Reviewerly/exHarmony huggingface dataset:
An Accurate Polyp Segmentation Framework via Feature Secondary Fusion

Pre-segmentation of potential polyps can effectively improve the diagnostic efficiency of clinical specialists and reduce misdiagnosis and missed diagnosis. A common practice in polyp segmentation is to use Feature Pyramid Network (FPN) for segmentation. The vanilla FPN uses an extremely simple element-wise summation approach for feature fusion, resulting in less effective feature interactions. To solve the above issue, we provide a Feature Secondary Fusion Module (FSFM) in FPN to boost the performance of polyp segmentation. Specifically, in the first fusion, we form a dual-branch architecture to incorporate low-level spatial information into the high-level features and inject high-level semantics into low-level features, respectively, which can significantly reduce information loss. In the second fusion, we apply the gate mechanism to select the informative feature, making the feature fusion more effective. Moreover, rigorous evaluations are carried out on multiple polyp segmentation benchmarks. According to the experimental findings, integrating FSFM into a feature pyramid network surpasses other cutting-edge approaches.
On the Dependable Operation of Bidirectional Encoder Representations from Transformers (BERT) in the Presence of Soft Errors

Transformers are widely used in Natural Language Processing (NLP) and computer vision; the Bidirectional Encoder Representations from Transformers (BERT) is one of the most popular pre-trained models for NLP applications. This paper considers the dependable operations of transformers using BERT and studies the impact of soft errors; as a case study, single-precision floating point weights are considered for emotion classification in text. Simulation by error injection is conducted to assess the impact of errors on different parts of the BERT model as well as the bits of the weights. The analysis of the results leads to the following findings: 1) there is a Critical Bit (CB) on which errors significantly affect the performance of the model; 2) errors that degrade the performance in many cases lead to a single class being output, so that BERT appears to generate a predetermined result regardless of the input. The analysis of the impact of errors is also performed on a layer, type, and head basis to evaluate the sensitivity as well as initial studies on error propagation and multiple bit flips.
An Intelligent Detector for Carrier Index Differential Chaos Shift Keying System

Inspired by the orthogonal time frequency space modulation, carrier index differential chaos shift keying (CI-DCSK) system has been proposed and attracted more and more attention because of its high-efficiency and low-complexity advantages. In this paper, a deep learning based intelligent detector for CI-DCSK system, referred to as DL-CI-DCSK detector, is proposed to realize more reliable transmission. The proposed detector inherits the advantages of neural network and traditional energy detection. The proposed DL-CI-DCSK detector first recovers the index bits using a neural network and then using the index bits to demodulate the modulation bits. The designed network structure mainly exploits the characteristics of long short-term memory unit and multiple fully connected layers to extract and integrate the correlation and features of the modulated signals. Simulation results show that the proposed intelligent detector can achieve better bit-error-rate (BER) performance than conventional detectors over multipath Rayleigh fading channels.
MNGLO-Net: A Cascade Network for Detecting Glomerulus and Classifying Pathological Types of Membranous Nephropathy

Membranous Nephropathy (MN) is a common adult nephrotic syndrome, whose key diagnostic process is Glomerular detection and classification. However, the development of MN diagnosis is impeded by the lack of pathologists and the complexity of glomerular pathological judgment. In this paper, we propose a two-stage cascade framework, MNGLO-Net, to identify three pathological types of glomeruli in our MNTG dataset. In first stage, NA-Net produces a detection of the glomerulus and classify the normal and abnormal types simultaneously. The resulting bounding box for each abnormal glomerulus is then used to crop a region of interest around the target in original image and the final feature maps of NA-Net to provide prior context to a refinement classification network, HL-Net, in second stage. Additionally, we propose the Color K-means guided Staining Transfer (CKST) module to solve the issue of single stain and differences among stains, and adopt the Prior knowledge guided Generating (PGAG) algorithm to deal with the challenge of significant shape difference. Experimental results show that MNGLO-Net reach the best results, i.e., 93.3% mean average precision (mAP) and 93.16% F1 score. Furthermore, we design the MN-GLOALY system to automatically produce pathological reports to promote the exploration of MN.
Learning Covariance-Based Multi-Scale Representation of Neuroimaging Measures for Alzheimer Classification

Stacking excessive layers in DNN results in highly underdetermined system when training samples are limited, which is very common in medical applications. In this regard, we present a framework capable of deriving an efficient high-dimensional space with reasonable increase in model size. This is done by utilizing a transform (i.e., convolution) that leverages scale-space theory with covariance structure. The overall model trains on this transform together with a downstream classifier (i.e., Fully Connected layer) to capture the optimal multi-scale representation of the original data which corresponds to task-specific components in a dual space. Experiments on neuroimaging measures from Alzheimer’s Disease Neuroimaging Initiative (ADNI) study show that our model performs better and converges faster than conventional models even when the model size is significantly reduced. The trained model is made interpretable using gradient information over the multi-scale transform to delineate personalized AD-specific regions in the brain.
Improved Multimodal Fusion for Small Datasets with Auxiliary Supervision

Prostate cancer is one of the leading causes of cancer-related death in men worldwide. Like many cancers, diagnosis involves expert integration of heterogeneous patient information such as imaging, clinical risk factors, and more. For this reason, there have been many recent efforts toward deep multimodal fusion of image and non-image data for clinical decision tasks. Many of these studies propose methods to fuse learned features from each patient modality, providing significant downstream improvements with techniques like cross-modal attention gating, Kronecker product fusion, orthogonality regularization, and more. While these enhanced fusion operations can improve upon feature concatenation, they often come with an extremely high learning capacity, meaning they are likely to overfit when applied even to small or low-dimensional datasets. Rather than designing a highly expressive fusion operation, we propose three simple methods for improved multimodal fusion with small datasets that aid optimization by generating auxiliary sources of supervision during training: extra supervision, clinical prediction, and dense fusion. We validate the proposed approaches on prostate cancer diagnosis from paired histopathology imaging and tabular clinical features. The proposed methods are straightforward to implement and can be applied to any classification task with paired image and non-image data.
Protection of Network Security Selector Secrecy in Outsourced Network Testing

With the emergence and fast development of cloud computing and outsourced services, more and more companies start to use managed security service providers (MSSP) as their security service team. This approach can save the budget on maintaining its own security teams and depend on professional security persons to protect the company infrastructures and intellectual property. However, this approach also gives the MSSP opportunities to honor only a part of the security service level agreement. To prevent this from happening, researchers propose to use outsourced network testing to verify the execution of the security policies. During this procedure, the end customer has to design network testing traffic and provide it to the testers. Since the testing traffic is designed based on the security rules and selectors, external testers could derive the customer network security setup, and conduct subsequent attacks based on the learned knowledge. To protect the network security configuration secrecy in outsourced testing, in this paper we propose different methods to hide the accurate information. For Regex-based security selectors, we propose to introduce fake testing traffic to confuse the testers. For exact match and range based selectors, we propose to use NAT VM to hide the accurate information. We conduct simulation to show the protection effectiveness under different scenarios. We also discuss the advantages of our approaches and the potential challenges.
ScaffML: A Quantum Behavioral Interface Specification Language for Scaffold

Ensuring the correctness of quantum programs is crucial for quantum software quality assurance. Although various effective verification methods exist for classical programs, they cannot be applied to quantum programs due to the fundamental differences in their execution logic, such as quantum superposition and entanglement. This calls for new methods to verify the correctness of quantum programs. In this paper, we present a behavioral interface specification language (BISL) called ScaffML for the quantum programming language Scaffold. ScaffML allows the specification of pre- and post-conditions for Scaffold modules and enables the mixing of assertions with Scaffold code, thereby facilitating debugging and verification of quantum programs. This paper discusses the goals and overall approach of ScaffML and describes the basic features of the language through examples. ScaffML provides an easy-to-use specification language for quantum programmers, supporting static analysis, run-time checking, and formal verification of Scaffold programs. Finally, we present several instances to illustrate the workflow and functionalities of ScaffML.
Visualizing Attractive Factors in Buddhist Statue Images Using Grad-CAM

In this study, we consider the problem of visualizing the attractive factors of Buddhist statue using a deep learning approach. For this purpose, we construct a Convolutional Neural Network (CNN) using the results of a questionnaire survey on the attractiveness of 553 Buddhist statue images conducted with 11 examinees as training data, and explore the attractive factors by visualizing the hidden layers of the CNN. For this visualization, we employ a method called as Gradient-weighted Class Activation Mapping (Grad-CAM), which combines feature maps and gradient information to represent important image regions contributing to the CNN output as heatmaps. The validity of the visualization results for these attractive factors is evaluated by experimental studies.
A Testing Pipeline for Quantum Computing Applications

As the scientific community is increasing the coverage of the quantum computing domain ranging from theoretical physics to financial applications, so does the number of quantum computing applications being developed for quantum computing hardware that is available today. As this hardware evolves towards increased capacity in terms of qubit number and increased fidelity, so does the complexity of quantum computing applications that implement the state-of-the-art of (hybrid) quantum algorithms. With this comes an increased demand for quantum software engineering practices where testing is a crucial part of the development process. Hence, a robust approach that addresses certain challenges and peculiarities related to the quantum computing domain is needed for the testing process. This paper proposes a standard-ready testing pipeline that covers the requirements of testing in a continuous development cycle as part of a modern quantum software engineering lifecycle.
Automated Region Learning Via Cell-Level Labels to Modify Cell Detection Process For Histopathological Images

Cell detection methods based on convolutional neural networks have been extensively studied. However, the distribution of histopathological cells is heterogeneous. Existing methods normally require region-level labeling as guidance to integrate regional information into the detection process. But the cost of manual regional labeling is higher as the variable shape and the unclear boundary of the region. In fact, different types of histopathological cells with such heterogeneity have certain distribution patterns, meaning that the spatial relationship of cell-level labels also implies regional information. Based on this, we propose a novel cell detection method modified by automated region learning via cell-level labels in histopathological images. We automatically obtain the true region of cell distribution from the cell-level labels based on the Alpha-shape algorithm to train a region-learning network. The results of the region-learning network are used to modify the cell detection process. Extensive experiments indicate the effectiveness of the proposed method.
Improving Machine Learning Robustness via Adversarial Training

As Machine Learning (ML) is increasingly used in solving various tasks in real-world applications, it is crucial to ensure that ML algorithms are robust to any potential worst-case noises, adversarial attacks, and highly unusual situations when they are designed. Studying ML robustness will significantly help in the design of ML algorithms. In this paper, we investigate ML robustness using adversarial training in centralized and decentralized environments, where ML training and testing are conducted in one or multiple computers. In the centralized environment, we achieve a test accuracy of 65.41% and 83.0% when classifying adversarial examples generated by Fast Gradient Sign Method and DeepFool, respectively. Comparing to existing studies, these results demonstrate an improvement of 18.41% for FGSM and 47% for DeepFool. In the decentralized environment, we study Federated learning (FL) robustness by using adversarial training with independent and identically distributed (IID) and non-IID data, respectively, where CIFAR-10 is used in this research. In the IID data case, our experimental results demonstrate that we can achieve such a robust accuracy that it is comparable to the one obtained in the centralized environment. Moreover, in the non-IID data case, the natural accuracy drops from 66.23% to 57.82%, and the robust accuracy decreases by 25% and 23.4% in C&W and Projected Gradient Descent (PGD) attacks, compared to the IID data case, respectively. We further propose an IID data-sharing approach, which allows for increasing the natural accuracy to 85.04% and the robust accuracy from 57% to 72% in C&W attacks and from 59% to 67% in PGD attacks.
LightESD: Fully-Automated and Lightweight Anomaly Detection Framework for Edge Computing

Anomaly detection is widely used in a broad range of domains from cybersecurity to manufacturing, finance, and so on. Deep learning based anomaly detection has recently drawn much attention because of its superior capability of recognizing complex data patterns and identifying outliers accurately. However, deep learning models are typically iteratively optimized in a central server with input data gathered from edge devices, and such data transfer between edge devices and the central server impose substantial overhead on the network and incur additional latency and energy consumption. To overcome this problem, we propose a fully-automated, lightweight, statistical learning based anomaly detection framework called LightESD. It is an on-device learning method without the need for data transfer between edge and server, and is extremely lightweight that most low-end edge devices can easily afford with negligible delay, CPU/memory utilization, and power consumption. Yet, it achieves highly competitive detection accuracy. Another salient feature is that it can auto-adapt to probably any dataset without manually setting or configuring model parameters or hyperparameters, which is a drawback of most existing methods. We focus on time series data due to its pervasiveness in edge applications such as IoT. Our evaluation demonstrates that LightESD outperforms other SOTA methods on detection accuracy, efficiency, and resource consumption. Additionally, its fully automated feature gives it another competitive advantage in terms of practical usability and generalizability.
Oriented Ship Detection Based on Expansion Deformation Rotation Representation

Existing mainstream two-stage oriented detection methods generate oriented proposals based on horizontal proposals as the circumscribed rectangle. Utilizing such generating strategy cannot obtain accurate oriented proposals due to the shape arbitrariness of ship targets. This paper proposes an effective and simple oriented proposal generating strategy, termed Expansion Deformation Rotation Representation (EDRR). EDRR introduces two expansion components that allow the oriented proposal has the potential to break through the constraints of the horizontal proposal. Then, a novel two-stage oriented ship detection framework named OSDet is proposed based on the EDRR strategy. To be specific, in the first stage, we use ReResNet-ReFPN to extract rotation invariant features and oriented RPN with EDRR strategy to generate accurate oriented candidates. Then, the oriented R-CNN head is utilized to refine oriented Rol features and complete the regression and recognition procedures in the second stage of the proposed OSDet. Extensive experiments on two challenging optical remote sensing datasets HRSC2016-MS and DOTA demonstrate that the proposed method can achieve competitive performance on the oriented ship detection task and have strong generalization capability on the aerial object detection task. Comparison with state-of-the-art methods indicate that the proposed OSDet achieves the best oriented ship/object detection performance, i.e., 77.50% and 77.14% mAP on HRSC2016-MS and DOTA datasets, respectively.
Model Pruning for Wireless Federated Learning with Heterogeneous Channels and Devices

Federated learning (FL) enables distributed model training, ensuring user privacy and reducing communication overheads. Model pruning further improves learning efficiency by removing weight connections in neural networks, increasing inference speed and reducing model storage size. While a larger pruning ratio shortens latency in each communication round, a larger number of communication rounds is needed for convergence. In this work, a training-based pruning ratio decision policy is proposed for wireless federated learning. By jointly minimizing average gradients and training latency with a given specific time budget, we optimize the pruning ratio for each device and the total number of training rounds. Numerical results demonstrate that the proposed algorithm achieves a faster convergence rate and lower latency compared to the existing approach.
Privacy and Anonymity For Multilayer Networks: A Reflection

Privacy of data as well as providing anonymization of data for various kinds of analysis have been addressed in the context of tabular transactional data which was mainstream. With the advent of the Internet and social networks, there is an emphasis on using different kinds of graphs for modeling and analysis. In addition to single graphs, the use of MultiLayer Networks (or MLNs) for modeling and analysis is becoming popular for complex data having multiple types of entities and relationships. They provide a better understanding of data as well as flexibility and efficiency of analysis.In this article, we understand the provenance of data privacy and some of the thinking on extending it to graph data models. We will focus on the issues of data privacy for models that are different from traditional data models and discuss alternatives. We will also consider privacy from a visualization perspective as we have developed a community Dashboard for MLN generation, analysis, and visualization based on our research.
Quantum Reinforcement Learning for Large-Scale Multi-Agent Decision-Making in Autonomous Aerial Networks

This paper addresses a new quantum computing-based multi-agent reinforcement learning (QMARL) algorithm which is inspired by the quantum neural network (QNN)-based centralized critic and multiple actor networks. The benefit of the proposed QMARL-based algorithm is in the action control dimension reduction where it can reduce the size into a logarithmic-scale when project value measure (PVM) is utilized. Therefore, our proposed QMARL-based algorithm is beneficial for massive-agent MARL training convergence. Moreover, the various applications of QMARL-based algorithms are presented in massive-scale unmanned aerial vehicle (UAV) networks. Lastly, our performance evaluation results verify that the proposed QMARL-based algorithm can successfully converge when massive-action dimensions should be utilized.
Quantum Circuits for Simulation of Optical and Photonic Devices

We provide and discuss examples of quantum circuits for simulating the behavior of optical and photonic devices, especially those present in quantum communication setups. Examples include decoherence effects in optical fibers, two-photon (Hong-Ou-Mandel) interference in a beamsplitter - including the case where multiple degrees of freedom are involved - and representation of weak coherent states. Moreover, we discuss the modelling of practical non-ideal characteristics of threshold single-photon detectors, namely, the quantum efficiency and the dark count rate.
SynthMix: Mixing Up Aligned Synthesis for Medical Cross-Modality Domain Adaptation

The adversarial methods showed advanced performance by producing synthetic images to mitigate the domain shift, a common problem due to the hardship of acquiring labelled data in medical field. Most existing studies focus on modifying the network architecture, but little has worked on the GAN training strategy. In this work, we propose SynthMix, an addon module with a natural yet effective training policy that can promote synthetic quality without altering the network architecture. Following the adversarial philosophy of GAN, we designed a mix-up synthesis scheme termed SynthMix. It coherently mixed up aligned images of real and synthetic samples to stimulate the generation of fine-grained features, examined by an associated Inspector for the domain-specific details. We evaluated our method on two segmentation benchmarks among three publicly available datasets, where our method showed a significant performance gain compared with existing state-of-the-art approaches. The code will be made publicly available.