Reviewerly/exHarmony huggingface dataset:
Residual‐based multi‐filter methodology for all‐source fault detection, exclusion, and performance monitoring

<h3>Abstract</h3> All-source navigation has become increasingly relevant over the past decade with the development of viable alternative sensor technologies. However, as the number and type of sensors informing a system increases, so does the probability of corrupting the system with sensor modeling errors, signal interference, and undetected faults. Though the latter of these has been extensively researched, the majority of existing approaches have constrained faults to biases and designed algorithms centered around the assumption of simultaneously redundant, synchronous sensors with valid measurement models, none of which are guaranteed for all-source systems. As part of an overall all-source assured or resilient navigation objective, this research contributes a fault- and sensor-agnostic fault detection and exclusion method that can provide the user with performance guarantees without constraining the statistical distribution of the fault. The proposed method is compared against normalized solution separation approaches using Monte-Carlo simulations in a 2D non-GPS navigation problem.
Capacity of Continuous Channels with Memory via Directed Information Neural Estimator

Calculating the capacity (with or without feedback) of channels with memory and continuous alphabets is a challenging task. It requires optimizing the directed information (DI) rate over all channel input distributions. The objective is a multi-letter expression, whose analytic solution is only known for a few specific cases. When no analytic solution is present or the channel model is unknown, there is no unified framework for calculating or even approximating capacity. This work proposes a novel capacity estimation algorithm that treats the channel as a `black-box', both when feedback is or is not present. The algorithm has two main ingredients: (i) a neural distribution transformer (NDT) model that shapes a noise variable into the channel input distribution, which we are able to sample, and (ii) the DI neural estimator (DINE) that estimates the communication rate of the current NDT model. These models are trained by an alternating maximization procedure to both estimate the channel capacity and obtain an NDT for the optimal input distribution. The method is demonstrated on the moving average additive Gaussian noise channel, where it is shown that both the capacity and feedback capacity are estimated without knowledge of the channel transition kernel. The proposed estimation framework opens the door to a myriad of capacity approximation results for continuous alphabet channels that were inaccessible until now.
InterSentiment: combining deep neural models on interaction and sentiment for review rating prediction

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model based on deep learning. InterSentiment is a specific instance of our proposed Deep Learning based Collaborative Filtering framework. The proposed model aims to learn the high-level representations combining user-product interaction and review sentiment, and jointly project them into the rating scores. Results of experiments conducted on IMDB and two Yelp datasets demonstrate clear advantage of our proposed approach over strong baseline methods.
The Capacity of Private Information Retrieval Under Arbitrary Collusion Patterns

We study the private information retrieval (PIR) problem under arbitrary collusion patterns for replicated databases. We find its capacity, which is the same as the capacity of the original PIR problem with the number of databases N replaced by a number S*. The number S* is the optimal solution to a linear programming problem that is a function of the collusion pattern. Hence, the collusion pattern affects the capacity of the PIR problem only through the number S*.
Light Field Image Quality Assessment: An Overview

The light field (LF) images have received significant attentions from both academia and industry, which allow a wide range of applications in the areas of computer vision and computer graphics, such as three-dimensional reconstruction, virtual reality, and so on. Considering that the human eyes are the final receivers of the LF images and the LF images are inevitably suffered from various distortions, image quality assessment (IQA) becomes an important issue for the LF images with the goal of evaluating the image quality of the LF images in accordance with the human visual system. This paper presents an up-to-date overview of the IQA specifically for the LF images, focusing on the formation of the LF images, the LF IQA databases and the existing LF IQA models.
Review on Autonomous Indoor Wheel Mobile Robot Navigation Systems

Indoor wheel mobile robot navigation system is still an open problem due to the presence of obstacles and the complexity of the environment. The path planning and localization play vital roles in obstacle avoidance and complete navigation of wheel mobile robot. In this paper, the most important methods are reviewed, by considering their advantages and limitations, especially for indoor mobile robots. Different low-cost navigation techniques for an indoor autonomous mobile robot are reviewed. This paper contributes to a comparison of approaches for collision-free and self-localizing indoor autonomous mobile robots, and recommendations towards low-cost but reliable solutions.
Time-range based sequential mining for survival prediction in prostate cancer

• Developed a prediction model for prostate cancer using dataset from Indian hospital. • Provides insights into survival predictors associated with cancer survivability. • Sequence mining along with classification techniques was employed for the study. • Sequence of treatments along with the time intervals improved the overall accuracy. • Time in treatments and performance attributes are vital predictors for cancer patient. Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival outcome of metastatic prostate cancers separately. Also, the treatments undertaken by the patients affect their overall survival. The present study tries to analyze the sequence of treatments given to the patients, along with the time intervals between each set of treatments. The time when medication needs to be changed may provide some useful insights into the survival outcome of the patients. A total of 407 metastatic prostate cancer patients’ data was collected and analyzed from an Indian tertiary care center. Popular sequence mining algorithms with exact order constraint have been applied to the treatment data. Appropriate time intervals were added in the resulted frequent sequences and fed to machine learning techniques along with other clinical data. The study suggests that the proposed methodology of the time range based sequence mining approach gave better results than the existing methods with 84.5% accuracy and 0.89 AUC. The time intervals in the existing sequence mining algorithms can give the clinicians some useful insights into the survival analysis and in determining the best lines of treatments for a particular patient.
ODIN

Recent advances in computer vision have led to a resurgence of interest in visual data analytics. Researchers are developing systems for effectively and efficiently analyzing visual data at scale. A significant challenge that these systems encounter lies in the drift in real-world visual data. For instance, a model for self-driving vehicles that is not trained on images containing snow does not work well when it encounters them in practice. This drift phenomenon limits the accuracy of models employed for visual data analytics. In this paper, we present a visual data analytics system, called Odin, that automatically detects and recovers from drift. Odin uses adversarial autoencoders to learn the distribution of high-dimensional images. We present an unsupervised algorithm for detecting drift by comparing the distributions of the given data against that of previously seen data. When Odin detects drift, it invokes a drift recovery algorithm to deploy specialized models tailored towards the novel data points. These specialized models outperform their non-specialized counterpart on accuracy, performance, and memory footprint. Lastly, we present a model selection algorithm for picking an ensemble of best-fit specialized models to process a given input. We evaluate the efficacy and efficiency of Odin on high-resolution dashboard camera videos captured under diverse environments from the Berkeley DeepDrive dataset. We demonstrate that Odin's models deliver 6X higher throughput, 2X higher accuracy, and 6X smaller memory footprint compared to a baseline system without automated drift detection and recovery.
Augmented Reality Smart Glasses in the Workplace: Safety and Security in the Fourth Industrial Revolution Era

Industry 4.0 is reinventing the way in which production is performed. Based on its eight pillars, I4.0 environments are adopting digital solutions in order to make production smart. One of these is the concept of augmented operator, which can act with the aid of digital tools to facilitate daily work. Augmented Reality can represent the turnkey. In this light, the aim of this research is to present a case study of a “security and safety” application through the use of AR smart glasses, tested in a real scenario. For our experiments, Vuzix Blade smart glasses have been tested in combination with a cloud-based architecture connected with an oil-extractor plant. The goal is to develop an AR application that allows to assist the operator during the working process. In particular, it acts as a guide system for the operator who wears glasses, provides remote support (remote operator) and, from a security point of view, sends real-time alerts in dangerous situations. The application has been validated after a number of practical tests carried out by specialised technicians who normally perform the work.
An efficient secure k nearest neighbor classification protocol with high‐dimensional features

k Nearest neighbor (kNN) classification algorithm is a prediction model which is widely used for real-life applications, such as healthcare, finance, computer vision, personalization recommendation and precision marketing. The arrival of data explosion era results in the significant increase of feature dimension, which also makes for the increase of privacy concern over the available samples and unlabeled data in the applications of machine learning. In this paper, we present a secure low communication overhead kNN classification protocol that is able to deal with high-dimensional features given in real numbers. First, to deal with feature values given in real numbers, we develop a specific data conversion algorithm, which is used in the chosen fully homomorphic scheme. This conversion algorithm is generic and applicable to other algorithms that need to handle real numbers using the fully homomorphic scheme. Second, we present a privacy-preserving euclidean distance protocol (PPEDP), which works with the Euclidean distance computation between two points given in real numbers in a high-dimensional space. Then, based on the novelty PPEDP and oblivious transfer, we propose a new classification approach, efficient secure kNN classification protocol, (ESkNN) with low communication overhead, which is appropriate for a sample set with high-dimensional features and real number feature values. Moreover, we implement ESkNN in C++. Experimental results show that ESkNN is several orders of magnitude faster in performance than existing works, and scales up to 18 000 feature dimension in a memory limited environment.
EBIT: Weakly-supervised image translation with edge and boundary enhancement

In this paper, targeting image translation between the thermal and visible domains, we propose a novel framework to enhance the edge and boundary features during translation. We tackle the unsupervised training task where sample image pairs from two domains are randomly chosen so the image content does not match, but we also take advantage of paired feature maps during the feature disentanglement process. This can be considered to be weakly-supervisedtraining. First, since thermal images usually have vague edges, we propose to apply a Canny operator to strengthen the edge features of the thermal images. Next, to define the correct object boundaries, we extract the boundary features from silhouette masks which are paired with the visible domain images. Then we disentangle the features of both domains into a domain-shared latent space and a domain-exclusive latent space. In the domain-shared latent space, the edge and boundary features extracted from the thermal and visible domains respectively act as domain-shared information which is used to render the edges and define the boundaries of the translated images. In the domain-exclusive latent space, domain-exclusive information such as colour is used to render the colour of objects of the translated images. In addition, we propose a pixel-wise adversarial loss rather than more traditional ones. The experimental results show that the proposed method has the ability to render realistic edge and colour features within the correct object boundaries and outperforms several state-of-the-art methods.
Joint feature representation and classification via adaptive graph semi-supervised nonnegative matrix factorization

As an effective feature representation method, non-negative matrix factorization (NMF) cannot utilize the label information sufficiently, which makes it not be suitable for the classification task. In this paper, we propose a joint feature representation and classification framework named adaptive graph semi-supervised nonnegative matrix factorization (AGSSNMF). Firstly, to enhance the discriminative ability of feature representation and accomplish the classification task, a regression model with nonnegative matrix factorization (called as RNMF) is proposed, which exploits the relation between the label information and feature representation. Secondly, to overcome the drawback of insufficient labels, an adaptive graph-based label propagation (refereed as AGLP) model is established, which adopts a local constraint to reflect the local structure of data. Then, we integrate RNMF and AGLP into a unified framework for feature representation and classification. Finally, an iterative optimization algorithm is used to solve the objective function. Extensive experiments show that the proposed framework has excellent performance compared with some well-known methods.
State-of-the-arts Violence Detection using ConvNets

Over the last few years, there has been a surge in developing automated systems that can monitor human activities in the public environment and recognize abnormalities like violent and suspicious events. Violence detection aims at identifying whether a violent action has occurred and has evolved as a popular topic in the field of image processing and computer vision. Effective techniques for accurate and intelligent video analysis are highly demanded. There are various methodologies which can detect such activities. This paper aims at reviewing various CNN based state-of-the-art techniques for violence detection. In this paper, we discuss variants of CNN, their application and datasets which play an indispensable part in the detection process.
Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk

This paper presents fraud detection problem as one of the most common problems in secure banking research field, due to its importance in reducing the losses of banks and e-transactions companies. Our work will include: applying the common classification algorithms such as logistic regression (LR), random forest (RF), alongside with modern classifiers with state-of-the-art results as XGBoost (XG) and CatBoost (CB), testing the effect of the unbalanced data through comparing their results with and without balancing, then focusing on the savings measure to test the effect of cost-sensitive wrapping of Bayes minimum risk (BMR), we will concentrate on using F1-score, AUC and Savings measures after using the traditional measures duo to their suitability to our problem. The results show that CB has the best savings (0.7158) alone, (0.971) when using SMOTE and (0.9762) with SMOTE and BMR, while XG has the best savings (0.757) when using BMR without SMOTE.
SpatialFlow: Bridging All Tasks for Panoptic Segmentation

Object location is fundamental to panoptic segmentation as it is related to all things and stuff in the image scene. Knowing the locations of objects in the image provides clues for segmenting and helps the network better understand the scene. How to integrate object location in both thing and stuff segmentation is a crucial problem. In this article, we propose spatial information flows to achieve this objective. The flows can bridge all sub-tasks in panoptic segmentation by delivering the object's spatial context from the box regression task to others. More importantly, we design four parallel sub-networks to get a preferable adaptation of object spatial information in sub-tasks. Upon the sub-networks and the flows, we present a location-aware and unified framework for panoptic segmentation, denoted as SpatialFlow. We perform a detailed ablation study on each component and conduct extensive experiments to prove the effectiveness of SpatialFlow. Furthermore, we achieve state-of-the-art results, which are 47.9 PQ and 62.5 PQ respectively on MS-COCO and Cityscapes panoptic benchmarks.
Robust kernelized graph-based learning

The studies of hidden complex structures in data have popularized the use of graph-based learning methods in semi-supervised and unsupervised learning tasks. Kernelized graph-based methods are proven to perform better, but these methods suffer from the issue of appropriate kernel selection. Instead of using multiple views, these methods generally use a single view. But multi-view methods need a proper weight assignment technique to each view in proportion to their contribution to the learning task. To solve this, a novel Self-weighted Multi-view Multiple Kernel Learning (SMVMKL) framework is proposed using multiple kernels on multiple views that automatically assigns appropriate weight to each kernel of each view without introducing an additional parameter. But the real-world data that is either noisy or corrupt with outliers which may effect the performance of the proposed SMVMKL method. To deal with this, a Robust Self-weighted Multi-view Multiple Kernel Learning (RSMVMKL) framework using the l2,1-norm has also been proposed that reduces the effect of outliers present in the data set. Both the proposed methods have been evaluated on multiple benchmark data sets and result in a performance comparable with the other state-of-the-art multi-view methods considered in this paper.
Continuous-domain assignment flows

Assignment flows denote a class of dynamical models for contextual data labelling (classification) on graphs. We derive a novel parametrisation of assignment flows that reveals how the underlying information geometry induces two processes for assignment regularisation and for gradually enforcing unambiguous decisions, respectively, that seamlessly interact when solving for the flow. Our result enables to characterise the dominant part of the assignment flow as a Riemannian gradient flow with respect to the underlying information geometry. We consider a continuous-domain formulation of the corresponding potential and develop a novel algorithm in terms of solving a sequence of linear elliptic partial differential equations (PDEs) subject to a simple convex constraint. Our result provides a basis for addressing learning problems by controlling such PDEs in future work.
Subgroup Perfect Codes in Cayley Graphs

Let $\Gamma$ be a graph with vertex set $V(\Gamma)$. A subset $C$ of $V(\Gamma)$ is called a perfect code in $\Gamma$ if $C$ is an independent set of $\Gamma$ and every vertex in $V(\Gamma)\setminus C$ is adjacent to exactly one vertex in $C$. A subset $C$ of a group $G$ is called a perfect code of $G$ if there exists a Cayley graph of $G$ which admits $C$ as a perfect code. A group $G$ is said to be code-perfect if every proper subgroup of $G$ is a perfect code of $G$. In this paper we prove that a group is code-perfect if and only if it has no elements of order 4. We also prove that a proper subgroup $H$ of an abelian group $G$ is a perfect code of $G$ if and only if the Sylow 2-subgroup of $H$ is a perfect code of the Sylow 2-subgroup of $G$. This reduces the problem of determining when a given subgroup of an abelian group is a perfect code to the case of abelian 2-groups. Finally, we determine all subgroup perfect codes in any generalized quaternion group.
Digging deeper

Transfer Learning is becoming increasingly important to the Human Activity Recognition community, as it enables algorithms to reuse what has already been learned from models. It promises shortened training times and increased classification results for new datasets and activity classes. However, the question of what exactly is transferred is not dealt with in detail in many of the recent publications, and it is furthermore often difficult to reproduce the presented results. Therefore we would like to contribute with this paper to the understanding of transfer learning for sensor-based human activity recognition. In our experiment use weight transfer to transfer models between two datasets, as well as between sensors from the same dataset. As source- and target- datasets PAMAP2 and Skoda Mini Checkpoint are used. The utilized network architecture is based on a DeepConvLSTM. The result of our investigation shows that transfer learning has to be considered in a very differentiated way, since the desired positive effects by applying the method depend very much on the data and also on the architecture used.
Deep Complementary Joint Model for Complex Scene Registration and Few-Shot Segmentation on Medical Images

Deep learning-based medical image registration and segmentation joint models utilize the complementarity (augmentation data or weakly supervised data from registration, region constraints from segmentation) to bring mutual improvement in complex scene and few-shot situation. However, further adoption of the joint models are hindered: 1) the diversity of augmentation data is reduced limiting the further enhancement of segmentation, 2) misaligned regions in weakly supervised data disturb the training process, 3) lack of label-based region constraints in few-shot situation limits the registration performance. We propose a novel Deep Complementary Joint Model (DeepRS) for complex scene registration and few-shot segmentation. We embed a perturbation factor in the registration to increase the activity of deformation thus maintaining the augmentation data diversity. We take a pixel-wise discriminator to extract alignment confidence maps which highlight aligned regions in weakly supervised data so the misaligned regions’ disturbance will be suppressed via weighting. The outputs from segmentation model are utilized to implement deep-based region constraints thus relieving the label requirements and bringing fine registration. Extensive experiments on the CT dataset of MM-WHS 2017 Challenge [42] show great advantages of our DeepRS that outperforms the existing state-of-the-art models.