22 - A photorealistic, richly metadata annotated dataset of urban environments

The VALERIE tool pipeline is a synthetic data generator [14] developed with the goal to contribute to the understanding of domain-specific factors that influence perception performance of DNNs (deep neural networks). This work was carried out under the German research project KI Absicherung in order to develop a methodology for the validation of DNNs in the context of pedestrian detection in urban environments for automated driving.