Reordering model adaptation remains a big challenge in statistical machine translation because reordering patterns of translation units often vary dramatically from one domain to another. In this paper, we propose a novel adaptive discriminative reordering model (DRM) based on structural learning, which can capture correspondences among reordering features from two different domains. Exploiting both in-domain and out-of-domain monolingual corpora, our model learns a shared feature representation for cross-domain phrase reordering. Incorporating features of this representation, the DRM trained on out-of-domain corpus generalizes better to in-domain data. Experiment results on the NIST Chinese-English translation task show that our approach significantly outperforms a variety of baselines.

It is notable In real world applications, data are often with multiple that strong modal features can lead to a better performance, modalities. Previous works assumed that each nevertheless, are more expensive, therefore a group of serialized modality contains sufficient information for target feature extraction methods were proposed. These methods and can be treated with equal importance. However, extract weak modal features firstly, and then extract more it is often that different modalities are of various strong modal features gradually to improve the performance importance in real tasks, e.g., the facial feature and reduce the overall cost as well. Marcialis et al.[2010] proposed is weak modality and the fingerprint feature is a serial fusion technique for multiple biometric modal strong modality in ID recognition. In this paper, we features through extracting gaits information and face information point out that different modalities should be treated step by step; Zhang et al.[2014] addressed the serialized with different strategies and propose the Auxiliary multi-modal learning techniques in a semi-supervised information Regularized Machine (ARM), which learning scenario. These methods handle strong and weak works by extracting the most discriminative feature modalities independently while leaving the fact of unsatisfied subspace of weak modality while regularizing the performance on weak modality unexplained.

Cognitive modelling can discover the latent characteristics of examinees for predicting their performance (i.e. scores) on each problem. As cognitive modelling is important for numerous applications, e.g. personalized remedy recommendation, some solutions have been designed in the literature. However, the problem of extracting information from both objective and subjective problems to get more precise and interpretable cognitive analysis is still underexplored. To this end, we propose a fuzzy cognitive diagnosis framework (FuzzyCDF) for examinees' cognitive modelling with both objective and subjective problems. Specifically, to handle the partially correct responses on subjective problems, we first fuzzify the skill proficiency of examinees. Then, we combine fuzzy set theory and educational hypotheses to model the examinees' mastery on the problems. Further, we simulate the generation of examination scores by considering both slip and guess factors. Extensive experiments on three real-world datasets prove that FuzzyCDF can predict examinee performance more effectively, and the output of FuzzyCDF is also interpretative.

Differential privacy preserving regression models guarantee protection against attempts to infer whether a subject was included in the training set used to derive a model. It is not designed to protect attribute privacy of a target individual when model inversion attacks are launched. In model inversion attacks, an adversary uses the released model to make predictions of sensitive attributes (used as input to the model) of a target individual when some background information about the target individual is available. Previous research showed that existing differential privacy mechanisms cannot effectively prevent model inversion attacks while retaining model efficacy. In this paper, we develop a novel approach which leverages the functional mechanism to perturb coefficients of the polynomial representation of the objective function but effectively balances the privacy budget for sensitive and non-sensitive attributes in learning the differential privacy preserving regression model. Theoretical analysis and empirical evaluations demonstrate our approach can effectively prevent model inversion attacks and retain model utility.

Active learning reduces the labeling cost by selectively querying the most valuable information from the annotator. It is essentially important for multi-label learning, where the labeling cost is rather high because each object may be associated with multiple labels. Existing multi-label active learning (MLAL) research mainly focuses on the task of selecting instances to be queried. In this paper, we disclose for the first time that the query type, which decides what information to query for the selected instance, is more important. Based on this observation, we propose a novel MLAL framework to query the relevance ordering of label pairs, which gets richer information from each query and requires less expertise of the annotator. By incorporating a simple selection strategy and a label ranking model into our framework, the proposed approach can reduce the labeling effort of annotators significantly. Experiments on 20 benchmark datasets and a manually labeled real data validate that our approach not only achieves superior performance on classification, but also provides accurate ranking for relevant labels.

Recently, several Web-scale knowledge harvesting systems have been built, each of which is competent at extracting information from certain types of data (e.g., unstructured text, structured tables on the web, etc.). In order to determine the response to a new query posed to such systems (e.g., is sugar a healthy food?), it is useful to integrate opinions from multiple systems. If a response is desired within a specific time budget (e.g., in less than 2 seconds), then maybe only a subset of these resources can be queried. In this paper, we address the problem of knowledge integration for on-demand time-budgeted query answering. We propose a new method, AskWorld, which learns a policy that chooses which queries to send to which resources, by accommodating varying budget constraints that are available only at query (test) time. Through extensive experiments on real world datasets, we demonstrate AskWorld’s capability in selecting most informative resources to query within test-time constraints, resulting in improved performance compared to competitive baselines.

Unlike other forms of spamming, it is difficult to collect a large amount of gold-standard labels for reviews Many existing methods on review spam detection by means of manual effort. Thus, most of these methods considering text content merely utilize simple text [Mukherjee et al., 2013; Li et al., 2013a; Sun et al., features such as content similarity. We explore a 2013] just rely on the ad-hoc or pseudo fake or non-fake novel idea of exploiting text generality for improving labels for model training, such as the labels annotated by spam detection. Besides, apart from the task the Amazon anonymous online workers [Ott et al., 2011; of review spam detection, although there have also Li et al., 2014]. On the other hand, some unsupervised been some works on identifying the review spammers methods have been proposed to detect the individual review (users) and the manipulated offerings (items), spammer [Mukherjee et al., 2013; Lim et al., 2010; no previous works have attempted to solve these Wang et al., 2011] and review spammer groups [Mukherjee et three tasks in a unified model. We have proposed al., 2012]. In addition, time series pattern [Xie et al., 2012], a unified probabilistic graphical model to detect rating distribution [Feng et al., 2012], reviewer graph [Wang et the suspicious review spams, the review spammers al., 2011], and reviewing burstiness [Fei et al., 2013] have also and the manipulated offerings in an unsupervised been applied to identify the review spams in an unsupervised manner.

We propose a new Bayesian model for reliable aggregatio of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user’s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the reporting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application.

Dynamic Programming (DP) over tree decompositions is a well-established method to solve problems — that are in general NP-hard — efficiently for instances of small treewidth. Experience shows that (i) heuristically computing a tree decomposition has negligible runtime compared to the DP step; (ii) DP algorithms exhibit a high variance in runtime when using different tree decompositions; in fact, given an instance of the problem at hand, even decompositions of the same width might yield extremely diverging runtimes. We thus propose here a novel and general method that is based on a selection of the best decomposition from an available pool of heuristically generated ones. For this purpose, we require machine learning techniques based on features of the decomposition rather than on the actual problem instance. We report on extensive experiments in different problem domains which show a significant speedup when choosing the tree decomposition according to this concept over simply using an arbitrary one of the same width.

Continuous optimization is an important problem in many areas of AI, including vision, robotics, probabilistic inference, and machine learning. Unfortunately, most real-world optimization problems are nonconvex, causing standard convex techniques to find only local optima, even with extensions like random restarts and simulated annealing. We observe that, in many cases, the local modes of the objective function have combinatorial structure, and thus ideas from combinatorial optimization can be brought to bear. Based on this, we propose a problem-decomposition approach to nonconvex optimization. Similarly to DPLL-style SAT solvers and recursive conditioning in probabilistic inference, our algorithm, RDIS, recursively sets variables so as to simplify and decompose the objective function into approximately independent sub-functions, until the remaining functions are simple enough to be optimized by standard techniques like gradient descent. The variables to set are chosen by graph partitioning, ensuring decomposition whenever possible. We show analytically that RDIS can solve a broad class of nonconvex optimization problems exponentially faster than gradient descent with random restarts. Experimentally, RDIS outperforms standard techniques on problems like structure from motion and protein folding.