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In this paper, we propose a control framework for intelligent surgical robots under the Remote Center of Motion (RCM). The goal of a surgical robot is to assist surgeons in performing complex surgeries. RCM constraint implies that the surgical tip attached to the end-effector of the surgical robot does not slide away from the point of the incision while performing surgery. Implementation of a control algorithm to comply with RCM constraints is a complicated task because of the nonlinear model of the surgical robots and stringent conditions of accuracy imposed by the patient’s safety. This paper proposes an optimization-driven approach to perform the surgical maneuver under RCM constraints. We then applied a bio-inspired optimization algorithm to solve the problem efficiently. For testing the performance of ZNNBAS, we used MATLAB to simulate a surgical procedure. A 7-DOF surgical robot (KUKA LBR IIWA 7) was used as a test bench for running the simulations. The simulation results show that the ZNNBAS is comparable with BAS, PSO, and GA and efficiently and robustly performed the task commanded maneuvers while enforcing the RCM constraints.

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant., Comment: This work is an extension of our COLING-2018 paper arXiv:1806.00807

Person reidentification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping views in a surveillance camera network. It has recently been validated that reranking can achieve remarkable performance improvements in person ReID systems. However, current reranking approaches either require feedback from users or suffer from burdensome computational costs. In this paper, we propose to exploit a density-adaptive smooth kernel technique to achieve efficient and effective reranking. Specifically, we adopt a smooth kernel function to formulate the neighbor relationships among data samples with a density-adaptive parameter. Based on this new formulation, we present two simple yet effective reranking methods, termed \emph{inverse} density-adaptive kernel based reranking (inv-DAKR) and \emph{bidirectional} density-adaptive kernel based reranking (bi-DAKR), in which the local density information in the vicinity of each gallery sample is elegantly exploited. Moreover, we extend the proposed inv-DAKR and bi-DAKR methods to incorporate the available extra probe samples and demonstrate that when and why these extra probe samples are able to improve the local neighborhood and thus further refine the ranking results. Extensive experiments are conducted on six benchmark datasets, including: PRID450s, VIPeR, CUHK03, GRID, Market-1501 and Mars. The experimental results demonstrate that our proposals are effective and efficient., 39 pages, 18 figures and 12 tables. This paper is an extended version of our preliminary work on ICPR 2018

Manual annotation of video segmentation datasets requires an immense amount of human effort, thus, reduction of human annotation costs is an active topic of research. While many papers deal with the propagation of masks through frames of a video, only a few results attempt to optimize annotation task selection. In this paper we present a deep learning based solution to the latter problem and train it using Reinforcement Learning. Our approach utilizes a modified version of the Dueling Deep Q-Network sharing weight parameters across the temporal axis of the video. This technique enables the trained agent to select annotation tasks from the whole video. We evaluate our annotation task selection method by means of a hierarchical supervoxel segmentation based mask propagation algorithm.

Facial expressions play an important role during communications, allowing information regarding the emotional state of an individual to be conveyed and inferred. Research suggests that automatic facial expression recognition is a promising avenue of enquiry in mental healthcare, as facial expressions can also reflect an individual's mental state. In order to develop user-friendly, low-cost and effective facial expression analysis systems for mental health care, this paper presents a novel deep convolution network based emotion analysis framework to support mental state detection and diagnosis. The proposed system is able to process facial images and interpret the temporal evolution of emotions through a new solution in which deep features are extracted from the Fully Connected Layer 6 of the AlexNet, with a standard Linear Discriminant Analysis Classifier exploited to obtain the final classification outcome. It is tested against 5 benchmarking databases, including JAFFE, KDEF,CK+, and databases with the images obtained ‘in the wild’ such as FER2013 and AffectNet. Compared with the other state-of-the-art methods, we observe that our method has overall higher accuracy of facial expression recognition. Additionally, when compared to the state-of-the-art deep learning algorithms such as Vgg16, GoogleNet, ResNet and AlexNet, the proposed method demonstrated better efficiency and has less device requirements. The experiments presented in this paper demonstrate that the proposed method outperforms the other methods in terms of accuracy and efficiency which suggests it could act as a smart, low-cost, user-friendly cognitive aid to detect, monitor, and diagnose the mental health of a patient through automatic facial expression analysis.

The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/ shallower) is better suitable for which kind of (deeper/ wider) datasets. To address these findings, we have performed experiments with three CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of the image classification problem. The source code of this research is available at https://github.com/shabbeersh/Impact-of-FC-layers., This paper is accepted for publication in Neurocomputing Journal

In online-learning, the data is incrementally received and the distributions from which it is drawn may keep changing over time. This phenomenon is widely known as concept drift. Such changes may affect the generalization of a learned model to future data. This problem may be exacerbated by the form of the drift itself changing over time. Quantitative measures to describe and analyze the concept drift have been proposed in previous work. A description composed from these measures is called a concept drift map. We believe that these maps could be useful for guiding how much knowledge in the old model should be forgotten. Therefore, this paper presents an adaptive online learning model that uses a concept drift map to regulate the forgetting factor of an extreme learning machine. Specifically, when a batch of new instances are labeled, the distribution of each class on each attribute is firstly estimated, and then it is compared with the distribution estimated in the previous batch to calculate the magnitude of concept drift, which is further used to regulate the forgetting factor and to update the learning model. Therefore, the novelty of this paper lies in that a quantitative distance metric between two distributions constructed on continuous attribute space is presented to construct concept drift map which can be further associated with the forgetting factor to make the learning model adapt the concept drift. Experimental results on several benchmark stream data sets show the proposed model is generally superior to several previous algorithms when classifying a variety of data streams subject to drift, indicating its effectiveness and feasibility.

Currently, knowledge discovery in databases is an essential first step when identifying valid, novel and useful patterns for decision making. There are many real-world scenarios, such as bankruptcy prediction, option pricing or medical diagnosis, where the classification models to be learned need to fulfill restrictions of monotonicity (i.e. the target class label should not decrease when input attributes values increase). For instance, it is rational to assume that a higher debt ratio of a company should never result in a lower level of bankruptcy risk. Consequently, there is a growing interest from the data mining research community concerning monotonic predictive models. This paper aims to present an overview of the literature in the field, analyzing existing techniques and proposing a taxonomy of the algorithms based on the type of model generated. For each method, we review the quality metrics considered in the evaluation and the different data sets and monotonic problems used in the analysis. In this way, this paper serves as an overview of monotonic classification research in specialized literature and can be used as a functional guide for the field.

Twin Support Vector Machines (TWSVMs) have emerged as an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets. The objective functions of the networks in the Twin NN are designed to realize the idea of the Twin SVM with non-parallel decision boudaries for the respective classes, while also being able to reduce model complexity. The Twin NN optimizes the feature map, allowing for better discrimination between classes. The paper also discusses an extension of the Twin NN for multiclass datasets. This architecture trains as many neural networks as the number of classes, and has the additional advantage that it does not have any hyper-parameter which requires tuning. Results presented in the paper demonstrate that the Twin NN generalizes well and scales well on large unbalanced datasets.

In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN’s further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration.

Being motivated by combining the advantages of hyperplane-based pattern analysis and fuzzy clustering techniques, we present in this paper a fuzzy mix-prototype (FMP) clustering for microarray data analysis. By integrating spherical and hyper-planar cluster prototypes, the FMP is capable of capturing latent data models with both spherical and non-spherical geometric structures. Our contributions of the paper can be summarized into three folds: first, the objective function of the FMP is formulated. Second, an iterative solution which minimizes the objective function under given constraints is derived. Third, the effectiveness of the proposed FMP is demonstrated through experiments on yeast and leukemia data sets.

In this paper, a self-organizing map (SOM) neural network is used to visualize corrective actions of failure modes and effects analysis (FMEA). SOM is a popular unsupervised neural network model that aims to produce a low-dimensional map (typically a two-dimensional map) for visualizing high-dimensional data. With regards to FMEA, it is a popular methodology to identify potential failure modes for a product or a process, to assess the risk associated with those failure modes, also, to identify and carry out corrective actions to address the most serious concerns. Despite the popularity of FMEA in a wide range of industries, two well-known shortcomings are the complexity of the FMEA worksheet and its intricacy of use. To the best of our knowledge, the use of computation techniques for solving the aforementioned shortcomings is limited. The use of SOM in FMEA is new. In this paper, corrective actions in FMEA are described in their severity, occurrence and detect scores. SOM is then used as a visualization aid for FMEA users to see the relationship among corrective actions via a map. Color information from the SOM map is then included to the FMEA worksheet for better visualization. In addition, a Risk Priority Number Interval is used to allow corrective actions to be evaluated and ordered in groups. Such approach provides a quick and easily understandable framework to elucidate important information from a complex FMEA worksheet; therefore facilitating the decision-making tasks by FMEA users. The significance of this study is two-fold, viz., the use of SOM as an effective neural network learning paradigm to facilitate FMEA implementations, and the use of a computational visualization approach to tackle the two well-known shortcomings of FMEA.

In today׳s society where audio–visual content is ubiquitous, violence detection in movies and Web videos has become a decisive functionality, e.g., for providing automated youth protection services. In this paper, we concentrate on two important aspects of video content analysis: Time efficiency and modeling of concepts (in this case, violence modeling). Traditional approaches to violent scene detection build on audio or visual features to model violence as a single concept in the feature space. Such modeling does not always provide a faithful representation of violence in terms of audio–visual features, as violence is not necessarily located compactly in the feature space. Consequently, in this paper, we target to close this gap. To this end, we present a solution which uses audio–visual features (MFCC-based audio and advanced motion features) and propose to model violence by means of multiple (sub)concepts. To cope with the heavy computations induced by the use of motion features, we perform a coarse-to-fine analysis, starting with a coarse-level analysis with time efficient audio features and pursuing with a fine-level analysis with advanced features when necessary. The results demonstrate the potential of the proposed approach on the standardized datasets of the latest editions of the MediaEval Affect in Multimedia: Violent Scenes Detection (VSD) task of 2014 and 2015.

In this paper, we examine the problem of learning sparse representations of visual patterns in the context of artificial and biological vision systems. There are a myriad of strategies for sparse coding that often result in similar feature properties for the learned feature set. Typically this results in a bank of Gabor-like or edge filters that are sensitive to a range of distinct angular and radial frequencies. The theory and experimentation that is presented in this paper serves to provide a better understanding of a number of specific properties related to low-level feature learning. This includes close examination of the role of phase pairing in complex cells, the role of depth information and its relationship to variation of intensity and chroma, and deriving hybrid features that borrow from both analytic forms and statistical methods. Together, these specific examples provide context for more general discussion of effective strategies for feature learning. In particular, we make the case that imposing additional constraints on mechanisms for feature learning inspired by biological vision systems can be useful in guiding constrained optimization towards convergence, or specific desirable computational properties for representation of visual input in artificial vision systems.

The emergence of ubiquitous sources of streaming data has given rise to the popularity of algorithms for online machine learning. In that context, Hoeffding trees represent the state-of-the-art algorithms for online classification. Their popularity stems in large part from their ability to process large quantities of data with a speed that goes beyond the processing power of any other streaming or batch learning algorithm. As a consequence, Hoeffding trees have often been used as base models of many ensemble learning algorithms for online classification. However, despite the existence of many algorithms for online classification, ensemble learning algorithms for online regression do not exist. In particular, the field of online any-time regression analysis seems to have experienced a serious lack of attention. In this paper, we address this issue through a study and an empirical evaluation of a set of online algorithms for regression, which includes the baseline Hoeffding-based regression trees, online option trees, and an online least mean squares filter. We also design, implement and evaluate two novel ensemble learning methods for online regression: online bagging with Hoeffding-based model trees, and an online RandomForest method in which we have used a randomized version of the online model tree learning algorithm as a basic building block. Within the study presented in this paper, we evaluate the proposed algorithms along several dimensions: predictive accuracy and quality of models, time and memory requirements, bias–variance and bias–variance–covariance decomposition of the error, and responsiveness to concept drift.

Probabilistic K-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, K. The contribution of this paper is to incorporate the uncertainty in K into the decision making, and consequently to provide improved classification with Bayesian model averaging. Indeed the problem of assessing the uncertainty in K can be viewed as one of statistical model selection which is one of the most important technical issues in the statistics and machine learning domain. In this paper, we develop a new functional approximation algorithm to reconstruct the density of the model (order) without relying on time consuming Monte Carlo simulations. In addition, the algorithms avoid cross validation by adopting Bayesian framework. The performance of the proposed approaches is evaluated on several real experimental datasets. HighlightsIt finds the optimal number of neighbours K for probabilistic KNN (PKNN).It reconstructs the density of K for the PKNN.It incorporates the uncertainty in K into the decision making.It provides improved PKNN classification using Bayesian model averaging.

This paper describes the algorithms used to estimate the state of consumption of a pump based on dynamic pressure or vibrations. To create algorithms, the author used computational intelligence methods in the form of neural networks. In order to perform the analysis, data analysis systems were designed based on three neural networks: multilayer perceptron neural network (MLP), generalized regression neural network (GRNN) and probabilistic neural network (PNN). Processing of the input signal in the final result of the analysis consisted of several steps. First, the measurement data were preprocessed (delete constant component, normalization, standardization, reduction, fast Fourier transform (FFT), etc.), and training and test sets were prepared using the matrices with the expected system answers. The last step was the analysis, consisting of design data analysis systems based on artificial neural networks and their learning and testing. On the basis of the obtained results the effectiveness of neural networks and the methods of pre-processing of the signals applied to approximate the state of consumption of the displacement pump were evaluated. Design systems were evaluated based on accuracy (generated error) and complexity (number of parameters and training time) criteria. The main contribution of the paper is to design and compare methods for pre-processing the signal, and to design and compare the effectiveness of the three neural networks in the diagnosis consumption of a positive displacement pump.

The advent of the Social Web has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. In pace with a global deluge of videos from billions of computers, smartphones, tablets, university projectors and security cameras, the amount of multimodal content on the Web has been growing exponentially, and with that comes the need for decoding such information into useful knowledge. In this paper, a multimodal affective data analysis framework is proposed to extract user opinion and emotions from video content. In particular, multiple kernel learning is used to combine visual, audio and textual modalities. The proposed framework outperforms the state-of-the-art model in multimodal sentiment analysis research with a margin of 10–13% and 3–5% accuracy on polarity detection and emotion recognition, respectively. The paper also proposes an extensive study on decision-level fusion.

In this paper, an intelligent swarm based-wavelet neural network for affective mobile designed is presented. The contribution on this paper is to develop a new intelligent particle swarm optimization (iPSO), where a fuzzy logic system developed based on human knowledge is proposed to determine the inertia weight for the swarm movement of the PSO and the control parameter of a newly introduced cross-mutated operation. The proposed iPSO is used to optimize the parameters of wavelet neural network. An affective design of mobile phones is used to evaluate the effectiveness of the proposed iPSO. It has been found that significantly better results in a statistical sense can be obtained by the iPSO comparing with the existing hybrid PSO methods.

Micro-expression is gaining more attention in both the scientific field and the mass media. It represents genuine emotions that people try to conceal, thus making it a promising cue for lie detection. Since micro-expressions are considered almost imperceptible to naked eyes, researchers have sought to automatically detect and recognize these fleeting facial expressions to help people make use of such deception cues. However, the lack of well-established micro-expression databases might be the biggest obstacle. Although several databases have been developed, there may exist some problems either in the approach of eliciting micro-expression or the labeling. We built a spontaneous micro-expression database with rigorous frame spotting, AU coding and micro-expression labeling. This paper introduces how the micro-expressions were elicited in a laboratory situation and how the database was built with the guide of psychology. In addition, this paper proposes issues that may help researchers effectively use micro-expression databases and improve micro-expression recognition. (C) 2014 Elsevier B.V. All rights reserved.

In this paper, we develop a face recognition system based on softcomputing techniques, which complies with privacy-by-design rules and defines a set of principles that are context-aware applications (including biometric sensors) and should contain to conform to European and US law. This paper deals with the necessity to consider legal issues concerning privacy or human rights in the development of biometric identification in ambient intelligence systems. Clearly, context-based services and ambient intelligence (and the most promising research area in Europe, namely ambient assisted living, ALL) call for a major research effort on new identification procedures. This work was supported in part by Projects CICYT TIN 2011-28620-C02-01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02. Publicado

This is the post-print version of the final paper published in Neurocomputing. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V. Spam, under a variety of shapes and forms, continues to inflict increased damage. Varying approaches including Support Vector Machine (SVM) techniques have been proposed for spam filter training and classification. However, SVM training is a computationally intensive process. This paper presents a MapReduce based parallel SVM algorithm for scalable spam filter training. By distributing, processing and optimizing the subsets of the training data across multiple participating computer nodes, the parallel SVM reduces the training time significantly. Ontology semantics are employed to minimize the impact of accuracy degradation when distributing the training data among a number of SVM classifiers. Experimental results show that ontology based augmentation improves the accuracy level of the parallel SVM beyond the original sequential counterpart.

This paper studies the problem of expertise matching with various constraints. Expertise matching, which aims to find the alignment between experts and queries, is a common problem in many applications such as conference paper-reviewer assignment, product-reviewer alignment, and product-endorser matching. Most existing methods formalize this problem as an information-retrieval problem and focus on finding a set of experts for each query independently. However, in real-world systems, various constraints are often needed to be considered. For example, in order to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: ''Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?'' This paper explores such an approach by formulating the expertise matching problem in a constraint-based optimization framework. In the proposed framework, the problem of expertise matching is linked to a convex cost flow problem, which guarantees an optimal solution under various constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems, namely conference paper-reviewer assignment and teacher-course assignment. Experimental results validate the effectiveness of the proposed approach. Based on the proposed method, we have also developed an online system for paper-reviewer suggestions, which has been used for paper-reviewer assignment in a top conference and feedbacks from the conference organizers are very positive.

Long-term time series prediction is a difficult task. This is due to accumulation of errors and inherent uncertainties of a long-term prediction, which leads to deteriorated estimates of the future instances. In order to make accurate predictions, this paper presents a methodology that uses input processing before building the model. Input processing is a necessary step due to the curse of dimensionality, where the aim is to reduce the number of input variables or features. In the paper, we consider the combination of the delta test and the genetic algorithm to obtain two aspects of reduction: scaling and projection. After input processing, two fast models are used to make the predictions: optimally pruned extreme learning machine and optimally pruned k-nearest neighbors. Both models have fast training times, which makes them suitable choice for direct strategy for long-term prediction. The methodology is tested on three different data sets: two time series competition data sets and one financial data set.

The problem of residual variance estimation consists of estimating the best possible generalization error obtainable by any model based on a finite sample of data. Even though it is a natural generalization of linear correlation, residual variance estimation in its general form has attracted relatively little attention in machine learning. In this paper, we examine four different residual variance estimators and analyze their properties both theoretically and experimentally to understand better their applicability in machine learning problems. The theoretical treatment differs from previous work by being based on a general formulation of the problem covering also heteroscedastic noise in contrary to previous work, which concentrates on homoscedastic and additive noise. In the second part of the paper, we demonstrate practical applications in input and model structure selection. The experimental results show that using residual variance estimators in these tasks gives good results often with a reduced computational complexity, while the nearest neighbor estimators are simple and easy to implement.

This paper presents an ensemble neural network and interval neutrosophic sets approach to the problem of binary classification. A bagging technique is applied to an ensemble of pairs of neural networks created to predict degree of truth membership, indeterminacy membership, and false membership values in the interval neutrosophic sets. In our approach, the error and vagueness are quantified in the classification process as well. A number of aggregation techniques are proposed in this paper. We applied our techniques to the classical benchmark problems including ionosphere, pima-Indians diabetes, and liver-disorders from the UCI machine learning repository. Our approaches improve the classification performance as compared to the existing techniques which applied only to the truth membership values. Furthermore, the proposed ensemble techniques also provide better results than those obtained from only a single pair of neural networks.

This perspective paper explores principles of unsupervised learning and how they relate to face recognition. Dependency coding and information maximization appear to be central principles in neural coding early in the visual system. These principles may be relevant to how we think about higher visual processes such as face recognition as well. The paper first reviews examples of dependency learning in biological vision, along with principles of optimal information transfer and information maximization. Next, we examine algorithms for face recognition by computer from a perspective of information maximization. The eigenface approach can be considered as an unsupervised system that learns the first- and second-order dependencies among face image pixels. Eigenfaces maximize information transfer only in the case where the input distributions are Gaussian. Independent component analysis (ICA) learns high-order dependencies in addition to first- and second-order relations, and maximizes information transfer for a more general set of input distributions. Face representations based on ICA gave better recognition performance than eigenfaces, supporting the theory that information maximization is a good strategy for high level visual functions such as face recognition. Finally, we review perceptual studies suggesting that dependency learning is relevant to human face perception as well, and present an information maximization account of perceptual effects such as the atypicality bias, and face adaptation aftereffects.

Named Entity Recognition(NER), one of the most fundamental problems in natural language processing, seeks to identify the boundaries and types of entities with specific meanings in natural language text. As an important international language, Chinese has uniqueness in many aspects, and Chinese NER (CNER) is receiving increasing attention. In this paper, we give a comprehensive survey of recent advances in CNER. We first introduce some preliminary knowledge, including the common datasets, tag schemes, evaluation metrics and difficulties of CNER. Then, we separately describe recent advances in traditional research and deep learning research of CNER, in which the CNER with deep learning is our focus. We summarize related works in a basic three-layer architecture, including character representation, context encoder, and context encoder and tag decoder. Meanwhile, the attention mechanism and adversarial-transfer learning methods based on this architecture are introduced. Finally, we present the future research trends and challenges of CNER.

The paper Ran and Hu (2014, Neurocomputing) examines identifiability and parameter redundancy in classes of models used in machine learning. This note discusses the results on global identifiability and also clarifies that the paper's results on parameter redundancy already exist in the paper Cole et al. (2010, Mathematical Biosciences).

International audience; Tensor data analysis is the evolutionary step of data analysis to more than two dimensions. Dealing with tensor data is often based on tensor decomposition methods. The present paper focuses on unsupervised learning and provides a python package referred to as TensorClus including novel co-clustering algorithms of three-way data. All proposed algorithms are based on the latent block models and suitable to different types of data, sparse or not. They are successfully evaluated on challenges in text mining, recommender systems, and hyperspectral image clustering. TensorClus is an open-source Python package that allows easy interaction with other python packages such as NumPy and TensorFlow; it also offers an interface with some tensor decomposition packages namely Tensorly and TensorD on the one hand, and on the other, the co-clustering package Coclust. Finally, it provides CPU and GPU compatibility. The TensorClus library is available at https://pypi.org/project/TensorClus/ 1 .

Although video summarization has achieved tremendous success benefiting from Recurrent Neural Networks (RNN), RNN-based methods neglect the global dependencies and multi-hop relationships among video frames, which limits the performance. Transformer is an effective model to deal with this problem, and surpasses RNN-based methods in several sequence modeling tasks, such as machine translation, video captioning, etc. Motivated by the great success of transformer and the natural structure of video (frame-shot-video), a hierarchical transformer is developed for video summarization, which can capture the dependencies among frame and shots, and summarize the video by exploiting the scene information formed by shots. Furthermore, we argue that both the audio and visual information are essential for the video summarization task. To integrate the two kinds of information, they are encoded in a two-stream scheme, and a multimodal fusion mechanism is developed based on the hierarchical transformer. In this paper, the proposed method is denoted as Hierarchical Multimodal Transformer (HMT). Practically, extensive experiments show that HMT achieves (F-measure: 0.441, Kendall’s τ : 0.079, Spearman’s ρ : 0.080) and (F-measure: 0.601, Kendall’s τ : 0.096, Spearman’s ρ : 0.107) on SumMe and TVsum, respectively. It surpasses most of the traditional, RNN-based and attention-based video summarization methods.

Learning hidden topics from data streams has become absolutely necessary but posed challenging problems such as concept drift as well as short and noisy data. Using prior knowledge to enrich a topic model is one of potential solutions to cope with these challenges. Prior knowledge that is derived from human knowledge (e.g. Wordnet) or a pre-trained model (e.g. Word2vec) is very valuable and useful to help topic models work better. However, in a streaming environment where data arrives continually and infinitely, existing studies are limited to exploiting these resources effectively. Especially, a knowledge graph, that contains meaningful word relations, is ignored. In this paper, to aim at exploiting a knowledge graph effectively, we propose a novel graph convolutional topic model (GCTM) which integrates graph convolutional networks (GCN) into a topic model and a learning method which learns the networks and the topic model simultaneously for data streams. In each minibatch, our method not only can exploit an external knowledge graph but also can balance the external and old knowledge to perform well on new data. We conduct extensive experiments to evaluate our method with both a human knowledge graph (Wordnet) and a graph built from pre-trained word embeddings (Word2vec). The experimental results show that our method achieves significantly better performances than state-of-the-art baselines in terms of probabilistic predictive measure and topic coherence. In particular, our method can work well when dealing with short texts as well as concept drift. The implementation of GCTM is available at \url{https://github.com/bachtranxuan/GCTM.git}.

Sentiment analysis in conversations has gained increasing attention in recent years for the growing amount of applications it can serve, e.g., sentiment analysis, recommender systems, and human-robot interaction. The main difference between conversational sentiment analysis and single sentence sentiment analysis is the existence of context information which may influence the sentiment of an utterance in a dialogue. How to effectively encode contextual information in dialogues, however, remains a challenge. Existing approaches employ complicated deep learning structures to distinguish different parties in a conversation and then model the context information. In this paper, we propose a fast, compact and parameter-efficient party-ignorant framework named bidirectional emotional recurrent unit for conversational sentiment analysis. In our system, a generalized neural tensor block followed by a two-channel classifier is designed to perform context compositionality and sentiment classification, respectively. Extensive experiments on three standard datasets demonstrate that our model outperforms the state of the art in most cases., 9 pages, 7 figures

This paper introduces latest advances in the subject of linear separability. New methods for testing linear separability are introduced. This is a very important area of work which can help simplify the topology of a neural network by using a single layer perceptron when the problem at hand is linearly separable. The research presented in this paper has allowed researchers to enhance the performance of the RDP neural network. It appears in one of the leading journals of Neural Networks.

Clustering categorical data remains a challenging problem in the era of big data, due to the difficulty in measuring dis/similarity meaningfully for categorical data and the high computational complexity of existing clustering algorithms that makes it difficult to be applied in practical use for big data mining applications. In this paper, we propose an integrated approach that incorporates the Locality-Sensitive Hashing (LSH) technique into the k -means-like clustering so as to make it capable of predicting the better initial clusters for boosting clustering effectiveness. To this end, we first utilize a data-driven dissimilarity measure for categorical data to construct a family of binary hash functions that are then used to generate the initial clusters. We also propose to use a nearest neighbor search at each iteration for cluster reassignment of data objects to improve the clustering complexity. These solutions are incorporated into the k -representatives algorithm resulting in the so-called LSH- k -representatives algorithm. Extensive experiments conducted on multiple real-world and synthetic datasets have demonstrated the effectiveness of the proposed method. It is shown that the newly developed algorithm yields comparable or better clustering results in comparison to the existing closely related works, yet it is significantly more efficient by a factor of between 2 × and 32 × .

Machine Reading Comprehension (MRC) is an active field in natural language processing with many successful developed models in recent years. Despite their high in-distribution accuracy, these models suffer from two issues: high training cost and low out-of-distribution accuracy. Even though some approaches have been presented to tackle the generalization problem, they have high, intolerable training costs. In this paper, we investigate the effect of ensemble learning as a light approach to improve out-of-distribution generalization of MRC systems by aggregating the outputs of some pre-trained base models without retraining a big model. After separately training the base models with different structures on different datasets, they are ensembled using weighting and stacking approaches in probabilistic and non-probabilistic settings. Three configurations are investigated including heterogeneous, homogeneous, and hybrid on eight datasets and six state-of-the-art models. We identify the important factors in the effectiveness of ensemble methods. Also, we compare the robustness of ensemble and fine-tuned models against data distribution shifts. The experimental results show the effectiveness and robustness of the ensemble approach in improving the out-of-distribution accuracy of MRC systems, especially when the base models are similar in accuracies.

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal’s, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

Heterogeneous Information Network (HIN) is a natural and general representation of data in recommender systems. Combining HIN and recommender systems can not only help model user behaviors but also make the recommendation results explainable by aligning the users/items with various types of entities in the network. Over the past few years, path-based reasoning models have shown great capacity in HIN-based recommendation. The basic idea of these models is to explore HIN with predefined path schemes. Despite their effectiveness, these models are often confronted with the following limitations: (1) Most prior path-based reasoning models only consider the influence of the predecessors on the subsequent nodes when modeling the sequences, and ignore the reciprocity between the nodes in a path; (2) The weights of nodes in the same path instance are usually assumed to be constant, whereas varied weights of nodes can bring more flexibility and lead to expressive modeling; (3) User-item interactions are noisy, but they are often indiscriminately exploited. To overcome the aforementioned issues, in this paper, we propose a novel path-based reasoning approach for recommendation over HIN. Concretely, we use a bidirectional LSTM to enable the two-way modeling of paths and capture the reciprocity between nodes. Then an attention mechanism is employed to learn the dynamical influence of nodes in different contexts. Finally, the adversarial regularization terms are imposed on the loss function of the model to mitigate the effects of noise and enhance HIN-based recommendation. Extensive experiments conducted on three public datasets show that our model outperforms the state-of-the-art baselines. The case study further demonstrates the feasibility of our model on the explainable recommendation task., 11 pages, 5 figures, Neurocomputing

With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of retrosynthesis is analogized to a process of language translation from the product to reactants. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. To make more chemically plausible constrains on the atom representation learning for better performance, in this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embeddings learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the vanilla Transformer model in test accuracy.

Knowledge distillation (KD) is widely applied in the training of efficient neural network. A compact model, which is trained to mimic the representation of a cumbersome model for the same task, generally obtains a better performance compared with being trained with the ground truth label. Previous KD-based works mainly focus on two aspects: (1) designing various feature representation for knowledge transfer; (2) introducing different training mechanism such as progressive learning or adversarial learning. In this paper, we revisit the standard KD and observe that training with teacher’s logits might suffer from incorrect and uncertain supervision. To tackle these problems, we propose two novel approaches to deal with incorrect logits and uncertain logits respectively, which are called Logits Adjustment (LA) and Dynamic Temperature Distillation (DTD). To be specific, LA rectifies the incorrect logits according to ground truth label and certain rules. While DTD treats the temperature of KD as a dynamic sample wise parameter rather than a static and global hyper-parameter, which actually notes the uncertainty for each sample’s logits. With iteratively updating the sample wise temperature, the student model could pay more attention on the samples that confuse the teacher model. Experiments on CIFAR-10/100, CINIC-10 and Tiny ImageNet verify that the proposed methods yield encouraging improvement compared with the standard KD. Furthermore, considering the simple implementations, LA and DTD can be easily attached to many KD-based frameworks and bring improvements without extra cost of training time and computing resources.

Detail-and-geometry richness is essential to bas-relief modelling. However, existing image-based and model-based bas-relief modelling techniques commonly suffer from detail monotony or geometry loss. In this paper, we introduce a new bas-relief modelling framework for detail abundance with visual attention based mask generation and geometry preservation, which benefits from our two key contributions. For detail richness, we propose a novel semantic neural network of normal transfer to enrich the texture styles on bas-reliefs. For geometry preservation, we introduce a normal decomposition scheme based on Domain Transfer Recursive Filter (DTRF). Experimental results demonstrate that our approach is advantageous on producing bas-relief modellings with both fine details and geometry preservation.

Hindsight Experience Replay (HER) is a multi-goal reinforcement learning algorithm for sparse reward functions. The algorithm treats every failure as a success for an alternative (virtual) goal that has been achieved in the episode. Virtual goals are randomly selected, irrespective of which are most instructive for the agent. In this paper, we present two improvements over the existing HER algorithm. First, we prioritize virtual goals from which the agent will learn more valuable information. We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals. Secondly, we reduce existing bias in HER by the removal of misleading samples. To test our algorithms, we built three challenging environments with sparse reward functions. Our empirical results in both environments show vast improvement in the final success rate and sample efficiency when compared to the original HER algorithm. A video showing experimental results is available at https://youtu.be/xjAiwJiSeLc.

To interact with humans in collaborative environments, machines need to be able to predict (i.e., anticipate) future events, and execute actions in a timely manner. However, the observation of the human limb movements may not be sufficient to anticipate their actions unambiguously. In this work, we consider two additional sources of information (i.e., context) over time, gaze, movement and object information, and study how these additional contextual cues improve the action anticipation performance. We address action anticipation as a classification task, where the model takes the available information as the input and predicts the most likely action. We propose to use the uncertainty about each prediction as an online decision-making criterion for action anticipation. Uncertainty is modeled as a stochastic process applied to a time-based neural network architecture, which improves the conventional class-likelihood (i.e., deterministic) criterion. The main contributions of this paper are four-fold: (i) We propose a novel and effective decision-making criterion that can be used to anticipate actions even in situations of high ambiguity; (ii) we propose a deep architecture that outperforms previous results in the action anticipation task when using the Acticipate collaborative dataset; (iii) we show that contextual information is important to disambiguate the interpretation of similar actions; and (iv) we also provide a formal description of three existing performance metrics that can be easily used to evaluate action anticipation models.Our results on the Acticipate dataset showed the importance of contextual information and the uncertainty criterion for action anticipation. We achieve an average accuracy of 98.75% in the anticipation task using only an average of 25% of observations., 27 pages, 16 figures, Neurocomputing

State-of-the-art deep neural networks (DNNs) typically consist of several layers of features representations, and especially rely on skip connections to avoid the difficulty of model optimization. Despite the proliferation of different DNN models that employ various forms of skip connections to achieve remarkable results on benchmarking datasets, a concrete explanation for the successful operation and improved generalization capability of these DNNs is surprisingly still lacking. In this paper, we focus on investigating the role of skip connections for training very deep DNNs. Our exposition directly provides interesting insights and new interpretations to the following important questions (i) why model optimization is easier (ii) why model generalization is better. Theoretical results reveal that skip connections allow DNNs to circumnavigate the singularity of latent representations that translate to optimization and generalization problems, which plague models without skip connections referred to as PlainNets. For substantiating our analysis, our investigation puts into context some of the most successful skip-connection based DNNs, which include residual networks (ResNets) and residual network with aggregated features (ResNeXt) in relation to PlainNets. Experimental evaluations of these models support the theoretical analysis.

Unsupervised domain adaptation aims at learning a classification model robust to data distribution shift between a labeled source domain and an unlabeled target domain. Most existing approaches have overlooked the multi-dimensional nature of visual data, building classification models in vector space. Meanwhile, the issue of limited training samples is rarely considered by previous methods, yet it is ubiquitous in practical visual applications. In this paper, we develop a structured discriminative tensor dictionary learning method (SDTDL), which enables domain matching in tensor space. SDTDL produces disentangled and transferable representations by explicitly separating domain-specific factor and class-specific factor in data. Classification is achieved based on sample reconstruction fidelity and distribution alignment, which is seamlessly integrated into tensor dictionary learning. We evaluate SDTDL on cross-domain object and digit recognition tasks, paying special attention to the scenarios of limited training samples and test beyond training sample set. Experimental results show that our method outperforms existing mainstream shallow approaches and representative deep learning methods by a significant margin.

In many real-world applications, only user-item interactions (one-class feedback) can be observed. The recommendation methods have been studied for personalized ranking with one-class feedback in recent years. Pairwise ranking methods have been widely used for dealing with the one-class problem with the assumption that users prefer their observed items over unobserved items. However, existing some items that users have not seen yet. It is unsuitable for treating all unobserved items of the user as negative feedback. In this paper, we propose a Prior-based Bayesian Pairwise Ranking (PBPR) model, which relaxes the simple pairwise preference assumption in previous works by further considering the pairwise preference between two unobserved items. Moreover, we calculate users' potential preference scores on unobserved items, i.e., prior information, based on historical interactions. The prior information can be used to measure the fine-grained preference difference between any two unobserved items of each user. Through extensive experiments on real-world datasets, we demonstrate the effectiveness of our proposed recommendation method.

Contemporary segmentation methods are usually based on deep fully convolutional networks (FCNs). However, the layer-by-layer convolutions with a growing receptive field is not good at capturing long-range contexts such as lane markers in the scene. In this paper, we address this issue by designing a distillation method that exploits label structure when training segmentation network. The intuition is that the ground-truth lane annotations themselves exhibit internal structure. We broadcast the structure hints throughout a teacher network, i.e., we train a teacher network that consumes a lane label map as input and attempts to replicate it as output. Then, the attention maps of the teacher network are adopted as supervisors of the student segmentation network. The teacher network, with label structure information embedded, knows distinctly where the convolution layers should pay visual attention into. The proposed method is named as Label-guided Attention Distillation (LGAD). It turns out that the student network learns significantly better with LGAD than when learning alone. As the teacher network is deprecated after training, our method do not increase the inference time. Note that LGAD can be easily incorporated in any lane segmentation network., Comment: Accepted to Neurocomputing 2021

Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states, and predict future actions from the hidden representations. It is well known that the recurrent pipeline is inefficient in capturing long-term information which may limit its performance in predication task. To address this problem, this paper proposes a simple yet efficient Temporal Transformer with Progressive Prediction (TTPP) framework, which repurposes a Transformer-style architecture to aggregate observed features, and then leverages a light-weight network to progressively predict future features and actions. Specifically, predicted features along with predicted probabilities are accumulated into the inputs of subsequent prediction. We evaluate our approach on three action datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. Additionally we also conduct a comprehensive study for several popular aggregation and prediction strategies. Extensive results show that TTPP not only outperforms the state-of-the-art methods but also more efficient.

In this paper, we propose a novel Attentive Multi-View Deep Subspace Nets (AMVDSN), which deeply explores underlying consistent and view-specific information from multiple views and fuse them by considering each view’s dynamic contribution obtained by attention mechanism. Unlike most multi-view subspace learning methods that they directly reconstruct data points on raw data or only consider consistency or complementarity when learning representation in deep or shallow space, our proposed method seeks to find a joint latent representation that explicitly considers both consensus and view-specific information among multiple views, and then performs subspace clustering on learned joint latent representation. Besides, different views contribute differently to representation learning, we therefore introduce attention mechanism to derive dynamic weight for each view, which performs much better than previous fusion methods in the field of multi-view subspace clustering. The proposed algorithm is intuitive and can be easily optimized just by using Stochastic Gradient Descent (SGD) because of the neural network framework, which also provides strong non-linear characterization capability compared with traditional subspace clustering approaches. The experimental results on seven real-world data sets have demonstrated the effectiveness of our proposed algorithm against some state-of-the-art subspace learning approaches.