Reviewerly/exHarmony huggingface dataset:
Surface Parameterization and Trajectory Generation on Regular Surfaces With Application in Robot-Guided Deposition Printing

In this work, we present a novel approach to design and carry out trajectories over regular curved surfaces. This has application in a number of robot path planning problems, including our primary interest in deposition printing. Existing solutions are often ad-hoc in terms of path generation and control of robot pose and velocity. Our approach provides a unified methodology for surface fitting from 3D surface measurements and mapping a curve from 2D onto a 3D surface with minimal distortion. Robot pose control is investigated to guide the end effector along the trajectory while maintaining a standoff distance and keeping the end effector normal to the surface. Simulations and experiments show the performance and necessity of our approach in the application of deposition printing on different 3D surfaces.
Role of the secondary visual cortex in HMAX model for object recognition

The models inspired by visual systems of life creatures (e.g., human, mammals, etc.) have been very successful in addressing object recognition tasks. For example, Hierarchical Model And X (HMAX) effectively recognizes different objects by modeling the V1, V4, and IT regions of the human visual system. Although HMAX is one of the superior models in the field of object recognition, its implementation has been limited due to some disadvantages such as the unrepeatability of the process under constant conditions, extreme redundancy, high computational load, and time-consuming. In this paper, we aim at revising the HMAX approach by adding the model of the secondary region (V2) in the human visual system which leads to removing the mentioned drawbacks of standard HMAX. The added layer selects repeatable and more informative features that increase the accuracy of the proposed method by avoiding the redundancy existing in the conventional approaches. Furthermore, this feature selection strategy considerably reduces the huge computational load. Another contribution of our model is highlighted when a small number of training images is available where our model can efficiently cope with this issue. We evaluate our proposed approach using Caltech5 and GRAZ-02 database as two famous benchmarks for object recognition tasks. Additionally, the results are compared with standard HMAX that validate and highlight the efficiency of the proposed method.
Maintenance Requests Labeling Using Machine Learning Classification

Follow-up maintenance reports are important and tedious work. Bug repository is usually used to store maintenance reports which could be: fault repair, Functionality addition or modification, or environmental adaptation. Labeling these maintenance reports can reduce the time and effort of handling them. We proposed an approach that classifies maintenance reports into different categories. We applied a machine learning pipeline to achieve classification. We reached up to 78% precision, 83% recall, and 79%F1-score. Adopting such an approach can speedup handling maintenance reports and increase user satisfaction.
RegiNet: Gradient guided multispectral image registration using convolutional neural networks

Multispectral image registration suffers from severe inconsistency between reference and target images. In this paper, we propose gradient guided multispectral image registration using convolutional neural networks, called RegiNet. We build an end-to-end network that directly produces the registration result from the input image pair. We use a gradient map of the reference image to guide the target image for registration. RegiNet first encodes the reference image and the gradient map of the target image separately, and then concatenates them to register the target image. For loss function, we use a structure loss to effectively capture gradient information from the reference image. Experimental results demonstrate that the proposed method successfully produces registration results as well as outperforms state-of-the-art ones in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).
Analysis of Features for Breast Cancer Recognition in Different Magnifications of Histopathological Images

Breast cancer is one of the most common diseases in women in the world. There are various imaging techniques employed in the diagnosis. The histological image analysis supported by computational systems has proved to be quite effective in diagnosing the disease. In this paper, we present an approach to quantify and classify tissue samples of the breast based on features extracted from the intensity histogram, co-occurrence matrix and the Shannon, Renyi, Tsallis and Kapoor entropies. The attribute set was employed to obtain the feature vectors which were evaluated as inputs to the random forest and sequential minimal optimization algorithms with the 10-fold cross-validation technique. In this study, we investigated the proposed approach with images obtained in four levels of magnification of the publicly available Breast Cancer Histopathological Database. In the feature selection stage, we investigated the correlation-Based feature selection, ReliefF, information gain, gain ratio, one-R and symmetrical uncertainty algorithms for evaluating the performance of the proposed approach. The proposed approach achieved significant results of AUC and accuracy for all cases analyzed. The proposed approach obtained 0.997 for AUC and 97.6% for the accuracy metric. These results are considered relevant and this approach is useful as an automated protocol for the diagnosis of breast histological tissue.
Robust requirements gathering for ontologies in smart water systems

Abstract Urban environments are urgently required to become smarter to overcome sustainability and resilience challenges whilst remaining economically viable. This involves a vast increase in the penetration of ICT resources, both physical and virtual, with the requirement to factor in built environment, socio-economic and human artefacts. This paper, therefore, proposes a methodology for eliciting, testing, and deploying, requirements in the field of urban cybernetics. This extends best practice requirements engineering principles to meet the demands of this growing niche. The paper follows a case study approach of applying the methodology in the smart water domain, where it achieves positive results. The approach not only heavily utilises iteration alongside domain experts, but also mandates the integration of technical domain experts to ensure software requirements are met. A key novelty of the approach is prioritising a balance between (a) knowledge engineers’ tenacity for logical accuracy, (b) software engineers’ need for speed, simplicity, and integration with other components, and (c) the domain experts’ needs to invoke ownership and hence nurture adoption of the resulting ontology.
Learning sparse features with lightweight ScatterNet for small sample training

Convolutional neural networks (CNNs) have recently achieved impressive performances in image processing tasks such as image classification and object recognition. However, CNNs typically have a large number of parameters, leading to their requirement of a large number of training samples to extract spatial features. To address these limitations, we propose a lightweight ScatterNet with the learnable weight matrix and sparse transformation such as scale transformation and translation to learn sparse filters. This filter based on ScatterNet uses He initialization algorithm and learns from input images which are viewed as two-directional sequential data in the initial stage of model training. A Strip-Recurrent module sweeps both horizontally and vertically across the image to compress feature matrices. Then, ScatterNet decomposes the above feature matrices as a learned mixture of different harmonic functions to integrate the spectral analysis into CNNs. Finally, we combine the sequential and spectral features to build our hybrid architectures to complete image classification and segmentation. These architectures can obtain good classification accuracy on both small and large training datasets. Our proposed method is evaluated at both layer and network levels on five widely-used benchmark datasets: MNIST, CIFAR-10, CIFAR-100, Small NORB and Tiny ImageNet. We also study other small sample problems such as medical image segmentation and image classification based on few-shot learning. Experiments show that our proposed layer and hybrid model achieves better accuracy for small sample training.
Unsupervised deep learning-based process monitoring methods

In this chapter, first we provide an overview of some of the shallow-machine learning approaches used in anomaly detection and outlier detection in data mining, namely data clustering techniques. Then, we give a brief description of two frequently used unsupervised machine learning algorithms for one-class classification or detection, namely one-class SVM and support vector data description (SVDD). Particular attention is paid to deep learning models. We present the commonly used deep learning models based on autoencoders (Variational Autoencoder, Denoising Autoencoder, and Contrastive Autoencoder), probabilistic models (Boltzmann Machine and Restricted Boltzmann Machine) and deep neural models (Deep Belief Network and Deep Boltzmann Machine), and we show their capacity and limitations. Finally, we merge the desirable properties of shallow learning approaches, such as one-class support vector machine and k-nearest neighbors and unsupervised deep-learning approaches to develop more sophisticated and efficient monitoring techniques.
Gaussian Process Online Learning With a Sparse Data Stream

Gaussian processes (GPs) have been exploited for various applications even including online learning. To learn time-varying hyperparameters from an information-limited sparse data stream, we consider the infinite-horizon Gaussian process (IHGP) with a low computational complexity. For example, the IHGP framework could provide efficient GP online learning with a sparse data stream in mobile devices. However, we show that the originally proposed IHGP has difficulty in learning time-varying hyperparameters online from the sparse data stream due to the numerically approximated gradient of the marginal likelihood function. In this paper, we show how to extend the IHGP in order to learn time-varying hyperparameters using a sparse and non-stationary data stream. In particular, our solution approach offers the exact gradient as the solution of a Lyapunov equation. Therefore, our approach achieves better performance with a sparse data stream while still keeping the computational complexity low. Finally, we present the comparison results to demonstrate that our approach outperforms the originally proposed IHGP on practical applications with sparse data streams. To demonstrate the effectiveness of our approach, we consider a multi-rate sensor fusion or an interpolation problem where slow vision systems need to be combined with other fast sensory units for feedback control in the field of autonomous driving. In particular, we apply our approach to vehicle lateral position error estimation together with a deep learning model for autonomous driving using non-stationary lateral position error signals in a model-free and data-driven fashion.
A New One Time Password Generation Method for Financial Transactions with Randomness Analysis

All of us know that one time passwords (OTPs) are a part of life in today's scenario. We have to use them for authentication in many applications such as financial transactions, e-commerce websites, and in various government and nongovernment websites, etc. The strength of any OTP is uniqueness and it must not be predictable in any case. The method of OTP generation should be simple but safe enough to avoid any vulnerability. So a simple and unique OTP generation mechanism using RSA (Rivest, Shamir, Adleman) based string is presented in this paper with complete randomness analysis which also solves the associated problem of Syndicate bank's token based OTP procedure.
Tailoring Term Truncations for Electronic Structure Calculations Using a Linear Combination of Unitaries

A highly anticipated use of quantum computers is the simulation of complex quantum systems including molecules and other many-body systems. One promising method involves directly applying a linear combination of unitaries (LCU) to approximate a Taylor series by truncating after some order. Here we present an adaptation of that method, optimized for Hamiltonians with terms of widely varying magnitude, as is commonly the case in electronic structure calculations. We show that it is more efficient to apply LCU using a truncation that retains larger magnitude terms as determined by an iterative procedure. We obtain bounds on the simulation error for this generalized truncated Taylor method, and for a range of molecular simulations, we report these bounds as well as exact numerical results. We find that our adaptive method can typically improve the simulation accuracy by an order of magnitude, for a given circuit depth.
Predicting User Posting Activities in Online Health Communities with Deep Learning

Online health communities (OHCs) represent a great source of social support for patients and their caregivers. Better predictions of user activities in OHCs can help improve user engagement and retention, which are important to manage and sustain a successful OHC. This article proposes a general framework to predict OHC user posting activities. Deep learning methods are adopted to learn from users’ temporal trajectories in both the volumes and content of posts published over time. Experiments based on data from a popular OHC for cancer survivors demonstrate that the proposed approach can improve the performance of user activity predictions. In addition, several topics of users’ posts are found to have strong impact on predicting users’ activities in the OHC.
Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

Dynamic scenes that contain both object motion and egomotion are a challenge for monocular visual odometry (VO). Another issue with monocular VO is the scale ambiguity, i.e. these methods cannot estimate scene depth and camera motion in real scale. Here, we propose a learning based approach to predict camera motion parameters directly from optic flow, by marginalizing depthmap variations and outliers. This is achieved by learning a sparse overcomplete basis set of egomotion in an autoencoder network, which is able to eliminate irrelevant components of optic flow for the task of camera parameter or motionfield estimation. The model is trained using a sparsity regularizer and a supervised egomotion loss, and achieves the state-of-the-art performances on trajectory prediction and camera rotation prediction tasks on KITTI and Virtual KITTI datasets, respectively. The sparse latent space egomotion representation learned by the model is robust and requires only 5% of the hidden layer neurons to maintain the best trajectory prediction accuracy on KITTI dataset. Additionally, in presence of depth information, the proposed method demonstrates faithful object velocity prediction for wide range of object sizes and speeds by global compensation of predicted egomotion and a divisive normalization procedure.
Learning wavelet coefficients for face super-resolution

Abstract Face image super-resolution imaging is an important technology which can be utilized in crime scene investigations and public security. Modern CNN-based super-resolution produces excellent results in terms of peak signal-to-noise ratio and the structural similarity index (SSIM). However, perceptual quality is generally poor, and the details of the facial features are lost. To overcome this problem, we propose a novel deep neural network to predict the super-resolution wavelet coefficients in order to obtain clearer facial images. Firstly, this paper uses prior knowledge of face images to manually emphases relevant facial features with more attention. Then, a linear low-rank convolution in the network is used. Finally, image edge features from canny detector are applied to enhance super-resolution images during training. The experimental results show that the proposed method can achieve competitive PSNR and SSIM and produces images with much higher perceptual quality.
Nearest neighbour search over encrypted data using intel SGX

Content-based image retrieval (CBIR) retrieves desired digital images from large databases. “Content-based” means that the search analyzes the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with images. It has different applications in different domains such as crime prevention, intellectual property, medical diagnosis, and face finding. Based on the aforementioned applications, there is a desideratum to retrieve users who have content-wise similar images, e.g., patients to speedup the diagnosis process or individuals to identify an unidentified criminals or group individuals with similar interests. Most image owners are outsourcing their images to the cloud because of low storage and computation costs. Although existing encryption mechanisms protect images from unauthorized access, these techniques increase the computational complexity of executing arbitrary functions on the outsourced images. In this paper, our focus is to efficiently and privately identify users who have content-wise similar encrypted images stored in the cloud. The proposed scheme utilizes the Intel Software Guard Extensions (Intel SGX) architecture, the Convolutional Neural Network (CNN), and Locality Sensitive Hashing (LSH) for minhash signatures. Considering symmetric encryption, we experimentally show that the proposed approach is only five times slower than the plaintext approach with just one round of interaction with the cloud server.
Uncoded Placement With Linear Sub-Messages for Private Information Retrieval From Storage Constrained Databases

We propose capacity-achieving schemes for private information retrieval (PIR) from uncoded databases (DBs) with both homogeneous and heterogeneous storage constraints. In the PIR setting, a user queries a set of DBs to privately download a message, where privacy implies that no one DB can infer which message the user desires. In general, a PIR scheme is comprised of storage placement and delivery designs. Previous works have derived the capacity, or infimum download cost, of PIR with uncoded storage placement and sufficient conditions of storage placement to meet capacity. However, the currently proposed storage placement designs require splitting each message into an exponential number of sub-messages with respect to the number of DBs. In this work, when DBs have the same storage constraint, we propose two simple storage placement designs that satisfy the capacity conditions. Then, for more general heterogeneous storage constraints, we translate the storage placement design process into a “filling problem”. We design an iterative algorithm to solve the filling problem where, in each iteration, messages are partitioned into sub-messages and stored at subsets of DBs. All of our proposed storage placement designs require a number of sub-messages per message at most equal to the number of DBs.
Context prior-based with residual learning for face detection: A deep convolutional encoder–decoder network

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder–decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder–decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://github.com/zzxcoder/EvaluationFHEDN.
Manuscripts Image Retrieval Using Deep Learning Incorporating a Variety of Fusion Levels

The instantaneous search and retrieval of the most relevant images to a specific query image is a desirable application for all digital libraries.The automatic extraction and classification according to the most distinguishable features, is a crucial step to detect the similarities among images successfully.This study introduces a novel approach that utilizes a fusion model for classifying and retrieving historical Arabic manuscripts' images.To accomplish our goal, the images are first classified according to their extracted deep learning visual features utilizing a pre-trained convolutional neural network.Then, the texts written in the manuscripts' images are extracted and pre-processed to classify the images according to their textual features using an optimized bidirectional LSTM deep learning model with attention and batch normalization layers.Finally, both the visual and textual deep learning models are fused at three different fusion-levels named: decision-level, features-level, and score-level.The score-level fusion model resulted in a considerable improvement of each model used individually.Extensive experimentation and evaluation of the proposed fusion method on the collected ancient Arabic manuscripts dataset proved its robustness against other stateof-the-art methods recording 99% classification accuracy and 98% mean accuracy on the top-10 image retrieval.
On Multiview Robustness of 3D Adversarial Attacks

Nowadays deep neural networks have been applied widely in many applications of computer vision including medical diagnosis and self-driving cars. However, deep neural networks are threatened by adversarial examples usually in which image pixels were perturbed unnoticeable to humans but enough to fool the deep networks. Compared to 2D image adversarial examples, 3D adversarial models are less invasive in the process of attacks, and thus more realistic. There have been many research works on generating 3D adversarial examples. In this paper, we study the robustness of 3D adversarial attacks when the victim camera is placed at different viewpoints. In particular, we find a method to create 3D adversarial examples that can achieve 100% attack success rate from all viewpoints with any integer spherical coordinates. Our method is simple as we only perturb the texture space. We create 3D models with realistic textures using 3D reconstruction from multiple uncalibrated images. With the help of a differentiable renderer, we then apply gradient based optimization to compute texture perturbations based on a set of rendered images, i.e., training dataset. Our extensive experiments show that even only including 1% of all possible rendered images in training, we can still achieve 99.9% attack success rate with the trained texture perturbations. Furthermore, our thorough experiments show high transferability of the multiview robustness of our 3D adversraial attacks across various state-of-the-art deep neural network models.