Reviewerly/exHarmony huggingface dataset:
BI-RADS-NET-V2: A Composite Multi-Task Neural Network for Computer-Aided Diagnosis of Breast Cancer in Ultrasound Images With Semantic and Quantitative Explanations

Computer-aided Diagnosis (CADx) based on explainable artificial intelligence (XAI) can gain the trust of radiologists and effectively improve diagnosis accuracy and consultation efficiency. This paper proposes BI-RADS-Net-V2, a novel machine learning approach for fully automatic breast cancer diagnosis in ultrasound images. The BI-RADS-Net-V2 can accurately distinguish malignant tumors from benign ones and provides both semantic and quantitative explanations. The explanations are provided in terms of clinically proven morphological features used by clinicians for diagnosis and reporting mass findings, i.e., Breast Imaging Reporting and Data System (BI-RADS). The experiments on 1,192 Breast Ultrasound (BUS) images indicate that the proposed method improves the diagnosis accuracy by taking full advantage of the medical knowledge in BI-RADS while providing both semantic and quantitative explanations for the decision.
LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images

Light field (LF) images containing information for multiple views have numerous applications, which can be severely affected by low-light imaging. Recent learning-based methods for low-light enhancement have some disadvantages, such as a lack of noise suppression, complex training process and poor performance in extremely low-light conditions. To tackle these deficiencies while fully utilizing the multi-view information, we propose an efficient Low-light Restoration Transformer (LRT) for LF images, with multiple heads to perform intermediate tasks within a single network, including denoising, luminance adjustment, refinement and detail enhancement, achieving progressive restoration from small scale to full scale. Moreover, we design an angular transformer block with an efficient view-token scheme to model the global angular dependencies, and a multi-scale spatial transformer block to encode the multi-scale local and global information within each view. To address the issue of insufficient training data, we formulate a synthesis pipeline by simulating the major noise sources with the estimated noise parameters of LF camera. Experimental results demonstrate that our method achieves the state-of-the-art performance on low-light LF restoration with high efficiency.
A Hybrid Quantum Wasserstein GAN with Applications to Option Pricing

This paper conducts an in-depth exploration of harnessing Machine Learning techniques in the context of Quantum methods for Option Pricing. We develop and implement a novel Hybrid Quantum Wasserstein GAN for loading arbitrary distributions from a classical state into a quantum state, which is of interest beyond its financial applications. In particular, our hybrid methodology eliminates several potential sources of instability and has superior performance compared to fully quantum generative models if the target distribution is classical.Our qWGAN can be used to capture the probability distribution of an asset at maturity, and transmute it into a quantum state as demonstrated on synthetic as well as real data experiments. In an Option Pricing context we present a full pipeline using this methodology and leverage the Iterative Quantum Amplitude Estimation algorithm to derive the expected option payoff, ensuring a quadratic enhancement in error scaling compared to traditional methods.
Towards Blockchain-based Adaptive Trust Management in Wireless Sensor Networks

Blockchain is an emerging technology that can be utilized to enhance Wireless Sensor Networks (WSNs) security level in various Internet of Things (IoT) applications. However, the implementation of blockchain in WSNs remains a challenging task due to the high demands for processing and communication. WSNs are prone to different types of cyber-attacks and the sensor nodes can be compromised or become unreliable as they are the primary data sources. To address this issue, it is crucial to incorporate a blockchain-based trust mechanism that guarantees the selection of trusted sources of data-gathering. This paper proposes a lightweight blockchain-based adaptive trust management mechanism that maintains nodes' trust and facilitates the identification of malicious nodes. A trust smart contract is introduced to evaluate the trustworthiness of each sensor node based on its behavior during the network operation using a set of assessment metrics such as node status, Transmitted Signal Strength, Packet Sending Rate, Packet Forwarding Rate, and Forwarding Delay. The proposed mechanism is shown to help mitigate potential cyber-attacks and maintain trustworthiness among sensor nodes.
Probing Numeracy and Logic of Language Models of Code

Machine learning techniques have found a widespread use in the software engineering community. In particular, language models (LMs) trained on code form the backbone of a majority of these applications, spanning tasks such as code completion, summarization, refactoring, execution prediction, and test generation. These tasks require reasoning about both the syntax and semantics of code. Recent work has shown that language models learn to capture the syntactic properties of code, but it is unclear to what extent they can reason about the semantics of code. In this work, we explore the ability of 3 language models of code to reason about a specific kind of semantics: numerical and logical properties of code. We propose several probing tasks to test the numerical and logical reasoning abilities of these models. We find that the models we explore - CodeBERT, GraphCodeBERT and CodeGen do indeed learn many numerical and logical properties of code, such as finding maximum in a list of numbers, comparing numbers, evaluating boolean expressions and representing numbers. They do not perform as well on complex tasks such as evaluating arithmetic expressions and substituting variables in such expressions. Our results indicate that while these models hold promise, there is a lot of room for improvement of their numeric and logical reasoning abilities.
A Feature Engineering Approach for Detecting Cyberbullying in Bangla Text Using Machine Learning

Cyberbullying has become an alarming issue with the prodigious rise of different social media platforms. The consequences of cyberbullying are uncountable and thus it should be detected and prevented immediately. Previous research has demonstrated great accuracy in detecting textual cyberbullying data. But the detection of the severity level of cyberbullying specially in Bangla text is yet to be performed. To mitigate this gap, this research is conducted to assess the severity level of Bangla cyberbullying text using feature engineering approach. We performed feature engineering to introduce new features such as PMI-SO (Pointwise Mutual Information - Semantic Orientation) and domain specific features to perform the detection. Two datasets were created in order to detect the severity level and to generate the features. The datasets contained 9721 and 33736 Bangla texts respectively. The Severity Dataset was annotated as low, medium, high and non-bullying. Final performance analysis showed that XG Boost classifier was the most accurate classifier with 87 % accuracy. The performance of SVM was followed by Logistic Regression, SVM and Naive Bayes classifiers. These classifiers obtained 85%, 81 % and 77% accuracy respectively.
Edge Guided GANs With Multi-Scale Contrastive Learning for Semantic Image Synthesis

We propose a novel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</u> dge guided <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">g</u> enerative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> dversarial <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</u> etwork with <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u> ontrastive learning (ECGAN) for the challenging semantic image synthesis task. Although considerable improvements have been achieved by the community in the recent period, the quality of synthesized images is far from satisfactory due to three largely unresolved challenges. 1) The semantic labels do not provide detailed structural information, making it challenging to synthesize local details and structures; 2) The widely adopted CNN operations such as convolution, down-sampling, and normalization usually cause spatial resolution loss and thus cannot fully preserve the original semantic information, leading to semantically inconsistent results (e.g., missing small objects); 3) Existing semantic image synthesis methods focus on modeling “local” semantic information from a single input semantic layout. However, they ignore “global” semantic information of multiple input semantic layouts, i.e., semantic cross-relations between pixels across different input layouts. To tackle 1), we propose to use the edge as an intermediate representation which is further adopted to guide image generation via a proposed attention guided edge transfer module. Edge information is produced by a convolutional generator and introduces detailed structure information. To tackle 2), we design an effective module to selectively highlight class-dependent feature maps according to the original semantic layout to preserve the semantic information. To tackle 3), inspired by current methods in contrastive learning, we propose a novel contrastive learning method, which aims to enforce pixel embeddings belonging to the same semantic class to generate more similar image content than those from different classes. We further propose a novel multi-scale contrastive learning method that aims to push same-class features from different scales closer together being able to capture more semantic relations by explicitly exploring the structures of labeled pixels from multiple input semantic layouts from different scales. Experiments on three challenging datasets show that our methods achieve significantly better results than state-of-the-art approaches. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Ha0Tang/ECGAN</uri> .
ADCL: Adversarial Distilled Contrastive Learning on lightweight models for self-supervised image classification

With the development of modern sensors, numerous images are collected in edge application scenarios; however, their utilization is quite expensive because a massive effort is required to label them for further usage. Self-supervised learning, with no need for labeled data, shows great potential in this context; however, notable performance degradations can be observed when training lightweight networks which are essential in edge implementation. We propose an effective distillation method called Adversarial Distilled Contrastive Learning (ADCL) to mitigate this issue. Specifically, we introduced knowledge distillation into self-supervised learning to transfer underlying feature clustering relations from teacher models to shallow models. We adopted an online-updated rather than a pretrained teacher model to realize convenient implementation to specific data domains. An adversarial loss item was introduced to alleviate unstable optimization caused by an online trained teacher by forcing the teacher model to find feature relations beyond the recognition of the student model. Compared with other self-supervised knowledge distillation methods that maintain data queues consisting of positive and negative examples, the asymmetric contrastive learning method was employed to further relieve the memory bottleneck during training. The experimental results prove the effectiveness of our method. When ResNet-50 is used as a teacher to teach ResNet-18 on ImageNet, ADCL achieves top-1 accuracies of 60.3% , which surpasses other knowledge distillation methods with online teachers and is comparable to approaches using pretrained teachers and data queues.
L’Accademia della Crusca e il linguaggio di genere

The article takes stock of the interventions of the Accademia della Crusca on the theme of gender language, retracing the history of the various problems that have emerged over time, and explains the reasons why schwa and asterisk cannot be accepted as recommended solutions.
User authentication and access control to blockchain-based forensic log data

Abstract For dispute resolution in daily life, tamper-proof data storage and retrieval of log data are important with the incorporation of trustworthy access control for the related users and devices, while giving access to confidential data to the relevant users and maintaining data persistency are two major challenges in information security. This research uses blockchain data structure to maintain data persistency. On the other hand, we propose protocols for the authentication of users (persons and devices) to edge server and edge server to main server. Our proposed framework also provides access to forensic users according to their relevant roles and privilege attributes. For the access control of forensic users, a hybrid attribute and role-based access control (ARBAC) module added with the framework. The proposed framework is composed of an immutable blockchain-based data storage with endpoint authentication and attribute role-based user access control system. We simulate authentication protocols of the framework in AVISPA. Our result analysis shows that several security issues can efficiently be dealt with by the proposed framework.
Experimenting with Extreme Learning Machine for Biomedical Image Classification

Currently, deep learning networks, with particular regard to convolutional neural network models, are typically exploited for biomedical image classification. One of the disadvantages of deep learning is that is extremely expensive to train due to complex data models. Extreme learning machine has recently emerged which, as shown in experimental studies, can produce an acceptable predictive performance in several classification tasks, and at a much lower training cost compared to deep learning networks that are trained by backpropagation. We propose a method devoted to exploring the possibility of considering extreme learning machines for biomedical classification tasks. Binary and multiclass classification in four case studies are considered to demonstrate the effectiveness of extreme learning machine, considering the biomedical images acquired with the dermatoscope and with the blood cell microscope, showing that the extreme learning machine can be successfully applied for biomedical image classification.
The Application of Portable X-ray Fluorescence (pXRF) for Elemental Analysis of Sediment Samples in the Laboratory and Its Influencing Factors

Several techniques, such as chemical methods and inductively coupled plasma mass spectrometry (ICP-MS), are available to accurately determine element content. However, they are time-consuming, labor-intensive, or expensive. Portable X-ray fluorescence spectrometry (pXRF) can be applied in various scenarios, with significantly higher efficiency and cost-effectiveness than laboratory methods. However, it also has limitations such as lower detection capability, relatively high detection limits, and lower accuracy than laboratory methods. In this study, we focused on applying pXRF to determine the elemental content of sediment samples and investigate its use in mineral exploration. A variety of factors influencing the results of pXRF analysis were analyzed. Our results showed that pXRF could detect more than 30 elements in stream sediments. The reliability of pXRF’s measurements was affected by factors such as the kind of element, sediment particle size, sample grinding treatment, count time, averaged element content, standard deviation of content, and range of content variation. The combination of pXRF analysis and laboratory analysis of partial samples is adequate for establishing a multi-element content inference equation. With this equation, it is possible to effectively infer the content gradient of elements, which will provide valuable support for mineral resource exploration.
Assessing GAN-Based Generative Modeling on Skin Lesions Images

The lack of sufficiently large open medical databases is one of the biggest challenges in AI-powered healthcare. Synthetic data created using Generative Adversarial Networks (GANs) appears to be a good solution to mitigate the issues with privacy policies. The other type of cure is decentralized protocol across multiple medical institutions without exchanging local data samples. In this paper, we explored unconditional and conditional GANs in centralized and decentralized settings. The centralized setting imitates studies on large but highly unbalanced skin lesion dataset, while the decentralized one simulates a more realistic hospital scenario with three institutions. We evaluated models' performance in terms of fidelity, diversity, speed of training, and predictive ability of classifiers trained on the generated synthetic data. In addition we provided explainability through exploration of latent space and embeddings projection focused both on global and local explanations. Calculated distance between real images and their projections in the latent space proved the authenticity and generalization of trained GANs, which is one of the main concerns in this type of applications. The open source code for conducted studies is publicly available at \url{https://github.com/aidotse/stylegan2-ada-pytorch}.
Anomaly Detection in Orthopedic Musculoskeletal Radiographs Using Deep Learning

In this paper, we investigate anomaly detection in orthopedics musculoskeletal radiographs using deep learning. We examine thirteen models from the most powerful neural network families: generative adversarial networks (GANs), autoencoders (AEs) and convolutional neural network (CNN). The main goal is to detect anomalies in musculoskeletal radiographs using the Stanford Musculoskeletal Radiographs (MURA) dataset. The results of the examined models were compared to several recent studies. Generally, CNN models achieve the best score of 0.822 which is a very promising result, competitive for expert radiologists performance.
A Post-quantum Secure Aggregation for Federated Learning

Federated learning is a privacy-preserving machine learning paradigm consisting of local model training, global model aggregation, and global model distribution. Recent research found that model aggregation can reveal the participants’ privacy. To preserve the participants’ privacy, multi-party computation-based secure aggregation is used in federated learning with mobile device participants. The character of the mobile device requires that secure aggregation can be efficient in computation and robust to the dropout. However, prior works need multi rounds, increase computation cost related to the dropped participants, and fails to resist quantum attacks. To solve these issues, we propose a 3-round post-quantum secure protocol for federated learning. In the proposed protocol, single-masking generated by homomorphic Pseudorandom Generator based on learning with round encrypts single user's model. After all the encrypted models are aggregated fast, additively homomorphic decryption based on Shamir secret share guarantee the robustness and performance induced by the dropped participants. All message exchange is based on a post-quantum secure channel constructed with the first post-quantum cryptography standard, Kyber KEM. In post-quantum security, security analysis demonstrates that the proposed protocol can preserve privacy under the sem-honest adversaries setting, and the experimental results show higher running time efficiency.
RepSGD: Channel Pruning Using Reparamerization for Accelerating Convolutional Neural Networks

Channel pruning is a popular method for compressing convolutional neural networks (CNNs) while maintaining acceptable accuracy. Most existing channel pruning methods use the approach of zeroing unnecessary filters and then removing them. To address the limitations of existing approaches, methods of creating forcibly filter redundancy and then removing redundant filters have been proposed without heuristic knowledge. However, these methods also use a deformed gradient to make filters identical, and performance degradation is inevitable because the parameters cannot be updated using the original gradients. To solve these problems, this study proposes RepSGD, which can compress CNNs simply and efficiently. RepSGD inserts a new point-wise convolution layer after the existing standard convolution layer. Subsequently, only new point-wise convolution layers are trained to produce filter redundancy (i.e., to make the filters identical), whereas the standard convolution layers are trained using the original gradient. After training, RepSGD merges two consecutive convolution layers into one convolution layer. Subsequently, the redundant filters in the merged convolution layer are pruned. Because RepSGD does not change the original architecture of the CNN, additional inference computation is not required, and it is possible to support training from scratch. In addition, using the original gradient in RepSGD optimizes the objective function of the CNNs better. We show that RepSGD outperforms existing pruning methods in various models and datasets through extensive experiments.
A High-Speed and Self-calibratable True Random Number Generator (TRNG) based on Unified Selector-RRAM

In this study, a high-speed and self-calibratable true random number generator (TRNG) is demonstrated based on a unified selector-RRAM device with dual switching modes. In fast selector mode (∼35 ns), the V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</inf> fluctuation is the entropy for the high-speed TRNG. However, V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</inf> has large device-to-device (D2D) variation, which leads to unstable switching probability. By adjusting the resistance of RRAM, the D2D variation of V <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</inf> can be substantially regulated through self-calibration ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$10\times \sigma/\mu$</tex> improvement), enabling the reliable deployment of TRNG.
Conditional Expectation Network for SHAP

A very popular model-agnostic technique for explaining predictive models is the SHapley Additive exPlanation (SHAP). There is a conditional version and an unconditional one of SHAP, the latter is also known as interventional SHAP. Except for tree-based methods, usually the unconditional version is used (for computational reasons). We provide a (surrogate) neural network approach which allows us to efficiently calculate the conditional version for both neural networks and other regression models, and which properly considers the dependence structure in the feature components. This proposal is also useful to provide drop1 and anova analyses in complex regression models which are similar to their generalized linear model (GLM) counterparts, and we provide a partial dependence plot (PDP) counterpart that considers the right dependence structure in the feature components.