Reviewerly/exHarmony huggingface dataset:
Artin-Schreier curves given by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msub><mml:mrow><mml:mi mathvariant="double-struck">F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:math>-linearized polynomials

Let Fq be a finite field with q elements, where q is a power of an odd prime p. In this paper we associate circulant matrices and quadratic forms with the Artin-Schreier curve yq−y=x⋅F(x)−λ, where F(x) is a Fq-linearized polynomial and λ∈Fq. Our results provide a characterization of the number of affine rational points of this curve in the extension Fqr of Fq, for gcd⁡(q,r)=1. In the case F(x)=xqi−x we give a complete description of the number of affine rational points in terms of Legendre symbols and quadratic characters.
Data Management Systems for the Hierarchical Edge

In recent years, there has been an exponential increase in the generation of data at the edge of the network. The International Data Corporation (IDC) estimates that the Global Datasphere, which was 33 zettabytes in 2018, will rise to 175 zettabytes by 2025, and there will be more than 150 billion connected devices worldwide [10]. The Internet of Things (IoT) segment is expected to experience the fastest growth, with data creation at the edge of the network projected to increase almost twice as fast as in the cloud. As a result, worldwide spending on edge computing is forecasted to reach 317 billion by 2026, as per IDC projections [1].
Captivates: A Smart Eyeglass Platform for Across-Context Physiological Measurements

We present Captivates, an open-source smartglasses system designed for long-term, in-the-wild psychophysiological monitoring at scale. Captivates integrate many underutilized physiological sensors in a streamlined package, including temple and nose temperature measurement, blink detection, head motion tracking, activity classification, 3D localization, and head pose estimation. Captivates was designed with an emphasis on (1) manufacturing and scalability, so we can easily support large-scale user studies for ourselves and offer the platform as a generalized tool for ambulatory psychophysiology research; (2) robustness and battery life, so long-term studies result in trustworthy data across an individual's entire day in natural environments without supervision or recharge; and (3) aesthetics and comfort, so people can wear them in their normal daily contexts without self-consciousness or changes in behavior.
Mining frequent items from high-dimensional set-valued data under local differential privacy protection

Mining frequent items from high-dimensional historical data (set-valued data) from massive users can extract the most routine data, playing a vital role in data mining. However, frequent item mining requires the collection of a large amount of user data, posing the potential risk of leaking user privacy. Local differential privacy is a mainstream privacy-preserving technique. It has been widely used for user privacy protection in various scenarios as it provides strict privacy protection. Mining frequent items from high-dimensional set-valued data under local differential privacy preservation has recently attracted much attention from researchers. Existing works usually use random sampling to reduce the communication cost of publishing perturbed data but hardly guarantee frequent item mining accuracy. This is because they sample minimal items from each user’s high-dimensional set-value data (e.g., LDPMiner samples one item), making it difficult to focus on the scope of frequent item mining. The motivation of this paper to solve the above problem is that the frequent items mined from the data of a portion of users (e.g., half of the users) are similar to those mined from the data of the global users. Therefore, we randomly divide users into two groups and mine the set of candidate frequent items from the first group of users. Then, we focus on the candidate set in the second group of users and mine frequent items from it. Besides, we observe that the larger the sample size of user data, the better the frequent item mining accuracy and, subsequently, the higher the communication cost. Therefore, we randomly group the contents and randomly sample from each group, thus improving the frequent item mining accuracy by publishing more data than existing works. On this basis, we adaptively perturb the sampled group data according to the communication cost to trade off the communication cost and frequent item mining accuracy. Finally, we analyze the privacy and utility of our method theoretically. The experiments with state-of-the-art methods such as FIML, SVIM, and LDPMiner show that our proposed method improves about 15% in accuracy and 10% in utility for mining frequent items in high-dimensional set-valued data.
TMSDNet: Transformer with multi‐scale dense network for single and multi‐view 3D reconstruction

Abstract 3D reconstruction is a long‐standing problem. Recently, a number of studies have emerged that utilize transformers for 3D reconstruction, and these approaches have demonstrated strong performance. However, transformer‐based 3D reconstruction methods tend to establish the transformation relationship between the 2D image and the 3D voxel space directly using transformers or rely solely on the powerful feature extraction capabilities of transformers. They ignore the crucial role played by deep multi‐scale representation of the object in the voxel feature domain, which can provide extensive global shape and local detail information about the object in a multi‐scale manner. In this article, we propose a novel framework TMSDNet (transformer with multi‐scale dense network) for single‐view and multi‐view 3D reconstruction with transformer to solve this problem. Based on our well‐designed combined‐transformer Block, which is canonical encoder–decoder architecture, voxel features with spatial order can be extracted from the input image, which are used to further extract multi‐scale global features in parallel using a multi‐scale residual attention module. Furthermore, a residual dense attention block is introduced for deep local features extraction and adaptive fusion. Finally, the reconstructed objects are produced with the voxel reconstruction block. Experiment results on the benchmarks such as ShapeNet and Pix3D datasets demonstrate that TMSDNet outperforms the existing state‐of‐the‐art reconstruction methods substantially.
A novel approach for bias mitigation of gender classification algorithms using consistency regularization

Published research has confirmed the bias of automated face-based gender classification algorithms across gender-racial groups. Specifically, unequal accuracy rates were obtained for women and dark-skinned people for face-based automated gender classification algorithms. To mitigate the bias of gender classification and other facial-analysis-based algorithms in general, the vision community has proposed several techniques. However, most of the existing bias mitigation techniques suffer from a lack of generalizability, need a demographically-annotated training set, are application-specific, and often offer a trade-off between fairness and classification accuracy. This means that fairness is often obtained at the cost of a reduction in the classification accuracy of the best-performing demographic sub-group. In this paper, we propose a novel bias mitigation technique that leverages the power of semantic preserving augmentations at the image- and feature-level in a self-consistency setting for the downstream gender classification task. Thorough experimental validation on gender-annotated facial image datasets confirms the efficacy of our bias mitigation technique in improving overall gender classification accuracy as well as reducing bias across all gender-racial groups over state-of-the-art bias mitigation techniques. Specifically, our proposed technique obtained a reduction in the bias by an average of 30% over existing bias mitigation techniques as well as an improvement in the overall classification accuracy of about 5% over the baseline gender classifier. Therefore, resulting in state-of-the-art generalization performance in the intra- and cross-dataset evaluations. Additionally, our proposed technique operates in the absence of demographic labels and is application agnostic, compared to most of the existing bias mitigation techniques.
<scp>ARJUNA</scp>: An accessible pin entry model in smartphones for persons with low vision

Abstract User authentication is the cornerstone of providing secure access to the services in the digital ecosystem. Critical applications such as Banking use Personal Identification Number (PIN)‐based passwords and One Time Passwords (OTP), for transactions to strengthen security. However, persons with visual impairments have often been a soft target for cyber attacks as it is comparatively simpler to make them victims of various attacks. This paper proposes a model titled Accessible Robust Just‐in‐time Numerical Authentication (ARJUNA) to assist PIN entry in smartphones for people with visual impairments. This study has explored several cyber‐attacks on visually impaired users and proposed the ARJUNA model that provides a robust braille‐inspired PIN input for people with visual impairments. The proposed model is implemented as an Android application that users can easily access. Several experiments are conducted with visually impaired users to study the model's performance. The proposed ARJUNA model has shown supportive results in the user study. The robustness of ARJUNA against various attacks is also explored in this article.
Urdu Text Reuse Detection at Phrasal level using Sentence Transformer-based approach

Text reuse is a process of creating new text(s) from pre-existing text(s). In recent years, Urdu Text Reuse Detection (UTRD) has gained the attention of researchers because the text is readily available in digital format all over the internet and can be copied or paraphrased from another source without proper attribution, which makes it easier to reuse but hard to detect. Text Reuse Detection (TRD) has many potential applications in Plagiarism detection, Paraphrase detection, Paraphrase generation, and Analysis of text reuse in web content. In recent years, Urdu Text Reuse Detection (UTRD) has gained the attention of researchers because the text is readily available in digital format all over the internet and can be copied or paraphrased from another source without proper attribution, which makes it easier to reuse but hard to detect. In previous studies, the problem of UTRD has been explored at the sentence level (Hafeez, 2022; Hafeez et al., 2023), sentence/passage level (Sameen et al., 2017), and document level (Sharjeel et al., 2017), along with benchmark corpora and approaches. However, the problem of UTRD has not been explored at the Phrasal level with respect to corpora and approaches. To fulfill this research gap, this research study has made a major contribution by developing a large benchmark manually annotated corpus of 25,001 text pairs at two levels of a rewrite: (1) Derived = 15,105 and (2) Non-Derived = 9896. In addition, we have developed, applied, evaluated, and compared baseline approaches (N-gram Overlap and Word Embedding-based approaches) with proposed Sentence Transformer-based approaches on the proposed UTRD-Phr-23 Corpus. As another contribution, we proposed a novel Sentence Transformers-based model (using a combination of eight different Sentence Transformers (ST) including paraphrase- multilingual-mpnet-base-v2, distiluse-base-multilingual-cased-v, paraphrase-multilingual-MiniLM-L12-v2, LaBSE, xlm-r-distilroberta-base-paraphrase-v1, xlm-r-100langs-bert-base-nli-mean-tokens, xlm-r-bert- base-nli-stsb-mean-tokens, and xlm-r-100langs-bert-base-nli-stsb-mean-tokens). Our proposed model outperforms with an F1 score of 0.63 compared to the best results obtained using N-gram Overlap (baseline) approach (F1 = 0.53).
Towards a successful secure software acquisition

Security is a critical attribute of software quality. Organizations invest considerable sums of money in protecting their assets. Despite investing in secure infrastructure, organizations remain prone to security risks and cyberattacks that exploit security flaws. Many factors contribute to the challenges related to software security, e.g., the exponential increase in Internet-enabled applications, threats from hackers, and the susceptibility of inexperienced Internet users. Moreover, organizations tend to procure off-the-shelf software from third-party suppliers. However, gaining a complete understanding of ways to assess suppliers’ readiness to provide secure software before selecting a supplier is imperative. We have developed a readiness model for secure software acquisition (RMSSA) to help software organizations select suppliers who can provide secure software. We employed state-of-the-art techniques based on systematic literature review to determine the best practices undertaken by organizations in terms of acquiring secure software, which depends on six core security knowledge areas: confidentiality, integrity, availability, authorization, authentication, and accountability. We evaluated the RMSSA theoretically and in a practical environment based on three case studies with software organizations. Our findings can guide software organizations in selecting the supplier who can develop secure software. The proposed RMSSA can be used to evaluate suppliers’ readiness to provide secure software.
Few-shot forgery detection via Guided Adversarial Interpolation

The increase in face manipulation models has led to a critical issue in society—the synthesis of realistic visual media. With the emergence of new forgery approaches at an unprecedented rate, existing forgery detection methods suffer from significant performance drops when applied to unseen novel forgery approaches. In this work, we address the few-shot forgery detection problem by (1) designing a comprehensive benchmark based on coverage analysis among various forgery approaches, and (2) proposing Guided Adversarial Interpolation (GAI). Our key insight is that there exist transferable distribution characteristics between majority and minority forgery classes.1 Specifically, we enhance the discriminative ability against novel forgery approaches via adversarially interpolating the forgery artifacts of the minority samples to the majority samples under the guidance of a teacher network. Unlike the standard re-balancing method which usually results in over-fitting to minority classes, our method simultaneously takes account of the diversity of majority information as well as the significance of minority information. Extensive experiments demonstrate that our GAI achieves state-of-the-art performances on the established few-shot forgery detection benchmark. Notably, our method is also validated to be robust to choices of majority and minority forgery approaches.
Transfer Learning Dehazing Network by Gaussian Process Mapping

Haze seriously interferes with the high-quality imaging process, resulting in a significant difference from a haze-free scene to a hazy status. This phenomenon makes dehazing challenging. To make real data dehazing effective, we propose a novel Transfer Learning Dehazing Network (TLDN) with Gaussian Process Mapping (GPM) for the single image dehazing in this paper. Under our observation, we believe there is a correlation between hazy and haze-free scenes in the middle hidden channels. Therefore, we suppose a transfer functional relationship in some latent features encoded by two kinds of networks and embedded into a latent vector space. The two encoder–decoder networks are introduced to learn the corresponding high-dimensional feature representation between pairs of hazy and haze-free images. These features would be employed to build a mapping relationship under the Gaussian process regression. Finally, experimental results on synthetic data and real-world samples demonstrate the effectiveness of our proposed method.
SCFL: Mitigating backdoor attacks in federated learning based on SVD and clustering

Federated learning (FL) is a distributed machine learning paradigm that enables scattered clients to collaboratively train a shared global model. FL is suitable for privacy-preserving applications due to keeping the training data decentralized. However, FL is susceptible to backdoor attacks which attempt to embed backdoor triggers into the global model during the training process, and later activate them to cause a desired misclassification. In this paper, to effectively defend against backdoor attacks in the FL system, we propose SCFL including three parts: first, the Singular Value Decomposition (SVD) technique is used to extract the significant features of model updates; second, the k-means clustering algorithm is used to cluster the significant features; finally, cosine similarity is used to measure the distance between two model updates and the optimal clients are selected to aggregate the global model after clipping. Unlike most robust algorithms, SCFL does not limit the number of attackers to be less than that of benign clients, nor does restrict the data distribution among all clients to be independent and identically distributed (IID). Moreover, SCFL does not require any auxiliary information outside of the learning process. We conduct extensive experiments including various types of backdoor attacks. Experimental results demonstrate that SCFL can effectively defend against these backdoor attacks and outperform the existing state-of-the-art algorithms.
Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection Approach to Aid Elderly People

Over the last few decades, the processes of mobile communications and the Internet of Things (IoT) have been established to collect human and environmental data for a variety of smart applications and services. Remote monitoring of disabled and elderly persons living in smart homes was most difficult because of possible accidents which can take place due to day-to-day work like falls. Fall signifies a major health problem for elderly people. When the condition is not alerted in time, then this causes death or impairment in the elderly which decreases the quality of life. For elderly persons, falls can be assumed to be the main cause for the demise of posttraumatic complications. Therefore, early detection of elderly persons’ falls in smart homes is required for increasing their survival chances or offering vital support. Therefore, the study presents a Chameleon Swarm Algorithm with Improved Fuzzy Deep Learning for Fall Detection (CSA-IDFLFD) technique. The CSA-IDFLFD technique helps elderly persons with the identification of fall actions and improves their quality of life. The CSA-IDFLFD technique involves two phases of operations. In the initial phase, the CSA-IDFLFD technique involves the design of the IDFL model for the identification and classification of fall events. Next, in the second phase, the parameters related to the IDFL method can be optimally selected by the design of CSA. To validate the performance of the CSA-IDFLFD technique in the fall detection (FD) process, a widespread experimental evaluation process takes place. The extensive outcome stated the improved detection results of the CSA-IDFLFD technique.
Computer Security Active Defense Technology Based on Bayesian Model

Abstract The existing active defense technology based on game theory is mainly based on static games. This method can not adapt to the dynamic change of the attacker’s attacking intent and tactics. Based on the non-cooperative and non-zero-sum dynamic game theory, a complete Bayesian system of emotional game bodies is established. Use the “virtual node” to transform the attack and defense chart into a tree structure of the attack and defense game. An attack and defense strategy suitable for integrity and incompleteness is given. The experimental simulation results show that the seven comprehensibilities and usability of Nash equilibrium solutions optimized by Bayesian subgames are well obtained
Hierarchical quantum circuit representations for neural architecture search

Abstract Quantum circuit algorithms often require architectural design choices analogous to those made in constructing neural and tensor networks. These tend to be hierarchical, modular and exhibit repeating patterns. Neural Architecture Search (NAS) attempts to automate neural network design through learning network architecture and achieves state-of-the-art performance. We propose a framework for representing quantum circuit architectures using techniques from NAS, which enables search space design and architecture search. We use this framework to justify the importance of circuit architecture in quantum machine learning by generating a family of Quantum Convolutional Neural Networks (QCNNs) and evaluating them on a music genre classification dataset, GTZAN. Furthermore, we employ a genetic algorithm to perform Quantum Phase Recognition (QPR) as an example of architecture search with our representation. Finally, we implement the framework as an open-source Python package to enable dynamic circuit creation and facilitate circuit search space design for NAS.
The weaponisation of free speech under the Morrison government

In recent years, the concept of ‘free speech’ appears to have reached a position of significant prominence in public debate. In this context, we ask how the concept of free speech was used discursively by the Morrison-led government. Utilising a political discourse analytic approach, and examining texts from 2019 to 2021, we argue that, and show how, the government engaged in multiple discourses around free speech that were incompatible, inconsistent and incoherent. These inconsistencies and discontinuities were so significant as to render this a ‘weaponisation’ of free speech discourse. Thus, the article both reveals the complexities of the Morrison government’s discursive positioning of free speech, and renders sensible the otherwise nonsensical way in which ‘free speech’ was used to garner support for a range of public policies.
Supervised Dimensionality Reduction of Proportional Data Using Exponential Family Distributions

Most well-known supervised dimensionality reduction algorithms suffer from the curse of dimensionality while handling high-dimensional sparse data due to ill-conditioned second-order statistics matrices. They also do not deal with multi-modal data properly since they construct neighborhood graphs that do not discriminate between multi-modal classes of data and single-modal ones. In this paper, a novel method that mitigates the above problems is proposed. In this method, assuming the data is from two classes, they are projected into the low-dimensional space in the first step which removes sparsity from the data and reduces the time complexity of any operation drastically afterwards. These projected data are modeled using a mixture of exponential family distributions for each class, allowing the modeling of multi-modal data. A measure for the similarity between the two projected classes is used as an objective function for constructing an optimization problem, which is then solved using a heuristic search algorithm to find the best separating projection. The conducted experiments show that the proposed method outperforms the rest of the compared algorithms and provides a robust effective solution to the problem of dimensionality reduction even in the presence of multi-modal and sparse data.