Reviewerly/exHarmony huggingface dataset:
Blockchain Concepts on Computer Vision With Human-Computer Interaction and Secured Data-Sharing Framework

Presently, the technological developments in the field of human-computer interaction (HCI) have shown that developers are developing cognitive vision systems that provide normal and effective operating mechanism for smart sensors, and the privacy should be maintained in the course of data transfer. Blockchain technology received significant interest to remove third-party business providers, to introduce HCI rapidly, and for secure information sharing in the network. Therefore this paper presents blockchain assisted cognitive vision systems for human-computer interaction and secured data sharing (BCVS-DS) framework. Cognitive vision systems use the information from various sensors that is used to handle and joined by blending techniques. The secured data sharing (SDS) method is flexible and efficiently manages permission by spreading various user characteristics to multiple authorization centers. Experimental results are tested for BCVS-DS by AVEC dataset. BCVS-DS achieves the highest classification accuracy of 94.32%.
Verb Classification Across Languages

Recent developments in language modeling have enabled large text encoders to derive a wealth of linguistic information from raw text corpora without supervision. Their success across natural language processing (NLP) tasks has called into question the role of man-made computational resources, such as verb lexicons, in supporting modern NLP. Still, probing analyses have concurrently exposed the limitations of the knowledge possessed by the large neural architectures, revealing them to be clever task solvers rather than self-taught linguists. Can human-designed lexical resources still help fill their knowledge gaps? Focusing on verb classification, we discuss approaches to generating verb classes multilingually and weigh the relative benefits of undertaking expensive lexicographic work and outsourcing the task to untrained native speakers. Then, we consider the evidence for the utility of augmenting pretrained language models with external verb knowledge and ponder the ways in which human expertise can continue to benefit multilingual NLP.
Human Arm Motion Capture Using IMU Sensors

The human arm is a system which is constrained and yet can produce an orientation of each of its segments in order for the end effector, in this case, the hand, to be moved to any coordinate inside its sphere of operation. Different segments or parts of the arm (upper arm, forearm and hand) can enable a fixed set of rotational motions about certain axis with a constrained space for movement to achieve movement in three dimensions. This behaviour falls somewhere between a parallel robot and a six degrees of freedom robotic manipulator. Thus, determining the orientation of the individual segments can help determine the position of the hand due to the rigid coupling witnessed between the different segments in the form of joints as well as the fact concerning the cascaded movement of segments in response to change in orientation of one of the segments. This paper lays down a concept which uses quaternions representing rotation to calculate the orientation and determine the three-dimensional position of the hand. The kinematic quantities required to calculate orientation is obtained from Inertial Measurement Units (IMUs) deployed on each segment of the arm to fetch the relevant kinematic data associated with the respective arm segment.
The Correntropy Kalman Filter: A Robust Estimator for GPS Applications

India suffers from low satellite visibility. If any GPS receiver is operated in urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. Among all these factors, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel kinematic positioning algorithm is proposed designated as the Correntropy Kalman Filter (CKF) that adopts the robust correntropy criterion as the optimality criterion instead of using the well-known minimum mean square error (MMSE). A novel fixed-point algorithm is then used to update the posterior estimates. A sufficient condition that guarantees the convergence of the fixed-point algorithm is also given. The proposed algorithm results are then compared with the Least Square Estimator (LSE), traditional Kalman Filter (KF), and Extended Kalman Filter (EKF) algorithms. Results prove that the proposed CKF algorithm exhibits significant improvement in position estimation compared to the other three recursive algorithms (i.e., LSE, KF, and EKF). And Statistical position Accuracy Measures (SAM) like DRMS, CEP, SEP, etc. are additionally used for performance evaluation of the proposed algorithm.
Single-Step Ahead Solar Irradiance Forecasting Using Hybrid WT-PSO-Based Neural Network

Solar irradiance forecasting accuracy is a major concern for grid stability, and it has been studied from various angles including precision and complexity. So, in order to enhance the forecasting accuracy, this paper proposed a hybrid solar irradiation forecasting machine in which wavelet transform is used to deconstruct the original data into a set of understandable series, and particle swarm optimization is utilized to solve the correlation problem by selecting the input parameters (weight and biases) properly whereas feed forward neural network is trained by meaningful series. For the proposed work, 3-year data from 2012 to 2014 have been collected from National Solar Radiation Database for a Delhi location. The proposed model is trained using 2-year data and tested for 1 year on different seasons by following the single-step ahead. The proposed model performance is determined in terms of Mean Absolute Percentage Error (MAPE) and Mean Absolute Error (MAE). The developed model performance indicates that it outperforms the Naïve Predictor, standalone neural network model and particle swarm optimization-based neural network model in terms of precision and complexity.
Trajectory Privacy Protection Based on Sensitive Stay Area Replacement in Publishing

In data opening and sharing, a trajectory privacy protection based on sensitive stay area replacement (SSAR) is proposed in order to improve the availability of protected trajectory while maintaining the same security. Firstly, the stay areas are extracted by analyzing the movement characteristics. Secondly, the sensitive stay areas are obtained according to the user-defined sensitive semantic positions. Then, the sensitive areas are constructed according to the user's privacy requirement needs to randomly select the substitution. Finally, part of the sampling positions in the sensitive area are reset. The parameter selection experiment and the comparison experiment with other schemes show that the trajectory similarity is improved by more than 35% in SSAR. That is to say, SSAR can greatly improve the availability of protected trajectory on the basis of ensuring the safety degree.
Classification Algorithm for Heterogeneous Network Data Streams Based on Big Data Active Learning

Data classification is one of the main tasks in the current data mining field, and the existing network data triage algorithms have problems such as too small a proportion of labeled samples, a large amount of noise, and redundant data, which lead to low classification accuracy of data stream implementation. Network embedding can effectively improve these problems, but the network embedding itself has problems such as capturing relational honor and ambiguity. This study proposes a SNN-RODE based LapRLS heterogeneous network data classification algorithm to achieve deep embedding of structure and semantics among nodes by constructing a multitask SNN and selecting dead song datasets to perform mining tasks to train the neural network. Then a semisupervised learning classifier based on Laplace regular least squares regression model is designed to use the relative support difference function as the decision method and optimize the function. The simulation experimental results show that the SNN-RODE-LapRLS algorithm improves the performance by 14%-51% over the mainstream classification algorithms, and the consumption time meets the demand of real-time classification.
The AI Laocoön: Art and the Artificial Imagination, or Survival Aesthetics in the Anthropocene

Abstract The art-as-research described in this article—a project named The Drowned World, after J.G. Ballard’s dystopian novel of the same name—is a reflection on environmental collapse and its technical representation in the Anthropocene. Is imagination being subsumed by the artificial? Are the sociotechnic hyperobjects of artificial intelligence and global warming chimera of the imagination, or are they certainties emergent from a desire for virtuality? In The Drowned World project, the author proposes to employ text-driven image synthesis as an aesthetic apparatus obscuring the distinction between imagination (potentiality) and virtuality (artificiality) to offer a computational poetics of a world drowning in data, an imaginative ecology of the virtual sublime.
Visualizing Spatial Gaze Data in the Perception of 3D Objects

Abstract In response to the rapid development of mobile eye tracking, this paper proposes methods for the visualization of gaze patterns in three dimensions. Methods are divided into two basic categories, beginning with translations from 2D graphic conventions to those that incorporate 3D mesh geometries. They are first discussed in principle and thereafter demonstrated on actual gaze data of sculptural reproductions. The tracing of eye movements of objects in the round required consideration of variables typically unaccounted for in conventional eye-tracking visualization, most importantly with regards to the moving position of the viewing eye. A major advantage of 3D visualizations is the capacity to present gaze information simultaneously on obverse, reverse, and oblique surfaces of perceptual attention. The included methods provided for effective communication of a subject’s perceptual distribution over potentially complex stimulus geometries.
Experimental Environments and the Aesthetic Experience of Metabolic Processes

Abstract This article explores the aesthetic experience of metabolic processes in two projects: the newly designed Jade Eco Park in Taiwan, by architect Philippe Rahm, and the virtual reality environment Give and Take, developed by Desiree Foerster, Michaela Büsse, Sarah Hermanutz, and Andreas Rau. The authors argue that a heightened sensitivity to atmospheric and metabolic processes enabled by these projects brings to the foreground a different mode of aesthetic perception, toward how we experience and not only what we feel. They propose further that an aesthetics of metabolism allows us to become more sensitive to our own bodily involvement with the world and other sentient beings with whom humans share the world.
Entangled Realities: Emerging Performances of Relating Humans, Sonatars, and Spaces

Abstract This article investigates music performances with audiovisual components in extended reality setups that are not score based, but composed, emerging from a discursive process of interrelating human performers and computational counterparts such as virtual performance partners, virtual instruments, and potentials related to particular spaces. Rooted in notions of earlier avant-garde music and digital games, the performances are understood to emerge from a bottom-up process of relating human-technological agencies. A dichotomy of preexistent physical and virtual realities that would be extended or mixed is consequently replaced by a notion of entangled realities, as the artistic reality of the artwork emerges with the performance.
Online optical inspection of electrolytic copper plate based on digital moiré

The application of automatic inspection plays an important role in improving the quality of industrial products. Aiming at nodules on the surface of electrolytic copper plate, this article introduces a comprehensive optical online inspection method. In order to obtain dynamic information of product and overcome the interference of random factors such as large-curvature deformation, position deviation and tilt, the system integrates a variety of single technologies including digital moiré, image recognition and spatial transformation. The online test results show that the system can effectively extract the information of nodules and automatically classify the products according to the set threshold.
The application of nature-inspired optimization algorithms on the modern management: A systematic literature review and bibliometric analysis

Abstract With the expanding adoption of technology and intelligent applications in every aspect of our life, energy, resource, data, and product management are all improving. So, modern management has recently surged to cope with modern societies. Numerous optimization approaches and algorithms are used to effectively optimize the literature while taking into account its many restrictions. With their dependability and superior solution quality for overcoming the numerous barriers to generation, distribution, integration, and management, nature-inspired meta-heuristic optimization algorithms have stood out among these methods. Hence, this article aims to review the application of nature-inspired optimization algorithms to modern management. Besides, the created clusters introduce the top authors in this field. The results showed that nature-inspired optimization algorithms contribute significantly to cost, resource, and energy efficiency. The genetic algorithm is also the most important and widely used method in the previous literature.
Interactions of Framing and Timing in Nudging Online Game Security

Previous studies on applying the framing effects to cybersecurity nudges have shown that slight wording adjustments can predictably alter users’ security and privacy preferences. However, the applications mostly focus on the Reference-dependent preference, whereas limited information is available about another important embodiment, the Pseudo-certainty effect. In addition, it is also suggested that the framing effects are sensitive to the context, whereas known contributing contextual factors are limited. To solve the problems, we examine framing effects in cybersecurity in depth by designing two framing nudges based on the Pseudo-certainty effect and exploring their interactions with a contextual factor, timing. A between-subjects field experiment with 182 participants was conducted on a self-developed game to investigate the effectiveness of the framing nudges and timing in improving online game security. The participants were prompted to decide whether to set a game password. The results indicate that the framing of security warnings significantly affects users’ security decisions and behaviors, although their effects are moderated by the timing at which the warnings are presented. Finally, we conclude with practical security warning design implications.
Pairing-free certificateless blind signature scheme for smart grid

The smart grid is an emerging power system that can realize the information exchange between users and power companies in two-way communication and adjust power companies’ power supply according to the real-time power requests from smart meters on the users’ side. However, since malicious attackers can eavesdrop on two-way communication to collect and transmit information, they may leak users’ information. Therefore, this paper proposes a new certificateless blind signature (CLBS) scheme with batch verification function and improves a power request system model for the smart grid to protect users’ privacy. In addition, the proposed CLBS scheme simplifies the certificate management process in the traditional public key cryptosystem and eliminates the hidden key escrow in the identity-based public key cryptosystem. The unforgeability of the scheme is proved through the random oracle model, and the scheme’s security can be reduced to the intractability of the elliptic curve discrete logarithm problem (ECDLP). The analysis results show that our scheme does not use the time-consuming bilinear pairing operation, and has obvious advantages in computing performance compared with the existing signature schemes.
An improved algorithm for saccadic intrusion qualification under air traffic control situation

Saccadic intrusions are saccadic deviations that interrupt fixations. Saccadic intrusions have the unique characteristics of regular saccades and a small amplitude and round trip, providing a foundation for accurately identifying and quantifying saccadic intrusions. However, currently, rapid and accurate identification algorithms for saccadic intrusions of air traffic controllers are lacking. This study aimed to improve Tokuda's quantification algorithm for saccadic eye movement deviation in terms of data preprocessing, saccadic eye movement identification, and saccade amplitude threshold. The eye movement data were recorded using an eye tracker at a frequency of 100 Hz and obtained from the control simulation experiment. A total of 20 participants with basic control knowledge participated in this study, and finally 54 sets of data were obtained. The analysis of eye movement data showed that the saccadic intrusion quantification value of 36 of 54 sets of data obtained by the improved algorithm was closer to the value output by the manual identification method compared with the original algorithm. The results showed that these improvements had positive effects on the algorithm. • The improved algorithm can handle more data loss cases. • The improved algorithm uses the adaptive threshold method of eye movement speed. • An upper limit of eye movement amplitude was set based on ATCers' characteristics.
Virtual Reality in Stereometry Training

A new stereoscopic system is presented in this chapter for training in stereometry. Virtual reality systems are two types: immersion and non-immersion. An example of a environments with immersion is the desktop computer in which the virtual world is displayed on a special stereoscopic monitor through 3D glasses. The HMD (Head Mounted Display) virtual helmet is an example of an immersion system. The new stereoscopic system can also implement both types of 3D technology. At the same time, the software can generate objects in the .obj file extension to print to a 3D printer or add to a virtual reality device. The 3D technology is one of the fastest-growing. Over the last decade, it has found application in almost all spheres of society, including education. In learning stereometry, a problem may arise in drawing the geometric figures, unlike planimetry, where the drawing 100% coincides with its original. The problem can be solved with the help of specialized 3D software.
Review of Kalman Filter Developments in Analytical Engineering Design

This chapter discusses using the Kalman Filter (KF) for the analytical design in such engineering applications, as a closed loop control system, which often can be considered as time-invariant and linear (LTI). The chapter discuses designing a navigation accelerometer with the electric spring. This is a typical example of the closed loop, negative feedback control. Two approaches are used: A-conventional, empirical and B-analytical with KF. The consideration of both of them opens a comprehensive understanding of the system dynamics and its potentials. The discussion is based on the suboptimal form of the KF-Filter with Bounded Growth of Memory (FBGM), proposed by the author.
Bayesian multiple changepoint detection with missing data and its application to the magnitude‐frequency distributions

Abstract The detection of abrupt changes in an evolving pattern of time series in the presence of missing data still poses a challenge to real applications. We formulate the multiple changepoint problem into a latent Markov model on a countably infinite state space. For efficiency‐enhancing, we propose a partially collapsed Gibbs sampler for the inference of the joint posterior of the number of changepoints and their locations. Variants of Viterbi algorithms are suggested for obtaining the MAP estimates of random changepoints in the presence of missing data, which provides better performances in these varying‐dimensional problems. The method is generally applicable for multiple changepoint detection under a variety of missing data mechanism. The method is applied to a case study of the magnitude‐frequency distribution of the 2010 Darfield M7.1 earthquake sequence in New Zealand. We find out some unusual features of the seismic b ‐value in the Darfield earthquake sequence. It is noted that two changepoints are detected and in contrast to the background seismic b ‐value, relatively low b ‐values in the early aftershock propagation period are identified. We suggest that this might be a forewarning of potentially devastatingly strong aftershocks. The advance in the method of b ‐value changepoint detection will enhance our understanding of earthquake occurrence and potentially lead to improved risk forecasting.
A visual model of L2 motivation as a complex dynamic system

Abstract Learner motivation is an essential ingredient for successful language learning. This article investigates the motivating and demotivating factors affecting the motivation of fifty-eight EAP learners’ L2 learning. Guided by complex dynamic systems theory, with a dual focus on both what factors are important and what role these play in the L2 motivational system, this exploratory study utilized network analysis to map out how motivational factors connect to each other and form a L2 motivation factor network. It was found that motivating and demotivating factors, through the connections they share, exert an influence not only on each other, but on the whole motivational system. Based on the findings, suggestions are provided for classroom teachers for how they might steer learners’ motivational systems towards more motivated states, including introducing new factors into the system, enhancing connectivity, and increasing learners’ awareness of the complex and dynamic nature of motivation.
Investigating Sign Language Interpreter Rendering and Guiding Methods in Virtual Reality 360-Degree Content

As sign languages are often the native languages for members of the Deaf community, text captioning can be inaccessible, highlighting the importance of sign language interpretation. This research explores sign language in virtual reality 360-degree three degrees of freedom videos, exploring two rendering modes, fixed-position and always-visible, and two visual guiding methods, arrows and radar. Findings from testing with eight participants indicates that fixed-position rendering provides participants with a greater sense of presence than always-visible rendering, whilst always-visible rendering produces less of a blocking effect. Arrows appear more usable than radar for visually guiding participants to active speakers, including providing a higher level of sign language understanding. Future research is needed to validate these findings with six degrees of freedom content.