Reviewerly/exHarmony huggingface dataset:
A Method to Improve the Design of Virtual Reality Games in Healthcare Applied to Increase Physical Activity in Patients with Type 2 Diabetes Mellitus

In recent years, there has been an increase in research related to specific applications or features of virtual reality tools for health care. Each work highlights their findings related to the specific case on which the research was focusing. The present study is a proposal of a design method for virtual reality games for healthcare applications. Virtual reality allows the integration of real physical activity, required for health treatments, and a virtual world that improves the engagement of the patient. The design method is based on the needs that a health treatment faces in terms of motivational activities and challenges to include in a virtual reality scenario, obtained from the investigation of previous works. A treatment benefiting from user exercise was selected as the case to fully describe the design method. Therefore, this paper also describes a virtual reality tool developed to help in the treatment of chronic diseases, such as type 2 diabetes. The tool was used by a group of patients. To evaluate the method and the virtual reality application, two types of tests were applied: a clinical test adapted from previous literature and a usability test focused on the virtual environment. Clinical tests showed low stress and good physical health, while usability tests showed engagement.
ULAN: A Universal Local Adversarial Network for SAR Target Recognition Based on Layer-Wise Relevance Propagation

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks easily fail in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the area in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-white-box attack network called Universal Local Adversarial Network (ULAN) to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed method, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method effectively prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.
Semi-Global Matching Assisted Absolute Phase Unwrapping

Measuring speed is a critical factor to reduce motion artifacts for dynamic scene capture. Phase-shifting methods have the advantage of providing high-accuracy and dense 3D point clouds, but the phase unwrapping process affects the measurement speed. This paper presents an absolute phase unwrapping method capable of using only three speckle-embedded phase-shifted patterns for high-speed three-dimensional (3D) shape measurement on a single-camera, single-projector structured light system. The proposed method obtains the wrapped phase of the object from the speckle-embedded three-step phase-shifted patterns. Next, it utilizes the Semi-Global Matching (SGM) algorithm to establish the coarse correspondence between the image of the object with the embedded speckle pattern and the pre-obtained image of a flat surface with the same embedded speckle pattern. Then, a computational framework uses the coarse correspondence information to determine the fringe order pixel by pixel. The experimental results demonstrated that the proposed method can achieve high-speed and high-quality 3D measurements of complex scenes.
Analysis of Profiles of Family Educational Situations during COVID-19 Lockdown in the Valencian Community (Spain)

Due to the pandemic (COVID-19), the education system in Spain was forced to close for three months, creating an unprecedented situation: improvised distance schooling. Family characteristics and their life situations with Information and Communication Technology use would be aspects to be studied as educational conditioning factors. This paper presents the ways in which a representative sample of families in the Valencian Community (Spain) assumed the education of their children during the lockdown. Mixed methods (quantitative -surveys-/qualitative -focus groups-) are used. Multivariate profiles are studied (k-means cluster) that summarise the life circumstances, represented by composite indicators resulting from the families’ responses to specific items describing their way of life and educational performance. Associated variables, such as demographic or life situation characteristics, are analyzed for each profile. Some gaps (described by indicators that synthesize the functioning of the families) are observed due to life circumstances that correspond not only to vulnerable groups but also to upper-middle-level families.
Labeling Expert: A New Multi-Network Anomaly Detection Architecture Based on LNN-RLSTM

In network edge computing scenarios, close monitoring of network data and anomaly detection is critical for Internet services. Although a variety of anomaly detectors have been proposed by many scholars, few of these take into account the anomalies of the data in business logic. Expert labeling of business logic exceptions is also very important for detection. Most exception detection algorithms focus on problems, such as numerical exceptions, missed exceptions and false exceptions, but they ignore the existence of business logic exceptions, which brings a whole new challenge to exception detection. Moreover, anomaly detection in the context of big data is limited to the need to manually adjust detector parameters and thresholds, which is constrained by the physiological limits of operators. In this paper, a neural network algorithm based on the combination of Labeling Neural Network and Relevant Long Short-Term Memory Neural Network is proposed. This is a semi-supervised exception detection algorithm that can be readily extended with business logic exception types. The self-learning performance of this multi-network is better adapted to the big data anomaly detection scenario, which further improves the efficiency and accuracy of network data anomaly detection and considers business scenario-based anomaly data detection. The results show that the algorithm achieves 96% detection accuracy and 97% recall rate, which are consistent with the business logic anomaly fragments marked by experts. Both theoretical analysis and simulation experiments verify its effectiveness.
Developing a BIM Single Source of Truth Prototype Using Blockchain Technology

Blockchain technology has been proposed as a potential solution for coordinating information and trust to aid the development of a single source of the truth data model, going beyond peer-to-peer cash transactions. It is, therefore, argued that the construction supply chain (CSC) will resolve issues related to the lack of reliable platforms for construction and asset management operations once blockchain technology and Building Information Modelling (BIM) are integrated. Though there is no longer any debate about the importance of integrating blockchain technology with BIM, there is still a lack of academic literature on its proof of concept. This study aims to create a thorough proof of concept for integrating blockchain technology and BIM for supply chain data delivery. It demonstrated a step-by-step methodology starting from understanding the current business scenario and proposing logical system architecture, followed by selecting a blockchain platform, designing system architecture related to technologies, prototyping, and evaluating through a virtual business scenario. The software prototype presented in this paper helps establish the technological viability of a single source of the truth data model for integrating blockchain technology and BIM. The supply chain data delivery for handover was considered in this software prototype. However, the process used to create this software prototype can be replicated in future work on blockchain technology-based built environment applications or digital transformation in the built environment research.
Instructional Design of an Integrative Online Business English Course for Master’s Students of a Technical University

The article deals with the instructional design of an integrative Business English course for master’s students of a technical university (a case study of the Saint Petersburg Mining University) for blended and/or flexible learning. The main goal is to design a course that can be used as a full-fledged online course in asynchronous learning and is at the same time adjustable to the existing offline Business English course. The research uses methods such as observation, focus groups, surveying, empirical research and analytical and descriptive methods. The authors see a solution in a special instructional design based on the integration of traditional teaching approaches in offline learning, information technology and elements of infotainment and edutainment. The article presents the results of the target audience analysis and the needs analysis, outlines the structure of the course, specifies approaches to enhance motivation in master’s students and presents an integrative system of assessment and evaluation of the learners’ knowledge and skills. The key features of the instructional design of the Business English course suggested include exposure to professional scenarios, learners’ reflection, multiple instruments encouraging learners’ cognitive activity and performance and an opportunity to apply their knowledge to actual performance rather than summative assessment.
Integrated Image Processing Toolset for Tracking Direction of Metal Grain Deformation

Grain boundaries (GBs), which are among the mechanical properties of a material, are a microstructural aspect that contributes to the overall behavior of metal. A deep understanding of the behavior of the GBs’ deformation, dislocation, and fracture will encourage the rapid development of new materials and lead to the better operation and maintenance of materials during their designed lifetimes. In this study, an integrated image processing toolset is proposed to provide an expeditious approach to extracting GBs, tracking their location, and identifying their internal deformation. This toolset consists of three integrated algorithms: image stitching, grain matching, and boundary extraction. The algorithms are designed to simultaneously integrate high and low spatial resolution images for gathering high-precision boundary coordinates and effectively reconstructing a view of the entire material surface for the tracing of the grain location. This significantly reduces the time needed to acquire the dataset owing to the ability of the low spatial resolution lens to capture wider areas as the base image. The high spatial resolution lens compensates for any weakness of the base image by capturing views of specific sections, thereby increasing the observation flexibility. One application successfully described in this paper is tracking the direction of the metal grain deformation in global coordinates by stacking a specific grain before and after the deformation. This allows observers to calculate the direction of the grain deformation by comparing the overlapping areas after the material experiences a load. Ultimately, this toolset is expected to lead to further applications in terms of observing fascinating phenomena in materials science and engineering.
Research on Human-Computer Interaction Technology of Large-Scale High-Resolution Display Wall System

As an effective solution for data visualization and analysis, the large-scale high-resolution display wall system has been widely used in various scientific research fields. On the basis of investigating existing system cases and research results, this paper introduces the SHU-VAS system (60 screens, 120 megapixels) for data visualization and analysis. In order to improve the efficiency of human-computer interaction in large-scale high-definition display wall systems, we propose an interaction framework based on gesture and double-precision pointing technology. During the interaction process, an adaptive mode switching method based on user action features is used to switch between rough and precise control modes. In the evaluation experiments, we analyzed the characteristics of different interaction methods in movement and navigation interaction tasks, and verified the effectiveness of the gesture-based interaction framework proposed in this paper.
Accuracy of Hidden Markov Models in Identifying Alterations in Movement Patterns during Biceps-Curl Weight-Lifting Exercise

This paper presents a comparison of mathematical and cinematic motion analysis regarding the accuracy of the detection of alterations in the patterns of positional sequence during biceps-curl lifting exercise. Two different methods, one with and one without metric data from the environment, were used to identify the changes. Ten volunteers performed a standing biceps-curl exercise with additional loads. A smartphone recorded their movements in the sagittal plane, providing information on joints and barbell sequential position changes during each lift attempt. An analysis of variance revealed significant differences in joint position (p &lt; 0.05) among executions with three different loads. Hidden Markov models were trained with data from the bi-dimensional coordinates of the joint positional sequence to identify meaningful alteration with load increment. Tests of agreement tests between the results provided by the models with the environmental measurements, as well as those from image coordinates, were performed. The results demonstrated that it is possible to efficiently detect changes in the patterns of positional sequence with and without the necessity of measurement and/or environmental control, reaching an agreement of 86% between each other, and 100% and 86% for each respective method to the results of ANOVA. The method developed in this study illustrates the viability of smartphone camera use for identifying positional adjustments due to the inability to control limbs in an adequate range of motion with increasing load during a lifting task.
Evaluating preparedness in using blockchains for electronic health record systems

Purpose An emerging technology in the primary stages of its life cycle is the blockchain. This research paper aims to evaluate the preparedness of hospitals in using blockchain technology in their electronic health record (EHR) systems. Design/methodology/approach In the initial stage, 15 criteria relating to preparedness in using blockchain in EHR systems were identified from the literature and divided into five criteria, namely, technological, legal, financial, environmental and organizational. Then, 17 experts from various specialized fields were invited to form expert panels. After validating the criteria identified by the expert panels, the weights of the criteria were determined through the fuzzy best-worst multicriteria decision-making method. Following that, the preparedness of ten selected hospitals in Tehran to use blockchain in their EHR systems was assessed via the weighted aggregated sum product assessment method. Finally, using sensitivity analysis and examining different scenarios, the robustness of the results of the proposed approach was validated. Findings According to expert judgments, the legal criterion (32%) was deemed the most important factor in the preparedness to use blockchain in EHR systems followed by technological (28%), financial (17%), organizational (13%) and environmental (9%) criteria. A sensitivity analysis showed that the proposed approach offers good strength and robustness in evaluating the selected hospitals. Originality/value This study can be useful in developing knowledge in the field of technology management for evaluating blockchain implementation in the health-care industry using a novel, coherent and robust approach. In addition, the proposed approach provides comprehensive insight for decision-makers on assessing preparedness in deploying blockchain technology in EHR systems.
ePoSt: Practical and Client-Friendly Proof of Storage-Time

Data availability is the major concern of outsourced data storage services. While reliability is promoted by many storage providers, it is infeasible for users to verify these claims. Proofs of Storage-Time are proposed to address this issue: they allow a prover to convince a verifier that the prover indeed stores the outsourced data continuously during the whole storage period. These protocols, however, either fail to guarantee the actual duration of data possession, or require the client to perform a computationally expensive storing process for each file, marking them far from being practical. We present <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {ePoSt}$ </tex-math></inline-formula> , the first secure, stateless and efficient Proof of Storage-Time protocol with public verifiability. Not only does it ensure continuous data availability, but it also minimizes the cost of the client, which is crucial for real-world deployment. Specifically, processing a 1GB file for outsourcing, in anticipation for 3 proof/verify interactions, each attesting a period of 1 year at 25 minutes intervals requires only 66.34 minutes. In contrast, state-of-the-art solution requires 51 hours of processing to prepare a file for outsourcing. Proof size and verification remains at a reasonable cost: a proof is of size 48.82KB, and can be verified in 36.05 ms. Furthermore, our solution enjoys public verifiability and remains stateless. The former allows the data owner to outsource the verification process, and the latter allows unlimited number of proofs and verification to be conducted after the file has been stored. These properties make <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathsf {ePoSt}$ </tex-math></inline-formula> particularly suitable for new business models such as decentralised storage networks (e.g., Filecoin).
An Area-Efficient Accelerator for Non-Maximum Suppression

Non-maximum suppression (NMS) is an essential part of the post-processing of object detectors. Most object detection models require NMS algorithms to filter overlapping candidate boxes belonging to the same object and reserve the box that best represents the object as the object representative box. However, the hardware for the standard NMS algorithm has some disadvantages, such as high complexity (especially when box numbers are significant), high latency, large area, and high power consumption. To solve these problems, we propose an efficient parallel hardware architecture, which uses a new sorting circuit with a ping-pong buffer and a new retention mechanism of the candidate box for the new NMS algorithm called distance over side-NMS (DoS-NMS). This architecture uses a PE group with a voting mechanism to simplify the algorithm for reducing the latency and area. Additionally, the PE group computes the DoS (non-intersection over union) of the candidate box and multiple object representatives in parallel, significantly reducing algorithm complexity and memory access cost. Experiments indicated that the algorithm runs on the chip with area of 0.75mm2, power consumption of 68.41 mW, and normalized area efficiency that is 3.72 and 4.84 times higher than the two state-of-the-art methods, respectively.
Hardness and approximation of multiple sequence alignment with column score

Multiple Sequence Alignment (MSA for short) is a well known problem in the field of computational biology. In order to evaluate the quality of a solution, many different scoring functions have been introduced, the most widely used being the Sum-of-Pairs score (SP-score). It is known that computing the best MSA under the SP-score measure is NP-hard. In this paper, we introduce a variant of the Column score (defined in Thompson et al. 1999), which we refer to as Selective Column score: Given a symbol a∈Σ, the score of the i-th column is one if and only if all symbols of the same column are a, and otherwise zero. The a-column score of an alignment is then the number of columns made of only character a. We show that finding the optimal MSA under the Selective Column Score is NP-hard for all alphabets of size |Σ|≥2, and that the associated maximization problem is poly-APX-hard. We also give an approximation algorithm that almost matches the inapproximability bound.
Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

With the majority of mobile robot path planning methods being focused on obstacle avoidance, this paper, studies the problem of Navigation Among Movable Obstacles (NAMO) in an unknown environment, with static (i.e., that cannot be moved by a robot) and movable (i.e., that can be moved by a robot) objects. In particular, we focus on a specific instance of the NAMO problem in which the obstacles have to be moved to predefined storage zones. To tackle this problem, we propose an online planning algorithm that allows the robot to reach the desired goal position while detecting movable objects with the objective to push them towards storage zones to shorten the planned path. Moreover, we tackle the challenging problem where an obstacle might block the movability of another one, and thus, a combined displacement plan needs to be applied. To demonstrate the new algorithm’s correctness and efficiency, we report experimental results on various challenging path planning scenarios. The presented method has significantly better time performance than the baseline, while also introducing multiple novel functionalities for the NAMO problem.
The retention of information in virtual reality based engineering simulations

The use of VR in engineering education provides a means for contextualising learning through authentic and engaging scenarios, e.g. the touring and operation of a chemical plant. This paper presents the design of an immersive VR simulation of an existing pilot plant in the School and its evaluation through a randomised non-inferiority trial to test student learning compared to a more traditional learning medium. Specifically, the study evaluates information retention (i.e. memory and understanding) when using the VR simulation and compares this to information retention when presented as a multimedia instructional video. Student self-efficacy in plant operation and design (e.g. equipment function and operation), as well as engagement in the learning activity, are also evaluated. The results indicate that both the VR and multimedia-based activities lead to comparable data retention and student self-efficacy. Moreover, students enjoyed the VR experience and welcomed the addition of the resource to their educational materials.