Reviewerly/exHarmony huggingface dataset:
Hierarchical One-Class Classifier With Within-Class Scatter-Based Autoencoders

Autoencoding is a vital branch of representation learning in deep neural networks (DNNs). The extreme learning machine-based autoencoder (ELM-AE) has been recently developed and has gained popularity for its fast learning speed and ease of implementation. However, the ELM-AE uses random hidden node parameters without tuning, which may generate meaningless encoded features. In this brief, we first propose a within-class scatter information constraint-based AE (WSI-AE) that minimizes both the reconstruction error and the within-class scatter of the encoded features. We then build stacked WSI-AEs into a one-class classification (OCC) algorithm based on the hierarchical regularized least-squared method. The effectiveness of our approach was experimentally demonstrated in comparisons with several state-of-the-art AEs and OCC algorithms. The evaluations were performed on several benchmark data sets.
Extended Object Tracking Using Random Matrix With Skewness

For extended object tracking, the random matrix approach is a computationally efficient framework that is capable of estimating the kinematic state, and extension of the object jointly, and thus is gaining momentum in recent years. Existing random matrix approaches have an underlying assumption that scatter centers are symmetrically distributed around the centroid. In many real scenarios, however, they are often distributed on particular portions of the object since these parts reflect more radar energy, and measurement distributions over an object are skewed. To effectively describe such a phenomenon, this paper proposes a new measurement model using a skew normal distribution. Based on the proposed model, a variational Bayesian approach is derived to recursively estimate the kinematic state, and the extension through convergent iterations. The resultant algorithm inherits the simplicity of the random matrix approach. To cope with the possible abrupt change of kinematic state, extension, and measurement distribution over an object (especially the skewness) when a target maneuvers, a multiple model approach is presented in the information theoretic interacting multiple model framework. Effectiveness of the proposed algorithms is evaluated using simulated data, and real experimental data. Results show that the proposed algorithms outperform the existing random matrix methods when measurement distributions are skewed.
Dual subspace discriminative projection learning

In this paper, we propose a dual subspace discriminative projection learning (DSDPL) framework for multi-category image classification. Our approach reflects the notion that images are composed of class-shared information, class-specific information, and sparse noise. Unlike traditional subspace learning methods, DSDPL serves to decompose original high dimensional data, via learned projection matrices, into class-shared and class-specific subspaces. The learned projection matrices are jointly constrained with l2,1 sparse norm and LDA terms while the reconstructive properties of DSDPL reduce information loss, leading to greater stability within low dimensional subspaces. Regression-based terms are also included to facilitate a more robust classification approach, using extracted class-specific features for better classification. Our approach is examined on five different datasets for face, object and scene classifications. Experimental results demonstrate not only the superiority and versatility of DSDPL over current benchmark approaches, but also a more robust classification approach with low sample size training data.
Inclusive and engaged HCI

column Share on Inclusive and engaged HCI Author: Gillian R. Hayes University of California, Irvine University of California, IrvineView Profile Authors Info & Claims InteractionsVolume 27Issue 2March - April 2020 pp 26–31https://doi.org/10.1145/3378561Published:25 February 2020Publication History 13citation1,373DownloadsMetricsTotal Citations13Total Downloads1,373Last 12 Months421Last 6 weeks33 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A Modern View on Forward Security

Abstract Forward security ensures that compromise of entities today does not impact the security of cryptographic primitives employed in the past. Such a form of security is regarded as increasingly important in the modern world due to the existence of adversaries with mass storage capabilities and powerful infiltration abilities. Although the idea of forward security has been known for over 30 years, current understanding of what it really should mean is limited due to the prevalence of new techniques and inconsistent terminology. We survey existing methods for achieving forward security for different cryptographic primitives and propose new definitions and terminology aimed at a unified treatment of the notion.
A hybrid algorithm based on MOSFLA and GA for multi-UAVs plant protection task assignment and sequencing optimization

We propose a Multi-Objective Shuffled Frog-Leaping Algorithm (MOSFLA) and Genetic Algorithm (GA) based task assignment and sequencing method, for multi-Unmanned Aerial Vehicles (multi-UAVs) plant-protection operation optimization. Based on full coverage of spray area, a dual decision model of non-operation flight distance and total operation time is developed considering energy consumption and operation efficiency. The proposed optimization method is hybridized using MOSFLA and GA: we first use modified MOSFLA for multi-UAVs operation assignment optimization, shrinking multi-UAVs operation cost including fields allocation, non-operation flight distance and operation time difference; we then employ GA for fields operation sequencing optimization, reducing the total operation time. Considering multi-UAVs’ take-off preparation delay effect, we established a latency time calculation model to determine total operation time for multi-UAVs. The test results show that: ① the non-operation flight distance cost for multi-UAVs using MOSFLA is less than that of single-UAV, which also performs better than that for multi-UAVs in traditional modes; ② the total operation time shrinks by using GA with known assignment matrix (including MOSFLA), which could save over 20 min compared with traditional modes; ③ the total operation time cost in MOSFLA–GA is less than that in traditional modes and optimized traditional modes, with same UAV number and preparation time for take-off; ④ the minimum iteration when assignment fitness value of MOSFLA attains maximum is small (< 20); the minimum iteration when sequencing fitness value of GA attains maximum is less than 100, but is 5 in certain cases.
Unsupervised multi-granular Chinese word segmentation and term discovery via graph partition

This study aims at realizing unsupervised term discovery in Chinese electronic health records (EHRs) by using the word segmentation technique. The existing supervised algorithms do not perform satisfactorily in the case of EHRs, as annotated medical data are scarce. We propose an unsupervised segmentation method (GTS) based on the graph partition principle, whose multi-granular segmentation capability can help realize efficient term discovery. A sentence is converted to an undirected graph, with the edge weights based on n-gram statistics, and ratio cut is used to split the sentence into words. The graph partition is solved efficiently via dynamic programming, and multi-granularity is realized by setting different partition numbers. A BERT-based discriminator is trained using generated samples to verify the correctness of the word boundaries. The words that are not recorded in existing dictionaries are retained as potential medical terms. We compared the GTS approach with mature segmentation systems for both word segmentation and term discovery. MD students manually segmented Chinese EHRs at fine and coarse granularity levels and reviewed the term discovery results. The proposed unsupervised method outperformed all the competing algorithms in the word segmentation task. In term discovery, GTS outperformed the best baseline by 17 percentage points (a 47% relative percentage of increment) on F1-score. In the absence of annotated training data, the graph partition technique can effectively use the corpus statistics and even expert knowledge to realize unsupervised word segmentation of EHRs. Multi-granular segmentation can be used to provide potential medical terms of various lengths with high accuracy.
Topographic property of backpropagation artificial neural network: From human functional connectivity network to artificial neural network

The human brain network is one of the most complex data processing centers so far and shows several topographic features such as small worldness, modularity, and high efficiency. Inspired by the human brain network, the artificial neural network (ANN) method was proposed and has become an important engineering tool for pattern recognition. A recent study suggested the similarity between the human brain network and ANN. Would the ANN show topographic features that are similar to those of the human brain network? In the present study, we first constructed a simple backpropagation ANN (BP-ANN) with one hidden layer, which is one of the simplest, representative ANN, based on CIFAR10 and CIFAR100 datasets. Then, we explored the topographic features of a network with artificial neurons. We found that artificial neurons of the BP-ANN showed a high local efficiency and modularity similar to the human brain network. However, the global efficiency of artificial neurons of the BP-ANN was comparable with those of a random network. Our results showed that the best model (high accuracy and no overfitting) of a large-scale BP-ANN during epochs of training showed the lowest local and global efficiency. These results might imply that after epochs of training, neurons of the BP-ANN form local highly intra-connected functional modules but lack cooperation at the global level. The topography structure of the BP-ANN also shows the potential to become an early-stop marker during training.
Locally private frequency estimation of physical symptoms for infectious disease analysis in Internet of Medical Things

Frequency estimation of physical symptoms for peoples is the most direct way to analyze and predict infectious diseases. In Internet of medical Things (IoMT), it is efficient and convenient for users to report their physical symptoms to hospitals or disease prevention departments by various mobile devices. Unfortunately, it usually brings leakage risk of these symptoms since data receivers may be untrusted. As a strong metric for health privacy, local differential privacy (LDP) requires that users should perturb their symptoms to prevent the risk. However, the widely-used data structure called sketch for frequency estimation does not satisfy the specified requirement. In this paper, we firstly define the problem of frequency estimation of physical symptoms under LDP. Then, we propose four different protocols, i.e., CMS-LDP, FCS-LDP, CS-LDP and FAS-LDP to solve the above problem. Next, we demonstrate that the designed protocols satisfy LDP and unbiased estimation. We also present two approaches to implement the key component (i.e., universal hash functions) of protocols. Finally, we conduct experiments to evaluate four protocols on two real-world datasets, representing two different distributions of physical symptoms. The results show that CMS-LDP and CS-LDP have relatively optimal utility for frequency estimation of physical symptoms in IoMT.
pyFUME: a Python Package for Fuzzy Model Estimation

Living in the era of "data deluge" demands for an increase in the application and development of machine learning methods, both in basic and applied research. Among these methods, in the last decades fuzzy inference systems carved out their own niche as (light) grey box models, which are considered more interpretable and transparent than other commonly employed methods, such as artificial neural networks. Although commercially distributed alternatives are available, software able to assist practitioners and researchers in each step of the estimation of a fuzzy model from data are still limited in scope and applicability. This is especially true when looking at software developed in Python, a programming language that quickly gained popularity among data scientists and it is often considered their language of choice. To fill this gap, we introduce pyFUME, a Python library for automatically estimating fuzzy models from data. pyFUME contains a set of classes and methods to estimate the antecedent sets and the consequent parameters of a Takagi-Sugeno fuzzy model from data, and then create an executable fuzzy model exploiting the Simpful library. pyFUME can be beneficial to practitioners, thanks to its pre-implemented and user-friendly pipelines, but also to researchers that want to fine-tune each step of the estimation process.
Data curation as collective action during <scp>COVID</scp>‐19

Abstract In this commentary, the authors, an international group data curation researchers and educators, reflect on some of the challenges and opportunities for data curation in the wake of the COVID‐19 pandemic. We focus on some topics of particular interest to the information science community: data infrastructures for scholarly communication and research, the politicization of data curation and visualization for public‐facing “dashboards,” and human subjects research and policies. We conclude with some areas of opportunity and need, including broader and richer data curation education in the information schools, the establishment of better data management policy implementations by research funders, the award of formal academic credit for data curation activities and data sharing, and engagement in cooperative action around data ethics and security.
Maneuvering Ability-Based Weighted Potential Field Framework for Multi-USV Navigation, Guidance, and Control

Abstract Numerous types of unmanned surface vehicles (USVs) are currently available for different applications with a wide spectrum of maneuvering capabilities. We present a generalized multi-USV navigation, guidance, and control framework adaptable to specific USV maneuvering response capabilities for dynamic obstacle avoidance. The proposed method integrates offline optimal path planning with a safety distance constrained A* algorithm, and an online extended adaptively weighted (EAW) artificial potential field-based path following approach with dynamic collision avoidance, based on USV maneuvering response times. The framework adaptively weighs inter-USV interaction, waypoint following, and collision avoidance based on USV maneuvering capabilities. The EAW system allows USVs with fast maneuvering abilities to react late and slow USVs to react sooner to oncoming moving obstacles gradually, with a carefully designed series of repulsive potential with diminishing weighting along the predicted path of detected moving obstacles, such that a smooth path is followed by the USV group with reduced cross-track error and reduced maneuvering effort. We emphasize the importance of such requirements in constrained and busy maritime environments such as narrow channels in busy harbors. Simulation results validate the proposed EAW artificial potential field framework for different sized multi-USV teams showing reduced cross-track error and maneuvering effort compared to the unweighted or traditional approach, for both slow- and fast-maneuvering multi-USV teams.
HUNA: A Method of Hierarchical Unsupervised Network Alignment for IoT

With the advent of the era of the Internet of Things (IoT), a large number of interconnected smart devices form a huge network. The network can be abstracted as a graph, and we propose to identify similar IoT devices in different networks by graph alignment. However, most methods rely on prelabeled cross-network node pairs such as anchor links, which are difficult to obtain due to personal privacy and security restrictions, especially in IoT. In addition, existing network entity alignment methods focus on individual pairs of nodes but ignore the tightly connected group structure in the network, which is a significant feature of IoT devices. In this article, we propose a method of hierarchical unsupervised network alignment (HUNA) to identify similar IoT devices in different networks by a deep learning approach. First, we propose an unsupervised network alignment method based on cycle adversarial networks (UNA), which utilizes the adversarial characteristics of cycle adversarial networks to achieve entity alignment under unsupervised conditions. Second, we further expand the model by carefully designing the group structure aggregation optimization module to aggregate the nodes with closely related attributes and structures into a coarse-grained node and align the coarse-grained nodes. Finally, we evaluate HUNA with real and synthetic data sets. Experimental results show that this method can improve the accuracy of node alignment by 10% and perform well in terms of parameter sensitivity.
Mobile-assisted Language learning to enhance L2 reading comprehension: a selection of implementation studies between 2012–2017

ABSTRACTMobile-assisted language learning (MALL) is a fast-developing field that is constantly growing and evolving in education. There is a lack of publications focusing on implementation studies of MALL as a means to enhance second language/foreign language (L2) reading comprehension. Building on Burston's (2013). Mobile-Assisted Language Learning: a Selected Annotated Bibliography of Implementation Studies 1994–2012. Language Learning & Technology, 17(173), 157–225. https://doi.org/10.1080/0022027032000276961] comprehensive historical background of MALL applications from 1994 to 2012, the current article aims to provide a thorough review of a selection of implementation studies between 2012 and 2017, which focus on L2 reading comprehension through the use of mobile devices. Thus, the current article will provide an overview of the main journal articles, book chapters and conference proceedings which investigate the use of mobile devices and tablets to improve L2 reading comprehension published in the last six years.
Anchor-Based Multiview Subspace Clustering With Diversity Regularization

Multiview clustering has attracted much attention due to its ability to aggregate various source information and many advanced approaches have been proposed in the literature. However, there are still two major issues that need to be further explored: i) how to efficiently handle large-scale data; ii) how to effectively incorporate the complementary multiple sources. In this article, we fulfill a unified multiview subspace clustering model termed anchor-based multiview subspace clustering with diversity regularization by seamlessly optimizing subspace learning and multiview fusion. First, we efficiently evaluate the self-expression similarity matrix based on sampling anchor points to reduce the high time complexities in former methods. A regularization term is further imposed to encourage high independence and diversity of each view. In addition, we theoretically analyze the time complexity of the proposed algorithm. Comprehensive experiments on several benchmark datasets demonstrate that our proposed model consistently outperforms over the state-of-the-art techniques.
Improving deep‐learning‐based fault localization with resampling

Abstract Many fault localization approaches recently utilize deep learning to learn an effective localization model showing a fresh perspective with promising results. However, localization models are generally learned from class imbalance datasets; that is, the number of failing test cases is much fewer than passing test cases. It may be highly susceptible to affect the accuracy of learned localization models. Thus, in this paper, we explore using data resampling to reduce the negative effect of the imbalanced class problem and improve the accuracy of learned models of deep‐learning‐based fault localization. Specifically, for deep‐learning‐based fault localization, its learning feature may require duplicate essential data to enhance the weak but beneficial experience incurred by the class imbalance datasets. We leverage the property of test cases (i.e., passing or failing) to identify failing test cases as the duplicate essential data and propose an iterative oversampling approach to resample failing test cases for producing a class balanced test suite. We apply the test case resampling to representative localization models using deep learning. Our empirical results on eight large‐sized programs with real faults and four large‐sized programs with seeded faults show that the test case resampling significantly improves fault localization effectiveness.
A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images

Nuclei instance segmentation within microscopy images is a fundamental task in the pathology work-flow, based on that the meaningful nuclear features can be extracted and multiple biological related analysis can be performed.However, this task is still challenging because of the large variability among different types of nuclei.Although deep learning(DL) based methods have achieved state-of-the-art results in nuclei instance segmentation tasks, these methods are usually focus on improving the accuracy and require support of powerful computing resources.In this paper, we joint the detection and segmentation simultaneously, and propose a fast and accurate box-based nuclei instance segmentation method.Mainly, we employ a fusion module based on the feature pyramid network(FPN) to combine the complementary information of the shallow layers with deep layers for detection the nuclear location by bounding boxes.Subsequently, we crop the feature maps according to the bounding boxes and feed the cropped patches into an U-net architecture as a guide to separate clustered nuclei.The experiments show that the proposed approach outperforms prior state-of-the-art methods, not only on accuracy but also on speed.The source code will be released at: https://github.com/QUAPNH/Nucleiseg.
Saliency Detection Using Deep Features and Affinity-Based Robust Background Subtraction

Most existing saliency methods measure fore- ground saliency by using the contrast of a foreground region to its local context, or boundary priors and spatial compactness. These methods are not powerful enough to extract a precise salient region from noisy and cluttered backgrounds. To evaluate the contrast of salient and background regions effectively, we consider high-level features from both supervised and unsupervised methods. We propose an affinity-based robust background subtraction technique and maximum attention map using a pre-trained convolution neural network. This affinity-based technique uses pixel similarities to propagate the values of salient pixels among foreground and background regions and their union. The salient pixel value controls the foreground and background information by using multiple pixel affinities. The maximum attention map is derived from the convolution neural network using features of the Pooling and Relu layers. This method can detect salient regions from images that have noisy and cluttered backgrounds. Our experimental results demonstrate the effectiveness of the proposed approach on six different saliency data sets and benchmarks and show that it improves the quality of detection beyond current saliency detection methods.