Reviewerly/exHarmony huggingface dataset:
Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning

Despite the recent developments in the field of cross-modal retrieval, there has been less research focusing on low-resource languages due to the lack of manually annotated datasets. In this paper, we propose a noise-robust cross-lingual cross-modal retrieval method for low-resource languages. To this end, we use Machine Translation (MT) to construct pseudo-parallel sentence pairs for low-resource languages. However, as MT is not perfect, it tends to introduce noise during translation, rendering textual embeddings corrupted and thereby compromising the retrieval performance. To alleviate this, we introduce a multi-view self-distillation method to learn noise-robust target-language representations, which employs a cross-attention module to generate soft pseudo-targets to provide direct supervision from the similarity-based view and feature-based view. Besides, inspired by the back-translation in unsupervised MT, we minimize the semantic discrepancies between origin sentences and back-translated sentences to further improve the noise robustness of the textual encoder. Extensive experiments are conducted on three video-text and image-text cross-modal retrieval benchmarks across different languages, and the results demonstrate that our method significantly improves the overall performance without using extra human-labeled data. In addition, equipped with a pre-trained visual encoder from a recent vision and language pre-training framework, i.e., CLIP, our model achieves a significant performance gain, showing that our method is compatible with popular pre-training models. Code and data are available at https://github.com/HuiGuanLab/nrccr.
Breast lesion detection using an anchor-free network from ultrasound images with segmentation-based enhancement

The survival rate of breast cancer patients is closely related to the pathological stage of cancer. The earlier the pathological stage, the higher the survival rate. Breast ultrasound is a commonly used breast cancer screening or diagnosis method, with simple operation, no ionizing radiation, and real-time imaging. However, ultrasound also has the disadvantages of high noise, strong artifacts, low contrast between tissue structures, which affect the effective screening of breast cancer. Therefore, we propose a deep learning based breast ultrasound detection system to assist doctors in the diagnosis of breast cancer. The system implements the automatic localization of breast cancer lesions and the diagnosis of benign and malignant lesions. The method consists of two steps: 1. Contrast enhancement of breast ultrasound images using segmentation-based enhancement methods. 2. An anchor-free network was used to detect and classify breast lesions. Our proposed method achieves a mean average precision (mAP) of 0.902 on the datasets used in our experiment. In detecting benign and malignant tumors, precision is 0.917 and 0.888, and recall is 0.980 and 0.963, respectively. Our proposed method outperforms other image enhancement methods and an anchor-based detection method. We propose a breast ultrasound image detection system for breast cancer detection. The system can locate and diagnose benign and malignant breast lesions. The test results on single dataset and mixed dataset show that the proposed method has good performance.
Towards generalized morphing attack detection by learning residuals

Face recognition systems (FRS) are vulnerable to different kinds of attacks. Morphing attack combines multiple face images to obtain a single face image that can verify equally against all contributing subjects. Various Morphing Attack Detection (MAD) algorithms have been proposed in recent years albeit limited generalizability. We present a new approach for MAD in this work with better generalization than state-of-the-art (SOTA) algorithms. We propose an end-to-end multi-stage encoder-decoder network for learning the residuals of morphing process to detect attacks. Leveraging the residuals, we learn an efficient classifier using cross-entropy loss and asymmetric loss. The use of asymmetric loss in our approach is motivated by imbalanced distribution of morphs and bona fides. An extensive set of experiments are conducted on five different datasets consisting of two landmark based and three Generative Adversarial Network (GAN) based morphs in various settings such as digital, print-scan and print-scan-compression. We first demonstrate a near-ideal performance of the proposed MAD with Detection Equal Error Rate (D-EER) of 0% in the best case and 2.58% in the worst case in the digital domain in closed-set protocol, i.e., known attacks. Further, we demonstrate the applicability of the proposed approach on 60 different combinations where the testing set contains unknown morphing attacks in open-set protocol to illustrate the generalization ability of our proposed approach. Through training the proposed approach on landmark-based morph generation data alone, we obtain an EER of 3.59% in the best case and 12.89% in the worst case for morphed images in the digital domain, reducing the error rates from 45.67% and 30.23% respectively, in open-set protocol. We further present an extensive analysis of the proposed approach through Class Activation Maps (CAM) to explain the decisions using by making use of three complementary CAM analysis.
Fuzzy Semantics for Arbitrary-Shaped Scene Text Detection

To robustly detect arbitrary-shaped scene texts, bottom-up methods are widely explored for their flexibility. Due to the highly homogeneous texture and cluttered distribution of scene texts, it is nontrivial for segmentation-based methods to discover the separatrixes between adjacent instances. To effectively separate nearby texts, many methods adopt the seed expansion strategy that segments shrunken text regions as seed areas, and then iteratively expands the seed areas into intact text regions. In seek of a more straightforward way that does not rely on seed area segmentation and avoid possible error accumulation brought by iterative processing, we propose a redundancy removal strategy. In this work, we directly explore two types of fuzzy semantics-text and separatrix-that do not possess specific boundaries, and separate cluttered instances by excluding the separatrix pixels from text regions. To deal with the fuzzy semantic boundaries, we also conduct reliability analysis in both optimization and inference stage to suppress false positive pixels at ambiguous locations. Experiments on benchmark datasets demonstrate the effectiveness of our method.
Development and Evaluation of a Novel Deep-Learning-Based Framework for the Classification of Renal Histopathology Images

Kidney cancer has several types, with renal cell carcinoma (RCC) being the most prevalent and severe type, accounting for more than 85% of adult patients. The manual analysis of whole slide images (WSI) of renal tissues is the primary tool for RCC diagnosis and prognosis. However, the manual identification of RCC is time-consuming and prone to inter-subject variability. In this paper, we aim to distinguish between benign tissue and malignant RCC tumors and identify the tumor subtypes to support medical therapy management. We propose a novel multiscale weakly-supervised deep learning approach for RCC subtyping. Our system starts by applying the RGB-histogram specification stain normalization on the whole slide images to eliminate the effect of the color variations on the system performance. Then, we follow the multiple instance learning approach by dividing the input data into multiple overlapping patches to maintain the tissue connectivity. Finally, we train three multiscale convolutional neural networks (CNNs) and apply decision fusion to their predicted results to obtain the final classification decision. Our dataset comprises four classes of renal tissues: non-RCC renal parenchyma, non-RCC fat tissues, clear cell RCC (ccRCC), and clear cell papillary RCC (ccpRCC). The developed system demonstrates a high classification accuracy and sensitivity on the RCC biopsy samples at the slide level. Following a leave-one-subject-out cross-validation approach, the developed RCC subtype classification system achieves an overall classification accuracy of 93.0% ± 4.9%, a sensitivity of 91.3% ± 10.7%, and a high classification specificity of 95.6% ± 5.2%, in distinguishing ccRCC from ccpRCC or non-RCC tissues. Furthermore, our method outperformed the state-of-the-art Resnet-50 model.
An Enhanced Context-based Emotion Detection Model using RoBERTa

Emotions are integral in conveying information in a particular context. For example, the most basic questions can have multiple answers, and the only way to pinpoint the correct answer for a particular question is by understanding the proper context behind it. Therefore, emotion detection plays a crucial part in a deeper understanding of the subject, whether in conversation or text. In this paper, the proposed approach EmoRoBERTa is an attempt to build a more robust emotion detection model, which can be implemented in various NLP tasks such as semantic and propaganda analysis, that involve the heavy usage of emotions. The approach involves combining a pretrained, fine-tuned RoBERTa model, and fitting it on a GoEmotions dataset, which includes twenty-seven different emotions and a neutral one, the most detailed taxonomy currently present. The model was then tested on three different emotion taxonomies and rendered desirable results, a higher MARCO-F1 score than the state-of-the-art model currently being used.
A Virtual Reality Study Investigating the Effect of Cybersickness on the Relationship Between Vection and Presence Across Environments with Varying Levels of Ecological Relevance

In the absence of physical motion, people sometimes experience the illusory sensation of self-motion which is known as vection. Vection research could contribute to the improvement of the fidelity of simulators as vection and presence appear to be positively related. However, when utilizing virtual reality technology for simulators, visually-induced motion sickness (VIMS) in the form of Cybersickness (CS) sometimes co-occurs with the experience of vection. Nonetheless, the relationship between vection and CS is not evident. Past research mainly investigated the relationship between the vection and CS using environments with a certain level of ecological relevance. Herein we investigated whether CS negatively affects the relationship between vection and presence across different virtual environments with varying levels of ecological relevance. We immersed twenty-nine participants visually and audibly in virtual environments and after each trial participants reported their vection intensity, CS, and presence. Our results showed that the relationship between vection intensity and presence was unaffected by CS. We conclude that the relationship between vection and presence is unaffected by CS with low levels of discomfort.
PinchText: One-Handed Text Entry Technique Combining Pinch Gestures and Hand Positions for Head-Mounted Displays

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3D Cascade RCNN: High Quality Object Detection in Point Clouds

Recent progress on 2D object detection has featured Cascade RCNN, which capitalizes on a sequence of cascade detectors to progressively improve proposal quality, towards high-quality object detection. However, there has not been evidence in support of building such cascade structures for 3D object detection, a challenging detection scenario with highly sparse LiDAR point clouds. In this work, we present a simple yet effective cascade architecture, named 3D Cascade RCNN, that allocates multiple detectors based on the voxelized point clouds in a cascade paradigm, pursuing higher quality 3D object detector progressively. Furthermore, we quantitatively define the sparsity level of the points within 3D bounding box of each object as the point completeness score, which is exploited as the task weight for each proposal to guide the learning of each stage detector. The spirit behind is to assign higher weights for high-quality proposals with relatively complete point distribution, while down-weight the proposals with extremely sparse points that often incur noise during training. This design of completeness-aware re-weighting elegantly upgrades the cascade paradigm to be better applicable for the sparse input data, without increasing any FLOP budgets. Through extensive experiments on both the KITTI dataset and Waymo Open Dataset, we validate the superiority of our proposed 3D Cascade RCNN, when comparing to state-of-the-art 3D object detection techniques. The source code is publicly available at https://github.com/caiqi/Cascasde-3D.
DFSNE-Net: Deviant feature sensitive noise estimate network for low-dose CT denoising

Computed tomography (CT) has radiation problems, and high-quality CT scanning is usually accompanied by high doses of radiation that can be harmful to humans, so low-dose CT denoising has received extensive academic attention.In this paper, firstly, the concept of deviant features is proposed which lead to the hypothesis of correlation between noise and deviant features. Secondly, in order to estimate the noise in CT, a new method of deviant feature perception and downsampling is proposed. Specifically, the deviant feature perception module based on the multi-scale convolutional cooperative (MSC-DFPM) and Filtering module based on the self-information space attention(SISA-FM) are proposed, and construct the deviant feature sensitive noise estimate network (DFSNE-Net), then a balanced loss function and training strategy adapted to the DFSNE-Net are proposed. Finally, noise distribution normalization based on skewness and kurtosis(SK-NDN) and low credible noise suppression based on confidence interval(CI-LCNS) as the noise optimization methods are proposed to optimize the estimated noise of DFSNE-Net, which is applied to the denoising task of CT.Experiments and results demonstrate that our proposed denoising method in this paper obtains a better denoising effect than the current state-of-the-art denoising methods in different evaluation indicator, which proves the hypothesis that noise is strongly correlated with deviant features. It is also proved that the denoising method proposed in this paper can achieve the denoising task for different doses of CT.
Base-2 Softmax Function: Suitability for Training and Efficient Hardware Implementation

The softmax function is widely used in deep neural networks (DNNs), its hardware performance plays an important role in the training and inference of DNN accelerators. However, due to the complexity of the traditional softmax, the existing hardware architectures are resource-consuming or have low precision. In order to address the challenges, we study a base-2 softmax function in terms of its suitability for neural network training and efficient hardware implementation. Compared to the classical base- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula> softmax function, the base-2 softmax function is a new softmax function that uses 2 as the exponential base instead of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula> . From the aspects of mathematical derivation and software simulation, we first demonstrate the feasibility and good accuracy of the base-2 softmax function in the application of neural network training. Then, we use the symmetric-mapping lookup table (SM-LUT) method to design a low-complexity architecture but with high precision to implement it. Under TSMC 28nm CMOS technology, an example design of our architecture has the area of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5676 ~\mu m^{2}$ </tex-math></inline-formula> and the power consumption of 13.12 mW for circuit synthesis at the frequency of 3 GHz. Compared with the latest works, our architecture achieves the best performance and efficiency.
Monte-Carlo Robot Path Planning

Path planning is a crucial algorithmic approach for designing robot behaviors. Sampling-based approaches, like rapidly exploring random trees (RRTs) or probabilistic roadmaps, are prominent algorithmic solutions for path planning problems. Despite its exponential convergence rate, RRT can only find suboptimal paths. On the other hand, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text {RRT}^*$</tex-math></inline-formula> , a widely-used extension to RRT, guarantees probabilistic completeness for finding optimal paths but suffers in practice from slow convergence in complex environments. Furthermore, real-world robotic environments are often partially observable or with poorly described dynamics, casting the application of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text {RRT}^*$</tex-math></inline-formula> in complex tasks suboptimal. This letter studies a novel algorithmic formulation of the popular Monte-Carlo tree search (MCTS) algorithm for robot path planning. Notably, we study Monte-Carlo Path Planning (MCPP) by analyzing and proving, on the one part, its exponential convergence rate to the optimal path in fully observable Markov decision processes (MDPs), and on the other part, its probabilistic completeness for finding feasible paths in partially observable MDPs (POMDPs) assuming limited distance observability (proof sketch). Our algorithmic contribution allows us to employ recently proposed variants of MCTS with different exploration strategies for robot path planning. Our experimental evaluations in simulated 2D and 3D environments with a 7 degrees of freedom (DOF) manipulator, as well as in a real-world robot path planning task, demonstrate the superiority of MCPP in POMDP tasks.
Fully integrated four-channel wavelength-division multiplexed QKD receiver

Quantum key distribution (QKD) enables secure communication even in the presence of advanced quantum computers. However, scaling up discrete-variable QKD to high key rates remains a challenge due to the lossy nature of quantum communication channels and the use of weak coherent states. Photonic integration and massive parallelization are crucial steps toward the goal of high-throughput secret-key distribution. We present a fully integrated photonic chip on silicon nitride featuring a four-channel wavelength-division demultiplexed QKD receiver circuit including state-of-the-art waveguide-integrated superconducting nanowire single-photon detectors (SNSPDs). With a proof-of-principle setup operated at a clock rate of 3.35 GHz, we achieve a total secret-key rate of up to 12.17 Mbit/s at 10 dB channel attenuation with low detector-induced error rates. The QKD receiver architecture is massively scalable and constitutes a foundation for high-rate many-channel QKD transmission.
Multiple Mechanisms to Strengthen the Ability of YOLOv5s for Real-Time Identification of Vehicle Type

Identifying the type of vehicle on the road is a challenging task, especially in the natural environment with all its complexities, such that the traditional architecture for object detection requires an excessively large amount of computation. Such lightweight networks as MobileNet are fast but cannot satisfy the performance-related requirements of this task. Improving the detection-related performance of small networks is, thus, an outstanding challenge. In this paper, we use YOLOv5s as the backbone network to propose a large-scale convolutional fusion module called the ghost cross-stage partial network (G_CSP), which can integrate large-scale information from different feature maps to identify vehicles on the road. We use the convolutional triplet attention network (C_TA) module to extract attention-based information from different dimensions. We also optimize the original spatial pyramid pooling fast (SPPF) module and use the dilated convolution to increase the capability of the network to extract information. The optimized module is called the DSPPF. The results of extensive experiments on the bdd100K, VOC2012 + 2007, and VOC2019 datasets showed that the improved YOLOv5s network performs well and can be used on mobile devices in real time.
Microservices Configurations and the Impact on the Performance in Cloud Native Environments

Cloud-native rethinks the application architecture by embracing a micro-service approach, where each microservice is packaged into containers to run in a centralized or an edge cloud. When deploying the container running the micro-service, the tenant has to specify the amount of CPU and memory limit to run their workload. However, it is not straightforward for a tenant to know in advance the computing amount that allows running the microservice optimally. This will impact the service performances and the infrastructure provider, particularly if the resource overprovisioning approach is used. To overcome this issue, we conduct in this paper an experimental study aiming to detect if a tenant’s configuration allows running its service optimally. We run several experiments on a cloud-native platform, using different types of applications under different resource configurations. The obtained results provide insights on how to detect and correct performance degradation due to misconfiguration of the service resource.
C<sup>2</sup>‐GAN: Content‐consistent generative adversarial networks for unsupervised domain adaptation in medical image segmentation

In clinical practice, medical image analysis has played a key role in disease diagnosis. One of the important steps is to perform an accurate organ or tissue segmentation for assisting medical professionals in making correct diagnoses. Despite the tremendous progress in the deep learning-based medical image segmentation approaches, they often fail to generalize to test datasets due to distribution discrepancies across domains. Recent advances aligning the domain gaps by using bi-directional GANs (e.g., CycleGAN) have shown promising results, but the strict constraints of the cycle consistency hamper these methods from yielding better performance. The purpose of this study is to propose a novel bi-directional GAN-based segmentation model with fewer constraints on the cycle consistency to improve the generalized segmentation results.We propose a novel unsupervised domain adaptation approach by designing content-consistent generative adversarial networks ( C2-GAN$\text{C}^2\text{-GAN}$ ) for medical image segmentation. First, we introduce content consistency instead of cycle consistency to relax the constraint of the invertibility map to encourage the synthetic domain generated with a large domain transportation distance. The synthetic domain is thus pulled close to the target domain for the reduction of domain discrepancy. Second, we suggest a novel style transfer loss based on the difference in low-frequency magnitude to further mitigate the appearance shifts across domains.We validate our proposed approach on three public X-ray datasets, including the Montgomery, JSRT, and Shenzhen datasets. For an accurate evaluation, we randomly divided the images of each dataset into 70% for training, 10% for evaluation, and 20% for testing. The mean Dice was 95.73 ± 0.22%, 95.16 ± 1.42% for JSRT and Shenzhen datasets, respectively. For the recall and precision metrics, our model also achieved better or comparable performance than the state-of-the-art CycleGAN-based UDA approaches.The experimental results validate the effectiveness of our method in mitigating the domain gaps and improving generalized segmentation results for X-ray image segmentation.