Reviewerly/exHarmony huggingface dataset:
General quantum correlation from nonreal values of Kirkwood–Dirac quasiprobability over orthonormal product bases

Abstract We propose a characterization and a quantification of the general quantum correlation which is exhibited even by a separable (unentangled) mixed bipartite state in terms of the nonclassical values of the associated Kirkwood–Dirac (KD) quasiprobability. Such a general quantum correlation, wherein entanglement is a subset, is not only intriguing from a fundamental point of view, but it has also been recognized as a resource in a variety of schemes of quantum information processing and quantum technology. Given a bipartite state, we construct a quantity based on the imaginary part the associated KD quasiprobability defined over a pair of orthonormal product bases and an optimization procedure over all pairs of such bases. We show that it satisfies certain requirements expected for a quantifier of general quantum correlations. It gives a lower bound to the total sum of the quantum standard deviation of all the elements of the product (local) basis, minimized over all such bases. It suggests an interpretation as the minimum genuine quantum share of uncertainty in all local von-Neumann projective measurements. Moreover, it is a faithful witness for entanglement and measurement-induced nonlocality of pure bipartite states. We then discuss a variational scheme for its estimation, and based on this, we offer information theoretical meanings of the general quantum correlation. Our results suggest a deep connection between the nonclassical concept of general quantum correlation and the nonclassical values of the KD quasiprobability and the associated strange weak values.
Kerr‐Nonlinearity Enhanced Photon Blockades via Driving a Δ‐Type Atom

Abstract The single‐ and two‐photon blockade effects in an optical cavity with a nonlinear Kerr medium, which is coupled with a three‐level atom, forming a Δ‐type transition configuration and closed‐loop coupling are investigated by introducing two driving fields in the atom‐cavity system. It is found that single‐ and two‐photon blockades can be remarkably enhanced by the Kerr nonlinearity, however, the parameter regimes governing two blockades are distinctly different. It is demonstrated that a large Kerr nonlinearity makes more contribution to the single‐photon blockade, as compared to the two‐photon blockade effect. Furthermore, by varying the atom‐cavity coupling strength, single‐ and two‐photon blockades can alternatingly occur. Additionally, the region where two‐photon blockade occurs is widened at the cost of increased Kerr nonlinearity and atom‐cavity coupling. This work provides an alternative way to manipulate the few‐photon states and has potential applications in generating single‐photon or two‐photon sources.
Logics with Probabilistic Team Semantics and the Boolean Negation

We study the expressivity and the complexity of various logics in probabilistic team semantics with the Boolean negation. In particular, we study the extension of probabilistic independence logic with the Boolean negation, and a recently introduced logic FOPT. We give a comprehensive picture of the relative expressivity of these logics together with the most studied logics in probabilistic team semantics setting, as well as relating their expressivity to a numerical variant of second-order logic. In addition, we introduce novel entropy atoms and show that the extension of first-order logic by entropy atoms subsumes probabilistic independence logic. Finally, we obtain some results on the complexity of model checking, validity, and satisfiability of our logics.
Cryptocurrency as an investment or speculation: a bibliometric review study

Purpose The study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time. Design/methodology/approach The study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer. Findings The study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme. Originality/value The current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.
Discovery, Quantitative Recurrence, and Inhibition of Motion-Blur Hysteresis Phenomenon in Visual Tracking Displacement Detection

Motion blur is common in video tracking and detection, and severe motion blur can lead to failure in tracking and detection. In this work, a motion-blur hysteresis phenomenon (MBHP) was discovered, which has an impact on tracking and detection accuracy as well as image annotation. In order to accurately quantify MBHP, this paper proposes a motion-blur dataset construction method based on a motion-blur operator (MBO) generation method and self-similar object images, and designs APSF, a MBO generation method. The optimized sub-pixel estimation method of the point spread function (SPEPSF) is used to demonstrate the accuracy and robustness of the APSF method, showing the maximum error (ME) of APSF to be smaller than others (reduced by 86%, when motion-blur length > 20, motion-blur angle = 0), and the mean square error (MSE) of APSF to be smaller than others (reduced by 65.67% when motion-blur angle = 0). A fast image matching method based on a fast correlation response coefficient (FAST-PCC) and improved KCF were used with the motion-blur dataset to quantify MBHP. The results show that MBHP exists significantly when the motion blur changes and the error caused by MBHP is close to half of the difference of the motion-blur length between two consecutive frames. A general flow chart of visual tracking displacement detection with error compensation for MBHP was designed, and three methods for calculating compensation values were proposed: compensation values based on inter-frame displacement estimation error, SPEPSF, and no-reference image quality assessment (NR-IQA) indicators. Additionally, the implementation experiments showed that this error can be reduced by more than 96%.
Using immersive technologies to enhance the student learning experience

Purpose The purpose of this paper is to evaluate undergraduate student perceptions about the application of immersive technologies for enhancing the student learning experience. This study assesses the viewpoints of students from varying multidisciplinary backgrounds about whether immersive technologies can enhance their learning experience and increase their level of engagement in the context of higher educational delivery. Design/methodology/approach The research used a case study methodology adopting a questionnaire-based research mixed methods approach. In total, 83 participants completed the questionnaire. The purpose of the research was to evaluate and interpret students’ perspectives at higher educational level about the use of immersive technologies towards enhancing their learning experience. There was also a focus on remote educational delivery due to the legacy of COVID-19. Findings The findings suggest that there is still more empirical work to be undertaken regarding the application of immersive technologies in higher education. The study revealed that there are immersive benefits though preference for face-to-face teaching remains popular. The negative connotations associated with immersive technology use in higher education, (e.g. virtual reality), such as cost of equipment and motion sickness, substantiates the themes identified in the academic literature. Originality/value The study explores a diversity of immersive technologies and their application in higher education (HE) contexts. Findings indicate that although there are acknowledged pedagogical benefits of immersive technology use in HE prevalent barriers remain that require further empirical research if immersive technology use is to be universally used in the sector.
Content Analysis of Libraries’ Instagram Posts: Cultural Collection, Activities, and Preservation of Cultural Heritage

Abstract Libraries’ roles and contributions in promoting and raising awareness of culture and cultural heritage to support the sustainability of cultural life can be strengthened by utilizing social media platforms, including Instagram. However, there is a gap in studies and research relating to how academic libraries reflect their cultural functions through social media, i.e., Instagram. This paper provides a content analysis of academic libraries’ Instagram accounts at three academic libraries located in the United States, Canada, and the United Kingdom. These libraries represent their universities’ concerns with promoting sustainable development goals, specifically Sustainable Development Goal 11 (make cities and human settlements inclusive, safe, resilient, and sustainable). This study analyzed and categorized the Instagram posts of academic libraries related to culture and cultural heritage to answer the following research question: how do academic libraries reflect their cultural functions through social media, i.e., Instagram? The results show that the academic libraries studied here considered reflecting their cultural functions through social media by informing users about various cultural events, collections, facts, and news on Instagram.
Thinking Participatory Design work-shops in the presence of cosmopolitics

This paper is a written version of a presentation held at DASTS 2022 as part of a panel session organized by Mike Michael, Alex Wilkie, Michael Guggenheim and the author. The idea behind the panel, inspired by laboratory studies in STS, was to propose a focus on workshops as ‘worldbuilding’ events, which implies a close scrutiny of how workshops are constructed and carried out and to what effect. Along the lines of Annemarie Mol’s ontological politics and Isabelle Stengers’ cosmopolitics, workshops are not just spaces in which we explore and experiment with ideas and concepts that may or may not be realized at some point: they shape reality from the get-go. In the paper, I thus discuss Participatory Design (PD) workshops by way of Isabelle Stengers’ cosmopolitical and constructivist thinking. My discussion is based on a few but key publications from the field of PD such as the Handbook of Participatory Design (Simonsen &amp; Robertson, 2012).
Classification of Alzheimer's Disease with Deep Learning on Eye-tracking Data

Existing research has shown the potential of classifying Alzheimer's Disease (AD) from eye-tracking (ET) data with classifiers that rely on task-specific engineered features. In this paper, we investigate whether we can improve on existing results by using a Deep Learning classifier trained end-to-end on raw ET data. This classifier (VTNet) uses a GRU and a CNN in parallel to leverage both visual (V) and temporal (T) representations of ET data and was previously used to detect user confusion while processing visual displays. A main challenge in applying VTNet to our target AD classification task is that the available ET data sequences are much longer than those used in the previous confusion detection task, pushing the limits of what is manageable by LSTM-based models. We discuss how we address this challenge and show that VTNet outperforms the state-of-the-art approaches in AD classification, providing encouraging evidence on the generality of this model to make predictions from ET data.
A lightweight privacy-preserving scheme using pixel block mixing for facial image classification in deep learning

The training of state-of-the-art deep learning models generally requires significant high-quality data, including personal and sensitive data. To ensure privacy of the sensitive data used in training deep learning models, many methods have been designed by the research community. However, it has been observed that many privacy-preserving approaches for image-based deep learning model training incur significant time in the processing of images and/or have low accuracy on trained models when a large number of images is used for classification tasks. Hence, in this paper, we propose a lightweight and efficient approach to preserve image privacy while maintaining the availability of the training set. Specifically, we design the pixel block mixing algorithm for facial image classification privacy preservation in deep learning. Experimental findings show that the models trained by the mixed training set generated by the proposed algorithm maintain their availability. The comparison results of the structural similarity index measure between images in the new training set and the original training set show that our scheme preserves image privacy. Our evaluations also reveal that the data augmentation can be applied to the mixed training set to improve the training effectiveness. We also demonstrate it is computationally challenging for attackers to restore the mixed training set to the original one.
Unsupervised Deep Hashing with Fine-grained Similarity-preserving Contrastive Learning for Image Retrieval

Unsupervised deep hashing has demonstrated significant advancements with the development of contrastive learning. However, most of previous methods have been hindered by insufficient similarity mining using global-only image representations. This has led to interference from background or non-interest objects during similarity reconstruction and contrastive learning. To address this limitation, we propose a novel unsupervised deep hashing framework named Fine-grained Similarity-preserving Contrastive learning Hashing (FSCH), which explores fine-grained semantic similarity among different images and their augmented views more comprehensively. It mainly comprises two modules: the global-local fine-grained similarity consistency preservation module and the local fine-grained similarity contrast preservation module. Specifically, we reconstruct local pairwise similarity structures by matching fine-grained patches, in conjunction with global similarity structures based on global hash codes cosine similarity, to generate hash codes with the ability to preserve global-local similarity consistency. Moreover, the preservation of local fine-grained similarity among augmented views is accomplished through the common regional features mutual representation between patches, then we enhance the discriminability of hash codes by mitigating the potential features difference during contrastive learning. Experimental results on four benchmark datasets demonstrate that our FSCH achieves an excellent retrieval performance compared to state-of-the-art unsupervised hashing methods.
Virtual Reality to Augmented Reality: Need of the Hour in Human Resource Management

&lt;div&gt;Augmented reality is the need of the hour for Human Resource Management in this era of globalization wherein the world has become flat and businesses have no boundaries. This global business has made varied functions to work in the virtual world full of ease. The functions not only include finance, marketing, logistics, and supply chain but also Human Resource Management has taken a face towards the virtual world for its functions like recruitment, selection, performance management, competency management, or training. The function started with virtual reality and has eventually made a paradigm shift towards augmented reality. The chapter presents the evolution, applications, and challenges of VR and AR with respect to HRM.&lt;br&gt;&lt;/div&gt;
Improving Peer Assessment Validity and Reliability Through a Fuzzy Coherence Measure

Classical evaluation methods, assessments, exams, and so forth accentuate the perception of one against all, professor versus learners. Including students in the assessment process, allows transforming the professor from an opponent to a critical friend, with the role of helping students to recognize both their strengths and weaknesses. However, assessing peers is not as easy as it may appear, since reliability and validity issues still represent a major concern. To improve reliability, this study proposes a consensus-reaching measure that reduces the impact of inconsistent evaluations (harsh or overvalued) through minimizing iteratively the sum of weighted incoherencies. Furthermore, it improves validity by computing the similarity of each assessment to the professor's evaluation. Moreover, it is easier for students to assess with linguistic grades “Good,” “Fair,” etc., than with classical numbers, hence fuzzy logic is used to encompass the inherent uncertainty of students’ assessments. This pilot study included 23 students in the 4th year project management course where learners assessed their peers' oral presentations. Results suggest that the used measures permit achieving high reliability even with students from different cultures.
Quantified effects of the laser-seeding attack in quantum key distribution

Quantum key distribution (QKD) enables private communications with information-theoretic security. To guarantee the practical security of QKD, it is essential that QKD systems are implemented in accordance with theoretical requirements and are robust with regard to side-channel attacks. Here we study a prominent attack on QKD transmitters known as the laser-seeding attack (LSA). It consists in injecting photons into the laser of the transmitter in an attempt to modify the outgoing light in some way that is beneficial to the eavesdropper. In this work we measure the response of a QKD transmitter to the LSA as a function of the optical power injected, allowing us to quantify the level of optical attenuation required to mitigate the attack. Further, we use a laser-rate-equation model to numerically simulate the effects of the LSA on a gain-switched laser. With this model we are able to reproduce previous experimental results, as well as generate new insight into the LSA by examining the effects of the LSA when the QKD transmitter is operated with different laser current driving parameters.
Reservoir Computing System With HZO/Si FeFETs in Parallel Configuration: Experimental Demonstration of Speech Classification

We study a reservoir computing (RC) system with ferroelectric field-effect transistors (FeFETs) in a parallel configuration and develop various schemes in a speech classification task. Experimental drain-source, and substrate output currents of a FeFET are used for temporal reservoir state vectors in response to a time-series input signal at a corresponding frequency channel and their different characteristics accelerate the information extraction capability to effectively enhance the performance. Adjustable weights in the readout part are trained by Ridge regression. Finally, we achieved the highest classification accuracy of 98.1%. Our systematic approaches find important knowledge toward the system design establishment of FeFET-based RC for versatile application.
Self-Awakened Particle Swarm Optimization BN Structure Learning Algorithm Based on Search Space Constraint

To obtain the optimal Bayesian network (BN) structure, researchers often use the hybrid learning algorithm that combines the constraint-based (CB) method and the score-and-search (SS) method. This hybrid method has the problem that the search efficiency could be improved due to the ample search space. The search process quickly falls into the local optimal solution, unable to obtain the global optimal. Based on this, the Particle Swarm Optimization (PSO) algorithm based on the search space constraint process is proposed. In the first stage, the method uses dynamic adjustment factors to constrain the structure search space and enrich the diversity of the initial particles. In the second stage, the update mechanism is redefined, so that each step of the update process is consistent with the current structure which forms a one-to-one correspondence. At the same time, the “self-awakened” mechanism is added to prevent precocious particles from being part of the best. After the fitness value of the particle converges prematurely, the activation operation makes the particles jump out of the local optimal values to prevent the algorithm from converging too quickly into the local optimum. Finally, the standard network dataset was compared with other algorithms. The experimental results showed that the algorithm could find the optimal solution at a small number of iterations and a more accurate network structure to verify the algorithm’s effectiveness.
Meaning by Courtesy: LLM-Generated Texts and the Illusion of Content

Click to increase image sizeClick to decrease image sizeThis article refers to:AUTOGEN: A Personalized Large Language Model for Academic Enhancement—Ethics and Proof of Principle DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Notes1 The phrase riffs on the title of Baker (Citation1987).2 Cappelen and Dever’s (Citation2021) example of an AI-generated credit score rating is helpful here. Lucie’s mortgage application is rejected, due to a credit score of 550. When she enquires into what this score means, and what it has to do with her credit-worthiness, she can at best get an answer that involves a mathematical description of input data and a description of how the data is processed through the successive layers of a neural network. But this is not satisfying to her: “How, she wonders, can a bunch of mathematical transformations, none of which in particular can be tied to any meaningful assessment of her credit-worthiness, somehow all add up to saying something about whether she should get a loan?” (2021: 9; emphasis added). Note that this does not dispute the accuracy of the rating algorithm. It only asks what it could mean for the score to have a semantic content – to be saying something about her credit worthiness – given the manner in which it was generated.3 See Nagel (Citation2014, Chapter 6) for a discussion.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.