Knowledge graph embedding, which aims at learning the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness, which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited performance of embedding related applications. To solve this problem, this article presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. Two major innovations of our article are presented here. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Via the proposed additional smoothing factor, RTGE can preserve both structural information and evolutionary patterns of a given graph. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally aware information, which further improves the adaptive ability of the proposed algorithm and plays an essential role in obtaining superior performance in various tasks. Extensive experiments conducted on multiple benchmark tasks show that RTGE can increase performance in entity/relationship/temporal scoping prediction tasks.

Classical algorithms in recommender systems (RS) mainly emphasis on achieving high accuracy and thus recommend items precisely matching a user's past choices. However, the user may gradually lose interest and crave something more inspiring. In psychology, curiosity is a critical human nature and can be efficient bootstrap exploratory behaviors, thus this phenomenon can be explained as insufficient stimulation to induce curiosity regard to recommended items. Inspired from the above, this work proposes a Curiosity-drive Recommendation Framework (CdRF) which incorporates a highly innovative Stimulus-evoked Curiosity mechanism (SeCM) together with a basic accuracy-oriented algorithm via Borda count. In SeCM, we first estimate the stimulus intensity appearing on each item for each user and then model personalized curiosity among the calculated intensities using Wundt curve. For the target user, the output of CdRF is a ranked list of N N items which are both relevant and highly curiousness. We conduct extensive experiments using four public datasets to evaluate the performance of each specification of SeCM as well as the whole framework CdRF. The results reveal that SeCM can flexibly generate diversified items and CdRF can increase diversity in terms of ILS, Newness and AD while compromising very little Precision. This kind of research also offers a way to understand both individual differences in curiosity and how curiosity contributes to item exploration at the level of RS.

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.

The issue of data imbalance occurs in many real-world applications especially in medical diagnosis, where normal cases are usually much more than the abnormal cases. To alleviate this issue, one of the most important approaches is the oversampling method, which seeks to synthesize minority class samples to balance the numbers of different classes. However, existing methods barely consider global geometric information involved in the distribution of minority class samples, and thus may incur distribution mismatching between real and synthetic samples. In this paper, relying on optimal transport (Villani 2008), we propose an oversampling method by exploiting global geometric information of data to make synthetic samples follow a similar distribution to that of minority class samples. Moreover, we introduce a novel regularization based on synthetic samples and shift the distribution of minority class samples according to loss information. Experiments on toy and real-world data sets demonstrate the efficacy of our proposed method in terms of multiple metrics.

Information processing of guide dog robot requires expensive computing resources to meet real-time performance. We propose an edge computing framework based on Intel Up-Squared board and neural compute stick. Image processing and real-time control are performed on the framework. In addition, the voice recognition and commanding are also implemented, which are processed on Amazon cloud service. Vision, speech, and control services are integrated by ASU VIPLE (Visual IoT/Robotics Programming Language Environment). A prototype is developed, which implements the guide dog's obstacle avoidance, traffic sign recognition and following, and voice interaction with human.

In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called O nline H eterogeneous K nowledge T ransition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.

Heterogeneous domain adaptation (HDA) aims to exploit knowledge from a heterogeneous source domain to improve the learning performance in a target domain. Since the feature spaces of the source and target domains are different, the transferring of knowledge is extremely difficult. In this paper, we propose a novel semi-supervised algorithm for HDA by exploiting the theory of optimal transport (OT), a powerful tool originally designed for aligning two different distributions. To match the samples between heterogeneous domains, we propose to preserve the semantic consistency between heterogeneous domains by incorporating label information into the entropic Gromov-Wasserstein discrepancy, which is a metric in OT for different metric spaces, resulting in a new semi-supervised scheme. Via the new scheme, the target and transported source samples with the same label are enforced to follow similar distributions. Lastly, based on the Kullback-Leibler metric, we develop an efficient algorithm to optimize the resultant problem. Comprehensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of our proposed method.

In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.

Domain adaptation aims to reduce the effort on collecting and annotating target data by leveraging knowledge from a different source domain. The domain adaptation problem will become extremely challenging when the feature spaces of the source and target domains are different, which is also known as the heterogeneous domain adaptation (HDA) problem. In this paper, we propose a novel HDA method to find the optimal discriminative correlation subspace for the source and target data. The discriminative correlation subspace is inherited from the canonical correlation subspace between the source and target data, and is further optimized to maximize the discriminative ability for the target domain classifier. We formulate a joint objective in order to simultaneously learn the discriminative correlation subspace and the target domain classifier. We then apply an alternating direction method of multiplier (ADMM) algorithm to address the resulting non-convex optimization problem. Comprehensive experiments on two real-world data sets demonstrate the effectiveness of the proposed method compared to the state-of-the-art methods.

The rapid growth of population has posed a challenge to people for discovering new followees in uni-directional social networks. Intuitively, a user's adoption of others as followees may motivated by her interest as well as social connection. Therefore, it is worth-while to consider both factors at the same time for better recommendations. Previous recommender works on implicit follow or not feedbacks become unqualified, mainly because of the coarse users' preferences inferring, which cannot distinguish whether one follows the other is based on her social connection or individual interest. In this paper, we present a new user recommendation method which is capable of recommending candidate followees who have similar interest and closer social connection relevant to a target user. As its core, a novel topic model namely UIS-LDA is designed to jointly model a user's preferences with respect to the set of latent interest topics and social topics. The experiments using Twitter dataset proves that our proposed method effective in improving the Precision, Conversion Rate F1 score and NDCG.

Transfer learning has been proven to be effective for the problems where training data from a source domain and test data from a target domain are drawn from different distributions. To reduce the distribution divergence between the source domain and the target domain, many previous studies have been focused on designing and optimizing objective functions with the Euclidean distance to measure dissimilarity between instances. However, in some real-world applications, the Euclidean distance may be inappropriate to capture the intrinsic similarity or dissimilarity between instances. To deal with this issue, in this paper, we propose a metric transfer learning framework (MTLF) to encode metric learning in transfer learning. In MTLF, instance weights are learned and exploited to bridge the distributions of different domains, while Mahalanobis distance is learned simultaneously to maximize the intra-class distances and minimize the inter-class distances for the target domain. Unlike previous work where instance weights and Mahalanobis distance are trained in a pipelined framework that potentially leads to error propagation across different components, MTLF attempts to learn instance weights and a Mahalanobis distance in a parallel framework to make knowledge transfer across domains more effective. Furthermore, we develop general solutions to both classification and regression problems on top of MTLF, respectively. We conduct extensive experiments on several real-world datasets on object recognition, handwriting recognition, and WiFi location to verify the effectiveness of MTLF compared with a number of state-of-the-art methods.

Medical ultrasound imaging is a typical clinical diagnostic method which has been widely applied to heart movement monitoring, fetal development status tracking, and liver interventional surgery, etc. Among various medical ultrasound imaging algorithms, delay-and-sum algorithm is the most common algorithm which can be found in almost every medical ultrasonic scanner. The execution of delay-and-sum beamforming algorithm is not time-consuming typically. However, when constructing a medical imaging cloud service, the computing speed of the delay-and-sum algorithm on the cloud server has to be much faster than the computing speed of the algorithm implementation on regular ultrasonic scanner processor, so as to overcome the cloud data transfer speed constraint on the real-time imaging requirement for medical ultrasound. As such, this paper demonstrated the implementation of the parallel delay-and-sum beamforming algorithm on the FPGA with coherent accelerator processor interface. Furthermore, the performance evaluation results were also demonstrated in the paper.

Query-URL relevance, measuring the relevance of each retrieved URL with respect to a given query, is one of the fundamental criteria to evaluate the performance of commercial search engines. The traditional way to collect reliable and accurate query-URL relevance requires multiple annotators to provide their individual judgments based on their subjective expertise (e.g., understanding of user intents). In this case, the annotators’ subjectivity reflected in each annotator individual judgment (AIJ) inevitably affects the quality of the ground truth relevance (GTR). But to the best of our knowledge, the potential impact of AIJs on estimating GTRs has not been studied and exploited quantitatively by existing work. This article first studies how multiple AIJs and GTRs are correlated. Our empirical studies find that the multiple AIJs possibly provide more cues to improve the accuracy of estimating GTRs. Inspired by this finding, we then propose a novel approach to integrating the multiple AIJs with the features characterizing query-URL pairs for estimating GTRs more accurately. Furthermore, we conduct experiments in a commercial search engine—Baidu.com—and report significant gains in terms of the normalized discounted cumulative gains.

Term weighting schemes have been widely used in information retrieval and text categorization models. In this paper, we first investigate into the limitations of several state-of-the-art term weighting schemes in the context of text categorization tasks. Considering that category-specific terms are more useful to discriminate different categories, and these terms tend to have smaller entropy with respect to these categories, we then explore the relationship between a term's discriminating power and its entropy with respect to a set of categories. To this end, we propose two entropy-based term weighting schemes (i.e., tf.dc and tf.bdc) which measure the discriminating power of a term based on its global distributional concentration in the categories of a corpus. To demonstrate the effectiveness of the proposed term weighting schemes, we compare them with seven state-of-the-art schemes on a long-text corpus and a short-text corpus respectively. Our experimental results show that the proposed schemes outperform the state-of-the-art schemes in text categorization tasks with KNN and SVM.

Collective classification (CC) is a task to jointly classifying related instances of network data. Enabling CC usually improves the performance of predictive models on fully-labeled training networks with large amount of labeled data. However, acquiring such labels can be difficult and costly, and learning a CC classifier with only a few labeled data can lead to poor performance. On the other hand, there are usually large amount of unlabeled data available in practical. This naturally motivates semi-supervised collective classification (SSCC) approaches for leveraging the unlabeled data to improve CC from a sparsely-labeled network. In this paper, we propose a novel non-negative matrix factorization (NMF) based SSCC algorithm, called NMF-SSCC, to effectively learn a data representation by exploiting both labeled and unlabeled data on the network. Our idea is to use matrix factorization to obtain a compact representation of network data which uncovers the class discrimination of the data inferred from the labeled instances and simultaneously respects the intrinsic network structure. To achieve this, we design a new matrix factorization objective function and incorporate a label matrix factorization term as well as a network regularization term into it. An efficient optimization algorithm using the multiplicative updating rules is then developed to solve the new objective function. To further boost the predicting performance, we extend the proposed NMF-SSCC method into an ensemble scheme, called NMFE-SSCC, in terms of building a classification ensemble with a set of NMF-SSCC collective classifiers using different constructed latent graphs. Each NMF-SSCC classifier is learnt from one latent graph generated with various latent linkages for effectively label propagation. Experimental results on real-world data sets have demonstrated the effectiveness of the new methods.

Object recognition and modeling have classically been studied separately, but practically, they are two closely correlated aspects. In this paper, by exploring the interrelations, we propose a framework to address these two problems at the same time, which we call simultaneous recognition and modeling. Differing from traditional recognition process which consists of off-line object model learning and on-line recognition procedures, our method is solely online. Starting with an empty object database, we incrementally build up object models while at the same time using these models to identify newly observed object views. In the proposed framework, objects are modeled as view graphs and a probabilistic observation model is presented. Both the appearance and the spatial structure of the object are examined, and a formulation based on maximum likelihood estimation is developed. Joint object recognition and modeling are achieved by solving the optimization problem. To evaluate the framework, we have developed a method for simultaneously learning multiple 3-D object models directly from the cluttered indoor environment and tested it using several everyday scenes. Experimental results demonstrate that the framework can cope with the recognition and modeling problem together nicely.

Collaborative filtering (CF) is an important and popular technology for recommender systems. However, current CF methods suffer from such problems as data sparsity, recommendation inaccuracy, and big-error in predictions. In this paper, we borrow ideas of object typicality from cognitive psychology and propose a novel typicality-based collaborative filtering recommendation method named TyCo. A distinct feature of typicality-based CF is that it finds "neighbors" of users based on user typicality degrees in user groups (instead of the corated items of users, or common users of items, as in traditional CF). To the best of our knowledge, there has been no prior work on investigating CF recommendation by combining object typicality. TyCo outperforms many CF recommendation methods on recommendation accuracy (in terms of MAE) with an improvement of at least 6.35 percent in Movielens data set, especially with sparse training data (9.89 percent improvement on MAE) and has lower time cost than other CF methods. Further, it can obtain more accurate predictions with less number of big-error predictions.

3D model building is an essential topic in both computer vision and robotic community. Usually this is achieved with alignment methods which try to place the data into a common reference frame by finding out the transformations between them. Traditional local alignment approaches, such as ICP and its variants, mainly concern about pairwise constraints and leave out the transformation consistency requirements implied by data sequence. However, these consistencies are important clues to increase the precision of alignment. To use this information better, we present Local Transformation Consistent Alignment (LTCA), which takes triple of frames as input and simultaneously optimize the transformations between them so that local consistencies can be maintained. To evaluate the proposed method, we apply it to the 3D modeling problem with RGB-D image data as input. Comparisons with the state-of-the-art pairwise alignment- Generalized ICP are also made. Experimental results show that more precise alignment results can be obtained when consistency constraints are kept.

Recently, some systems allow users to rate and annotate resources, e.g., Movie Lens, and we consider that it provides a way to identify favor tags and annoying tags of a user by integrating user's rating and tags. In this paper, we reveal and elaborate on the limitations of current works on user profiling for personalized search in collaborative tagging systems. Then we propose a new multi-level user profiling model by integrating tags and ratings to achieve personalized search, which can reflect not only the user's favor but also a user's nuisances. To the best of our knowledge, this is the first effort to integrate the ratings and tags to model multi-level user profiles for personalized search.

In cognitive psychology, typicality refers to the degree of goodness of objects as exemplars in concepts. In this paper, we apply the idea of typicality analysis from cognitive psychology to query answering, and propose a novel method to answer typicality queries effectively based on theories in cognitive psychology. The proposed method adopts multi-prototype concept modeling and basic level category detection. By a systematic empirical evaluation using real data sets, we verify the accuracy and the effectiveness of our method on answering typicality queries.

Place and object recognition are two fundamental problems for mobile robot to understand its surroundings. In the field of computer vision it has been acknowledged that context plays an important role in image parsing, but in most of the researches contextual information is only used in one direction and little attention is paid to the relative pose context between objects and local features. We observe, however, place and object can serve as context to each other, that is the recognition of one facilitates the recognition of the other. In this paper, a new hierarchical random field which can encode multiple kinds of context including co-occurrence context, temporal context and relative pose context is proposed for simultaneous place and object recognition with a mobile platform. And a new kind of relative pose context, which is scale and rotation invariant, is defined to improve the stability of pose-encoded context. Experimental results with a mobile robot prove that the proposed method significantly improve the precision of the place and object recognition in familiar and unfamiliar environments.