Reviewerly/exHarmony huggingface dataset:
Dual-stream Representation Fusion Learning for accurate medical image segmentation

Accurate segmenting regions of interest in various medical images are essential to clinical research and applications. Although deep learning-based methods have achieved good results, the fully automated segmentation results still need to be refined on the tininess, complexities, and irregularities of lesion shapes. To address this issue, we propose a Dual-stream Representation Fusion Learning (DRFL) paradigm for accurate clinical segmentation, including Dual-stream Fusion Module, Representation Fusion Transformer Module and Peakiness Fusion Attention Module. Specifically, Dual-stream Fusion Module can simultaneously generate binary masks and high-resolution images with segmentation stream and super-resolution stream that share a feature extractor, then both prediction outputs are merged as the input of Fusion Module to further improve the performance of the network for generating the final segmentation result; Representation Fusion Transformer Module is lightweight to fuse high-resolution representation and fine-grained structure representation; Peakiness Fusion Attention Module can capture more salient features while fusing more spatial information to improve the performance of the network. The effectiveness of our dual-stream representation fusion learning is validated on different medical image segmentation tasks, and extensive experiments show that our DRFL outperforms the state-of-the-art methods in segmentation quality of lung nodule segmentation, lung segmentation, cell contour segmentation, and prostate segmentation. Our code is available at https://github.com/Rongtao-Xu/RepresentationLearning/tree/main/DRFL-EAAI2023.
Multiclass blood cancer classification using deep CNN with optimized features

Breast cancer, lung cancer, skin cancer, and blood malignancies such as leukemia and lymphoma are just a few instances of cancer, which is a collection of cells that proliferate uncontrollably within the body. Acute lymphoblastic leukemia is of one the significant form of malignancy. The hematologists frequently makes an oversight while determining a blood cancer diagnosis, which requires an excessive amount of time. Thus, this research reflects on a novel method for the grouping of the leukemia with the aid of the modern technologies like Machine Learning and Deep Learning. The proposed research pipeline is occupied into some interconnected parts like dataset building, feature extraction with pre-trained Convolutional Neural Network (CNN) architectures from each individual images of blood cells, and classification with the conventional classifiers. The dataset for this study is divided into two identical categories, Benign and Malignant, and then reshaped into four significant classes, each with three subtypes of malignant, namely, Benign, Early Pre-B, Pre-B, and Pro-B. The research first extracts the features from the individual images with CNN models and then transfers the extracted features to the features selections such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and SVC Feature Selectors along with two nature inspired algorithms like Particle Swarm Optimization (PSO) and Cat Swarm Optimization (CSO). After that, research has applied the seven Machine Learning classifiers to accomplish the multi-class malignant classification. To assess the efficacy of the proposed architecture a set of experimental data have been enumerated and interpreted accordingly. The study discovered a maximum accuracy of 98.43% when solely using pre-trained CNN and classifiers. Nevertheless, after incorporating PSO and CSO, the proposed model achieved the highest accuracy of 99.84% by integrating the ResNet50 CNN architecture, SVC feature selector, and LR classifiers. Although the model has a higher accuracy rate, it does have some drawbacks. However, the proposed model may also be helpful for real-world blood cancer classification.
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications

Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology. While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pin-pointing how the contrastive objective will behave differently due to the characteristics of histopathology data. Using SimCLR and H&amp;E stained images as a representative setting for contrastive self-supervised learning in histopathology, we bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications. Code and trained models are available at &lt;a href='https://github.com/k-stacke/ssl-pathology'&gt;https://github.com/k-stacke/ssl-pathology&lt;/a&gt;
Blockchain-enabled digital assets tokenization for cyber-physical traceability in E-commerce logistics financing

E-commerce logistics financing drives growth in small and medium-sized logistics companies. Despite the popularity, its adoption faces vexing challenges in supply chains, such as (i) asymmetry arising from information silos, (ii) hidden centrality in existing information technology solutions, and (iii) opaqueness in information ownership. Blockchain technology, with its native constructs, such as shared ledger, smart contracts, and tokens, is deemed promising to alleviate these concerns. Hence, this study introduces a blockchain-enabled cyber-physical traceability system for logistics financing based on digital asset tokenization. The tokenization of digital assets brings visibility and traceability into the supply chain's cyber (information flow) and physical (logistics flow) transactions. Subsequently, we (i) introduce the design and implementation of the proposed traceability system and (ii) demonstrate the application of the proposed system in logistics financing. Some of the resulting implications include: (i) The information sharing through blockchain technology brings resolution into the operations in the supply chain for logistics financing. (ii) The system minimizes the required upfront expenditure and provides flexible access to computing resources to promote the system's adoption by the stakeholders.
Lightweight and Progressively-Scalable Networks for Semantic Segmentation

Multi-scale learning frameworks have been regarded as a capable class of models to boost semantic segmentation. The problem nevertheless is not trivial especially for the real-world deployments, which often demand high efficiency in inference latency. In this paper, we thoroughly analyze the design of convolutional blocks (the type of convolutions and the number of channels in convolutions), and the ways of interactions across multiple scales, all from lightweight standpoint for semantic segmentation. With such in-depth comparisons, we conclude three principles, and accordingly devise Lightweight and Progressively-Scalable Networks (LPS-Net) that novelly expands the network complexity in a greedy manner. Technically, LPS-Net first capitalizes on the principles to build a tiny network. Then, LPS-Net progressively scales the tiny network to larger ones by expanding a single dimension (the number of convolutional blocks, the number of channels, or the input resolution) at one time to meet the best speed/accuracy tradeoff. Extensive experiments conducted on three datasets consistently demonstrate the superiority of LPS-Net over several efficient semantic segmentation methods. More remarkably, our LPS-Net achieves 73.4% mIoU on Cityscapes test set, with the speed of 413.5FPS on an NVIDIA GTX 1080Ti, leading to a performance improvement by 1.5% and a 65% speed-up against the state-of-the-art STDC. Code is available at https://github.com/YihengZhang-CV/LPS-Net .
Aspect-Pair Supervised Contrastive Learning for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task, which identifies the sentiment polarity of a specific aspect in a sentence. In general, the syntactic dependency and semantic information between aspects and their contexts are modeled using deep neural networks. However, most of the existing methods treat specific aspects in a sentence independently, while ignoring the sentiment relationships between multiple aspects. In this work, we propose an Aspect-Pair Supervised Contrastive Learning (APSCL) model to capture the latent relationships between multiple aspects in the sentiment subspace. Through experiments, we approve that in the embedding space, the representation discrepancy of aspect-pairs in the same relation category is narrowed while the embedding representation of aspect-pairs in different relation categories is pushed away. Then the aspect feature representation is enhanced through the relationship optimization between aspects. Furthermore, the relation categories between aspects are established in terms of the existing label attributes of aspects, and no additional corpus is needed. The extensive experiments on public datasets SemEval 2014 and MAMs show that the proposed framework APSCL is able to improve up to 2.29% on accuracy and 4.18% on F1 score over top-1 baseline models. Moreover, our framework can also be adapted to other benchmark models.
Health Mention Classification from User-Generated Reviews Using Machine Learning Techniques

The advancements in information and communication technologies contributed greatly to the development of social media and other platforms where people express their opinions and experiences. There are several platforms such as drugs.com where people rate pharmaceutical drugs and also give comments and reviews on the drugs they use and their side effects. It is important to analyze such reviews to find out the sentiment, opinions, drug efficacy, and most importantly, adverse drug reactions. Health mention classification deals with classifying such user-generated text into different classes of health mentions such as obesity, anxiety, and more. This work uses machine learning approaches for classifying health mentions from the publicly available health-mention dataset. Both the shallow machine learning algorithms and deep learning approaches with pre-trained embeddings have been implemented and the performances were compared with respect to the precision, recall, and f1-score. The experimental results show that machine learning approaches will be a good choice for automatically classifying health mentions from the large amount of user-generated drug reviews that may help different stakeholders of the healthcare industry to better understand the market and consumers.
A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.
Improving the probabilistic quantum teleportation efficiency of arbitrary superposed coherent state using multipartite even and odd j-spin coherent states as resource

Quantum teleportation is one of the most important techniques for quantum information secure transmission. Using preshared entanglement, quantum teleportation is designed as a basic key in many quantum information tasks and features prominently in quantum technologies, especially in quantum communication. In this work, we provide a new probabilistic teleportation scheme for arbitrary superposed coherent states by employing the multipartite even and odd j-spin coherent states as the entangled resource connecting Alice (sender) and Bob (receiver). Here, Alice possesses both even and odd spin coherent states and makes repeated GHZ states measurements (GHZSMs) on the pair of spins, consisting of (1) the unknown spin state and (2) one of the two coherent spin states, taken alternately, until reaching a quantum teleportation with maximal average fidelity. We provide the relationship between the entanglement amount of the shared state, quantified by the concurrence, with the teleportation fidelity and the success probability of the teleported target state up to the $$n\textrm{th}$$ repeated attempt. In this scheme, we show that the perfect quantum teleportation can be done even with a non-maximally entangled state. Furthermore, this repeated GHZSMs attempt process significantly increases both the average fidelity of the teleported state and the probability of a successful run of the probabilistic protocol. Also on our results, we show that the j-spin number, the target state parameter and the overlap between coherent states provide important additional control parameters that can be adjusted to maximize the teleportation efficiency.
Evaluation of Virtual Reality Application in Construction Teaching: A Comparative Study of Undergraduates

Construction courses are characterized by a combination of theoretical and practical knowledge; however, the teaching of practical knowledge is often absent due to safety and cost considerations. VR can improve the teaching of practical knowledge by facilitating interactions between teachers and students through virtual means, regardless of location, which is a weakness of current lecture-based teaching, especially in the COVID-19 era. Therefore, this paper aims to evaluate the effect and discuss the prospect of VR in construction teaching, with a comparative study of 50 students who were evenly divided into two groups and taught using traditional teaching and VR teaching, respectively. This experiment shows that VR teaching improves the students’ learning enthusiasm and satisfaction, especially in terms of practical knowledge. Additionally, students believe the combination of traditional and VR teachings can be more helpful in construction teaching. The findings of this research strengthened the advantages of VR in delivering practical knowledge in construction teaching.
Multi-modal cross-attention network for Alzheimer’s disease diagnosis with multi-modality data

Alzheimer's disease (AD) is a neurodegenerative disorder, the most common cause of dementia, so the accurate diagnosis of AD and its prodromal stage mild cognitive impairment (MCI) is significant. Recent studies have demonstrated that multiple neuroimaging and biological measures contain complementary information for diagnosis. Many existing multi-modal models based on deep learning simply concatenate each modality's features despite substantial differences in representation spaces. In this paper, we propose a novel multi-modal cross-attention AD diagnosis (MCAD) framework to learn the interaction between modalities for better playing their complementary roles for AD diagnosis with multi-modal data including structural magnetic resonance imaging (sMRI), fluorodeoxyglucose-positron emission tomography (FDG-PET) and cerebrospinal fluid (CSF) biomarkers. Specifically, the imaging and non-imaging representations are learned by the image encoder based on cascaded dilated convolutions and CSF encoder, respectively. Then, a multi-modal interaction module is introduced, which takes advantage of cross-modal attention to integrate imaging and non-imaging information and reinforce relationships between these modalities. Moreover, an extensive objective function is designed to reduce the discrepancy between modalities for effectively fusing the features of multi-modal data, which could further improve the diagnosis performance. We evaluate the effectiveness of our proposed method on the ADNI dataset, and the extensive experiments demonstrate that our MCAD achieves superior performance for multiple AD-related classification tasks, compared to several competing methods. Also, we investigate the importance of cross-attention and the contribution of each modality to the diagnostics performance. The experimental results demonstrate that combining multi-modality data via cross-attention is helpful for accurate AD diagnosis.
URTSegNet: A real-time segmentation network of unstructured road at night based on thermal infrared images for autonomous robot system

When it comes to poor lighting conditions such as night, backlight, haze, etc., the detection method of unstructured roads based on visible light cameras often fails. Therefore, this paper aims to improve the detection performance of unstructured roads based on thermal infrared images for autonomous robot systems. For this purpose, we propose a real-time network for unstructured road segmentation at night based on thermal infrared images (URTSegNet) in this paper. Like BiSeNet V2, we adopt a two-branch structure, including Detail Branch and Semantic Branch, to extract spatial detail and contextual features. The Detail Branch maintains a small receptive field and high capacity and adjusts the computational cost by employing depth-wise separable convolutions. In the Semantic Branch, we modify Stem Block to obtain more information because of the characteristics of infrared images. Dilated convolution expands the receptive field while keeping the number of parameters unchanged. Finally, we design a more concise and efficient Aggregation Block using a gate mechanism. An infrared unstructured road dataset, including total darkness at night, low light, backlight, haze, and other scenes, is collected to get the real-world dataset and effectively evaluate performance. We got 97.5% Intersection over Union (IoU) and reached an inference speed of 129 FPS on a single NVIDIA GTX 1660S card on our dataset. In addition, extensive evaluation experiments are conducted on two public datasets to verify the generalization ability of the model and the role of each module. This research addresses the problem of road detection for unmanned platforms in unstructured environments at night or under poor lighting conditions. The algorithm has high accuracy and a fast inference speed so that it can be implemented on various embedded devices.
Global- and local-aware feature augmentation with semantic orthogonality for few-shot image classification

As for few-shot image classification, recently, some works revisit the standard transfer learning paradigm, i.e., pre-training and fine-tuning, and have achieved some success. However, we find that this kind of methods heavily relies on a naive image-level data augmentation (e.g., cropping and flipping) at the fine-tuning stage, which will easily suffer from the overfitting problem because of the limited-data regime. To tackle this issue, in this paper, we attempt to perform a novel feature-level semantic augmentation at the fine-tuning stage and propose a Global- and Local-aware Feature Augmentation method (GLFA) from both the channel- and spatial-wise perspectives. In addition, at the pre-training stage, we further propose a Semantic Orthogonal Learning Framework (SOLF) to make the learned feature channels more independently, orthogonal and diverse. Extensive experiments demonstrate that the proposed method can obtain significant performance improvements over the state of the arts. Code is available at https://github.com/onlyyao/GLFA-SOLF.
A New Global Pooling Method for Deep Neural Networks: Global Average of Top-K Max-Pooling

Global Pooling (GP) is one of the important layers in deep neural networks.GP significantly reduces the number of model parameters by summarizing the feature maps and enables a reduction in the computational cost of training.The most commonly used GP methods are global max pooling (GMP) and global average pooling (GAP).The GMP method produces successful results in experimental studies but has a tendency to overfit training data and may not generalize well to test data.On the other hand, the GAP method takes into account all activations in the pooling region, which reduces the effect of high activation areas and causes a decrease in model performance.In this study, a GP method called global average of top-k max pooling (GAMP) is proposed, which returns the average of the highest k activations in the feature map and allows for mixing the two methods mentioned.The proposed method is compared quantitatively with other GP methods using different models, i.e., Custom and VGG16-based and different datasets, i.e., CIFAR10 and CIFAR100.The experimental results show that the proposed GAMP method provides better image classification accuracy than the other GP methods.When the Custom model is used, the proposed GAMP method provides a classification accuracy of 1.29% higher on the CIFAR10 dataset and 1.72% higher on the CIFAR100 dataset compared to the method with the closest performance.