Reviewerly/exHarmony huggingface dataset:
Graph edit distance: Restrictions to be a metric

In the presentation of the graph edit distance in 1983 and other newer bibliography, authors state that it is necessary to apply the distance restrictions (non-negativity, identity of indiscernible elements, symmetry and triangle inequality) to each of the edit functions (insertion, deletion and substitution of nodes and edges) involved in the process of computing the graph edit distance to make the graph edit distance a true distance. Moreover, graph edit distance algorithms presented in the last three decades have been based on mapping the edit path that transforms a graph into the other one into a bijection of the graphs in which some null nodes have been added. In this paper, we show that the triangle inequality does not need to be imposed in each edit function if the graph edit distance is defined through an edit path; however, it is necessary if it is defined as a graph bijection. This is an important finding since the triangle inequality is the only restriction that relates different edit functions and concerns the process of tuning the edit functions given a specific application. Hence, on one hand, it would encourage research to define new algorithms based on the edit path instead of the graph bijection and, on the other hand, use edit functions without the restriction, for instance, that the sum of the costs of insertion and deletion of a pair of nodes has to be larger or equal than the cost of substituting them, which could increase the recognition ratio of a concrete application.
Noise in boson sampling and the threshold of efficient classical simulatability

We study the quantum to classical transition in Boson Sampling by analysing how $N$-boson interference is affected by inevitable noise in an experimental setup. We adopt the Gaussian noise model of Kalai and Kindler for Boson Sampling and show that it appears from some realistic experimental imperfections. We reveal a connection between noise in Boson Sampling and partial distinguishability of bosons, which allows us to prove efficient classical simulatability of noisy no-collision Boson Sampling with finite noise amplitude $\epsilon$, i.e., $\epsilon = \Omega(1)$ as $N\to \infty$. On the other hand, using an equivalent representation of network noise as losses of bosons compensated by random (dark) counts of detectors, it is proven that for noise amplitude inversely proportional to total number of bosons, i.e., $\epsilon=O(1/N)$, noisy no-collision Boson Sampling is as hard to simulate classically as in the noiseless case. Moreover, the ratio of ``noise clicks" (lost bosons compensated by dark counts) to the total number of bosons $N$ vanishes as $N\to \infty$ for arbitrarily small noise amplitude, i.e., $\epsilon = o(1)$ as $N\to \infty$, hence, we conjecture that such a noisy Boson Sampling is also hard to simulate classically. The results significantly relax sufficient condition on noise in a network components, such as two-mode beam splitters, for classical hardness of experimental Boson Sampling.
Design, development, and evaluation of a local sensor-based gait phase recognition system using a logistic model decision tree for orthosis-control

Functionality and versatility of microprocessor-controlled stance-control knee-ankle-foot orthoses (M-SCKAFO) are dictated by their embedded control systems. Proper gait phase recognition (GPR) is required to enable these devices to provide sufficient knee-control at the appropriate time, thereby reducing the incidence of knee-collapse and fall events. Ideally, the M-SCKAFO sensor system would be local to the thigh and knee, to facilitate innovative orthosis designs that allow more flexibility for ankle joint selection and other orthosis components. We hypothesized that machine learning with local sensor signals from the thigh and knee could effectively distinguish gait phases across different walking conditions (i.e., surface levels, walking speeds) and that performance would improve with gait phase transition criteria (i.e., current states depend on previous states).A logistic model decision tree (LMT) classifier was trained and tested (five-fold cross-validation) on gait data that included knee flexion angle, thigh-segment angular velocity, and thigh-segment acceleration. Twenty features were extracted from 0.1 s sliding windows for 30 able-bodied participants that walked on different surfaces (level, down-slope, up-slope, right cross-slope, left cross-slope) at a various walking speeds (self-paced (1.33 m/s, SD = 0.04 m/s), 0.8, 0.6, 0.4 m/s). The LMT-based GPR model was also tested with another validation set containing similar features and surfaces from 12 able-bodied volunteers at self-paced walking speeds (1.41 m/s, SD = 0.34 m/s). A "Transition Sequence Verification and Correction" (TSVC) algorithm was applied to correct for continuous class prediction and to improve GPR performance.The LMT had a tree size of 1643 with 822 leaf nodes, with a logistic regression model at each leaf node. The local sensor LMT-based GPR model identified loading response, push-off, swing, and terminal swing phases with overall classification accuracy of 98.38 for the initial training set (five-fold cross-validation) and 90.60% for the validation set. Applying TSVC increased classification accuracy to 98.72% for the initial training set and 98.61% for the validation set. Sensitivity, specificity, precision, F-score, and Matthew's correlation coefficient results suggest strong evidence for the feasibility of an LMT-based GPR system for real-time orthosis control.The novel machine learning GPR model that used sensor features local to the thigh and knee was viable for dynamic knee-ankle-foot orthosis-control. This highly accurate GPR model was generalizable when combined with TSVC. Our approach could reduce sensor system complexity as compared with other M-SCKAFO approaches, thereby enabling customizable advantages for end-users through modular unit orthosis designs.
Digital image correlation strain measurement of thick adherend shear test specimen joined with an epoxy film adhesive

Structural bonding and bonded repairs of composite materials become more and more important. Understanding the strain within the bondline leads to suitable bonding design. For new design approaches the strain distribution within the bondline has to be analyzed. Thus, often finite element analysis (FE) are used. However, a huge challenge is the availability of reliable material properties for the adhesives and their validation. Previous work has shown that it is possible to measure the small displacements resulting within thin epoxy film adhesives using high resolution digital image correlation (DIC). In this work a 2D DIC setup with a high resolution consumer camera is used to visualize the strain distribution within the bondline over the length of the joint as well as over the adhesive thickness. Therefore, single lap joints with thick aluminum adherends according to ASTM D 5656 are manufactured and tested. Local 2D DIC strain measurements are performed and analyzed. Two different camera setups are used and compared. The evaluation provides reliable material data and enables a look insight the bondline. The results of the full field strain data measured with DIC are compared with numerical simulations. Thus, material models as well as chosen parameters for the adhesive are validated. Compared to extensometers, giving only point-wise information for fixed measuring points, the DIC allows a virtual point-wise inspection along the complete bondline. Furthermore, it allows measuring close to the bondline to reduce the influence of adherend deformation.
The modified sufficient conditions for echo state property and parameter optimization of leaky integrator echo state network

Leaky integrator echo state network (Leaky-ESN), an improved echo state network, is suitable for learning very slow dynamic systems and replaying the learnt system at different speeds. To ensure the echo state property of Leaky-ESN, spectral radius of reservoir connection weight matrix needs to be less than leaking rate, which means that the output feedback of Leaky-ESN is actually ignored. Leaky-ESN without output feedback is equivalent to being a feedforward type neural network. Therefore, in this paper, we consider Leaky-ESN model with output feedback. Firstly, we give the modified sufficient conditions for echo state property, which are actually some inequality constraints about the control parameters (mainly leaking rate, spectral radius of reservoir connection weight matrix and the scaling of output feedback) and the maximum singular values of feedback and output connection weight matrices. Secondly, to reduce the influence of the initial values of the control parameters on Leaky-ESN, we use the barrier method to optimize the control parameters. The barrier method can convert the constrained optimization problem into the unconstrained optimization problem. Newton’s method and its improve method, for example, eigenvalue modification methods, are further used to solve the unconstrained optimization problem and then obtain the control parameters of Leaky-ESN. Finally, Mackey–Glass chaotic time series prediction is selected to validate the method proposed in this paper. Simulation results show that the method proposed in this paper can make Leaky-ESN model more stable and higher approximation precision.
The Bernoulli wavelets operational matrix of integration and its applications for the solution of linear and nonlinear problems in calculus of variations

In this paper, a new method for solving some classes of linear and nonlinear problems in calculus of variations is presented. The method is based upon Bernoulli wavelets. The operational matrices of integration and product of Bernoulli wavelets are calculated. These matrices are then utilized to reduce the calculus of variations problems to the solution of algebraic equations. Illustrative examples are included to demonstrate the validity and applicability of the technique.
Quality-Driven Detection and Resolution of Metamodel Smells

In model-driven engineering, analogously to any software development practice, metamodel design must be accurate and performed by considering relevant quality factors, including maintainability, reusability, and understandability.The quality of metamodels might be compromised by the introduction of smells that can be the result of inappropriate design decisions.Detecting and resolving metamodel smells are a complex task.The existing approaches deal with this problem by supporting the identification and resolution of smells without providing the means to explicitly trace them with the quality attributes that can be potentially affected.In this paper, we present an approach to defining extensible catalogues of metamodel smells.Each smell can be linked to the corresponding quality attributes.Such links are exploited to automatically select only those smells that have to be necessarily resolved for enhancing the quality factors that are of interest for the modeler.The implementation of the approach is based on the Edelta language, and it has been validated on a corpus of metamodels retrieved from a publicly available repository.
Multi-Scale Context Attention Network for Stereo Matching

Recently, many works for stereo matching with convolutional neural networks have gained satisfactory performance. However, it is still an urgent challenge to deal with ill-posed regions and improve details in disparity maps. To address these problems, we propose a multi-scale context attention network with three main modules: atrous spatial pyramid pooling attention, richer convolutional features, and attention mechanism. First, we propose an atrous spatial pyramid pooling attention module to capture context information by the aggregating context in different scales, meanwhile take advantage of the attention mechanism to selectively emphasize informative features and suppress fewer ones. Then, the richer convolutional module is proposed to bring useful detail information for the network. Additionally, attention mechanism is used to pick out informative features for disparity refinement sub-network. Furthermore, we design a point-specific loss function strategy to perform online hard point mining, which helps the network to improve the accuracy of disparity maps. The experiments on the FlyingThings3D and KITTI 2015 benchmark demonstrate that the proposed method can achieve state-of-the-art performance.
An empirical study on predicting cloud incidents

With the increasing rate of adoption and growth of cloud computing services, businesses have been shifting their information technology (IT) infrastructure to the cloud. Although cloud vendors promise high availability and reliability of their cloud services, cloud-related incidents involving outages and service disruptions remain a challenge. Understanding cloud incidents and the ability to predict them would be helpful in deciding how to manage and circumvent future incidents. In this study, we propose a hybrid model that employs machine learning and time series methods to forecast cloud incidents. We evaluate the proposed model using a sample of 2261 incidents collected from two cloud providers namely, Netflix and Hulu. Unique to this study is that our model relies solely on historical data that is independent of the underlying cloud infrastructure. Results suggest that the proposed hybrid model outperforms individual forecasting models: neural network, time series and random forest. Results also reveal important temporal insights from the proposed model and highlights the practical relevance of historical data to forecast and manage cloud incidents.
Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms

Hierarchical Multi-Label Classification is a challenging classification task where the classes are hierarchically structured, with superclass and subclass relationships. It is a very common task, for instance, in Protein Function Prediction, where a protein can simultaneously perform multiple functions. In these tasks it is very difficult to achieve a high predictive performance, since hundreds or even thousands of classes with imbalanced data distributions have to be considered. In addition, the models should ideally be easily interpretable to allow the validation of the knowledge extracted from the data. This work proposes and investigates the use of Genetic Algorithms to induce rules that are both hierarchical and multi-label. Several experiments with different fitness functions and genetic operators are preformed to obtain different Hierarchical Multi-Label Classification rules. The different proposed configurations of Genetic Algorithms are evaluated together with state-of-the-art methods for HMC rule induction based on Ant Colony Optimization and Predictive Clustering Trees, using many datasets related to the Protein Function Prediction task. The experimental results show that it is possible to recommend the best configuration in terms of predictive performance and model interpretability.
Categorical Metadata Representation for Customized Text Classification

The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. This information has been used to modify parts of the model (e.g., word embeddings, attention mechanisms) such that results can be customized according to the metadata. We observe that current representation methods for categorical metadata, which are devised for human consumption, are not as effective as claimed in popular classification methods, outperformed even by simple concatenation of categorical features in the final layer of the sentence encoder. We conjecture that categorical features are harder to represent for machine use, as available context only indirectly describes the category, and even such context is often scarce (for tail category). To this end, we propose using basis vectors to effectively incorporate categorical metadata on various parts of a neural-based model. This additionally decreases the number of parameters dramatically, especially when the number of categorical features is large. Extensive experiments on various data sets with different properties are performed and show that through our method, we can represent categorical metadata more effectively to customize parts of the model, including unexplored ones, and increase the performance of the model greatly.
Head pose estimation with soft labels using regularized convolutional neural network

Head pose estimation has many wide applications such as driver monitoring, attention recognition and multi-view facial analysis. Most of the previous works routinely utilize detected face regions to further estimate head pose with hard labels, which limits to explore more discriminative texture information and tends to over-fit. In this paper, we present a novel framework to alleviate this problem, which takes entire images as input and constructs soft labels using a Gaussian distribution function as supervision information, and then introduces a regularized convolutional neural network architecture that is optimized by two types of similarity measure functions: Kullback–Leibler divergence loss and Jeffreys divergence loss. The regularized architecture includes four modules: one backbone net for learning common features, two parallel branches named sub-net1 and sub-net2 for learning complementary features and one feature fusion module, namely, fused net. The architecture is trained in an alternately training fashion, making the learned model more robust and stable. Extensive experiments have been carried out on three public datasets: Pointing04, CAS-PEAL-R1 and CMU Multi-PIE. The results show that our method achieves a significant improvement in performance compared to the state of the art. The best accuracy on the three datasets we achieve are 85.77%, 99.19% and 99.88%, respectively.
Building an Omnidirectional 3-D Color Laser Ranging System Through a Novel Calibration Method

Three-dimensional (3-D) color laser ranging technology plays a crucial role in many applications. This paper develops a new omnidirectional 3-D color laser ranging system. It consists of a two-dimensional (2-D) laser rangefinder (LRF), a color camera, and a rotating platform. Both the 2-D LRF and the camera rotate with the rotating platform to collect line point clouds and images synchronously. The line point clouds and the images are then fused into a 3-D color point cloud by a novel calibration method of a 2-D LRF and a camera based on an improved checkerboard pattern with rectangle holes. In the calibration, boundary constraint and mean approximation are deployed to accurately compute the centers of rectangle holes from the raw sensor data based on data correction. Then, the data association between the 2-D LRF and the camera is directly established to determine their geometric mapping relationship. These steps make the calibration process simple, accurate, and reliable. The experiments show that the proposed calibration method is accurate, robust to noise, and suitable for different geometric structures, and the developed 3-D color laser ranging system has good performance for both indoor and outdoor scenes.
Blockchain-Based Privacy Preserving Deep Learning

Smart mobile devices have access to huge amounts of data appropriate to deep learning models, which in turn can significantly improve the end-user experience on mobile devices. But massive data collection required for machine learning introduce obvious privacy issues. To this end, the notion of federated learning (FL) was proposed, which leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. However, in many applications one or more Byzantine devices may suffice to let current coordination learning mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in federated learning systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology to establish secure learning coordination mechanisms and to identify and exclude Byzantine members. We studied the performance of our blockchain-based approach in a collective deep-learning scenario both in the presence and absence of Byzantine devices and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine devices are part of the members.
A Novel Quantum-Inspired Fuzzy Based Neural Network for Data Classification

The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter ( <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> ) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> , which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.
A FRT - SVD Based Blind Medical Watermarking Technique for Telemedicine Applications

In this article, a blind and robust medical image watermarking technique based on Finite Ridgelet Transform (FRT) and Singular Value Decomposition (SVD) is proposed. A host medical image is first transformed into 16 × 16 non-overlapping blocks and then ridgelet transform is applied on the individual blocks to obtain sets of ridgelet coefficients. SVD is then applied on these sets, to obtain the corresponding U, S and V matrix. The watermark information is embedded into the host medical image by modification of the value of the significant elements of U matrix. This proposed technique is tested on various types of medical images such as X-ray and CT scan. The simulation results revealed that this technique provides better imperceptibility, with an average PSNR being 42.95 dB for all test medical images. This technique also overcomes the limitation of the existing technique which is applicable on only the Region of Interest (ROI) of the medical image.