Reviewerly/exHarmony huggingface dataset:
THE IMAGE OF WOMEN IN THE NOVEL WOMEN CRYING TO THE BLACK MOON BY DIAN PURNOMO

This research aims to describe the image of the main female character in the novel Woman Who Cried to the Black Moon by Dian Purnomo. The method used in this research is descriptive qualitative. The results of this study are women's self-image in the form of character or the formation of women who are also responsible for their potential as individual beings; Physical image in the form of a physical image or a woman's body, which includes beauty, posture, and beauty, like the figure of a mage who has the physical characteristics of a woman who comes from Eastern Indonesia; a psychic image which states that women are psychological beings who think, feel, and aspire. Women were created by straight psychics who are carried away by feelings, also by magi who always feel sad when separated from their friends, social image of the role of women in the family and community environment. Within the family circle, women have roles as children, homemakers, and wives. Based on research conducted on the novel Woman Crying to the Black Moon by Dian Purnomo, it can be concluded that the image of women in it is self-image, physical image, psychological image, and social image.
Development and optimization of platforms of wise information technology of med based on medical internet of things and blockchain technology

Abstract As society evolves and the aging population grows, there is an increasing demand for healthcare. Traditional medical service models no longer suffice, leading to issues like uneven distribution of medical resources and varying medical standards. Wise Information Technology in Medicine (WITMED) platforms have emerged in response to this challenge. This study addresses the limitations of conventional medical service models, including resource imbalance, cumbersome procedures, and low informatization. Leveraging Medical Internet of Things (MIoT) and blockchain technology, we researched the development and enhancement of WITMED platforms. To assess the effectiveness of MIoT and blockchain‐based WITMED compared to traditional medical platforms, we evaluated data security, data interoperability, platform response time, and user satisfaction scores. The findings indicate that MIoT and blockchain‐based WITMED outperformed traditional platforms with a 0.13 higher user satisfaction score, improved data security and interoperability, and faster response times. WITMED based on MIoT and blockchain enhances efficiency, convenience, and personalization of medical services, ultimately elevating healthcare quality. Optimizing this platform should focus on improving user experience, data processing capabilities, performance, stability, response time, privacy protection, scalability, interoperability, and technical proficiency to provide superior medical services to users.
Interpolation Based Reversible Data Hiding using Pixel Intensity Classes

In this article, we suggest a new interpolation technique as well as a novel Reversible Data Hiding (RDH) approach for up scaling the actual image and concealing sensitive information within the up scaled/interpolated image. This data hiding strategy takes into account the features of the Human Visual System (HVS) when concealing the secret data in order to prevent detection of the private data even after extensive embedding. The private data bits are adaptively embedded into the picture cell based on its values in the suggested hiding strategy after grouping different pixel intensity ranges. As a result, the proposed approach can preserve the stego-visual image’s quality. According to experimental findings, the proposed interpolation approach achieves PSNRs of over 40 dB for all experimental images. The outcomes further demonstrate that the suggested data concealing strategy outperforms every other interpolation-based data hiding scheme existing in use.
Fixed point logics and definable topological properties

Abstract Modal logic enjoys topological semantics that may be traced back to McKinsey and Tarski, and the classification of topological spaces via modal axioms is a lively area of research. In the past two decades, there has been interest in extending topological modal logic to the language of the mu-calculus, but previously no class of topological spaces was known to be mu-calculus definable that was not already modally definable. In this paper, we show that the full mu-calculus is indeed more expressive than standard modal logic, in the sense that there are classes of topological spaces (and weakly transitive Kripke frames), which are mu-definable but not modally definable. The classes we exhibit satisfy a modally definable property outside of their perfect core, and thus we dub them imperfect spaces. We show that the mu-calculus is sound and complete for these classes. Our examples are minimal in the sense that they use a single instance of a greatest fixed point, and we show that least fixed points alone do not suffice to define any class of spaces that is not already modally definable.
Entropic property of randomized QAOA circuits

Abstract Quantum approximate optimization algorithm (QAOA) aims to solve discrete optimization problems by sampling bitstrings using a parameterized quantum circuit. The circuit parameters (angles) are optimized in the way that minimizes the cost Hamiltonian expectation value. Recently, general statistical properties of QAOA output probability distributions have begun to be studied. In contrast to the conventional approach, we analyse QAOA circuits with random angles. We provide analytical equations for probabilities and the numerical evidence that for unweighted Max-Cut problems on connected graphs such sampling always gives higher entropy of energy distribution than uniform random sampling of bitstrings. We also analyse the probability to obtain the global optima, which appears to be higher on average than for random sampling.
Universal Highly Effective Entanglement Purification with Single‐Copy Hyperentangled State

Abstract Entanglement purification is a powerful way to protect the quality of entangled system from channel noise and environment noise. A universal highly effective entanglement purification protocol with single‐copy hyperentangled state (H‐UHEPP) is proposed for distillable mixed entangled states with bilateral controlled‐not gate operations, bilateral asymmetrical rotation operations and single photon Bell‐state measurements, where all of these operations and measurements are easy to implement in polarization, spatial and time‐bin degrees of freedom (DOFs) of photon system. This H‐UHEPP, which requires only one pair of hyperentangled photons and simple unitary operations on three DOFs of a photon, can significantly enhance the fidelity of the purified state compared with the previous works. As a consequence, H‐UHEPP can speed up the entanglement purification process and reduce the resource consumption, which makes it useful in future quantum information processing.
Why can’t you be all talk to get things done? Consumer acceptance of voice-assisted products

Purpose Voice assistant technology represents one of the most radical artificial intelligence innovations. Drawing on the processing fluency theory and consumer learning literature, this study aims to explore how consumer acceptance of new products is influenced by voice assistant function (VAF), along with the impacts of role clarity and learning modality. Design/methodology/approach Four between-subjects experimental studies were conducted. Study 1 tested the main effect of VAF on consumer acceptance. Study 2 included role clarity as a mediator between VAF and consumer acceptance. Study 3 examined the moderation effect of learning modality and contrasted the effectiveness of experiential and verbal learning in helping increase consumer acceptance. Study 4, as a post hoc study, tested serial mediations to validate whether processing fluency was indeed the mechanism explaining the indirect relationship between VAF and consumer acceptance via role clarity. Findings The negative impact of VAF on consumer acceptance was demonstrated in all four studies. Studies 2 and 3 showed VAF decreased role clarity which further influenced consumer acceptance. Moreover, Study 3 evidenced that experiential learning was more effective than verbal learning in increasing consumer acceptance of voice-assisted products via role clarity. Study 4 demonstrated that VAF decreased role clarity, which in turn decreased processing fluency, leading to lower consumer acceptance. Originality/value This research views the usage of voice-assisted products as a coproduction process between consumers and the VAF. Accordingly, findings provide novel insights into processing fluency of tasks assisted by VAF through the lens of role clarity and learning modality, which enriches the understanding of potential barriers and opportunities for consumers to accept voice-assisted products.
Planning Workflow Executions over the Edge-to-Cloud Continuum

In the ever growing field of Data Science, Cloud Computing is a well established computational paradigm, while Edge Computing is an emerging and promising alternative when it comes to the novel challenges introduced in the Internet of Things (IoT) landscape. The combination of the two as a unified paradigm forms the Edge-to-Cloud continuum, which allows the execution of applications and services to span both edge and cloud resources in a transparent way. In such heterogeneous and volatile environments, the scheduling of data intensive workloads is a difficult task, usually performed manually and requiring careful and educated decisions on the type of devices used to optimally exploit the underlying hardware and achieve any user-defined higher level policy. In this paper we present the EC-Planner, a planning component for Edge-Cloud environments, which can make intelligent, automated decisions both on how and where to map arbitrary data analytics tasks to the underlying heterogeneous infrastructure, which may consist of a mix of devices and processing units, including CPUs and hardware accelerators both in the Cloud and at the Edge.
Subject Validity Issues and Potential Solutions in the Era of Metaverse–Starting with the Concern Over Property Rights in Crypto Art

In a pre-digital epoch, humans possessed the material means of production through material labor, which allowed them to exercise subjective agency and establish their existence as subjects through the acquisition of property rights. As digital technology has advanced, our digital being now forms an integral element of our fundamental human existence. In the age of the “metaverse,” we must confront and provide an answer to the topic of how to thrive better in the digital virtual world. The rise of Crypto Art, now imbued with a tangible worth by the incorporation of Blockchain technology and Non-Fungible Tokens in the artistic sphere, has opened new vistas for us to verify our subjects within the “metaverse”, thereby securing the liberty of our digital existence. However, crypto art, as digital labor under the techno-capitalist system, inevitably brings about the problem of alienation of digital laborers and their products, impacting the vulnerable subjects in the “metauniverse”. The communitarian mechanism and spirit of consensus inherent within the blockchain present potential pathways towards resolution. In addressing this predicament, crypto art must first transform into a societal intermediary committed to fostering a community centered around the sharing of digital resources. This would empower digital workers to genuinely possess their labor and acquire property rights on a public and communal platform, ultimately leading to the restoration of subjectivity.
Privacy‐preserving graph publishing with disentangled variational information bottleneck

Summary Social networks collect enormous amounts of user personal and behavioral data, which could threaten users' privacy if published or shared directly. Privacy‐preserving graph publishing (PPGP) can make user data available while protecting private information. For this purpose, in PPGP, anonymization methods like perturbation and generalization are commonly used. However, traditional anonymization methods are challenging in balancing high‐level privacy and utility, ineffective at defending against both various link and hybrid inference attacks, as well as vulnerable to graph neural network (GNN)‐based attacks. To solve those problems, we present a novel privacy‐disentangled approach that disentangles private and non‐private information for a better privacy‐utility trade‐off. Moreover, we propose a unified graph deep learning framework for PPGP, denoted privacy‐disentangled variational information bottleneck (PDVIB). Using low‐dimensional perturbations, the model generates an anonymized graph to defend against various inference attacks, including GNN‐based attacks. Particularly, the model fits various privacy settings by employing adjustable perturbations at the node level. With three real‐world datasets, PDVIB is demonstrated to generate robust anonymous graphs that defend against various privacy inference attacks while maintaining the utility of non‐private information.
Fitting Data on a Grain of Rice

Coresets are among the most successful compression paradigms. For clustering, a coreset B of a point set A preserves the clustering cost for any candidate solution C. In general, we are interested in finding a B that is as small as possible. In this overview, we will survey techniques for constructing coresets for clustering problems, their applications, and potential future directions.
Detection of Fake Currency Using Machine Learning Models

The goal of this research is to determine whether a given cash sample is genuine or counterfeit. Based on the colours, widths, and serial numbers described, several conventional procedures and methods exist for identifying counterfeit cash. Image processing proposes a number of machine learning techniques with a false-identity detection success rate of 99.9 percent for paper cash in today's era of modern computing. In algorithm-based techniques for detection and identification, various entities such as color, form, paper width, and image filtering on the note play a crucial role. This research proposes the application of K-Nearest Neighbors (KNN) followed by image processing as an effective method for spotting counterfeit money. KNN is favored for use in computer vision problems due to its outstanding accuracy, particularly when dealing with small datasets. This approach leverages the strengths of KNN in handling limited data to enhance the precision and reliability of counterfeit money detection. The accurate facts and information on entities and attributes associated to currency have been compiled in this banknote authentication dataset, which was developed using advanced computational and mathematical methodologies. AI calculations and picture handling are utilized for information handling and information extraction to accomplish an elevated degree of exactness and accuracy.
Health and toxicity in content moderation: the discursive work of justification

Within academia, industry, and government, the terms ‘health’ and ‘toxicity’ are widely used to describe and justify decisions around online content and its removal. However, the meanings of these terms are assumed to be self-evident and therefore are rarely examined. This article turns a critical eye to the health and toxicity metaphor to unpack its hidden political work. We trace the metaphor through three different discourses: the historical political economy of the term, the usage by cultural elites in the last two decades, and finally through its contemporary instrumental usage by volunteer content moderators on Facebook. By linking these discourses together, we argue that the metaphor of health and toxicity serves as a means for justification and legitimacy under contemporary neoliberalized orders that typically chafe at modes of public intervention and the language of democratic statecraft. Rather than elucidating the challenges of online content, we find that the metaphor often serves to obfuscate or sidestep the hardest problems in democratic governance. This analysis therefore has practical significance for researchers, policymakers, journalists, and other speakers that publicly traffic in this discourse at large.
A Deep Multi-Tasking Approach Leveraging on Cited-Citing Paper Relationship For Citation Intent Classification

Abstract Citations are crucial artifacts to provide additional information to the reader to comprehend the research under concern. There are different roles that citations play in scientific discourse. Correctly identifying the intent of the citations finds applications ranging from predicting scholarly impact, finding idea propagation, to text summarization. With the rapid growth in scientific literature, the need for automated methods to classify citations is now growing intense. However, we can only fully understand the intent of a citation if we look at the citation context in the citing paper and also the primary purpose of the cited article. In this work, we propose a neural multi-task learning framework that harnesses the structural information of the research papers and the cited paper’s information for the effective classification of citation intents. We analyze the impact of three auxiliary tasks on the performance of our approach for citation classification. Our experiments on three benchmark citation classification datasets show that incorporating cited paper information (title) shows that our deep neural model achieves a new state-of-the-art on the ACL-ARC dataset with an absolute increase of 5.3% in the F1 score over the previous best model. We also achieve comparable performance with respect to the best-performing systems in the SDP 2021 3C Shared task on Citation Context Classification. We make our codes available at https://github.com/Tirthankar-Ghosal/citationclassification-SCIM
dCNN/dCAM: anomaly precursors discovery in multivariate time series with deep convolutional neural networks

Abstract Detection of defects and identification of symptoms in monitoring industrial systems is a widely studied problem with applications in a wide range of domains. Most of the monitored information extracted from systems corresponds to data series (or time series), where the evolution of values through one or multiple dimensions directly illustrates its health state. Thus, an automatic anomaly detection method in data series becomes crucial. In this article, we propose a novel method based on a convolutional neural network to detect precursors of anomalies in multivariate data series. Our contribution is twofold: We first describe a new convolutional architecture dedicated to multivariate data series classification; We then propose a novel method that returns dCAM, a dimension-wise Class Activation Map specifically designed for multivariate time series that can be used to identify precursors when used for classifying normal and abnormal data series. Experiments with several synthetic datasets demonstrate that dCAM is more accurate than previous classification approaches and a viable solution for discriminant feature discovery and classification explanation in multivariate time series. We then experimentally evaluate our approach on a real and challenging use case dedicated to identifying vibration precursors on pumps in nuclear power plants.
Stochastic performance analysis of a time‐of‐arrival quantum random number generator

Abstract The performance analysis of a quantum random number generator (QRNG), operating based on the interarrival time differences between consecutive photon detections from a coherent light source, is presented. The proposed analysis approach accurately takes into account the physical properties of the single‐photon detection systems, such as discretised time measurement, the correlations induced by the asynchronous arrival of photons with respect to the time resolution grid, and the dead time after observations and provides the QRNG's relevant performance measures, such as the joint distribution of bits, lag‐ r correlations, the bit generation overhead and the bit generation time. Analysis results are verified by computer simulations.
A Comprehensive Analysis of Provider Fraud Detection through Machine Learning

This research paper presents a comprehensive analysis of healthcare provider fraud detection and analysis using machine learning, drawing insights from diverse literature surveys. The study employs a systematic approach to evaluate methodologies and insights from various academic fields. Leveraging the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, the research synthesizes findings from 27 relevant studies out of 450 articles. The focus lies on characterizing healthcare fraud, emphasizing addressing the limitations and gaps identified in existing literature. The paper introduces a Sequential Forward Selection (SFS) method and SMOTE oversampling for fraud detection, utilizing K-Nearest Neighbors, Artificial Neural Network, Linear Discriminant Analysis, and Gradient Boosting Machine, Classification using a bagging classifier and a stacking meta-estimator. It is recommended to use the Stacking aggregator because it is statistically significant. This study intends to offer insightful information to researchers, legislators, and healthcare practitioners by resolving issues with and gaps in existing methodologies.
A correspondence selection method based on same object and same position constraints

Establishing robust correspondences between two images is important for computer version tasks. However, in the real scene incorrect correspondences are inevitable no matter what kind of correspondence matching algorithms are adopted due to some complex factors, such as illumination, occlusion, and so on. To reduce the number of incorrect correspondences, an algorithm with the same object and same position constraints (SOSPC), is proposed to remove wrong correspondences from the given putative correspondences in this paper. The algorithm is based on the fact that in the given image pairs correct correspondences locate at the same position on the same objects. To select the correspondences on the same objects, an object matching method based on the correspondences selected by GMS is proposed. To select the correspondences on the correct positions, an iterative fundamental matrix estimation method based on clustering is presented. The experimental results have validated the effectiveness of the same object and the same position constraints, and the method achieves the state-of-art performance on five datasets.