Reviewerly/exHarmony huggingface dataset:
Deep Learning on Private Data

Emerging complex deep neural networks require vast amounts of data to achieve high precision. However, the information is often collected from user logs and personal data. In this article, we summarize recent cryptographic methodologies for provably privacy-preserving deep learning and inference.
Thermal quantum correlations in a two-qubit Heisenberg XXZ spin-$\tfrac{1}{2}$ chain under an inhomogeneous magnetic field

The thermal quantum correlations in a two-qubit one-dimensional XXZ Heisenberg spin- chain subjected to an inhomogeneous magnetic field are investigated. The concurrence, as thermal entanglement quantifier, and the discord-like quantifiers, namely the trace distance discord and local quantum uncertainty, are computed. Their dependence on the anisotropy parameter γ, uniform magnetic field B, coupling constant J and nonuniform magnetic field b are presented in detail. The mentioned quantifiers behaviors in terms of this parameters are compared and discussed. The results show that TDD is always much stronger than the concurrence and LQU, whereas the latter two show a swinging behavior depending on system parameters. Also, we have shown that the nonuniform magnetic field can reduce the effects of temperature on thermal correlations. Furthermore, the thermal correlations, for fixed values of b, can be enhanced in the antiferromagnetic system in comparison to the ferromagnetic one. In addition, the quantum correlations amounts in the ferromagnetic system are deteriorated when γ decreases, but they are improved when the system is antiferromagnetic.
Teaching on a Budget in Multi-Agent Deep Reinforcement Learning

Deep Reinforcement Learning (RL) algorithms can solve complex sequential decision tasks successfully. However, they have a major drawback of having poor sample efficiency which can often be tackled by knowledge reuse. In Multi-Agent Reinforcement Learning (MARL) this drawback becomes worse, but at the same time, a new set of opportunities to leverage knowledge are also presented through agent interactions. One promising approach among these is peer-to-peer action advising through a teacher-student framework. Despite being introduced for single-agent RL originally, recent studies show that it can also be applied to multi-agent scenarios with promising empirical results. However, studies in this line of research are currently very limited. In this paper, we propose heuristics-based action advising techniques in cooperative decentralised MARL, using a nonlinear function approximation based task-level policy. By adopting Random Network Distillation technique, we devise a measurement for agents to assess their knowledge in any given state and be able to initiate the teacher-student dynamics with no prior role assumptions. Experimental results in a gridworld environment show that such an approach may indeed be useful and needs to be further investigated.
Event-triggered minimax state estimation with a relative entropy constraint

In this paper, we consider an event-triggered minimax state estimation problem for uncertain systems subject to a relative entropy constraint. This minimax estimation problem is formulated as an equivalent event-triggered linear exponential quadratic Gaussian problem. It is then shown that this problem can be solved via dynamic programming and a newly defined information state. As the solution to this dynamic programming problem is computationally intractable, a one-step event-triggered minimax estimation problem is further formulated and solved, where an a posteriori relative entropy is introduced as a measure of the discrepancy between probability measures. The resulting estimator is shown to evolve in recursive closed-form expressions. For the multi-sensor system scenario, a one-step event-triggered minimax estimator is also presented in a sequential fusion way. Finally, comparative simulation examples are provided to illustrate the performance of the proposed one-step event-triggered minimax estimators.
Automatic motion planning for complex welding problems by considering angular redundancy

Automatic motion planning in complex environment is significant in manufacturing. This paper presents an off-line collision-free motion planning algorithm by considering the task redundancy existing in manufacturing. The paper takes a typical welding technique as an example, which mainly aims at solving the complex continuous welding motion planning problems. In the proposed algorithm, the angular redundancy existing in the welding process is fully considered for planning and optimizing the welding torch path by minimizing the torch angular cost. Besides, some strategies have been introduced to improve the efficiency of the proposed algorithm, such as the heuristic region sampling strategy based on Gaussian sampling, which is adopted to guide planning. Midpoint collision checking strategy is employed to improve the efficiency of the collision checking. The proposed algorithm is very effective in solving the complex welding motion planning problems, such as in the welding environment where the weld seam is situated in the narrow passage or the dense obstacles. The experiments are carried out to verify that our proposed algorithm is feasible in the relevant scenarios. All the experimental results show that not only the proposed algorithm could find a feasible collision-free path in the different complex environments if any path exists, but also the torch angle could be optimized with the increase of iteration.
Pose estimator and tracker using temporal flow maps for limbs

For human pose estimation in videos, it is significant how to use temporal information between frames. In this paper, we propose temporal flow maps for limbs (TML) and a multi-stride method to estimate and track human poses. The proposed temporal flow maps are unit vectors describing the limbs' movements. We constructed a network to learn both spatial information and temporal information end-to-end. Spatial information such as joint heatmaps and part affinity fields is regressed in the spatial network part, and the TML is regressed in the temporal network part. We also propose a data augmentation method to learn various types of TML better. The proposed multi-stride method expands the data by randomly selecting two frames within a defined range. We demonstrate that the proposed method efficiently estimates and tracks human poses on the PoseTrack 2017 and 2018 datasets.
Keystroke Analysis for User Identification using Deep Neural Networks

The current authentication systems based on password and pin code are not enough to guarantee attacks from malicious users. For this reason, in the last years, several studies are proposed with the aim to identify the users basing on their typing dynamics. In this paper, we propose a deep neural network architecture aimed to discriminate between different users using a set of keystroke features. The idea behind the proposed method is to identify the users silently and continuously during their typing on a monitored system. To perform such user identification effectively, we propose a feature model able to capture the typing style that is specific to each given user. The proposed approach is evaluated on a large dataset derived by integrating two real-world datasets from existing studies. The merged dataset contains a total of 1530 different users each writing a set of different typing samples. Several deep neural networks, with an increasing number of hidden layers and two different sets of features, are tested with the aim to find the best configuration. The final best classifier scores a precision equal to 0.997, a recall equal to 0.99 and an accuracy equal to 99% using an MLP deep neural network with 9 hidden layers. Finally, the performances obtained by using the deep learning approach are also compared with the performance of traditional decision-trees machine learning algorithm, attesting the effectiveness of the deep learning-based classifiers in the domain of keystroke analysis.
Imbalance learning using heterogeneous ensembles

In binary classification, class-imbalance problem occurs when the number of samples in one class is much larger than that of the other class. In such cases, the performance of a classifier is generally poor on the minority class. Classifier ensembles are used to tackle this problem where each member is trained using a different balanced dataset that is computed by randomly undersampling the majority class and/or randomly oversampling the minority. Although the primary target of imbalance learning is the minority class, downsampling-based schemes employ the same minority sample set for all members whereas oversampling the minority is challenging due to its unclear structure. On the other hand, heterogeneous ensembles utilizing multiple learning algorithms have a higher potential in generating diverse members than homogeneous ones. In this study, the use of heterogeneous ensembles for imbalance learning is addressed. Experiments are conducted on 66 datasets to explore the relation between the heterogeneity of the ensemble and performance scores using AUC and F1 measures. The results obtained have shown that the performance scores improve as the number of classification methods is increased from one to five. Moreover, when compared with homogeneous ensembles, significantly higher scores are achieved using heterogeneous ones. Also, it is observed that multiple balancing schemes contribute to the performance scores of some homogeneous and heterogeneous ensembles. However, the improvements are not significant for either approach.
Zero-shot Image Recognition Using Relational Matching, Adaptation and Calibration

Zero-shot learning (ZSL) for image classification focuses on recognizing novel categories that have no labeled data available for training. The learning is generally carried out with the help of mid-level semantic descriptors associated with each class. This semantic-descriptor space is generally shared by both seen and unseen categories. However, ZSL suffers from hubness, domain discrepancy and biased-ness towards seen classes. To tackle these problems, we propose a three-step approach to zero-shot learning. Firstly, a mapping is learned from the semantic-descriptor space to the image-feature space. This mapping learns to minimize both one-to-one and pairwise distances between semantic embeddings and the image features of the corresponding classes. Secondly, we propose test-time domain adaptation to adapt the semantic embedding of the unseen classes to the test data. This is achieved by finding correspondences between the semantic descriptors and the image features. Thirdly, we propose scaled calibration on the classification scores of the seen classes. This is necessary because the ZSL model is biased towards seen classes as the unseen classes are not used in the training. Finally, to validate the proposed three-step approach, we performed experiments on four benchmark datasets where the proposed method outperformed previous results. We also studied and analyzed the performance of each component of our proposed ZSL framework.
Optical analog computing devices designed by deep neural network

We proposed a multilayered spatial optical differentiator designing method by use of the deep neural network (DNN). After trained for approximately 30 h, the DNN is able to predict the reflection coefficient of a 12-layer multilayer film with high fidelity (validation mean squared error < 2.4×10−4). As a useful example, a second-order spatial optical differentiator was then designed. Compared with the general optimization method, the machine learning could help to quickly generate a wavefront computing device at an about 6-times faster speed. The performance of the designed device is confirmed from the comparison with the theoretical ideal operation output. Another first-order spatial optical differentiator was also designed to validate the generality of the method. The results indicate that the DNN may have a bright future in designing devices capable of all kinds of complex time-space wavefront mathematical operation, in particular based on the multilayer material systems.
Interpretable Deep Convolutional Fuzzy Classifier

While deep learning has proven to be a powerful new tool for modeling and predicting a wide variety of complex phenomena, those models remain incomprehensible black boxes. This is a critical impediment to the widespread deployment of deep learning technology, as decades of research have found that users simply will not trust (i.e., make decisions based on) a model whose solutions cannot be explained. Fuzzy systems, on the other hand, are by design much more easily understood. In this article, we propose to create more comprehensible deep networks by hybridizing them with fuzzy logic. Our proposed architecture first employs a convolutional neural network as an automated feature extractor and then performs a fuzzy clustering in the derived feature space. After hardening the clusters, we employ Rocchio's algorithm to classify the data points. Experiments on three datasets show that the automated feature extraction substantially improves the accuracy of the fuzzy classifier, and while the substitution of a fuzzy classifier slightly decreases the network's performance, we are able to introduce an effective interpretation mechanism.
Crime Analysis and Prediction Using Fuzzy C-Means Algorithm

Crime analysis is methodological approach for identify the crime areas. The crime areas are mainly based on the crime type these identified crime areas are helpful to reduce the crime rate. This can be very easy to identify the crime areas, based on this process the crime rate can be analyzed. With the increasing of computer systems the crime data analysts can help to the crime investigators to analyze the crime. Based on the clustering and preprocessing extract the crime areas from a structured data. The cause of occurrences of crimes like crime details of person and other factors we are focusing mainly on crime factors of previous years. This system is mainly focus on in which area the crime will occur, does not focus on the identify the criminal. In the existing system naive bayes classification was used In the present system, the fuzzy C-Means algorithm will be use to cluster the crime data for total cognizable crimes such as Kidnapping, murder, Theft, Burglary, cheating, crime against women, robbery and other such crimes.
GAN-Based Image Enrichment in Digital Pathology Boosts Segmentation Accuracy

We introduce the idea of 'image enrichment' whereby the information content of images is increased in order to enhance segmentation accuracy. Unlike in data augmentation, the focus is not on increasing the number of training samples (by adding new virtual samples), but on increasing the information for each sample. For this purpose, we use a GAN-based image-to-image translation approach to generate corresponding virtual samples from a given (original) image. The virtual samples are then merged with the original sample to create a multi-channel image, which serves as the enriched image. We train and test a segmentation network on enriched images showing kidney pathology and obtain segmentation scores exhibiting an improvement compared to conventional processing of the original images only. We perform an extensive evaluation and discuss the reasons for the improvement.
Combining Numerical Weather Predictions and Satellite Data for PV Energy Nowcasting

The increasing presence of photovoltaic (PV) generation in the energy mix demands improved forecasting tools which can be updated in an almost continuous basis. Satellite-based information lends itself naturally to this purpose and here it is used to nowcast hourly PV energy production for horizons up to six hours over Peninsular Spain and two islands, Majorca in the Mediterranean Sea and Tenerife in the Atlantic Ocean. This paper compares a single model-based on same-day numerical weather prediction (NWP) with hourly refreshed models which either only use satellite-based measurements or which combine both NWP same-day forecasts and satellite data. As shown in the experiments, the satellite-NWP combination gives very good nowcasting results, clearly superior to those achievable separately by either approach.
A non-revisiting genetic algorithm based on a novel binary space partition tree

As one of the most popular evolutionary algorithms for solving complex optimization problems, genetic algorithm has been extensively studied in the last three decades. Since genetic algorithm is a stochastic algorithm that may revisit duplicated solutions, it could suffer from low convergence speed on some real-world problems making algorithms likely to get trapped into local optimums. To address this issue, this paper proposes a non-revisiting genetic algorithm with a novel binary space partition (BSP) tree. The proposed BSP tree records all the generated solutions, which enables the algorithm to quickly determine whether a newly generated solution is duplicated or not. Moreover, the proposed algorithm fine-tunes the solutions according to the topology of the BSP tree in each generation, and thus can improve the population diversity and convergence speed. In comparison to six representative evolutionary algorithms, the proposed non-revisiting genetic algorithm exhibits better overall performance on eight benchmark problems, the power system fault diagnosis problem, and the molecular signatures selection problem.
Tubular Structure Segmentation Using Spatial Fully Connected Network with Radial Distance Loss for 3D Medical Images

This paper presents a new spatial fully connected tubular network for 3D tubular-structure segmentation. Automatic and complete segmentation of intricate tubular structures remains an unsolved challenge in the medical image analysis. Airways and vasculature pose high demands on medical image analysis as they are elongated fine structures with calibers ranging from several tens of voxels to voxel-level resolution, branching in deeply multi-scale fashion, and with complex topological and spatial relationships. Most machine/deep learning approaches are based on intensity features and ignore spatial consistency across the network that are otherwise distinct in tubular structures. In this work, we introduce 3D slice-by-slice convolutional layers in a U-Net architecture to capture the spatial information of elongated structures. Furthermore, we present a novel loss function, coined radial distance loss, specifically designed for tubular structures. The commonly used methods of cross-entropy loss and generalized Dice loss are sensitive to volumetric variation. However, in tiny tubular structure segmentation, topological errors are as important as volumetric errors. The proposed radial distance loss places higher weight to the centerline, and this weight decreases along the radial direction. Radial distance loss can help networks focus more attention on tiny structures than on thicker tubular structures. We perform experiments on bronchus segmentation on 3D CT images. The experimental results show that compared to the baseline U-Net, our proposed network achieved improvement about 24% and 30% in Dice index and centerline over ratio.
Deep Segmentation-Emendation Model for Gland Instance Segmentation

Accurate and automated gland instance segmentation on histology microscopy images can assist pathologists to diagnose the malignancy degree of colorectal adenocarcinoma. To address this problem, many deep convolutional neural network (DCNN) based methods have been proposed, most of which aim to generate better segmentation by improving the model structure and loss function. Few of them, however, focus on further emendating the inferred predictions, thus missing a chance to refine the obtained segmentation results. In this paper, we propose the deep segmentation-emendation (DSE) model for gland instance segmentation. This model consists of a segmentation network (Seg-Net) and an emendation network (Eme-Net). The Seg-Net is dedicated to generating segmentation results, and the Eme-Net learns to predict the inconsistency between the ground truth and the segmentation results generated by Seg-Net. The predictions made by Eme-Net can in turn be used to refine the segmentation result. We evaluated our DSE model against five recent deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset and against two deep learning models on the colorectal adenocarcinoma (CRAG) dataset. Our results indicate that using Eme-Net results in significant improvement in segmentation accuracy, and the proposed DSE model is able to substantially outperform all the rest models in gland instance segmentation on both datasets.
Heavy Hitters and the Structure of Local Privacy

We present a new locally differentially private algorithm for the heavy hitters problem that achieves optimal worst-case error as a function of all standardly considered parameters. Prior work obtained error rates that depend optimally on the number of users, the size of the domain, and the privacy parameter but depend sub-optimally on the failure probability. We strengthen existing lower bounds on the error to incorporate the failure probability and show that our new upper bound is tight with respect to this parameter as well. Our lower bound is based on a new understanding of the structure of locally private protocols. We further develop these ideas to obtain the following general results beyond heavy hitters. • Advanced Grouposition : In the local model, group privacy for k users degrades proportionally to ≈√ k instead of linearly in k as in the central model. Stronger group privacy yields improved max-information guarantees, as well as stronger lower bounds (via “packing arguments”), over the central model. • Building on a transformation of Bassily and Smith (STOC 2015), we give a generic transformation from any non-interactive approximate-private local protocol into a pure-private local protocol. Again in contrast with the central model, this shows that we cannot obtain more accurate algorithms by moving from pure to approximate local privacy.
Vehicle Detection in Aerial Images Based on Lightweight Deep Convolutional Network and Generative Adversarial Network

Vehicle detection in aerial images is a challenging task and plays an important role in a wide range of applications. Traditional detection algorithms are based on sliding-window searching and shallow-learning-based features, which limits the ability to represent features and generates a lot of computational costs. Recently, with the successful application of convolutional neural network in computer vision, many state-of-the-art detectors have been developed based on deep CNNs. However, these CNN-based models still face some difficulties and challenges in vehicle detection in aerial images. Firstly, the CNN-based detection model requires extensive calculations during training and detection, and the accuracy of detection for small objects is not high. In addition, deep learning models often require a large amount of sample data to train a robust detection model, while the annotated data of aerial vehicles is limited. In this study, we propose a lightweight deep convolutional neural network detection model named LD-CNNs. The detection algorithm not only greatly reduces the computational costs of the model, but also significantly improves the accuracy of the detection. What's more, in order to cope with the problem of insufficient training samples, we develop a multi-condition constrained generative adversarial network named MC-GAN, which can effectively generate samples. The detection performance of the proposed model has been evaluated on the Munich public dataset and the collected dataset respectively. The results show that on the Munich dataset, the proposed method achieves 86.9% on mAP (mean average precision), F1-score is 0.875, and the detection time is 1.64s on Nvidia Titan XP. At present, these detection indicators have reached state-of-the-art level in vehicle detection of aerial images.