bluuebunny/crossref_metadata_2025 huggingface dataset:
Improved Deep Embedded Clustering with Local Structure Preservation
Authors: Guo Xifeng , Gao Long , Liu Xinwang , Yin Jianping
Published: July 2017

Deep clustering learns deep feature representations that favor clustering task using neural networks. Some pioneering work proposes to simultaneously learn embedded features and perform clustering by explicitly defining a clustering oriented loss. Though promising performance has been demonstrated in various applications, we observe that a vital ingredient has been overlooked by these work that the defined clustering loss may corrupt feature space, which leads to non-representative meaningless features and this in turn hurts clustering performance. To address this issue, in this paper, we propose the Improved Deep Embedded Clustering (IDEC) algorithm to take care of data structure preservation. Specifically, we manipulate feature space to scatter data points using a clustering loss as guidance. To constrain the manipulation and maintain the local structure of data generating distribution, an under-complete autoencoder is applied. By integrating the clustering loss and autoencoder's reconstruction loss, IDEC can jointly optimize cluster labels assignment and learn features that are suitable for clustering with local structure preservation. The resultant optimization problem can be effectively solved by mini-batch stochastic gradient descent and backpropagation. Experiments on image and text datasets empirically validate the importance of local structure preservation and the effectiveness of our algorithm.
Instability Prediction in Power Systems using Recurrent Neural Networks
Authors: Gupta Ankita , Gurrala Gurunath , Sastry Pidaparthy S
Published: July 2017

Recurrent Neural Networks (RNNs) can model temporal dependencies in time series well. In this paper we present an interesting application of stacked Gated Recurrent Unit (GRU) based RNN for early prediction of imminent instability in a power system based on normal measurements of power system variables over time. In a power system, disturbances like a fault can result in transient instability which may lead to blackouts. Early pre-

diction of any such contingency can aid the operator to take timely preventive control actions. In recent times some machine learning techniques such as SVMs have been proposed to predict such instability. However, these approaches assume availability of accurate fault information like its occurrence and clearance instants which is impractical. In this paper we propose an Online Monitoring System (OMS), which is a GRU based RNN, that continuously keeps predicting the current status based on past measurements. Through extensive simulations using a standard 118-bus system, the effectiveness of the proposed system is demonstrated. We also show how we can use PCA and predictions from the RNN to identify the most critical generator that leads to transient instability.
Storage Fit Learning with Unlabeled Data
Authors: Hou Bo-Jian , Zhang Lijun , Zhou Zhi-Hua
Published: July 2017

By using abundant unlabeled data, semi-supervised learning approaches have been found very useful in various tasks. Existing approaches, however, neglect the fact that the storage available for the learning process is different under different situations, and thus, the learning approaches should be flexible subject to the storage budget limit. In this paper, we focus on graph-based semi-supervised learning and propose two storage fit learning approaches which can adjust their behaviors to different storage budgets. Specifically, we utilize techniques of low-rank matrix approximation to find a low-rank approximator of the similarity matrix so as to reduce the space complexity. The first approach is based on stochastic optimization, which is an iterative approach that converges to the optimal low-rank approximator globally. The second approach is based on Nystrom method, which can find a good low-rank approximator efficiently and is suitable for real-time applications. Experiments on classification tasks show that the proposed methods can fit dynamically different storage budgets and obtain good performances in different scenarios.
Multiwinner Rules on Paths From k-Borda to Chamberlin–Courant
Authors: Faliszewski Piotr , Skowron Piotr , Slinko Arkadii , Talmon Nimrod
Published: July 2017

The classical multiwinner rules are designed for particular purposes. For example, variants of k-Borda are used to find k best competitors in judging contests while the Chamberlin-Courant rule is

used to select a diverse set of k products. These rules represent two extremes of the multiwinner world. At times, however, one might need to find an appropriate trade-off between these two extremes. We explore continuous transitions from k-Borda to Chamberlin-Courant and study intermediate rules.
DeepStory: Video Story QA by Deep Embedded Memory Networks
Authors: Kim Kyung-Min , Heo Min-Oh , Choi Seong-Ho , Zhang Byoung-Tak
Published: July 2017

Question-answering (QA) on video contents is a significant challenge for achieving human-level intelligence as it involves both vision and language in real-world settings. Here we demonstrate the possibility of an AI agent performing video story QA by learning from a large amount of cartoon videos. We develop a video-story learning model, i.e. Deep Embedded Memory Networks (DEMN), to reconstruct stories from a joint scene-dialogue video stream using a latent embedding space of observed data. The video stories are stored in a long-term memory component. For a given question, an LSTM-based attention model uses the long-term memory to recall the best question-story-answer triplet by focusing on specific words containing key information. We trained the DEMN on a novel QA dataset of children’s cartoon video series, Pororo. The dataset contains 16,066 scene-dialogue pairs of 20.5-hour videos, 27,328 fine-grained sentences for scene description, and 8,913 story-related QA pairs. Our experimental results show that the DEMN outperforms other QA models. This is mainly due to 1) the reconstruction of video stories in a scene-dialogue combined form that utilize the latent embedding and 2) attention. DEMN also achieved state-of-the-art results on the MovieQA benchmark.
Improving the Generalization Performance of Multi-class SVM via Angular Regularization
Authors: Li Jianxin , Zhou Haoyi , Xie Pengtao , Zhang Yingchun
Published: July 2017

In multi-class support vector machine (MSVM) for classification, one core issue is to regularize the coefficient vectors to reduce overfitting. Various regularizers have been proposed such as L2, L1, and trace norm. In this paper, we introduce a new type of regularization approach -- angular regularization, that encourages the coefficient vectors to have larger angles such that class regions can be widen to flexibly accommodate unseen samples. We propose a novel angular regularizer based on the singular values of the coefficient matrix, where the uniformity of singular values reduces the correlation among different classes and drives the angles between coefficient vectors to increase. In generalization error analysis, we show that decreasing this regularizer effectively reduces generalization error bound. On various datasets, we demonstrate the efficacy of the regularizer in reducing overfitting.
Radar: Residual Analysis for Anomaly Detection in Attributed Networks
Authors: Li Jundong , Dani Harsh , Hu Xia , Liu Huan
Published: July 2017

Attributed networks are pervasive in different domains, ranging from social networks, gene regulatory networks to financial transaction networks. This kind of rich network representation presents challenges for anomaly detection due to the heterogeneity of two data representations. A vast majority of existing algorithms assume certain properties of anomalies are given a prior. Since various types of anomalies in real-world attributed networks co-exist, the assumption that priori knowledge regarding anomalies is available does not hold. In this paper, we investigate the problem of anomaly detection in attributed networks generally from a residual analysis perspective, which has been shown to be effective in traditional anomaly detection problems. However, it is a non-trivial task in attributed networks as interactions among instances complicate the residual modeling process. Methodologically, we propose a learning framework to characterize the residuals of attribute information and its coherence with network information for anomaly detection. By learning and analyzing the residuals, we detect anomalies whose behaviors are singularly different from the majority. Experiments on real datasets show the effectiveness and generality of the proposed framework.
Deep Neural Networks for High Dimension, Low Sample Size Data
Authors: Liu Bo , Wei Ying , Zhang Yu , Yang Qiang
Published: July 2017

Deep neural networks (DNN) have achieved breakthroughs in applications with large sample size. However, when facing high dimension, low sample size (HDLSS) data, such as the phenotype prediction problem using genetic data in bioinformatics, DNN suffers from overfitting and high-variance gradients. In this paper, we propose a DNN model tailored for the HDLSS data, named Deep Neural Pursuit (DNP). DNP selects a subset of high dimensional features for the alleviation of overfitting and takes the average over multiple dropouts to calculate gradients with low variance. As the first DNN method applied on the HDLSS data, DNP enjoys the advantages of the high nonlinearity, the robustness to high dimensionality, the capability of learning from a small number of samples, the stability in feature selection, and the end-to-end training. We demonstrate these advantages of DNP via empirical results on both synthetic and real-world biological datasets.
DeepFacade: A Deep Learning Approach to Facade Parsing
Authors: Liu Hantang , Zhang Jialiang , Zhu Jianke , Hoi Steven C. H.
Published: July 2017

The parsing of building facades is a key component to the problem of 3D street

scenes reconstruction, which is long desired in computer vision. In this

paper, we propose a deep learning based method for segmenting a facade into

semantic categories. Man-made structures often present the characteristic of

symmetry. Based on this observation, we propose a symmetric regularizer for

training the neural network. Our proposed method can make use of both the

power of deep neural networks and the structure of man-made architectures. We

also propose a method to refine the segmentation results using bounding boxes

generated by the Region Proposal Network. We test our method by training a

FCN-8s network with the novel loss function. Experimental results show that

our method has outperformed previous state-of-the-art methods significantly on

both the ECP dataset and the eTRIMS dataset. As far as we know, we are the

first to employ end-to-end deep convolutional neural network on full image

scale in the task of building facades parsing.
WALKING WALKing walking: Action Recognition from Action Echoes
Authors: Ma Qianli , Shen Lifeng , Chen Enhuan , Tian Shuai , Wang Jiabing , Cottrell Garrison W.
Published: July 2017

Recognizing human actions represented by 3D trajectories of skeleton joints is a challenging machine learning task. In this paper, the 3D skeleton sequences are regarded as multivariate time series, and their dynamics and multiscale features are efficiently learned from action echo states. Specifically, first the skeleton data from the limbs and trunk are projected into five high dimensional nonlinear spaces, that are randomly generated by five dynamic, training-free recurrent networks, i.e., the reservoirs of echo state networks (ESNs). In this way, the history of the time series is represented as nonlinear echo states of actions. We then use a single multiscale convolutional layer to extract multiscale features from the echo states, and maintain multiscale temporal invariance by a max-over-time pooling layer. We propose two multi-step fusion strategies to integrate the spatial information over the five parts of the human physical structure. Finally, we learn the label distribution using softmax. With one training-free recurrent layer and only layer of convolution, our Convolutional Echo State Network (ConvESN) is a very efficient end-to-end model, and achieves state-of-the-art performance on four skeleton benchmark data sets.
Learning Feature Engineering for Classification
Authors: Nargesian Fatemeh , Samulowitz Horst , Khurana Udayan , Khalil Elias B. , Turaga Deepak
Published: July 2017

Feature engineering is the task of improving predictive modelling performance on a dataset by transforming its feature space. Existing approaches to automate this process rely on either transformed feature space exploration through evaluation-guided search, or explicit expansion of datasets with all transformed features followed by feature selection. Such approaches incur high computational costs in runtime and/or memory. We present a novel technique, called Learning Feature Engineering (LFE), for automating feature engineering in classification tasks. LFE is based on learning the effectiveness of applying a transformation (e.g., arithmetic or aggregate operators) on numerical features, from past feature engineering experiences. Given a new dataset, LFE recommends a set of useful transformations to be applied on features without relying on model evaluation or explicit feature expansion and selection. Using a collection of datasets, we train a set of neural networks, which aim at predicting the transformation that impacts classification performance positively. Our empirical results show that LFE outperforms other feature engineering approaches for an overwhelming majority (89%) of the datasets from various sources while incurring a substantially lower computational cost.
CHARDA: Causal Hybrid Automata Recovery via Dynamic Analysis
Authors: Summerville Adam , Osborn Joseph , Mateas Michael
Published: July 2017

We propose and evaluate a new technique for learning hybrid automata automatically by observing the runtime behavior of a dynamical system.Working from a sequence of continuous state values and predicates about the environment, CHARDA recovers the distinct dynamic modes, learns a model for each mode from a given set of templates, and postulates \textit{causal} guard conditions which trigger transitions between modes.Our main contribution is the use of information-theoretic measures (1)~as a cost function for data segmentation and model selction to penalize over-fitting and (2)~to determine the likely causes of each transition.CHARDA is easily extended with different classes of model templates, fitting methods, or predicates.In our experiments on a complex videogame character, CHARDA successfully discovers a reasonable over-approximation of the character's true behaviors.Our results also compare favorably against recent work in automatically learning probabilistic timed automata in an aircraft domain: CHARDA exactly learns the modes of these simpler automata.
Bayesian Dynamic Mode Decomposition
Authors: Takeishi Naoya , Kawahara Yoshinobu , Tabei Yasuo , Yairi Takehisa
Published: July 2017

Dynamic mode decomposition (DMD) is a data-driven method for calculating a modal representation of a nonlinear dynamical system, and it has been utilized in various fields of science and engineering. In this paper, we propose Bayesian DMD, which provides a principled way to transfer the advantages of the Bayesian formulation into DMD. To this end, we first develop a probabilistic model corresponding to DMD, and then, provide the Gibbs sampler for the posterior inference in Bayesian DMD. Moreover, as a specific example, we discuss the case of using a sparsity-promoting prior for an automatic determination of the number of dynamic modes. We investigate the empirical performance of Bayesian DMD using synthetic and real-world datasets.
COBRA: A Fast and Simple Method for Active Clustering with Pairwise Constraints
Authors: Van Craenendonck Toon , Dumancic Sebastijan , Blockeel Hendrik
Published: July 2017

Clustering is inherently ill-posed: there often exist multiple valid clusterings of a single dataset, and without any additional information a clustering system has no way of knowing which clustering it should produce. This motivates the use of constraints in clustering, as they allow users to communicate their interests to the clustering system. Active constraint-based clustering algorithms select the most useful constraints to query, aiming to produce a good clustering using as few constraints as possible. We propose COBRA, an active method that first over-clusters the data by running K-means with a $K$ that is intended to be too large, and subsequently merges the resulting small clusters into larger ones based on pairwise constraints. In its merging step, COBRA is able to keep the number of pairwise queries low by maximally exploiting constraint transitivity and entailment. We experimentally show that COBRA outperforms the state of the art in terms of clustering quality and runtime, without requiring the number of clusters in advance.
Dual Inference for Machine Learning
Authors: Xia Yingce , Bian Jiang , Qin Tao , Yu Nenghai , Liu Tie-Yan
Published: July 2017

Recent years have witnessed the rapid development of machine learning in solving artificial intelligence (AI) tasks in many domains, including translation, speech, image, etc. Within these domains, AI tasks are usually not independent. As a specific type of relationship, structural duality does exist between many pairs of AI tasks, such as translation from one language to another vs. its opposite direction, speech recognition vs. speech synthetization, image classification vs. image generation, etc. The importance of such duality has been magnified by some recent studies, which revealed that it can boost the learning of two tasks in the dual form. However, there has been little investigation on how to leverage this invaluable relationship into the inference stage of AI tasks. In this paper, we propose a general framework of dual inference which can take advantage of both existing models from two dual tasks, without re-training, to conduct inference for one individual task. Empirical studies on three pairs of specific dual tasks, including machine translation, sentiment analysis, and image processing have illustrated that dual inference can significantly improve the performance of each of individual tasks.
Dynamic Multi-View Hashing for Online Image Retrieval
Authors: Xie Liang , Shen Jialie , Han Jungong , Zhu Lei , Shao Ling
Published: July 2017

Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.
Image-embodied Knowledge Representation Learning
Authors: Xie Ruobing , Liu Zhiyuan , Luan Huanbo , Sun Maosong
Published: July 2017

Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.
Multi-Positive and Unlabeled Learning
Authors: Xu Yixing , Xu Chang , Xu Chao , Tao Dacheng
Published: July 2017

The positive and unlabeled (PU) learning problem focuses on learning a classifier from positive and unlabeled data. Some methods have been developed to solve the PU learning problem. However, they are often limited in practical applications, since only binary classes are involved and cannot easily be adapted to multi-class data. Here we propose a one-step method that directly enables multi-class model to be trained using the given input multi-class data and that predicts the label based on the model decision. Specifically, we construct different convex loss functions for labeled and unlabeled data to learn a discriminant function F. The theoretical analysis on the generalization error bound shows that it is no worse than k√k times of the fully supervised multi-class classification methods when the size of the data in k classes is of the same order. Finally, our experimental results demonstrate the significance and effectiveness of the proposed algorithm in synthetic and real-world datasets.
Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network
Authors: Yang Jufeng , She Dongyu , Sun Ming
Published: July 2017

Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in convolutional neural networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction. While the proposed method prefers to the distribution dataset with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i.e., Emotion6, Flickr_LDL, Twitter_LDL, and the largest single emotion dataset, i.e., Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.
Learning to Read Irregular Text with Attention Mechanisms
Authors: Yang Xiao , He Dafang , Zhou Zihan , Kifer Daniel , Giles C. Lee
Published: July 2017

We present a robust end-to-end neural-based model to attentively recognize text in natural images. Particularly, we focus on accurately identifying irregular (perspectively distorted or curved) text, which has not been well addressed in the previous literature. Previous research on text reading often works with regular (horizontal and frontal) text and does not adequately generalize to processing text with perspective distortion or curving effects. Our work proposes to overcome this difficulty by introducing two learning components: (1) an auxiliary dense character detection task that helps to learn text specific visual patterns, (2) an alignment loss that provides guidance to the training of an attention model. We show with experiments that these two components are crucial for achieving fast convergence and high classification accuracy for irregular text recognition. Our model outperforms previous work on two irregular-text datasets: SVT-Perspective and CUTE80, and is also highly-competitive on several regular-text datasets containing primarily horizontal and frontal text.
Link Prediction with Spatial and Temporal Consistency in Dynamic Networks
Authors: Yu Wenchao , Cheng Wei , Aggarwal Charu C , Chen Haifeng , Wang Wei
Published: July 2017

Dynamic networks are ubiquitous. Link prediction in dynamic networks has attracted tremendous research interests. Many models have been developed to predict links that may emerge in the immediate future from the past evolution of the networks. There are two key factors: 1) a node is more likely to form a link in the near future with another node within its close proximity, rather than with a random node; 2) a dynamic network usually evolves smoothly. Existing approaches seldom unify these two factors to strive for the spatial and temporal consistency in a dynamic network. To address this limitation, in this paper, we propose a link prediction model with spatial and temporal consistency (LIST), to predict links in a sequence of networks over time. LIST characterizes the network dynamics as a function of time, which integrates the spatial topology of network at each timestamp and the temporal network evolution. Comparing to existing approaches, LIST has two advantages: 1) LIST uses a generic model to express the network structure as a function of time, which makes it also suitable for a wide variety of temporal network analysis problems beyond the focus of this paper; 2) by retaining the spatial and temporal consistency, LIST yields better prediction performance. Extensive experiments on four real datasets demonstrate the effectiveness of the LIST model.
Single-Pass PCA of Large High-Dimensional Data
Authors: Yu Wenjian , Gu Yu , Li Jian , Liu Shenghua , Li Yaohang
Published: July 2017

Principal component analysis (PCA) is a fundamental dimension reduction tool in statistics and machine learning. For large and high-dimensional data, computing the PCA (i.e., the top singular vectors of the data matrix) becomes a challenging task. In this work, a single-pass randomized algorithm is proposed to compute PCA with only one pass over the data. It is suitable for processing extremely large and high-dimensional data stored in slow memory (hard disk) or the data generated in a streaming fashion. Experiments with synthetic and real data validate the algorithm's accuracy, which has orders of magnitude smaller error than an existing single-pass algorithm. For a set of high-dimensional data stored as a 150 GB file, the algorithm is able to compute the first 50 principal components in just 24 minutes on a typical 24-core computer, with less than 1 GB memory cost.
Video Question Answering via Hierarchical Spatio-Temporal Attention Networks
Authors: Zhao Zhou , Yang Qifan , Cai Deng , He Xiaofei , Zhuang Yueting
Published: July 2017

Open-ended video question answering is a challenging problem in visual information retrieval, which automatically generates the natural language answer from the referenced video content according to the question. However, the existing visual question answering works only focus on the static image, which may be ineffectively applied to video question answering due to the temporal dynamics of video contents. In this paper, we consider the problem of open-ended video question answering from the viewpoint of spatio-temporal attentional encoder-decoder learning framework. We propose the hierarchical spatio-temporal attention network for learning the joint representation of the dynamic video contents according to the given question. We then develop the encoder-decoder learning method with reasoning recurrent neural networks for open-ended video question answering. We construct a large-scale video question answering dataset. The extensive experiments show the effectiveness of our method.
Networked Fairness in Cake Cutting
Authors: Bei Xiaohui , Qiao Youming , Zhang Shengyu
Published: July 2017

We introduce a graphical framework for fair division in cake cutting, where comparisons between agents are limited by an underlying network structure. We generalize the classical fairness notions of envy-freeness and proportionality in this graphical setting. An allocation is called envy-free on a graph if no agent envies any of her neighbor's share, and is called proportional on a graph if every agent values her own share no less than the average among her neighbors, with respect to her own measure. These generalizations enable new research directions in developing simple and efficient algorithms that can produce fair allocations under specific graph structures.

On the algorithmic frontier, we first propose a moving-knife algorithm that outputs an envy-free allocation on trees. The algorithm is significantly simpler than the discrete and bounded envy-free algorithm introduced in [Aziz and Mackenzie, 2016] for compete graphs. Next, we give a discrete and bounded algorithm for computing a proportional allocation on transitive closure of trees, a class of graphs by taking a rooted tree and connecting all its ancestor-descendant pairs.
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
Authors: Ma Xingjun , Wijewickrema Sudanthi , Zhou Shuo , Zhou Yun , Mhammedi Zakaria , O'Leary Stephen , Bailey James
Published: July 2017

Simulation-based training (SBT) is gaining popularity as a low-cost and convenient training technique in a vast range of applications. However, for a SBT platform to be fully utilized as an effective training tool, it is essential that feedback on performance is provided automatically in real-time during training. It is the aim of this paper to develop an efficient and effective feedback generation method for the provision of real-time feedback in SBT. Existing methods either have low effectiveness in improving novice skills or suffer from low efficiency, resulting in their inability to be used in real-time. In this paper, we propose a neural network based method to generate feedback using the adversarial technique. The proposed method utilizes a bounded adversarial update to minimize a L1 regularized loss via back-propagation. We empirically show that the proposed method can be used to generate simple, yet effective feedback. Also, it was observed to have high effectiveness and efficiency when compared to existing methods, thus making it a promising option for real-time feedback generation in SBT.
Multimodal Storytelling via Generative Adversarial Imitation Learning
Authors: Chen Zhiqian , Zhang Xuchao , Boedihardjo Arnold P. , Dai Jing , Lu Chang-Tien
Published: July 2017

Deriving event storylines is an effective summarization method to succinctly organize extensive information, which can significantly alleviate the pain of information overload. The critical challenge is the lack of widely recognized definition of storyline metric. Prior studies have developed various approaches based on different assumptions about users' interests. These works can extract interesting patterns, but their assumptions do not guarantee that the derived patterns will match users' preference. On the other hand, their exclusiveness of single modality source misses cross-modality information. This paper proposes a method, multimodal imitation learning via Generative Adversarial Networks(MIL-GAN), to directly model users' interests as reflected by various data. In particular, the proposed model addresses the critical challenge by imitating users' demonstrated storylines. Our proposed model is designed to learn the reward patterns given user-provided storylines and then applies the learned policy to unseen data. The proposed approach is demonstrated to be capable of acquiring the user's implicit intent and outperforming competing methods by a substantial margin with a user study.
Iterative Entity Alignment via Joint Knowledge Embeddings
Authors: Zhu Hao , Xie Ruobing , Liu Zhiyuan , Sun Maosong
Published: July 2017

Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature construction to complete alignment. In this paper, we present a novel approach for entity alignment via joint knowledge embeddings. Our method jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities. During this process, we can align entities according to their semantic distance in this joint semantic space. More specifically, we present an iterative and parameter sharing method to improve alignment performance. Experiment results on real-world datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment, and can further improve knowledge graph completion performance on various KGs with the favor of joint knowledge embeddings.
Learning to Hallucinate Face Images via Component Generation and Enhancement
Authors: Song Yibing , Zhang Jiawei , He Shengfeng , Bao Linchao , Yang Qingxiong
Published: July 2017

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.
Image Gradient-based Joint Direct Visual Odometry for Stereo Camera
Authors: Zhu Jianke
Published: July 2017

Visual odometry is an important research problem for computer vision and robotics. In general, the feature-based visual odometry methods heavily rely on the accurate correspondences between local salient points, while the direct approaches could make full use of whole image and perform dense 3D reconstruction simultaneously. However, the direct visual odometry usually suffers from the drawback of getting stuck at local optimum especially with large displacement, which may lead to the inferior results. To tackle this critical problem, we propose a novel scheme for stereo odometry in this paper, which is able to improve the convergence with more accurate pose. The key of our approach is a dual Jacobian optimization that is fused into a multi-scale pyramid scheme. Moreover, we introduce a gradient-based feature representation, which enjoys the merit of being robust to illumination changes. Furthermore, a joint direct odometry approach is proposed to incorporate the information from the last frame and previous keyframes. We have conducted the experimental evaluation on the challenging KITTI odometry benchmark, whose promising results show that the proposed algorithm is very effective for stereo visual odometry.
Robust Quadratic Programming for Price Optimization
Authors: Yabe Akihiro , Ito Shinji , Fujimaki Ryohei
Published: July 2017

The goal of price optimization is to maximize total revenue by adjusting the prices of products, on the basis of predicted sales numbers that are functions of pricing strategies. Recent advances in demand modeling using machine learning raise a new challenge in price

optimization, i.e., how to manage statistical errors in estimation. In this paper, we show that uncertainty in recently-proposed prescriptive price optimization frameworks can be represented by a matrix normal distribution. For this particular uncertainty, we propose novel robust quadratic programming algorithms for conservative lower-bound maximization. We offer an asymptotic probabilistic guarantee of conservativeness of our formulation. Our experiments on both artificial and actual price data show that our robust price optimization allows users to determine best risk-return trade-offs and to explore safe, profitable price strategies.
Algorithmic Bias in Autonomous Systems
Authors: Danks David , London Alex John
Published: July 2017

Algorithms play a key role in the functioning of autonomous systems, and so concerns have periodically been raised about the possibility of algorithmic bias. However, debates in this area have been hampered by different meanings and uses of the term, "bias." It is sometimes used as a purely descriptive term, sometimes as a pejorative term, and such variations can promote confusion and hamper discussions about when and how to respond to algorithmic bias. In this paper, we first provide a taxonomy of different types and sources of algorithmic bias, with a focus on their different impacts on the proper functioning of autonomous systems. We then use this taxonomy to distinguish between algorithmic biases that are neutral or unobjectionable, and those that are problematic in some way and require a response. In some cases, there are technological or algorithmic adjustments that developers can use to compensate for problematic bias. In other cases, however, responses require adjustments by the agent, whether human or autonomous system, who uses the results of the algorithm. There is no "one size fits all" solution to algorithmic bias.
Responsible Autonomy
Authors: Dignum Virginia
Published: July 2017

As intelligent systems are increasingly making decisions that directly affect society, perhaps the most important upcoming research direction in AI is to rethink the ethical implications of their actions. Means are needed to integrate moral, societal and legal values with technological developments in AI, both during the design process as well as part of the deliberation algorithms employed by these systems. In this paper, we describe leading ethics theories and propose alternative ways to ensure ethical behavior by artificial systems.

Given that ethics are dependent on the socio-cultural context and are often only implicit in deliberation processes, methodologies are needed to elicit the values held by designers and stakeholders, and to make these explicit leading to better understanding and trust on artificial autonomous systems.
On Automating the Doctrine of Double Effect
Authors: Govindarajulu Naveen Sundar , Bringsjord Selmer
Published: July 2017

The doctrine of double effect (DDE) is a long-studied ethical principle that governs when actions that have both positive and negative effects are to be allowed. The goal in this paper is to automate DDE. We briefly present DDE, and use a first-order modal logic, the deontic cognitive event calculus, as our framework to formalize the doctrine. We present formalizations of increasingly stronger versions of the principle, including what is known as the doctrine of triple effect. We then use our framework to simulate successfully scenarios that have been used to test the presence of the principle in human subjects. Our framework can be used in two different modes. One can use it to build DDE-compliant autonomous systems from scratch, or one can use it to verify that a given AI system is DDE-complaint, by applying a DDE layer on an existing system or model. For the latter mode, the underlying AI system can be built using any architecture (planners, deep neural networks, bayesian networks, knowledge-representation systems, or a hybrid); as long as the system exposes a few parameters in its model, such verification is possible. The role of the DDE layer here is akin to a (dynamic or static) software verifier that examines existing software modules. Finally, we end by sketching initial work on how one can apply our DDE layer to the STRIPS-style planning model, and to a modified POMDP model. This is preliminary work to illustrate the feasibility of the second mode, and we hope that our initial sketches can be useful for other researchers in incorporating DDE in their own frameworks.
Lessons from the Amazon Picking Challenge: Four Aspects of Building Robotic Systems
Authors: Eppner Clemens , Höfer Sebastian , Jonschkowski Rico , Martín-Martín Roberto , Sieverling Arne , Wall Vincent , Brock Oliver
Published: July 2017

We describe the winning entry to the Amazon Picking Challenge 2015. From the experience of building this system and competing, we derive several conclusions: (1) We suggest to characterize robotic system building along four key aspects, each of them spanning a spectrum of solutions - modularity vs. integration, generality vs. assumptions, computation vs. embodiment, and planning vs. feedback. (2) To understand which region of each spectrum most adequately addresses which robotic problem, we must explore the full spectrum of possible approaches. (3) For manipulation problems in unstructured environments, certain regions of each spectrum match the problem most adequately, and should be exploited further. This is supported by the fact that our solution deviated from the majority of the other challenge entries along each of the spectra.

This is an abridged version of a conference publication.
Summary: Multi-Agent Path Finding with Kinematic Constraints
Authors: Hönig Wolfgang , Kumar T. K. Satish , Cohen Liron , Ma Hang , Xu Hong , Ayanian Nora , Koenig Sven
Published: July 2017

Multi-Agent Path Finding (MAPF) is well studied in both AI and robotics. Given a discretized environment and agents with assigned start and goal locations, MAPF solvers from AI find collision-free paths for hundreds of agents with user-provided sub-optimality guarantees. However, they ignore that actual robots are subject to kinematic constraints (such as velocity limits) and suffer from imperfect plan-execution capabilities. We therefore introduce MAPF-POST to postprocess the output of a MAPF solver in polynomial time to create a plan-execution schedule that can be executed on robots. This schedule works on non-holonomic robots, considers kinematic constraints, provides a guaranteed safety distance between robots, and exploits slack to avoid time-intensive replanning in many cases. We evaluate MAPF-POST in simulation and on differential-drive robots, showcasing the practicality of our approach.
Towards Trust, Transparency and Liability in AI / AS systems
Authors: Thelisson Eva
Published: July 2017

The research problem being investigated in this article is how to develop governance mechanisms and collective decision-making processes at a global level for Artificial Intelligence systems (AI) and Autonomous systems (AS), which would enhance confidence in AI and AS.
Libratus: The Superhuman AI for No-Limit Poker
Authors: Brown Noam , Sandholm Tuomas
Published: July 2017

No-limit Texas Hold'em is the most popular variant of poker in the world. Heads-up no-limit Texas Hold'em is the main benchmark challenge for AI in imperfect-information games. We present Libratus, the first - and so far only - AI to defeat top human professionals in that game. Libratus's architecture features three main modules, each of which has new algorithms: pre-computing a solution to an abstraction of the game which provides a high-level blueprint for the strategy of the AI, a new nested subgame-solving algorithm which repeatedly calculates a more detailed strategy as play progresses, and a self-improving module which augments the pre-computed blueprint over time.
Efficient Computation of Extensions for Dynamic Abstract Argumentation Frameworks: An Incremental Approach
Authors: Alfano Gianvincenzo , Greco Sergio , Parisi Francesco
Published: July 2017

Abstract argumentation frameworks (AFs) are a well-known formalism for modelling and deciding many argumentation problems. Computational issues and evaluation algorithms have been deeply investigated for static AFs, whose structure does not change over the time. However, AFs are often dynamic as a consequence of the fact that argumentation is inherently dynamic.

In this paper, we tackle the problem of incrementally computing extensions for dynamic AFs: given an initial extension and an update (or a set of updates), we devise a technique for computing an extension of the updated AF under four well-known semantics (i.e., complete, preferred, stable, and grounded). The idea is to identify a reduced (updated) AF sufficient to compute an extension of the whole AF and use state-of-the-art algorithms to recompute an extension of the reduced AF only. The experiments reveal that, for all semantics considered and using different solvers, the incremental technique is on average two orders of magnitude faster than computing the semantics from scratch.
From Decimation to Local Search and Back: A New Approach to MaxSAT
Authors: Cai Shaowei , Luo Chuan , Zhang Haochen
Published: July 2017

Maximum Satisfiability (MaxSAT) is an important NP-hard combinatorial optimization problem with many applications and MaxSAT solving has attracted much interest. This work proposes a new incomplete approach to MaxSAT. We propose a novel decimation algorithm for MaxSAT, and then combine it with a local search algorithm. Our approach works by interleaving between the decimation algorithm and the local search algorithm, with useful information passed between them. Experiments show that our solver DeciLS achieves state of the art performance on all unweighted benchmarks from the MaxSAT Evaluation 2016. Moreover, compared to SAT-based MaxSAT solvers which dominate industrial benchmarks for years, it performs better on industrial benchmarks and significantly better on application formulas from SAT Competition. We also extend this approach to (Weighted) Partial MaxSAT, and the resulting solvers significantly improve local search solvers on crafted and industrial benchmarks, and are complementary (better on WPMS crafted benchmarks) to SAT-based solvers.
Acceptability Semantics for Weighted Argumentation Frameworks
Authors: Amgoud Leila , Ben-Naim Jonathan , Doder Dragan , Vesic Srdjan
Published: July 2017

The paper studies semantics that evaluate arguments in argumentation graphs, where

each argument has a basic strength, and may be attacked by other arguments. It starts

by defining a set of principles, each of which is a property that a semantics could

satisfy. It provides the first formal analysis and comparison of existing semantics.

Finally, it defines three novel semantics that satisfy more principles than existing ones.
CARTOGRAFÍAS ACÚSTICAS PARA UNA FANTASÍA LUSA: RECONFIGURACIONES SIMBÓLICAS DEL AUTO DA BARCA DO INFERNO DE GIL VICENTE DESDE LA PROPUESTA MUSICAL DE MADREDEUS
Authors: Villegas-Restrepo Juan E.
Published: July 2017

This essay analyzes the theatrical drama Auto da Barca do Inferno by Portuguese playwright and musician Gil Vicente (1465-1536?) in light of the acoustic-literary theme of the song “O Pastor”, written by the also Portuguese music band Madredeus (1985-). With emphasis on the analysis of pastoral figure, the double allegory of the drama’s plot, the ambiguity of song’s tonalities, and the tireless recurrence to a third poetic imaginary presented to the reader-listener in the shape of a boat, it will try to show how the song questions the rigid parapet established by the dogmatic and imperialist Manichaeism of the Vincentian drama.