bluuebunny/crossref_metadata_2025 huggingface dataset:
Learning Out-of-Vocabulary Words in Intelligent Personal Agents
Authors: Ray Avik , Shen Yilin , Jin Hongxia
Published: July 2018

Semantic parsers play a vital role in intelligent agents to convert natural language instructions to an actionable logical form representation. However, after deployment, these parsers suffer from poor accuracy on encountering out-of-vocabulary (OOV) words, or significant accuracy drop on previously supported instructions after retraining. Achieving both goals simultaneously is non-trivial. In this paper, we propose novel neural networks based parsers to learn OOV words; one incorporating a new hybrid paraphrase generation model, and an enhanced sequence-to-sequence model. Extensive experiments on both benchmark and custom datasets show our new parsers achieve significant accuracy gain on OOV words and phrases, and in the meanwhile learn OOV words while maintaining accuracy on previously supported instructions.
Preference Orders on Families of Sets - When Can Impossibility Results Be Avoided?
Authors: Maly Jan , Truszczynski Miroslaw , Woltran Stefan
Published: July 2018

Lifting a preference order on elements of some universe to a preference order on subsets of this universe is often guided by postulated properties the lifted order should have. Well-known impossibility results pose severe limits on when such liftings exist if all non-empty subsets of the universe are to be ordered. The extent to which these negative results carry over to other families of sets is not known. In this paper, we consider families of sets that induce connected subgraphs in graphs. For such families, common in applications, we study whether lifted orders satisfying the well-studied axioms of dominance and (strict) independence exist for every or, in another setting, only for some underlying order on elements (strong and weak orderability). We characterize families that are strongly and weakly orderable under dominance and strict independence, and obtain a tight bound on the class of families that are strongly orderable under dominance and independence.
Joint Posterior Revision of NLP Annotations via Ontological Knowledge
Authors: Rospocher Marco , Corcoglioniti Francesco
Published: July 2018

Different well-established NLP tasks contribute to elicit the semantics of entities mentioned in natural language text, such as Named Entity Recognition and Classification (NERC) and Entity Linking (EL). However, combining the outcomes of these tasks may result in NLP annotations --- such as a NERC organization linked by EL to a person --- that are unlikely or contradictory when interpreted in the light of common world knowledge about the entities these annotations refer to. We thus propose a general probabilistic model that explicitly captures the relations between multiple NLP annotations for an entity mention, the ontological entity classes implied by those annotations, and the background ontological knowledge those classes may be consistent with. We use the model to estimate the posterior probability of NLP annotations given their confidences (prior probabilities) and the ontological knowledge, and consequently revise the best annotation choice performed by the NLP tools. In a concrete scenario with two state-of-the-art tools for NERC and EL, we experimentally show on three reference datasets that for these tasks, the joint annotation revision performed by the model consistently improves on the original results of the tools.
Functional Partitioning of Ontologies for Natural Language Query Completion in Question Answering Systems
Authors: Sen Jaydeep , Mittal Ashish , Saha Diptikalyan , Sankaranarayanan Karthik
Published: July 2018

Query completion systems are well studied in the context of information retrieval systems that handle keyword queries. However, Natural Language Interface to Databases (NLIDB) systems that focus on syntactically correct and semantically complete queries to obtain high precision answers require a fundamentally different approach to the query completion problem as opposed to IR systems. To the best of our knowledge, we are first to focus on the problem of query completion for NLIDB systems. In particular, we introduce a novel concept of functional partitioning of an ontology and then design algorithms to intelligently use the components obtained from functional partitioning to extend a state-of-the-art NLIDB system to produce accurate and semantically meaningful query completions in the absence of query logs. We test the proposed query completion framework on multiple benchmark datasets and demonstrate the efficacy of our technique empirically.
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Authors: Shen Tao , Zhou Tianyi , Long Guodong , Jiang Jing , Wang Sen , Zhang Chengqi
Published: July 2018

Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called "reinforced sequence sampling (RSS)", selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA.  It achieves state-of-the-art performance on both the Stanford Natural Language Inference (SNLI) and the Sentences Involving Compositional Knowledge (SICK) datasets.
Listen, Think and Listen Again: Capturing Top-down Auditory Attention for Speaker-independent Speech Separation
Authors: Shi Jing , Xu Jiaming , Liu Guangcan , Xu Bo
Published: July 2018

Recent deep learning methods have made significant progress in multi-talker mixed speech separation. However, most existing models adopt a driftless strategy to separate all the speech channels rather than selectively attend the target one. As a result, those frameworks may be failed to offer a satisfactory solution in complex auditory scene where the number of input sounds is usually uncertain and even dynamic. In this paper, we present a novel neural network based structure motivated by the top-down attention behavior of human when facing complicated acoustical scene. Different from previous works, our method constructs an inference-attention structure to predict interested candidates and extract each speech channel of them. Our work gets rid of the limitation that the number of channels must be given or the high computation complexity for label permutation problem. We evaluated our model on the WSJ0 mixed-speech tasks. In all the experiments, our model gets highly competitive to reach and even outperform the baselines.
Joint Learning Embeddings for Chinese Words and their Components via Ladder Structured Networks
Authors: Song Yan , Shi Shuming , Li Jing
Published: July 2018

The components, such as characters and radicals, of a Chinese word are important sources to help in capturing semantic information of the word. In this paper, we propose a novel framework, namely, ladder structured networks (LSN), which contains three layers representing word, character and radical and learns their embeddings synchronously. LSN captures not only the relations among words, but also the relations among their component characters and radicals, as well as the relations across layers. Each layer in LSN is pluggable so that any particular type of unit (word, character, radical) can be removed and the LSN is thus adjusted for particular types of inputs. In evaluating our framework, we use word similarity as the intrinsic evaluation and part-of-speech tagging and document classification as extrinsic evaluations. Experimental results confirm the validity of our approach and show superiority of our approach over previous work.
Bootstrapping Entity Alignment with Knowledge Graph Embedding
Authors: Sun Zequn , Hu Wei , Zhang Qingheng , Qu Yuzhong
Published: July 2018

Embedding-based entity alignment represents different knowledge graphs (KGs) as low-dimensional embeddings and finds entity alignment by measuring the similarities between entity embeddings. Existing approaches have achieved promising results, however, they are still challenged by the lack of enough prior alignment as labeled training data. In this paper, we propose a bootstrapping approach to embedding-based entity alignment. It iteratively labels likely entity alignment as training data for learning alignment-oriented KG embeddings. Furthermore, it employs an alignment editing method to reduce error accumulation during iterations. Our experiments on real-world datasets showed that the proposed approach significantly outperformed the state-of-the-art embedding-based ones for entity alignment. The proposed alignment-oriented KG embedding, bootstrapping process and alignment editing method all contributed to the performance improvement.
Multiway Attention Networks for Modeling Sentence Pairs
Authors: Tan Chuanqi , Wei Furu , Wang Wenhui , Lv Weifeng , Zhou Ming
Published: July 2018

Modeling sentence pairs plays the vital role for judging the relationship between two sentences, such as paraphrase identification, natural language inference, and answer sentence selection. Previous work achieves very promising results using neural networks with attention mechanism. In this paper, we propose the multiway attention networks which employ multiple attention functions to match sentence pairs under the matching-aggregation framework. Specifically, we design four attention functions to match words in corresponding sentences. Then, we aggregate the matching information from each function, and combine the information from all functions to obtain the final representation. Experimental results demonstrate that the proposed multiway attention networks improve the result on the Quora Question Pairs, SNLI, MultiNLI, and answer sentence selection task on the SQuAD dataset.
Get The Point of My Utterance! Learning Towards Effective Responses with Multi-Head Attention Mechanism
Authors: Tao Chongyang , Gao Shen , Shang Mingyue , Wu Wei , Zhao Dongyan , Yan Rui
Published: July 2018

Attention mechanism has become a popular and widely used component in sequence-to-sequence models. However, previous research on neural generative dialogue systems always generates universal responses, and the attention distribution learned by the model always attends to the same semantic aspect. To solve this problem, in this paper, we propose a novel Multi-Head Attention Mechanism (MHAM) for generative dialog systems, which aims at capturing multiple semantic aspects from the user utterance. Further, a regularizer is formulated to force different attention heads to concentrate on certain aspects. The proposed mechanism leads to more informative, diverse, and relevant response generated. Experimental results show that our proposed model outperforms several strong baselines.
Hermitian Co-Attention Networks for Text Matching in Asymmetrical Domains
Authors: Tay Yi , Luu Anh Tuan , Hui Siu Cheung
Published: July 2018

Co-Attentions are highly effective attention mechanisms for text matching applications. Co-Attention enables the learning of pairwise attentions, i.e., learning to attend based on computing word-level affinity scores between two documents. However, text matching problems can exist in either symmetrical or asymmetrical domains. For example, paraphrase identification is a symmetrical task while question-answer matching and entailment classification are considered asymmetrical domains. In this paper, we argue that Co-Attention models in asymmetrical domains require different treatment as opposed to symmetrical domains, i.e., a concept of word-level directionality should be incorporated while learning word-level similarity scores. Hence, the standard inner product in real space commonly adopted in co-attention is not suitable. This paper leverages attractive properties of the complex vector space and proposes a co-attention mechanism based on the complex-valued inner product (Hermitian products). Unlike the real dot product, the dot product in complex space is asymmetric because the first item is conjugated. Aside from modeling and encoding directionality, our proposed approach also enhances the representation learning process. Extensive experiments on five text matching benchmark datasets demonstrate the effectiveness of our approach.
One "Ruler" for All Languages: Multi-Lingual Dialogue Evaluation with Adversarial Multi-Task Learning
Authors: Tong Xiaowei , Fu Zhenxin , Shang Mingyue , Zhao Dongyan , Yan Rui
Published: July 2018

Automatic evaluating the performance of Open-domain dialogue system is a challenging problem. Recent work in neural network-based metrics has shown promising opportunities for automatic dialogue evaluation. However, existing methods mainly focus on monolingual evaluation, in which the trained metric is not flexible enough to transfer across different languages. To address this issue, we propose an adversarial multi-task neural metric (ADVMT) for multi-lingual dialogue evaluation, with shared feature extraction across languages. We evaluate the proposed model in two different languages. Experiments show that the adversarial multi-task neural metric achieves a high correlation with human annotation, which yields better performance than monolingual ones and various existing metrics.
Transformable Convolutional Neural Network for Text Classification
Authors: Xiao Liqiang , Zhang Honglun , Chen Wenqing , Wang Yongkun , Jin Yaohui
Published: July 2018

Convolutional neural networks (CNNs) have shown their promising performance for natural language processing tasks, which extract n-grams as features to represent the input. However, n-gram based CNNs are inherently limited to fixed geometric structure and cannot proactively adapt to the transformations of features. In this paper, we propose two modules to provide CNNs with the flexibility for complex features and the adaptability for transformation, namely, transformable convolution and transformable pooling. Our method fuses dynamic and static deviations to redistribute the sampling locations, which can capture both current and global transformations. Our modules can be easily integrated by other models to generate new transformable networks. We test proposed modules on two state-of-the-art models, and the results demonstrate that our modules can effectively adapt to the feature transformation in text classification.
The Price of Usability: Designing Operationalizable Strategies for Security Games
Authors: Mc Carthy Sara Marie , Laan Corine M. , Wang Kai , Vayanos Phebe , Sinha Arunesh , Tambe Milind
Published: July 2018

We consider the problem of allocating scarce security resources among heterogeneous targets to thwart a possible attack. It is well known that deterministic solutions to this problem being highly predictable are severely suboptimal. To mitigate this predictability, the game-theoretic security game model was proposed which randomizes over pure (deterministic) strategies, causing confusion in the adversary. Unfortunately, such mixed strategies typically involve randomizing over a large number of strategies, requiring security personnel to be familiar with numerous protocols, making them hard to operationalize. Motivated by these practical considerations, we propose an easy to use approach for computing  strategies that are easy to operationalize and that bridge the gap between the static solution and the optimal mixed strategy. These strategies only randomize over an optimally chosen subset of pure strategies whose cardinality is selected by the defender, enabling them to conveniently tune the trade-off between ease of operationalization and efficiency using a single design parameter. We show that the problem of computing such operationalizable strategies is NP-hard, formulate it as a mixed-integer optimization problem, provide an algorithm for computing epsilon-optimal equilibria, and an efficient heuristic. We evaluate the performance of our approach on the problem of screening for threats at airport checkpoints and show that the Price of Usability, i.e., the loss in optimality to obtain a strategy that is easier to operationalize, is typically not high.
Ensemble Neural Relation Extraction with Adaptive Boosting
Authors: Yang Dongdong , Wang Senzhang , Li Zhoujun
Published: July 2018

Relation extraction has been widely studied to extract new relational facts from open corpus. Previous relation extraction methods are faced with the problem of wrong labels and noisy data, which substantially decrease the performance of the model. In this paper, we propose an ensemble neural network model - Adaptive Boosting LSTMs with Attention, to more effectively perform relation extraction. Specifically, our model first employs the recursive neural network LSTMs to embed each sentence. Then we import attention into LSTMs by considering that the words in a sentence do not contribute equally to the semantic meaning of the sentence. Next via adaptive boosting, we build strategically several such neural classifiers. By ensembling multiple such LSTM classifiers with adaptive boosting, we could build a more effective and robust joint ensemble neural networks based relation extractor. Experiment results on real dataset demonstrate the superior performance of the proposed model, improving F1-score by about 8% compared to the state-of-the-art models.
Chinese Poetry Generation with a Working Memory Model
Authors: Yi Xiaoyuan , Sun Maosong , Li Ruoyu , Yang Zonghan
Published: July 2018

As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge. In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line. After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both automatic and human evaluation results show that our model outperforms current state-of-the-art methods.
Biased Random Walk based Social Regularization for Word Embeddings
Authors: Zeng Ziqian , Liu Xin , Song Yangqiu
Published: July 2018

Nowadays, people publish a lot of natural language texts on social media.

Socialized word embeddings (SWE) has been proposed to deal with two phenomena of language use: everyone has his/her own personal characteristics of language use and socially connected users are likely to use language in similar ways.

We observe that the spread of language use is transitive.

Namely, one user can affect his/her friends and the friends can also affect their friends.

However, SWE modeled the transitivity implicitly.

The social regularization in SWE only applies to one-hop neighbors and thus users outside the one-hop social circle will not be affected directly.

In this work, we adopt random walk methods to generate paths on the social graph to model the transitivity explicitly.

Each user on a path will be affected by his/her adjacent user(s) on the path.

Moreover, according to the update mechanism of SWE, fewer friends a user has, fewer update opportunities he/she can get.

Hence, we propose a biased random walk method to provide these users with more update opportunities.

Experiments show that our random walk based social regularizations perform better on sentiment classification.
Text Emotion Distribution Learning via Multi-Task Convolutional Neural Network
Authors: Zhang Yuxiang , Fu Jiamei , She Dongyu , Zhang Ying , Wang Senzhang , Yang Jufeng
Published: July 2018

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users’ emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexiconbased strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches.
Dynamically Forming a Group of Human Forecasters and Machine Forecaster for Forecasting Economic Indicators
Authors: Miyoshi Takahiro , Matsubara Shigeo
Published: July 2018

How can human forecasts and a machine forecast be combined in inflation forecast tasks? A machine-learning-based forecaster makes a forecast based on a statistical model constructed from past time-series data, while humans take varied information such as economic policies into account. Combination methods for different forecasts have been studied such as ensemble and consensus methods. These methods, however, always use the same manner of combination regardless of the situation (input), which makes it difficult to use the advantages of different types of forecasters. To overcome this drawback, we propose an ensemble method for estimating the expected error of a machine forecast and dynamically determining the optimal number of humans included in the ensemble. We evaluated the proposed method by using the seven datasets on U.S. inflation and confirmed that it attained the highest forecast accuracy for four datasets and the same accuracy as the highest one of traditional methods for two datasets.
Neural Networks Incorporating Unlabeled and Partially-labeled Data for Cross-domain Chinese Word Segmentation
Authors: Zhao Lujun , Zhang Qi , Wang Peng , Liu Xiaoyu
Published: July 2018

Most existing Chinese word segmentation (CWS) methods are usually supervised. Hence, large-scale annotated domain-specific datasets are needed for training. In this paper, we seek to address the problem of CWS for the resource-poor domains that lack annotated data. A novel neural network model is proposed to incorporate unlabeled and partially-labeled data. To make use of unlabeled data, we combine a bidirectional LSTM segmentation model with two character-level language models using a gate mechanism. These language models can capture co-occurrence information. To make use of partially-labeled data, we modify the original cross entropy loss function of RNN. Experimental results demonstrate that the method performs well on CWS tasks in a series of domains.
Novel Structural Parameters for Acyclic Planning Using Tree Embeddings
Authors: Bäckström Christer , Jonsson Peter , Ordyniak Sebastian
Published: July 2018

We introduce two novel structural parameters for acyclic planning (planning restricted to instances with acyclic causal graphs): up-depth and down-depth. We show that cost-optimal acyclic planning restricted to instances with bounded domain size and bounded up- or down-depth can be solved in polynomial time. For example, many of the tractable subclasses based on polytrees are covered by our result. We analyze the parameterized complexity of planning with bounded up- and down-depth: in a certain sense, down-depth has better computational properties than up-depth. Finally, we show that computing up- and down-depth are fixed-parameter tractable problems, just as many other structural parameters that are used in computer science. We view our results as a natural step towards understanding the complexity of acyclic planning with bounded treewidth and other parameters.
LP Heuristics over Conjunctions: Compilation, Convergence, Nogood Learning
Authors: Steinmetz Marcel , Hoffmann Joerg
Published: July 2018

Two strands of research in classical planning are LP heuristics and conjunctions to improve approximations. Combinations of the two have also been explored. Here, we focus on convergence properties, forcing the LP heuristic to equal the perfect heuristic h* in the limit. We show that, under reasonable assumptions, partial variable merges are strictly dominated by the compilation Pi^C of explicit conjunctions, and that both render the state equation heuristic equal to h* for a suitable set C of conjunctions. We show that consistent potential heuristics can be computed from a variant of Pi^C, and that such heuristics can represent h* for suitable C. As an application of these convergence properties, we consider sound nogood learning in state space search, via refining the set C. We design a suitable refinement method to this end. Experiments on IPC benchmarks show significant performance improvements in several domains.
Admissible Abstractions for Near-optimal Task and Motion Planning
Authors: Vega-Brown William , Roy Nicholas
Published: July 2018

We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan.  We then derive admissible abstractions for two motion planning domains with continuous state.  We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain.  These bounds guide the search for a plan while maintaining performance guarantees.  We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner.  Using our abstractions, we find near-optimal motion plans in planning problems involving 10^13 states without using a separate task planner.
GraspNet: An Efficient Convolutional Neural Network for Real-time Grasp Detection for Low-powered Devices
Authors: Asif Umar , Tang Jianbin , Harrer Stefan
Published: July 2018

Recent research on grasp detection has focused on improving accuracy through deep CNN models, but at the cost of large memory and computational resources. In this paper, we propose an efficient CNN architecture which produces high grasp detection accuracy in real-time while maintaining a compact model design. To achieve this, we introduce a CNN architecture termed GraspNet which has two main branches: i) An encoder branch which downsamples an input image using our novel Dilated Dense Fire (DDF) modules - squeeze and dilated convolutions with dense residual connections. ii) A decoder branch which upsamples the output of the encoder branch to the original image size using deconvolutions and fuse connections. We evaluated GraspNet for grasp detection using offline datasets and a real-world robotic grasping setup. In experiments, we show that GraspNet achieves superior grasp detection accuracy compared to the stateof-the-art computation-efficient CNN models with real-time inference speed on embedded GPU hardware (Nvidia Jetson TX1), making it suitable for low-powered devices.
Online, Interactive User Guidance for High-dimensional, Constrained Motion Planning
Authors: Islam Fahad , Salzman Oren , Likhachev Maxim
Published: July 2018

We consider the problem of planning a collision-free path for a high-dimensional robot. Specifically, we suggest a planning framework where a motion-planning algorithm can obtain guidance from a user. In contrast to existing approaches that try to speed up planning by incorporating experiences or demonstrations ahead of planning, we suggest to seek user guidance only when the planner identifies that it ceases to make significant progress towards the goal. Guidance is provided in the form of an intermediate configuration q^, which is used to bias the planner to go through q^. We demonstrate our approach for the case where the planning algorithm is Multi-Heuristic A* (MHA*) and the robot is a 34-DOF humanoid. We show that our approach allows to compute highly-constrained paths with little domain knowledge. Without our approach, solving such problems requires carefully-crafted domain-dependent heuristics.
Parameterised Queries and Lifted Query Answering
Authors: Braun Tanya , Möller Ralf
Published: July 2018

A standard approach for inference in probabilistic formalisms with first-order constructs is lifted variable elimination (LVE) for single queries. To handle multiple queries efficiently, the lifted junction tree algorithm (LJT) employs a first-order cluster representation of a model and LVE as a subroutine. Both algorithms answer conjunctive queries of propositional random variables, shattering the model on the query, which causes unnecessary groundings for conjunctive queries of interchangeable variables. This paper presents parameterised queries as a means to avoid groundings, applying the lifting idea to queries. Parameterised queries enable LVE and LJT to compute answers faster, while compactly representing queries and answers.
A Graphical Criterion for Effect Identification in Equivalence Classes of Causal Diagrams
Authors: Jaber Amin , Zhang Jiji , Bareinboim Elias
Published: July 2018

Computing the effects of interventions from observational data is an important task encountered in many data-driven sciences. The problem is addressed by identifying the post-interventional distribution with an expression that involves only quantities estimable from the pre-interventional distribution over observed variables, given some knowledge about the causal structure. In this work, we relax the requirement of having a fully specified causal structure and study the identifiability of effects with a singleton intervention (X), supposing that the structure is known only up to an equivalence class of causal diagrams, which is the output of standard structural learning algorithms (e.g., FCI). We derive a necessary and sufficient graphical criterion for the identifiability of the effect of X on all observed variables. We further establish a sufficient graphical criterion to identify the effect of X on a subset of the observed variables, and prove that it is strictly more powerful than the current state-of-the-art result on this problem.
Policy Optimization with Second-Order Advantage Information
Authors: Li Jiajin , Wang Baoxiang , Zhang Shengyu
Published: July 2018

Policy optimization on high-dimensional continuous control tasks exhibits its difficulty caused by the large variance of the policy gradient estimators. We present the action subspace dependent gradient (ASDG) estimator which incorporates the Rao-Blackwell theorem (RB) and Control Variates (CV) into a unified framework to reduce the variance. To invoke RB, our proposed algorithm (POSA) learns the underlying factorization structure among the action space based on the second-order advantage information. POSA captures the quadratic information explicitly and efficiently by utilizing the wide \& deep architecture. Empirical studies show that our proposed approach demonstrates the performance improvements on high-dimensional synthetic settings and OpenAI Gym's MuJoCo continuous control tasks.
Ex-post IR Dynamic Auctions with Cost-per-Action Payments
Authors: Shen Weiran , Wang Zihe , Zuo Song
Published: July 2018

Motivated by online ad auctions, we consider a repeated auction between one seller and many buyers, where each buyer only has an estimation of her value in each period until she actually receives the item in that period. The seller is allowed to conduct a dynamic auction but must guarantee ex-post individual rationality.

In this paper, we use a structure that we call credit accounts to enable a general reduction from any incentive compatible and ex-ante individual rational dynamic auction to an approximate incentive compatible and ex-post individually rational dynamic auction with credit accounts. Our reduction obtains stronger individual rationality guarantees at the cost of weaker incentive compatibility. Surprisingly, our reduction works without any common knowledge assumption. Finally, as a complement to our reduction, we prove that there is no non-trivial auction that is exactly incentive compatible and ex-post individually rational under this setting.
Building Sparse Deep Feedforward Networks using Tree Receptive Fields
Authors: Li Xiaopeng , Chen Zhourong , Zhang Nevin L.
Published: July 2018

Sparse connectivity is an important factor behind the success of convolutional neural networks and recurrent neural networks. In this paper, we consider the problem of learning sparse connectivity for feedforward neural networks (FNNs). The key idea is that a unit should be connected to a small number of units at the next level below that are strongly correlated. We use Chow-Liu's algorithm to learn a tree-structured probabilistic model for the units at the current level, use the tree to identify subsets of units that are strongly correlated, and introduce a new unit with receptive field over the subsets. The procedure is repeated on the new units to build multiple layers of hidden units. The resulting model is called a TRF-net. Empirical results show that, when compared to dense FNNs, TRF-net achieves better or comparable classification performance with much fewer parameters and sparser structures. They are also more interpretable.
Mixed Causal Structure Discovery with Application to Prescriptive Pricing
Authors: Wenjuan Wei , Lu Feng , Chunchen Liu
Published: July 2018

Prescriptive pricing is one of the most advanced pricing techniques, which derives the optimal price strategy to maximize the future profit/revenue by carrying out a two-stage process, demand modeling and price optimization.Demand modeling tries to reveal price-demand laws by discovering causal relationships among demands, prices, and objective factors, which is the foundation of price optimization.Existing methods either use regression or causal learning for uncovering the price-demand relations, but suffer from pain points in either accuracy/efficiency or mixed data type processing, while all of these are actual requirements in practical pricing scenarios.This paper proposes a novel demand modeling technique for practical usage.Speaking concretely, we propose a new locally consistent information criterion named MIC,and derive MIC-based inference algorithms for an accurate recovery of causal structure on mixed factor space.Experiments on simulate/real datasets show the superiority of our new approach in both price-demand law recovery and demand forecasting, as well as show promising performance in supporting optimal pricing.
Evolving AI from Research to Real Life – Some Challenges and Suggestions
Authors: Mannarswamy Sandya , Roy Shourya
Published: July 2018

Artificial Intelligence (AI) has come a long way from the stages of being just scientific fiction or academic research curiosity to a point, where it is poised to impact human life significantly. AI driven applications such as autonomous vehicles, medical diagnostics, conversational agents etc. are becoming a reality. In this position paper, we argue that there are certain challenges AI still needs to overcome in its evolution from Research to Real Life. We outline some of these challenges and our suggestions to address them. We provide pointers to similar issues and their resolutions in disciplines such as psychology and medicine from which AI community can leverage the learning. More importantly, this paper is intended to focus the attention of AI research community on translating AI research efforts into real world deployments.
Search Progress and Potentially Expanded States in Greedy Best-First Search
Authors: Heusner Manuel , Keller Thomas , Helmert Malte
Published: July 2018

A classical result in optimal search shows that A* with an admissible and consistent heuristic expands every state whose f-value is below the optimal solution cost and no state whose f-value is above the optimal solution cost. For satisficing search algorithms, a similarly clear understanding is currently lacking. We examine the search behavior of greedy best-first search (GBFS) in order to make progress towards such an understanding.

We introduce the concept of high-water mark benches, which separate the search space into areas that are searched by a GBFS algorithm in sequence. High-water mark benches allow us to exactly determine the set of states that are expanded by at least one GBFS tie-breaking strategy and give us a clearer understanding of search progress.
Inhibition of Occluded Facial Regions for Distance-Based Face Recognition
Authors: López Sánchez Daniel , Corchado Juan M. , González Arrieta Angelica
Published: July 2018

This work focuses on the design and validation of a CBR system for efficient face recognition under partial occlusion conditions. The proposed CBR system is based on a classical distance-based classification method, modified to increase its robustness to partial occlusion. This is achieved by using a novel dissimilarity function which discards features coming from occluded facial regions. In addition, we explore the integration of an efficient dimensionality reduction method into the proposed framework to reduce computational cost. We present experimental results showing that the proposed CBR system outperforms classical methods of similar computational requirements in the task of face recognition under partial occlusion.
Distributing Frank-Wolfe via Map-Reduce
Authors: Moharrer Armin , Ioannidis Stratis
Published: July 2018

We identify structural properties under which a

convex optimization over the simplex can be massively

parallelized via map-reduce operations using

the Frank-Wolfe (FW) algorithm. A broad class

of problems, e.g., Convex Approximation, Experimental

Designs, and Adaboost, can be tackled this

way. We implement FW over Apache Spark, and

solve problems with 20 million variables using 350

cores in 79 minutes; the same operation takes 165

hours when executed serially.
The Intricacies of Three-Valued Extensional Semantics for Higher-Order Logic Programs
Authors: Rondogiannis Panos , Symeonidou Ioanna
Published: July 2018

In this paper we examine the problem of providing a purely extensional three-valued semantics for higher-order logic programs with negation. We demonstrate that a technique that was proposed by M. Bezem for providing extensional semantics to positive higher-order logic programs, fails when applied to higher-order logic programs with negation. On the positive side, we demonstrate that for stratified higher-order logic programs, extensionality is indeed achieved by the technique. We analyze the reasons of the failure of extensionality in the general case, arguing that a three-valued setting can not distinguish between certain predicates that appear to have a different behaviour inside a program context, but which happen to be identical as three-valued relations.
Tamper-Proof Privacy Auditing for Artificial Intelligence Systems
Authors: Sutton Andrew , Samavi Reza
Published: July 2018

Privacy audit logs are used to capture the actions of participants in a data sharing environment in order for auditors to check compliance with privacy policies. However, collusion may occur between the auditors and participants to obfuscate actions that should be recorded in the audit logs. In this paper, we propose a Linked Data based method of utilizing blockchain technology to create tamper-proof audit logs that provide proof of log manipulation and non-repudiation.
A Conversational Approach to Process-oriented Case-based Reasoning
Authors: Zeyen Christian , Müller Gilbert , Bergmann Ralph
Published: July 2018

Process-oriented case-based reasoning (POCBR) supports workflow modeling by retrieving and adapting workflows that have proved useful in the past. Current approaches typically require users to specify detailed queries, which can be a demanding task. Conversational case-based reasoning (CCBR) particularly addresses this problem by proposing methods that incrementally elicit the relevant features of the target problem in an interactive dialog. However, no CCBR approaches exist that are applicable for workflow cases that go beyond attribute-value representations such as labeled graphs. This paper closes this gap and presents a conversational POCBR approach (C-POCBR) in which questions related to structural properties of the workflow cases are generated automatically. An evaluation with cooking workflows indicates that C-POCBR can reduce the communication effort for users during retrieval.
Time Series Chains: A Novel Tool for Time Series Data Mining
Authors: Zhu Yan , Imamura Makoto , Nikovski Daniel , Keogh Eamonn
Published: July 2018

Since their introduction over a decade ago, time se-ries motifs have become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. In this work we introduce Time Series Chains, which are related to, but distinct from, time series motifs. Informally, time series chains are a temporally ordered set of subsequence patterns, such that each pattern is similar to the pattern that preceded it, but the first and last patterns are arbi-trarily dissimilar. In the discrete space, this is simi-lar to extracting the text chain “hit, hot, dot, dog” from a paragraph. The first and last words have nothing in common, yet they are connected by a chain of words with a small mutual difference. Time Series Chains can capture the evolution of systems, and help predict the future. As such, they potentially have implications for prognostics. In this work, we introduce a robust definition of time series chains, and a scalable algorithm that allows us to discover them in massive datasets.
Maintenance of Case Bases: Current Algorithms after Fifty Years
Authors: Juarez Jose M. , Craw Susan , Lopez-Delgado J. Ricardo , Campos Manuel
Published: July 2018

Case-Based Reasoning (CBR) learns new knowledge from data and so can cope with changing environments. CBR is very different from model-based systems since it can learn incrementally as new data is available, storing new cases in its case-base. This means that it can benefit from readily available new data, but also case-base maintenance (CBM) is essential to manage the cases, deleting and compacting the case-base. In the 50th anniversary of CNN (considered the first CBM algorithm), new CBM methods are proposed to deal with the new requirements of Big Data scenarios. In this paper, we present an accessible historic perspective of CBM and we classify and analyse the most recent approaches to deal with these requirements.
Boosting Combinatorial Problem Modeling with Machine Learning
Authors: Lombardi Michele , Milano Michela
Published: July 2018

In the past few years, the area of Machine Learning (ML) has witnessed tremendous advancements, becoming a pervasive technology in a wide range of applications. One area that can significantly benefit from the use of ML is Combinatorial Optimization. The three pillars of constraint satisfaction and optimization problem solving, i.e., modeling, search, and optimization, can exploit ML techniques to boost their accuracy, efficiency and effectiveness. In this survey we focus on the modeling component, whose effectiveness is crucial for solving the problem. The modeling activity has been traditionally shaped by optimization and domain experts, interacting to provide realistic results. Machine Learning techniques can tremendously ease the process, and exploit the available data to either create models or refine expert-designed ones. In this survey we cover approaches that have been recently proposed to enhance the modeling process by learning either single constraints, objective functions, or the whole model. We highlight common themes to multiple approaches and draw connections with related fields of research.
Incentive-Compatible Mechanisms for Norm Monitoring in Open Multi-Agent Systems (Extended Abstract)
Authors: Alechina Natasha , Halpern Joseph Y. , Kash Ian A. , Logan Brian
Published: July 2018

We consider the problem of detecting norm violations in open multi-agent systems (MAS). In this extended abstract, we outline the approach of [Alechina et al., 2018], and show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations.
Solving Multi-Agent Path Finding on Strongly Biconnected Digraphs (Extended Abstract)
Authors: Botea Adi , Bonusi Davide , Surynek Pavel
Published: July 2018

We present and evaluate diBOX, an algorithm for

multi-agent path finding on strongly biconnected

directed graphs. diBOX runs in polynomial time,

computes suboptimal solutions and is complete for

instances on strongly biconnected digraphs with at

least two unoccupied positions. A detailed empirical

analysis shows a good scalability for diBOX.
From Feature to Paradigm: Deep Learning in Machine Translation (Extended Abstract)
Authors: Costa-jussà Marta R.
Published: July 2018

In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feed-forward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations.

This extended abstract focuses on describing the foundational works on the neural MT approach; mentioning its strengths and weaknesses; and including an analysis of the corresponding challenges and future work. The full manuscript [Costa-jussà, 2018] describes, in addition, how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring; and on describing the new neural MT approach together with recent approaches on using subword, characters and training with multilingual

languages, among others.