Reviewerly/exHarmony huggingface dataset:
Understanding the Micro-Behaviors of Hardware Offloaded Network Stacks with Lumina

Hardware offloaded network stacks are widely adopted in modern datacenters to meet the demand for high throughput, ultra-low latency and low CPU overhead. To fully leverage their exceptional performance, users need to have a deep understanding of their behaviors. Despite many efforts on testing software network stacks, hardware network stacks impose unique challenges to testing tools due to their kernel bypass nature and high performance.
Exploring the New Frontier: Decentralized Financial Services

Decentralized Finance (DeFi) marks a transformative shift in financial services, harnessing the power of distributed ledger and smart contract technologies. Despite only five years of development, DeFi has rapidly matured, pioneering state-of-the-art financial products, a stratified ecosystem, and a myriad of innovative vertical and horizontal protocol integrations. Its distinctive characteristics establish DeFi as a crucible for innovation, carrying potential to spur advancements across various service sectors. However, the research community has yet to fully explore this burgeoning field. In this manuscript, we navigate the complexities of this emerging service system, analyzing 362 notable Ethereum-based DeFi initiatives. Our study reveals a robust and dynamic growth trajectory, the advent of a multi-tiered ecosystem, and a sophisticated network of primary market investors. We conclude by highlighting promising avenues for future exploration in DeFi and service system research.
A Novel Aging-Based Proof of Stake Consensus Mechanism

Proof-of-Work (PoW) is currently the most popular consensus mechanism used in blockchain platforms. However, the significant amount of energy required by PoW has seriously undermined its effectiveness. Proof of Stake (PoS) is an alternative consensus mechanism that addresses the energy consumption issue of PoW. Unfortunately, the PoS mechanism may also lead to several issues, including a low level of decentralization, unfairness, and security issues in the blockchain. To address these concerns, we propose an aging-based version of PoS that enhances the decentralization level of the network by providing validation opportunities to a broader group of nodes. In addition to the nodes’ stake, aging-based PoS takes into account the age of nodes as a deciding factor in achieving fairness. Furthermore, aging-based PoS incorporates a punishment mechanism to prevent nodes from engaging in malicious activities on the blockchain. Our results demonstrate that aging-based PoS offers high decentralization, fairness, and security for current blockchain systems.
Single-flux-quantum-based qubit control with tunable driving strength

Single-flux-quantum (SFQ) circuits have great potential in building cryogenic quantum-classical interfaces for scaling up superconducting quantum processors. SFQ-based quantum gates have been designed and realized. However, current control schemes are difficult to tune the driving strength to qubits, which restricts the gate length and usually induces leakage to unwanted levels. In this study, we design the scheme and corresponding pulse generator circuit to continuously adjust the driving strength by coupling SFQ pulses with variable intervals. This scheme not only provides a way to adjust the SFQ-based gate length, but also proposes the possibility to tune the driving strength envelope. Simulations show that our scheme can suppress leakage to unwanted levels and reduce the error of SFQ-based Clifford gates by more than an order of magnitude.
Understanding community perception and acceptability of interactive voice response survey method: findings from formative research in Bangladesh.

Abstract Mobile phone technology’s expansion in Bangladesh has ushered in its use for rapid population-based data collection. This study was conducted to assess community perceptions on the acceptability, usability and challenges of participating in mobile phone surveys (MPS) on non-communicable disease risk factors through interactive voice response (IVR) in Bangladesh. Three user groups and seven focus group discussions with different communities, along with seven key informant interviews with relevant experts, were conducted in Bangla. Interviews were audio-recorded and analysed manually using thematic analysis. Participants indicated their unwillingness to receive calls during their busy hours. They suggested shorter duration calls and two sessions to complete longer duration surveys. They recommended that incentives can increase call completion, and previous notification through voice or text message might improve this further. Previous experience with scam calls and mobile balance depletion resulted in their hesitancy to receive calls from unknown numbers. Respondents showed concerns about sharing sensitive issues like alcohol consumption. Providing responses by key presses, less familiarity with smartphone use, availability and strength of mobile networks were identified as challenges for IVR surveys. The study emphasized careful consideration of survey duration and burden, trustworthiness, general and technical literacy and socio-cultural factors to increase MPS participation.
The use of pasung media to improve collaboration and learning outcomes food chains class V SDN Mojorejo 1 Batu

This Classroom Action Research aimed to find out the application of PASUNG media (connecting boards) to improve collaboration and learning outcomes for fifth-grade students at SDN Mojorejo 1 Batu City. The research method used was the classroom action research method. The subjects of this study were fifth-grade students at SDN Mojorejo 1 Batu City. Data on collaboration and learning outcomes were obtained from evaluation test results and student group activities through collaboration observation sheets. Based on the data obtained, learning using PASUNG media (connecting boards) can improve collaboration and student learning outcomes which can be seen from the results of student products in the form of miniature food chains. The results of student collaboration from Cycle I increased from 75% to 81% in Cycle II. Meanwhile, the average learning outcomes data of students in Cycle I was 73%, which increased to 85% in Cycle II. Based on the learning activities using PASUNG (Papan Sambung) media and creating a mini food chain project by the students, it can be concluded that the collaboration and learning outcomes of the students improved.
Online Optimization Algorithms for Edge Computing-based Cloud Gaming User Experience

Cloud gaming is an online gaming paradigm that uses portable devices as clients and offloads the running of game engines to servers through network connections. Emerging edge computing can provide low-latency, high-bandwidth network services and is gradually replacing cloud computing as the mainstream network architecture for cloud gaming. User-perceived latency and fluency are two key factors that determine the user experience in cloud gaming. User-perceived latency refers to the time difference between the input and the corresponding video frame displayed on the screen, while fluency is negatively correlated with the total duration of freezing. These two parameters are directly determined by the playing status of the game video, and they are inherently contradictory under given network conditions. This work proposes a novel client-side video latency optimization scheme that determines the video segment to be fetched and played from the client video butter at each time point based on a quantitative user experience model. This optimization scheme balances the perceived latency and fluency to enhance the gaming experience. The corresponding optimization problem is formulated as an online optimization problem, and two heuristic online optimization algorithms are proposed. Simulation experiments are carried out under different parameter settings, and the results show that the two algorithms proposed in this paper have an average performance improvement of 15.73% compared to the comparison algorithm.
FPGA Based Memory Security and Error Correction System for RISC-V Processor

With the rapid development of integrated circuits, the application of processor has become more and more widespread, and the information and data needs to be dealt with have become more complex, which brings great challenges to its security and dependability. As one of the most essential parts, memory affects the security and dependability of the processor. In terms of security, instruction set randomization is considered to be the effective means against code injection which is one of the common ways for hackers to attack memory. In terms of dependability, error correction codes are implemented to reduce soft errors like single-event upsets and multiple-bit upsets. These two methods are of great significance for improving the security and dependability of memory. With the rapid revolution of open-source hardware, RISCV instruction set architecture has been popular in academia and industry. However, the research on the security and dependability of processor based on RISC-V is still in its infancy. It is also hard for related studies to take into account little overhead in hardware resources, power consumption and performance while improving security or dependability. In this paper, we propose a FPGA based memory security and error correction systems for the RISC-V processor. The system integrates instruction set randomization and error correction codes to improve the security and dependability of processor while ensuring the performance of processor with less hardware overhead and power consumption.
Lung CT Synthesis Using GANs with Conditional Normalization on Registered Ultrashort Echo-Time MRI

In clinical practice, the modality of choice for lung diagnosis is usually computed tomography (CT), which exposes patients to ionizing radiations and could potentially affect patients’ health. Conversely, MR scan is considered safe and non-invasive but seems challenging due to the low proton density of the lungs and respiratory artifacts. Recently, ultrashort echo-time (UTE) MRI has been developed for lung assessment and shows promising results. In this work, we propose generating 2D synthetic CT slices from UTE MR slices, to improve the image quality and interpretability. Lung MR and CT volumes of 110 patients acquired on the same day were registered using an accurate edge-based non-rigid registration method. We trained and compared paired state-of-the-art generative models based on adversarial, feature-matching and perceptual losses, and also evaluated the impact of conditional batch normalization, namely SPADE [17], on image synthesis. Quantitative and qualitative evaluations showed that this approach was able to synthesize CT images that closely approximate ground truth CT images, and also enables the use of algorithms originally designed for real CT.
3D Coronary Vessel Reconstruction from Bi-Plane Angiography Using Graph Convolutional Networks

X-ray coronary angiography (XCA) is used to assess coronary artery disease and provides valuable information on lesion morphology and severity. However, XCA images are 2D and therefore limit visualisation of the vessel. 3D reconstruction of coronary vessels is possible using multiple views, however lumen border detection in current software is performed manually resulting in limited reproducibility and slow processing time. In this study we propose 3DAngioNet, a novel deep learning (DL) system that enables rapid 3D vessel mesh reconstruction using 2D XCA images from two views. Our approach learns a coarse mesh template using an EfficientB3-UNet segmentation network and projection geometries, and deforms it using a graph convolutional network. 3DAngioNet outperforms similar automated reconstruction methods, offers improved efficiency, and enables modelling of bifurcated vessels. The approach was validated using state-of-the-art software verified by skilled cardiologists.
Quantitative Biomarkers Reproducibility Using Generative Adversarial Approaches in Reduced to Conventional Dose CT

In recent years, several techniques for image-to-image translation by means of generative adversarial neural networks (GAN) have been proposed to learn mapping characteristics between a source and a target domain. In particular, in the medical imaging field conditional GAN frameworks with paired samples (cGAN) and unconditional cycle-consistent GANs with unpaired data (CycleGAN) have been demonstrated as a powerful scheme to model non-linear mappings that produce realistic target images from different modality sources. When proposing the usage and adaptation of these frameworks for medical image synthesis, quantitative and qualitative validation are usually performed by assessing the similarity between synthetic and target images in terms of metrics such as mean absolute error (MAE) or structural similarity (SSIM) index. However, an evaluation of clinically relevant markers showing that diagnostic information is not overlooked in the translation process is often missing. In this work, we aim at demonstrating the importance of validating medical image-to-image translation techniques by assessing their effect on the measurement of clinically relevant metrics and biomarkers. We implemented both a conditional and an unconditional approach to synthesize conventional dose chest CT scans from reduced dose CT and show that while both visually and in terms of traditional metrics the network appears to successfully minimize perceptual discrepancies, these methods are not reliable to systematically reproduce quantitative measurements of various chest biomarkers.
Multi-task Residual Cross-attention Network for Tumor Segmentation and Lymph Node Metastasis Prediction in Cervical Cancer

Tumor localization and lymph node metastasis (LNM) diagnosis are two important tasks for gynecologist to make decisions in cervical cancer treatments. Aiming to develop an accurate and convenient diagnosis system, we propose a multi-task residual cross-attention network named MRCNet for tumor segmentation and LNM prediction. Specifically, we tackle task correlation with underlying related supervision information, and capture multi-level features by multi-scale convolutional neural network, which equipped with cross-attention module concerning spatial and channel dimensions to emphasize meaningful features. A total of 1123 cervical cancer patients from 13 centers in China are collected to assess the architecture, 2 centers of them were set as an external testing cohort. The experimental results demonstrate the promising inference performance and generalization ability of our MRCNet for both segmentation and classification tasks, which can help doctors make judgments about treatment measures.
Transformer-based Characterization of Breast Lesions in Handheld Ultrasound Images WITH Classification Inconsistency Measure

Ultrasound imaging combined with computer aided diagnosis methods has shown promise in improving the availability of breast cancer screening in low-and-middle- income countries. In this study, we employ a vision transformer-based architecture to discriminate between benign and malignant lesions in breast ultrasound images obtained through a handheld device. We further introduce a metric, the classification inconsistency rate (CIR), that quantifies the uncertainty of the model for a queried breast ultrasound image. The trained model provided a test accuracy and area under the ROC curve (AUC) equal to 89.3 % and 0.95, respectively. These metrics increased to 94.4% and 0.96, respectively, by excluding uncertain predictions based on the CIR values. Fisher exact test indicated that false predictions had significantly higher probability of providing CIR>0, compared to true predictions (p-value<0.00001). The trained model with the uncertainty measure, are expected to assist in breast cancer diagnosis using portable ultrasound scanners in remote settings.
Enabling Collagen Quantification on HE-Stained Slides through Stain Deconvolution and Restained HE-HES

In histology, the presence of collagen in the extra-cellular matrix has both diagnostic and prognostic value for cancer malignancy, and can be highlighted by adding Saffron (S) to a routine Hematoxylin and Eosin (HE) staining. However, Saffron is not usually added because of the additional cost and because pathologists are accustomed to HE, with the exception of France-based laboratories. In this paper, we show that it is possible to quantify the collagen content from the HE image alone and to digitally create an HES image. To do so, we trained a UNet to predict the Saffron densities from HE images. We created a dataset of registered, restained HE-HES slides and we extracted the Saffron concentrations as ground truth using stain deconvolution on the HES images. Our model reached a Mean Absolute Error of 0.0668 ± 0.0002 (Saffron values between 0 and 1) on a 3-fold testing set. We hope our approach can aid in improving the clinical workflow while reducing reagent costs for laboratories.
GrNT: Gate-Regularized Network Training for Improving Multi-Scale Fusion in Medical Image Segmentation

Multi-scale fusion is a key for semantic segmentation of medical images. Recent deep learning methods added network complexity to achieve better multi-scale fusion results. However, given the already very expressive architectures (e.g., U-Net), an interesting question is whether additional complexity is necessary for achieving more robust multi-scale fusion performance. In this paper, we proposed a new method for improving the multi-scale fusion performance of a medical image segmentation model. We create a set of binary gates at fusing locations that allow us to control forward and backward flows in training. A gate on-off schedule is imposed so that high-scale (level) features could drive the generation of segmentation, while low-scale features serve as complimentary for reconstructing shape details. Our method is effective, easy to implement, and occurs no extra cost when deploying the trained model. Experiments show that our gate-regularized network training (GrNT) improves widely adopted models (e.g., U-Net, Attention U-Net, and DenseVoxNet) on three segmentation datasets (2017 ISIC Skin Lesion segmentation (2D), MM-WHS CT (3D), and 2016 HVSMR (3D)).
Swin-SFTNet : Spatial Feature Expansion and Aggregation Using Swin Transformer for Whole Breast Micro-Mass Segmentation

Incorporating various mass shapes and sizes in training deep learning architectures has made breast mass segmentation challenging. Moreover, manual segmentation of masses of irregular shapes is time-consuming and error-prone. Though Deep Neural Network has shown outstanding performance in breast mass segmentation, it fails in segmenting micro-masses. In this paper, we propose a novel U-net-shaped transformer-based architecture, called Swin-SFTNet, that outperforms state-of-the-art architectures in breast mammography-based micro-mass segmentation. Firstly to capture the global context, we designed a novel Spatial Feature Expansion and Aggregation Block(SFEA) that transforms sequential linear patches into a structured spatial feature. Next, we combine it with the local linear features extracted by the swin transformer block to improve overall accuracy. We also incorporate a novel embedding loss that calculates similarities between linear feature embeddings of the encoder and decoder blocks. With this approach, we achieve higher segmentation dice over the state-of-the-art by 3.10% on CBIS-DDSM, 3.81% on InBreast, and 3.13% on CBIS pre-trained model on the InBreast test data set.
Cervical Cell Classification Using Multi-Scale Feature Fusion and Channel-Wise Cross-Attention

Cervical cancer is one of the prevalent malignant tumors in women, and accurate cervical cell classification is clinically significant for early screening of cervical cancer. In this paper, we propose a novel cervical cell classification method based on multi-scale feature fusion and channel-wise cross-attention. Specifically, the multi-scale cell features are combined from the perspective of channels, and then the fused multi-scale features are fed into multi-head channel-wise cross-attention to explore the channel dependencies and non-local semantic information, which are encoded into the high-level CNN features through Multi-Layer Perceptron (MLP) with residual structure. More importantly, the Re-Attention is applied to exploit the correlation among different attention heads. Experiments on three public cervical cell datasets, SIPaKMeD, Herlev and Motic, demonstrate the effectiveness of the method for cervical cell classification.
Federated Linear Mixed Effects Modeling for Voxel-Based Morphometry

We propose a federated linear mixed-effects (LME) model to perform a large-scale analysis of data gathered from different collaborations without the need to pool or share the actual data. This method is efficient as it overcomes the hurdles of data privacy for sharing and has lower bandwidth and memory requirements for analysis than the centralized modeling approach. Our decentralized LME approach is optimized for neuroimaging data. We evaluate our model using features extracted from structural magnetic resonance imaging (sMRI) data. Results demonstrate that federated (decentralized) LME models achieve similar performance compared to the models trained with all the data in one location.
Denoising Diffusion Medical Models

In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentations on a small number of annotated datasets as well as other massive unlabeled datasets with no supervision. Radio-graph/segmentation pairs are generated jointly by the DDMM sampling process in probabilistic mode. As a result, a vanilla UNet that uses this data augmentation for segmentation task outperforms other similarly data-centric approaches.
Region and Spatial Aware Anomaly Detection for Fundus Images

Recently anomaly detection has drawn much attention in diagnosing ocular diseases. Most existing anomaly detection research in fundus images has relatively large anomaly scores in the salient retinal structures, such as blood vessels, optical cups and discs. In this paper, we propose a Region and Spatial Aware Anomaly Detection (ReSAD) method for fundus images, which obtains local region and long-range spatial information to reduce the false positives in the normal structure. ReSAD transfers a pre-trained model to extract the features of normal fundus images and applies the Region-and-Spatial-Aware feature Combination module (ReSC) for pixel-level features to build a memory bank. In the testing phase, ReSAD uses the memory bank to determine out-of-distribution samples as abnormalities. Our method significantly outperforms the existing anomaly detection methods for fundus images on two publicly benchmark datasets.
Calibration of A Regression Network Based on the Predictive Variance with Applications to Medical Images

Calibrating regression neural networks is crucial in medical imaging applications where the decision-making depends on the predicted confidence. In this study we propose a calibration procedure for regression networks that is based on scaling the predictive variance. The amount of scaling depends on the input and is a function of the predictive variance computed by the network. We thus find an entire calibration function instead of a single parameter. We report extensive experiments on a variety of image datasets and network architectures. Our approach achieves state-of-the-art results with a guarantee that the prediction accuracy is not altered.