Reviewerly/exHarmony huggingface dataset:
Cloud provider profit-aware and triadic concept analysis-based data replication strategy for tenant performance improvement

Effective data management is very challenging to cloud providers, whose business model relies on maintaining an economic profit while satisfying the tenants' performance requirements. To address these challenges, many data replication strategies have been proposed. In this paper, we propose a new dynamic data replication strategy for cloud systems called RCPP1. In order to satisfy performance requirements, the proposed strategy exploits the valuable knowledge extracted from the tenants' past access history. Therefore, it uses the mathematical triadic concept analysis approach to determine correlated data to be replicated. Furthermore, the cloud provider's profit is taken into account. Hence, an economic model is proposed to estimate the revenues and expenditures of the provider. Experimental studies show the efficiency and effectiveness of RCPP compared to state-of-the-art strategies. RCPP is indeed proven able to reduce the total expenditures of the cloud provider significantly while achieving better performances.
Interval type-2 fuzzy brain emotional control design for the synchronization of 4D nonlinear hyperchaotic systems

This research provides a novel intelligent control structure for 4D nonlinear hyperchaotic systems. This is a hybrid design containing a new interval type-2 fuzzy fourfold function-link brain emotional controller and a smooth robust controller. It comprises a fuzzy inference system and three subnetworks. The subnetworks are a new fourfold function-link network, a type-2 fuzzy amygdala network and a type-2 fuzzy prefrontal cortex network that decrease the synchronization errors efficiently, follow the reference signal well, and achieve good performance. Two Lyapunov stability functions are utilized to get the adaptive laws, and they are applied to online tune the parameters of the system. The proposed design is used to synchronize two 4D nonlinear hyperchaotic systems and the simulation results are given to demonstrate its superiority and effectiveness.
Conflicts of Laws and Codes

Blockchain technology promises to perform tasks that have traditionally been assigned to the law and the realm of legal institutions. Smart contracts create agreements that are both automatable by computers and enforceable via the tamper-proof execution of computer codes. Based on such smart contracts, some providers of blockchain technologies offer ‘to act as a digital jurisdiction’. The promise seems to be that law of the relevant jurisdiction is entirely substituted by the rules codified in the blockchain. However, even if it has often been argued that the ‘Code Is Law’, the law is not—and arguably never will be—entirely redundant. Therefore, the challenge is to identify the boundaries of such digital jurisdictions by clarifying the relationship between law and code and to develop new principles for conflicts of laws or rather principles for the conflict of laws and codes.
From Learning to Assessment. Utilizing Blockchain Technologies in Gaming Environments to Secure Learning Outcomes and Test Results

This study, pursues the following three goals, namely the introduction and discussion regarding Blockchain technologies in education in general and serious games in particular; a definition and proposal of a category system for digital games with the aim not only to teach but also to assess; and a description of the serious game Gallery Defender, one of the very first games which maps grades/certificates for the player/learner as well as further information for the teacher on Blockchain. This game is currently in the middle of an iterative design process and the authors describe the used Blockchain approach of the first iteration of the game to inspire further developments in this direction, especially for the Maltese audience, as Malta is perceived as the leading EU country in the field of Blockchain regulation.
Impact of English-Language and French-Language Student and Faculty Populations on the Use of Online Databases in a Bilingual Canadian University

This article reports the results of a quantitative study comparing usage rates between English- and French-language online databases at the J.N. Desmarais Library of Laurentian University, Canada. For Canadian institutions of higher learning, the comparison between English- and French-language services is particularly relevant in that English and French are considered to be the country’s two official languages. Overall, French-language online databases are used at very different rates by French-language student and faculty populations at Laurentian University compared to English-language students and faculty’s use of English-language databases. Where English-language student and faculty populations exhibit consistently strong and positive relationships with English-language online database use, French-language student and faculty populations exhibit the complete opposite with a consistently negative relationship with French-language online databases.
Designing enhanced augmented reality tourism experiences: a multi-stakeholder approach

Augmented reality (AR) has gained increased popularity in the tourism sector, for its ability to create enhanced tourist experiences. This, coupled with the proliferation of mobile technologies has increased pressure for tourism organisations to design enhanced tourist experiences. Whilst much research attention has focused on the potential of AR, exploring areas such as adoption, acceptance and usability, there is a lack in research identifying constructs to inform the design of valuable AR application experiences. Whilst research identifies the need for AR design to adopt a user-centred, rather than technology-driven approach, past studies have failed to address the needs of both internal and external stakeholder groups. To provide a holistic understanding, recognising differences in perception between stakeholder groups, a multi-stakeholder approach was adopted. A small museum in the UK is used to identify key constructs to inform the effective design of enhanced AR tourism experiences. Fifty interviews were held with five stakeholder groups, revealing four AR design constructs; visitor, organisational, stakeholder and economic value, in addition to a number of design themes. Findings reinforce the importance of stakeholder involvement, and outline constructs to inform the design of AR experiences, extending the existing pool of knowledge and bridging a gap in current research.
Tourism Destination Competitiveness in UAE: The Role of Strategic Leadership and Strategic Planning Effectiveness

Building competitive organizations requires a conscious management and a strong and effective leadership that recognizes the importance of sustainable development, and how to be applied in business organizations. The aim of the research is to test the association among Strategic leadership (SL), strategic planning effectiveness (SPE) toward tourism organization competitiveness (TOC) in UAE tourism industry. A quantitative research approach has been conducted for the study and survey-based method has followed in the research and distribute questionnaire among employee to collect data. The questionnaire has developed into five point Likert scale and structured questionnaires were distributed among UAE tourism employees. This study finding has showed direct and indirect relationships influences among variables. SL influence positively towards SPE, and TOC. Managerial and theoretical implications are introduced.
LocalFace: Learning significant local features for deep face recognition

As a widely mentioned topic in face recognition, the margin-based loss function enhances the discriminability of face recognition models by applying margin between class decision boundaries. However, there is still room to improve the representation of face features. Local face feature extraction has been employed in traditional face recognition methods, but with the increase of network depth in deep learning, the traditional approach requires a large number of computational resources. In this paper, we propose a novel face recognition architecture called LocalFace to extract local face features. First, by analyzing the distribution of significant features in face images, we propose an efficient face fixed-point local feature extraction approach and improve this method to propose a more effective face dynamic local feature extraction scheme. Subsequently, we propose a block-based random occlusion method for the limitations of the random face occlusion method to better simulate the occlusion situation in real scenes. In the end, we present a detailed discussion on the channel attention method that is more appropriate for face recognition and classification tasks. Our method enhances the representation of face features by ensembling local features into global features without extra parameters, which is efficient and easy to implement. Extensive experiments on various benchmarks demonstrate the superiority of our LocalFace, and part of the experimental results achieve SOTA results.
Hype as a Factor on the Global Market: The Case of Bitcoin

The impact of Bitcoin-related Google queries, Facebook likes, reposts and comments on Bitcoin price is analyzed with the help of ARDL and GARCH models. Our results have led us to the following conclusions. Firstly, a sharp increase in Bitcoin’s popularity or hype, which manifested itself through a rise in the number of Bitcoin-related Google queries, has resulted in an increase in Bitcoin price. This effect corresponds to the description of the ‘collective hysteria’ that spread in the online community and was triggered by the increasing volatility of the Bitcoin market. Secondly, we found that Bitcoin’s popularity among ordinary Internet users has a positive impact in low-volatile and highly volatile rising markets but a negative one in a highly volatile falling market. Thirdly, Bitcoin’s popularity among informed Internet users has a negative impact on Bitcoin price in a period of low volatility. Fourthly, uninformed users’ trust in Bitcoin has a positive influence on Bitcoin price in low-volatile and highly volatile falling markets. Finally, the main factors that shape the Bitcoin market are trust and popularity.
An intelligent feature selection approach with systolic tree structures for efficient association rules in big data environment

Frequent Pattern Mining (FPM) is regularly used in data mining applications for identifying objects of interest that frequent transactional databases. Traditional ARM algorithms work on minimum levels of support and confidence metrics which have to be defined and are subjective. This work attempts to solve this issue by proposing techniques for the automatic determination of these metrics. The Kernel Possibilistic Fuzzy Local Information C-Means (KPFLICM) Clustering and K-means Clustering-based Feature Selection algorithm for Association Rules (KCFSAR) is used where FPMs are based on Systolic Tree Structures (STS). These structures aim to achieve better accuracy by mimicking internal memory structures of the Frequent Pattern Growths (FPG) algorithm. Moreover, Chaotic Butterfly Optimization Method (CBOAs) is used to obtain the optimal support value and CBOA subsequently discovers global optimal solutions. This work's suggested scheme demonstrates its efficacy and superiority in experimental findings when compared to other approaches in terms of feature subset sizes, accuracies, execution times, and memory consumptions.
Hybrid calibration procedure for structured light field system

• A hybrid calibration method for structured light field system combining stereo vision method and phase to coordinate mapping method. • The subview of light field system is treated as pinhole model to build auxiliary Fringe Projection Profilometry. • Light ray fitting and plane fitting are implemented to improve the calibration accuracy. A typical structured light field system consists of a light field camera and a pattern projector. By combining Fringe Projection Profilometry and light field imaging technology, the phase distribution characteristics of each position and each angle of the scene under test could be obtained, thus providing more information dimensions, and therefore improving the robustness and dynamic range of three-dimensional (3D) measurement. System calibration is a decisive factor for high-efficiency and high precision measurement. However, the complicated structure and working method make the calibration of structured light field system usually costly and cumbersome. In this paper, we propose a hybrid calibration approach combining the flexibility of the stereo vision method and high precision characteristics of the phase-coordinate mapping method. The feasibility of the proposed method has been verified in actual experiments with typical 3D targets.
Computer Vision and Multi-Object Tracking for Traffic Measurement from Campus Monitoring Cameras

Conventional methods of traffic monitoring primarily rely on sensors, on-board GPS, and human observation, which are generally resource-intensive, slow, and subject to implementation limits. Computer vision (particularly convolutional neural networks) is a more flexible, convenient, and cost-effective alternative to extract traffic information from traffic videos. This paper presents a methodology to extract traffic volume counts from cameras on Texas A&M University (TAMU) campus. Particularly, an object detector model, namely YOLOv5 (You Only Look Once), is integrated with an object tracking algorithm (Deep-SORT) to track vehicles in camera views. These tracks are projected using homography transformation from the local coordinate system of the camera onto an orthogonal map (world coordinates) along with information such as unique vehicle ID, type, position, and time stamp. This data is used to count vehicles that cross the pre-defined study zones (lanes) on different directions. A total of 60 samples (each 1 minute long) recorded during a 24-h period on TAMU campus are manually annotated and used to benchmark model predictions, resulting in a traffic volume count accuracy ranging from 68% to 93%. Moreover, the best input pixel resolution is determined to be 1,280 × 1,280 by measuring the average precision for each class of interest.
Exercise and Performance Learning Assistant System for Self-Practice Dynamic Yoga by OpenPose and Fuzzy Based Design

In this study, a learning assistant system based on OpenPose and fuzzy technologies is studied for self-practice dynamic Yoga at home. The skeleton information of the human body is extracted through OpenPose. In the pre-processing, the initial frame and key frames of the instructor are selected with Euclidean distance. In the system processing, the initial frame and key frames are used to determine the difference between the instructor and the user. The vector and the angle are calculated with the key points of the instructor and the user. The angle differences between them are used as the inputs of the scoring system based on fuzzy theory to calculate each score. This system synchronizes the time domain of the instructor and the user to effectively, and it correctly selects the user's corresponding frame, and then the system greatly reduces the amount of calculations without losing accuracy. The experimental results show that the proposed system can effectively detect and score the corresponding video frames between the user and the instructor.
Marfusion: An Attention-Based Multimodal Fusion Model for Human Activity Recognition in Real-World Scenarios

Human Activity Recognition(HAR) plays an important role in the field of ubiquitous computing, which can benefit various human-centric applications such as smart homes, health monitoring, and aging systems. Human Activity Recognition mainly leverages smartphones and wearable devices to collect sensory signals labeled with activity annotations and train machine learning models to recognize individuals’ activity automatically. In order to deploy the Human Activity Recognition model in real-world scenarios, however, there are two major barriers. Firstly, sensor data and activity labels are traditionally collected using special experimental equipment in a controlled environment, which means fitting models trained with these datasets may result in poor generalization to real-life scenarios. Secondly, existing studies focus on single or a few modalities of sensor readings, which neglect useful information and its relations existing in multimodal sensor data. To tackle these issues, we propose a novel activity recognition model for multimodal sensory data fusion: Marfusion, and an experimental data collection platform for HAR tasks in real-world scenarios: MarSense. Specifically, Marfusion extensively uses a convolution structure to extract sensory features for each modality of the smartphone sensor and then fuse the multimodal features using the attention mechanism. MarSense can automatically collect a large amount of smartphone sensor data via smartphones among multiple users in their natural-used conditions and environment. To evaluate our proposed platform and model, we conduct a data collection experiment in real-life among university students and then compare our Marfusion model with several other state-of-the-art models on the collected datasets. Experimental Results do not only indicate that the proposed platform collected Human Activity Recognition data in the real-world scenario successfully, but also verify the advantages of the Marfusion model compared to existing models in Human Activity Recognition.
Full-Body Motion Capture-Based Virtual Reality Multi-Remote Collaboration System

Various realistic collaboration technologies have emerged in the context of the COVID-19 pandemic. However, as existing virtual reality (VR) collaboration systems generally employ an inverse kinematic method using a head-mounted display and controller, the user and character cannot be accurately matched. Accordingly, the immersion level of the VR experience is low. In this study, we propose a VR remote collaboration system that uses motion capture to improve immersion. The system uses a VR character in which a user wearing motion capture equipment performs the same operations as the user. Nevertheless, an error can occur in the virtual environment when the sizes of the actual motion capture user and virtual character are different. To reduce this error, a technique for synchronizing the size of the character according to the user’s body was implemented and tested. The experimental results show that the error between the heights of the test subject and virtual character was 0.465 cm on average. To verify that the implementation of the motion-capture-based VR remote collaboration system is possible, we confirm that three motion-capture users can collaborate remotely using a photon server.
Fringe pattern demodulation using Zernike polynomials and a<i>l</i>1-norm regularized extended Kalman filter

A novel algorithm for closed fringe demodulation for an absolute phase estimation, to the best of our knowledge, is proposed. The two-dimensional phase is represented as a weighted linear combination of a certain number of Zernike polynomials (ZPs). Essentially, the problem of phase estimation is converted into the estimation of ZP coefficients. The task of ZP coefficient estimation is performed based on a state space model. Due to the nonlinear dependence of the fringe intensity measurement model on the ZP coefficients, the extended Kalman filter (EKF) is used for the state estimation. A pseudo-measurement model is considered based on the state vector sparsity constraint to improve the convergence performance of the EKF. Simulation and experimental results are provided to demonstrate the noise robustness and the practical applicability of the proposed method.
Genuine multipartite entanglement of quantum states in the multiple-copy scenario

Genuine multipartite entanglement (GME) is considered a powerful form of entanglement since it corresponds to those states that are not biseparable, i.e. a mixture of partially separable states across different bipartitions of the parties. In this work we study this phenomenon in the multiple-copy regime, where many perfect copies of a given state can be produced and controlled. In this scenario the above definition leads to subtle intricacies as biseparable states can be GME-activatable, i.e. several copies of a biseparable state can display GME. We show that the set of GME-activatable states admits a simple characterization: a state is GME-activatable if and only if it is not partially separable across one bipartition of the parties. This leads to the second question of whether there is a general upper bound in the number of copies that needs to be considered in order to observe the activation of GME, which we answer in the negative. In particular, by providing an explicit construction, we prove that for any number of parties and any number <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi><mml:mo>&amp;#x2208;</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi mathvariant="double-struck">N</mml:mi></mml:mrow></mml:math> there exist GME-activatable multipartite states of fixed (i.e. independent of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>) local dimensions such that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> copies of them remain biseparable.
HATP/EHDA: A Robot Task Planner Anticipating and Eliciting Human Decisions and Actions

The variety and complexity of tasks autonomous robots can tackle is constantly increasing, yet we seldom see robots collaborating with humans. Indeed, humans are either requested for punctual help or are given the lead on the whole task. We propose a human-aware task planning approach allowing the robot to plan for a task while also considering and emulating the human decision, action, and reaction processes. Our approach, named Human-Aware Task Planner with Emulation of Human Decisions and Actions (HATP/EHDA), is based on the exploration of multiple hierarchical tasks networks albeit differently whether the agent is considered to be controllable (the robot) or uncontrollable (the human). We present the rationale of our approach along with a formalization and show its potential on an illustrative example.
Toughness arising from inherent strength of polymers

This study carries out spatial-resolved optical birefringence observations to quantify, for mode I (tensile opening) loading, the stress intensification at crack tip of brittle and ductile glassy polymers (polymethyl methacrylate—PMMA, polyethylene terephthalate—PET) as well as one type of elastomer: (ethylene propylene diene monomer—EPDM). We measure the stress buildup in a precut specimen by correlating retardation with the corresponding tensile stress. Given the adequate spatial resolution under 10μm and natural bluntness of intentional through-cuts, we are able to show that (a) during drawing at different stages up to the onset of fracture in precut PMMA and EPDM, the local stress saturates, namely, ceases to increase as r−1/2 upon approaching the cut tip (with r reaching rss in a range of 0.05–0.15 mm), (b) tip stress σtip, i.e., the tensile stress in the stress saturation zone (r ≤ rss), linearly grows with (operationally defined) stress intensity factor KI until fracture, reaching a level below the breaking stress σb observed of uncut specimen. Thus, the inherent strength σF(inh) under plane strain, taken to be the tip stress at fracture is only comparable to σb. Moreover, a characteristic length P, involved in the observed linearity between KI and σtip, i.e., in KI=σtipP1/2, is found to be comparable to 2πrss. Here rss appears to depend on the tip sharpness, which may be characterized by a radius of curvature ρtip. Thus, toughness given by the critical stress intensity factor KIc is determined by the product of σF(inh) and ρtip1/2, and the critical energy release rate GIc is given by the product of specific work of fracture wF = [σF(inh)]2/2E and ρtip.