Reviewerly/exHarmony huggingface dataset:
A quantum blockchain-enabled framework for secure private electronic medical records in Internet of Medical Things

• Novel quantum blockchain with proprietary data structure and its implementation. • The block hash value stored as one qubit with parallel computing capability. • Quantum electronic medical records guarantee data security and privacy by resisting quantum attacks. There is a rising need for healthcare as a result of rising public awareness of health issues. Electronic medical records include extremely confidential and sensitive information, and blockchain technology can enable the safe exchange of these records across various medical organizations. The current blockchain system is susceptible to quantum computer attacks, nevertheless, as a result of the advent of quantum computers. This research designs a novel distributed quantum electronic medical record system and suggests a new private quantum blockchain network based on security considerations. The blocks in this quantum blockchain’s data structure are linked via entangled states. The time stamp is automatically formed by connecting quantum blocks with controlled activities, which lowers the amount of storage space needed. Each block’s hash value is recorded using just one qubit. The quantum information processing is detailed in depth in the quantum electronic medical record protocol. Every medical record can be tracked, and the security and privacy of electronic medical records in Internet of Medical Things systems can be guaranteed. The protocol also ditches the traditional encryption and digital signature algorithms in favor of a quantum authentication system. According to the mathematical analysis, the quantum blockchain network has strong security against attacks from quantum computers since it can withstand External attack, Intercept-Measure-Repeat attack and Entanglement-Measure attack. The quantum circuit diagram for deriving the hash value is provided, along with the correctness and traceability analysis of the quantum block. The comparison between the proposed quantum blockchain model and a few other quantum blockchain models is also included.
RETRACTED: Transformer-induced graph reasoning for multimodal semantic segmentation in remote sensing

As a large amount of earth observation data is available on a global scale, it becomes possible to apply multimodal semantic segmentation technology to remote sensing scene analysis. However, the diversity of objects in large-scale scenes and the cross-modal gap between different images are still challenging in practical applications. To address these problems, we propose a Transformer-Induced Hierarchical Graph Network (GraFNet) for multimodal semantic segmentation in remote sensing scenes, which promotes the exploration of potential intra- and inter-modal relations by introducing a new modeling paradigm. Different from existing methods, GraFNet parses multimodal remote sensing images into semantic topological graphs, and exploits the structural information of land cover categories to learn joint representations. Specifically, an attentive heterogeneous information aggregation mechanism is presented to parse diverse objects in remote sensing scenes into semantic entities, and capture modality-specific object–object interaction patterns in a topology-aware environment. In addition, modality hierarchical dependency modeling is introduced to encode the interactive representation of cross-modal objects, and distinguish the modality-specific contribution to improve cross-modal compatibility. Extensive experiments on several multimodal remote sensing datasets demonstrate that the proposed GraFNet outperforms the state-of-the-art approaches, achieving F 1 /mIoU accuracy 91.1%/82.4% on the ISPRS Vaihingen dataset, 93.4%/88.4% on ISPRS Potsdam dataset, and 91.8%/84.0% on the MSAW dataset.
High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets

Breast cancer is a significant cause of cancer fatality among women all over the world. Hence the detection of this disease at the initial stage works as a boon to the patient so that proper treatment can be provided. We have developed five new deep hybrid convolutional neural network-based breast cancer detection frameworks in this work. The proposed hybrid schemes exhibit better performance than the respective base classifiers keeping the combined benefits of both the networks. In addition, a probability-based weight factor ( w ) and threshold value ( β ) play a crucial role in making an efficient hybridization. Experimentally selected optimum threshold value ( β ) makes the system faster and more accurate. More importantly, unlike traditional deep learning methods, the proposed framework yields excellent performance even in small datasets. The proposed scheme is validated with datasets of two different breast cancer modalities: mini-DDSM (mammogram), BUSI and BUS2 (ultrasound). The experimental results demonstrate the superiority of the proposed ShuffleNet-ResNet scheme over the current state-of-the-art methods in all the mentioned datasets. Moreover, the proposed scheme achieves the accuracy of 99.17%, 98.00% for abnormality and malignancy detection in mini-DDSM respectively, and 96.52%, 93.18% for abnormality and malignancy detection BUSI datasets, respectively. BUS2 delivers 98.13% accuracy for malignancy detection. • Five new hybrid CNN frameworks have been proposed for breast cancer detection. • The combined benefits of classifier 1 and classifier 2 improve performance. • Emphasis is given to both abnormality and malignancy detection. • The efficacy of the system is validated using mammogram and ultrasound databases. • Moreover, the proposed ShuffleNet-ResNet framework gives the best performance.
Feature Aggregation and Propagation Network for Camouflaged Object Detection

Camouflaged object detection (COD) aims to detect/segment camouflaged objects embedded in the environment, which has attracted increasing attention over the past decades. Although several COD methods have been developed, they still suffer from unsatisfactory performance due to the intrinsic similarities between the foreground objects and background surroundings. In this paper, we propose a novel Feature Aggregation and Propagation Network (FAP-Net) for camouflaged object detection. Specifically, we propose a Boundary Guidance Module (BGM) to explicitly model the boundary characteristic, which can provide boundary-enhanced features to boost the COD performance. To capture the scale variations of the camouflaged objects, we propose a Multi-scale Feature Aggregation Module (MFAM) to characterize the multi-scale information from each layer and obtain the aggregated feature representations. Furthermore, we propose a Cross-level Fusion and Propagation Module (CFPM). In the CFPM, the feature fusion part can effectively integrate the features from adjacent layers to exploit the cross-level correlations, and the feature propagation part can transmit valuable context information from the encoder to the decoder network via a gate unit. Finally, we formulate a unified and end-to-end trainable framework where cross-level features can be effectively fused and propagated for capturing rich context information. Extensive experiments on three benchmark camouflaged datasets demonstrate that our FAP-Net outperforms other state-of-the-art COD models. Moreover, our model can be extended to the polyp segmentation task, and the comparison results further validate the effectiveness of the proposed model in segmenting polyps. The source code and results will be released at https://github.com/taozh2017/FAPNet.
Young’s double-slit experiment optimizer : A novel metaheuristic optimization algorithm for global and constraint optimization problems

Due to the global progress, the optimization problems are becoming more and more complex. Hence, deterministic and heuristic approaches are no longer adequate for dealing with such sophisticated problems. subsequently, metaheuristics have recently emerged as an effective alternative for addressing the optimization problems. This paper proposes a novel metaheuristic called Young’s Double-Slit Experiment (YDSE) optimizer, derived from a physical backdrop. The YDSE optimizer is inspired by Young’s double-slit experiment, which is regarded as one of the most well-known classical physics experiments, revealing the wave nature of light. In YDSE optimizer, each fringe represents a possible solution in the population. Many concepts are modeled from the experiment, such as monochromatic light waves, Huygens’ principle, constructive and destructive interference, wave intensity, amplitude, and path difference. The YDSE optimizer strikes a balance between exploration and exploitation by selecting either a constructive interference or a destructive interference based on the order number of the fringe. During the optimization process, the solution moves in search space based on its order number. If the solution has an odd number, it moves in the dark regions towards the central bright region, which is expected to contain the optimal solution. The algorithm exploits the promising areas in the bright fringe areas, which are assumed to contain the optimum. The performance of the YDSE optimizer is compared with another twelve metaheuristics using CEC 2014, CEC 2017, and CEC 2022. The benchmarks cover different unimodal, multimodal, hybrid, and composite test functions. Also, we consider ten constrained and unconstrained engineering optimization design problems. YDSE proved its superiority over the CEC 2014 and CEC 2017 winners, such as L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA. The results and the statistical analysis demonstrated the outperformance of the proposed YDSE optimizer at a 95% confidence interval. • YDSE optimizer is a novel metaheuristic inspired by Young’s double-slit experiment. • We compare YDSE to existing algorithms using CEC 2014, 2017, 2022 benchmarks. • Ten optimization problems are considered from the engineering field. • The statistical analysis demonstrated the superiority of the proposed YDSE. • YDSE optimizer outperforms L-SHADE, LSHADE-cnEpSin, and LSHADE-SPACMA.
6d Rotation Representation For Unconstrained Head Pose Estimation

In this paper, we present a method for unconstrained end-to-end head pose estimation. We address the problem of ambiguous rotation labels by introducing the rotation matrix formalism for our ground truth data and propose a continuous 6D rotation matrix representation for efficient and robust direct regression. This way, our method can learn the full rotation appearance which is contrary to previous approaches that restrict the pose prediction to a narrow-angle for satisfactory results. In addition, we propose a geodesic distance-based loss to penalize our network with respect to the SO(3) manifold geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that our proposed method significantly outperforms other state-of-the-art methods by up to 20\%. We open-source our training and testing code along with our pre-trained models: https://github.com/thohemp/6DRepNet.
Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand

The utilization of chatbots has grown in popularity in recent years, leading to an increasing interest among academics and practitioners. This study investigates the effect of chatbot language style on customers' continuance usage intention and attitude toward brand. Two scenario-based experiments were conducted to examine the underlying mechanism. The results show that when chatbots adopt an informal (vs. formal) language style, customers’ continuance usage intention and brand attitude increase through the mediating role of parasocial interaction. Further, this study identifies brand affiliation as a pertinent moderator, such that the effect of chatbot language style is attenuated for people who have no prior relationship with the brand. The findings contribute to the existing chatbot literature and offer practical implications for brand managers to develop optimal language strategies when deploying chatbots in e-commerce.
Unsupervised Domain Adaptation for Nighttime Aerial Tracking

Previous advances in object tracking mostly reported on favorable illumination circumstances while neglecting performance at nighttime, which significantly impeded the development of related aerial robot applications. This work instead develops a novel unsupervised domain adaptation framework for nighttime aerial tracking (named UDAT). Specifically, a unique object discovery approach is provided to generate training patches from raw nighttime tracking videos. To tackle the domain discrepancy, we employ a Transformer-based bridging layer post to the feature extractor to align image features from both domains. With a Transformer day/night feature discriminator, the day-time tracking model is adversarially trained to track at night. Moreover, we construct a pioneering benchmark namely NAT2021 for unsupervised domain adaptive night-time tracking, which comprises a test set of 180 manually annotated tracking sequences and a train set of over 276k unlabelled nighttime tracking frames. Exhaustive experiments demonstrate the robustness and domain adaptability of the proposed framework in nighttime aerial tracking. The code and benchmark are available at https://github.com/vision4robotics/UDAT.
Pointly-Supervised Instance Segmentation

We propose an embarrassingly simple point annotation scheme to collect weak supervision for instance segmentation. In addition to bounding boxes, we collect binary labels for a set of points uniformly sampled inside each bounding box. We show that the existing instance segmentation models developed for full mask supervision can be seamlessly trained with point-based supervision collected via our scheme. Remarkably, Mask R-CNN trained on COCO, PASCAL VOC, Cityscapes, and LVIS with only 10 annotated random points per object achieves 94%−98% of its fully-supervised performance, setting a strong baseline for weakly-supervised instance segmentation. The new point annotation scheme is approximately 5 times faster than annotating full object masks, making high-quality instance segmentation more accessible in practice. Inspired by the point-based annotation form, we propose a modification to PointRend instance segmentation module. For each object, the new architecture, called Implicit PointRend, generates parameters for a function that makes the final point-level mask prediction. Implicit PointRend is more straightforward and uses a single point-level mask loss. Our experiments show that the new module is more suitable for the point-based supervision. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Project page: https://bowenc0221.github.io/point-sup
Omnivore: A Single Model for Many Visual Modalities

Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our ‘OMNIVORE’ model leverages the flexibility of transformer-based architectures and is trained jointly on classification tasks from different modalities. Omnivoreis simple to train, uses off-the-shelf standard datasets, and performs at-par or better than modality-specific models of the same size. A single Omnivoremodel obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN RGB-D. After finetuning, our models outperform prior work on a variety of vision tasks and generalize across modalities. OMNIVORE's shared visual representation naturally enables cross-modal recognition without access to correspondences between modalities. We hope our results motivate researchers to model visual modalities together.
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Abstract A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.1
Occlusion-Aware Cost Constructor for Light Field Depth Estimation

Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation methods construct matching cost by sequentially shifting each sub-aperture image (SAI) with a series of pre-defined offsets, which is complex and time-consuming. In this paper, we propose a simple and fast cost constructor to construct matching cost for LF depth estimation. Our cost constructor is composed by a series of convolutions with specifically designed dilation rates. By applying our cost constructor to SAI arrays, pixels under predefined disparities can be integrated and matching cost can be constructed without using any shifting operation. More importantly, the proposed cost constructor is occlusion-aware and can handle occlusions by dynamically modulating pixels from different views. Based on the proposed cost constructor, we develop a deep network for LF depth estimation. Our network ranks first on the commonly used 4D LF benchmark in terms of the mean square error (MSE), and achieves a faster running time than other state-of-the-art methods.
Deblurring via Stochastic Refinement

Image deblurring is an ill-posed problem with multiple plausible solutions for a given input image. However, most existing methods produce a deterministic estimate of the clean image and are trained to minimize pixel-level distortion. These metrics are known to be poorly correlated with human perception, and often lead to unrealistic reconstructions. We present an alternative framework for blind deblurring based on conditional diffusion models. Unlike existing techniques, we train a stochastic sampler that refines the output of a deterministic predictor and is capable of producing a diverse set of plausible reconstructions for a given input. This leads to a significant improvement in perceptual quality over existing state-of-the-art methods across multiple standard benchmarks. Our predict-and-refine approach also enables much more efficient sampling compared to typical diffusion models. Combined with a carefully tuned network architecture and inference procedure, our method is competitive in terms of distortion metrics such as PSNR. These results show clear benefits of our diffusion-based method for deblurring and challenge the widely used strategy of producing a single, deterministic reconstruction.
Ranking-Based Siamese Visual Tracking

Current Siamese-based trackers mainly formulate the visual tracking into two independent subtasks, including classification and localization. They learn the classification subnetwork by processing each sample separately and neglect the relationship among positive and negative samples. Moreover, such tracking paradigm takes only the classification confidence of proposals for the final prediction, which may yield the misalignment between classification and localization. To resolve these issues, this paper proposes a ranking-based optimization algorithm to explore the relationship among different proposals. To this end, we introduce two ranking losses, including the classification one and the IoU-guided one, as optimization constraints. The classification ranking loss can ensure that positive samples rank higher than hard negative ones, i.e., distractors, so that the trackers can select the foreground samples successfully without being fooled by the distractors. The IoU-guided ranking loss aims to align classification confidence scores with the Intersection over Union(IoU) of the corresponding localization prediction for positive samples, enabling the well-localized prediction to be represented by high classification confidence. Specifically, the proposed two ranking losses are compatible with most Siamese trackers and incur no additional computation for inference. Extensive experiments on seven tracking benchmarks, including OTB100, UAV123, TC128, VOT2016, NFS30, GOT-10k and LaSOT, demonstrate the effectiveness of the proposed ranking-based optimization algorithm. The code and raw results are available at https://github.com/sansanfree/RBO.
VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks

Recently, fine-tuning language models pre-trained on large text corpora have provided huge improvements on vision-and-language (V&L) tasks as well as on pure language tasks. However, fine-tuning the entire parameter set of pre-trained models becomes impractical since the model size is growing rapidly. Hence, in this paper, we introduce adapter-based parameter-efficient transfer learning techniques to V&L models such as VL-BART and VL-T5. We evaluate our methods in a unified multi-task setup on both image-text and video-text benchmarks. For the image-text tasks, we use four diverse V&L datasets: VQAv2, GQA, NLVR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , and MSCOCO image captioning. For video-text tasks, we use TVQA, How2QA, TVC, and YC2C. With careful training and thorough experiments, we benchmark three popular adapter-based methods (Adapter, Hyperformer, Compacter) against the standard full fine-tuning and the recently proposed prompt-tuning approach. We also enhance the efficiency and performance of adapters by sharing their weights to attain knowledge across tasks. Our results demonstrate that training the adapter with the weight-sharing technique (4.18% of total parameters for image-text tasks and 3.39% for video-text tasks) can match the performance of fine-tuning the entire model. Lastly, we present a comprehensive analysis including the combination of adapter and task-specific prompts and the impact of V&L pre-training on adapters. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The code for our CVPR 2022 paper is available at: https://github.com/ylsung/VL_adapter.