bluuebunny/crossref_metadata_2025 huggingface dataset:
Adaptive Manifold Regularized Matrix Factorization for Data Clustering
Authors: Zhang Lefei , Zhang Qian , Du Bo , You Jane , Tao Dacheng
Published: July 2017

Data clustering is the task to group the data samples into certain clusters based on the relationships of samples and structures hidden in data, and it is a fundamental and important topic in data mining and machine learning areas. In the literature, the spectral clustering is one of the most popular approaches and has many variants in recent years. However, the performance of spectral clustering is determined by the affinity matrix, which is always computed by a predefined model (e.g., Gaussian kernel function) with carefully tuned parameters combination, and may far from optimal in practice. In this paper, we propose to consider the observed data clustering as a robust matrix factorization point of view, and learn an affinity matrix simultaneously to regularize the proposed matrix factorization. The solution of the proposed adaptive manifold regularized matrix factorization (AMRMF) is reached by a novel Augmented Lagrangian Multiplier (ALM) based algorithm. The experimental results on standard clustering datasets demonstrate the superior performance over the exist alternatives.
Learning Discriminative Recommendation Systems with Side Information
Authors: Zhao Feipeng , Guo Yuhong
Published: July 2017

Top-N recommendation systems are useful in many real world applications such as E-commerce platforms. Most previous methods produce top-N recommendations based on the observed user purchase or recommendation activities. Recently, it has been noticed that side information that describes the items can be produced from auxiliary sources and help to improve the performance of top-N recommendation systems; e.g., side information of the items can be collected from the item reviews. In this paper, we propose a joint discriminative prediction model that exploits both the partially observed user-item recommendation matrix and the item-based side information to build top-N recommendation systems. This joint model aggregates observed user-item recommendation activities to produce the missing user-item recommendation scores while simultaneously training a linear regression model to predict the user-item recommendation scores from auxiliary item features. We evaluate the proposed approach on a number of recommendation datasets. The experimental results show that the proposed joint model is very effective for producing top-N recommendation systems.
TUCH: Turning Cross-view Hashing into Single-view Hashing via Generative Adversarial Nets
Authors: Zhao Xin , Ding Guiguang , Guo Yuchen , Han Jungong , Gao Yue
Published: July 2017

Cross-view retrieval, which focuses on searching images as response to text queries or vice versa, has received increasing attention recently. Cross-view hashing is to efficiently solve the cross-view retrieval problem with binary hash codes. Most existing works on cross-view hashing exploit multi-view embedding method to tackle this problem, which inevitably causes the information loss in both image and text domains. Inspired by the Generative Adversarial Nets (GANs), this paper presents a new model that is able to Turn Cross-view Hashing into single-view hashing (TUCH), thus enabling the information of image to be preserved as much as possible. TUCH is a novel deep architecture that integrates a language model network T for text feature extraction, a generator network G to generate fake images from text feature and a hashing network H for learning hashing functions to generate compact binary codes. Our architecture effectively unifies joint generative adversarial learning and cross-view hashing. Extensive empirical evidence shows that our TUCH approach achieves state-of-the-art results, especially on text to image retrieval, based on image-sentences datasets, i.e. standard IAPRTC-12 and large-scale Microsoft COCO.
Link Prediction via Ranking Metric Dual-Level Attention Network Learning
Authors: Zhao Zhou , Gao Ben , Zheng Vincent W. , Cai Deng , He Xiaofei , Zhuang Yueting
Published: July 2017

Link prediction is a challenging problem for complex network analysis, arising in many disciplines such as social networks and telecommunication networks. Currently, many existing approaches estimate the proximity of the link endpoints for link prediction from their feature or the local neighborhood around them, which suffer from the localized view of network connections and insufficiency of discriminative feature representation. In this paper, we consider the problem of link prediction from the viewpoint of learning discriminative path-based proximity ranking metric embedding. We propose a novel ranking metric network learning framework by jointly exploiting both node-level and path-level attentional proximity of the endpoints for link prediction. We then develop the path-based dual-level reasoning attentional learning method with recurrent neural network for proximity ranking metric embedding. The extensive experiments on two large-scale datasets show that our method achieves better performance than other state-of-the-art solutions to the problem.
Binary Linear Compression for Multi-label Classification
Authors: Zhou Wen-Ji , Yu Yang , Zhang Min-Ling
Published: July 2017

In multi-label classification tasks, labels are commonly related with each other. It has been well recognized that utilizing label relationship is essential to multi-label learning. One way to utilizing label relationship is to map labels to a lower-dimensional space of uncorrelated labels, where the relationship could be encoded in the mapping. Previous linear mapping methods commonly result in regression subproblems in the lower-dimensional label space. In this paper, we disclose that mappings to a low-dimensional multi-label regression problem can be worse than mapping to a classification problem, since regression requires more complex model than classification. We then propose the binary linear compression (BILC) method that results in a binary

label space, leading to classification subproblems. Experiments on several multi-label datasets show that, employing classification in the embedded space results in much simpler models than regression, leading to smaller structure risk. The proposed methods are also shown to be superior to some state-of-the-art approaches.
Dependency Exploitation: A Unified CNN-RNN Approach for Visual Emotion Recognition
Authors: Zhu Xinge , Li Liang , Zhang Weigang , Rao Tianrong , Xu Min , Huang Qingming , Xu Dong
Published: July 2017

Visual emotion recognition aims to associate images with appropriate emotions. There are different visual stimuli that can affect human emotion from low-level to high-level, such as color, texture, part, object, etc. However, most existing methods treat different levels of features as independent entity without having effective method for feature fusion. In this paper, we propose a unified CNN-RNN model to predict the emotion based on the fused features from different levels by exploiting the dependency among them. Our proposed architecture leverages convolutional neural network (CNN) with multiple layers to extract different levels of features with in a multi-task learning framework, in which two related loss functions are introduced to learn the feature representation. Considering the dependencies within the low-level and high-level features, a new bidirectional recurrent neural network (RNN) is proposed to integrate the learned features from different layers in the CNN model. Extensive experiments on both Internet images and art photo datasets demonstrate that our method outperforms the state-of-the-art methods with at least 7% performance improvement.
Core Stability in Hedonic Games among Friends and Enemies: Impact of Neutrals
Authors: Ohta Kazunori , Barrot Nathanaël , Ismaili Anisse , Sakurai Yuko , Yokoo Makoto
Published: July 2017

We investigate hedonic games under enemies aversion and friends appreciation, where every agent considers other agents as either a friend or an enemy. We extend these simple preferences by allowing each agent to also consider other agents to be neutral. Neutrals have no impact on her preference, as in a graphical hedonic game.Surprisingly, we discover that neutral agents do not simplify matters, but cause complexity. We prove that the core can be empty under enemies aversion and the strict core can be empty under friends appreciation. Furthermore, we show that under both preferences, deciding whether the strict core is non-empty, is NP^NP-complete. This complexity extends to the core under enemies aversion. We also show that under friends appreciation, we can always find a core stable coalition structure in polynomial time.
Modeling Physicians' Utterances to Explore Diagnostic Decision-making
Authors: Guo Xuan , Li Rui , Yu Qi , Haake Anne
Published: July 2017

Diagnostic error prevention is a long-established but specialized topic in clinical and psychological research. In this paper, we contribute to the field by exploring diagnostic decision-making via modeling physicians' utterances of medical concepts during image-based diagnoses. We conduct experiments to collect verbal narratives from dermatologists while they are examining and describing dermatology images towards diagnoses. We propose a hierarchical probabilistic framework to learn domain-specific patterns from the medical concepts in these narratives. The discovered patterns match the diagnostic units of thought identified by domain experts. These meaningful patterns uncover physicians' diagnostic decision-making processes while parsing the image content. Our evaluation shows that these patterns provide key information to classify narratives by diagnostic correctness levels.
Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach
Authors: Poddar Lahari , Hsu Wynne , Lee Mong Li
Published: July 2017

User opinions expressed in the form of ratings can influence an individual's view of an item.

However, the true quality of an item is often obfuscated by user biases, and it is not obvious from the observed ratings

the importance different users place on different aspects of an item.

We propose a probabilistic modeling of the observed aspect ratings to infer (i) each user's aspect bias and (ii) latent intrinsic quality of an item. We model multi-aspect ratings as ordered discrete data and encode the dependency between different aspects by using a latent Gaussian structure. We handle the Gaussian-Categorical non-conjugacy using a stick-breaking formulation coupled with P\'{o}lya-Gamma auxiliary variable augmentation for a simple, fully Bayesian inference. On two real world datasets, we demonstrate the predictive ability of our model and its effectiveness in learning explainable user biases to provide insights towards a more reliable product quality estimation.
Cognitive-Inspired Conversational-Strategy Reasoner for Socially-Aware Agents
Authors: Romero Oscar J. , Zhao Ran , Cassell Justine
Published: July 2017

In this work we propose a novel module for a dialogue system that allows a conversational agent to utter phrases that do not just meet the system's task intentions, but also work towards achieving the system's social intentions. The module - a Social Reasoner - takes the task goals the system must achieve and decides the appropriate conversational style and strategy with which the dialogue system describes the information the user desires so as to boost the strength of the relationship between the user and system (rapport), and therefore the user's engagement and willingness to divulge the information the agent needs to efficiently and effectively achieve the user's goals. Our Social Reasoner is inspired both by analysis of empirical data of friends and stranger dyads engaged in a task, and by prior literature in fields as diverse as reasoning processes in cognitive and social psychology, decision-making, sociolinguistics and conversational analysis. Our experiments demonstrated that, when using the Social Reasoner in a Dialogue System, the rapport level between the user and system increases in more than 35% in comparison with those cases where no Social Reasoner is used.
Leveraging Human Knowledge in Tabular Reinforcement Learning: A Study of Human Subjects
Authors: Rosenfeld Ariel , Taylor Matthew E. , Kraus Sarit
Published: July 2017

Reinforcement Learning (RL) can be extremely effective in solving complex, real-world problems. However, injecting human knowledge into an RL agent may require extensive effort on the human designer's part. To date, human factors are generally not considered in the development and evaluation of possible approaches. In this paper, we propose and evaluate a novel method, based on human psychology literature, which we show to be both effective and efficient, for both expert and non-expert designers, in injecting human knowledge for speeding up tabular RL.
Depression Detection via Harvesting Social Media: A Multimodal Dictionary Learning Solution
Authors: Shen Guangyao , Jia Jia , Nie Liqiang , Feng Fuli , Zhang Cunjun , Hu Tianrui , Chua Tat-Seng , Zhu Wenwu
Published: July 2017

Depression is a major contributor to the overall global burden of diseases. Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases. Inspired by these, our work aims to make timely depression detection via harvesting social media data. We construct well-labeled depression and non-depression dataset on Twitter, and extract six depression-related feature groups covering not only the clinical depression criteria, but also online behaviors on social media. With these feature groups, we propose a multimodal depressive dictionary learning model to detect the depressed users on Twitter. A series of experiments are conducted to validate this model, which outperforms (+3% to +10%) several baselines. Finally, we analyze a large-scale dataset on Twitter to reveal the underlying online behaviors between depressed and non-depressed users.
Don't Bury your Head in Warnings: A Game-Theoretic Approach for Intelligent Allocation of Cyber-security Alerts
Authors: Schlenker Aaron , Xu Haifeng , Guirguis Mina , Kiekintveld Christopher , Sinha Arunesh , Tambe Milind , Sonya Solomon , Balderas Darryl , Dunstatter Noah
Published: July 2017

In recent years, there have been a number of successful cyber attacks on enterprise networks by malicious actors which have caused severe damage. These networks have Intrusion Detection and Prevention Systems in place to protect them, but they are notorious for producing a high volume of alerts. These alerts must be investigated by cyber analysts to determine whether they are an attack or benign. Unfortunately, there are magnitude more alerts generated than there are cyber analysts to investigate them. This trend is expected to continue into the future creating a need for tools which find optimal assignments of the incoming alerts to analysts in the presence of a strategic adversary. We address this challenge with the four following contributions: (1) a cyber screening game (CSG) model for the cyber network protection domain, (2) an NP-hardness proof for computing the optimal strategy for the defender, (3) an algorithm that finds the optimal allocation of experts to alerts in the CSG, and (4) heuristic improvements for computing allocations in CSGs that accomplishes significant scale-up which we show empirically to closely match the solution quality of the optimal algorithm.
Online Reputation Fraud Campaign Detection in User Ratings
Authors: Xu Chang , Zhang Jie , Sun Zhu
Published: July 2017

Reputation fraud campaigns (RFCs) distort the reputations of rated items, by generating fake ratings through multiple spammers. One effective way of detecting RFCs is to characterize their collective behaviors based on rating histories.However, these campaigns are constantly evolving and changing tactics to evade detection.For example, they can launch early attacks on the items to quickly dominate the reputations.They can also whitewash themselves through creating new accounts for subsequent attacks.It is thus challenging for existing approaches working on historical data to promptly react to such emerging fraud activities.In this paper, we conduct RFC detection in online fashion, so as to spot campaign activities as early as possible.This leads to a unified and scalable optimization framework, FraudScan, that can adapt to emerging fraud patterns over time.Empirical analysis on two real-world datasets validates the effectiveness and efficiency of the proposed framework.
No Time to Observe: Adaptive Influence Maximization with Partial Feedback
Authors: Yuan Jing , Tang Shaojie
Published: July 2017

Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: full-feedback model or zero-feedback model. In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy. In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed. Full-feedback model has better performance but potentially huge delay, zero-feedback model has zero delay but poorer performance since it does not utilize the observation that may be made during the seeding process. To fill the gap between these two models, we propose partial-feedback model, which allows us to select a seed at any intermediate stage. We develop a novel alpha-greedy policy that achieves a bounded approximation ratio.
Socialized Word Embeddings
Authors: Zeng Ziqian , Yin Yichun , Song Yangqiu , Zhang Ming
Published: July 2017

Word embeddings have attracted a lot of attention. On social media, each user’s language use can be significantly affected by the user’s friends. In this paper, we propose a socialized word embedding algorithm which can consider both user’s personal characteristics of language use and the user’s social relationship on social media. To incorporate personal characteristics, we propose to use a user vector to represent each user. Then for each user, the word embeddings are trained based on each user’s corpus by combining the global word vectors and local user vector. To incorporate social relationship, we add a regularization term to impose similarity between two friends. In this way, we can train the global word vectors and user vectors jointly. To demonstrate the effectiveness, we used the latest large-scale Yelp data to train our vectors, and designed several experiments to show how user vectors affect the results.
A Causal Framework for Discovering and Removing Direct and Indirect Discrimination
Authors: Zhang Lu , Wu Yongkai , Wu Xintao
Published: July 2017

In this paper, we investigate the problem of discovering both direct and indirect discrimination from the historical data, and removing the discriminatory effects before the data is used for predictive analysis (e.g., building classifiers). The main drawback of existing methods is that they cannot distinguish the part of influence that is really caused by discrimination from all correlated influences. In our approach, we make use of the causal network to capture the causal structure of the data. Then we model direct and indirect discrimination as the path-specific effects, which accurately identify the two types of discrimination as the causal effects transmitted along different paths in the network. Based on that, we propose an effective algorithm for discovering direct and indirect discrimination, as well as an algorithm for precisely removing both types of discrimination while retaining good data utility. Experiments using the real dataset show the effectiveness of our approaches.
Posted Pricing sans Discrimination
Authors: Sekar Shreyas
Published: July 2017

In the quest for market mechanisms that are easy to implement, yet close to optimal, few seem as viable as posted pricing. Despite the growing body of impressive results, the performance of most posted price mechanisms however, rely crucially on "price discrimination" when multiple copies of a good are available. For the more general case with non-linear production costs on each good, hardly anything is known for general multi-good markets. With this in mind, we study the problem of social welfare maximization in a Bayesian setting where the seller can produce any number of copies of a good but faces convex production costs for the same. Our central contribution is a structured framework for decision making and static item pricing in the face of uncertainty and production costs, i.e., the seller decides how much to produce and posts a single price per good that is common to all buyers, the buyers arrive sequentially and purchase utility maximizing bundles of goods. The framework yields constant factor approximations to the optimum welfare when buyer valuations are fractionally subadditive, extends to more general valuations and also settings where the seller is completely oblivious to buyer valuations. Our work presents the first known results for non-discriminatory pricing in environments with non-linear costs where we only have access to stochastic information regarding buyer preferences. At a high level, our results imply that it is often possible to obtain good guarantees without discriminating against buyers, i.e., charging them differently for the same good.
A Feature-Enriched Neural Model for Joint Chinese Word Segmentation and Part-of-Speech Tagging
Authors: Chen Xinchi , Qiu Xipeng , Huang Xuanjing
Published: July 2017

Recently, neural network models for natural language processing tasks have been increasingly focused on for their ability of alleviating the burden of manual feature engineering. However, the previous neural models cannot extract the complicated feature compositions as the traditional methods with discrete features. In this work, we propose a feature-enriched neural model for joint Chinese word segmentation and part-of-speech tagging task. Specifically, to simulate the feature templates of traditional discrete feature based models, we use different filters to model the complex compositional features with convolutional and pooling layer, and then utilize long distance dependency information with recurrent layer. Experimental results on five different datasets show the effectiveness of our proposed model.
Stance Classification with Target-specific Neural Attention
Authors: Du Jiachen , Xu Ruifeng , He Yulan , Gui Lin
Published: July 2017

Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-the-art performance.
SWIM: A Simple Word Interaction Model for Implicit Discourse Relation Recognition
Authors: Lei Wenqiang , Wang Xuancong , Liu Meichun , Ilievski Ilija , He Xiangnan , Kan Min-Yen
Published: July 2017

Capturing the semantic interaction of pairs of words across arguments and proper argument representation are both crucial issues in implicit discourse relation recognition. The current state-of-the-art represents arguments as distributional vectors that are computed via bi-directional Long Short-Term Memory networks (BiLSTMs), known to have significant model complexity.In contrast, we demonstrate that word-weighted averaging can encode argument representation which can incorporate word pair information efficiently. By saving an order of magnitude in parameters, our proposed model achieves equivalent performance, but trains seven times faster.
MAT: A Multimodal Attentive Translator for Image Captioning
Authors: Liu Chang , Sun Fuchun , Wang Changhu , Wang Feng , Yuille Alan
Published: July 2017

In this work we formulate the problem of image captioning as a multimodal translation task. Analogous to machine translation, we present a sequence-to-sequence recurrent neural networks (RNN) model for image caption generation. Different from most existing work where the whole image is represented by convolutional neural network (CNN) feature, we propose to represent the input image as a sequence of detected objects which feeds as the source sequence of the RNN model. In this way, the sequential representation of an image can be naturally translated to a sequence of words, as the target sequence of the RNN model. To represent the image in a sequential way, we extract the objects features in the image and arrange them in a order using convolutional neural networks. To further leverage the visual information from the encoded objects, a sequential attention layer is introduced to selectively attend to the objects that are related to generate corresponding words in the sentences. Extensive experiments are conducted to validate the proposed approach on popular benchmark dataset, i.e., MS COCO, and the proposed model surpasses the state-of-the-art methods in all metrics following the dataset splits of previous work. The proposed approach is also evaluated by the evaluation server of MS COCO captioning challenge, and achieves very competitive results, e.g., a CIDEr of 1.029 (c5) and 1.064 (c40).
A Structural Representation Learning for Multi-relational Networks
Authors: Liu Lin , Li Xin , Cheung William K. , Xu Chengcheng
Published: July 2017

Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.
Synchronisation Games on Hypergraphs
Authors: Simon Sunil , Wojtczak Dominik
Published: July 2017

We study a strategic game model on hypergraphs where players, modelled by nodes, try to coordinate or anti-coordinate their choices within certain groups of players, modelled by hyperedges. We show this model to be a strict generalisation of symmetric additively separable hedonic games to the hypergraph setting and that such games always have a pure Nash equilibrium, which can be computed in pseudo-polynomial time. Moreover, in the pure coordination setting, we show that a strong equilibrium exists and can be computed in polynomial time when the game possesses a certain acyclic structure.
Automatic Assessment of Absolute Sentence Complexity
Authors: Stajner Sanja , Ponzetto Simone Paolo , Stuckenschmidt Heiner
Published: July 2017

Lexically and syntactically simpler sentences result in shorter reading time and better understanding in many people. However, no reliable systems for automatic assessment of absolute sentence complexity have been proposed so far. Instead, the assessment is usually done manually, requiring expert human annotators. To address this problem, we first define the sentence complexity assessment as a five-level classification task, and build a ‘gold standard’ dataset. Next, we propose robust systems for sentence complexity assessment, using a novel set of features based on leveraging lexical properties of freely available corpora, and investigate the impact of the feature type and corpus size on the classification performance.
Finding Prototypes of Answers for Improving Answer Sentence Selection
Authors: Tam Wai Lok , Han Namgi , Navarro-Horñiacek Juan Ignacio , Miyao Yusuke
Published: July 2017

Answer sentence selection has been widely adopted recently for benchmarking techniques in Question Answering. Previous proposals for the task are essentially general solutions taking the form of neural networks that measure semantic similarity. In contrast, the present paper describes a simple technique to take advantage of such general-purpose tools for dealing with questions and answer sentences without changing the base system. The technique involves replacing wh-words in input questions with a word denoting the prototype of all answers. These transformed questions are passed as input to an existing neural network built for measuring semantic similarity. This technique is evaluated on two different neural network architectures over two datasets: TrecQA and WikiQA. Results of our experiments show improvement in overall accuracy across most question types we are interested in: `who', `when' and `where'-type questions.
From Neural Sentence Summarization to Headline Generation: A Coarse-to-Fine Approach
Authors: Tan Jiwei , Wan Xiaojun , Xiao Jianguo
Published: July 2017

Headline generation is a task of abstractive text summarization, and previously suffers from the immaturity of natural language generation techniques. Recent success of neural sentence summarization models shows the capacity of generating informative, fluent headlines conditioned on selected recapitulative sentences. In this paper, we investigate the extension of sentence summarization models to the document headline generation task. The challenge is that extending the sentence summarization model to consider more document information will mostly confuse the model and hurt the performance. In this paper, we propose a coarse-to-fine approach, which first identifies the important sentences of a document using document summarization techniques, and then exploits a multi-sentence summarization model with hierarchical attention to leverage the important sentences for headline generation. Experimental results on a large real dataset demonstrate the proposed approach significantly improves the performance of neural sentence summarization models on the headline generation task.
Multi-Modal Word Synset Induction
Authors: Thomason Jesse , Mooney Raymond J.
Published: July 2017

A word in natural language can be polysemous, having multiple meanings, as well as synonymous, meaning the same thing as other words. Word sense induction attempts to find the senses of polysemous words. Synonymy detection attempts to find when two words are interchangeable. We combine these tasks, first inducing word senses and then detecting similar senses to form word-sense synonym sets (synsets) in an unsupervised fashion. Given pairs of images and text with noun phrase labels, we perform synset induction to produce collections of underlying concepts described by one or more noun phrases. We find that considering multi-modal features from both visual and textual context yields better induced synsets than using either context alone. Human evaluations show that our unsupervised, multi-modally induced synsets are comparable in quality to annotation-assisted ImageNet synsets, achieving about 84% of ImageNet synsets' approval.
Learning Sentence Representation with Guidance of Human Attention
Authors: Wang Shaonan , Zhang Jiajun , Zong Chengqing
Published: July 2017

Recently, much progress has been made in learning general-purpose sentence representations that can be used across domains. However, most of the existing models typically treat each word in a sentence equally. In contrast, extensive studies have proven that human read sentences efficiently by making a sequence of fixation and saccades. This motivates us to improve sentence representations by assigning different weights to the vectors of the component words, which can be treated as an attention mechanism on single sentences. To that end, we propose two novel attention models, in which the attention weights are derived using significant predictors of human reading time, i.e., Surprisal, POS tags and CCG supertags. The extensive experiments demonstrate that the proposed methods significantly improve upon the state-of-the-art sentence representation models.
Learning to Identify Ambiguous and Misleading News Headlines
Authors: Wei Wei , Wan Xiaojun
Published: July 2017

Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines ``clickbaits'' and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.
Symbolic Priors for RNN-based Semantic Parsing
Authors: Xiao Chunyang , Dymetman Marc , Gardent Claire
Published: July 2017

Seq2seq models based on Recurrent Neural Networks

(RNNs) have recently received a lot of attention

in the domain of Semantic Parsing. While

in principle they can be trained directly on pairs

(natural language utterances, logical forms), their

performance is limited by the amount of available

data. To alleviate this problem, we propose to

exploit various sources of prior knowledge: the

well-formedness of the logical forms is modeled

by a weighted context-free grammar; the likelihood

that certain entities present in the input utterance

are also present in the logical form is modeled by

weighted finite-state automata. The grammar and

automata are combined together through an efficient

intersection algorithm to form a soft guide

(“background”) to the RNN.We test our method on

an extension of the Overnight dataset and show that

it not only strongly improves over an RNN baseline,

but also outperforms non-RNN models based

on rich sets of hand-crafted features.
Fast Parallel Training of Neural Language Models
Authors: Xiao Tong , Zhu Jingbo , Liu Tongran , Zhang Chunliang
Published: July 2017

Training neural language models (NLMs) is very time consuming and we need parallelization for system speedup. However, standard training methods have poor scalability across multiple devices (e.g., GPUs) due to the huge time cost required to transmit data for gradient sharing in the back-propagation process. In this paper we present a sampling-based approach to reducing data transmission for better scaling of NLMs. As a ''bonus'', the resulting model also improves the training speed on a single device. Our approach yields significant speed improvements on a recurrent neural network-based language model. On four NVIDIA GTX1080 GPUs, it achieves a speedup of 2.1+ times over the standard asynchronous stochastic gradient descent baseline, yet with no increase in perplexity. This is even 4.2 times faster than the naive single GPU counterpart.
Attachment Centrality for Weighted Graphs
Authors: Sosnowska Jadwiga , Skibski Oskar
Published: July 2017

Measuring how central nodes are in terms of connecting a network has recently received increasing attention in the literature. While a few dedicated centrality measures have been proposed, Skibski et al. [2016] showed that the Attachment Centrality is the only one that satisfies certain natural axioms desirable for connectivity. Unfortunately, the Attachment Centrality is defined only for unweighted graphs which makes this measure ill-fitted for various applications. For instance, covert networks are typically weighted, where the weights carry additional intelligence available about criminals or terrorists and the links between them. To analyse such settings, in this paper we extend the Attachment Centrality to node-weighted and edge-weighted graphs. By an axiomatic analysis, we show that the Attachment Centrality is closely related to the Degree Centrality in weighted graphs.
Efficient Optimal Search under Expensive Edge Cost Computation
Authors: Asai Masataro , Kishimoto Akihiro , Botea Adi , Marinescu Radu , M. Daly Elizabeth , Kotoulas Spyros
Published: July 2017

Optimal heuristic search has been successful in many domains, including journey planning, route planning and puzzle solving. Existing work typically assumes that the cost of each action can easily be obtained. However, in many problems, the exact edge cost is expensive to compute. Existing search algorithms face a significant performance bottleneck, due to an excessive overhead associated with dynamically calculating exact edge costs.

We present DEA*, an algorithm for problems with expensive edge cost computations. DEA* combines heuristic edge cost evaluations with delayed node expansions, reducing the number of exact edge computations. We formally prove that DEA* is optimal and it is efficient with respect to the number of exact edge cost computations.

We empirically evaluate DEA* on multiple-worker routing problems where the exact edge cost is calculated by invoking an external multi-modal journey planning engine. The results demonstrate the effectiveness of our ideas in reducing the computational time and improving the solving ability. In addition, we show the advantages of DEA* in domain-independent planning, where we simulate that accurate edge costs are expensive to compute.
Faster Conflict Generation for Dynamic Controllability
Authors: Bhargava Nikhil , Vaquero Tiago , Williams Brian
Published: July 2017

In this paper, we focus on speeding up the temporal plan relaxation problem for dynamically controllable systems. We take a look at the current best-known algorithm for determining dynamic controllability and augment it to efficiently generate conflicts when the network is deemed uncontrollable. Our work preserves the O(n^3) runtime of the best available dynamic controllability checker and improves on the previous best runtime of O(n^4) for extracting dynamic controllability conflicts. We then turn our attention to temporal plan relaxation tasks and show how we can leverage our work on conflicts and the structure of the network to efficiently make incremental updates intended to restore dynamic controllability by relaxing constraints. Our new algorithm, RelaxIDC, has the same asymptotic runtime as previous algorithms but sees dramatic empirical improvements over the course of repeated dynamic controllability checks.
Online Optimization of Video-Ad Allocation
Authors: Sumita Hanna , Kawase Yasushi , Fujita Sumio , Fukunaga Takuro
Published: July 2017

In this paper, we study the video advertising in the context of internet advertising. Video advertising is a rapidly growing industry, but its computational aspects have not yet been investigated. A difference between video advertising and traditional display advertising is that the former requires more time to be viewed. In contrast to a traditional display advertisement, a video advertisement has no influence over a user unless the user watches it for a certain amount of time. Previous studies have not considered the length of video advertisements, and time spent by users to watch them. Motivated by this observation, we formulate a new online optimization problem for optimizing the allocation of video advertisements, and we develop a nearly (1 − 1/e)-competitive algorithm for finding an envy-free allocation of video advertisements.
Purely Declarative Action Descriptions are Overrated: Classical Planning with Simulators
Authors: Francès Guillem , Ramírez Miquel , Lipovetzky Nir , Geffner Hector
Published: July 2017

Classical planning is concerned with problems where a goal needs to be reached

from a known initial state by doing actions with deterministic, known effects.

Classical planners, however, deal only with classical problems that can be

expressed in declarative planning languages such as STRIPS or PDDL. This

prevents their use on problems that are not easy to model declaratively or

whose dynamics are given via simulations. Simulators do not provide a

declarative representation of actions, but simply return successor states.

The question we address in this paper is: can a planner that has access to

the structure of states and goals only, approach the performance of planners

that also have access to the structure of actions expressed in PDDL?

To answer this, we develop domain-independent, black box planning algorithms

that completely ignore action structure, and show that they match the

performance of state-of-the-art classical planners on the standard planning

benchmarks. Effective black box algorithms open up new possibilities for

modeling and for expressing control knowledge, which we also illustrate.
Factorized Asymptotic Bayesian Policy Search for POMDPs
Authors: Imaizumi Masaaki , Fujimaki Ryohei
Published: July 2017

This paper proposes a novel direct policy search (DPS) method with model selection for partially observed Markov decision processes (POMDPs). DPSs have been standard for learning POMDPs due to their computational efficiency and natural ability to maximize total rewards. An important open challenge for the best use of DPS methods is model selection, i.e., determination of the proper dimensionality of hidden states and complexity of policy functions, to mitigate overfitting in highly-flexible model representations of POMDPs. This paper bridges Bayesian inference and reward maximization and derives marginalized weighted log-likelihood~(MWL) for POMDPs which takes both advantages of Bayesian model selection and DPS. Then we propose factorized asymptotic Bayesian policy search (FABPS) to explore the model and the policy which maximizes MWL by expanding recently-developed factorized asymptotic Bayesian inference. Experimental results show that FABPS outperforms state-of-the-art model selection methods for POMDPs, with respect both to model selection and to expected total rewards.
Landmarks for Numeric Planning Problems
Authors: Scala Enrico , Haslum Patrik , Magazzeni Daniele , Thiébaux Sylvie
Published: July 2017

The paper generalises the notion of landmarks for reasoning about planning problems involving propositional and numeric variables. Intuitively, numeric landmarks are regions in the metric space defined by the problem whose crossing is necessary for its resolution. The paper proposes a relaxation-based method for their automated extraction directly from the problem structure, and shows how to exploit them to infer what we call disjunctive and additive hybrid action landmarks. The justification of such a disjunctive representation results from the intertwined propositional and numeric structure of the problem. The paper exercises their use in two novel admissible LP-Based numeric heuristics, and reports experiments on cost-optimal numeric planning problems. Results show the heuristics are more informed and effective than previous work for problems involving a higher number of (sub)goals.
Search and Learn: On Dead-End Detectors, the Traps they Set, and Trap Learning
Authors: Steinmetz Marcel , Hoffmann Jörg
Published: July 2017

A key technique for proving unsolvability in classical planning are dead-end detectors \Delta: effectively testable criteria sufficient for unsolvability, pruning (some) unsolvable states during search. Related to this, a recent proposal is the identification of traps prior to search, compact representations of non-goal state sets T that cannot be escaped. Here, we create new synergy across these ideas. We define a generalized concept of traps, relative to a given dead-end detector \Delta, where T can be escaped, but only into dead-end states detected by \Delta. We show how to learn compact representations of such T during search, extending the reach of \Delta. Our experiments show that this can be quite beneficial. It improves coverage for many unsolvable benchmark planning domains and dead-end detectors \Delta, in particular on resource-constrained domains where it outperforms the state of the art.
Mechanism Design for Strategic Project Scheduling
Authors: Varakantham Pradeep , Fu Na
Published: July 2017

Organizing large scale projects (e.g., Conferences, IT Shows, F1 race) requires precise scheduling of multiple dependent tasks on common resources where multiple selfish entities are competing to execute the individual tasks. In this paper, we consider a well studied and rich scheduling model referred to as RCPSP (Resource Constrained Project Scheduling Problem). The key change to this model that we consider in this paper is the presence of selfish entities competing to perform individual tasks with the aim of maximizing their own utility. Due to the selfish entities in play, the goal of the scheduling problem is no longer only to minimize makespan for the entire project, but rather, to maximize social welfare while ensuring incentive compatibility and economic efficiency. We show that traditional VCG mechanism is not incentive compatible in this context and hence we provide two new practical mechanisms that extend on VCG. These new mechanisms referred to as Individual Completion based Payments (ICP) and Social Completion based Payments (SCP) provide strong theoretical properties including strategy proofness.
Manipulating Gale-Shapley Algorithm: Preserving Stability and Remaining Inconspicuous
Authors: Vaish Rohit , Garg Dinesh
Published: July 2017

We study the problem of manipulation of the men-proposing Gale-Shapley algorithm by a single woman via permutation of her true preference list. Our contribution is threefold: First, we show that the matching induced by an optimal manipulation is stable with respect to the true preferences. Second, we identify a class of optimal manipulations called inconspicuous manipulations which, in addition to preserving stability, are also nearly identical to the true preference list of the manipulator (making the manipulation hard to be detected). Third, for optimal inconspicuous manipulations, we strengthen the stability result by showing that the entire stable lattice of the manipulated instance is contained inside the original lattice.​
Hierarchical Task Network Planning with Task Insertion and State Constraints
Authors: Xiao Zhanhao , Herzig Andreas , Perrussel Laurent , Wan Hai , Su Xiaoheng
Published: July 2017

We extend hierarchical task network planning with task insertion (TIHTN) by introducing state constraints, called TIHTNS. We show that just as for TIHTN planning, all solutions of the TIHTNS planning problem can be obtained by acyclic decomposition and task insertion, entailing that its plan-existence problem is decidable without any restriction on decomposition methods. We also prove that the extension by state constraints does not increase the complexity of the plan-existence problem, which stays 2-NEXPTIME-complete, based on an acyclic progression operator. In addition, we show that TIHTNS planning covers not only the original TIHTN planning but also hierarchy-relaxed hierarchical goal network planning.