Reviewerly/exHarmony huggingface dataset:
Evaluation of Internal Models in Autonomous Learning

Internal models (IMs) can represent relations between sensors and actuators in natural and artificial agents. In autonomous robots, the adaptation of IMs and the adaptation of the behavior are interdependent processes which have been studied under paradigms for self-organization of behavior such as homeokinesis. We compare the effect of various types of IMs on the generation of behavior in order to evaluate model quality across different behaviors. The considered IMs differ in the degree of flexibility and expressivity related to, respectively, learning speed and structural complexity of the model. We show that the different IMs generate different error characteristics which in turn lead to variations of the self-generated behavior of the robot. Due to the tradeoff between error minimization and complexity of the explored environment, we compare the models in the sense of Pareto optimality. Among the linear and nonlinear models that we analyze, echo-state networks achieve a particularly high performance which we explain as a result of the combination of fast learning and complex internal dynamics. More generally, we provide evidence that Pareto optimization is preferable in autonomous learning as it allows that a special solution can be negotiated in any particular environment.
Analysis of edge and corner points using parabolic dictionaries

Last decade saw the creation of a number of directional representation dictionaries that desire to address the weaknesses of the classical wavelet transform that arise due to its limited capacity for the analysis of edge-like features of two-dimensional signals. Salient features of these dictionaries are directional selectivity and anisotropic treatment of the axes, achieved through the parabolic scaling law. In this paper we will examine the adequacy of such dictionaries for the analysis of edge- and corner-like features of 2D regions through a comprehensive framework for directional parabolic dictionaries, called the continuous parabolic molecules. This work builds on a family of earlier studies and aims to give a broader perspective through the level of generality.
Nonrigid registration of cardiac DSCT images by integrating intensity and point features

In order to obtain accurate anatomical information for the whole heart, we propose a nonrigid registration method combining mutual information with the marching cubes method. Certain points are regarded as prior knowledge of the shape landmarks of cardiac structures. The registration process for the heart image can be divided into two steps: coarse alignment and accurate registration. The coarse alignment uses affine transformations to localize and center the image of the heart, and the accurate registration uses a B-spline method to constrain the deformation field. Mutual information combined with feature point pairs are used as the similarity measure function. All 15-dimensional feature descriptors are used to identify matched point pairs between marching cubes points in atlas intensity images and other points in the neighborhood of target images needing segmentation. Adaptive stochastic gradient descent optimization is used to obtain optimal registration parameters. Two groups of experiments show that the proposed method achieves higher registration accuracy than traditional ones based only on mutual information. They indicate that accurate anatomical information of the whole cardiac structure can be obtained.
Graph matching approach and generalized median graph for automatic labeling of cortical sulci with parallel and distributed algorithms

The human brain cortex is very complex structure containing folds (gyri) and fissures (sulci) that were the subject of our study in this paper. The sulcus is one of the most important features in order to know the different functions areas of the brain. The exact identification of sulci on human brain using MRI images is helpful in many studies and applications related to brain diseases and human behavior. Automatic labeling of cortical sulci with all this complexity and inter-subject variability, this is considered non-trivial task. In this paper, we have proposed a new graph based approach of automatic labeling of cortical sulci with parallel and distributed algorithms using graph matching and generalized median graph. The graph matching is very important in many studies and applications such as pattern recognition and classification. The generalized median graph of a set of graphs is a way to represent a set of graphs by a comprehensive graph that minimizes the sum of the distances to all graphs. We have used the characteristics of shape, orientation and location to describe the sulci. The results that we have obtained prove that our approach is accurate and acceptable in this field which uses the graph matching for automatic labeling of cortical sulci.
Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds

Conditional kernel mean embeddings are nonparametric models that encode conditional expectations in a reproducing kernel Hilbert space. While they provide a flexible and powerful framework for probabilistic inference, their performance is highly dependent on the choice of kernel and regularization hyperparameters. Nevertheless, current hyperparameter tuning methods predominantly rely on expensive cross validation or heuristics that is not optimized for the inference task. For conditional kernel mean embeddings with categorical targets and arbitrary inputs, we propose a hyperparameter learning framework based on Rademacher complexity bounds to prevent overfitting by balancing data fit against model complexity. Our approach only requires batch updates, allowing scalable kernel hyperparameter tuning without invoking kernel approximations. Experiments demonstrate that our learning framework outperforms competing methods, and can be further extended to incorporate and learn deep neural network weights to improve generalization. (Source code available at: https://github.com/Kelvin-Hsu/cake ).
Improving Performance of NMT Using Semantic Concept of WordNet Synset

Neural machine translation (NMT) has shown promising progress in recent years. However, for reducing the computational complexity, NMT typically needs to limit its vocabulary scale to a fixed or relatively acceptable size, which leads to the problem of rare word and out-of-vocabulary (OOV). In this paper, we present that the semantic concept information of word can help NMT learn better semantic representation of word and improve the translation accuracy. The key idea is to utilize the external semantic knowledge base WordNet to replace rare words and OOVs with their semantic concepts of WordNet synsets. More specifically, we propose two semantic similarity models to obtain the most similar concepts of rare words and OOVs. Experimental results on 4 translation tasks (We verify the effectiveness of our method on four translation tasks, including English-to- German, German-to-English, English-to-Chinese and Chinese-to-English.) show that our method outperforms the baseline RNNSearch by 2.38–2.88 BLEU points. Furthermore, the proposed hybrid method by combining BPE and our proposed method can also gain 0.39–0.97 BLEU points improvement over BPE. Experiments and analysis presented in this study also demonstrate that the proposed method can significantly improve translation quality of OOVs in NMT.
Knowledge triple mining via multi-task learning

Recent years have witnessed the rapid development of knowledge bases (KBs) such as WordNet, Yago and DBpedia, which are useful resources in AI-related applications. However, most of the existing KBs are suffering from incompleteness and manually adding knowledge into KBs is inefficient. Therefore, automatically mining knowledge becomes a critical issue. To this end, in this paper, we propose to develop a model (S2 AMT) to extract knowledge triples, such as <Barack Obama, wife, Michelle Obama>, from the Internet and add them to KBs to support many downstream applications. Particularly, because the seed instances1 for every relation is difficult to obtain, our model is capable of mining knowledge triples with limited available seed instances. To be more specific, we treat the knowledge triple mining task for each relation as a single task and use multi-task learning (MTL) algorithms to solve the problem, because MTL algorithms can often get better results than single-task learning (STL) ones with limited training data. Moreover, since finding proper task groups is a fatal problem in MTL which can directly influences the final results, we adopt a clustering algorithm to find proper task groups to further improve the performance. Finally, we conduct extensive experiments on real-world data sets and the experimental results clearly validate the performance of our MTL algorithms against STL ones.
Multidomain Features Fusion for Zero-Shot Learning

Given a novel class instance, the purpose of zero-shot learning (ZSL) is to learn a model to classify the instance by seen samples and semantic information transcending class boundaries. The difficulty lies in how to find a suitable space for zero-shot recognition. The previous approaches use semantic space or visual space as classification space. These methods, which typically learn visual-semantic or semantic-visual mapping and directly exploit the output of the mapping function to measure similarity to classify new categories, do not adequately consider the complementarity and distribution gap of multiple domain information. In this paper, we propose to learn a multidomain information fusion space by a joint learning framework. Specifically, we consider the fusion space as a shared space in which different domain features can be recovered by simple linear transformation. By learning a n-way classifier of fusion space from the seen class samples, we also obtain the discriminative information of the similarity space to make the fusion representation more separable. Extensive experiments on popular benchmark datasets manifest that our approach achieves state-of-the-art performances in both supervised and unsupervised ZSL tasks.
Understanding Fake Faces

Face recognition research is one of the most active topics in computer vision (CV), and deep neural networks (DNN) are now filling the gap between human-level and computer-driven performance levels in face verification algorithms. However, although the performance gap appears to be narrowing in terms of accuracy-based expectations, a curious question has arisen; specifically, Face understanding of AI is really close to that of human? In the present study, in an effort to confirm the brain-driven concept, we conduct image-based detection, classification, and generation using an in-house created fake face database. This database has two configurations: (i) false positive face detections produced using both the Viola Jones (VJ) method and convolutional neural networks (CNN), and (ii) simulacra that have fundamental characteristics that resemble faces but are completely artificial. The results show a level of suggestive knowledge that indicates the continuing existence of a gap between the capabilities of recent vision-based face recognition algorithms and human-level performance. On a positive note, however, we have obtained knowledge that will advance the progress of face-understanding models.
Grouping and time-series notifying of periodic data in a real-time streaming system for smart toy claw machine

Traditional online claw machine has defects such as video delay, which affects the accuracy of the operation when the operator operates the claw machine. When there are too many viewers on-line that watch the operation of the smart toy claw machine by the operator, the video will be a delay. The traditional online claw machine is lack of interactive features. It also cannot increase the number of viewers and create discussion topics to attract more consumers. This paper proposes a real-time streaming system for a smart toy game machine. It can improve the above shortcomings and reduce costs and improve network management efficiency. The player can operate and control the device without delay based on the proposed framework. This system will allow the large number of viewer ends that do not need to control the online smart toy claw machine to view the real-time video smoothly. This system allows an increase in the number of the viewers end and let the viewers end creates discussion topics that attract more consumers. In this study, we propose a grouping and time-series (GT) notifying of periodic data of customer-premise equipment (CPE) WAN management protocol (called GT_CWMP). The network traffic and server processing time can be reduced 98% and 89% compared to the normal and random methods, respectively.
Shape Basis Interpretation for Monocular Deformable 3-D Reconstruction

In this paper, we propose a novel interpretable shape model to encode object nonrigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these precomputed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and nonrigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.
MASON: A Model AgnoStic ObjectNess Framework

This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision. The proposed method 'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to generate category-independent and model-agnostic heat maps for any image. The network is not explicitly trained for the task, and hence, can be used off-the-shelf in tandem with any other network or task. We show that this framework scales to a wide variety of images, and illustrate the effectiveness of MASON in three varied application contexts.
Multiple description image compression based on multiwavelets

Multiple description coding (MDC) is one of the source coding techniques to alleviate the problems of packet loss in the network. The decoder estimates the lost signals from received ones, based on the certain statistical correlation between descriptions. However, this correlation also leads to compression redundancy at the same time. Therefore, how to make efficient use of the introduced correlation has great importance in practical MDC approaches. In this paper, we propose a multiple description image coding scenario based on balanced multiwavelets. Two simple and effective methods to reconstruct the original image from partial descriptions are suggested. Furthermore, optimization criterion corresponding to this multiwavelet based system is provided. According to this criterion, we can choose appropriate multifilter banks to satisfy different demands. Experimental results show that the optimized multifilter banks in a simulated transform coding environment perform very well.
Uncertain Timestamps in Linear State Estimation

We consider a linear state estimation problem, where, in addition to the usual timestamped measurements, observations with uncertain timestamps are available. Such observations could, e.g., come from traces left by a target in a tracking scenario or from witnesses of an event and have the potential to improve the estimation accuracy significantly. We derive the posterior distribution and point estimators for a linear Gaussian smoothing formulation of this problem and illustrate with two numerical examples.
Identity-based revocation system: Enhanced security model and scalable bounded IBRS construction with short parameters

Identity-based revocation system (IBRS) produces a ciphertext based on the revoked identities such that this ciphertext can only be decrypted by the private keys of non-revoked identities. IBRS can be classified into unbounded IBRS and bounded IBRS, depending on whether to set a maximal revocation number (e.g., N) or not. Compared to unbounded IBRS, existing bounded IBRS schemes have the advantage of short ciphertexts, however, they cannot support an increase of N and have to fix a large N to provide the revocation. This compromises the computational efficiency, which is linear in N. To bridge this gap, we are interested in exploring whether N can be dynamic. Precisely, N is initially set as a small number and increased into a larger one when more than N identities are revoked, while this increase does not affect other non-revoked users in the system. In this paper, motivated by the security of bounded IBRS with a dynamic N, we define an enhanced security model, which allows the corruption of any polynomial number (e.g., n) of private keys, independent of N. It augments existing security models by running different challenges when n ≤ N and n > N, respectively. The enhanced security model suits both unbounded IBRS and bounded IBRS with a general description that can be “empty” for unbounded IBRS or a special N for bounded IBRS. Then, we propose a scalable bounded IBRS scheme and are able to prove its security in an enhanced security model. The increase of N incurs no changing on all of the existing private keys and the only cost is a slight expansion of the master public keys. Our proposed scheme also has short ciphertexts and private keys, comparable to the state-of-the-art (bounded) IBRS schemes.
Deep Learning for Opinion Mining

In this chapter, through introducing the deep learning and relation between deep learning and artificial intelligence, and especially machine learning, the authors discuss machine learning and deep learning techniques, the literature focuses on applied deep learning techniques for extracting opinions. It can be found that opinion mining without using deep learning is not meaningful. In this way, authors mention the history of deep learning and appearance of it and some important and useful deep learning algorithms for opinion mining; learning methods and customized deep learning techniques for opinion mining will also be described to understand how these algorithms and techniques are used as an applicable solution. Future trends of deep learning in opinion mining are introduced through some clues about the applications and future usages of deep learning and opinion mining and how intelligent agents develop automatic deep learning. Finally, authors have summarized different sections of the chapter at conclusion.
Direct coupling coherent quantum observers with discounted mean square performance criteria and penalized back-action

This paper is concerned with quantum harmonic oscillators consisting of a quantum plant and a directly coupled coherent quantum observer. We employ discounted quadratic performance criteria in the form of exponentially weighted time averages of the second-order moments of the system variables. Small-gain-theorem bounds are obtained for the back-action of the observer on the covariance dynamics of the plant in terms of the plant–observer coupling. A coherent quantum filtering (CQF) problem is formulated as the minimization of the discounted mean square of an estimation error, with which the dynamic variables of the observer approximate those of the plant. The cost functional also involves a quadratic penalty on the plant–observer coupling matrix in order to mitigate the back-action effect. For the discounted mean square optimal CQF problem with penalized back-action, we establish the first-order necessary conditions of optimality in the form of algebraic matrix equations. By using the Hamiltonian structure of the Heisenberg dynamics and Lie-algebraic techniques, this set of equations is represented in a more explicit form for equally dimensioned plant and observer. For a class of such observers with autonomous estimation error dynamics, we obtain a solution of the CQF problem and outline a homotopy method. The computation of the performance criteria and the observer synthesis are illustrated by numerical examples.
High dynamic range image compression based on visual saliency

High dynamic range (HDR) image has larger luminance range than conventional low dynamic range (LDR) image, which is more consistent with human visual system (HVS). Recently, JPEG committee releases a new HDR image compression standard JPEG XT. It decomposes input HDR image into base layer and extension layer. However, this method doesn’t make full use of HVS, causing waste of bits on imperceptible regions to human eyes. In this paper, a visual saliency based HDR image compression scheme is proposed. The saliency map of tone mapped HDR image is first extracted, then is used to guide extension layer encoding. The compression quality is adaptive to the saliency of the coding region of the image. Extensive experimental results show that our method outperforms JPEG XT profile A, B, C, and offers the JPEG compatibility at the same time. Moreover, our method can provide progressive coding of extension layer.
On overabundant words and their application to biological sequence analysis

The observed frequency of the longest proper prefix, the longest proper suffix, and the longest infix of a word w in a given sequence x can be used for classifying w as avoided or overabundant. The definitions used for the expectation and deviation of w in this statistical model were described and biologically justified by Brendel et al. (1986) [1]. We have very recently introduced a time-optimal algorithm for computing all avoided words of a given sequence over an integer alphabet (2017) [2]. In this article, we extend this study by presenting an O(n)-time and O(n)-space algorithm for computing all overabundant words in a sequence x of length n over an integer alphabet. Our main result is based on a new non-trivial combinatorial property of the suffix tree T of x: the number of distinct factors of x whose longest infix is the label of an explicit node of T is no more than 3n−4. We further show that the presented algorithm is time-optimal by proving that O(n) is a tight upper bound for the number of overabundant words. Finally, we present experimental results, using both synthetic and real data, which justify the effectiveness and efficiency of our approach in practical terms.