Reviewerly/exHarmony huggingface dataset:
Deep Randomized Neural Networks

Randomized Neural Networks explore the behavior of neural systems where the majority of connections are fixed, either in a stochastic or a deterministic fashion. Typical examples of such systems consist of multi-layered neural network architectures where the connections to the hidden layer(s) are left untrained after initialization. Limiting the training algorithms to operate on a reduced set of weights inherently characterizes the class of Randomized Neural Networks with a number of intriguing features. Among them, the extreme efficiency of the resulting learning processes is undoubtedly a striking advantage with respect to fully trained architectures. Besides, despite the involved simplifications, randomized neural systems possess remarkable properties both in practice, achieving state-of-the-art results in multiple domains, and theoretically, allowing to analyze intrinsic properties of neural architectures (e.g. before training of the hidden layers' connections). In recent years, the study of Randomized Neural Networks has been extended towards deep architectures, opening new research directions to the design of effective yet extremely efficient deep learning models in vectorial as well as in more complex data domains. This chapter surveys all the major aspects regarding the design and analysis of Randomized Neural Networks, and some of the key results with respect to their approximation capabilities. In particular, we first introduce the fundamentals of randomized neural models in the context of feed-forward networks (i.e., Random Vector Functional Link and equivalent models) and convolutional filters, before moving to the case of recurrent systems (i.e., Reservoir Computing networks). For both, we focus specifically on recent results in the domain of deep randomized systems, and (for recurrent models) their application to structured domains.
Auto-weighted multi-view clustering via spectral embedding

As is well-known, multi-view clustering has attracted considerable attention since many benchmark data sets exist heterogeneous features. Previous multi-view spectral clustering methods mainly contain two steps: 1) constructing multiple similarity graphs; 2) performing K-means (KM) clustering. The two-step strategy cannot acquire optimal results since the clustering performance highly relies on the constructed similarity graphs. To address this drawback, a unified framework named as an Auto-weighted Multi-view Clustering via Spectral Embedding (AMCSE) is presented. In the new proposed method, it can consider the clustering capacity heterogeneity of different views as well as directly obtain the clustering results. More importantly, the unified framework can make multiple graph learning guide the clustering result discretization, while the latter is in turn to conduct to learn better spectral embedding. A series of experiments are conducted on six real-world data sets, and the clustering results verify that the proposed method is not only effective but also efficient, comparing with state-of-the-art graph-based multi-view clustering approaches.
Optimality Study of Existing Quantum Computing Layout Synthesis Tools

Layout synthesis, an important step in quantum computing, processes quantum circuits to satisfy device layout constraints. In this paper, we construct QUEKO benchmarks for this problem, which have known optimal depths and gate counts. We use QUEKO to evaluate the optimality of current layout synthesis tools, including Cirq from Google, Qiskit from IBM, $\mathsf{t}|\mathsf{ket}\rangle$ from Cambridge Quantum Computing, and recent academic work. To our surprise, despite over a decade of research and development by academia and industry on compilation and synthesis for quantum circuits, we are still able to demonstrate large optimality gaps: 1.5-12x on average on a smaller device and 5-45x on average on a larger device. This suggests substantial room for improvement of the efficiency of quantum computer by better layout synthesis tools. Finally, we also prove the NP-completeness of the layout synthesis problem for quantum computing. We have made the QUEKO benchmarks open-source.
Robust normal vector estimation in 3D point clouds through iterative principal component analysis

This paper introduces a robust normal vector estimator for point cloud data. It can handle sharp features as well as smooth areas. Our method is based on the inclusion of a robust estimator into a Principal Component Analysis in the neighborhood of the studied point, so that it can detect and reject outliers automatically during the estimation. A projection process ensures robustness against noise. Two automatic initializations are computed, leading to independent optimizations making the algorithm robust to neighborhood anisotropy around sharp features. An evaluation has been carried out in which the algorithm is compared to state-of-the-art methods. The results show that it is more robust against low and/or non-uniform samplings, high noise levels and outliers. Moreover, our algorithm is fast relative to existing methods handling sharp features. The code is available on the website: https://projet.liris.cnrs.fr/pcr/, and integrated in the platform: https://github.com/MEPP-team/MEPP2.
CarePre

Clinical decision support systems (CDSS) are widely used to assist with medical decision making. However, CDSS typically require manually curated rules and other data which are difficult to maintain and keep up-to-date. Recent systems leverage advanced deep learning techniques and electronic health records (EHR) to provide more timely and precise results. Many of these techniques have been developed with a common focus on predicting upcoming medical events. However, while the prediction results from these approaches are promising, their value is limited by their lack of interpretability. To address this challenge, we introduce CarePre, an intelligent clinical decision assistance system. The system extends a state-of-the-art deep learning model to predict upcoming diagnosis events for a focal patient based on his/her historical medical records. The system includes an interactive framework together with intuitive visualizations designed to support the diagnosis, treatment outcome analysis, and the interpretation of the analysis results. We demonstrate the effectiveness and usefulness of CarePre system by reporting results from a quantities evaluation of the prediction algorithm and a case study and three interviews with senior physicians.
PlotQA: Reasoning over Scientific Plots

Existing synthetic datasets (Figure QA, DVQA) for reasoning over plots do not contain variability in data labels, real-valued data, or complex reasoning questions. Consequently, proposed models for these datasets do not fully address the challenge of reasoning over plots. In particular, they assume that the answer comes either from a small fixed size vocabulary or from a bounding box within the image. However, in practice, this is an unrealistic assumption because many questions require reasoning and thus have real-valued answers which appear neither in a small fixed size vocabulary nor in the image. In this work, we aim to bridge this gap between existing datasets and real-world plots. Specifically, we propose Plot QA with 28.9 million question-answer pairs over 224,377 plots on data from real-world sources and questions based on crowd-sourced question templates. Further, 80.76% of the out-of-vocabulary (OOV) questions in PlotQA have answers that are not in a fixed vocabulary. Analysis of existing models on Plot QA reveals that they cannot deal with OOV questions: their overall accuracy on our dataset is in single digits. This is not surprising given that these models were not designed for such questions. As a step towards a more holistic model which can address fixed vocabulary as well as OOV questions, we propose a hybrid approach: Specific questions are answered by choosing the answer from a fixed vocabulary or by extracting it from a predicted bounding box in the plot, while other questions are answered with a table question-answering engine which is fed with a structured table generated by detecting visual elements from the image. On the existing DVQA dataset, our model has an accuracy of 58%, significantly improving on the highest reported accuracy of 46%. On Plot QA, our model has an accuracy of 22.52%, which is significantly better than state of the art models.
Reaction diffusion system prediction based on convolutional neural network

Abstract The reaction-diffusion system is naturally used in chemistry to represent substances reacting and diffusing over the spatial domain. Its solution illustrates the underlying process of a chemical reaction and displays diverse spatial patterns of the substances. Numerical methods like finite element method (FEM) are widely used to derive the approximate solution for the reaction-diffusion system. However, these methods require long computation time and huge computation resources when the system becomes complex. In this paper, we study the physics of a two-dimensional one-component reaction-diffusion system by using machine learning. An encoder-decoder based convolutional neural network (CNN) is designed and trained to directly predict the concentration distribution, bypassing the expensive FEM calculation process. Different simulation parameters, boundary conditions, geometry configurations and time are considered as the input features of the proposed learning model. In particular, the trained CNN model manages to learn the time-dependent behaviour of the reaction-diffusion system through the input time feature. Thus, the model is capable of providing concentration prediction at certain time directly with high test accuracy (mean relative error &lt;3.04%) and 300 times faster than the traditional FEM. Our CNN-based learning model provides a rapid and accurate tool for predicting the concentration distribution of the reaction-diffusion system.
A copy-move image forgery detection technique based on tetrolet transform

Copy-move forgery is a common type of forgery in digital images. In copy-move forgery, one part of the image is replicated within the same image, generally at different location. For revival of trustworthiness of images, there is a need to develop an efficient and robust technique to detect such forgeries. This paper proposes a new copy-move image forgery detection technique based on Tetrolet transform. In this technique, initially the input image is divided into overlapping blocks, then four low-pass coefficients and twelve high-pass coefficients are extracted from each block by applying Tetrolet transform. Feature vectors are then sorted lexicographically, and similar blocks are identified by matching the extracted Tetrolet features. Experimental results show that the proposed technique can detect and locate the duplicated regions in the images very accurately, even when the copied regions have undergone some post-processing operations blurring, color reduction, adjustment of brightness and contrast, rotation, scaling, JPEG compression. In addition, it is also observed that the proposed technique is able to detect very small duplicated regions and multiple forgery cases, even when image is smooth.
Mechanical MNIST: A benchmark dataset for mechanical metamodels

Metamodels, or models of models, map defined model inputs to defined model outputs. Typically, metamodels are constructed by generating a dataset through sampling a direct model and training a machine learning algorithm to predict a limited number of model outputs from varying model inputs. When metamodels are constructed to be computationally cheap, they are an invaluable tool for applications ranging from topology optimization, to uncertainty quantification, to multi-scale simulation. By nature, a given metamodel will be tailored to a specific dataset. However, the most pragmatic metamodel type and structure will often be general to larger classes of problems. At present, the most pragmatic metamodel selection for dealing with mechanical data has not been thoroughly explored. Drawing inspiration from the benchmark datasets available to the computer vision research community, we introduce a benchmark data set (Mechanical MNIST) for constructing metamodels of heterogeneous material undergoing large deformation. We then show examples of how our benchmark dataset can be used, and establish baseline metamodel performance. Because our dataset is readily available, it will enable the direct quantitative comparison between different metamodeling approaches in a pragmatic manner. We anticipate that it will enable the broader community of researchers to develop improved metamodeling techniques for mechanical data that will surpass the baseline performance that we show here.
Prediction of total organic carbon at Rumaila oil field, Southern Iraq using conventional well logs and machine learning algorithms

Total organic carbon (TOC) is an important parameter for assessing the hydrocarbon potential of source rocks. The standard method for analysis of TOC is the Rock-Eval pyrolysis on cutting and core samples. The coring process is always expensive and time consuming. Therefore, researchers around the world focused on developing techniques to estimate TOC and other organic parameters from readily available well logs data that are almost available in all wells. In this study, we evaluated the use of three machine learning models namely, random forest (RF), rotation forest (rF), k nearest neighbors (KNN) to estimate TOC based on conventional well logs data. The well logs involved gamma ray, acoustic, density, neutron, and deep resistivity. The efficacy of the models was tested against the most widely used backpropagation artificial neutral network (BPANN) and support vector regression (SVR) models. North Rumaila oilfield in southern Iraq was taken as a case study. The models were trained and tested using data from two wells in the field, namely R-167 and R-172. The number of TOC measurements used for training and testing were 40 (R-167) and 18 (R-172), respectively. The efficacy of the used algorithms was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and correlation of determination (R2). The models are also visually compared using Taylor diagram and violin plot to distinguish the best performance model. Results indicated the KNN was the best followed by RF and then rF. The worst performance models were BPANN and SVR models. This study confirmed the ability of machine learning models for building efficient model for estimating TOC from readily available borehole logs data without the need for very expensive coring process.
A crossover operator for improving the efficiency of permutation-based genetic algorithms

Crossover is one of the most important operators in a genetic algorithm by which offspring production for the next generation is performed. There are a number of crossover operators for each type of chromosome representation of solutions that are closely related to different types of optimisation problems. Crossover operation in genetic algorithms, aimed at solving permutation-based combinatorial optimisation problems, is more computationally expensive compared to other cases. This is mainly caused by the fact that no duplicate numbers are allowed in a chromosome and therefore offspring legalisation is needed after each substring exchange. Under these conditions, the time required for performing crossover operation increases significantly with increasing chromosome size, which may deeply affect the efficiency of these genetic algorithms. In this paper, a genetic algorithm that uses path representation for chromosomes and benefits from an alternative form of the well-known partially mapped crossover is proposed. The results of numerical experiments performed on a set of benchmark problems clearly show that the use of this crossover operator can significantly increase the efficiency of permutation-based genetic algorithms and also help in producing good quality solutions.
Deep Saliency Hashing for Fine-Grained Retrieval

In recent years, hashing methods have been proved to be effective and efficient for large-scale Web media search. However, the existing general hashing methods have limited discriminative power for describing fine-grained objects that share similar overall appearance but have a subtle difference. To solve this problem, we for the first time introduce the attention mechanism to the learning of fine-grained hashing codes. Specifically, we propose a novel deep hashing model, named deep saliency hashing (DSaH), which automatically mines salient regions and learns semantic-preserving hashing codes simultaneously. DSaH is a two-step end-to-end model consisting of an attention network and a hashing network. Our loss function contains three basic components, including the semantic loss, the saliency loss, and the quantization loss. As the core of DSaH, the saliency loss guides the attention network to mine discriminative regions from pairs of images.We conduct extensive experiments on both fine-grained and general retrieval datasets for performance evaluation. Experimental results on fine-grained datasets, including Oxford Flowers, Stanford Dogs, and CUB Birds demonstrate that our DSaH performs the best for the fine-grained retrieval task and beats the strongest competitor (DTQ) by approximately 10% on both Stanford Dogs and CUB Birds. DSaH is also comparable to several state-of-the-art hashing methods on CIFAR-10 and NUS-WIDE.
CNN and HOG based comparison study for complete occlusion handling in human tracking

Applications in the Human Machine Interaction (HMI) field are increasing since people and robots share the same environment and perform activities together. Therefore, robust detection and strong tracking methods are required. This study is about human detection and tracking. For detection, two different approaches, Histogram of Oriented Gradients (HOG)-Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are used. The video used in the application is recorded in a noisy environment, and in some frames, the human is completely covered by other objects in the environment, i.e. exposed to complete occlusion. For this reason, Kalman Filter (KF) and Particle Filter (PF) based tracking is performed. Different from these two traditional filters, a hybrid Kalman-Particle Filter (KPF) has also been proposed. The contribution of this study is to compare CNN with HOG-SVM, which is described as the most successful human detection method. As a result of the study, it was found that CNN provides successful results especially in case of occlusion. This study is also important in terms of proposing a hybrid KPF and comparing the filters in the case of complete occlusion. The results showed that for human tracking, CNN using KF performed better performance throughout the video. The proposed KPF also outperformed PF and this superiority became much more prominent in the case of complete occlusion.
Lung tumor segmentation using improved region growing algorithm

The goal of this project is to achieve an accurate segmentation of the pulmonary tumors besides shortening the time and increasing the accuracy. Here, improved region growing (IRG) algorithm is introduced in order to segment the lung tumor with a sufficient accuracy in a shorter time compared to the other basics methods. This comprehensive algorithm was applied on 4 patients CT images and the results of the various steps on segmentation improvement shown 98% accuracy as compared to the basic algorithm. The combination of "multipoint growth start" produced a desirable outcome in accurately bounding the tumor. The proposed algorithm improved tumor identification by less than 13% along with a sufficient percentage of compliance accuracy.
Quantum Circuits for Dynamic Runtime Assertions in Quantum Computation

In this paper, we propose quantum circuits for runtime assertions, which can be used for both software debugging and error detection. Runtime assertion is challenging in quantum computing for two key reasons. First, a quantum bit (qubit) cannot be copied, which is known as the non-cloning theorem. Second, when a qubit is measured, its superposition state collapses into a classical state, losing the inherent parallel information. In this paper, we overcome these challenges with runtime computation through ancilla qubits, which are used to indirectly collect the information of the qubits of interest. We design quantum circuits to assert classical states, entanglement, and superposition states. Our experimental results show that they are effective in debugging as well as improving the success rate for various quantum algorithms on IBM Q quantum computers.
Challenges and Solutions in Autonomous Driving: A Blockchain Approach

The autonomous driving era is coming with tremendous potential to revolutionize transportation systems with improved safety, comfort, and intelligence on the road. Integrating connected autonomous vehicles (CAVs) and rising social networking, autonomous vehicular social networks (AVSNs) can facilitate the data dissemination in safety-critical and entertainment-related applications for autonomous driving. However, with time-varying, delay-constrained, and location-dependent characteristics in autonomous driving, it faces great challenges to incentivize CAVs to disseminate massive content in AVSNs. Meanwhile, attackers may not only disseminate fake information to confuse the network, but also pose security vulnerabilities and privacy issues for CAVs. In this article, we investigate secure and incentive content delivery in AVSNs based on the advanced blockchain. Specifically, we first present a blockchain-enabled AVSN framework to safeguard content delivery. Then, we investigate the task-based and credit-based reputation models to evaluate the trustworthiness of CAVs and RSUs, respectively. The reputation increase and task reward are explored as incentives for CAVs delivering reliable content. In addition, a novel proof of reputation consensus protocol is devised to efficiently reach consensus in blockchain-enabled AVSNs while motivating RSUs to behave honestly. Experimental results indicate that the proposed framework outperforms the existing approaches, delivering vehicular content more reliably and securely. Finally, future research directions in this emerging area are discussed.
Virtual Museums as Learning Agents

Virtual solutions for exhibiting museum collections are no longer a novelty, as such experiences already exist in the world, but the remote use of museum collections for learning purposes has so far not been widely used in the educational environment. This article analyzes virtual museum applications by evaluating them from a learning perspective, including 25 criteria in the evaluation rubric divided into three groups: (i) Technical performance; (ii) information architecture; and (iii) educational value. This will enable educators to select the most appropriate material for their specific learning purpose and to plan the most appropriate learning strategies by organizing training sessions to acquire knowledge that can be enhanced by museum information and teaching students digital skills in evaluating information available in the digital environment, analyzing its pros and cons to teach them how to develop new innovative solutions. The research is carried out from a phenomenological perspective; to be more precise, virtual museums are analyzed using the principles of transcendental design and a hermeneutic design is used to interpret the resulting data. A total of 36 applications of virtual museums were analyzed, whereupon the results were compiled using static data analysis software, while 13 applications were used for the hermeneutic data analysis. The results suggest that the strength of virtual museums is in information architecture, but less attention is paid to the educational value of the material, which points to the need to change the principles of virtual museum design and emphasizes the role of teachers in using virtual museums as learning agents.