Reviewerly/exHarmony huggingface dataset:
Mitotic Nuclei Detection in Breast Histopathology Images using YOLOv4

World Health Organization (WHO) has reported that breast cancer is the most often found cancer in women and it is adversary affecting millions of women all around the world. Early detection and real-time screening can immensely assist the patient. Mitotic nuclei detection in breast histopathology images plays a critical function to evaluate the aggressiveness of the cancer malignancy. Cancer is identified by pathologists by analyzing histopathology images of tissues and determines numerous biomarkers. Since there is only minute variation among mitotic and not mitotic cells, this procedure is tedious, time-consuming, and instinctive. Various image processing techniques and deep learning models had been proposed to automate the procedure of detecting mitotic cells from the histopathology images. Traditional techniques commonly perform nuclei segmentation followed by classification which calls for immoderate computational resources. These models also lack expected accuracy due to the shortage of proper balanced datasets and errors during image staining. In this paper, we define the challenges as an object detection task, wherein the mitotic nuclei are directly predicted without nuclei segmentation in a single step using YOLOv4, which is a fast-operating object detection model. The model was trained with 506 mitosis instances from the openly available MITOS-ATYPIA-14 grand challenge dataset that comprises hematoxylin and eosin (H&E) stained breast histopathology images annotated by experienced pathologists. The outcome suggests that the YOLOv4 model with RGB images as input offers an F-measure of 0.73 and can be used as a dependable and much less computationally exhaustive approach among the prevailing ones.
Simplifying Paragraph-Level Question Generation via Transformer Language Models

Question Generation (QG) is an important task in Natural Language Processing (NLP) that involves generating questions automatically when given a context paragraph. While many techniques exist for the task of QG, they employ complex model architectures, extensive features, and additional mechanisms to boost model performance. In this work, we show that transformer-based finetuning techniques can be used to create robust question generation systems using only a single pretrained language model, without the use of additional mechanisms, answer metadata, and extensive features. Our best model outperforms previous more complex RNN-based Seq2Seq models, with an 8.62 and a 14.27 increase in METEOR and ROUGE_L scores, respectively. We show that it also performs on par with Seq2Seq models that employ answer-awareness and other special mechanisms, despite being only a single-model system. We analyze how various factors affect the model's performance, such as input data formatting, the length of the context paragraphs, and the use of answer-awareness. Lastly, we also look into the model's failure modes and identify possible reasons why the model fails.
FastStereoNet: A Fast Neural Architecture Search for Improving the Inference of Disparity Estimation on Resource-Limited Platforms

Convolutional neural networks (CNNs) provide the best accuracy for disparity estimation. However, CNNs are computationally expensive, making them unfavorable for resource-limited devices with real-time constraints. Recent advances in neural architectures search (NAS) promise opportunities in automated optimization for disparity estimation. However, the main challenge of the NAS methods is the significant amount of computing time to explore a vast search space [e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.6\times 10^{29}$ </tex-math></inline-formula> ] and costly training candidates. To reduce the NAS computational demand, many proxy-based NAS methods have been proposed. Despite their success, most of them are designed for comparatively small-scale learning tasks. In this article, we propose a fast NAS method, called FastStereoNet, to enable resource-aware NAS within an intractably large search space. FastStereoNet automatically searches for hardware-friendly CNN architectures based on late acceptance hill climbing (LAHC), followed by simulated annealing (SA). FastStereoNet also employs a fine-tuning with a transferred weights mechanism to improve the convergence of the search process. The collection of these ideas provides competitive results in terms of search time and strikes a balance between accuracy and efficiency. Compared to the state of the art, FastStereoNet provides <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.25\times $ </tex-math></inline-formula> reduction in search time and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$44.4\times $ </tex-math></inline-formula> reduction in model size. These benefits are attained while yielding a comparable accuracy that enables seamless deployment of disparity estimation on resource-limited devices. Finally, FastStereoNet significantly improves the perception quality of disparity estimation deployed on field-programmable gate array and Intel Neural Compute Stick 2 accelerator in a significantly less onerous manner.
Multi-Label Image Classification via Category Prototype Compositional Learning

Real-world images are often compositions of multiple objects with different categories, scales, poses and locations. Adding nonexistent objects to an image (composing) or removing existent objects from an image (decomposing) leads to higher discrepancy in appearance, which reveals an important but long-neglected compositional nature of multi-label images. In light of this observation, we propose a novel end-to-end compositional learning framework named Category Prototype Compositional Learning (CPCL) to model such compositional nature for multi-label image classification. In CPCL, each image is represented by a collection of category-related features used to eliminate the negative effects from location information. Then, a compositional learning module is introduced to compose and decompose the category-related features with their corresponding category prototypes, which are derived from the semantic representations of categories. If the image has the given object, the output after composing should be closer to the original input than the output after decomposing. Contrarily, if the image does not have the given object, the output after decomposing should be closer to the original input than the output after composing. We introduce the Transformed Appearance Distance (TAD) to measure the appearance change between the composed and decomposed features relative to the category-related features with respect to each category. Finally, multi-label image classification is accomplished by performing a TAD-based metric learning. Experimental results on three multi-label image classification benchmarks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , NUS-WIDE, MS-COCO and VOC 2007, validate the effectiveness and superiority of our work in comparison with the state-of-the-arts. The source codes of our model have been released on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ZFT-CQU/CPCL</uri> .
Human skeleton representation for 3D action recognition based on complex network coding and LSTM

3D skeleton sequences contain more effective and discriminative information than RGB video and are more suitable for human action recognition. Accurate extraction of human skeleton information is the key to the high accuracy of action recognition. Considering the correlation between joint points, in this work, we first propose a skeleton feature extraction method based on complex network. The relationship between human skeleton points in each frame is coded as a network. The changes of action over time are described by a time series network composed of skeleton points. Network topology attributes are used as feature vectors, complex network coding and LSTM are combined to recognize human actions. The method was verified on the NTU RGB + D60, MSR Action3D and UTKinect-Action3D dataset, and have achieved good performance, respectively. It shows that the method of extracting skeleton features based on complex network can properly identify different actions. This method that considers the temporal information and the relationship between skeletons at the same time plays an important role in the accurate recognition of human actions.
Ensemble Neuroevolution-Based Approach for Multivariate Time Series Anomaly Detection

Multivariate time series anomaly detection is a very common problem in the field of failure prevention. Fast prevention means lower repair costs and losses. The amount of sensors in novel industry systems makes the anomaly detection process quite difficult for humans. Algorithms which automates the process of detecting anomalies are crucial in modern failure-prevention systems. Therefore, many machine and deep learning models have been designed to address this problem. Mostly, they are autoencoder-based architectures with some generative adversarial elements. In this work, a framework is shown which incorporates neuroevolution methods to boost the anomaly-detection scores of new and already known models. The presented approach adapts evolution strategies for evolving ensemble model, in which every single model works on a subgroup of data sensors. The next goal of neuroevolution is to optimise architecture and hyperparameters like window size, the number of layers, layer depths, etc. The proposed framework shows that it is possible to boost most of the anomaly detection deep learning models in a reasonable time and a fully automated mode. The tests were run on SWAT and WADI datasets. To our knowledge, this is the first approach in which an ensemble deep learning anomaly detection model is built in a fully automatic way using a neuroevolution strategy.
An Improved Animal Migration Optimization Algorithm to Train the Feed-Forward Artificial Neural Networks

The most important and demanding part of the artificial neural network is the training process which involves finding the most suitable values for the weights in the network architecture, a challenging optimization problem. Gradient approaches and the meta-heuristic approaches are two methods extensively used to optimize the weights in the network. Gradient approaches have serious disadvantages including getting stuck in local optima, inadequate exploration, etc. To overcome these disadvantages, meta-heuristic approaches are preferred in training the artificial neural network instead of gradient methods. Therefore, in this study, an improved animal migration optimization algorithm with the Lévy flight feature was proposed to train the multilayer perceptron. The proposed hybrid algorithm is named IAMO-MLP. The main contributions of this article are that the IAMO algorithm was developed, the IAMO-MLP algorithm can successfully escape from local optima, and the initial positions did not affect the performance of the IAMO-MLP algorithm. The enhanced algorithm was tested and validated against a wider set of benchmark functions and indicated that it substantially outperformed the original implementation. Afterward, the IAMO-MLP was compared with ten algorithms on five classification problems (xor, balloon, iris, breast cancer, and heart) and one real-world problem in terms of mean squared error, classification accuracy, and nonparametric statistical Friedman test. According to the results, the IAMO was successful in training the multilayer perceptron.
High-Performance FPGA-based Accelerator for Bayesian Neural Networks

Neural networks (NNs) have demonstrated their potential in a wide range of applications such as image recognition, decision making or recommendation systems. However, standard NNs are unable to capture their model uncertainty which is crucial for many safety-critical applications including healthcare and autonomous vehicles. In comparison, Bayesian neural networks (BNNs) are able to express uncertainty in their prediction via a mathematical grounding. Nevertheless, BNNs have not been as widely used in industrial practice, mainly because of their expensive computational cost and limited hardware performance. This work proposes a novel FPGA based hardware architecture to accelerate BNNs inferred through Monte Carlo Dropout. Compared with other state-of-the-art BNN accelerators, the proposed accelerator can achieve up to 4 times higher energy efficiency and 9 times better compute efficiency. Considering partial Bayesian inference, an automatic framework is proposed, which explores the trade-off between hardware and algorithmic performance. Extensive experiments are conducted to demonstrate that our proposed framework can effectively find the optimal points in the design space.
Dynamic Scheduling for Heterogeneous Federated Learning in Private 5G Edge Networks

Private 5G edge networks support secure and private service, spectrum flexibility, and edge intelligence. In this paper, we aim to design a dynamic scheduling policy to explore the spectrum flexibility for heterogeneous federated learning (FL) in private 5G edge networks. Particularly, FL is implemented with multiple communication rounds, in each of which the scheduled device receives the global model from the edge server, updates its local model, and sends the updated local model to the edge server for global aggregation. The heterogeneity in FL comes from unbalanced data sizes across devices and diverse device capabilities. In this regard, we start with the convergence analysis of FL to determine the role of unbalanced data sizes in the learning performance. Then, based on the fact that diverse device capabilities make the completion times of local updates asynchronous, we adopt the sequential transmission for global aggregation. On this basis, we formulate a heterogeneity-aware dynamic scheduling problem to minimize the global loss function, with the consideration of straggler and limited device energy issues. By solving the formulated problem, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u> ynam <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</u> c <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sc</u> heduling alg <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> rithm (DISCO), to make an intelligent decision on the set and order of scheduled devices in each communication round. Theoretical analysis reveals that under certain conditions, the learning performance and energy constraints can be guaranteed in the DISCO. Finally, we demonstrate the superiority of the DISCO through numerical and experimental results, respectively.
Federated or Split? A Performance and Privacy Analysis of Hybrid Split and Federated Learning Architectures

Mobile phones, wearable devices, and other sensors produce every day a large amount of distributed and sensitive data. Classical machine learning approaches process these large datasets usually on a single machine, training complex models to obtain useful predictions. To better preserve user and data privacy and at the same time guarantee high performance, distributed machine learning techniques such as Federated and Split Learning have been recently proposed. Both of these distributed learning architectures have merits but also drawbacks. In this work, we analyze such tradeoffs and propose a new hybrid Federated Split Learning architecture, to combine the benefits of both in terms of efficiency and privacy. Our evaluation shows how Federated Split Learning may reduce the computational power required for each client running a Federated Learning and enable Split Learning parallelization while maintaining a high prediction accuracy with unbalanced datasets during training. Furthermore, FSL provides a better accuracy-privacy tradeoff in specific privacy approaches compared to Parallel Split Learning.
eID and Self-Sovereign Identity Usage: An Overview

The COVID-19 pandemic helped countries to increase the use of their mobile eID solutions. These are based on traditional identity management systems, which suffer from weaknesses, such as the reliance on a central entity to provide the identity data and the lack of control of the user over her or his data. The introduction of self-sovereign identity (SSI) for e-government systems can strengthen the privacy of the citizens while enabling identification also for the weakest. To successfully initiate SSI, different factors have to be taken into account. In order to have a clear understanding of the challenges, but also lessons learned, we provide an overview of existing solutions and projects and conducted an analysis of their experiences. Based on a taxonomy, we identified strong points, as well as encountered challenges. The contribution of this paper is threefold: First, we enhanced existing taxonomies based on the literature for further evaluations. Second, we analyzed eID solutions for lessons learned. Third, we evaluated more recently started SSI projects in different states of their lifecycle. This led to a comprehensive discussion of the lessons learned and challenges to address, as well as further findings.
Privacy-preserving genotype imputation with fully homomorphic encryption

Genotype imputation is the inference of unknown genotypes using known population structure observed in large genomic datasets; it can further our understanding of phenotype-genotype relationships and is useful for QTL mapping and GWASs. However, the compute-intensive nature of genotype imputation can overwhelm local servers for computation and storage. Hence, many researchers are moving toward using cloud services, raising privacy concerns. We address these concerns by developing an efficient, privacy-preserving algorithm called p-Impute. Our method uses homomorphic encryption, allowing calculations on ciphertext, thereby avoiding the decryption of private genotypes in the cloud. It is similar to k-nearest neighbor approaches, inferring missing genotypes in a genomic block based on the SNP genotypes of genetically related individuals in the same block. Our results demonstrate accuracy in agreement with the state-of-the-art plaintext solutions. Moreover, p-Impute is scalable to real-world applications as its memory and time requirements increase linearly with the increasing number of samples. p-Impute is freely available for download here: https://doi.org/10.5281/zenodo.5542001.
Integration of Spatial and Frequency Domain Encryption for Digital Images

Transmission of multimedia data such as images, videos, and audio over the Internet is risky due to cyberattacks.To overcome the security issues, several encryption schemes are proposed over the last few decades which also possess few vulnerabilities such as time inefficiency and weak security.In this research, to provide the highest level of security to the digital data, chaos is incorporated for the scrambling of rows and columns of the plaintext image.Further, a noisy image is generated based on the chaotic logistic map and the suitable initial conditions which are selected based on the analysis performed.For the reduction of the encryption computational time, a Discrete Wavelet Transform (DWT) is used in which only low-frequency bands are encrypted because most of the plaintext information lies in such frequency bands.To gauge the performance of the proposed encryption scheme, several security tests such as entropy, correlation, energy, peak signal to noise ratio, mean square error, keyspace, and key sensitivity analysis, noise-resistant, and cropping attack analyses are performed.From the cropping and noise attack analysis, we have found that the proposed encryption algorithm can decrypt the plaintext image with negligible loss of information but the content of the plaintext image can be visualized.
Optimal Time Difference-Based TDCP-GPS/IMU Navigation Using Graph Optimization

Global positioning system (GPS) has been widely used to estimate the absolute position of outdoor vehicles. Global positioning system and inertial measurement unit (IMU) can be integrated to deliver an enhanced effect over the individual system. GPS/IMU navigation solution that utilizes time-differenced carrier phase (TDCP) as observation can achieve high-precision positioning. The traditional TDCP-GPS/IMU navigation methods are compromised by the loss of constraints, especially when cycle slip occurs. This paper proposes an optimal time difference-based TDCP-GPS/IMU navigation using graph optimization. Different from current methods, an optimal time difference strategy is designed to formulate integer-free constraints that span multiple epochs in a time window. Each satellite can be fully used to restrain the accumulation errors even through cycle slip happens. Then the integer-free constraints are fused with relative constraints from IMU pre-integration and TDCP between two successive epochs using graph optimization. Field tests are carried out to validate the performance of the proposed method. The results show that the proposed method can make the most of window length constraints to improve the positioning performance compared with traditional methods.
Learning latent features with local channel drop network for vehicle re-identification

Vehicle re-identification targets to find the target vehicle images in a large dataset which is composed of vehicle images from multiple non-overlapping cameras. Due to the various illumination, viewpoints and resolutions, it is challenging to find the right vehicle images accurately. Most existing works put emphasis on learning strong features by exploiting the attention parts in vehicle images, which leads to some small important cues being suppressed by these significant parts. Hence, a local channel drop network (LCDNet) is proposed in this paper, which focuses on seeking the latent features by releasing the constraint of most attentive features. Specially, besides the normal local feature learning network, LCDNet consists of an attentive local feature learning branch that drops some regions to promote learning the attentive features of local regions. Besides, the batch ranking loss is introduced to split the samples into two groups in a batch and regularize them by enforcing a margin, which ensures the model to learn meaningful features to distinct vehicles. Moreover, to further calculate the similarity of various images, the paper proposes a multi-distance based ranking method to achieve more accurate results. Experiments on several benchmark datasets validate the effectiveness of the proposed method.
Antiferromagnetic spatial photonic Ising machine through optoelectronic correlation computing

Recently, spatial photonic Ising machines (SPIM) have been demonstrated to compute the minima of Hamiltonians for large-scale spin systems. Here we propose to implement an antiferromagnetic model through optoelectronic correlation computing with SPIM. Also we exploit the gauge transformation which enables encoding the spins and the interaction strengths in a single phase-only spatial light modulator. With a simple setup, we experimentally show the ground state search of an antiferromagnetic model with $40000$ spins in number-partitioning problem. Thus such an optoelectronic computing exhibits great programmability and scalability for the practical applications of studying statistical systems and combinatorial optimization problems.