Reviewerly/exHarmony huggingface dataset:
Deriving Multiple-Layer Information from a Motion-Sensing Mattress for Precision Care

Bed is often the personal care unit in hospitals, nursing homes, and individuals' homes. Rich care-related information can be derived from the sensing data from bed. Patient fall is a significant issue in hospitals, many of which are related to getting in and/or out of bed. To prevent bed falls, a motion-sensing mattress was developed for bed-exit detection. A machine learning algorithm deployed on the chip in the control box of the mattress identified the in-bed postures based on the on/off pressure pattern of 30 sensing areas to capture the users' bed-exit intention. This study aimed to explore how sleep-related data derived from the on/off status of 30 sensing areas of this motion-sensing mattress can be used for multiple layers of precision care information, including wellbeing status on the dashboard and big data analysis for living pattern clustering. This study describes how multiple layers of personalized care-related information are further derived from the motion-sensing mattress, including real-time in-bed/off-bed status, daily records, sleep quality, prolonged pressure areas, and long-term living patterns. Twenty-four mattresses and the smart mattress care system (SMCS) were installed in a dementia nursing home in Taiwan for a field trial. Residents' on-bed/off-bed data were collected for 12 weeks from August to October 2021. The SMCS was developed to display care-related information via an integrated dashboard as well as sending reminders to caregivers when detecting events such as bed exits and changes in patients' sleep and living patterns. The ultimate goal is to support caregivers with precision care, reduce their care burden, and increase the quality of care. At the end of the field trial, we interviewed four caregivers for their subjective opinions about whether and how the SMCS helped their work. The caregivers' main responses included that the SMCS helped caregivers notice the abnormal situation for people with dementia, communicate with family members of the residents, confirm medication adjustments, and whether the standard care procedure was appropriately conducted. Future studies are suggested to focus on integrated care strategy recommendations based on users' personalized sleep-related data.
Stimulating the visit of a physical museum through a virtual one

Museums are more than a place to preserve collections of pieces. They offer serious leisure activities that can be incremented by using virtual reality tools. Thus, this study aims to analyse (i) the effect of virtual atmospheric cues on serious leisure in a virtual museum environment, and (ii) the impact of serious leisure on the desire to visit the physical museum. Results demonstrated that virtual atmospheric cues exercise a significant effect on the perception of vividness and presence. Both vividness and presence influence serious leisure and this, in turn, affects serious leisure in a virtual museum environment.
Conceptual modeling as language design

Abstract Conceptual modeling is a broad practice encompassing knowledge organization, domain modeling, and knowledge representation. It is best understood not as a scientific process of discovery, but as a constructive process of language design. This constructive process involves both explicating differences of meaning implicit in some discursive tradition and revising those differences to improve that tradition. Understood this way, conceptual modeling can serve as the basis for a paradigm of information research and practice that does not reproduce fundamental asymmetries between researchers and the people they study, or between practitioners and the people they serve. Progress within this paradigm will involve combining methods from what have up to now been different traditions or modes of conceptual modeling. It will require paying closer attention to the historical and structural dimensions of discursive traditions and reimagining the function of critique.
A complex solution to an unsolvable problem?

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Dynamic Data Integrity Auditing Based on Hierarchical Merkle Hash Tree in Cloud Storage

In cloud storage mode, users lose physical control over their data. To enhance the security of outsourced data, it is vital to audit the data integrity of the data owners. However, most of the current audit protocols have a single application scenario and cannot accommodate the actual needs of individuals and enterprises. In this research, a safe and efficient auditing scheme is proposed that is based on a hierarchical Merkle tree. On the one hand, we use a hierarchical authentication data structure and local signature aggregation technique to reduce the scale of the Merkle tree. In addition, authoritative nodes are introduced to reduce the length of the authentication path and improve the update efficiency. On the other hand, we introduce a monitoring mechanism that is based on the original data integrity auditing model to analyze the cloud data, which improves the transparency and credibility of cloud service providers. In addition, we achieve incomplete data recovery through log analysis, which greatly reduces the number of replicas of files under the premise of multi-copy auditing, reduces the burden on cloud service providers, and improves the fairness of audit protocols. The theoretical analysis and experimental comparison prove that the method is secure and efficient. It can effectively reduce the computational overhead and storage overhead in integrity auditing.
Optical Neural Network Architecture for Deep Learning with Temporal Synthetic Dimension

The physical concept of synthetic dimensions has recently been introduced into optics. The fundamental physics and applications are not yet fully understood, and this report explores an approach to optical neural networks using synthetic dimension in time domain, by theoretically proposing to utilize a single resonator network, where the arrival times of optical pulses are interconnected to construct a temporal synthetic dimension. The set of pulses in each roundtrip therefore provides the sites in each layer in the optical neural network, and can be linearly transformed with splitters and delay lines, including the phase modulators, when pulses circulate inside the network. Such linear transformation can be arbitrarily controlled by applied modulation phases, which serve as the building block of the neural network together with a nonlinear component for pulses. We validate the functionality of the proposed optical neural network for the deep learning purpose with examples handwritten digit recognition and optical pulse train distribution classification problems. This proof of principle computational work explores the new concept of developing a photonics-based machine learning in a single ring network using synthetic dimensions, which allows flexibility and easiness of reconfiguration with complex functionality in achieving desired optical tasks.
Dynamics of two-qubit quantum nonlocality in a Heisenberg chain model with the intrinsic decoherence

This paper investigates the dynamics of two-spin nonlocality generation in a Heisenberg XXX chain with Dzyaloshinskii-Moriya (DM) and Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interactions. We analyze the two-spin nonlocality dynamics by using uncertainty-induced nonlocality, maximal Bell function, and log-negativity. We demonstrate that a separable two-spin Heisenberg XXX chain state, induced by two-spin antiferromagnetic interaction as well as x-component of DM and KSEA interactions, could evolve to maximal two-spin nonlocality state. The ability of preserving the maximal uncertainty-induced nonlocality can be enhanced by increasing the coupling strength of the spin-spin interaction coupling. The hierarchy principle is maintained for the two-spin Bell nonlocality and log-negativity entanglement. The two-spin log-negativity dynamics exhibits the phenomena of sudden death and birth. The sudden-death phenomenon is due to the intrinsic decoherence, which also causes a reduction in the two-spin nonlocality. While the sudden-birth phenomenon is due to two-spin antiferromagnetic interaction as well as x-component of DM and KSEA interactions. The two-spin uncertainty-induced nonlocality is more robust, against the intrinsic decoherence, than the other types of the nonlocality. The results indicate that by boosting the two-spin antiferromagnetic interaction, the produced nonlocality (resulting from the DM and KSEA x-component interactions) can be shielded from the intrinsic decoherence effect.
Segmentation of multiple Organs‐at‐Risk associated with brain tumors based on coarse‐to‐fine stratified networks

Abstract Background Delineation of Organs‐at‐Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time‐consuming, labor‐intensive and affected by inter‐ and intra‐observer variability, a robust and efficient automatic segmentation algorithm is highly desirable for improving the efficiency and repeatability of OAR delineation. Purpose Automatic segmentation of OARs in medical images is challenged by low contrast, various shapes and imbalanced sizes of different organs. We aim to overcome these challenges and develop a high‐performance method for automatic segmentation of 10 OARs required in radiotherapy planning for brain tumors. Methods A novel two‐stage segmentation framework is proposed, where a coarse and simultaneous localization of all the target organs is obtained in the first stage, and a fine segmentation is achieved for each organ, respectively, in the second stage. To deal with organs with various sizes and shapes, a stratified segmentation strategy is proposed, where a High‐ and Low‐Resolution Residual Network (HLRNet) that consists of a multiresolution branch and a high‐resolution branch is introduced to segment medium‐sized organs, and a High‐Resolution Residual Network (HRRNet) is used to segment small organs. In addition, a label fusion strategy is proposed to better deal with symmetric pairs of organs like the left and right cochleas and lacrimal glands. Results Our method was validated on the dataset of MICCAI ABCs 2020 challenge for OAR segmentation. It obtained an average Dice of 75.8% for 10 OARs, and significantly outperformed several state‐of‐the‐art models including nnU‐Net (71.6%) and FocusNet (72.4%). Our proposed HLRNet and HRRNet improved the segmentation accuracy for medium‐sized and small organs, respectively. The label fusion strategy led to higher accuracy for symmetric pairs of organs. Conclusions Our proposed method is effective for the segmentation of OARs of brain tumors, with a better performance than existing methods, especially on medium‐sized and small organs. It has a potential for improving the efficiency of radiotherapy planning with high segmentation accuracy.
Uses and abuses of User-Centered Design: literature review and proposal of a tool for evaluating ethics in the design of mobile applications

The aim of this paper is to study the implications and ethical variables of User-Centered Design (UCD) methods in the design of mobile applications. We carried out a review of the literature on ethics in User-Centered Design within the field of mobile applications. We studied three disciplines that are applied in the design of mobile applications and that include UCD in their definition: Human–Computer Interaction, User Experience, and Design for Behavior Change.The analysis has allowed us, firstly, to identify different ethical approaches to applying UCD, and secondly, to propose a new tool for analyzing the ethical implications of UCD taking into account two variables: alignment with the user's interests and will, and the type of influence the design generates. This paper shows that UCD methods are not always used for the benefit of the user, and asserts the ethical responsibility of design processes involving how users interact with mobile technologies as well as the importance of media education so that citizens become technologically responsible.
Geographically weighted regression (GWR) and Prediction-area (P-A) plot to generate enhanced geochemical signatures for mineral exploration targeting

The geochemical exploration provides an effective and direct spatial evidence for identifying undiscovered deposits. Geochemical data are spatial data with many attributes, such as closure effect, collinearity, multivariate, spatial correlation and heterogeneity. The data processing method considering the attributes of geochemical data is very important for accurately identification of the undiscovered deposits. In this paper, a data-driven mineral exploration targeting method was proposed, first log-ratio transformation was performed to eliminate the closure effect, and then central standardization was applied to avoid collinearity. Then, the geographically weighted regression (GWR) was carried out with rock-forming oxides and immobile elements as dependent variables and ore-forming elements as independent variables, which considering the spatial attributes of geochemical data, and the residuals were regarded as the geochemical evidences. Afterwards, associated elements for mineralization were selected and the P-A plot was used to evaluate the relative importance of ore-forming elements (residual errors) and associated elements to indicate the locations of the deposits of the type sought, and finally an enhanced data-driven multi-element geochemical signature was generated for mineral exploration targeting. The geochemical anomalies extraction effect was significantly enhanced compared with traditional method. The method considering the multi-attribute characteristics of geochemical data provided an effective geochemical proxy for mineral exploration targeting, which was of great significance for improving the geochemical exploration theory and prospecting practice.
Assessing the cognition of movement trajectory visualizations: interpreting speed and direction

This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variables in communicating movement parameters along trajectories, such as speed and direction. To do so, a web-based survey is conducted to evaluate the understanding of movement visualizations by a nonspecialist audience. This and future studies contribute fundamental insights into the cognition of movement visualizations and inspire new methods for the empirical evaluation of geovisualizations.
<i>Elastic Power Utilization</i> in Sustainable Micro Cloud Data Centers

Efficient utilization of renewable energy when powering Cloud Data Centers is a challenging problem due to the variable and intermittent nature of both workload demand and renewable energy supply. This work aims to develop an innovative dynamic resource management algorithm to provide energy flexibility to data center operators for shaping their energy demand to match renewable energy supply. We present a novel framework, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Elastic Power Utilization</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EPU</i> ), to serve this purpose. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EPU</i> utilizes energy source information to dynamically manage data center resources for matching the renewable energy supply with the energy demand to serve the workload. We propose a resource management algorithm that exploits overbooking, consolidation and migration of virtual machines (VMs) to implement the power elasticity required by the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EPU</i> framework. We compare our approach to a state-of-the-art algorithm and baseline approaches with three different workloads. The results from extensive simulations show that our proposed algorithm outperforms the state-of-the-art approach in saving brown energy by 23.1%, 21.3%, and 27.0% for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Google</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Wikipedia</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Nectar</i> workloads, respectively.
Revisiting Core Maintenance for Dynamic Hypergraphs

Core maintenance for dynamic hypergraphs has been receiving an increasing attention. However, existing works mainly focus on the insertion/deletion of hyperedges. This article revisits the problem from the view of vertices change. We study core maintenance when the vertices are inserted/deleted into/from specific hyperedges in the hypergraph, which is a challenging task since the deletion of the vertex may increase the core numbers and the insertion of the vertex may decrease the core numbers. We discuss in detail the possible changes of core numbers in different situations. For the insertion/deletion of vertices contained by a single hyperedge, we design sequential algorithms to discover the vertices whose core numbers have changed. Compared with static recomputation (Leng et al. 2013) and LYCLC (Luo et al. 2021) algorithms, our sequential algorithms can accelerate more than 1,000× and 12× at most in the processing time, respectively. For the insertion/deletion of vertices contained by different hyperedges, we find that core numbers of all vertices change 1 at most if these hyperedges form a matching. We design parallel algorithms that divide a matching into different sets based on their core numbers and allot a thread to each set. Experiments show that our parallel algorithms have good stability, scalability, and parallelism. Compared with the parallel static algorithm (Gabert et al. 2021) and the parallel dynamic algorithm GPC (Gabert et al. 2021), our parallel algorithms with 32 threads can accelerate 33× and 22× at most in the processing time, respectively.
Quorum sensing centered bacterial horde algorithm for global optimization

Summary This article proposes an intellectual quorum sensing‐centered bacterial horde optimization (QBHO) exert for assessing and authenticating the optimization problems. Quorum detection realizes the finest place of the bacteria by apprehending the most awful point within the hunt area. This mechanism is utilized in this study for problem‐solving. With this bacterial behavior and location perceptive, the superlative optimum result is obtained. In this anticipated QBHO algorithm, the competence of bacteria is enhanced to be beneficial so that results can be enhanced. The fundamental group‐built optimization procedures for instance, particle swarm, ant cluster, biogeography‐built algorithm, simulated bee constellation procedure and typical bacterial herd optimization through the distinctive considerations remain simulated and then associated through the intended practice for its ratification of results with QBHO. The projected QBHO practice is exploited to ensue with the seven‐typical benchmark with unimodal and multimodal trial functions intended to prove its practicability and ideality. The realized findings imply that the suggested QBHO approach surpasses the deemed optimization procedures for complex standard functions. Furthermore, the intended procedure might be appropriate to several engineering challenges, notably intended for intricate concurrent optimization problems.
Spectral Determinant of the Two‐Photon Quantum Rabi Model

The various generalized spectral determinants (G-functions) of the two-photon quantum Rabi model are analyzed with emphasis on the qualitative aspects of the regular spectrum. Whereas all of them yield at least a subset of the exact regular eigenvalues, only the G-function proposed by Chen et al. in 2012 exhibits an explicitly known pole structure which dictates the approach to the collapse point. We derive this function rigorously employing the $\mathbb{Z}_4$-symmetry of the model and show that its zeros correspond to the complete regular spectrum.
An attention‐based representation learning model for multiple relational knowledge graph

Abstract Knowledge graph embedding models are used to learn low‐dimensional representations of entities and relations in knowledge graphs. In this paper, we propose Multi‐RAttE, an attention‐based learning method for multiple relational knowledge graph embedding representation, which divides the information transfer in the knowledge graph into cross‐relational information transfer and relation‐specific information transfer, and divides the embedding of knowledge graph entities into structural embedding and multi‐relational embedding for joint learning. To objectively analyse the performance of the Multi‐RAttE model, we select two typical datasets and different representative baseline models for experimental evaluation on several tasks such as link prediction, multi‐relation prediction and node classification. The experimental results show that the Multi‐RAttE model improves 8% over the state‐of‐the‐art model Composition‐based Multi‐Relational Graph Convolutional Networks (CompGCN) in terms of Hits@1 metric on the link prediction task on FB15k‐237 dataset; on the multi‐relation prediction task, the accuracy improves by 1.8% and 3.7% in the auc metric and F1 metric, respectively. The experimental results have proved that the Multi‐RAttE model can effectively perform the representation of multiple relations.
Decision-making based on an improved visual analytics approach for emotion prediction

Visual Analytics approach allows driving informed and effective decision-making. It assists decision-makers to visually interact with large amount of data and to computationally learn valuable hidden patterns in that data, which improve the decision quality. In this article, we introduce an enhanced visual analytics model combining cognitive-based visual analysis to data mining-based automatic analysis. As emotions are strongly related to human behaviour and society, emotion prediction is widely considered by decision making activities. Unlike speech and facial expressions modalities, EEG (electroencephalogram) has the advantage of being able to record information about the internal emotional state that is not always translated by perceptible external manifestations. For this reason, we applied the proposed cognitive approach on EEG data to demonstrate its efficiency for predicting emotional reaction to films. For automatic analysis, we developed the Echo State Network (ESN) technique considered as an efficient machine learning solution due to its straightforward training procedure and high modelling ability for handling time-series problems. Finally, utility and usability tests were performed to evaluate the developed prototype.