Reviewerly/exHarmony huggingface dataset:
Detecting Text in the Wild with Deep Character Embedding Network

Most text detection methods hypothesize texts are horizontal or multi-oriented and thus define quadrangles as the basic detection unit. However, text in the wild is usually perspectively distorted or curved, which can not be easily tackled by existing approaches. In this paper, we propose a deep character embedding network (CENet) which simultaneously predicts the bounding boxes of characters and their embedding vectors, thus making text detection a simple clustering task in the character embedding space. The proposed method does not require strong assumptions of forming a straight line on general text detection, which provides flexibility on arbitrarily curved or perspectively distorted text. For character detection task, a dense prediction subnetwork is designed to obtain the confidence score and bounding boxes of characters. For character embedding task, a subnet is trained with contrastive loss to project detected characters into embedding space. The two tasks share a backbone CNN from which the multi-scale feature maps are extracted. The final text regions can be easily achieved by a thresholding process on character confidence and embedding distance of character pairs. We evaluated our method on ICDAR13, ICDAR15, MSRA-TD500, and Total Text. The proposed method achieves state-of-the-art or comparable performance on all of the datasets, and shows a substantial improvement in the irregular-text datasets, i.e. Total-Text.
Anonymous Single Sign-On With Proxy Re-Verification

An anonymous single sign-on (ASSO) scheme allows users to access multiple services anonymously using one credential. We propose a new ASSO scheme, where users can access services anonymously through the use of anonymous credentials and unlinkably through the provision of designated verifiers. Notably, verifiers cannot link a user's service requests even if they collude. The novelty is that when a designated verifier is unavailable, a central authority can authorize new verifiers to authenticate the user on behalf of the original verifier. Furthermore, a central verifier can also be authorized to de-anonymize users and trace their service requests. We formalize the scheme along with a security proof and provide an empirical evaluation of its performance. This scheme can be applied to smart ticketing where minimizing the collection of personal information of users is increasingly important to transport organizations due to privacy regulations such as general data protection regulations (GDPRs).
Multiple kernel dimensionality reduction based on linear regression virtual reconstruction for image set classification

In this paper, we propose a novel multiple kernel dimensionality reduction (DR) method based on linear regression reconstruction mechanism for image set based classification. The proposed method aims to learn an optimal kernel automatically from the multiple base kernels and a projection transformation such that in the projected low dimensional subspace, the between-class reconstruction error denoted by the distance of the between-class reconstructed virtual samples is maximized and the within-class reconstruction error denoted by the distance of the within-class reconstructed virtual samples is minimized. Therefore, the compactness of reconstructed within-class virtual samples is enhanced, and between-class reconstructed virtual samples are better separated. This feature extraction scheme can best work with the corresponding classification strategy, which will naturally enhance the classification performance. By employing the method of trace ratio maximization, we also develop a framework to solve the resulting nonconvex optimization problem efficiently. Extensive experiments on benchmark image set datasets well demonstrate the effectiveness of the proposed method compared with other set based methods.
Boundary-sensitive Network for Portrait Segmentation

Portrait segmentation has gained more and more attractions in recent years due to the popularity of selfie images. Compared to general semantic segmentation problems, portrait segmentation focuses on facial areas with higher requirements especially over the boundaries. To improve the performance of portrait segmentation, we propose a boundary-sensitive deep neural network (BSN) for better accuracy among the portrait boundaries. BSN introduces three novel techniques. First, an individual boundary-sensitive mask is proposed by dilating the contour line and assigning the boundary pixels with multi-class labels. Second, a global boundary-sensitive mask is employed as a position sensitive prior to further constrain the overall shape of the segmentation map. Third, we train a boundary-sensitive attribute classifier jointly with the segmentation network to reinforce the network with semantic boundary shape information. We have evaluated BSN on the state-of-the-art public portrait segmentation datasets, i.e., the PFCN dataset, as well as the portrait images collected from other three popular image segmentation datasets: COCO, COCO-Stuff, and PASCAL VOC. Our method achieves the superior quantitative and qualitative performance over state-of-the-arts on the evaluated datasets, especially obtains better visualization effect on the portrait boundary region.
“I Didn’t Understand, I´m Really Not Very Smart”—How Design of a Digital Tutee’s Self-Efficacy Affects Conversation and Student Behavior in a Digital Math Game

How should a pedagogical agent in educational software be designed to support student learning? This question is complex seeing as there are many types of pedagogical agents and design features, and the effect on different student groups can vary. In this paper we explore the effects of designing a pedagogical agent’s self-efficacy in order to see what effects this has on students´ interaction with it. We have analyzed chat logs from an educational math game incorporating an agent, which acts as a digital tutee. The tutee expresses high or low self-efficacy through feedback given in the chat. This has been performed in relation to the students own self-efficacy. Our previous results indicated that it is more beneficial to design a digital tutee with low self-efficacy than one with high self-efficacy. In this paper, these results are further explored and explained in terms of an increase in the protégé effect and a reverse role modelling effect, whereby the students encourage digital tutees with low self-efficacy. However, there are indications of potential drawbacks that should be further investigated. Some students expressed frustration with the digital tutee with low self-efficacy. A future direction could be to look at more adaptive agents that change their self-efficacy over time as they learn.
Beyond remote control: Exploring natural gesture inputs for smart TV systems

This paper reports on work that explores natural gesture inputs from end-users for television use in a typical living room setting. First, we derive a set of 19 user-defined freehand gestures for regular TV control tasks. This study helps us to determine the gestures preferred by TV viewers and red uce the risk of developing an unusable, ineffective system. Then, based on this user-defined gesture set, we propose a unified framework to address specific problems in a complex real-world TV viewing environment, including 1) the automatic exclusion of many meaningless daily actions by TV viewers, 2) the capability to recognize both static and dynamic gestures simultaneously, as well as one- and two-handed gestures simultaneously, and 3) the continuous recognition of multiple dynamic gestures in the air (e.g., a channel switching gesture for channel 127). Experimental results show that our approach allows users to interact with TV-based applications more flexibly and effectively, with improved user experience and user satisfaction. Finally, we highlight the implications of our work for the design and development of related freehand gesture applications.
Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval

We propose an unsupervised hashing method, exploiting a shallow neural network, that aims to produce binary codes that preserve the ranking induced by an original real-valued representation. This is motivated by the emergence of small-world graph-based approximate search methods that rely on local neighborhood ranking. We formalize the training process in an intuitive way by considering each training sample as a query and aiming to obtain a ranking of a random subset of the training set using the hash codes that is the same as the ranking using the original features. We also explore the use of a decoder to obtain an approximated reconstruction of the original features. At test time, we retrieve the most promising database samples using only the hash codes and perform re-ranking using the reconstructed features, thus allowing the complete elimination of the original real-valued features and the associated high memory cost. Experiments conducted on publicly available large-scale datasets show that our method consistently outperforms all compared state-of-the-art unsupervised hashing methods and that the reconstruction procedure can effectively boost the search accuracy with a minimal constant additional cost.
Unpaired Thermal to Visible Spectrum Transfer Using Adversarial Training

Thermal Infrared (TIR) cameras are gaining popularity in many computer vision applications due to their ability to operate under low-light conditions. Images produced by TIR cameras are usually difficult for humans to perceive visually, which limits their usability. Several methods in the literature were proposed to address this problem by transforming TIR images into realistic visible spectrum (VIS) images. However, existing TIR-VIS datasets suffer from imperfect alignment between TIR-VIS image pairs which degrades the performance of supervised methods. We tackle this problem by learning this transformation using an unsupervised Generative Adversarial Network (GAN) which trains on unpaired TIR and VIS images. When trained and evaluated on KAIST-MS dataset, our proposed methods was shown to produce significantly more realistic and sharp VIS images than the existing state-of-the-art supervised methods. In addition, our proposed method was shown to generalize very well when evaluated on a new dataset of new environments.
Style-adaptive photo aesthetic rating via convolutional neural networks and multi-task learning

Photo aesthetic rating aims at automatically and precisely evaluating the pictorial aesthetic score. Recently, great progresses has been achieved in this area due to the fabulous ability of convolutional neural networks (CNNs). Existing deep approaches try to train CNNs from a large set of photos with score annotations. However, the considerable cost in collecting score annotations limits the generalization of these approaches to other types of media. To combat this limitation, we propose a novel photo aesthetic rating architecture. Our method comprises three modules: a CNN based feature extractor, a style classifier, and a group of style-specific aesthetic prediction models. First, we propose to train a CNN in the standard binary aesthetic classification task and use it to extract aesthetic-aware features, because binary labels are easy to access. Afterwards, we use a support vector machine (SVM) to formulate the style classifier. Besides, we explored a multi-task learning (MTL) approach to jointly learn the style-specific rating models, which will further improve the generalization ability. Additionally, to address the imbalance in the distribution of styles and ratings, we adopted the strategy of data augmentation and selective sampling. Finally, we estimate the rating of an input photo by combining the estimates of the style-specific rating models according to the outputs of the style classifier. Experiments conducted on the AVA database show that the proposed method is considerably comparative to state-of-the-art approaches.
On Enactability of Agent Interaction Protocols: Towards a Unified Approach

Interactions between agents are usually designed from a global viewpoint. However, the implementation of a multi-agent interaction is distributed. It is well known that this difference between the specification and the implementation levels can introduce problems, allowing designers to specify protocols from a global viewpoint that cannot be implemented as a collection of individual agents. This leads naturally to the question of whether a given (global) protocol is enactable, namely, whether it can be implemented in a distributed way. We consider this question in the powerful setting of trace expressions, considering a range of message ordering interpretations (specifying what it means to say that an interaction step occurs before another), and a range of possible constraints on the semantics of message delivery, corresponding to different properties of the underlying communication middleware. We provide a definition of enactability, along with an implementation of the definition that is applied to a number of example protocols.
Structured Light Three-Dimensional Measurement Based on Machine Learning

The three-dimensional measurement of structured light is commonly used and has widespread applications in many industries. In this study, machine learning is used for structured light 3D measurement to recover the phase distribution of the measured object by employing two machine learning models. Without phase shift, the measurement operational complexity and computation time decline renders real-time measurement possible. Finally, a grating-based structured light measurement system is constructed, and machine learning is used to recover the phase. The calculated phase of distribution is wrapped in only one dimension and not in two dimensions, as in other methods. The measurement error is observed to be under 1%.
Content-based histopathological image retrieval using multi-scale and multichannel decoder based LTP

The paper proposes a content based histopathological image retrieval scheme based on multi-scale, multichannel decoded local ternary pattern features and VLAD coding. The scheme analyzes an image into multiple scales wherein a decoder based local ternary pattern is utilized to extract upper and lower texture images at each level. A dense patch based histogram representation is obtained at each scale which is followed by VLAD coding and power-law normalization. Simulations performed on a standard 20-class histopathological dataset. The proposed scheme shows considerable improvement over state-of-the-art techniques.
Stochastic global optimization algorithms: A systematic formal approach

Up to now, different algorithmic methods have been developed to find ’good’ solutions (near to the optimal solution) for global optimization problems that cannot be solved using analytical methods. In particular, algorithmic methods such as differential evolution, evolutionary algorithms, and hill climbing belong to the class of Stochastic Global Optimization Algorithms (SGoals). In general, an SGoal iteratively applies stochastic operations to a set of candidate solutions (population), following, in some cases, a heuristic/metaheuristic. Although some research works have tried to formalize SGoals using Markov kernels, such formalization is not general and sometimes is blurred. In this paper, we propose a comprehensive, systematic and formal approach for studying SGoals. First, we present concepts of probability theory that are required to perform such formalization and we demonstrate that the swapping, sorting, and permutation functions, among others, can be represented by Markov kernels. Then, we introduce the joint Markov kernel as a way of characterizing the combination of stochastic methods. Next, we define the optimization space, a σ-algebra that contains ϵ-optimal states, and develop Markov kernels for stochastic methods like swapping, sorting and permutation. Finally, we introduce sufficient convergence conditions for SGoals and present some popular SGoals in terms of the developed theory.
Image super-resolution using TV priori guided convolutional network

We propose a TV priori guided deep learning method for single image super-resolution (SR). The new up-sample method based on TV priori information, new learning algorithm and neural networks architecture are embraced in our TV Priori Guided Convolutional Neural Network which directly learns an end-to-end mapping between the low/high-resolution images. There are three aspects of the innovation of our algorithm. First, unlike most super-resolution algorithms, an input image is up-sampled via Moms interpolation before they fed into the network. Then, we use a set of discrete TV council's templates to extract priori information. Second, we put priori information into non-local regression frame, and propose a non-local TV interpolation; Third, non-local TV interpolated images are fed into the network. We conduct a number of experiments to evaluate the effectiveness of TV priori guided CNN.
Light Field Image Compression via CNN-Based EPI Super-Resolution and Decoder-Side Quality Enhancement

Because of the capacity of capturing both the spatial and angular information of the light rays simultaneously, light field images (LFIs) contain richer scene information compared with conventional images, but at the cost of huge volume. This paper proposes a novel LFI sparse compression framework driven by convolutional neural network (CNN). The epipolar plane image (EPI) super-resolution is for compensating the information loss caused by sparse sampling and the decoder-side sub-aperture images (SAIs) quality enhancement is for compensating the information loss caused by lossy compression. Specifically, we choose those SAIs both in odd rows and odd columns as our key SAIs and compress them using standard video encoder. For those non-key SAIs, we predict them using decompressed key SAIs by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super-resolved by a CNN and the outputs, high-resolution EPIs, are used to rebuild the dense SAIs. Moreover, in order to improve the quality of the predicted SAI, we add decoder-side quality enhancement before prediction. We propose a multi-scale dense residual network (MSDRN) to implement both EPI super-resolution and quality enhancement. Transfer learning strategies are used to improve the training performance of quality enhancement. The experimental results show the superior performance of the proposed framework over existing methods in terms of rate-distortion performance.
An ensemble learning model based on Bayesian model combination for solar energy prediction

To improve the reliability of solar irradiance prediction methods, an ensemble learning method based on the Bayesian model combination has been developed in this paper for solar utilization systems. First, a novel data sampling method has been proposed, including the advantages of clustering and cross validation, which can effectively ensure that the training subsets are different from each other and can cover a variety of different meteorological samples. Second, an ensemble learning model with multiple base learners has been designed. Each training subset is utilized to train the corresponding base learner. Then, a novel Bayesian model combination strategy expands hypothesis space E on Bayesian model averaging, which is applied to frame the combination strategy based on the accuracy of each base learner on the validation set. The prediction values of multiple learners are framed through the model combination strategy. Thus, a novel ensemble learning model based on Bayesian model combination has been established. Finally, experiments are carried out and the proposed method is compared with the Artificial Neural Network (ANN), K-means (Radial Basis Function) RBF, Support Vector Machine (SVM), and Multikernel SVM. The annual average mean absolute error of the ensemble learning method based on Bayesian model combination is reduced by 0.0374 MJ×m−2 compared with the ensemble learning method. The annual average mean absolute error of the proposed method is reduced by 42.6%, 38.2%, 52%, and 48.7%, respectively, compared with ANN, K-means RBF, SVM, and Multikernel SVM. The effectiveness as well as the reliability of the proposed method in solar energy prediction have been found to perform better and have verified our approach.
A study of divisive clustering with Hausdorff distances for interval data

Clustering methods are becoming key as analysts try to understand what knowledge is buried inside contemporary large data sets. This article analyzes the impact of six different Hausdorff distances on sets of multivariate interval data (where, for each dimension, an interval is defined as an observation [a, b] with a ≤ b and with a and b taking values on the real line R1), used as the basis for Chavent’s [15, 16] divisive clustering algorithm. Advantages and disadvantages are summarized for each distance. Comparisons with two other distances for interval data, the Gowda–Diday and Ichino–Yaguchi measures are included. All have specific strengths depending on the type of data present. Global normalization of a distance is not recommended; and care needs to be made when using local normalizations to ensure the features of the underlying data sets are revealed. The study is based on sets of simulated data, and on a real data set.
A Probabilistic Framework to Node-level Anomaly Detection in Communication Networks

In this paper we consider the task of detecting abnormal communication volume occurring at node-level in communication networks. The signal of the communication activity is modeled by means of a clique stream: each occurring communication event is instantaneous, and activates an undirected subgraph spanning over a set of equally participating nodes. We present a probabilistic framework to model and assess the communication volume observed at any single node. Specifically, we employ non-parametric regression to learn the probability that a node takes part in a certain event knowing the set of other nodes that are involved. On the top of that, we present a concentration inequality around the estimated volume of events in which a node could participate, which in turn allows us to build an efficient and interpretable anomaly scoring function. Finally, the superior performance of the proposed approach is empirically demonstrated in real-world sensor network data, as well as using synthetic communication activity that is in accordance with that latter setting.