Reviewerly/exHarmony huggingface dataset:
Modulated Entangled Photon Source for Quantum Secret Sharing

Passive entangled photon sources, in which only one state is optimized for are suitable for two-party quantum key distribution (QKD). However, quantum based protocols that involve more participants require a more flexible photon source. In particular, quantum secret sharing (QSS) can be implemented using two-photon states only, whereby the source acts as an active participant [1].
DIRECTED READING-THINKING ACTIVITY (DRTA) STRATEGY IN READING CLASS AT SMA NEGERI 2 KOTA TERNATE

This study aims to determine whether the Directed Reading Thinking Activity (DRTA) strategy can improve students' reading comprehension at SMA Negeri 2 Kota Ternate. The research method used is pre-experimental design. Which consists of pre-test, treatment, and post-test design. The population in this study were students of class X-G SMA Negeri 2 Kota Ternate. Using a convenience sampling technique involving one X-G class. as the sample, 30 students at class. The results of the study show that the Directed Reading Thinking Activity (DRTA) strategy can improve students' reading comprehension in Class X-G at SMA Negeri 2 Kota Ternate. It is proven that the Pre-Test score is 48.73 and the Post-Test value is 68.77. Based on the difference in scores obtained by students in reading comprehension, it showed an increase from the pre-test to the post-test where the increase in students' reading comprehension increased. Significant improvement in student achievement. Thus, the null hypothesis (H0) is not accepted, while the alternative hypothesis (Hi) is accepted. Therefore the researcher concluded that. The Directed Reading Thinking Activity (DRTA) strategy is effective in learning reading comprehension. because students understand more easily with predictions that identify main ideas and supporting details and can be seen from the results of better student reading scores.
Model of a Magnetic Waveguide for a Neural Network

The present work focuses on a novel mechanism for controlling the spin-wave (magnonic) signals in the ferromagnetic films that is promising to be a basic element for the realization of artificial synapses in hardware-based artificial neural networks. The general idea of this control is based on the effect of vanadium dioxide films exhibiting a metal–insulator transition on the propagation of magnon wave packets. A model of an artificial synapse has been built. In particular, various levels of microwave attenuation of spin waves transmitted through the investigated structure has been observed due to a variation of the conductivity of the vanadium dioxide film within a narrow temperature range. This feature makes it possible to control the strength of the connections between the individual neurons by dynamic controllability of an amplitude of the transmitted magnon signal.
CStrCRL: Cross-view Contrastive Learning through Gated GCN with Strong Augmentations for Skeleton Recognition

Contrastive learning has been widely embraced for its notable success along with two augmentation methods— normal and strong augmentations—in skeleton action recognition. Existing methods gain performance largely by customizing normal augmentations while bypassing strong augmentations that riches in motion patterns. To make up for the blank, we propose a novel framework, called CStrCRL, acquiring view-invariant and discriminative features from strong augmentations by leveraging contrastive learning. Specifically, to avoid the fragility of skeleton data adversely affecting the model after applying strong augmentations, we use consistency learning to maximize the similarity between strongly and normally augmented views. Furthermore, we employ cross-view learning on strong and normal augmentations for eliminating uncertainty feature boundaries learned by the model. Moreover, we design a new backbone, termed GatedStrNet, for discriminating valid and invalid features contained in strong augmented views. Finally, extensive experiments on NTU 60/120 and PKUMMD II demonstrate that the proposed method bridges the performance gap between normal and strong augmentations on contrastive learning of skeleton recognition. Notably, with a single stream input, CStrCRL achieves accuracies of 78.93% and 84.04% on the NTU60 Xsub and Xview datasets. Our source code can be found at: https://github.com/RHu-main/CStrCRL.
Speeding Up a Time-Delay Photonic Reservoir

Photonic reservoir computing facilitates high-speed analog information processing, promising significant speed-ups of computationally expensive machine learning tasks. In this work, we use a semiconductor laser with delayed feedback as a time-delay photonic reservoir and train only a linear output layer to solve time series prediction tasks. The reservoir laser is optically driven by an injection laser that encodes the input data using time-multiplexing. Therefore, a randomly drawn step function called mask modulates the input data establishing a neural network of so-called virtual nodes encoded in time [1], [2]. The mask is periodic to the input time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> and has a step duration of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> , referred to as node separation. These two timescales and the reservoir delay time <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tau$</tex> are crucial for the virtual network's computational capabilities [3] and its data rate given by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1/T$</tex> . Instead of choosing the input time close to the delay time, in this work, we demonstrate in two benchmark tasks that the input time can be set much smaller than the delay time while improving the performance. Additionally, this yields a significant speed-up of the reservoir's data rate even when the delay, e.g., realized by optical fibers, can not be easily reduced.
Expansion-based Hill-climbing

This paper investigates the influence of adaptive walks heuristics within local searches, by studying to what extent a wiser choice among improving neighbors influences the expected quality of the attained local optima. To this aim, we specifically focus on hill-climbers and introduce the maximum expansion pivoting rule, which selects the improving neighbor having the highest number of improving neighbors. Experiments show that having one step ahead of information allows for wiser neighbor choices, leading to better local optima. As the best improvement climber, a maximum expansion climber selects a particular search trajectory among all possible first improvement trajectories, however, it significantly increases the expected quality of the trajectory. On the other hand, the computational overhead makes this heuristic less valuable when included in an iterated search process, where repeating fast random hill-climbings from random starting points still allows a better exploration of the search space. This paper, therefore, extends previous studies on the relative efficiency of hill-climbing pivoting rules, by focusing on the original maximum expansion selection criterion. Results suggest that the achievement of good local optima combined with the use of approximation techniques and problem specificities could lead to the design of effective advanced metaheuristics that exploit the maximum expansion principle.
Measurement Based CV Parametrized Quantum Circuits

Multimode Gaussian states can be represented as complex undirected graphs [1] and such a representation allows for manipulation of the said graphs given measurements on some of the nodes of the graph. The resulting graph due to such measurements on a partition of of the original graph can thus be parametrized by the measurement parametres. Given a multipartite resource state, for example a cluster state, one can effect large unitaries by measuring part of the resource state. Here we use a deterministically generated 2D CV cluster state [2] to generate large Haar Random unitaries. In particular, we show the implementation of a 48 mode Haar-random unitary with a very high fidelity to the theoretical prediction exceeding previous attempts [3], [4]. Half of the modes of a 96 mode 2D cluster state's graph is measured using a homodyne detector while another homodyne detector is used to characterize the resulting graph. The angles for the measured modes are sampled uniformly randomly from a uniform distribution over angles in the range [ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$-0.5\pi,\ 0.5\pi$</tex> ] which is essentially sampling from the Haar measure over U(1). The resulting covariance matrix is then decomposed into an effective symplectic matrix using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sigma=\frac{1}{2}SS^{T}$</tex> , which is further decomposed into a corresponding unitary using first a Bloch-Messiah decomposition and then a rectangular decomposition. The phase and amplitude distributions are shown in the following image also compared to the theoretical distribution [3].
A 3-Layer Injection-Locked Multimode Semiconductor Laser Neural Network

Optical hardware implementations of artificial neural networks (ANNs) have become a hot topic of research due to the inherent parallelism, potentially high speed and energy efficiency of optics [1]. Semiconductor laser networks are of specific interest as they are highly non-linear systems, which can be modulated at high throughput [2]. As such, photonic ANNs can be realized by leveraging the spatial degrees of freedom of multimode, large-area vertical cavity surface emitting lasers (LA-VCSELs) to implement the ANN's neurons. Here, we demonstrate how to extend that approach and to realize deep photonic ANNs based on LA-VCSELs [3].
Order of Magnitude Increase in Storage Time of Brillouin-based Memory

With the recent developments in photonic computing platforms, the need of novel optical memories becomes increasingly important [1]. Optoacoustic memories have shown great potential due to their strong light-matter interaction, small footprint and high bandwidth [2]. These coherent and volatile memories can be controlled entirely optically, but so far their intrinsic storage time is limited to approximately 10 ns [3].
Ultrafast Boltzmann Sampling Using Photonic Ising Machines for Machine Learning

Ising machines have emerged as a promising computational concept that specializes in solving resource intensive optimization problems. Their operation is based on mapping a problem's cost function to a network of coupled Ising spins whose energy is described by the Ising Hamiltonian. Such a spin system can be emulated on a network of bistable analog oscillators and the tendency of this system to evolve to the lowest energy configuration is then used to find optimal solutions of a problem orders of magnitude faster than digital computers. In optics, photonic Ising machines based on coupled lasers, degenerate optical parametric oscillators and polariton condensates have recently demonstrated potential performance gains over digital hardware. As an alternative to these complex systems, we have developed a photonic Ising machine based on opto-electronic oscillators [1], which can utilize telecom-grade components and potentially yields high bandwidths of 40 GHz. We have shown that this opto-electronic Ising machine can have similar and in specific cases even significantly better performance on solving combinatorial optimization tasks as compared to the state of the art.
Shaped Supercontinuum as a Neural Network Computing Element

Optical computing is being developed as a supplement or even potential replacement for semiconductor electronic computing. Neural networks computing may be particularly compatible with optics as it involves performing many parallel, nonlinear operations on a set of inputs to calculate a set of outputs [1]. This is like supercontinuum generation, where almost any subtle adjustment of the seed pulse can significantly change the pulse spectral or temporal shape. Here, we incorporate a shaped supercontinuum source into a neural network, performing standard classification and autoencoding tasks by measuring shaped supercontinuum spectra.
Forecasting Extreme Events from Nonlocal Partial Information in a Spatially Extended Microcavity Laser

The forecasting of high-dimensional, spatiotemporal nonlinear systems has made tremendous progress with the advent of model-free machine learning techniques [1], [2]. Particularly, the forecasting of extreme events (EE), which are rare and intense phenomena arising in a lot of physical and natural complex systems [3], can have great impact in these fields. Unfortunately, a precise forecasting often relies on the precise knowledge of information on the past state of the system which is not always possible to achieve in real systems: only partial information is available for learning and forecasting in some cases. This can be due to poor temporal or spatial samplings, to physical variables difficult to measure or to access, or to noise.
Improving the Performance of Photonic Delay-Based Reservoir Computing by Phase Modulating the Input Signal

Photonic reservoir computing (RC) has been effectively used for solving various complex problems, such as speech recognition, time-series predictions or non-linear channel equalization tasks. Such RC systems are straightforward to train when compared to other neural network architectures, and have the advantage of high-speed performance, low-energy consumption and the possibility of high inherent parallelism [1]–[3].
Virtual and augmented reality and pre-service teachers: Makers from muggles?

This study examined the impact of a brief immersive experience with virtual reality (VR) on pre-service teachers' self-efficacy and attitudes towards technology in education. The study found that although pre-service teachers were aware of VR and augmented reality (AR) technologies, they lacked experience using them. The intervention had a positive impact on their beliefs and confidence in using innovative information and communications technology in the classroom. The findings suggest that brief interventions can serve as a means for pre-service teachers to evaluate their digital skills and develop an action plan to enhance them. Additionally, the study highlights the potential barriers to implementation faced by teachers, including the pace of technological change, lack of embedding time and funding constraints. This research contributes to the limited literature on the use of VR in teacher education and suggests that immersive experiences with technology can foster positive attitudes towards innovation, curiosity and skill development. The study provides implications for teacher education programs and policymakers regarding the potential of VR and AR technologies in education and the importance of supporting teachers in developing their digital skills. Implications for practice or policy: Teacher education providers should consider including immersive experiences with VR and AR to increase pre-service teachers’ awareness and evaluation of their potential to support learning. Pre-service teachers and those responsible for supporting them can use VR experiences as a means to evaluate their level of digital skill and identify an action plan to develop and/or update such skills as appropriate.
INFORMATION TECHNOLOGY IN SURVEILLANCE OF EMPLOYEE PERFORMANCE

This research paper examines the role of Information Technology in monitoring employee performance in companies. Organizations use performance management software that helps employee performance through key performance indicators, goal setting and performance reviews. It identifies the aids, issues, and effects of employing IT-based monitoring systems to identify employee lexical efficiency. Through the literature review, this study explores the technologies utilized in monitoring employee performance and outlines the research approach, encompassing case studies, surveys, interviews with HR professionals and employees. The result approaches that IT-based performance monitoring can steer organizational translucency, simplify input mechanisms along with concerns related to data privacy, employee morale, and potential ethical issues. Organizations makes use of time and attendance systems to track employee hours worked and absences which helps in monitoring employee punctuality, attendance patterns, and compliance to work schedules. The discussion makes an impact in fostering a performance-oriented culture and employee engagement, while acknowledging the need for clear communication and transparent policies. In conclusion, this research paper highlights that while IT offers invaluable tools to monitor employee performance, a balanced approach is essential to ensure both organizational efficiency and employee well-being. Organizations must carefully consider the ethical and privacy implications of using IT-based systems and prioritize employee involvement in decision-making processes to create a positive and productive work environment. KEYWORDS: Employee Performance, Surveillance, Workplace Monitoring, Employee Monitoring, Digital Surveillance, Performance Evaluation, Privacy Concerns, Ethics of Workplace Monitoring, Electronic Monitoring, Cybersecurity, Employee Privacy Rights, Surveillance Technologies, Workplace Transparency, Organizational Performance.
Service, game and livelihood, the new dimensions of neutralization techniques in Internet Romance Fraud: Extending the Neutralization Theory in Modern Internet crimes.

Internet romance fraud is a serious international crime made possible by the use of the internet and social media dating sites. It is a relationship fraud based on mutual love shared between the victims and their offender partners, without the victim knowing that the offenders have criminal intent to defraud the victim. This crime affect victims in developed countries who are referred to as ‘clients’ by their offenders’ partners. Offenders use neutralization techniques to rationalize and justify their deviant behavior by deflecting the quilt associated with this crime. This study surveyed 320 respondents in validated internet romance fraud (IRF) hotspots. Variance-based partial least square (SmartPLS) with structural equation modeling (SEM) was used to investigate casual relationships among the various. The findings indicate internet romance fraudsters” (IRF) use neutralization methods (“condemn of the condemners”, “denial of injury”, “denial of victim,” “denial of responsibility”, game, service and livelihood) to justify their crimes. The offenders do not use defense of necessity and metaphor of the ledger to rationalize their crimes against their victims. The evidence from this study suggests new dimensions of neutralization techniques (game, service and livelihood) have been introduced by criminals to justify their crimes. The empirical evidence from this study suggests that crime intention in the context of IRF is carried on to the actualization of the crime, a novelty in IS research. The implication of this suggests that criminals are using new techniques in emerging crimes (IRF). Law enforcement and prosecution should arm themselves to deal with the new way of crime justification and rationalization.
IMPACT OF DIGITALISATION AMONG THE RURAL YOUTH- A STUDY WITH VIJAYAPURA DISTRICT

Present Generation Youth are technology savvy than knowledge savvy. Technology has become an important part of human life especially among youth. It is widely accepted fact that youth use more technologies than other age group people. The Government of India launched digital India Campaign to provide Government services electronically by improving online infrastructure and with the objective of achieving digital empowerment in the country. As urban areas youth have easy access to technology, internet connection, they can get the benefits of digitalisation. In India nearly 66% of the population is residing in rural areas. Still today some rural areas are facing the problem of lack of access to information technology, lack of awareness, poor internet connection, lack of digital literacy etc. The aim of Government will be successful, if people in rural areas will start to use and gain benefits of the digital services. Thus, this research paper seeks to analyse the awareness level and impact of digitalisation among rural youth of Vijayapura District.
The Effects of Learner Autonomy on Writing in EFL

The purpose of this study was to investigate whether and to what extent autonomic and non-autonomous variables affect students' intrinsic motivation and satisfaction with these activities. Students’ academic performance in English as a Foreign Language (EFL) writing classes was examined to uncover any potential connections between learner autonomy (LA) and academic performance. An analysis of statistical significance was performed using a paired sample t-test on the resultant pairs of student perceptions. Based on the results of this study, the perception of autonomous activities among students is positive. Additionally, they performed better in such EFL writing contexts because of their autonomous activities. Based on the results of the present study, instructors should incorporate autonomous activities into the teaching process in EFL writing classes. EFL writing classrooms may provide students with the opportunity to play a more active role than a passive one, with the ability to effectively contribute to developing syllabuses and curriculum, which can increase their intrinsic motivation and improve their academic performance. As a result, students are motivated to take responsibility for their actions.
How to Deal With Social Media Misinformation

The Membership Management ReportVolume 19, Issue 10 p. 2-2 Association Communications How to Deal With Social Media Misinformation First published: 03 September 2023 https://doi.org/10.1002/mmr.32204Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume19, Issue10October 2023Pages 2-2 RelatedInformation
Study on Korean Language Teaching and Learning Based on Collaborative Learning: Focus on the Designing Korean Collaborative Class Through a Analysis of Student's Perception

The purpose of this study is to present a basic framework for Korean class design based on collaborative learning. Through the analysis of previous studies, a basic framework was presented. And based on the basic framework, a Korean reading class were designed and conducted for learners. After the class, a perception of the participating learners were investigated. As a result, in order to facilitate collaborative learning, procedures and strategies that increase learners' understanding and participation in collaborative learning must be considered from the design stage. In addition, a device for the instructor to properly intervene is needed. As a result, in this study, a basic framework for Korean class design based on collaborative learning was revised to reflect these findings. And the further studies on verify the effectiveness of the basic framework need to be conducted.