Reviewerly/exHarmony huggingface dataset:
Feature-Aligned Single-Stage Rotation Object Detection With Continuous Boundary

Recently, rotation detection has gained much attention and shown its potential for accurate localization in remote sensing scenes. However, the objects in remote sensing images have a variety of directions, sizes, and aspect ratios, which makes it difficult to locate and classify objects. Therefore, the object detection task is still facing great challenges in the field of remote sensing. In this paper, we propose a novel single-stage detector, which includes feature alignment block (FAB), double regression branches (DRBs), and a circumcircle rotation box (CRB). FAB utilizes the deformable convolution to flexibly obtain the features of different aspect ratio objects, and aligns the regression features with the corresponding classification features through fusion. Consequently, it can make the extracted feature information have stronger discrimination, which is conducive to improving the object position and classification accuracy. DRBs consist of a main regression branch and an auxiliary regression branch. The main regression branch is used to fine-tune the result of the auxiliary regression branch to obtain a more accurate regression result. Moreover, to eliminate the boundary discontinuity problem faced by regression-based detectors, we construct CRB through designing an angle of rotation and the radius of the circumcircle. Extensive experiments and visual analysis are conducted on three public benchmarks, i.e., DOTA, HRSC2016, and DIOR-R. The results show that our proposed method has excellent localization and classification performance for oriented objects on all the representative datasets.
Evolutionary Construction of Perfectly Balanced Boolean Functions

Finding Boolean functions suitable for cryptographic primitives is a complex combinatorial optimization problem, since they must satisfy several properties to resist cryptanalytic attacks, and the space is very large, which grows super exponentially with the number of input variables. Recent research has focused on the study of Boolean functions that satisfy properties on restricted sets of inputs due to their importance in the development of the FLIP stream cipher. In this paper, we consider one such property, perfect balancedness, and investigate the use of Genetic Programming (GP) and Genetic Algorithms (GA) to construct Boolean functions that satisfy this property along with a good nonlinearity profile. We formulate the related optimization problem and define two encodings for the candidate solutions, namely the truth table and the weightwise balanced representations. Somewhat surprisingly, the results show that GA with the weightwise balanced representation outperforms GP with the classical truth table phenotype in finding highly nonlinear Weightwise Perfectly Balanced (WPB) functions. This is in stark contrast to previous findings on the evolution of balanced Boolean functions, where GP always performs best.
Semantic segmentation of explosive volcanic plumes through deep learning

Tracking explosive volcanic phenomena can provide important information for hazard monitoring and volcano research. Perhaps the simplest forms of monitoring instruments are visible-wavelength cameras, which are routinely deployed on volcanoes around the globe. Here, we present the development of deep learning models, based on convolutional neural networks (CNNs), to perform semantic segmentation of explosive volcanic plumes on visible imagery, therefore classifying each pixel of an image as either explosive plume or not explosive plume. We have developed 3 models, each with average validation accuracies of >97% under 10-fold cross-validation; although we do highlight that, due to the limited training and validation dataset, this value is likely an overestimate of real-world performance. We then present model deployment for automated retrieval of plume height, rise speed and propagation direction, all parameters which can have great utility particularly in ash dispersion modelling and associated aviation hazard identification. The 3 trained models are freely available for download at https://doi.org/10.15131/shef.data.17061509.
SemG-TS: Abstractive Arabic Text Summarization Using Semantic Graph Embedding

This study proposes a novel semantic graph embedding-based abstractive text summarization technique for the Arabic language, namely SemG-TS. SemG-TS employs a deep neural network to produce the abstractive summary. A set of experiments were conducted to evaluate the performance of SemG-TS and to compare the results to those of a popular baseline word embedding technique called word2vec. A new dataset was collected for the experiments. Two evaluation methodologies were followed in the experiments: automatic and human evaluations. The Rouge evaluation measure was used for the automatic evaluation, while for the human evaluation, Arabic native speakers were tasked to evaluate the relevancy, similarity, readability, and overall satisfaction of the generated summaries. The obtained results prove the superiority of SemG-TS.
Optimizing machine learning inference queries with correlative proxy models

We consider accelerating machine learning (ML) inference queries on unstructured datasets. Expensive operators such as feature extractors and classifiers are deployed as user-defined functions (UDFs), which are not penetrable with classic query optimization techniques such as predicate push-down. Recent optimization schemes (e.g., Probabilistic Predicates or PP) assume independence among the query predicates, build a proxy model for each predicate offline, and rewrite a new query by injecting these cheap proxy models in the front of the expensive ML UDFs. In such a manner, unlikely inputs that do not satisfy query predicates are filtered early to bypass the ML UDFs. We show that enforcing the independence assumption in this context may result in sub-optimal plans. In this paper, we propose CORE, a query optimizer that better exploits the predicate correlations and accelerates ML inference queries. Our solution builds the proxy models online for a new query and leverages a branch-and-bound search process to reduce the building costs. Results on three real-world text, image and video datasets show that CORE improves the query throughput by up to 63% compared to PP and up to 80% compared to running the queries as it is.
A fuzzy content-based group recommender system with dynamic selection of the aggregation functions

Recommender systems are currently software tools that are focused on providing users with the best choices in an overloaded search space of possible options. Hence, group recommender systems have recently become an important trend in recommendation, because they aim at recommending a special type of items so-called social items, that tend to be consumed in groups such as TV programs, travel packages, etc. Among the different types of algorithms applied for group recommender systems, this paper is focused on content-based group recommender systems, as a novel group recommendation paradigm that exploits item features in the recommendation generation process. Specifically, our goal is to introduce a new content-based group recommendation approach, based on the recommendation aggregation paradigm whose main novelty is the development of a dynamic selection process of the aggregation scheme. Such an approach is centered on the identification of group's characteristics that are matching with the most appropriate function to use in the individual recommendation aggregation step. To perform such a matching, it is proposed a fuzzy decision tree induction process. The experimental evaluation shows that this scheme improves the recommendation performance of previous content-based group recommendation approaches, as well as it serves a starting point for further research based on this dynamic selection paradigm.
Introduction to Quantum-Resistant Blockchain

Quantum-resistant blockchains refer to cryptographic processes that are resistant to attacks via quantum computers. Present public-key algorithms depend on the difficulty of deciphering the discrete log and factorization problem of large prime numbers. Shor's algorithm can be used to break the hash signatures by quantum computers. Therefore, it is necessary for the development of a post-quantum secure signature scheme or quantum-resistant blockchain for post-quantum blockchain security. This chapter will discuss the impact quantum computers are predicted to have on public key cryptography based on the following topics: quantum computers, public key cryptography, quantum threat to PKI, Shor's and Grover's algorithms, post-quantum cryptography, and quantum-resistant blockchain.
Influences of virtual reality on design creativity and design thinking

This study explores the influences of virtual reality (VR) on creative design performance and design thinking processes. In this pretest−posttest experiment, nonequivalent groups of 180 seventh-grade students from a junior high school in Taipei were enrolled as research participants. For interior design lesson activities, the experimental and control groups were taught using VR applications and multimedia lectures, respectively, after which their creative design performance and design thinking processes were assessed and compared. The main conclusions are as follows: VR has a significant and positive influence on the creative design process (particularly the design and planning, testing and modification, and thinking and sharing stages), but it did not influence the data collection stage; VR has moderate effects on the functional integrity, technical requirements, sophistication and esthetics, and originality and novelty of creative design outcomes; and VR users have comprehensive design thinking processes that transition smoothly from empathy to problem definition, creative ideation, prototyping, and testing and verification. The experimental group significantly outperformed the control group in empathy.
ConvNeXt-Based Fine-Grained Image Classification and Bilinear Attention Mechanism Model

Thus far, few studies have been conducted on fine-grained classification tasks for the latest convolutional neural network ConvNeXt, and no effective optimization method has been made available. To achieve more accurate fine-grained classification, this paper proposes two attention embedding methods based on ConvNeXt network and designs a new bilinear CBAM; simultaneously, a multiscale, multi-perspective and all-around attention framework is proposed, which is then applied in ConvNeXt. Experimental verification shows that the accuracy rate of the improved ConvNeXt for fine-grained image classification reaches 87.8%, 91.2%, and 93.2% on fine-grained classification datasets CUB-200-2011, Stanford Cars, and FGVC Aircraft, respectively, showing increases of 2.7%, 0.3% and 0.4%, respectively, compared to those of the original network without optimization, and increases of 3.7%, 8.0% and 2.0%, respectively, compared to those of the traditional BCNN. In addition, ablation experiments are set up to verify the effectiveness of the proposed attention framework.
Do birds of a feather flock together? Evidence from time-varying herding behaviour of bitcoin and foreign exchange majors during Covid-19

This paper analyses herding behaviour within bitcoin and foreign exchange majors before and during the Covid-19 pandemic. We utilise both static and time-varying parameter regression herding measures to assess herding intensity based on hourly and daily frequencies, covering the period from 1 March 2018 to 28 February 2022. Our hourly static and time-varying model results indicate the absence of herding (hence, the presence of anti-herding behaviour) within bitcoin and the foreign exchange majors before and during Covid-19. In daily herding analyses, however, while we do not find evidence of herding within bitcoin or the foreign exchange majors, we do observe strong time-varying herding within the foreign exchange majors after excluding bitcoin both before and during Covid-19, and during both up- and down-market days. We conclude that herding behaviour between foreign exchange majors tends to be time-varying and horizon-dependent. Our results could be useful for bitcoin and foreign exchange investors, traders, researchers and regulators, helping them to strengthen their understanding of herding behaviour before and during periods of market stress such as the period of Covid-19.
Bibliometric and sentiment analysis with machine learning on the scientific contribution of Professor Srinivasa Sourirajan

Prof. Srinivasa Sourirajan is remembered by the desalination and membrane community as the “Father of Reverse Osmosis”. He passed away at the age of 98 peacefully in his beloved city Ottawa (Canada). His legacy will be remembered by the scientific community “membrane science, membrane processes, desalination and engineering”. His research studies were not only novel, but also very creative and even visionary. He offered a priceless gift to humanity by bringing clean water to all those in need through the presentation of reverse osmosis technology together with its appropriate membranes for water treatment, including desalination. This technology has now gained worldwide interest as it is able to produce clean water at a lower cost compared to other separation processes. His scientific contribution also pioneered other research areas. He developed novel research methodologies in geophysics while in catalysis he produced unleaded gasoline to help with the smog issue. He was nominated for the Nobel Prize three times. Prof. Sourirajan had also an exceptional humanitarian attribute. He played a significant role in bringing the Indian community to Ottawa. In the present paper we apply machine learning for his extraordinary and original scientific contribution. The results reveal how influential scientist he was.
Differential cryptanalysis of WARP

WARP is an energy-efficient lightweight block cipher that is currently the smallest 128-bit block cipher in terms of hardware. It was proposed by Banik et al. in SAC 2020 as a lightweight replacement for AES-128 without changing the mode of operation. This paper proposes key-recovery attacks on WARP based on differential cryptanalysis in single and related-key settings. We searched for differential trails for up to 20 rounds of WARP, with the first 19 having optimal differential probabilities. We also found that the cipher has a strong differential effect, whereby 16 to 20-round differentials have substantially higher probabilities than their corresponding individual trails. A 23-round key-recovery attack was then realized using an 18-round differential distinguisher. Next, we formulated an automatic boomerang search using SMT that relies on the Feistel Boomerang Connectivity Table to identify valid switches. We designed the search as an add-on to the CryptoSMT tool, making it applicable to other Feistel-like ciphers such as TWINE and LBlock-s. For WARP, we found a 21-round boomerang distinguisher which was used in a 24-round rectangle attack. In the related-key setting, we describe a family of 2-round iterative differential trails, which we used in a practical related-key attack on the full 41-round WARP.
A survey of automated methods for biomedical text simplification

Plain language in medicine has long been advocated as a way to improve patient understanding and engagement. As the field of Natural Language Processing has progressed, increasingly sophisticated methods have been explored for the automatic simplification of existing biomedical text for consumers. We survey the literature in this area with the goals of characterizing approaches and applications, summarizing existing resources, and identifying remaining challenges.We search English language literature using lists of synonyms for both the task (eg, "text simplification") and the domain (eg, "biomedical"), and searching for all pairs of these synonyms using Google Scholar, Semantic Scholar, PubMed, ACL Anthology, and DBLP. We expand search terms based on results and further include any pertinent papers not in the search results but cited by those that are.We find 45 papers that we deem relevant to the automatic simplification of biomedical text, with data spanning 7 natural languages. Of these (nonexclusively), 32 describe tools or methods, 13 present data sets or resources, and 9 describe impacts on human comprehension. Of the tools or methods, 22 are chiefly procedural and 10 are chiefly neural.Though neural methods hold promise for this task, scarcity of parallel data has led to continued development of procedural methods. Various low-resource mitigations have been proposed to advance neural methods, including paragraph-level and unsupervised models and augmentation of neural models with procedural elements drawing from knowledge bases. However, high-quality parallel data will likely be crucial for developing fully automated biomedical text simplification.
Query efficient black-box adversarial attack on deep neural networks

Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, yet they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting). As plenty of machine learning models have been deployed via online services that only provide query outputs from inaccessible models (e.g., Google Cloud Vision API2), black-box adversarial attacks raise critical security concerns in practice rather than white-box ones. However, existing query-based black-box adversarial attacks often require excessive model queries to maintain a high attack success rate. Therefore, in order to improve query efficiency, we explore the distribution of adversarial examples around benign inputs with the help of image structure information characterized by a Neural Process, and propose a Neural Process based black-box adversarial attack (NP-Attack) in this paper. Our proposed NP-Attack could be further boosted when applied with surrogate models or tiling tricks. Extensive experiments show that NP-Attack could greatly decrease the query counts under the black-box setting.
MFF: Multi-modal feature fusion for zero-shot learning

Generative Zero-Shot Learning (ZSL) methods generally generate pseudo-samples/features based on the semantic description information of unseen classes, thereby transforming ZSL tasks into traditional supervised learning tasks. Under this learning paradigm, the quality of pseudo-samples/features guided by the classes’ semantic description information is the key to the success of the model. However, the semantic description information used in the existing generative methods is mainly the low-dimensional representation (e.g., attributes) of classes, which leads to the low quality of the generated pseudo-samples/features and may aggravate the problem of domain shift. To alleviate this problem, we introduce the visual principal component feature, which is extracted by a principal component analysis network, to make up for the deficiency of using only semantic description information and propose a novel Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN) based generative method for ZSL, which we call Multi-modal Feature Fusion algorithm (MFF). In MFF, the input of different modal information enables VAE better fit the original data distribution and the proposed alignment loss ensures the consistency of the generated visual features and the corresponding semantic features. With the help of high-quality pseudo-samples/features, the ZSL model can make more accurate predictions for unseen classes. Extensive experiments on five public datasets demonstrate that our proposed algorithm outperforms several state-of-the-art methods under both ZSL and generalized ZSL settings.
Label-informed cardiac magnetic resonance image synthesis through conditional generative adversarial networks

Synthesis of a large set of high-quality medical images with variability in anatomical representation and image appearance has the potential to provide solutions for tackling the scarcity of properly annotated data in medical image analysis research. In this paper, we propose a novel framework consisting of image segmentation and synthesis based on mask-conditional GANs for generating high-fidelity and diverse Cardiac Magnetic Resonance (CMR) images. The framework consists of two modules: i) a segmentation module trained using a physics-based simulated database of CMR images to provide multi-tissue labels on real CMR images, and ii) a synthesis module trained using pairs of real CMR images and corresponding multi-tissue labels, to translate input segmentation masks to realistic-looking cardiac images. The anatomy of synthesized images is based on labels, whereas the appearance is learned from the training images. We investigate the effects of the number of tissue labels, quantity of training data, and multi-vendor data on the quality of the synthesized images. Furthermore, we evaluate the effectiveness and usability of the synthetic data for a downstream task of training a deep-learning model for cardiac cavity segmentation in the scenarios of data replacement and augmentation. The results of the replacement study indicate that segmentation models trained with only synthetic data can achieve comparable performance to the baseline model trained with real data, indicating that the synthetic data captures the essential characteristics of its real counterpart. Furthermore, we demonstrate that augmenting real with synthetic data during training can significantly improve both the Dice score (maximum increase of 4%) and Hausdorff Distance (maximum reduction of 40%) for cavity segmentation, suggesting a good potential to aid in tackling medical data scarcity.
Utility-Based Bayesian Personalized Treatment Selection for Advanced Breast Cancer

A Bayesian method is proposed for personalized treatment selection in settings where data are available from a randomized clinical trial with two or more outcomes. The motivating application is a randomized trial that compared letrozole plus bevacizimab to letrozole alone as first-line therapy for hormone receptor positive advanced breast cancer. The combination treatment arm had larger median progression-free survival time, but also a higher rate of severe toxicities. This suggests that the risk-benefit trade-off between these two outcomes should play a central role in selecting each patient's treatment, particularly since older patients are less likely to tolerate severe toxicities. To quantify the desirability of each possible outcome combination for an individual patient, we elicited from breast cancer oncologists a utility function that varied with age. The utility was used as an explicit criterion for quantifying risk-benefit trade-offs when making personalized treatment selections. A Bayesian nonparametric multivariate regression model with a dependent Dirichlet process prior was fit to the trial data. Under the fitted model, a new patient's treatment can be selected based on the posterior predictive utility distribution. For the breast cancer trial dataset, the optimal treatment depends on the patient's age, with the combination preferable for patients 70 years or younger and the single agent preferable for patients older than 70.