Reviewerly/exHarmony huggingface dataset:
SIFT-CNN: When Convolutional Neural Networks Meet Dense SIFT Descriptors for Image and Sequence Classification

Despite the success of hand-crafted features in computer visioning for many years, nowadays, this has been replaced by end-to-end learnable features that are extracted from deep convolutional neural networks (CNNs). Whilst CNNs can learn robust features directly from image pixels, they require large amounts of samples and extreme augmentations. On the contrary, hand-crafted features, like SIFT, exhibit several interesting properties as they can provide local rotation invariance. In this work, a novel scheme combining the strengths of SIFT descriptors with CNNs, namely SIFT-CNN, is presented. Given a single-channel image, one SIFT descriptor is computed for every pixel, and thus, every pixel is represented as an M-dimensional histogram, which ultimately results in an M-channel image. Thus, the SIFT image is generated from the SIFT descriptors for all the pixels in a single-channel image, while at the same time, the original spatial size is preserved. Next, a CNN is trained to utilize these M-channel images as inputs by operating directly on the multiscale SIFT images with the regular convolution processes. Since these images incorporate spatial relations between the histograms of the SIFT descriptors, the CNN is guided to learn features from local gradient information of images that otherwise can be neglected. In this manner, the SIFT-CNN implicitly acquires a local rotation invariance property, which is desired for problems where local areas within the image can be rotated without affecting the overall classification result of the respective image. Some of these problems refer to indirect immunofluorescence (IIF) cell image classification, ground-based all-sky image-cloud classification and human lip-reading classification. The results for the popular datasets related to the three different aforementioned problems indicate that the proposed SIFT-CNN can improve the performance and surpasses the corresponding CNNs trained directly on pixel values in various challenging tasks due to its robustness in local rotations. Our findings highlight the importance of the input image representation in the overall efficiency of a data-driven system.
Fidelity Overhead for Nonlocal Measurements in Variational Quantum Algorithms

Measuring quantum observables by grouping terms that can be rotated to sums of only products of Pauli ẑ operators (Ising form) is proven to be efficient in near term quantum computing algorithms. This approach requires extra unitary transformations to rotate the state of interest so that the measurement of a fragment's Ising form would be equivalent to the measurement of the fragment for the unrotated state. These extra rotations allow one to perform a fewer number of measurements by grouping more terms into the measurable fragments with a lower overall estimator variance. However, previous estimations of the number of measurements did not take into account nonunit fidelity of quantum gates implementing the additional transformations. Through a circuit fidelity reduction, additional transformations introduce extra uncertainty and increase the needed number of measurements. Here we consider a simple model for errors introduced by additional gates needed in schemes involving groupings of commuting Pauli products. For a set of molecular electronic Hamiltonians, we confirm that the numbers of measurements in schemes using nonlocal qubit rotations are still lower than those in their local qubit rotation counterparts, even after accounting for uncertainties introduced by additional gates.
Crash 2 Squash: An Autonomous Drone for the Traversal of Narrow Passageways

Access and exploration of confined and cluttered spaces is a major challenge in search and rescue, maintenance of infrastructures, and environmental monitoring. However, existing drones can only access passageways that are 30% narrower of their size. Herein, a drone that can squeeze its way through arbitrarily long passages that are half its width is presented. This is achieved by developing a quadrotor that synergistically embodies a soft foldable frame, multimodal mobility, and autonomous navigation. The drone exploits visual perception to detect the entrance of the gap and aerial mobility to align and fly toward it. The entry is made possible by the soft design of the frame, which passively folds without breaking when the drone flies and then collides at a controlled speed with the entrance of the passage, i.e., the “crash to squash” entry maneuver. Once inside, the quadrotor uses terrestrial locomotion for the traversal. The mechanical design of the drone and the performance of the “crash to squash” entry maneuver in passageways of different sizes are experimentally characterized. Finally, the control method is validated by indoor autonomous flights.
Blockchain-Based Supply Chain System for Olive Fields Using WSNs

The agricultural domain in developing countries is mostly dictated by archaic rules based on traditions and inherited practices. With the evolution of digitalization and technology, it seems essential to apply new technologies to the agricultural field. Among the technologies to be exploited in agriculture, we mention sensors, IoT, WSN, cloud, blockchain, etc. We talk about smart agriculture in this case. In this paper, we propose a platform secured by blockchain for monitoring and securing production. This platform uses IoT connected sensors to track and save data. Our system is used to monitor the production process of olive trees. The goal is to track everything that enters and leaves our olive tree production from fertilizers, insecticides, and fortifiers to olives, trimming etc. The blockchain via its decentralized system allow a secure, irreversible, and clear monitoring. A dashboard allow us to highlight the changes while facilitating the work of farmers. Our prototype will be embedded via a Raspberry Pi 4 platform.
Semi-Supervised Authentically Distorted Image Quality Assessment With Consistency-Preserving Dual-Branch Convolutional Neural Network

Recently, convolutional neural networks (CNNs) have provided a favoured prospect for authentically distorted image quality assessment (IQA). For good performance, most existing CNN-based methods rely on a large amount of labeled data for training, which is time-consuming and cumbersome to collect. By simultaneously exploiting few labeled data and many unlabeled data, we make a pioneering attempt to propose a semi-supervised framework (termed SSLIQA) with consistency-preserving dual-branch CNN for authentically distorted IQA in this paper. The proposed SSLIQA introduces a consistency-preserving strategy and transfers two kinds of consistency knowledge from the teacher branch to the student branch. Concretely, SSLIQA utilizes the sample prediction consistency to train the student to mimic output activations of individual examples represented by the teacher. Considering that subjects often refer to previous analogous cases to make scoring decisions, SSLIQA computes the semantic relation among different samples in a batch and encourages the consistency of sample semantic relation between two branches to explore extra quality-related information. Benefiting from the consistency-preserving strategy, we can exploit numerous unlabeled data to improve network's effectiveness and generalization. Experimental results on three authentically distorted IQA databases show that the proposed SSLIQA is stably effective under different student-teacher combinations and different labeled-to-unlabeled data ratios. In addition, it points out a new way on how to achieve higher performance with a smaller network.
Generating Hypergraph-Based High-Order Representations of Whole-Slide Histopathological Images for Survival Prediction

Patient survival prediction based on gigapixel whole-slide histopathological images (WSIs) has become increasingly prevalent in recent years. A key challenge of this task is achieving an informative survival-specific global representation from those WSIs with highly complicated data correlation. This article proposes a multi-hypergraph based learning framework, called "HGSurvNet," to tackle this challenge. HGSurvNet achieves an effective high-order global representation of WSIs via multilateral correlation modeling in multiple spaces and a general hypergraph convolution network. It has the ability to alleviate over-fitting issues caused by the lack of training data by using a new convolution structure called hypergraph max-mask convolution. Extensive validation experiments were conducted on three widely-used carcinoma datasets: Lung Squamous Cell Carcinoma (LUSC), Glioblastoma Multiforme (GBM), and National Lung Screening Trial (NLST). Quantitative analysis demonstrated that the proposed method consistently outperforms state-of-the-art methods, coupled with the Bayesian Concordance Readjust loss. We also demonstrate the individual effectiveness of each module of the proposed framework and its application potential for pathology diagnosis and reporting empowered by its interpretability potential.
Impact of Outliers and Dimensionality Reduction on the Performance of Predictive Models for Medical Disease Diagnosis

Numerical electronic health records often come with numerous features and outliers. These features are usually indicators of medical diseases. To prevent poor model performance associated with the curse of dimensionality, these features must be reduced with only the most important ones retained. Also, outliers may result in poor generalization and performance of a machine learning model. This study investigates the impact of dimensionality reduction and outliers on machine learning models for medical disease diagnosis. After fitting datasets to the models, the impact of outliers and dimensionality reduction was evaluated using performance evaluation metrics. The accuracy for all models across the Wisconsin breast and heart dataset illustrates a minimum of 1.5% increase compared to when PCA was not implemented. Also, the precision, recall, and f1 score values show significant improvement. The naïve Bayes model recorded an accuracy of 73.6% in the absence of outliers and 72.7% in the presence of outliers when the Pima Indian diabetes dataset was used. This is less than 1% improvement in the model’s performance. The result indicates that dimensionality reduction significantly impacts the performance of models in diagnosing breast cancer and heart disease. Furthermore, outliers did not significantly impact the model’s performance when using Pima Indian and stroke datasets. Finally, this study depicts that problems associated with the curse of dimensionality persist with machine learning models in diagnosing medical diseases.
Auto-scaling containerized cloud applications: A workload-driven approach

Today, cloud computing presents new business opportunities as it offers various technological advantages including elastic computing and efficient pricing strategies. Although, cloud users have access to large amount of resources, it is yet a challenging task to efficiently manage the hardware resources in the cloud environment. In this article, we present PACE (Performance-aware Auto-scaler for Cloud Elasticity), a framework for auto-scaling containerized cloud applications based on workload demand. The framework offers a) reactive auto-scaling using threshold-based rules to avoid application failures during intensive workload tasks and b) proactive auto-scaling using convolutional neural networks (CNN) and K-means to generate elastic scaling policies that incorporate future workload demands. The experimental analysis is based on the Yahoo! Cloud Serving Benchmark (YCSB) executed in Redis containers deployed on the Google Cloud Platform. The proposed framework can automatically adjust cloud resources to satisfy workload demand and ensure Quality of Service (QoS) requirements.
Hybrid image embedding technique using Steganographic Signcryption and IWT-GWO methods

Image Steganography is the process of hiding information, which can be text, image or video inside a cover image. Recent steganography research hasn't addressed the problem of loss of secret information during extraction. Hence, to reduce information loss, the Integer Wavelet Transform (IWT) is used in this paper, and encryption algorithms are used to secure the secret image . The cover image is first converted into frequency domain using IWT, and then the best pixel positions of the cover image are selected using the Gray Wolf Optimization (GWO) algorithm to embed the secret image. The secret image is encrypted using the signcryption algorithm before being embedded into the cover image, which generates the stego image. In order to extract the secret image from the stego image, the inverse of IWT and unsigncryption methods were used. This method is implemented in the MATLAB working platform, and the results are analysed to demonstrate the system's effectiveness. From the results, one can infer that the proposed technique improved the PSNR by 2.5%, and error has been reduced by greater extent as compared to the existing methods.
The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition

Concerns regarding inappropriate leakage of sensitive personal information as well as unauthorized data use are increasing with the growth of genomic data repositories. Therefore, privacy and security of genomic data have become increasingly important and need to be studied. With many proposed protection techniques, their applicability in support of biomedical research should be well understood. For this purpose, we have organized a community effort in the past 8 years through the integrating data for analysis, anonymization and sharing consortium to address this practical challenge. In this article, we summarize our experience from these competitions, report lessons learned from the events in 2020/2021 as examples, and discuss potential future research directions in this emerging field.
Fuzzy support vector machine with graph for classifying imbalanced datasets

Since support vector machine (SVM) considers all the training samples equally, it suffers from the problems of noise/outliers and class imbalance. Although many fuzzy support vector machines (FSVMs) have been proposed to suppress the effect of noise/outliers and class imbalance, most of them ignore the impact of the curse of dimensionality on the discriminative performance of fuzzy membership function and do not give the fuzzy membership function corresponding to the kernel space, which seriously reduces the performance of FSVM. To solve these problems, we propose the fuzzy support vector machine with graph (GraphFSVM) in this paper. Specifically, we first design a graph-based fuzzy membership function to accurately assess the importance of samples in original feature space and prove that the function can mine discriminative information between samples in high-dimensional data. Additionally, since the data distribution in kernel space is different from those in the original feature space, a method is provided to calculate the fuzzy membership function in the kernel space. Finally, the GraphFSVM model analyzes samples of each class independently, this suppresses the effect of class imbalance. Following the above principles, we design the graph-based fuzzy support vector machine and propose a detailed optimization method. Experimental results on UCI, gene expression, and image datasets show that the GraphFSVM has better generalization and robustness than other state-of-the-art methods.
Transformer and group parallel axial attention co-encoder for medical image segmentation

U-Net has become baseline standard in the medical image segmentation tasks, but it has limitations in explicitly modeling long-term dependencies. Transformer has the ability to capture long-term relevance through its internal self-attention. However, Transformer is committed to modeling the correlation of all elements, but its awareness of local foreground information is not significant. Since medical images are often presented as regional blocks, local information is equally important. In this paper, we propose the GPA-TUNet by considering local and global information synthetically. Specifically, we propose a new attention mechanism to highlight local foreground information, called group parallel axial attention (GPA). Furthermore, we effectively combine GPA with Transformer in encoder part of model. It can not only highlight the foreground information of samples, but also reduce the negative influence of background information on the segmentation results. Meanwhile, we introduced the sMLP block to improve the global modeling capability of network. Sparse connectivity and weight sharing are well achieved by applying it. Extensive experiments on public datasets confirm the excellent performance of our proposed GPA-TUNet. In particular, on Synapse and ACDC datasets, mean DSC(%) reached 80.37% and 90.37% respectively, mean HD95(mm) reached 20.55 and 1.23 respectively.
Immersive VR versus BIM for AEC Team Collaboration in Remote 3D Coordination Processes

Building Information Modeling (BIM) and Virtual Reality (VR) are both tools for collaboration and communication, yet questions still exist as to how and in what ways these tools support technical communication and team decision-making. This paper presents the results of an experimental research study that examined multidisciplinary Architecture, Engineering, and Construction (AEC) team collaboration efficiency in remote asynchronous and synchronous communication methods for 3D coordination processes by comparing BIM and immersive VR both with markup tools. Team collaboration efficiency was measured by Shared Understanding, a psychological method based on Mental Models. The findings revealed that the immersive experience in VR and its markup tool capabilities, which enabled users to draw in a 360-degree environment, supported team communication more than the BIM markup tool features, which allowed only one user to draw on a shared 2D screenshot of the model. However, efficient team collaboration in VR required the members to properly guide each other in the 360-degree environment; otherwise, some members were not able to follow the conversations.
Occlusion-aware light field depth estimation with view attention

Depth estimation for light field images is crucial in light field applications such as image-based rendering and refocusing. Previous learning-based methods com bining neural network with cost volume can achieve accurate depth estimation but fail in regions with occlusion. In this paper, a two-stage attention-based occlusion-aware light field depth estimation network is proposed. In the initial depth estimation stage, the sub-aperture images are divided into four groups based on different view directions and four initial cost volumes are constructed using the feature maps from each group to aggregate initial depth maps. Then in the refined depth estimation stage, the four aggregated volumes from the initial stage are fused into one based on the view attention, where features of views with less occlusion are highly weighted to provide more effective information. Experiment results demonstrate that the proposed method can accomplish robust and accurate depth estimation in the presence of occlusion, which ranks the first place on 4D light field benchmark in terms of most accuracy metrics.
Stochastic distributed learning with gradient quantization and double-variance reduction

We consider distributed optimization over several devices, each sending incremental model updates to a central server. This setting is considered, for instance, in federated learning. Various schemes have been designed to compress the model updates in order to reduce the overall communication cost. However, existing methods suffer from a significant slowdown due to additional variance ω>0 coming from the compression operator and as a result, only converge sublinearly. What is needed is a variance reduction technique for taming the variance introduced by compression. We propose the first methods that achieve linear convergence for arbitrary compression operators. For strongly convex functions with condition number κ, distributed among n machines with a finite-sum structure, each worker having less than m components, we also (i) give analysis for the weakly convex and the non-convex cases and (ii) verify in experiments that our novel variance reduced schemes are more efficient than the baselines. Moreover, we show theoretically that as the number of devices increases, higher compression levels are possible without this affecting the overall number of communications in comparison with methods that do not perform any compression. This leads to a significant reduction in communication cost. Our general analysis allows to pick the most suitable compression for each problem, finding the right balance between additional variance and communication savings. Finally, we also (iii) give analysis for arbitrary quantized updates.
Recent Progress of Neuromorphic Computing Based on Silicon Photonics: Electronic–Photonic Co-Design, Device, and Architecture

The rapid development of neural networks has led to tremendous applications in image segmentation, speech recognition, and medical image diagnosis, etc. Among various hardware implementations of neural networks, silicon photonics is considered one of the most promising approaches due to its CMOS compatibility, accessible integration platforms, mature fabrication techniques, and abundant optical components. In addition, neuromorphic computing based on silicon photonics can provide massively parallel processing and high-speed operations with low power consumption, thus enabling further exploration of neural networks. Here, we focused on the development of neuromorphic computing based on silicon photonics, introducing this field from the perspective of electronic–photonic co-design and presenting the architecture and algorithm theory. Finally, we discussed the prospects and challenges of neuromorphic silicon photonics.
Knowledge Graphs and Explainable AI in Healthcare

Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable.