Reviewerly/exHarmony huggingface dataset:
Supervised discriminant Isomap with maximum margin graph regularization for dimensionality reduction

As one of the most popular nonlinear dimensionality reduction methods, Isomap has been widely used in pattern recognition and machine learning. However, Isomap has the following problems: (1) Isomap is an unsupervised dimensionality reduction method, it cannot use class label information to obtain discriminative low dimensional embedding for classification; (2) The embedding performance of Isomap is sensitive to neighborhood size parameter; (3) Isomap cannot deal with outside new data by direct embedding. In this paper, a novel dimensionality reduction method called supervised discriminant Isomap is proposed to solve the first two problems mentioned above. Specifically, first, raw data points are partitioned into different manifolds by using their class label information. Then, supervised discriminant Isomap aims at seeking an optimal nonlinear subspace to preserve the geometrical structure of each manifold according to the Isomap criterion, and to enhance the discriminating capability by maximizing the distances between data points of different classes and the maximum margin graph regularization term. Finally, the corresponding optimization problems are solved by using eigen-decomposition algorithm. Further, we extend supervised discriminant Isomap to a linear dimensionality reduction method called supervised discriminant Isomap projection for handling the above three problems. Moreover, our approaches have three important characteristics: (1) Proposed methods adaptively estimate the local neighborhood surrounding each sample based on data density and similarity; (2) The objective functions of proposed methods can maximize margins between the each classes in the dimension-reduced feature space; (3) The objective functions of proposed methods have closed-form solutions. Furthermore, our methods can capture more discriminative information from raw data than other Isomap based methods. Extensive experiments on nine data sets demonstrate that the proposed methods are superior to the related state-of-the-art methods.
Three Years Later: A Study of MAC Address Randomization In Mobile Devices And When It Succeeds

Abstract Mobile device manufacturers and operating system developers increasingly deploy MAC address randomization to protect user privacy and prevent adversaries from tracking persistent hardware identifiers. Early MAC address randomization implementations suffered from logic bugs and information leakages that defeated the privacy benefits realized by using temporary, random addresses, allowing devices and users to be tracked in the wild. Recent work either assumes these implementation flaws continue to exist in modern MAC address randomization implementations, or considers only dated software or small numbers of devices. In this work, we revisit MAC address randomization by performing a cross-sectional study of 160 models of mobile phones, including modern devices released subsequent to previous studies. We tested each of these phones in a lab setting to determine whether it uses randomization, under what conditions it randomizes its MAC address, and whether it mitigates known tracking vulnerabilities. Our results show that, although very new phones with updated operating systems generally provide a high degree of privacy to their users, there are still many phones in wide use today that do not effectively prevent tracking.
Fokker--Planck Particle Systems for Bayesian Inference: Computational Approaches

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 2 December 2019Accepted: 22 January 2021Published online: 26 April 2021KeywordsBayesian inverse problems, Fokker--Planck equation, gradient flow, affine invariance, gradient-free sampling methods, localizationAMS Subject Headings65N21, 62F15, 65N75, 65C30, 90C56Publication DataISSN (online): 2166-2525Publisher: Society for Industrial and Applied MathematicsCODEN: sjuqa3
Optimized Data Storage Method for Sharding-Based Blockchain

COVID-19 virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.
Hyperparameter self-tuning for data streams

The number of Internet of Things devices generating data streams is expected to grow exponentially with the support of emergent technologies such as 5G networks. Therefore, the online processing of these data streams requires the design and development of suitable machine learning algorithms, able to learn online, as data is generated. Like their batch-learning counterparts, stream-based learning algorithms require careful hyperparameter settings. However, this problem is exacerbated in online learning settings, especially with the occurrence of concept drifts, which frequently require the reconfiguration of hyperparameters. In this article, we present SSPT, an extension of the Self Parameter Tuning (SPT) optimisation algorithm for data streams. We apply the Nelder–Mead algorithm to dynamically-sized samples, converging to optimal settings in a single pass over data while using a relatively small number of hyperparameter configurations. In addition, our proposal automatically readjusts hyperparameters when concept drift occurs. To assess the effectiveness of SSPT, the algorithm is evaluated with three different machine learning problems: recommendation, regression, and classification. Experiments with well-known data sets show that the proposed algorithm can outperform previous hyperparameter tuning efforts by human experts. Results also show that SSPT converges significantly faster and presents at least similar accuracy when compared with the previous double-pass version of the SPT algorithm.
Automatic Detection of Causality in Requirement Artifacts: The CiRA Approach

[Context & motivation:] System behavior is often expressed by causal relations in requirements (e.g., If event 1, then event 2). Automatically extracting this embedded causal knowledge supports not only reasoning about requirements dependencies, but also various automated engineering tasks such as seamless derivation of test cases. However, causality extraction from natural language (NL) is still an open research challenge as existing approaches fail to extract causality with reasonable performance. [Question/problem:] We understand causality extraction from requirements as a two-step problem: First, we need to detect if requirements have causal properties or not. Second, we need to understand and extract their causal relations. At present, though, we lack knowledge about the form and complexity of causality in requirements, which is necessary to develop a suitable approach addressing these two problems. [Principal ideas/results:] We conduct an exploratory case study with 14,983 sentences from 53 requirements documents originating from 18 different domains and shed light on the form and complexity of causality in requirements. Based on our findings, we develop a tool-supported approach for causality detection (CiRA, standing for Causality in Requirement Artifacts). This constitutes a first step towards causality extraction from NL requirements. [Contribution:] We report on a case study and the resulting tool-supported approach for causality detection in requirements. Our case study corroborates, among other things, that causality is, in fact, a widely used linguistic pattern to describe system behavior, as about a third of the analyzed sentences are causal. We further demonstrate that our tool CiRA achieves a macro-F $$_{1}$$ score of 82% on real word data and that it outperforms related approaches with an average gain of 11.06% in macro-Recall and 11.43% in macro-Precision. Finally, we disclose our open data sets as well as our tool to foster the discourse on the automatic detection of causality in the RE community.
Decontextualization: Making Sentences Stand-Alone

Abstract Models for question answering, dialogue agents, and summarization often interpret the meaning of a sentence in a rich context and use that meaning in a new context. Taking excerpts of text can be problematic, as key pieces may not be explicit in a local window. We isolate and define the problem of sentence decontextualization: taking a sentence together with its context and rewriting it to be interpretable out of context, while preserving its meaning. We describe an annotation procedure, collect data on the Wikipedia corpus, and use the data to train models to automatically decontextualize sentences. We present preliminary studies that show the value of sentence decontextualization in a user-facing task, and as preprocessing for systems that perform document understanding. We argue that decontextualization is an important subtask in many downstream applications, and that the definitions and resources provided can benefit tasks that operate on sentences that occur in a richer context.
A survey of traditional and advanced automatic modulation classification techniques, challenges, and some novel trends

Summary Automatic modulation classification (AMC) is an important stage in intelligent wireless communication receivers. It is a necessary process after signal detection, and before demodulation. It plays a vital role in various applications. Blind modulation classification is a very difficult task without information about the transmitted signal and the receiver parameters like carrier frequency, signal power, timing information, phase offset, existence of frequency‐selective multipath fading, and time‐varying channels in real‐world applications. The AMC methods are divided into traditional and advanced methods. Traditional methods include likelihood‐based (LB) and feature‐based (FB) methods. The advanced methods include deep learning (DL) methods. In addition, the AMC methods are used to classify different modulation schemes such as ASK, PSK, FSK, PAM, and QAM with different orders and different signal‐to‐noise ratios (SNRs). This paper focuses on summarizing the AMC methoods, comparing between them, presenting the commercial software packages for AMC, and finally considering the new challenges in the implementation of AMC.
Thor: A Deep Learning Approach for Face Mask Detection to Prevent the COVID-19 Pandemic

With the rapid worldwide spread of Coronavirus , wearing face masks in public becomes a necessity to mitigate the transmission of this or other pandemics.However, with the lack of on-ground automated prevention measures, depending on humans to enforce face mask-wearing policies in universities and other organizational buildings, is a very costly and time-consuming measure.Without addressing this challenge, mitigating highly airborne transmittable diseases will be impractical, and the time to react will continue to increase.Considering the high personnel traffic in buildings and the effectiveness of countermeasures, that is, detecting and offering unmasked personnel with surgical masks, our aim in this paper is to develop automated detection of unmasked personnel in public spaces in order to respond by providing a surgical mask to them to promptly remedy the situation.Our approach consists of three key components.The first component utilizes a deep learning architecture that integrates deep residual learning (ResNet-50) with Feature Pyramid Network (FPN) to detect the existence of human subjects in the videos (or video feed).The second component utilizes Multi-Task Convolutional Neural Networks (MT-CNN) to detect and extract human faces from these videos.For the third component, we construct and train a convolutional neural network classifier to detect masked and unmasked human subjects.Our techniques were implemented in a mobile robot, Thor, and evaluated using a dataset of videos collected by the robot from public spaces of an educational institute in the U.S. Our evaluation results show that Thor is very accurate achieving an F1 score of 87.7% with a recall of 99.2% in a variety of situations, a reasonable accuracy given the challenging dataset and the problem domain.
Bridging the Gap Between 2D and 3D Contexts in CT Volume for Liver and Tumor Segmentation

Automatic liver and tumor segmentation remain a challenging topic, which subjects to the exploration of 2D and 3D contexts in CT volume. Existing methods are either only focus on the 2D context by treating the CT volume as many independent image slices (but ignore the useful temporal information between adjacent slices), or just explore the 3D context lied in many little voxels (but damage the spatial detail in each slice). These factors lead an inadequate context exploration together for automatic liver and tumor segmentation. In this paper, we propose a novel full-context convolution neural network to bridge the gap between 2D and 3D contexts. The proposed network can utilize the temporal information along the Z axis in CT volume while retaining the spatial detail in each slice. Specifically, a 2D spatial network for intra-slice features extraction and a 3D temporal network for inter-slice features extraction are proposed separately and then are guided by the squeeze-and-excitation layer that allows the flow of 2D context and 3D temporal information. To address the severe class imbalance issue in the CT volume and meanwhile improve the segmentation performance, a loss function consisting of weighted cross-entropy and jaccard distance is proposed. During the network training, the 2D and 3D contexts are learned jointly in an end-to-end way. The proposed network achieves competitive results on the Liver Tumor Segmentation Challenge (LiTS) and the 3D-IRCADB datasets. This method should be a new promising paradigm to explore the contexts for liver and tumor segmentation.
Verifiably safe exploration for end-to-end reinforcement learning

Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first approach toward enforcing formal safety constraints on end-to-end policies with visual inputs. Our approach draws on recent advances in object detection and automated reasoning for hybrid dynamical systems. The approach is evaluated on a novel benchmark that emphasizes the challenge of safely exploring in the presence of hard constraints. Our benchmark draws from several proposed problem sets for safe learning and includes problems that emphasize challenges such as reward signals that are not aligned with safety constraints. On each of these benchmark problems, our algorithm completely avoids unsafe behavior while remaining competitive at optimizing for as much reward as is safe. We characterize safety constraints in terms of a refinement relation on Markov decision processes - rather than directly constraining the reinforcement learning algorithm so that it only takes safe actions, we instead refine the environment so that only safe actions are defined in the environment's transition structure. This has pragmatic system design benefits and, more importantly, provides a clean conceptual setting in which we are able to prove important safety and efficiency properties. These allow us to transform the constrained optimization problem of acting safely in the original environment into an unconstrained optimization in a refined environment.
Hierarchical Density-Aware Dehazing Network

The commonly used atmospheric model in image dehazing cannot hold in real cases. Although deep end-to-end networks were presented to solve this problem by disregarding the physical model, the transmission map in the atmospheric model contains significant haze density information, which cannot simply be ignored. In this article, we propose a novel hierarchical density-aware dehazing network, which consists of a the densely connected pyramid encoder, a density generator, and a Laplacian pyramid decoder. The proposed network incorporates density estimation but alleviates the constraint of the atmospheric model. The predicted haze density then guides the Laplacian pyramid decoder to generate a haze-free image in a coarse-to-fine fashion. In addition, we introduce a multiscale discriminator to preserve global and local consistency for dehazing. We conduct extensive experiments on natural and synthetic hazy images, which prove that the proposed model performs favorably against the state-of-the-art dehazing approaches.
Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.
Extreme learning machine for multivariate reservoir characterization

Reservoir characterization is one of the most important tasks in oil and gas field exploration and development. Different parameters reflect the relevant information of oil and gas fields from diversified aspects. We design a new reservoir characterization framework by introducing extreme learning machine (ELM) that is one of the state-of-the-art methods in machine learning. It is a single hidden layer feedforward neural (SLFN) network, while the input weight and the bias value of the hidden layer are randomly assigned and kept fixed for simplifying the calculation. Based on ELM, we achieve simultaneous prediction of multiple reservoir parameters (including lithofacies, porosity, shale content and saturation etc.) only through one training step. In order to combat overfitting when the number of hidden nodes is inappropriate or the training samples are inadequate, we extend the method by using biased dropout and dropconnect operations to regularize ELM. We describe the new method in detail and analyze its performance with varying input parameters. It is evaluated on well and seismic datasets by exploiting elastic attributes as training input. Compared with traditional SLFN-based method, ELM-based method uses less computational resources and costs less time on training without losing accuracy. The biased dropout and dropconnect operations further enhance the generalization ability.
Adversarial Entropy Optimization for Unsupervised Domain Adaptation

Domain adaptation is proposed to deal with the challenging problem where the probability distribution of the training source is different from the testing target. Recently, adversarial learning has become the dominating technique for domain adaptation. Usually, adversarial domain adaptation methods simultaneously train a feature learner and a domain discriminator to learn domain-invariant features. Accordingly, how to effectively train the domain-adversarial model to learn domain-invariant features becomes a challenge in the community. To this end, we propose in this article a novel domain adaptation scheme named adversarial entropy optimization (AEO) to address the challenge. Specifically, we minimize the entropy when samples are from the independent distributions of source domain or target domain to improve the discriminability of the model. At the same time, we maximize the entropy when features are from the combined distribution of source domain and target domain so that the domain discriminator can be confused and the transferability of representations can be promoted. This minimax regime is well matched with the core idea of adversarial learning, empowering our model with transferability as well as discriminability for domain adaptation tasks. Also, AEO is flexible and compatible with different deep networks and domain adaptation frameworks. Experiments on five data sets show that our method can achieve state-of-the-art performance across diverse domain adaptation tasks.
Visual Attention Dehazing Network with Multi-level Features Refinement and Fusion

Image dehazing is very important for many computer vision tasks. However, typical CNN-based methods learn a direct mapping from a hazy image to a clear image, ignoring relevant haze priors and multi-level features. In this paper, a new Visual Attention Dehazing Network (VADN) with multi-level refinement and fusion is proposed, which leverages a haze attention map as a haze relevant prior and learns complementary haze information among multi-level features. The VADN contains a feature extraction network, a recurrent refinement network and an encoder-decoder network. The feature extraction network captures the multi-level features. The recurrent refinement network generates and refines the haze attention map by taking low-level features and high-level features as inputs alternatively. Then, the haze attention map is injected into the encoder-decoder network to obtain the clear image with the help of complementary information learned from informative multi-level features. The experimental results demonstrate that the average PSNR of VADN is 32.50 dB which outperforms most state-of-the-art methods by up to 5.14 dB. Besides, the run time of VADN is 0.067 s, only 55% of the run time spent by the recent enhanced pix2pix dehazing network.