Reviewerly/exHarmony huggingface dataset:
A Bayesian approach for subgroup analysis

Several penalization approaches have been developed to identify homogeneous subgroups based on a regression model with subject-specific intercepts in subgroup analysis. These methods often apply concave penalty functions to pairwise comparisons of the intercepts, such that the subjects with similar intercept values are assigned to the same group, which is very similar to the procedure of the penalization approaches for variable selection. Since the Bayesian methods are commonly used in variable selection, it is worth considering the corresponding approaches to subgroup analysis in the Bayesian framework. In this paper, a Bayesian hierarchical model with appropriate prior structures is developed for the pairwise differences of intercepts based on a regression model with subject-specific intercepts, which can automatically detect and identify homogeneous subgroups. A Gibbs sampling algorithm is also provided to select the hyperparameter and estimate the intercepts and coefficients of the covariates simultaneously, which is computationally efficient for pairwise comparisons compared to the time-consuming procedures for parameter estimation of the penalization methods (e.g., alternating direction method of multiplier) in the case of large sample sizes. The effectiveness and usefulness of the proposed Bayesian method are evaluated through simulation studies and analysis of a Cleveland Heart Disease Dataset.
A robust transfer deep stochastic configuration network for industrial data modeling

Abstract A robust transfer deep stochastic configuration network for industrial data modeling is proposed to address challenging problems such as the presence of outliers (or noise) and conditional drift of the data model due to changes in working conditions. Assuming that outliers follow the t-distribution, the maximum a posteriori estimation is employed to evaluate the read-out weights, and the expectation maximization algorithm is used to iteratively optimize the hyperparameters of the distribution. Moreover, the knowledge contained in the data are expressed in the form of the model structure, connection weights and outlier distribution, and a knowledge-data-based robust transfer strategy is developed to offset the impact of insufficient training data on the learning performance of a deep stochastic configuration network with a new working condition. Comparative experiments are carried out using the historical furnace temperature data of a municipal solid waste incineration plant in China. The results show that the proposed method performs more favorably in robust data modeling and mitigates the impact of changes in working conditions on the applicability and accuracy.
Print Books and Ebooks: The New Equilibrium in an Academic Library

ABSTRACTABSTRACTThis article looks back at ALIA's prediction from 2013 that 'library print and ebook collections will establish a 50:50 equilibrium by 2020 and that this balance will be maintained for the foreseeable future' in the light of the experience at Curtin University Library. It revisits the findings of an article on the same topic published in 2015, and documents the situation at Curtin University Library as at 2022. It describes changes in the ebook environment and user attitudes over the past 6 years, as well as developments in the Library's acquisition strategies and methods to increase ebook holdings, and the current state of the collection. The article finally provides a likely projection of the ebook and print book holdings situation at Curtin Library in 2050.KEYWORDS: Ebookscollection developmentlibrary spacesacademic libraries Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsDavid WellsDavid Wells is a Deputy Director, Collections, Systems & Infrastructure, at Curtin University Library. He has many years of engagement with collection development and collection maintenance initiatives and has published several articles on collections, ebook adoption and usage, library discovery systems and how users interact with them, and on the assessment and analysis of materials availability.Anita SallenbachAnita Sallenbach is a Coordinator, Collection Development, at Curtin University Library. Her responsibilities include the selection and renewals process of information resources, the management of ebook products and the evaluation of different acquisition models, such as Patron Driven Acquisition and Evidence-Based Acquisition. She has also been closely involved in the improvement of discovery and accessibility of Australian Indigenous content in the collection.Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.
A stochastic contraction mapping theorem

In this paper we define contractive and nonexpansive properties for adapted stochastic processes which can be used to deduce limiting properties. In general, nonexpansive processes possess finite limits while contractive processes converge to zero a.e. A general method for obtaining convergence rates is proposed. These techniques are applied to a number of important processes, including stochastic approximation, least-squares estimation of controlled linear models and Q-learning.
Coherence controlled generation of Gaussian quantum discord in a quantum beat laser

Abstract Quantum discord, an appropriate measure of quantum correlation, is analyzed in a two-mode Gaussian state of the cavity field evolved by a system of quantum beat laser. In the laser system, two arbitrary single-mode Gaussian states of the cavity fields and an external classical field couple to the atomic lasing medium. We calculate the time-dependent expression for the quantum discord both analytically and numerically, by following the reduced density matrix equation of the resulting cavity field. In this framework, we investigate the generation and control of Gaussian quantum discord in the two-mode Gaussian state of the cavity field at the output in terms of the purity and non-classicality of the two input cavity modes, the Rabi frequency of the classical driving field, the relative phase of the coupling parameters, and the damping rates of the cavity modes. The behaviour of quantum discord appears oscillatory due to the quantum beats in the output cavity field developed by the classical driving field in the medium as time passes. Moreover, we find that quantum discord switches on and off while adjusting the relative phase of the coupling parameters. Analysis of the analytical results agrees well with our numerical simulations.
Classical SPICE simulation of superconducting quantum circuits

Abstract Quantum computing has been developed for many physical systems, and superconducting qubits are now in the integration phase. To efficiently design a many qubit system, an appropriate circuit simulator is necessary. Despite the existence of simulators for circuits including Josephson junctions, simpler circuit simulators are desirable when considering integrated qubits controlled by millions of transistors. This study examines the application of conventional Simulation Program with Integrated Circuit Emphasis simulators to analyze transmission signals of the system composed of transmons in the dispersive regime. Further, the influences of device parameter variations caused by the fabrication process can be taken into the model.
Multiple hypothesis screening using mixtures of non‐local distributions with applications to genomic studies

The analysis of large‐scale datasets, especially in biomedical contexts, frequently involves a principled screening of multiple hypotheses. The celebrated two‐group model jointly models the distribution of the test statistics with mixtures of two competing densities, the null and the alternative distributions. We investigate the use of weighted densities and, in particular, non‐local densities as working alternative distributions, to enforce separation from the null and thus refine the screening procedure. We show how these weighted alternatives improve various operating characteristics, such as the Bayesian false discovery rate, of the resulting tests for a fixed mixture proportion with respect to a local, unweighted likelihood approach. Parametric and nonparametric model specifications are proposed, along with efficient samplers for posterior inference. By means of a simulation study, we exhibit how our model compares with both well‐established and state‐of‐the‐art alternatives in terms of various operating characteristics. Finally, to illustrate the versatility of our method, we conduct three differential expression analyses with publicly‐available datasets from genomic studies of heterogeneous nature.
Learning discriminative visual semantic embedding for zero-shot recognition

We present a novel zero-shot learning (ZSL) method that concentrates on strengthening the discriminative visual information of the semantic embedding space for recognizing object classes. To address the ZSL problem, many previous works strive to learn a transformation to bridge the visual features and semantic representations, while ignoring that the discriminative property of the semantic embedding space can benefit zero-shot prediction tasks. Among these existing approaches, human-defined attributes are typically employed to build up the mid-level semantics. However, the discriminative capability and completeness of manually defined attributes are hard to guarantee, which may easily cause semantic ambiguity. To alleviate this issue, we propose a discriminative visual semantic embedding (DVSE) model that formulates the ZSL problem as a supervised dictionary learning framework. The proposed method is capable of exploring a set of discriminative visual attributes and ensures knowledge transfer across categories. Moreover, a unified objective is introduced to generate an augmented semantic embedding space where these learned visual attributes and human-defined attributes are incorporated jointly for consolidating the visual cues of feature representations. Finally, we treat the DVSE model as an optimization problem and further propose an iterative solver. Extensive experiments on several challenging benchmark datasets demonstrate that the proposed method achieves favorable performances compared with state-of-the-art ZSL approaches.
KPDFI: Efficient data flow integrity based on key property against data corruption attack

Data corruption attack (DCA) poses a severe threat to computer systems, corrupting in-memory data to subvert the intended control/data flow and impose arbitrary behavior. Data-Flow Integrity (DFI) guarantees legal data memory write to prevent it. Unfortunately, DFIs for all data adopt imprecise analysis and generate frequent memory access. Although enforcing DFI for partial data implies fewer checks and less cost, it weakens security. Therefore, existing DFIs suffer from an unsolved paradox: protecting all data limits its performance, and protecting partial data degrades its security. This paper presents KPDFI, a DFI only for DCA-related data, to resolve this paradox. We first propose the Key Property (KP) based on the DCA and a KP-based data selection strategy to define the DCA-related data, called key_data. KPDFI ameliorates the redundancy of the key_data legal write sets with a more precise field-sensitive and context-sensitive pointer analysis and propagation analysis. Since DCA only makes the data flow of key_data abnormal, KPDFI requires code instrumentation of only a small portion of the program code for DFI checking. We implement a KPDFI enforcement framework based on LLVM. We conduct numerous assessments for KPDFI. The experimental results prove that KPDFI is a security-enhanced and lightweight approach that mitigates the widespread DCA with an acceptable performance overhead (9.53%).
Towards energy-efficient and time-sensitive task assignment in cross-silo federated learning

Federated learning (FL) is a novel distributed learning framework in which clients with models can collaboratively train a high-quality global model while maintaining the privacy of their local data. The heterogeneity of datasets makes the global aggregation and local training requirements of each client in the cross-silo scenario inconsistent. Although many efforts are dedicated to improving the efficiency of cross-silo FL, the task assignment design of cross-silo FL remains an open question. To optimize the efficiency of energy consumption and training time of cross-silo FL, we develop a novel Energy-efficient and Time-sensitive Task Assignment (ETTA) mechanism. Specifically, we model clients’ requirements for energy efficiency and training time in cross-silo FL as an optimization problem, and design a utility transfer function to maintain the honesty of clients and the stability of ETTA. In terms of energy efficiency maximization, we analyze the conditions of optimal solutions based on the equilibrium of marginal cost and income, and design an approximate algorithm for cross-silo FL scenarios that require lightweight mechanisms. In terms of training time minimization, we model this objective as a min–max problem and give its analytical solution with Pareto optimality. To ensure that the task assignment achieves incentive compatibility, we further design a utility transfer selection function to incentivize honest collaboration among selfish clients. Finally, we conduct extensive experiments on the performance of ETTA to demonstrate that it outperforms the state-of-the-art and can maintain stability in a dishonest environment.
The Effects of Classes on the Application of Emotional Regulation Task in Online Classes

The purpose of this study is to examine the effects of learning flow, self-determination motivation, academic emotional regulation, and self-directed learning ability, which are known to affect learners' positive academic achievement after applying emotion regulation strategies in non-face-to-face classes. Emotional regulation strategies were conducted for 64 college students in a non-face-to-face class for 10 weeks. Next, the Paried t-test was conducted to compare learning flow, self-determination motivation, academic emotional regulation, and self-directed learning ability before and after. As a result, learning flow, academic emotional regulation, and self-directed learning ability significantly increased after the application of emotion regulation tasks in non-face-to-face classes. This study shows that if learners focus more on positive emotions in non-face-to-face classes, they can intervene in the learning and psychological aspects of students.
A Study on Media Content for Global Citizenship in English Education: A Focus on the Documentary Side by Side: Out of a South Korean Orphanage and Into the World

The purpose of this study is to analyze the contents of the documentary, &lt;i&gt;Side by Side: Out of a South Korean Orphanage and into the World&lt;/i&gt; (Glenn &amp; Julie, 2018), as a form of media content for global citizenship education, and to examine the validity of its utilization. This documentary proposes an improved awareness of transnational adoption and a new awareness of the issue through creative directing and innovative screening methods. Utilizing media contents such as documentaries, students objectify social issues and materialize the recognition of problems and their solutions independently. This can be used as an effective approach to improve global citizenship in English education targeting the MZ generation who are actively open to media and digital environments. Above all, watching &lt;i&gt;Side by Side: Out of a South Korean Orphanage and into the World&lt;/i&gt; and discussing the adoption discourse will be an opportunity for students to develop their multicultural sensitivity and learn the virtues of being human. Furthermore, it will find whether this documentary is appropriate for practicing the core areas of global citizenship and global citizenship education presented by UNESCO (2015) and could be considered a basic study for future English class design and learning outcomes.
ICB FL: Implicit Class Balancing Towards Fairness in Federated Learning

Federated learning (FL) is a promising machine learning paradigm that allows many clients jointly train a model without sharing the raw data. As the standard FL has been designed from the server’s perspective, the unfairness issue may occur throughout the whole learning process including the global model optimization phase. Some existing works have attempted this issue to guarantee the global model achieves a similar accuracy across different classes (i.e., labels), but failed to consider the implicit classes (different representations of one label) under them in which the fairness issue persists. In this paper, we focus on the fairness issue in the global model optimization phase and mitigate the research gap by introducing the Implicit Class Balancing (ICB) Federated Learning framework with Single Class Training Scheme (SCTS). In ICB FL, the server first broadcasts the current global model and assigns a particular class (label) for each client. Then, each client locally trains the model only with the assigned class data (SCTS) and sends the gradient back to the server. The server subsequently performs unsupervised learning to identify the implicit classes and generates the balanced weight for each client. Finally, the server averages the gradient received with weights, and updates the global model. We evaluate our ICB FL in three datasets, and the experimental results show that our ICB FL can effectively enhance fairness across explicit and implicit classes.
Rug-pull malicious token detection on blockchain using supervised learning with feature engineering

The rapid development of blockchain and cryptocurrency in the past decade has created a huge demand for digital trading platforms. Popular decentralised exchanges (DEXs) such as Uniswap and PancakeSwap were created to address this market gap, facilitating cryptocurrency exchange without intermediaries and hence eliminating security and privacy issues associated with traditional centralised platforms. This, however, due to lack of regulation, results in the emergence of a host of damaging investment fraudulent schemes, including Ponzi, honey pot, pump-and-dump, and rug-pull.In this study, we aim to investigate the problem of detecting rug-pull on Uniswap using supervised learning. We aggregate a list of 23 features and propose the use of a hybrid feature selection technique to find the most relevant features for rug-pull. The classifier, using this refined set of features, outperforms the classifier in the previous studies and achieves an f1-score of 99%, a precision of 97% on non-malicious tokens, and a recall of 99% on malicious tokens. Additionally, we show that the XGBoost classifier, built using these proposed features, can distinguish scam tokens and newly listed tokens, which are often harder to differentiate as they have similar characteristics, and also propose a validation method.
Digital Forensics based on Federated Learning in IoT Environment

The prevalence of Internet of Things (IoT) devices and increasing cyber-attacks, especially Advanced Persistent Threats (APTs), require automating the forensics investigation process in the IoT networks. The effectiveness of existing digital forensic techniques and tools, when tasked with finding attack traces and their origins in IoT environments suffers due to the inherent volatility and heterogeneity of the IoT. To address these issues, we aim at developing a deep federated learning-based digital forensic method, which identifies cyber-attacks and examines their attributes in IoT networks. The proposed method is designed based on Federated Learning (FL) to preserve data privacy to learn multi-stage attack events and define their traces. Our method uses FL rounds to train a Convolutional Neural Network (CNN) model locally on IoT network data and share only the learned hyperparameters with the federated server instead of sharing the evidence data. The method is evaluated using the ToN-IoT dataset and compared with other two peer machine-learning models and a non-FL technique that uses the same Deep Learning (DL) model and settings. The results showed that the proposed method outperforms the other ones in discovering attacks by achieving 81.69% detection accuracy in less training time than the non-FL method when dropping the network flow identifiers features besides guaranteeing data privacy. The Proposed method would be deployed at the identification, examination and analysis phases of digital forensics to automate its process and offer forensically sound evidence that could be used in a court of law.
Task-based Parallelization Approach for Attacking the Supersingular Isogeny Path Problem

The hardness of the supersingular isogeny (SSI) path problem is a crucial underpinning of isogeny-based cryptography. While the isogeny graphs emanating from various isogeny-based schemes might look differently, they only differ by the isogeny degrees, which determine the number of edges from a vertex in the graph.
Developing an anomaly detection framework for Medicare claims

Detection rates of non-compliant activity in Australian provider medical claims are below international benchmarks, and new methods are required. Since the Department of Health requires interpretability and incorporation of expert feedback as key components of its decision support systems many existing fraud detection techniques are unsuitable. As part of an Industry PhD project we have developed several new anomaly detection techniques which have been implemented in a prototype software system for the Australian Government Department of Health. We discuss the goals of the project and outline our approach to rapid prototyping and implementing processes to achieve expert-validated improvements in detection rates.
Empowering Patients to Delegate and Revoke Access to Blockchain-based Electronic Health Records

Health Data sharing, particularly electronic health records (EHR) are challenging and critical when improving quality of care and healthcare services in the long run. Privacy and confidentiality concerns deter health data sharing, which is a missed opportunity to improve and deliver the quality of care and improve quality of life. We propose a Blockchain-based health data sharing framework to share electronic health records between patients and professionals in the national healthcare infrastructure e.g., MyHR which is readily available to utilise. A clinically validated healthcare scenario was used in a laboratory simulated environment to establish a secure Blockchain-based data sharing episode that should encourage a new trend to share electronic health records between patients and healthcare service providers via MyHR.
Exercise Speed Analysis Based on Head Mounted Display Orientation

Virtual Reality (VR) exergames are a useful tool for improving users’ exercise levels. However, they have some associated equipment requirements which are an impediment to adoption outside of specialised locations. In our research we aim to make VR exergames more accessible by removing such equipment requirements by determining users’ exercise intensity directly from affordable and widely available VR equipment such as a smartphone-based Head Mounted Displays (HMD).