Reviewerly/exHarmony huggingface dataset:
Probabilistic Diffusion for Interactive Image Segmentation

This paper presents an interactive image segmentation approach in which we formulate segmentation as a probabilistic estimation problem based on the prior user intention. Instead of directly measuring the relationship between pixels and labels, we first estimate the distances between pixel pairs and label pairs using a probabilistic framework. Then, binary probabilities with label pairs are naturally converted to unary probabilities with labels. The higher order relationship helps improve the robustness to user inputs. To improve segmentation accuracy, a likelihood learning framework is proposed to fuse the region and the boundary information of the image by imposing a smoothing constraint on the unary potentials. Furthermore, we establish an equivalence relationship between likelihood learning and likelihood diffusion and propose an iterative diffusion-based optimization strategy to maintain computational efficiency. Experiments on the Berkeley segmentation data set and Microsoft GrabCut database demonstrate that the proposed method can obtain better performance than the state-of-the-art methods.
Don't Trust The Locals: Investigating the Prevalence of Persistent Client-Side Cross-Site Scripting in the Wild

The Web has become highly interactive and an important driver for modern life, enabling information retrieval, social exchange, and online shopping.From the security perspective, Cross-Site Scripting (XSS) is one of the most nefarious attacks against Web clients.Research has long since focused on three categories of XSS: Reflected, Persistent, and DOMbased XSS.In this paper, we argue that our community must consider at least four important classes of XSS, and present the first systematic study of the threat of Persistent Client-Side XSS, caused by the insecure use of client-side storage.While the existence of this class has been acknowledged, especially by the non-academic community like OWASP, prior works have either only found such flaws as side effects of other analyses or focused on a limited set of applications to analyze.Therefore, the community lacks in-depth knowledge about the actual prevalence of Persistent Client-Side XSS in the wild.To close this research gap, we leverage taint tracking to identify suspicious flows from client-side persistent storage (Web Storage, cookies) to dangerous sinks (HTML, JavaScript, and script.src).We discuss two attacker models capable of injecting malicious payloads into storage, i.e., a Network Attacker capable of temporarily hijacking HTTP communication (e.g., in a public WiFi), and a Web Attacker who can leverage flows into storage or an existing reflected XSS flaw to persist their payload.With our taint-aware browser and these models in mind, we study the prevalence of Persistent Client-Side XSS in the Alexa Top 5,000 domains.We find that more than 8% of them have unfiltered data flows from persistent storage to a dangerous sink, which showcases the developers' inherent trust in the integrity of storage content.Even worse, if we only consider sites that make use of data originating from storage, 21% of the sites are vulnerable.For those sites with vulnerable flows from storage to sink, we find that at least 70% are directly exploitable by our attacker models.Finally, investigating the vulnerable flows originating from storage allows us to categorize them into four disjoint categories and propose appropriate mitigations.
Cross-View Local Structure Preserved Diversity and Consensus Learning for Multi-View Unsupervised Feature Selection

Multi-view unsupervised feature selection (MV-UFS) aims to select a feature subset from multi-view data without using the labels of samples. However, we observe that existing MV-UFS algorithms do not well consider the local structure of cross views and the diversity of different views, which could adversely affect the performance of subsequent learning tasks. In this paper, we propose a cross-view local structure preserved diversity and consensus semantic learning model for MV-UFS, termed CRV-DCL briefly, to address these issues. Specifically, we project each view of data into a common semantic label space which is composed of a consensus part and a diversity part, with the aim to capture both the common information and distinguishing knowledge across different views. Further, an inter-view similarity graph between each pairwise view and an intra-view similarity graph of each view are respectively constructed to preserve the local structure of data in different views and different samples in the same view. An l2,1-norm constraint is imposed on the feature projection matrix to select discriminative features. We carefully design an efficient algorithm with convergence guarantee to solve the resultant optimization problem. Extensive experimental study is conducted on six publicly real multi-view datasets and the experimental results well demonstrate the effectiveness of CRV-DCL.
A Faster Maximum-Likelihood Modulation Classification in Flat Fading Non-Gaussian Channels

In this letter, we use squared iterative method with parameter checking to accelerate the convergence rate of expectation/conditional maximization (ECM) algorithm when estimating the channel parameters blindly in flat fading non-Gaussian channels, and further, we proposed automatic modulation classification (AMC) in flat fading non-Gaussian channels based on the proposed maximum likelihood estimator. The numerical results show that the proposed method can accelerate the convergence rate of ECM algorithm, and AMC based on the proposed method is faster than that based on ECM, while the accuracy of the former shows nearly no loss compared with that of the latter.
Machine Learning, Natural Language Processing, and the Electronic Health Record: Innovations in Mental Health Services Research

An unprecedented amount of clinical information is now available via electronic health records (EHRs). These massive data sets have stimulated opportunities to adapt computational approaches to track and identify target areas for quality improvement in mental health care. In this column, three key areas of EHR data science are described: EHR phenotyping, natural language processing, and predictive modeling. For each of these computational approaches, case examples are provided to illustrate their role in mental health services research. Together, adaptation of these methods underscores the need for standardization and transparency while recognizing the opportunities and challenges ahead.
Whale Optimization Algorithm Based on Lamarckian Learning for Global Optimization Problems

Whale optimization algorithm (WOA) is a population-based meta-heuristic imitating the hunting behavior of humpback whales, which has been successfully applied to solve many real-world problems.Although WOA has a good convergence rate, it cannot achieve good results in finding the global optimal solution of high-dimensional complex optimization problems.The learning mechanism of Lamarckian evolutionism has the advantages of speeding up and strengthening local search.Through this learning mechanism, solutions with certain conditions can acquire higher adaptability with a higher probability by active learning.To enhance the global convergence speed and get better performance, this paper presents a WOA based on Lamarckian learning (WOALam) for solving high-dimensional function optimization problems.First, the population is initialized by good point set theory so that individuals can be evenly distributed in the solution space.Second, the upper confidence bound algorithm is used to calculate the development potential of the individual.Finally, based on the evolutionary theory of Lamarck, individuals with more development potentials are selected to perform the local enhanced search to improve the performance of the algorithm.The WOALam was compared with six variants of WOA on 44 benchmark functions.The experiments proved that the proposed algorithm can balance the global exploring ability and the exploiting ability well.It could obtain better results with fewer iterations and had good convergence speed and accuracy.
On the Dynamics of Classification Measures for Imbalanced and Streaming Data

As each imbalanced classification problem comes with its own set of challenges, the measure used to evaluate classifiers must be individually selected. To help researchers make this decision in an informed manner, experimental and theoretical investigations compare general properties of measures. However, existing studies do not analyze changes in measure behavior imposed by different imbalance ratios. Moreover, several characteristics of imbalanced data streams, such as the effect of dynamically changing class proportions, have not been thoroughly investigated from the perspective of different metrics. In this paper, we study measure dynamics by analyzing changes of measure values, distributions, and gradients with diverging class proportions. For this purpose, we visualize measure probability mass functions and gradients. In addition, we put forward a histogram-based normalization method that provides a unified, probabilistic interpretation of any measure over data sets with different class distributions. The results of analyzing eight popular classification measures show that the effect class proportions have on each measure is different and should be taken into account when evaluating classifiers. Apart from highlighting imbalance-related properties of each measure, our study shows a direct connection between class ratio changes and certain types of concept drift, which could be influential in designing new types of classifiers and drift detectors for imbalanced data streams.
Enhancing the Coexistence of LTE and Wi-Fi in Unlicensed Spectrum Through Convolutional Neural Networks

Over the last years, the ever-growing wireless traffic has pushed the mobile community to investigate solutions that can assist in more efficient management of the wireless spectrum. Towards this direction, the long-term evolution (LTE) operation in the unlicensed spectrum has been proposed. Targeting a global solution that respects the regional requirements, 3GPP announced the standard of LTE licensed assisted access (LAA). However, LTE LAA may result in unfair coexistence with Wi-Fi, especially when Wi-Fi does not use frame aggregation. Targeting a technique that enables fair channel access, the mLTE-U scheme has been proposed. According to mLTE-U, LTE uses a variable transmission opportunity, followed by a variable muting period that can be exploited by other networks to transmit. For the selection of the appropriate mLTE-U configuration, information about the dynamically changing wireless environment is required. To this end, this paper proposes a convolutional neural network (CNN) that is trained to perform identification of LTE and Wi-Fi transmissions. In addition, it can identify the hidden terminal effect caused by multiple LTE transmissions, multiple Wi-Fi transmissions, or concurrent LTE and Wi-Fi transmissions. The designed CNN has been trained and validated using commercial off-the-shelf LTE and Wi-Fi hardware equipment and for two wireless signal representations, namely, in-phase and quadrature samples and frequency domain representation through fast Fourier transform. The classification accuracy of the two resulting CNNs is tested for different signal to noise ratio values. The experimentation results show that the data representation affects the accuracy of CNN. The obtained information from CNN can be exploited by the mLTE-U scheme in order to provide fair coexistence between the two wireless technologies.
A Deep Generative Model for Graph Layout

Different layouts can characterize different aspects of the same graph. Finding a "good" layout of a graph is thus an important task for graph visualization. In practice, users often visualize a graph in multiple layouts by using different methods and varying parameter settings until they find a layout that best suits the purpose of the visualization. However, this trial-and-error process is often haphazard and time-consuming. To provide users with an intuitive way to navigate the layout design space, we present a technique to systematically visualize a graph in diverse layouts using deep generative models. We design an encoder-decoder architecture to learn a model from a collection of example layouts, where the encoder represents training examples in a latent space and the decoder produces layouts from the latent space. In particular, we train the model to construct a two-dimensional latent space for users to easily explore and generate various layouts. We demonstrate our approach through quantitative and qualitative evaluations of the generated layouts. The results of our evaluations show that our model is capable of learning and generalizing abstract concepts of graph layouts, not just memorizing the training examples. In summary, this paper presents a fundamentally new approach to graph visualization where a machine learning model learns to visualize a graph from examples without manually-defined heuristics.
Expression Analysis Based on Face Regions in Real-world Conditions

Facial emotion recognition is an essential and important aspect of the field of human-machine interaction. Past research on facial emotion recognition focuses on the laboratory environment. However, it faces many challenges in real-world conditions, i.e., illumination changes, large pose variations and partial or full occlusions. Those challenges lead to different face areas with different degrees of sharpness and completeness. Inspired by this fact, we focus on the authenticity of predictions generated by different <emotion, region> pairs. For example, if only the mouth areas are available and the emotion classifier predicts happiness, then there is a question of how to judge the authenticity of predictions. This problem can be converted into the contribution of different face areas to different emotions. In this paper, we divide the whole face into six areas: nose areas, mouth areas, eyes areas, nose to mouth areas, nose to eyes areas and mouth to eyes areas. To obtain more convincing results, our experiments are conducted on three different databases: facial expression recognition + ( FER+), real-world affective faces database (RAF-DB) and expression in-the-wild (ExpW) dataset. Through analysis of the classification accuracy, the confusion matrix and the class activation map (CAM), we can establish convincing results. To sum up, the contributions of this paper lie in two areas: 1) We visualize concerned areas of human faces in emotion recognition; 2) We analyze the contribution of different face areas to different emotions in real-world conditions through experimental analysis. Our findings can be combined with findings in psychology to promote the understanding of emotional expressions.
Weighted mean curvature

In image processing tasks, spatial priors are essential for robust computations, regularization, algorithmic design and Bayesian inference. In this paper, we introduce weighted mean curvature (WMC) as a novel image prior and present an efficient computation scheme for its discretization in practical image processing applications. We first demonstrate the favorable properties of WMC, such as sampling invariance, scale invariance, and contrast invariance with Gaussian noise model; and we show the relation of WMC to area regularization. We further propose an efficient computation scheme for discretized WMC, which is demonstrated herein to process over 33.2 giga-pixels/second on GPU. This scheme yields itself to a convolutional neural network representation. Finally, WMC is evaluated on synthetic and real images, showing its superiority quantitatively to total-variation and mean curvature.
High-sensitivity image encryption algorithm with random diffusion based on dynamic-coupled map lattices

This paper proposes a new high-sensitivity image encryption algorithm with random diffusion based on the spatiotemporal chaos of the Logistic-dynamic mixed linear-nonlinear coupled map lattices (LDMLNCML). The proposed LDMLNCML system possesses prominent cryptographic characteristics, which is very suitable for image encryption. The proposed image encryption algorithm adopts the strategy of random diffusion. Firstly, the pending sequence is generated according to the number of image pixels, and then two index chains are generated combining the conflict handling process. Finally, the cipher image is obtained by random diffusion. Index chains are highly sensitive to changes in position, and changes of the position of any one element will produce completely different index chains. The cipher value of each pixel in the diffusion phase depends on two random non-adjacent pixels and chaos interference value, which can greatly reduce the correlation between the adjacent pixels. Theoretical analysis and experimental results demonstrate the security and practicability of the cryptosystem.
Liver Tumor Segmentation Based on Multi-Scale Candidate Generation and Fractal Residual Network

Liver cancer is one of the most common cancers.Liver tumor segmentation is one of the most important steps in treating liver cancer.Accurate tumor segmentation on computed tomography (CT) images is a challenging task due to the variation of the tumor's shape, size, and location.To this end, this paper proposes a liver tumor segmentation method on CT volumes using multi-scale candidate generation method (MCG), 3D fractal residual network (3D FRN), and active contour model (ACM) in a coarse-to-fine manner.First, livers are segmented using 3D U-Net and then MCG is performed on these liver regions for obtaining tumor candidates (all superpixel blocks).Second, 3D FRN is proposed to further determine tumor regions, which is considered as coarse segmentation results.Finally, the ACM is used for tumor segmentation refinement.The proposed 3D MCG-FRN + ACM is trained using the 110 cases in the LiTS dataset and evaluated on a public liver tumor dataset of the 3DIRCADb with dice per case of 0.67.The experimentations and comparisons demonstrate the performance advantage of the 3D MCG-FRN + ACM compared to other segmentation methods.
Digital Literacies or Digital Competence: Conceptualizations in Nordic Curricula

This article examines how the concepts of digital literacies and digital competence are conceptualized in curricula for compulsory education within the Nordic countries. In 2006, the European Union defined digital competence as one of eight key competences for lifelong learning. The terms digital literacies and digital competence have since been used interchangeably, particularly in policy documents concerning education and the digitalization of educational systems and teaching. However, whether these concepts carry similar meanings, and are understood in a similar way, across languages and cultures is not self-evident. By taking the curricula in Sweden, Denmark, Finland, and Norway as examples, this article attempts to clarify similarities and differences in how the concepts are interpreted, as well as what implications this has for the digitalization of education. The analyses reveal that different terms are used in the curricula in the different countries, which are connected to themes or interdisciplinary issues to be incorporated into school subjects. The conceptualizations of the terms share a common emphasis on societal issues and a critical approach, highlighting a particular Nordic interpretation of digital literacies and digital competence.
Template Matching Using Time-Series Averaging and DTW With Dependent Warping for Online Signature Verification

Online signature verification has been widely applied in biometrics and forensics. Due to the recent demand on high-speed systems in this era of big data, to simultaneously improve its performance and calculation complexity, this study focuses on a single-template strategy that uses dynamic time warping (DTW) with dependent warping for online signature verification, and attempts to construct a novel time-series averaging method called Euclidean barycenter-based DTW barycenter averaging (EB-DBA). Specifically, this study proposes a single-template strategy using a mean template created by the EB-DBA to achieve higher performance at lower calculation complexity for online signature verification. The method’s discriminative power is enhanced upon the exploration of two DTW warping types, where it is found that the DTW with dependent warping exhibits better performance. The popular MCYT-100 dataset is utilized in the experiments, which confirms the effectiveness of the proposed method in simultaneously achieving lower error rate and lower calculation complexity, for online signature verification.
Learning to Control the Fine-grained Sentiment for Story Ending Generation

Automatic story ending generation is an interesting and challenging task in natural language generation.Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus on controlling the sentiment of story endings.This paper focuses on generating a story ending which meets the given fine-grained sentiment intensity.There are two major challenges to this task.First is the lack of story corpus which has fine-grained sentiment labels.Second is the difficulty of explicitly controlling sentiment intensity when generating endings.Therefore, we propose a generic and novel framework which consists of a sentiment analyzer and a sentimental generator, respectively addressing the two challenges.The sentiment analyzer adopts a series of methods to acquire sentiment intensities of the story dataset.The sentimental generator introduces the sentiment intensity into decoder via a Gaussian Kernel Layer to control the sentiment of the output.To the best of our knowledge, this is the first endeavor to control the fine-grained sentiment for story ending generation without manually annotating sentiment labels.Experiments show that our proposed framework can generate story endings which are not only more coherent and fluent but also able to meet the given sentiment intensity better. 1
Putting Evaluation in Context: Contextual Embeddings Improve Machine Translation Evaluation

Accurate, automatic evaluation of machine translation is critical for system tuning, and evaluating progress in the field.We proposed a simple unsupervised metric, and additional supervised metrics which rely on contextual word embeddings to encode the translation and reference sentences.We find that these models rival or surpass all existing metrics in the WMT 2017 sentence-level and systemlevel tracks, and our trained model has a substantially higher correlation with human judgements than all existing metrics on the WMT 2017 to-English sentence level dataset.