Reviewerly/exHarmony huggingface dataset:
Facing the challenges of metaverse: a systematic literature review from Social Sciences and Marketing and Communication

The metaverse is the conjunction and optimization of the possibilities of the Internet and technology at their best. It is a consequence of the development and evolution of digital society. Technological innovation, fundamentally oriented toward virtual reality, augmented reality, and mixed realities, contributes significantly to the creation of a solid foundation on which to build an entire universe of virtual worlds. This is a universe that, in turn, requires the creation of backbone content for narratives that attract and retain users by capturing their attention to promote a specific ecosystem that transfers the activities of the real world to a virtual one, either projected or recreated. This research is based on a systematic review of 402 articles and a qualitative analysis of 125 publications. It examines the trends in technology, application, and methodology pertaining to the metaverse in the social sciences field, namely marketing and communication and neuroscience, areas that contribute to the understanding of the social dimension of the metaverse phenomenon. Although there is abundant academic literature on the metaverse in computer science, this is not the case in the aforementioned disciplines. Given that the metaverse is destined to become the next Internet revolution, there is a race among countries and brands to position themselves within it, which is expected to intensify in the coming years. The metaverse can contribute to a wide variety of applications of a social nature, which is why it is a highly competitive tool for nations, companies, and academia, as well as the public and private media. The results indicate a technological transformation proposing a future that includes neuro-technologies based on brain–computer interfaces and the metaverse as the setting. This will occur alongside the solidification of the virtual ecosystem thanks to the emergence of digital natives and Gen Z, as well as the convergence of many different technologies and immersive and participatory content, in which the consumer is the provider, owner, and beneficiary.
Tracking of Fall Detection Using IMU Sensor: An IoHT Application

This research work has developed the methodology to detect the fall of a person during walking, running, and standing. The fall recovery capability & fall diagnosis information can be utilized to avoid the fall and to provide assistant after fall detection of elderly subjects and assess the various health metrics. A total of 20 subjects of [30 ±10] age group were considered for data collection. The fall diagnosis will help in designing an Internet of health things-based (IoHT) alert device or fall assistant device like airbag opening to support elderly subjects. All four directions were considered to observe the fall. It is observed that the vertical acceleration, angular velocity is helping us to detect fall and no-fall boundary. The separate boundary of fall & no-fall is calculated and presented for all three activities stand, walk, and run. Several deep learning models including the hybrid model is considered, but the long short-term memory (LSTM) model has provided the highest accuracy of 92.98% & an F1-score of 90.48% for fall detection. The allowable range of acceleration and angular velocity is provided in the form of 3D plots.
Construction and Local Equivalence of Dual-Unitary Operators: From Dynamical Maps to Quantum Combinatorial Designs

While quantum circuits built from two-particle dual-unitary (maximally entangled) operators serve as minimal models of typically nonintegrable many-body systems, the construction and characterization of dual-unitary operators themselves are only partially understood. A nonlinear map on the space of unitary operators has been proposed in Phys. Rev. Lett. 125, 070501 (2020) that results in operators being arbitrarily close to dual unitaries. Here, we study the map analytically for the two-qubit case describing the basins of attraction, fixed points, and rates of approach to dual unitaries. A subset of dual-unitary operators having maximum entangling power are 2-unitary operators or perfect tensors and these are equivalent to four-party absolutely maximally entangled states. It is known that they only exist if the local dimension is larger than d=2. We use the nonlinear map, and introduce stochastic variants of it, to construct explicit examples of new dual and 2-unitary operators. A necessary criterion for their local unitary equivalence to distinguish classes is also introduced and used to display various concrete results and a conjecture in d=3. It is known that orthogonal Latin squares provide a “classical combinatorial design” for constructing permutations that are 2-unitary. We extend the underlying design from classical to genuine quantum ones for general dual-unitary operators and give an example of what might be the smallest-sized genuinely quantum design of a 2-unitary in d=4.8 MoreReceived 4 June 2022Revised 16 October 2022Accepted 16 November 2022DOI:https://doi.org/10.1103/PRXQuantum.3.040331Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum entanglementQuantum gatesQuantum information theoryQuantum Information
Does distance matter? Embodiment and perception of personalized avatars in relation to the self-observation distance in virtual reality

Virtual reality applications employing avatar embodiment typically use virtual mirrors to allow users to perceive their digital selves not only from a first-person but also from a holistic third-person perspective. However, due to distance-related biases such as the distance compression effect or a reduced relative rendering resolution, the self-observation distance (SOD) between the user and the virtual mirror might influence how users perceive their embodied avatar. Our article systematically investigates the effects of a short (1 m), middle (2.5 m), and far (4 m) SOD between users and mirror on the perception of their personalized and self-embodied avatars. The avatars were photorealistic reconstructed using state-of-the-art photogrammetric methods. Thirty participants repeatedly faced their real-time animated self-embodied avatars in each of the three SOD conditions, where they were repeatedly altered in their body weight, and participants rated the 1) sense of embodiment, 2) body weight perception, and 3) affective appraisal towards their avatar. We found that the different SODs are unlikely to influence any of our measures except for the perceived body weight estimation difficulty. Here, the participants perceived the difficulty significantly higher for the farthest SOD. We further found that the participants’ self-esteem significantly impacted their ability to modify their avatar’s body weight to their current body weight and that it positively correlated with the perceived attractiveness of the avatar. Additionally, the participants’ concerns about their body shape affected how eerie they perceived their avatars. The participants’ self-esteem and concerns about their body shape influenced the perceived body weight estimation difficulty. We conclude that the virtual mirror in embodiment scenarios can be freely placed and varied at a distance of one to four meters from the user without expecting major effects on the perception of the avatar.
An Improved Sample Complexity Lower Bound for (Fidelity) Quantum State Tomography

We show that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="normal">&amp;#x03A9;</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>r</mml:mi><mml:mi>d</mml:mi><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>&amp;#x03F5;</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> copies of an unknown rank-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>r</mml:mi></mml:math>, dimension-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi></mml:math> quantum mixed state are necessary in order to learn a classical description with <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mn>1</mml:mn><mml:mo>&amp;#x2013;</mml:mo><mml:mi>&amp;#x03F5;</mml:mi></mml:math> fidelity. This improves upon the tomography lower bounds obtained by Haah, et al. and Wright (when closeness is measured with respect to the fidelity function).
Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation

In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. By representing the limited amount of supervisory information as a pairwise constraint matrix, we observe that the ideal affinity matrix for clustering shares the same low-rank structure as the ideal pairwise constraint matrix. Thus, we stack the two matrices into a 3-D tensor, where a global low-rank constraint is imposed to promote the affinity matrix construction and augment the initial pairwise constraints synchronously. Besides, we use the local geometry structure of input samples to complement the global low-rank prior to achieve better affinity matrix learning. The proposed model is formulated as a Laplacian graph regularized convex low-rank tensor representation problem, which is further solved with an alternative iterative algorithm. In addition, we propose to refine the affinity matrix with the augmented pairwise constraints. Comprehensive experimental results on eight commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GuanxingLu/Subspace-Clustering</uri> .
An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest

With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment. Due to the open characteristics of WSN, the security risks during information transmission are prominent, and network attack or intrusion is likely to occur. Therefore, effective anomaly detection is vital for IoT systems to keep the system safe. The original Isolation Forest algorithm is an anomaly detection algorithm with linear time complexity and has a better detection effect on perceptual data. However, there are also disadvantages such as strong randomness, low generalization performance, and insufficient stability. This paper proposes a data anomaly detection method named BS-iForest (box plot-sampled iForest) for wireless sensor networks based on a variant of Isolation Forest to address the problems. This method first uses the sub-dataset filtered by the box graph to train and construct trees. Then, isolation trees with higher accuracy are selected in the training set to form a base forest anomaly detector. Next, the base forest anomaly detector uses anomaly detection to judge data outliers during the next period. These experiments were performed on datasets collected from sensors deployed in a data center of a university, and the Breast Wisconsin (BreastW) dataset, showing the performance of the variant of the Isolation Forest algorithm. Compared with the traditional isolation forest, the area under the curve (AUC) increased by 1.5% and 7.7%, which verified that the proposed method outperforms the standard Isolation Forest algorithm with the two datasets we chose.
Science learning in 3D virtual environment multi-users online in basic education stage

Technological development is ever-evolving in all areas of life, its impact on the education field has emerged, and science is part of learning that teachers struggle with in their teaching methods. This study aimed to explore the effects of using a 3D virtual environment in learning science at the basic education stage and students’ attitudes towards their use. A quasi-experimental design was used in this study. A total of 99 students participated in the study, divided into two groups: an experimental group (n=50) and a control group (n=49). In this study, an achievement test and questionnaire were used as study tools after validating their validity and reliability. The data were analyzed using SPSS. Results showed that in the experimental groups that were taught science topics in 3D virtual environments, there were statistically significant differences compared to the control groups. In addition, students in the experimental group who were learning the topics in science textbooks in a 3D virtual environment showed positive attitudes toward it. The study recommends the use of 3D virtual environments in science education.
Does imagination compensate for the need for touch in 360-virtual shopping?

The inability to touch products is a fundamental shortcoming in online shopping because humans typically use the sense of touch to evaluate the utilitarian product functionality and to obtain hedonic sensory enjoyment, which the instrumental and autotelic need for touch capture. This study of 900 consumers looks at the interplay between need for touch and imagination to study how imagination compensates for the lack of touch when consumers shop in a 360-virtual store. The study finds that while telepresence of a 360-virtual store improves consumer attitudes toward virtual shopping, the need for hedonic sensory enjoyment – autotelic need for touch – significantly reduces this effect. Further, imagination can compensate for the need for touch; yet this finding holds only for the instrumental need for touch, not for the autotelic need for touch. Consequently, we conclude that imagination can compensate for the utilitarian need to touch products in a 360-virtual store.
Velocity pausing particle swarm optimization: a novel variant for global optimization

Abstract Particle swarm optimization (PSO) is one of the most well-regard metaheuristics with remarkable performance when solving diverse optimization problems. However, PSO faces two main problems that degrade its performance: slow convergence and local optima entrapment. In addition, the performance of this algorithm substantially degrades on high-dimensional problems. In the classical PSO, particles can move in each iteration with either slower or faster speed. This work proposes a novel idea called velocity pausing where particles in the proposed velocity pausing PSO (VPPSO) variant are supported by a third movement option that allows them to move with the same velocity as they did in the previous iteration. As a result, VPPSO has a higher potential to balance exploration and exploitation. To avoid the PSO premature convergence, VPPSO modifies the first term of the PSO velocity equation. In addition, the population of VPPSO is divided into two swarms to maintain diversity. The performance of VPPSO is validated on forty three benchmark functions and four real-world engineering problems. According to the Wilcoxon rank-sum and Friedman tests, VPPSO can significantly outperform seven prominent algorithms on most of the tested functions on both low- and high-dimensional cases. Due to its superior performance in solving complex high-dimensional problems, VPPSO can be applied to solve diverse real-world optimization problems. Moreover, the velocity pausing concept can be easily integrated with new or existing metaheuristic algorithms to enhance their performances. The Matlab code of VPPSO is available at: https://uk.mathworks.com/matlabcentral/fileexchange/119633-vppso .
Authority and solidarity on the Estonian COVID-19 signs: In line with the government's guidelines, we ask you to wear a mask

This article presents the results of a quantitative analysis of 900 Estonian COVID-19 door signs, which were studied to investigate the linguistic means of establishing and maintaining contact between the sign's author (institution) and the addressee (client). Malinowski's notion of “phatic communion” and Laver's notions of “self-oriented” and “other-oriented” utterances as means for expressing status relations—authority and solidarity—between the participants of the communication act were used to establish four types of grammatical person usage on the COVID-19 signs: (1) “neither 1st nor 2nd person”; (2) “1st person only”; (3) “2nd person only”, and (4) “both 1st and 2nd person”. Grammatical person of personal pronouns and verb forms were included. The presence and absence of two other means for expressing authority—the imperative mood and lexical expressions of authority—were analyzed within these four types of grammatical person usage. The most important difference emerged between the signs belonging to the types “2nd person only” (i.e., signs with only other-oriented 2nd person, without 1st person) and “both 1st and 2nd person” (i.e., signs with both self-oriented 1st person and other-oriented 2nd person). On the signs belonging to the type “2nd person only” that, relying on Laver, express the higher status of the sender of the message in relation to the receiver of the message, the authors of the signs use significantly more imperative mood and less refer to an authority outside the communication act, thus putting themselves in the role of authority. However, on the signs belonging to the type “both 1st and 2nd person” that, relying on Laver, express the solidarity of the sender of the message with the addressee, the authors of the signs seem less inclined to assume the role of authority (using less imperative mood) and rather call the reader of the sign to submit to some higher authority (using lexical expressions of authority, e.g., Vabariigi Valitsus “Government of the Republic”, Terviseamet “Health Board”, etc.) to which the author of the sign and the addressee are both in a subordinate position and, therefore, of equal status.
Master Data Management using Machine Learning Techniques: MDM Bot

&lt;p&gt;In today’s world of loosely coupled and distributed applications, communication and data centralization play a vital role. In large organizations, there are many applications and many teams that work on the data of customers, products, suppliers, and other business entities. Each application and team maintain a separate copy of the data, which means any application requiring this information needs to contact several other systems to obtain the most recent data for that entity. Therefore, this would increase the number of multiple lookups on various systems, resulting in an increase in the throughput of the application. To avoid this, the organization needs a centralized system called ”Master Data Management” that will maintain all the golden data across all the systems within the organization. Any system that is updated with new information will also be sent to MDM through events, and MDM will then be responsible for communicating with and updating the sub-systems within the organization’s Eco-System. Event-Driven Architecture will be used to communicate between distributed applications to achieve and implement Master Data Management. In recent years, there have been a lot of issues discovered in the MDM System between the data sync-up of all the sub-systems, resulting in a tedious process of identifying manually all the failures of the events, reprocessing the event information, correcting the data,and re-synchronizing the other sub-systems with all the previous ones. To avoid this manual process and to address the issues mentioned above, we introduce the ”MDM Bot” in this article. Artificial Intelligence and Machine Learning capability are included in the proposed MDM Bot.It is an excellent value-added Bot for an Enterprise System that helps and saves a lot of time and money.&lt;/p&gt;
VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application

Following advances in machine learning and deep learning processing, cyber security experts are committed to creating deep intelligent approaches for automatically detecting software vulnerabilities. Nowadays, many practices are for C and C++ programs, and methods rarely target PHP application. Moreover, many of these methods use LSTM (Long Short-Term Memory) but not GNN (Graph Neural Networks) to learn the token dependencies within the source code through different transformations. That may lose a lot of semantic information in terms of code representation. This article presents a novel Graph Neural Network vulnerability detection approach, VulEye, for PHP applications. VulEye can assist security researchers in finding vulnerabilities in PHP projects quickly. VulEye first constructs the PDG (Program Dependence Graph) of the PHP source code, slices PDG with sensitive functions contained in the source code into sub-graphs called SDG (Sub-Dependence Graph), and then makes SDG the model input to train with a Graph Neural Network model which contains three stack units with a GCN layer, Top-k pooling layer, and attention layer, and finally uses MLP (Multi-Layer Perceptron) and softmax as a classifier to predict if the SDG is vulnerable. We evaluated VulEye on the PHP vulnerability test suite in Software Assurance Reference Dataset. The experiment reports show that the best macro-average F1 score of the VulEye reached 99% in the binary classification task and 95% in the multi-classes classification task. VulEye achieved the best result compared with the existing open-source vulnerability detection implements and other state-of-art deep learning models. Moreover, VulEye can also locate the precise area of the flaw, since our SDG contains code slices closely related to vulnerabilities with a key triggering sensitive/sink function.
Benefits of Pod Dimensioning With Best-Effort Resources in Bare Metal Cloud Native Deployments

Container orchestration platforms automatically adjust resources to evolving traffic conditions. However, these scaling mechanisms are reactive and may lead to service degradation. Traditionally, resource dimensioning has been performed considering guaranteed (or request) resources. Recently, container orchestration platforms included the possibility of allocating idle (or limit) resources for a short time in a best-effort fashion. This letter analyzes the potential of using limit resources as a way to mitigate degradation while reducing the number of allocated request resources. Results show that a 25% CPU reduction can be achieved by relying on limit resources.
Linguistic Based Emotion Detection from Live Social Media Data Classification Using Metaheuristic Deep Learning Techniques

A crucial area of research that can reveal numerous useful insights is emotional recognition. Several visible ways, including speech, gestures, written material, and facial expressions, can be used to portray emotion. Natural language processing (NLP) and DL concepts are utilised in the content-based categorization problem that is at the core of emotion recognition in text documents.This research propose novel technique in linguistic based emotion detection by social media using metaheuristic deep learning architectures. Here the input has been collected as live social media data and processed for noise removal, smoothening and dimensionality reduction. Processed data has been extracted and classified using metaheuristic swarm regressive adversarial kernel component analysis. Experimental analysis has been carried out in terms of precision, accuracy, recall, F-1 score, RMSE and MAP for various social media dataset.