Reviewerly/exHarmony huggingface dataset:
OPT-RNN-DBSVM: OPTimal Recurrent Neural Network and Density-Based Support Vector Machine

When implementing SVMs, two major problems are encountered: (a) the number of local minima of dual-SVM increases exponentially with the number of samples and (b) the computer storage memory required for a regular quadratic programming solver increases exponentially as the problem size expands. The Kernel-Adatron family of algorithms, gaining attention recently, has allowed us to handle very large classification and regression problems. However, these methods treat different types of samples (i.e., noise, border, and core) in the same manner, which makes these algorithms search in unpromising areas and increases the number of iterations as well. This paper introduces a hybrid method to overcome such shortcomings, called the Optimal Recurrent Neural Network and Density-Based Support Vector Machine (Opt-RNN-DBSVM). This method consists of four steps: (a) the characterization of different samples, (b) the elimination of samples with a low probability of being a support vector, (c) the construction of an appropriate recurrent neural network to solve the dual-DBSVM based on an original energy function, and (d) finding the solution to the system of differential equations that govern the dynamics of the RNN, using the Euler–Cauchy method involving an optimal time step. Density-based preprocessing reduces the number of local minima in the dual-SVM. The RNN’s recurring architecture avoids the need to explore recently visited areas. With the optimal time step, the search moves from the current vectors to the best neighboring support vectors. It is demonstrated that RNN-SVM converges to feasible support vectors and Opt-RNN-DBSVM has very low time complexity compared to the RNN-SVM with a constant time step and the Kernel-Adatron algorithm–SVM. Several classification performance measures are used to compare Opt-RNN-DBSVM with different classification methods and the results obtained show the good performance of the proposed method.
Making sense of spoken plurals

Abstract Distributional semantics offers new ways to study the semantics of morphology. This study focuses on the semantics of noun singulars and their plural inflectional variants in English. Our goal is to compare two models for the conceptualization of plurality. One model (FRACSS) proposes that all singular-plural pairs should be taken into account when predicting plural semantics from singular semantics. The other model (CCA) argues that conceptualization for plurality depends primarily on the semantic class of the base word. We compare the two models on the basis of how well the speech signal of plural tokens in a large corpus of spoken American English aligns with the semantic vectors predicted by the two models. Two measures are employed: the performance of a form-to-meaning mapping and the correlations between form distances and meaning distances. Results converge on a superior alignment for CCA. Our results suggest that usage-based approaches to pluralization in which a given word’s own semantic neighborhood is given priority outperform theories according to which pluralization is conceptualized as a process building on high-level abstraction. We see that what has often been conceived of as a highly abstract concept, [+ plural ], is better captured via a family of mid-level partial generalizations.
Machine-Learning-Based Qubit Allocation for Error Reduction in Quantum Circuits

Quantum computing is a quickly growing field with great potential for future technology. Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations - qubit count and error vulnerability. Although quantum error correction (QEC) methods exist, they are not applicable to the current size of computers, requiring thousands of qubits, while current NISQ systems have hundreds at most. It is therefore imperative to improve the reliability of the circuits as much as possible to make them robust to the errors that will occur. One common approach is to adjust the compilation process of a circuit to create a final circuit with improved reliability. However, there are many decisions to be made when compiling that affect the final performance of the circuit, two of the most critical ones being the mapping of logical to physical qubits (the qubit allocation problem) and the movement of qubits to satisfy two-qubit gate adjacency requirements (the qubit routing problem). We focus on solving the qubit allocation problem and identifying initial layouts that reduce error. To identify these layouts, we combine reinforcement learning with a graph neural network (GNN)-based Q-network for analyzing both the connections and error rates of the graph-like backend of superconducting quantum computers to make mapping decisions, creating a Graph Neural Network Assisted Compilation (GNAQC) strategy. We provide both the circuit and the properties of the target backend as input to guide the decision-making process. We work with the IBM Qiskit API to compile and simulate our quantum circuits. We train the architecture using a set of four backends and six circuits and find that GNAQC generally outperforms pre-existing qubit allocation algorithms, increasing final relative output fidelity by roughly 12.7%.
SoK: Realistic adversarial attacks and defenses for intelligent network intrusion detection

Machine Learning (ML) can be incredibly valuable to automate anomaly detection and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is performed. However, despite the benefits of ML models, they are highly susceptible to adversarial cyber-attack examples specifically crafted to exploit them. A wide range of adversarial attacks have been created and researchers have worked on various defense strategies to safeguard ML models, but most were not intended for the specific constraints of a communication network and its communication protocols, so they may lead to unrealistic examples in the NID domain. This Systematization of Knowledge (SoK) consolidates and summarizes the state-of-the-art adversarial learning approaches that can generate realistic examples and could be used in real ML development and deployment scenarios with real network traffic flows. This SoK also describes the open challenges regarding the use of adversarial ML in the NID domain, defines the fundamental properties that are required for an adversarial example to be realistic, and provides guidelines for researchers to ensure that their future experiments are adequate for a real communication network.
Automated reading passage generation with OpenAI's large language model

The widespread usage of computer-based assessments and individualized learning platforms has increased demand for the rapid production of high-quality items. Automated item generation (AIG), the process of using item models to generate new items with the help of computer technology, was proposed to reduce reliance on human subject experts. While AIG has been used in test development, recent advances in machine learning algorithms offer the potential to enhance its efficiency further. This paper presents an innovative approach utilizing OpenAI's latest transformer-based language model, GPT-3, to generate reading passages. Existing reading passages were used in carefully engineered prompts to ensure the AI-generated text has similar content and structure to a fourth-grade reading passage. Multiple passages were generated for each prompt, and the final passage was selected based on Lexile score agreement with the original passage. To ensure accuracy, a human editor conducted a simple revision of the chosen passage, correcting any grammatical and factual errors. To evaluate the effectiveness of the AI-generated passages, human judges assessed their coherence and appropriateness for fourth-grade readers. The results indicated that GPT-3-produced passages closely resembled human-authored passages regarding coherence, appropriateness, and readability for the target audience. By combining GPT-3's capabilities with carefully designed prompts and human editing, this study demonstrates an efficient and effective method for generating reading passages. The findings highlight the potential of incorporating large language models into automated item generation, contributing to improved scalability and quality in educational assessment development.
The Improved Wavelet Denoising Scheme Based on Robust Principal Component Analysis for Distributed Fiber Acoustic Sensor

Aiming at the problems of large background noise of acoustic signals collected by distributed fiber acoustic sensor (DAS) system and unsatisfactory noise filtering effect of conventional filtering methods, in the study, an improved wavelet denoising scheme based on robust principal component analysis (RPCA) is proposed. The acoustic signal collected by DAS is first separated by the RPCA algorithm, and then the separated noisy signal is denoised by combining the improved wavelet threshold algorithm with a low-pass filter. The test results show that the proposed scheme can improve the signal-to-noise ratio (SNR) by 12–22 dB, and the perceptual evaluation of speech quality (PESQ) and correlation coefficient (CC) by 0.6–0.9 and 0.44–0.68 respectively, which can effectively improve the acoustic signal quality. The proposed scheme can accurately restore acoustic signals in noisy environments, which is of great significance in the fields of acoustic communication, recognition, and military reconnaissance.
StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis

Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for realistic image synthesis. While training and evaluating GAN becomes increasingly important, the current GAN research ecosystem does not provide reliable benchmarks for which the evaluation is conducted consistently and fairly. Furthermore, because there are few validated GAN implementations, researchers devote considerable time to reproducing baselines. We study the taxonomy of GAN approaches and present a new open-source library named StudioGAN. StudioGAN supports 7 GAN architectures, 9 conditioning methods, 4 adversarial losses, 12 regularization modules, 3 differentiable augmentations, 7 evaluation metrics, and 5 evaluation backbones. With our training and evaluation protocol, we present a large-scale benchmark using various datasets (CIFAR10, ImageNet, AFHQv2, FFHQ, and Baby/Papa/Granpa-ImageNet) and 3 different evaluation backbones (InceptionV3, SwAV, and Swin Transformer). Unlike other benchmarks used in the GAN community, we train representative GANs, including BigGAN and StyleGAN series in a unified training pipeline and quantify generation performance with 7 evaluation metrics. The benchmark evaluates other cutting-edge generative models (e.g., StyleGAN-XL, ADM, MaskGIT, and RQ-Transformer). StudioGAN provides GAN implementations, training, and evaluation scripts with the pre-trained weights. StudioGAN is available at https://github.com/POSTECH-CVLab/PyTorch-StudioGAN.
FsaNet: Frequency Self-Attention for Semantic Segmentation

Considering the spectral properties of images, we propose a new self-attention mechanism with highly reduced computational complexity, up to a linear rate. To better preserve edges while promoting similarity within objects, we propose individualized processes over different frequency bands. In particular, we study a case where the process is merely over low-frequency components. By ablation study, we show that low frequency self-attention can achieve very close or better performance relative to full frequency even without retraining the network. Accordingly, we design and embed novel plug-and-play modules to the head of a CNN network that we refer to as FsaNet. The frequency self-attention 1) requires only a few low frequency coefficients as input, 2) can be mathematically equivalent to spatial domain self-attention with linear structures, 3) simplifies token mapping ( 1×1 convolution) stage and token mixing stage simultaneously. We show that frequency self-attention requires 87.29% ~ 90.04% less memory, 96.13% ~ 98.07% less FLOPs, and 97.56% ~ 98.18% in run time than the regular self-attention. Compared to other ResNet101-based self-attention networks, FsaNet achieves a new state-of-the-art result (83.0% mIoU) on Cityscape test dataset and competitive results on ADE20k and VOCaug. FsaNet can also enhance MASK R-CNN for instance segmentation on COCO. In addition, utilizing the proposed module, Segformer can be boosted on a series of models with different scales, and Segformer-B5 can be improved even without retraining. Code is accessible at https://github.com/zfy-csu/FsaNet.
Underwater image enhancement method based on denoising diffusion probabilistic model

Underwater images often suffer from severe distortion, which seriously affects the image quality and the application. Current underwater image enhancement methods have poor generalization capability and cannot be adapted to all types of underwater images. In recent years, the diffusion model based on the denoising diffusion probabilistic model (DDPM) has achieved excellent results in various fields of computer vision. Inspired by the DDPM, an underwater image enhancement method based on the DDPM (UW-DDPM) was proposed in this paper. The UW-DDPM trained on paired datasets and utilized two U-Net networks to complete image denoising as well as image distribution transformation, which effectively improved the quality of underwater images. By testing on real underwater image datasets, UW-DDPM achieved better improvement in visual effects and evaluation metrics than the existing model.
An improved binary quantum-behaved particle swarm optimization algorithm for knapsack problems

The 0-1 knapsack problem is a typical NP-hard combinatorial optimization problem that is difficult to solve efficiently based on traditional optimization approaches. Binary quantum-behaved particle swarm optimization (BQPSO) algorithm is a simple yet efficient discrete optimization approach since it guarantees global search. However, BQPSO suffers from local optimum and slow convergence problems due to aggregation among particles, limiting its performance. To address this problem, an improved BQPSO (IBQPSO) algorithm is proposed to effectively solve the 0-1 knapsack problem. First, to address the discretization issues in existing QPSO algorithms, a mapping strategy based on the average position of all particles is introduced. Then, the transfer function is used to discretize the local attractors into binary vectors for crossover operations, which can guide individuals in the discrete space to the optimum. In addition, a new repair method is employed to address infeasible solutions, and a diversity maintenance mechanism is developed in IBQPSO to alleviate the local optima problem. The proposed algorithm is compared with ten state-of-the-art heuristic algorithms on a set of 0-1 knapsack problems with different scales. Experimental results show that IBQPSO has obvious superiority over the other ten algorithms in terms of convergence speed and search performance.
A constrained multiobjective differential evolution algorithm based on the fusion of two rankings

The tradeoff between objective functions and constraints is a key issue that needs to be addressed by constrained multiobjective optimization algorithms, and constraint handling techniques (CHTs) are an important technique for balancing objective functions and constraints. In this paper, a novel CHT that fuses two rankings is proposed. Specifically, each individual is assigned two rankings: one ranking calculated based on Pareto dominance (regardless of constraints) and another calculated based on the constrained dominance principle (CDP). The fitness value of an individual is the weighted sum of these two rankings, and the weight is related to the generation number and the proportion of feasible solutions in the current generation. Based on the proposed CHT, a constrained multiobjective differential evolution algorithm is proposed. To generate high-quality offspring, the proposed constrained multiobjective differential evolution algorithm combines four mutation operations as core components of the search algorithm. The proposed algorithm is compared with eight state-of-the-art algorithms in experiments with five test suites, and the experimental results show that the proposed algorithm performs significantly better than the eight state-of-the-art algorithms.
DocParser: End-to-end OCR-Free Information Extraction from Visually Rich Documents

Information Extraction from visually rich documents is a challenging task that has gained a lot of attention in recent years due to its importance in several document-control based applications and its widespread commercial value. The majority of the research work conducted on this topic to date follow a two-step pipeline. First, they read the text using an off-the-shelf Optical Character Recognition (OCR) engine, then, they extract the fields of interest from the obtained text. The main drawback of these approaches is their dependence on an external OCR system, which can negatively impact both performance and computational speed. Recent OCR-free methods were proposed to address the previous issues. Inspired by their promising results, we propose in this paper an OCR-free end-to-end information extraction model named DocParser. It differs from prior end-to-end approaches by its ability to better extract discriminative character features. DocParser achieves state-of-the-art results on various datasets, while still being faster than previous works.
American social media on the Russia-Ukraine war: A multimodal analysis

This research focuses on political narrative about the Russia-Ukraine war in American social media. It aims to investigate the type and configuration of multimodal semiotic resources used to present the events of this war and shape their perception by the audience, primarily American; The New York Times Telegram channel coverage of the 2022 Russian invasion as a case study. What kind of semiotic resources do American social media deploy to construct the narrative about the war? What are the dominant post formats, how they deploy information about the Russia-Ukraine war and construe its perception: the concepts of the war, of Ukraine as a victim of aggression and a fighter for independence, of Russia as a terrorist-state, and of their allies and opponents? Theoretically, this study is underpinned by a cognitive-semiotic, political linguistic and multimodal approach. In political media narrative, storytelling shapes perception of war realities: it frames and reframes the readers’ construal of the world, ascribes connotations to facts and events. A multimodal narrative involves different semiotic resources—verbal, graphic, audial, and cinematographic (in video). The configuration of these heterogeneous signs in a particular Telegram channel’s post defines its influence as predominantly rational or emotional. The findings show that the following post formats are typical for The New York Times Telegram channel: image followed by text and link to another site (the most frequent); gallery (a gallery of images) and quote (a quotation with its source/author as the title) (less frequent); video and audio (rarely used). They serve to accentuate, elaborate, and extend the information in the post. I argue that Telegram posts together with users’ emoji-reactions are a specific interactive genre of a multimodal political discourse. Emojis realize both pragmatic and cognitive functions: on the one hand, they are effective instruments to measure users’ reaction; on the other, they contribute to meaning-making by evaluating and making salient the concepts described. The dominance of rational and verified information in The New York Times Telegram channel creates the true image of Russia’s genocidal war against Ukraine and provides a fair assessment of related concepts.
Shadow estimation of gate-set properties from random sequences

With quantum computing devices increasing in scale and complexity, there is a growing need for tools that obtain precise diagnostic information about quantum operations. However, current quantum devices are only capable of short unstructured gate sequences followed by native measurements. We accept this limitation and turn it into a new paradigm for characterizing quantum gate-sets. A single experiment-random sequence estimation-solves a wealth of estimation problems, with all complexity moved to classical post-processing. We derive robust channel variants of shadow estimation with close-to-optimal performance guarantees and use these as a primitive for partial, compressive and full process tomography as well as the learning of Pauli noise. We discuss applications to the quantum gate engineering cycle, and propose novel methods for the optimization of quantum gates and diagnosing cross-talk.
Unsupervised domain adaptation for nuclei segmentation: Adapting from hematoxylin &amp; eosin stained slides to immunohistochemistry stained slides using a curriculum approach

Unsupervised domain adaptation (UDA) is a powerful approach in tackling domain discrepancies and reducing the burden of laborious and error-prone pixel-level annotations for instance segmentation. However, the domain adaptation strategies utilized in previous instance segmentation models pool all the labeled/detected instances together to train the instance-level GAN discriminator, which neglects the differences among multiple instance categories. Such pooling prevents UDA instance segmentation models from learning categorical correspondence between source and target domains for accurate instance classification; METHODS: To tackle this challenge, we propose an Instance Segmentation CycleGAN (ISC-GAN) algorithm for UDA multiclass-instance segmentation. We conduct extensive experiments on the multiclass nuclei recognition task to transfer knowledge from hematoxylin and eosin to immunohistochemistry stained pathology images. Specifically, we fuse CycleGAN with Mask R-CNN to learn categorical correspondence with image-level domain adaptation and virtual supervision. Moreover, we utilize Curriculum Learning to separate the learning process into two steps: (1) learning segmentation only on labeled source data, and (2) learning target domain segmentation with paired virtual labels generated by ISC-GAN. The performance was further improved through experiments with other strategies, including Shared Weights, Knowledge Distillation, and Expanded Source Data.Comparing to the baseline model or the three UDA instance detection and segmentation models, ISC-GAN illustrates the state-of-the-art performance, with 39.1% average precision and 48.7% average recall. The source codes of ISC-GAN are available at https://github.com/sdw95927/InstanceSegmentation-CycleGAN.ISC-GAN adapted knowledge from hematoxylin and eosin to immunohistochemistry stained pathology images, suggesting the potential for reducing the need for large annotated pathological image datasets in deep learning and computer vision tasks.