Reviewerly/exHarmony huggingface dataset:
Epistemological and Bibliometric Analysis of Ethics and Shared Responsibility—Health Policy and IoT Systems

The focus in this paper is placed on shared responsibility and ethics in health policy, specific to Internet of Things (IoT) devices in healthcare systems. The article assesses how the introduction of IoT brings risks to the security of medical systems. The justification for this research emerges from the opportunities emerging from digital technologies for medical services, but also creating a range of new cyber risks in the shared healthcare infrastructure. Such concerns are often not visible to individual departments in an integrated healthcare system. In addition, many healthcare organisations do not possess cyber skills and are faced with barriers to the adoption of smart manufacturing technologies, e.g., cost. These barriers trigger ethical concerns related to responsibility of cyber risks in shared healthcare systems.
MixFaceNets: Extremely Efficient Face Recognition Networks

In this paper, we present a set of extremely efficient and high throughput models for accurate face verification, Mix-FaceNets which are inspired by Mixed Depthwise Convolutional Kernels. Extensive experiment evaluations on Label Face in the Wild (LFW), Age-DB, MegaFace, and IARPA Janus Benchmarks IJB-B and IJB-C datasets have shown the effectiveness of our MixFaceNets for applications requiring extremely low computational complexity. Under the same level of computation complexity (≤ 500M FLOPs), our MixFaceNets outperform MobileFaceNets on all the evaluated datasets, achieving 99.60% accuracy on LFW, 97.05% accuracy on AgeDB-30, 93.60 TAR (at FAR1e-6) on MegaFace, 90.94 TAR (at FAR1e-4) on IJB-B and 93.08 TAR (at FAR1e-4) on IJB-C. With computational complexity between 500M and 1G FLOPs, our MixFaceNets achieved results comparable to the top-ranked models, while using significantly fewer FLOPs and less computation over-head, which proves the practical value of our proposed Mix-FaceNets. All training codes, pre-trained models, and training logs have been made available https://github.com/fdbtrs/mixfacenets.
Secure image encryption scheme using 4D-Hyperchaotic systems based reconfigurable pseudo-random number generator and S-Box

This paper introduces the design of a hardware efficient reconfigurable pseudorandom number generator (PRNG) using two different feedback controllers based four-dimensional (4D) hyperchaotic systems i.e. Hyperchaotic-1 and -2 to provide confidentiality for digital images. The parameter's value of these two hyperchaotic systems is set to be a specific value to get the benefits i.e. all the multiplications (except a few multiplications) are performed using hardwired shifting operations rather than the binary multiplications, which doesn't utilize any hardware resource. The ordinary differential equations (ODEs) of these two systems have been exploited to build a generic architecture that fits in a single architecture. The proposed architecture provides an opportunity to switch between two different 4D hyperchaotic systems depending on the required behavior. To ensure the security strength, that can be also used in the encryption process in which encrypt the input data up to two times successively, each time using a different PRNG configuration. The proposed reconfigurable PRNG has been designed using Verilog HDL, synthesized on the Xilinx tool using the Virtex-5 (XC5VLX50T) and Zynq (XC7Z045) FPGA, its analysis has been done using Matlab tool. It has been found that the proposed architecture of PRNG has the best hardware performance and good statistical properties as it passes all fifteen NIST statistical benchmark tests while it can operate at 79.101-MHz or 1898.424-Mbps and utilize only 0.036 %, 0.23 %, and 1.77 % from the Zynq (XC7Z045) FPGA's slice registers, slice LUTs, and DSP blocks respectively. Utilizing these PRNGs, we design two 16 × 16 substitution boxes (S-boxes). The proposed S-boxes fulfill the following criteria: Bijective, Balanced, Non-linearity, Dynamic Distance, Strict Avalanche Criterion (SAC) and BIC non-linearity criterion. To demonstrate these PRNGs and S-boxes, a new three different scheme of image encryption algorithms have been developed: a) Encryption using S-box-1, b) Encryption using S-box-2 and, c) Two times encryption using S-box-1 and S-box-2. To demonstrate that the proposed cryptosystem is highly secure, we perform the security analysis (in terms of the correlation coefficient, key space, NPCR, UACI, information entropy and image encryption quantitatively in terms of (MSE, PSNR and SSIM)).
A traversable wormhole teleportation protocol in the SYK model

A bstract In this paper, we propose a concrete teleportation protocol in the SYK model based on a particle traversing a wormhole. The required operations for the communication, and insertion and extraction of the qubit, are all simple operators in terms of the basic qubits. We determine the effectiveness of this protocol, and find a version achieves almost perfect fidelity. Many features of semiclassical traversable wormholes are manifested in this setup.
A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer

Tumor-infiltrating lymphocytes (TILs) are clinically significant in triple-negative breast cancer (TNBC). Although a standardized methodology for visual TILs assessment (VTA) exists, it has several inherent limitations. We established a deep learning-based computational TIL assessment (CTA) method broadly following VTA guideline and compared it with VTA for TNBC to determine the prognostic value of the CTA and a reasonable CTA workflow for clinical practice.We trained three deep neural networks for nuclei segmentation, nuclei classification and necrosis classification to establish a CTA workflow. The automatic TIL (aTIL) score generated was compared with manual TIL (mTIL) scores provided by three pathologists in an Asian (n = 184) and a Caucasian (n = 117) TNBC cohort to evaluate scoring concordance and prognostic value.The intraclass correlations (ICCs) between aTILs and mTILs varied from 0.40 to 0.70 in two cohorts. Multivariate Cox proportional hazards analysis revealed that the aTIL score was associated with disease free survival (DFS) in both cohorts, as either a continuous [hazard ratio (HR)=0.96, 95% CI 0.94-0.99] or dichotomous variable (HR=0.29, 95% CI 0.12-0.72). A higher C-index was observed in a composite mTIL/aTIL three-tier stratification model than in the dichotomous model, using either mTILs or aTILs alone.The current study provides a useful tool for stromal TIL assessment and prognosis evaluation for patients with TNBC. A workflow integrating both VTA and CTA may aid pathologists in performing risk management and decision-making tasks.National Natural Science Foundation of China, Guangdong Medical Research Foundation, Guangdong Natural Science Foundation.
RaPiD: AI Accelerator for Ultra-low Precision Training and Inference

The growing prevalence and computational demands of Artificial Intelligence (AI) workloads has led to widespread use of hardware accelerators in their execution. Scaling the performance of AI accelerators across generations is pivotal to their success in commercial deployments. The intrinsic error-resilient nature of AI workloads present a unique opportunity for performance/energy improvement through precision scaling. Motivated by the recent algorithmic advances in precision scaling for inference and training, we designed RaPiD <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a 4-core AI accelerator chip supporting a spectrum of precisions, namely, 16 and 8-bit floating-point and 4 and 2-bit fixed-point. The 36mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> RaPiD chip fabricated in 7nm EUV technology delivers a peak 3.5 TFLOPS/W in HFP8 mode and 16.5 TOPS/W in INT4 mode at nominal voltage. Using a performance model calibrated to within 1% of the measurement results, we evaluated DNN inference using 4-bit fixed-point representation for a 4-core 1 RaPiD chip system and DNN training using 8-bit floating point representation for a 768 TFLOPs AI system comprising 4 32-core RaPiD chips. Our results show INT4 inference for batch size of 1 achieves 3 - 13.5 (average 7) TOPS/W and FP8 training for a mini-batch of 512 achieves a sustained 102 - 588 (average 203) TFLOPS across a wide range of applications.
ORSI Salient Object Detection via Multiscale Joint Region and Boundary Model

Salient object detection (SOD) in optical remote sense images (ORSIs) is a valuable and challenging task. The factors in ORSI, such as background clutter, lighting shadows, imaging blur, and low resolution, significantly degrade the completeness and accuracy of salient objects. To handle this problem, we propose a novel model to learn robust multiscale region features of salient objects by simultaneously optimizing their boundaries. First, we extract multiscale region features of salient objects through a hierarchical attention module. Second, we generate the boundary features by combining the local cues and the global information generated by pyramid pooling. Finally, we embed the boundary features into region features at multiple scales. In particular, we design a joint learning scheme based on a bidirectional feature transformation to optimize boundary and region features simultaneously for accurate ORSI SOD. To provide a comprehensive evaluation platform, we construct a new dataset called ORSI-4199 for ORSI SOD. It contains 4199 finely annotated image pairs with diverse scenes, in which nine attributes (i.e., challenge types) are annotated to facilitate analyzing the strengths and weaknesses of SOD models from different perspectives. Extensive experiments on the public dataset ORSSD, EORRSD, and the newly created dataset ORSI-4199 show that the proposed approach achieves promising results against state-of-the-art methods. <uri>https://github.com/wchao1213/ORSI-SOD</uri>.
Non-Wearable IoT-Based Smart Ambient Behavior Observation System

A growing number of advanced smart systems and solutions are being designed for the elderly, helping them to live longer at home. These systems need to provide unobtrusive monitoring and safety for their users and information for the healthcare professionals and family members. Multi-modal sensor data enables the possibility for in-depth behavioral analysis. To gather multi-modal data, we propose an IoT-based smart ambient behavior observation system (SABOS). SABOS provides unobtrusive monitoring of daily living activities by utilizing various sensors integrated into the residential house. To reduce the amount of data, we present a data reduction algorithm. The data reduction algorithm effectively reduces over 90% of the submitted data with full recovery in the cloud. Data is sent to ThingSpeak for MATLAB visualization and analysis to generate graphical illustrations of daily living activities. In an emergency, an “if this then that” (IFTTT) service combined with ThingSpeak triggers an applet to send a defined message to a healthcare professional or a family member.
Adaptive Fusion of Multi-Scale YOLO for Pedestrian Detection

Although pedestrian detection technology is constantly improving, pedestrian detection remains challenging because of the uncertainty and diversity of pedestrians in different scales and of occluded pedestrian modes. This study followed the common framework of single-shot object detection and proposed a divide-and-rule method to solve the aforementioned problems. The proposed model introduced a segmentation function that can split pedestrians who do not overlap in one image into two subimages. By using a network architecture, multiresolution adaptive fusion was performed on the output of all images and subimages to generate the final detection result. This study conducted an extensive evaluation of several challenging pedestrian detection data sets and finally proved the effectiveness of the proposed model. In particular, the proposed model achieved the most advanced performance on data sets from Visual Object Classes 2012 (VOC 2012), the French Institute for Research in Computer Science and Automation, and the Swiss Federal Institute of Technology in Zurich and obtained the most competitive results in a triple-width VOC 2012 experiment carefully designed by the present study.
Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation

The success of supervised learning hinges on the assumption that the training and test data come from the same underlying distribution, which is often not valid in practice due to potential distribution shift. In light of this, most existing methods for unsupervised domain adaptation focus on achieving domain-invariant representations and small source domain error. However, recent works have shown that this is not sufficient to guarantee good generalization on the target domain, and in fact, is provably detrimental under label distribution shift. Furthermore, in many real-world applications it is often feasible to obtain a small amount of labeled data from the target domain and use them to facilitate model training with source data. Inspired by the above observations, in this paper we propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA). First, we provide a finite sample bound for both classification and regression problems under Semi-DA. The bound suggests a principled way to obtain target generalization, i.e., by aligning both the marginal and conditional distributions across domains in feature space. Motivated by this, we then introduce the LIRR algorithm for jointly Learning Invariant Representations and Risks. Finally, extensive experiments are conducted on both classification and regression tasks, which demonstrate that LIRR consistently achieves state-of-the-art performance and significant improvements compared with the methods that only learn invariant representations or invariant risks. Our code will be released at LIRR@github
Dual Focal Loss to address class imbalance in semantic segmentation

A common problem in pixelwise classification or semantic segmentation is class imbalance, which tends to reduce the classification accuracy of minority-class regions. An effective way to address this is to tune the loss function, particularly when Cross Entropy (CE), is used for classification. Although several CE variants have been reported in previous studies to address this problem, for example, Weighted Cross Entropy (WCE), Dual Cross Entropy (DCE), and Focal Loss (FL), each has their own limitations, such as introducing a vanishing gradient, penalizing negative classes inversely, or a sub-optimal loss weighting between classes. This limits their ability to improve classification accuracy or reduces their ease of use. Focal Loss has proven to be effective at balancing loss by increasing the loss on hard-to-classify classes. However, it tends to produce a vanishing gradient during backpropagation. To address these limitations, a Dual Focal Loss (DFL) function is proposed to improve the classification accuracy of the unbalanced classes in a dataset. The proposed loss function modifies the loss scaling method of FL to be effective against a vanishing gradient. In addition, inspired by DCE, a regularization term has also been added to DFL to constrain the negative class labels to further reduce the vanishing gradient effect and increase the loss on hard-to-classify classes. Experimental results show that DFL has better training performance, and provides greater accuracy compared to CE, WCE, FL and DCE in every test run conducted over a variety of different network models and datasets.
ASU-Net++: A nested U-Net with adaptive feature extractions for liver tumor segmentation

Locating tumors from medical images is of high importance in medical analysis and diagnosis. To tackle the complicated shape of tumors, we propose a multi-leves.l feature extraction neural network to automatically segment the data. Our proposed model is trained and tested with one liver tumor ultrasound and two CT datasets. We employ ++, a collaborative model that uses modified nested U-Net, as our backbone. The model is integrated with dilated dense short skip connections within convolution blocks to further improve the gradient flow and feature preservation. In addition, we modify the original Atrous Spatial Pyramid Pooling (ASPP) to an adaptive pooling structure for better compatibility with nested U-Net. Adaptive ASPP is designed to extract features from different levels and cover the increasing range of feature extraction with regard to the depth of the nested network. Our model showed its advantage in accurately segmenting different tumor sizes with complex edges and was able to generalize with small and diverse datasets. We further improved our model with the newly introduced AdaBelief optimizer and achieved a faster convergence rate. Segmentation results showed that the proposed model outperformed multiple network structures, and achieved a 0.9153 dice coefficient for the ultrasound dataset, a 0.9413 and a 0.9246 dice coefficient for the two CT datasets.
DeepSBD: A Deep Neural Network Model With Attention Mechanism for SocialBot Detection

Online Social Networks (OSNs) are witnessing sophisticated cyber threats, that are generally conducted using fake or compromised profiles. Automated agents (aka socialbots), a category of sophisticated and modern threat entities, are the native of the social media platforms and responsible for various modern weaponized information-related attacks, such as astroturfing, misinformation diffusion, and spamming. Detecting socialbots is a challenging and vital task due to their deceiving character of imitating human behavior. To this end, this paper presents an attention-aware deep neural network model, DeepSBD, for detecting socialbots on OSNs. The DeepSBD models users' behavior using profile, temporal, activity, and content information. It jointly models OSN users' behavior using Bidirectional Long Short Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures. It models profile, temporal, and activity information as sequences, which are fed to a two-layers stacked BiLSTM, whereas content information is fed to a deep CNN. We have evaluated DeepSBD over five real-world benchmark datasets and found that it performs significantly better in comparison to the state-of-the-arts and baseline methods. We have also analyzed the efficacy of DeepSBD at different ratios of socialbots and benign users and found that an imbalanced dataset moderately affects the classification accuracy. Finally, we have analyzed the discrimination power of different behavioral components, and it is found that both profile characteristics and content behavior are most impactful, whereas diurnal temporal behavior is the least effective for detecting socialbots on OSNs.
Anchor-based Plain Net for Mobile Image Super-Resolution

Along with the rapid development of real-world applications, higher requirements on the accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing methods have achieved remarkable success, the majority of them demand plenty of computational resources and large amount of RAM, and thus they can not be well applied to mobile device. In this paper, we aim at designing efficient architecture for 8-bit quantization and deploy it on mobile device. First, we conduct an experiment about meta-node latency by decomposing lightweight SR architectures, which determines the portable operations we can utilize. Then, we dig deeper into what kind of architecture is beneficial to 8-bit quantization and propose anchor-based plain net (ABPN). Finally, we adopt quantization-aware training strategy to further boost the performance. Our model can outperform 8-bit quantized FSRCNN by nearly 2dB in terms of PSNR, while satisfying realistic needs at the same time. Code is avaliable at https://github.com/NJU-Jet/SR_Mobile_Quantization.
Bitcoin price forecasting: A perspective of underlying blockchain transactions

Cryptocurrency price forecasting plays an important role in financial markets. Traditional approaches face two challenges: (1) it is difficult to ascertain the influential factors related to price forecasting; and (2) due to the 24/7 trading policy, cryptocurrencies’ prices face very large fluctuations, thus weakening the forecasting power of traditional models. To address these issues, we focus on Bitcoin and identify the influential factors related to its price forecasting from the perspective of underlying blockchain transactions. We then propose a price forecasting model WT-CATCN, which leverages Wavelet Transform (WT) and Casual Multi-Head Attention (CA) Temporal Convolutional Network (TCN), to forecast cryptocurrency prices. Our model can capture important positions of input sequences and model the correlations among different data features. Using real-world Bitcoin trading data, we test and compare WT-CATCN with other state-of-the-art price forecasting models. The experiment results show that our model improves the price forecasting performance by 25%.
Decentralized federated learning for extended sensing in 6G connected vehicles

Research on smart connected vehicles has recently targeted the integration of vehicle-to-everything (V2X) networks with Machine Learning (ML) tools and distributed decision making. Among these convergent paradigms, Federated Learning (FL) allows the vehicles to train a deep ML model collaboratively, by exchanging model parameters (i.e., neural network weights and biases), rather than raw sensor data, via V2X links. Early FL approaches resorted to a server-client architecture, where a Parameter Server (PS) acts as edge device to orchestrate the learning process. Novel FL tools, on the other hand, target fog architectures where the model parameters are mutually shared by vehicles and synchronized in a distributed manner via consensus algorithms. These tools do not rely on the PS, but take advantage of low-latency V2X links. In line with this recent research direction, in this paper we investigate distributed FL methods for augmenting the capability of road user/object classification based on Lidar data. More specifically, we propose a new modular, decentralized approach to FL, referred to as consensus-driven FL (C-FL), suitable for PointNet compliant deep ML architectures and Lidar point cloud processing for road actor classification. The C-FL process is evaluated by simulating a realistic V2X network, based on the Collective Perception Service (CPS), for mutual sharing of the PointNet model parameters. The performance validation considers the impact of the degree of connectivity of the vehicular network, the benefits of continual learning over heterogeneous training data, convergence time and loss/accuracy tradeoffs. Experimental results indicate that C-FL complies with the extended sensors use cases for high levels of driving automation, it provides a low-latency training service, compared with existing distributed ML approaches, and it outperforms ego learning with minimal bandwidth usage.