Reviewerly/exHarmony huggingface dataset:
Deep-Learning Based Super-Resolution for Low-Dose CT

Recently we proposed a texture transformer network for the image super-resolution (TTSR), a reference-based deep learning super-resolution method, to improve the spatial resolution and suppress the noise of computed tomography (CT) images, where its effectiveness was demonstrated using the XCAT phantom. In this work, we focus on developing TTSR for real patient CT data. The mapping from the noisy low-resolution CT (LRCT) images to the clean high-resolution CT (HRCT) images is learned through TTSR. First, we iteratively reconstruct HRCT (full-dose, full projection data, 512x512 image resolution) and LRCT (quarter dose, 1/4 of all projection data, 128x128 image resolution) images using the CT projection data from 10 patients. Then, the TTSR models are trained using LRCT and HRCT images from nine patients to establish the translation from LRCT images to HRCT images. Finally, the SR model performance is quantitatively evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using the test patient images. Cubic spline interpolation and a generative adversarial network (GAN) with cycle consistency ("GAN-CIRCLE") are used for comparison. TTSR can restore more details in SRCT images than interpolation and GAN-CIRCLE. The quantitative results of PSNR and SSIM also show that TTSR can achieve the best performance. We finally show that TTSR using only 1/4 of all projection data can outperform LDCT with advanced denoising using all projection data. This development paves a way for deep learning-based SR for CT with lessened requirement on the number of detectors and much reduced computation time.
Developing interpretable models and techniques for explainable AI in decision-making

The rapid proliferation of artificial intelligence (AI) technologies across various industries and decision-making processes has undeniably transformed the way of approaching complex problems and tasks. AI systems have proven their prowess in areas such as healthcare, finance, and autonomous systems, revolutionizing how decisions are made. Nevertheless, this proliferation of AI has raised critical concerns regarding the transparency, accountability, and fairness of these systems, as many of the state-of-the-art AI models often resemble complex black boxes. These intricate models, particularly deep learning neural networks, harbor non-linear relationships that are difficult for human users to decipher, thereby raising concerns about bias, fairness, and overall trustworthiness in AI-driven decisions. The urgency of this issue is underscored by the realization that AI should not merely be accurate; it should also be interpretable. Explainable AI (XAI) has emerged as a vital field of research, emphasizing the development of models and techniques that render AI systems comprehensible and transparent in their decision-making processes. This paper investigates into the relevance and significance of XAI across various domains, including healthcare, finance, and autonomous systems, where the ability to understand the rationale behind AI decisions is paramount. In healthcare, where AI assists in diagnosis and treatment, the interpretability of AI models is crucial for clinicians to make informed decisions. In finance, applications like credit scoring and investment analysis demand transparent AI to ensure fairness and accountability. In the realm of autonomous systems, transparency is indispensable to guarantee safety and compliance with regulations. Moreover, government agencies in areas such as law enforcement and social services require interpretable AI to maintain ethical standards and accountability. This paper also highlights the diverse array of research efforts in the XAI domain, spanning from model-specific interpretability methods to more general approaches aimed at unveiling complex AI models. Interpretable models like decision trees and rule-based systems have gained attention for their inherent transparency, while integrating explanation layers into deep neural networks strives to balance accuracy with interpretability. The study emphasizes the significance of this burgeoning field in bridging the gap between AI's advanced capabilities and human users' need for comprehensible AI systems. It seeks to contribute to this field by exploring the design, development, and practical applications of interpretable AI models and techniques, with the ultimate goal of enhancing the trust and understanding of AI-driven decisions.
Exploring advancements in deep learning for natural language processing tasks

This literature survey explores the transformative influence of deep learning on Natural Language Processing (NLP), revealing a dynamic interplay between these fields. Deep learning techniques, characterized by neural network architectures, have propelled NLP into a realm where machines not only comprehend but also generate human language. The survey covers various NLP applications, such as sentiment analysis, machine translation, text summarization, question answering, and speech recognition, scasing significant strides attributed to deep learning models like Transformer, BERT, GPT, and attention-based Sequence-to-Sequence models. These advancements have redefined the landscape of NLP tasks, setting new benchmarks for performance. ever, challenges persist, including limited data availability in certain languages, increasing model sizes, and ethical considerations related to bias and fairness. Overcoming these hurdles requires innovative approaches for data scarcity, the development of computationally efficient models, and a focus on ethical practices in research and application. This survey provides a comprehensive overview of the progress and obstacles in integrating deep learning with NLP, offering a roadmap for navigating this evolving domain.
Contrastive Learning for Ship Classification Using Real and Complex SAR Imagery

We explore the potential of contrastive learning for the classification of ships using real and complex Synthetic Aperture Radar (SAR) modalities. We employ a network that takes SAR images as input and learns a latent space for the classification of maritime objects, fishing and non-fishing vessels. In contrast with the prior work on SAR data, the model presented in this paper can operate entirely in the complex domain. We leverage existing work on the complex analogs of real-valued input, processing and output layers to deliver a contrastive learning approach that is capable of working with data that is naturally represented in the complex domain. We investigate different types of data augmentation that can be applied to real valued Ground Range Detected (GRD) images, complex valued Single Look Complex (SLC) data, and the magnitude component of SLC data and compare relative performance for contrastive learning. Our results on widely available SAR satellite data show our method can operate on complex-valued imagery without compromising accuracy compared to equivalent realvalued baselines.
Stochastic Dominant Cognitive Experience Guided Particle Swarm Optimization

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.
DAAN: A Dictionary-Based Adaptive Attention Network for Biomedical Named Entity Recognition with Chinese Electronic Medical Records

Biomedical named entity recognition (BNER) is a basic task of the extraction of medical information. The existing deep learning-based approaches usually represent the medical text by using words or characters. However, most of biomedical terms consist of many words (characters). Splitting them into many fragments (words or characters) while leveraging the attention mechanism to assign attention scores for each fragment maybe disperse the importance weight and cause a lower attention score for the biomedical terms. Therefore, this paper presents a dictionary-based adaptive attention network for BNER. Specifically, a biomedical dictionary is firstly constructed by integrating multiple existing medical resources. Secondly, building the guidance vectors by matching the electronic medical record (EMR) text to the constructed dictionary. Then, an adaptive attention strategy is presented to guide the attention mechanism to assign higher attention to the overall medical term by using the guidance vectors. We conduct extensive experiments on a real-world dataset, the results illuminate that our presented method outperforms all baselines.
AgreementLoss: A Data Pruning Metric For Stable Performance Over All Pruning Ratio

Data pruning is the method selecting a subset from an entire training dataset while maintaining the performance after training. Most pruning metrics measure the difficulty of each training sample for the target task and select the hardest samples first. The “hardest-first sampling” works well when the pruning ratio is low (the pruned dataset is large). However, while the pruning ratio increases, the performance decreases significantly and get worse than the random pruning. This is because the hardest samples are the special samples of the class, and a model trained on few hardest samples specializes in them and drops its performance. For solving this problem, we propose a new metric: AgreementLoss. This is computed from two classifier models trained on two datasets obtained by the disjoint decomposition of the original dataset. By the dataset decomposition, the predictions of the models are more different for a sample since either of them trains each sample in the original dataset. The metric is defined as the positive or negative loss value of the trained either model, depending on two predictions agree or disagree. This metric represents the difficulty of each sample, while extremely difficult “noisy” samples get lower scores by the negative sign. By selecting samples in descending order of the AgreementLoss scores, we can reduce the priority of the “noisy” samples. Experiments on various datasets show that our method gets stable performance over all pruning ratio and outperforms other metrics especially when the pruning ratio is high.
Truncated Quantile Critics Algorithm for Cryptocurrency Portfolio Optimization

This paper investigates portfolio management algorithm for the cryptocurrency market by using the TQC (Truncated Quantile Critics) algorithm. The study is based on the daily prices of cryptocurrencies. TQC is a deep reinforcement learning algorithm with the Actor-Critic architecture. It alleviates the overestimation problem of traditional value learning algorithm. In this paper, the data of cryptocurrencies are first processed as input to the networks. The inputs to the networks include not only the closing prices of cryptocurrencies, but also the relative strength index, moving average line, and moving average convergence divergence. Various metrics measuring algorithm returns and algorithm stability are used as evaluation criteria in this paper. In this paper, common deep reinforcement learning algorithms are compared. The experimental results show that the TQC algorithm has a highest return of 33.9 % during the test period, which is 3 %, 3 % and 15.6 % higher than A2C, PPO and DDPG respectively. And, the TQC algorithm has the highest stability of return, which is an important evaluation metric for portfolio management algorithms. Despite the high volatility of the cryptocurrency market, the performance of the TQC algorithm has remained relatively stable. This illustrates the positive effects of the TOC algorithm.
Design of a Quantum Self-Attention Neural Network on Quantum Circuits

This paper proposes a quantum self-attention neural network (QSAN) model that can be deployed on quantum circuits, providing a novel avenue to processing text classification tasks in natural language processing (NLP). The QSAN framework is established by integrating four basic blocks: the data preprocessing block, the quantum encoding block, the model design block, and the network optimization block. Simulation results demonstrate remarkable convergence and accuracy on various text classification datasets. In particular, the proposed QSAN surpasses the existing state-of-the-art quantum NLP (QNLP) model in terms of test accuracy.
Developing a Computer-Aided Diagnostic System for Breast Cancer Ultrasound Imaging

In this research we aim to develop a Computer-aided diagnostic system (CAD) for breast ultrasound imaging to enhance the early detection of breast cancer. CAD tools aid physicians in the diagnostic process leading to early detection and treatment of breast cancer. The proposed CAD tool comprises three stages, the first is enhancement of breast ultrasound imaging by applying custom segmentation deep learning architectures [U-Net, SK-U-Net, RDA-U-Net] to enhance the isolation of masses in Ultrasound images, then generation of new ultrasound images by employing Generative Adversarial Networks [DAGAN, DCGAN] to overcome data limitation in available data-sets, and the last stage is the detection and classification of breast cancer masses by a CNN model. The enhancement stage shows promising results with accuracy of 98 % overall accuracy, 92 % overall precision, and 85 % overall sensitivity.Clinical Relevance: Breast cancer is one of the deadly diseases in the world. The earlier the detection the higher the chances of recovery. Thus a CAD tool that facilitates the screening process of ultrasound images will aid physicians in the early detection and diagnosis of the disease.
PTS-LIC: Pruning Threshold Searching for Lightweight Learned Image Compression

Learned Image Compression (LIC), which uses neural networks to compress images, has experienced significant growth in recent years. The hyperprior-module-based LIC model has achieved higher performance than classical codecs. However, the LIC models are too heavy (in calculation and parameter amounts) to apply to edge devices. To solve this problem, some former papers focus on structural pruning for LIC models. However, they either cause noticeable performance decrement or neglect the appropriate pruning threshold for each LIC model. These problems keep their pruning results sub-optimal. This paper proposes a Pruning Threshold Searching on the hyperprior module for different-quality LIC models. Our method removes most parameters and calculations while keeping the performance the same as the models before pruning. We removed at least 49.8% of parameters and 28.5% of calculations for the Channel-Wise-Context-Model-based models and 29.1% of parameters for the Cheng-2020 models.
Toward Intention Discovery for Early Malice Detection in Cryptocurrency

Cryptocurrency's pseudo-anonymous nature makes it vulnerable to malicious activities. However, existing deep learning solutions lack interpretability and only support retrospective analysis of specific malice types. To address these challenges, we propose Intention-Monitor for early malice detection in Bitcoin. Our model, utilizing Decision-Tree based feature Selection and Complement (DT-SC), builds different feature sets for different malice types. The Status Proposal Module (SPM) and hierarchical self-attention predictor provide real-time global status and address label predictions. A survival module determines the stopping point and proposes the status sequence (intention). Our model detects various malicious activities with strong interpretability, outperforming state-of-the-art methods in extensive experiments on three real-world datasets. It also explains existing malicious patterns and identifies new suspicious characteristics through additional case studies.
Capturing Quantitative Data from UI Prototypes for AR and VR Using Online Remote User Testing

As the development of augmented reality (AR) and virtual reality (VR) applications is still limited to those with substantial amounts of technical knowledge, the prototyping and testing of user interface (UI) designs for AR and VR applications remotely proves difficult. Recent tools proposed for prototyping AR/VR applications focus on working toward increased fidelity of prototyping methods, but provide limited ability to easily collect objective quantitative data from user interactions with prototypes, especially in remote settings. In this paper, we present a remote usability study using Adobe XD rapid prototyping software integrated with Maze User Testing Software, that collect data for UI designs for an existing cross-platform software. The results were found by collecting task completion time, misclick rate, and user click-data using heatmaps. We discuss the results of our study, and show that objective quantitative data can be collected for AR/VR prototypes in remote testing settings to provide insightful usability feedback in early stages of interface design.
Pruning Based on Activation Function Outputs for Efficient Neural Networks

Deep Neural Networks (DNNs) are dominant in the field of machine learning. However, because DNN models have large computational complexity, implementation of DNN models on resource-limited equipment is challenging. Therefore, techniques of compressing DNN models without degrading their accuracy is desired. Pruning is one such technique that removes unimportant neurons (or channels). In this paper, we present Pruning with Output Error Minimization (POEM), a method that performs not only pruning but also reconstruction to compensate the error caused by pruning. The strength of POEM lies in its consistent criteria for neuron selection and reconstruction based on output error of the activation function, whereas the previous methods use ad hoc criteria or relax the problem to minimize error before the activation function. The experiments with the well-known DNN models (VGG-16, ResNet-18, MobileNet) and image recognition datasets (ImageNet, CUB-200-2011) were conducted. The results show that POEM significantly outperformed the previous methods in maintaining the accuracy of the compressed models.
Optical-Flow-Aided Self-Supervised Single View Depth Estimation from Monocular Videos

Recent advances in deep-learning-based accurate optical flow (OF) estimation have sparked interest in building hardware dedicated to it, making OF a real-time commodity for downstream computer vision tasks. Relative camera pose, and depth estimations (DE) are closely related to OF, but obtaining one from the others is not a trivial task. While previous works have attempted simultaneous learning of OF and DE, or utilized OF-nets only for camera pose prediction and sparse depth map supervision, we propose OF-aided self-supervised DE, that is, pre-computed OF is used as a network input. One of the main challenges in OF-aided DE is preventing the DE network from learning an incorrect OF magnitude prior, which is valid for rigid regions but breaks for scenes with dynamic objects or static camera sequences. We achieve OF-aided DE by instead transforming the input OF into a 3D surface normals space, which provides a well-informed geometrical input while being invariant to the OF magnitude. In combination with a new training strategy, we show that our models with OF-aided DE achieve state-of-the-art (SOTA) results on the KITTI dataset.
Subjective video quality assessment of immersive HDR content on head-mounted displays

High dynamic range (HDR) videos are known to provide better visual quality on HDR TV displays. Head-mounted displays (HMDs) are an integral part of immersive visual experiences. However, typical HMDs are equipped with standard dynamic range (SDR) displays, failing to show details in bright and dark areas of HDR content. Therefore, we aim to evaluate the perceptual quality improvement, in terms of mean opinion score (MOS), when observers view HDR instead of SDR content on immersive displays. We developed a pipeline to render 2D HDR and tone-mapped SDR immersive videos for HMDs. We conducted two single-stimulus subjective evaluation experiments to evaluate (1) the perceived visual difference when comparing one HDR scene with three tone-mapped SDR versions of it, and (2) how frame rates impact the perceptual quality of HDR immersive videos. Our results in MOS show that (1) there is a significant improvement of perceptual quality in HDR compared to tone-mapped SDR content, and (2) HDR immersive videos benefit much more from higher frame rates than SDR videos.
Overcoming Delayed Feedback via Overlook Decision Making

Reinforcement learning is one of the most general paradigms to solve sequential decision making issues on the assumption that the action selection and environmental feedback are instantaneous, however, unfortunately this assumption is rarely true with regard to such ubiquitous delays in real-world system which could degrade the performance of reinforcement learning algorithms. The most common solution to solve a fixed delay problem is to design a forward dynamic model which is used to predict the newest state by recursively iterating over long steps so that a predicted state can be got and it would be taken as the agent's observation to make the newest decision. However, there exists cumulative errors during the iterative process which make long-term prediction inaccurate and further affect agent's decision. Motivated by the goal to reduce cumulative errors, we propose a new algorithm named Multi-step Prediction model with Delayed Observation(MPDO), aiming at accurately predicting future state at longer horizons for better decision making. Our approach includes two parts: a multi-step prediction model and a strategy training based on proximal policy optimization algorithms(PPO). Our model only needs a small amount of data to conduct dynamic modeling quickly, and the accuracy of prediction and iteration speed are higher than traditional methods. Experiments on Gym and MuJoCo show that MPDO achieves higher performance in such different tasks with different delays compared with other state-of-the-art methods, which verify our method's effectiveness.