Reviewerly/exHarmony huggingface dataset:
Preliminary Research: Virtual Reality in Residential Aged Care to Reduce Apathy and Improve Mood

This study determines the feasibility of using virtual reality (VR) for residents with and without dementia in the residential aged care (RAC) environment (also referred to as nursing homes or long-term care). A mixed-methods study was conducted with 13 residents of varying cognitive capacity in a residential aged care facility (RACF) operated by a not-for-profit organization in Brisbane, Australia. Residents participated in one facilitated VR session, either as a group or individual session. Residents' mood and apathy were measured by the Observed Emotion Rating Scale (OERS) and the Person-Environment Apathy Rating Scale. Residents also completed a structured interview to provide their feedback after the VR session. In addition, four RACF staff members were interviewed about their experience of using the VR in residents and the ongoing feasibility. The experience of VR administered by a researcher and leisure and lifestyle coordinator as a leisure activity, significantly reduced apathy in residents (Z = -2.818, p = 0.005) through observations of increased facial expression, eye contact, physical engagement, verbal tone, and expression. The study did not find a VR impact on the OERS measures; no significant increase in fear/anxiety was observed. Reminiscence was clearly observed in six of the nine residents with the ability to verbally communicate. VR was found not to be helpful in residents during episodes of acute neuropsychiatric and behavioral symptoms. This study suggested feasibility of using fully immersive VR delivered by mobile phone technologies. It provides preliminary data for a controlled trial presently underway examining the effectiveness of VR as a group activity in RAC to improve mood states, behavioral symptoms, and pro re nata psychotropic medication use. Residents indicated that VR was enjoyable with low levels of physical and emotional discomfort reported or observed.
Applying Mobile Augmented Reality (AR) to Teach Interior Design Students in Layout Plans: Evaluation of Learning Effectiveness Based on the ARCS Model of Learning Motivation Theory

In this paper we present a mobile augmented reality (MAR) application supporting teaching activities in interior design. The application supports students in learning interior layout design, interior design symbols, and the effects of different design layout decisions. Utilizing the latest AR technology, users can place 3D models of virtual objects as e.g., chairs or tables on top of a design layout plan and interact with these on their mobile devices. Students can experience alternative design decision in real-time and increases the special perception of interior designs. Our system fully supports the import of interior deployment layouts and the generation of 3D models based on design artefacts based on typical design layout plan design symbols and allows the user to investigate different design alternatives. We applied John Keller's Attention, Relevance, Confidence, and Satisfaction (ARCS) learning motivation model to validate our solution to examine the students' willingness and verify the ability of students to improve learning through MAR technology. We compared a sample experimental group of N = 52 test-subjects with a sample of N = 48 candidates in a control group. Learning indicators as learning interest, confidence, satisfaction and effective have been utilized to assess the students' learning motivation through the use of MAR technology. The learning results have been determined by the independent sample t testing. The significance of the post-test had a p-value < 0.05 difference. The result of the study clearly shows that the reference group utilizing MAR technology as a learning aid show a higher learning effectiveness as the control group. Thus, we conclude that MAR technology does enhance students' learning ability for interior design and making appropriate design decisions.
Expert biases in technology foresight. Why they are a problem and how to mitigate them

The paper addresses an issue largely discussed in the field of Forecasting and in many future-oriented scientific and professional disciplines, but less frequently considered in the Foresight literature, particularly in the technology foresight field- i.e. the extent to which biases of human experts influence the foresight process. The paper reviews the literature on cognitive biases and identifies the main areas of technology foresight in which biases are most likely to materialize. It offers a number of examples in which these biases may indeed create distortions. It also reviews the potential impact of several recently introduced methods, in the field of technology foresight and in related areas, to mitigate the distortions and calls for future research in this new field of investigation.
On the effect of chaotic system in performance characteristics of chaos based s-box designs

Many cryptographic protocol have been realized using similarities between chaos and cryptology. One of the most successful examples of these protocols has been s-box structures. S-box structures are a popular topic because they are the only component that provides nonlinearity property in many encryption algorithms. Therefore, in the literature, chaos based s-box designs have been proposed for almost all chaotic system classes. However, the effect of chaotic systems on these designs has not been studied in all aspects. The purpose of this study is to address this problem. It has been shown in the study that strong s-box structures can be designed by using systems that do not display chaotic behavior. This result has revealed the necessity of reexamining many designs in chaos based cryptology literature. A raw literature analysis has shown that the number of studies related to chaos based s-box designs over the last decade is more than 250. Therefore, it is thought that study outputs will bring a different perspective for chaos based cryptology literature.
Eye-Tracking-Based Classification of Information Search Behavior Using Machine Learning: Evidence from Experiments in Physical Shops and Virtual Reality Shopping Environments

How can we tailor assistance systems, such as recommender systems or decision support systems, to consumers’ individual shopping motives? How can companies unobtrusively identify shopping motives without explicit user input? We demonstrate that eye movement data allow building reliable prediction models for identifying goal-directed and exploratory shopping motives. Our approach is validated in a real supermarket and in an immersive virtual reality supermarket. Several managerial implications of using gaze-based classification of information search behavior are discussed: First, the advent of virtual shopping environments makes using our approach straightforward as eye movement data are readily available in next-generation virtual reality devices. Virtual environments can be adapted to individual needs once shopping motives are identified and can be used to generate more emotionally engaging customer experiences. Second, identifying exploratory behavior offers opportunities for marketers to adapt marketing communication and interaction processes. Personalizing the shopping experience and profiling customers’ needs based on eye movement data promises to further increase conversion rates and customer satisfaction. Third, eye movement-based recommender systems do not need to interrupt consumers and thus do not take away attention from the purchase process. Finally, our paper outlines the technological basis of our approach and discusses the practical relevance of individual predictors.
Table Detection in Invoice Documents by Graph Neural Networks

Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.
Variational quantum unsampling on a quantum photonic processor

Quantum algorithms for Noisy Intermediate-Scale Quantum (NISQ) machines have recently emerged as new promising routes towards demonstrating near-term quantum advantage (or supremacy) over classical systems. In these systems samples are typically drawn from probability distributions which --- under plausible complexity-theoretic conjectures --- cannot be efficiently generated classically. Rather than first define a physical system and then determine computational features of the output state, we ask the converse question: given direct access to the quantum state, what features of the generating system can we efficiently learn? In this work we introduce the Variational Quantum Unsampling (VQU) protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuits outputs. In our approach one can variationally train a quantum operation to unravel the action of an unknown unitary on a known input state; essentially learning the inverse of the black-box quantum dynamics. While the principle of our approach is platform independent, its implementation will depend on the unique architecture of a specific quantum processor. Here, we experimentally demonstrate the VQU protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications; including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation.
Optimization and expansion of non-negative matrix factorization

Non-negative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence or to matrices with missing entries. Besides, most NMF research focuses only on blind decompositions: decomposition without utilizing prior knowledge. Finally, the lack of well-validated methodology for choosing the rank hyperparameters also raises concern on derived results.We adopt the idea of sequential coordinate-wise descent to NMF to increase the convergence rate. We demonstrate that NMF can handle missing values naturally and this property leads to a novel method to determine the rank hyperparameter. Further, we demonstrate some novel applications of NMF and show how to use masking to inject prior knowledge and desirable properties to achieve a more meaningful decomposition.We show through complexity analysis and experiments that our implementation converges faster than well-known methods. We also show that using NMF for tumour content deconvolution can achieve results similar to existing methods like ISOpure. Our proposed missing value imputation is more accurate than conventional methods like multiple imputation and comparable to missForest while achieving significantly better computational efficiency. Finally, we argue that the suggested rank tuning method based on missing value imputation is theoretically superior to existing methods. All algorithms are implemented in the R package NNLM, which is freely available on CRAN and Github.
Reinforcement Learning Based PHY Authentication for VANETs

Mobile edge computing in vehicular ad hoc networks (VANETs) suffers from rogue edge attacks due to the vehicle mobility and the network scale. In this paper, we present a physical authentication scheme to resist rogue edge attackers whose goal is to send spoofing signals to attack VANETs. This authentication scheme exploits the channel states of the shared ambient radio signals of the mobile device and its serving edge such as the onboard unit during the same moving trace and applies reinforcement learning to select the authentication modes and parameters. By applying transfer learning to save the learning time and applies deep learning to further improve the authentication performance, this scheme enables mobile devices in VANETs to optimize their authentication modes and parameters without being aware of the VANET channel model, the packet generation model, and the spoofing model. We provide the convergence bound such as the mobile device utility, evaluate the computational complexity of the physical authentication scheme, and verify the analysis results via simulations. Simulation and experimental results show that this scheme improves the authentication accuracy with reduced energy consumption against rogue edge attacks.
Deep learning and optimization algorithms for automatic breast cancer detection

Abstract Breast cancer is caused by the abnormal and rapid growth of breast cells. An early diagnosis can ensure an easier and effective treatment. A mass in the breast is a significant early sign of breast cancer, even though differentiating the cancerous mass's tissue from normal tissue for diagnosis is a difficult task for radiologists. The development of computer‐aided detection systems in recent years has led to nondestructive and efficient cancer diagnostic techniques. This paper proposes a comprehensive method to locate the cancerous region in the mammogram image. This method employs image noise reduction, optimal image segmentation based on the convolutional neural network, a grasshopper optimization algorithm, and optimized feature extraction and feature selection based on the grasshopper optimization algorithm, thereby improving precision and decreasing the computational cost. This method was applied to the Mammographic Image Analysis Society Digital Mammogram Database and Digital Database for Screening Mammography breast cancer databases and the simulation results were compared with 10 different state‐of‐the‐art methods to analyze the proposed system's efficiency. Final results showed that the proposed method had 96% Sensitivity, 93% Specificity, 85% PPV, 97% NPV, 92% accuracy, and better efficiency than other traditional methods in terms of Sensitivity, Specificity, PPV, NPV, and Accuracy.
A blockchain-enabled e-learning platform

The properties of a blockchain such as immutability, provenance, and peer-executed smart contracts could bring a new level of security, trust, and transparency to e-learning. In this paper, we introduce our proof-of-concept blockchain-based e-learning platform developed to increase transparency in assessments and facilitate curriculum personalisation in a higher education context. Most notably, our platform could automate assessments and issue credentials. We designed it to be pedagogically neutral and content-neutral in order to showcase the benefits of a blockchain back-end to end users such as students and teaching staff. Our evaluation suggests that our platform could increase trust in online education providers, assessment procedures, education history and credentials.
SD-UNet: Stripping down U-Net for Segmentation of Biomedical Images on Platforms with Low Computational Budgets

During image segmentation tasks in computer vision, achieving high accuracy performance while requiring fewer computations and faster inference is a big challenge. This is especially important in medical imaging tasks but one metric is usually compromised for the other. To address this problem, this paper presents an extremely fast, small and computationally effective deep neural network called Stripped-Down UNet (SD-UNet), designed for the segmentation of biomedical data on devices with limited computational resources. By making use of depthwise separable convolutions in the entire network, we design a lightweight deep convolutional neural network architecture inspired by the widely adapted U-Net model. In order to recover the expected performance degradation in the process, we introduce a weight standardization algorithm with the group normalization method. We demonstrate that SD-UNet has three major advantages including: (i) smaller model size (23x smaller than U-Net); (ii) 8x fewer parameters; and (iii) faster inference time with a computational complexity lower than 8M floating point operations (FLOPs). Experiments on the benchmark dataset of the Internatioanl Symposium on Biomedical Imaging (ISBI) challenge for segmentation of neuronal structures in electron microscopic (EM) stacks and the Medical Segmentation Decathlon (MSD) challenge brain tumor segmentation (BRATs) dataset show that the proposed model achieves comparable and sometimes better results compared to the current state-of-the-art.
BATCH: A Scalable Asymmetric Discrete Cross-Modal Hashing

Supervised cross-modal hashing has attracted much attention. However, there are still some challenges, e.g., how to effectively embed the label information into binary codes, how to avoid using a large similarity matrix and make a model scalable to large-scale datasets, how to efficiently solve the binary optimization problem. To address these challenges, in this paper, we present a novel supervised cross-modal hashing method, i.e., scalaBle Asymmetric discreTe Cross-modal Hashing, BATCH for short. It leverages collective matrix factorization to learn a common latent space for the labels and different modalities, and embeds the labels into binary codes by minimizing a distance-distance difference problem. Furthermore, it builds a connection between the common latent space and the hash codes by an asymmetric strategy. In the light of this, it can perform cross-modal retrieval and embed more similarity information into the binary codes. In addition, it introduces a quantization minimization term and orthogonal constraints into the optimization problem, and generates the binary codes discretely. Therefore, the quantization error and redundancy may be much reduced. Moreover, it is a two-step method, making the optimization simple and scalable to large-scale datasets. Extensive experimental results on three benchmark datasets demonstrate that BATCH outperforms some state-of-the-art cross-modal hashing methods in terms of accuracy and efficiency.
High-precision quantum algorithms for partial differential equations

Quantum computers can produce a quantum encoding of the solution of a system of differential equations exponentially faster than a classical algorithm can produce an explicit description. However, while high-precision quantum algorithms for linear ordinary differential equations are well established, the best previous quantum algorithms for linear partial differential equations (PDEs) have complexity <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">p</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">l</mml:mi><mml:mi mathvariant="normal">y</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>ϵ</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>ϵ</mml:mi></mml:math> is the error tolerance. By developing quantum algorithms based on adaptive-order finite difference methods and spectral methods, we improve the complexity of quantum algorithms for linear PDEs to be <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="normal">p</mml:mi><mml:mi mathvariant="normal">o</mml:mi><mml:mi mathvariant="normal">l</mml:mi><mml:mi mathvariant="normal">y</mml:mi></mml:mrow><mml:mo stretchy="false">(</mml:mo><mml:mi>d</mml:mi><mml:mo>,</mml:mo><mml:mi>log</mml:mi><mml:mo>⁡</mml:mo><mml:mo stretchy="false">(</mml:mo><mml:mn>1</mml:mn><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>ϵ</mml:mi><mml:mo stretchy="false">)</mml:mo><mml:mo stretchy="false">)</mml:mo></mml:math>, where <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math> is the spatial dimension. Our algorithms apply high-precision quantum linear system algorithms to systems whose condition numbers and approximation errors we bound. We develop a finite difference algorithm for the Poisson equation and a spectral algorithm for more general second-order elliptic equations.
Enhanced QoS-Based Model for Trust Assessment in Cloud Computing Environment

Trust management becomes an urgent requirement in the cloud environment and a trust relationship between service user and service provider is required. Trust is the estimation of the ability of cloud resources in completing a task based on some criteria such as availability, reliability, and resource processing power. In this paper, an enhanced QoS-based model for evaluating the trustworthiness of the cloud provider is introduced. The proposed model calculates the accumulative trust value which is updated dynamically at each transaction and reflects the current or latest transaction of the provider in the cloud. The trustworthiness of a cloud resource is evaluated based on its provider reputation history from user feedback ratings based on the covariance mathematical technique to evaluate the credibility of the user's feedback. The trustworthiness of a cloud resource is also evaluated by calculating the computing power of resources at run-time. Experimental results confirm the effect of user opinion and resources processing speed on trust value calculation, which in turn assesses the trustworthiness of the cloud provider. The simulation has been performed using the CloudSim with the platform Eclipse for developing the proposed model.
A novel optimal placing of solar farms utilizing multi-criteria decision-making (MCDA) and feature selection

Optimal planning of solar farm as a complex multi-criterion decision-making (MCDA) problem is to determine the optimal location to benefit the maximum potential of solar energy. In this research, GIS (geographic information systems)-MCDA methods in determining a suitable area to construct solar farms are applied. To reach this goal, fuzzy-AHP (analytical hierarchy process) and fuzzy-ANP (analytical network process) methods are utilized. Thereafter, the feature selection algorithm extracts important enough parameters to propel solving procedure in such sophisticated problem. The results indicate that combining the fuzzy-ANP method and feature selection algorithm leads to a more accurate method in comparison with AHP and ANP solely. More precisely, by decreasing DPTL and distance to road amounts in east and south of the study area, the fuzzy-AHP method indicates the unsuitable location for solar farm placement, however, decreasing these parameters makes the regions to be a good place for solar farms as the fuzzy-ANP method prove it. Finally, the results indicate that the north of the study area has the highest potential to be the host of solar farms.