Reviewerly/exHarmony huggingface dataset:
Near-optimal Differentially Private Client Selection in Federated Settings

We develop an iterative differentially private algorithm for client selection in federated settings. We consider a federated network wherein clients coordinate with a central server to complete a task; however, the clients decide whether to participate or not at a time step based on their preferences—local computation and probabilistic intent. The algorithm does not require client-to-client information exchange. The developed algorithm provides near-optimal values to the clients over long-term average participation with a certain differential privacy guarantee. Finally, we present the experimental results to check the algorithm’s efficacy.
The Minimax Risk in Testing the Histogram of Discrete Distributions for Uniformity under Missing Ball Alternatives

We study the problem of testing the goodness of fit of a discrete sample from many categories to the uniform distribution over the categories. As a class of alternative hypotheses, we consider the removal of an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</inf> ball of radius ϵ around the uniform rate sequence for p≤ 2. When the number of samples n and number of categories N go to infinity while ϵ is small, the minimax risk $R_ \in ^{\ast}$ in testing based on the sample’s histogram (number of absent categories, singletons, collisions, …) asymptotes to $2\Phi \left( { - n{N^{2 - 2/p}}{ \in ^2}/\sqrt {8N} } \right)$, with Φ(x) the normal CDF. This characterization allows the comparison of the many estimators previously proposed for this problem at the constant level, rather than at the rate of convergence of the risk or the scaling order of the sample complexity. The minimax test mostly relies on collisions in the very small sample limit but otherwise behaves like the chisquared test. Empirical studies over a range of problem parameters show that our estimate is accurate in finite samples and that the minimax test is significantly better than the chisquared test or a test that only uses collisions. Our analysis relies on the asymptotic normality of histogram ordinates, the equivalence between the minimax setting and a Bayesian setting, and the characterization of the least favorable prior by reducing a multi-dimensional optimization problem to a one-dimensional problem.
A Software Security Testing Model for Autonomous Systems

The autonomous system is an extremely complex and multidisciplinary system, and the overall goal of this paper is to study safety testing methods for autonomous systems to enhance the robustness of autonomous systems. In order to realize an autonomous system with high reliability and robustness, this paper summarizes the security defect detection methods for autonomous systems from the two dimensions of static analysis and dynamic testing and expounds on the syntax-level, semantic, and system-level testing methods for autonomous systems.
EFL TEACHERS’ VOICE ON THEIR PREFERRED STRATEGIES IN TEACHING EFL WRITING DURING THE PANDEMIC: INVESTIGATING THE ROLE OF TECHNOLOGY

This study endeavored to gain insights into English as a Foreign Language (EFL) preferred strategies in teaching EFL writing during the pandemic, particularly dealing with technology use. Six writing teachers from various universities in East Java, Indonesia, participated in this study. They were asked to respond to interview questions that were based on Lee’s (2017) five categories of technology use in a writing class and Chi’s (2009) Interactive-Constructive-Active-Passive (ICAP) taxonomy levels. Thematic analysis of the interview data revealed that five teachers managed to use technological tools in all five categories of technology usage, with the self- and peer-evaluation categories being the most frequently used. They were also able to conduct activities in all of the ICAP taxonomy levels, although some technical issues and students’ factors hindered the students from being fully engaged in the lessons. Lastly, the experience of integrating technology in the online writing class during the pandemic resulted in a moderately optimistic view by the teachers on technological integration in future onsite classes. It is recommended that the government and educational institutions provide support in terms of infrastructure, facilities, and professional development training in technology usage for teachers, especially for those who teach in remote regions.
Enabling Secure and Trustworthy Quantum Networks: Current State-of-the-Art, Key Challenges, and Potential Solutions

Quantum networks hold the promise of enabling secure and trustworthy communication and computation. However, they also pose significant challenges in terms of security, privacy, and trust. In this paper, we have explored the current state-of-the-art in the field of quantum network security, privacy, and trust, with a focus on quantum key distribution, quantum-resistant cryptography, quantum hacking, trust establishment, and privacy-enhancing technologies. We have identified key challenges and open research questions in these areas, and presented potential solutions to enable the realization of secure and trustworthy quantum networks. As quantum networks continue to evolve, addressing these challenges will be crucial to realizing their full potential for secure communication and computation.
Self-supervised Diffusion Model for Anomaly Segmentation in Medical Imaging

A powerful mechanism for detecting anomalies in a self-supervised manner was demonstrated by model training on normal data, which can then be used as a baseline for scoring anomalies. Recent studies on diffusion models (DMs) have shown superiority over generative adversarial networks (GANs) and have achieved better-quality sampling over variational autoencoders (VAEs). Owing to the inherent complexity of the systems being modeled and the increased sampling times in the long sequence, DMs do not scale well to high-resolution imagery or a large amount of training data. Furthermore, in anomaly detection, DMs based on the Gaussian process do not control the target anomaly size and fail to repair the anomaly image, which led us to the development of a simplex diffusion and selective denoising $$ ((SD)^2) $$ model. $$ (SD)^2 $$ does not require a full sequence of Markov chains in image reconstruction for anomaly detection, which reduces the time complexity, samples from the simplex noise diffusion process that have control over the anomaly size and are trained to reconstruct the selective features that help to repair the anomaly. $$ (SD)^2 $$ significantly outperformed the publicly available Brats2021 and Phenomena detection from X-ray image datasets compared to the self-supervised model. The source code is made publicly available at https://github.com/MAXNORM8650/SDSquare .
Onto-CARMEN: Ontology-driven approach for Cyber–Physical System Security Requirements meta-modelling and reasoning

In the last years, Cyber-physical systems (CPS) have attracted substantial mainstream, especially in the industrial sector, since they have become the focus of cyber-attacks. CPS are complex systems that encompass a great variety of hardware and software components with a countless number of configurations and features. For this reason, the construction, validation, and diagnosis of security in CPS become a major challenge. An invalid security requirement for the CPS can produce partial or incomplete configuration, even misconfigurations, and hence catastrophic consequences. Therefore, it is crucial to ensure the validation of the security requirements specification from the earlier design stages. To this end, Onto-CARMEN is proposed, a semantic approach that enables the automatic verification and diagnosis of security requirements according to the ENISA and OWASP recommendations. Our approach provides a mechanism for the specification of security requirements on top of ontologies, and automatic diagnosis through semantic axioms and SPARQL rules. The approach has been validated using security requirements from a real case study.
Risky Traffic Situation Detection and Classification Using Smartphones

Behind many traffic accidents, there are more frequent minor incidents (risky traffic situations) that may lead to severe accidents. Analyzing such minor incidents effectively reduces accidents, but the challenge is to design a method to collect and analyze such incident information. In this paper, we propose a novel platform that aggregates behavioral data from pedestrians and drivers using their smartphones and recognizes risky traffic situations from the aggregated data. We design a two-stage approach where the smartphones of pedestrians and vehicles act as local anomaly detectors for triggering the event detector and classifier in the post-stage at the cloud server to suppress the processing and communication overhead. We also introduce an unsupervised learning system to cope with unseen risky situations enabled by joint utilization of the autoencoder-based anomaly detector and the risky situation classifier. The evaluation is conducted through both simulation and real experiments. The simulation result shows the risky situation detector achieves an F-measure of 0.89. We also collected real data at a car driving course to evaluate the risky situation classifier. From the results, we have confirmed that the proposed method succeeded in classifying three risky traffic situations involving pedestrians and/or vehicles with an accuracy of 89.3%.
EDDMF: An Efficient Deep Discrepancy Measuring Framework for Full-Reference Light Field Image Quality Assessment

The increasing demand for immersive experience has greatly promoted the quality assessment research of Light Field Image (LFI). In this paper, we propose an efficient deep discrepancy measuring framework for full-reference light field image quality assessment. The main idea of the proposed framework is to efficiently evaluate the quality degradation of distorted LFIs by measuring the discrepancy between reference and distorted LFI patches. Firstly, a patch generation module is proposed to extract spatio-angular patches and sub-aperture patches from LFIs, which greatly reduces the computational cost. Then, we design a hierarchical discrepancy network based on convolutional neural networks to extract the hierarchical discrepancy features between reference and distorted spatio-angular patches. Besides, the local discrepancy features between reference and distorted sub-aperture patches are extracted as complementary features. After that, the angular-dominant hierarchical discrepancy features and the spatial-dominant local discrepancy features are combined to evaluate the patch quality. Finally, the quality of all patches is pooled to obtain the overall quality of distorted LFIs. To the best of our knowledge, the proposed framework is the first patch-based full-reference light field image quality assessment metric based on deep-learning technology. Experimental results on four representative LFI datasets show that our proposed framework achieves superior performance as well as lower computational complexity compared to other state-of-the-art metrics.
Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications

<h2>Abstract</h2> Recent introduction of digitalization in pathology has disrupted the field greatly with the potential to change the area immensely. Digital pathology has created the potential of applying advanced quantitative analysis and artificial intelligence (AI) to the domain. In this study, we present an overview of what pathology AI applications have the greatest potential of widespread adoption in the preclinical domain and subsequently, in the clinical setting. We also discuss the major challenges in AI adoption being faced by the field of digital and computational pathology. We review the research literature in the domain and present a detailed analysis of the most promising areas of digital and computational pathology AI research and identify applications that are likely to see the first adoptions of AI technology. Our analysis shows that certain areas and fields of application have received more attention and can potentially affect the field of digital and computational pathology more favorably, leading to the advancement of the field. We also present the main challenges that are faced by the field and provide a comparative analysis of various aspects that are likely to influence the field for the long term in the future.
Analysis and Design of Identity Authentication for IoT Devices in the Blockchain Using Hashing and Digital Signature Algorithms

This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes.
Random variate generation by fast numerical inversion in the varying parameter case

There are various general techniques to produce random variates of a probability distribution such as the rejection method or numerical approaches to invert the cumulative distribution function (CDF). Some of these methods work in a black-box fashion (i.e., a single piece of code can sample for a relatively large class of distributions) which allows to create generators easily even for nonstandard distributions. Numerical inversion has some desirable properties that make its application appealing. However, a setup step is typically required to compute an approximation of the inverse of the CDF. Hence, if the distribution depends on a shape parameter and small samples are required for many different parameters (varying parameter case), the cost of the setup step typically outweighs the marginal generation times of the small samples, rendering the inversion method very slow. This article presents a new approach that allows for the use of inversion in the varying parameter case, provided that a suitable transformation of the density can be found to avoid running the setup for every parameter. The method is applied to two distributions (ARGUS and alpha distribution) to demonstrate that the performance is very good in the varying parameter case.
A comparative analysis between Conformer-Transducer, Whisper, and wav2vec2 for improving the child speech recognition

Automatic Speech Recognition (ASR) systems have progressed significantly in their performance on adult speech data; however, transcribing child speech remains challenging due to the acoustic differences in the characteristics of child and adult voices. This work aims to explore the potential of adapting state-of-the-art Conformer-transducer models to child speech to improve child speech recognition performance. Furthermore, the results are compared with those of self-supervised wav2vec2 models and semi-supervised multi-domain Whisper models that were previously finetuned on the same data. We demonstrate that finetuning Conformer-transducer models on child speech yields significant improvements in ASR performance on child speech, compared to the non-finetuned models. We also show Whisper and wav2vec2 adaptation on different child speech datasets. Our detailed comparative analysis shows that wav2vec2 provides the most consistent performance improvements among the three methods studied.
BookGPT: A General Framework for Book Recommendation Empowered by Large Language Model

With the continuous development and change exhibited by large language model (LLM) technology, represented by generative pretrained transformers (GPTs), many classic scenarios in various fields have re-emerged with new opportunities. This paper takes ChatGPT as the modeling object, incorporates LLM technology into the typical book resource understanding and recommendation scenario for the first time, and puts it into practice. By building a ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT, this paper attempts to apply ChatGPT to recommendation modeling for three typical tasks: book rating recommendation, user rating recommendation, and the book summary recommendation; it also explores the feasibility of LLM technology in book recommendation scenarios. At the same time, based on different evaluation schemes for book recommendation tasks and the existing classic recommendation models, this paper discusses the advantages and disadvantages of the BookGPT in book recommendation scenarios and analyzes the opportunities and improvement directions for subsequent LLMs in these scenarios. The experimental research shows the following: (1) The BookGPT can achieve good recommendation results in existing classic book recommendation tasks. Especially in cases containing less information about the target object to be recommended, such as zero-shot or one-shot learning tasks, the performance of the BookGPT is close to or even better than that of the current classic book recommendation algorithms, and this method has great potential for improvement. (2) In text generation tasks such as book summary recommendation, the recommendation effect of the BookGPT model is better than that of the manual editing process of Douban Reading, and it can even perform personalized interpretable content recommendations based on readers’ attribute and identity information, making it more persuasive than interpretable one-size-fits-all recommendation models. Finally, we have open-sourced the relevant datasets and experimental codes, hoping that the exploratory program proposed in this paper can inspire the development of more LLMs to expand their applications and theoretical research prospects in the field of book recommendation and general recommendation tasks.
Blockchain Technology for Monitoring Energy Production for Reliable and Secure Big Data

The growing adoption of renewable energy sources and the need for more efficient and secure energy grids are revolutionizing the energy sector. Electricity monitoring becomes an issue of utmost importance, as current traditional energy meters have several problems in terms of lack of transparency, very high operational costs, and the possibility of being easily tampered with. This paper proposes a new system for electricity production metering that leverages blockchain and IoT for decentralized and secure data recording while protecting user privacy and reducing operational costs. The architecture results in improvements over the traditional energy meter. The system also contributes to the generation of big data that is reliable, traceable, error-proof, and highly resistant to cyber attacks. The architectural project outputs are a smart energy meter, a smart contract on the Ethereum blockchain, and a decentralized application to manage the information recording. The experimental prototype outcomes confirm the use of these new technologies to improve energy metering, enhancing efficiency, transparency, and traceability, with reduced costs and increased user privacy.
Designing Explainable Artificial Intelligence with Active Inference: A Framework for Transparent Introspection and Decision-Making

This paper investigates the prospect of developing human-interpretable, explainable artificial intelligence (AI) systems based on active inference and the free energy principle. We first provide a brief overview of active inference, and in particular, of how it applies to the modeling of decision-making, introspection, as well as the generation of overt and covert actions. We then discuss how active inference can be leveraged to design explainable AI systems, namely, by allowing us to model core features of "introspective" processes and by generating useful, human-interpretable models of the processes involved in decision-making. We propose an architecture for explainable AI systems using active inference. This architecture foregrounds the role of an explicit hierarchical generative model, the operation of which enables the AI system to track and explain the factors that contribute to its own decisions, and whose structure is designed to be interpretable and auditable by human users. We outline how this architecture can integrate diverse sources of information to make informed decisions in an auditable manner, mimicking or reproducing aspects of human-like consciousness and introspection. Finally, we discuss the implications of our findings for future research in AI, and the potential ethical considerations of developing AI systems with (the appearance of) introspective capabilities.
Self-consistent dynamical field theory of kernel evolution in wide neural networks <sup>*</sup>

Abstract We analyze feature learning in infinite-width neural networks trained with gradient flow through a self-consistent dynamical field theory. We construct a collection of deterministic dynamical order parameters which are inner-product kernels for hidden unit activations and gradients in each layer at pairs of time points, providing a reduced description of network activity through training. These kernel order parameters collectively define the hidden layer activation distribution, the evolution of the neural tangent kernel (NTK), and consequently, output predictions. We show that the field theory derivation recovers the recursive stochastic process of infinite-width feature learning networks obtained by Yang and Hu with tensor programs. For deep linear networks, these kernels satisfy a set of algebraic matrix equations. For nonlinear networks, we provide an alternating sampling procedure to self-consistently solve for the kernel order parameters. We provide comparisons of the self-consistent solution to various approximation schemes including the static NTK approximation, gradient independence assumption, and leading order perturbation theory, showing that each of these approximations can break down in regimes where general self-consistent solutions still provide an accurate description. Lastly, we provide experiments in more realistic settings which demonstrate that the loss and kernel dynamics of convolutional neural networks at fixed feature learning strength are preserved across different widths on a image classification task.
A Privacy-Preserving Authentication Scheme for a Blockchain-Based Energy Trading System

The adoption of renewable energies such as solar power, heat pumps, and wind power is on the rise, and individuals have started generating energy using their own solar panels. In recent years, many blockchain-based energy trading schemes have been proposed. However, existing schemes cannot fully address privacy issues and dependency on energy brokers during energy trading. In this paper, we propose a privacy-preserving authentication scheme for blockchain-based energy traders. An energy user encrypts a request message through lightweight attribute-based encryption, and only energy sellers who have proper attribute keys can decrypt and conduct further processes with the energy user. We analyze the proposed scheme using both informal and formal methods, such as the BAN logic, AVISPA simulation tool, and RoR model. Furthermore, we compare the computational and communication costs of our scheme with related schemes and show that the proposed scheme has competitive performance.