In this paper, we adopt a multimodal emotion recognition framework by combining eye movements and electroencephalography (EEG) to enhance emotion recognition. The main contributions of this paper are twofold. a) We investigate sixteen eye movements related to emotions and identify the intrinsic patterns of these eye movements for three emotional states: positive, neutral and negative. b) We examine various modality fusion strategies for integrating users external subconscious behaviors and internal cognitive states and reveal that the characteristics of eye movements and EEG are complementary to emotion recognition. Experiment results demonstrate that modality fusion could significantly improve emotion recognition accuracy in comparison with single modality. The best accuracy achieved by fuzzy integral fusion strategy is 87.59%, whereas the accuracies of solely using eye movements and EEG data are 77.80% and 78.51%, respectively.

Computer-aided translation (CAT) system is the most popular tool which helps human translators perform language translation efficiently. To further improve the efficiency, there is an increasing interest in applying the machine translation (MT) technology to upgrade CAT. Post-editing is a standard approach: human translators generate the translation by correcting MT outputs. In this paper, we propose a novel approach deeply integrating MT into CAT systems: a well-designed input method which makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses and n-best translation lists. Our proposed approach allows human translators to focus on choosing better translation results with less time rather than just complete translation themselves. The extensive experiments demonstrate that our method saves more than 14% time and over 33% keystrokes, and it improves the translation quality as well by more than 3 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.

Given a class of students, and a pool of questions in the domain of study, what subset will constitute a good exam? Millions of educators are dealing with this difficult problem worldwide, yet exams are still composed manually in non-systematic ways. In this work we present a novel algorithmic framework for exam composition. Our framework requires two input components: a student population represented by a distribution over overlay models, each consisting of a set of mastered abilities, or actions; and a target model ordering that, given any two student models, defines which should be given the higher grade. To determine the performance of a student model on a potential question, we test whether it satisfies a disjunctive action landmark, i.e., whether its abilities are sufficient to follow at least one solution path. We present a novel utility function for evaluating exams, using the described components. An exam is highly evaluated if it is expected to order the student population with high correlation to the target order.The merit of our algorithmic framework is exemplified with real auto-generated questions in the domain of middle-school algebra.

Learning from crowds , where the labels of data instances are collected using a crowdsourcing way, has attracted much attention during the past few years. In contrast to a typical crowdsourcing setting where all data instances are assigned to annotators for labeling,  active learning from crowds actively selects a subset of data instances and assigns them to the annotators, thereby reducing the cost of labeling. This paper goes a step further. Rather than assume all annotators must provide labels, we allow the annotators to express that they are unsure about the assigned data instances. By adding the “unsure” option, the workloads for the annotators are somewhat reduced, because saying “unsure” will be easier than trying to provide a crisp label for some difficult data instances. Moreover, it is safer to use “unsure” feedback than to use labels from reluctant annotators because the latter has more chance to be misleading. Furthermore, different annotators may experience difficulty in different data instances, and thus the unsure option provides a valuable ingredient for modeling crowds’ expertise. We propose the ALCU-SVM algorithm for this new learning problem. Experimental studies on simulated and real crowdsourcing data show that, by exploiting the unsure option, ALCU-SVM achieves very promising performance.

We describe a novel convolutional neural network architecture with k-max pooling layer that is able to successfully recover the structure of Chinese sentences. This network can capture active features for unseen segments of a sentence to measure how likely the segments are merged to be the constituents. Given an input sentence, after all the scores of possible segments are computed, an efficient dynamic programming parsing algorithm is used to find the globally optimal parse tree. A similar network is then applied to predict syntactic categories for every node in the parse tree. Our networks archived competitive performance to existing benchmark parsers on the CTB-5 dataset without any task-specific feature engineering.

The recently proposed Gaussian process dynamical models (GPDMs) have been successfully applied to time series modeling. There are four learning algorithms for GPDMs: maximizing a posterior (MAP), fixing the kernel hyperparameters α _ (Fix.α _ ), balanced GPDM (B-GPDM) and two-stage MAP (T.MAP), which are designed for model training with complete data. When data are incomplete, GPDMs reconstruct the missing data using a function of the latent variables before parameter updates, which, however, may cause cumulative errors. In this paper, we present four new algorithms (MAP+, Fix.α + , B-GPDM+ and T.MAP+) for learning GPDMs with incomplete training data and a new conditional model (CM+) for recovering incomplete test data. Our methods adopt the Bayesian framework and can fully and properly use the partially observed data. We conduct experiments on incomplete motion capture data (walk, run, swing and multiple-walker) and make comparisons with the existing four algorithms as well as k-NN, spline interpolation and VGPDS. Our methods perform much better on both training with incomplete data and recovering incomplete test data.

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.

Crowdsourcing machine translation shows advantages of lower expense in money to collect the translated data. Yet, when compared with translation by trained professionals, results collected from non-professional translators might yield low-quality outputs. A general solution for crowdsourcing practitioners is to employ a large amount of labor force to gather enough redundant data and then solicit from it. Actually we can further save money by avoid collecting bad translations. We propose to score Turkers by their authorities during observation, and then stop hiring the unqualified Turkers. In this way, we bring both opportunities and risks in crowdsourced translation: we can make it cheaper than cheaper while we might suffer from quality loss. In this paper, we propose a graph-based PageRank-HITS Hybrid model to distinguish authoritative workers from unreliable ones. The algorithm captures the intuition that good translation and good workers are mutually reinforced iteratively in the proposed frame. We demonstrate the algorithm will keep the performance while reduce work force and hence cut cost. We run experiments on the NIST 2009 Urdu-to-English evaluation set with Mechanical Turk, and quantitatively evaluate the performance in terms of BLEU score, Pearson correlation and real money.

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.