OpenRaiser/Intern-Atlas huggingface dataset:
Model Asset eXchange: Path to Ubiquitous Deep Learning Deployment
Authors: Alex Bozarth, Brendan Dwyer, Fei Hu, Daniel Jalova, Karthik Muthuraman, Nick Pentreath, Simon Plovyt, Gabriela de Queiroz, Saishruthi Swaminathan, Patrick Titzler, Xin Wu, Hong Xu, Frederick R. Reiss, Vijay Bommireddipalli
Venue: CIKM
Year: 2019

A recent trend observed in traditionally challenging fields such as computer vision and natural language processing has been the significant performance gains shown by deep learning (DL). In many different research fields, DL models have been evolving rapidly and become ubiquitous. Despite researchers' excitement, unfortunately, most software developers are not DL experts and oftentimes have a difficult time following the booming DL research outputs. As a result, it usually takes a significant amount of time for the latest superior DL models to prevail in industry. This issue is further exacerbated by the common use of sundry incompatible DL programming frameworks, such as Tensorflow, PyTorch, Theano, etc. To address this issue, we propose a system, called Model Asset Exchange (MAX), that avails developers of easy access to state-of-the-art DL models. Regardless of the underlying DL programming frameworks, it provides an open source Python library (called the MAX framework) that wraps DL models and unifies programming interfaces with our standardized RESTful APIs. These RESTful APIs enable developers to exploit the wrapped DL models for inference tasks without the need to fully understand different DL programming frameworks. Using MAX, we have wrapped and open-sourced more than 30 state-of-the-art DL models from various research fields, including computer vision, natural language processing and signal processing, etc. In the end, we selectively demonstrate two web applications that are built on top of MAX, as well as the process of adding a DL model to MAX.
Ontology-Mediated Queries over Probabilistic Data via Probabilistic Logic Programming
Authors: Timothy van Bremen, Anton Dries, Jean Christoph Jung
Venue: CIKM
Year: 2019

We study ontology-mediated querying over probabilistic data for the case when the ontology is formulated in EL(hdr), an expressive member of the EL family of description logics. We leverage techniques that have been developed (i) for classical ontology-mediated querying and (ii) for probabilistic logic programming and provide an implementation based on our findings. We include both theoretical considerations and an experimental evaluation of our approach.
Improved Compressed String Dictionaries
Authors: Nieves R. Brisaboa, Ana Cerdeira-Pena, Guillermo de Bernardo, Gonzalo Navarro
Venue: CIKM
Year: 2019

We introduce a new family of compressed data structures to efficiently store and query large string dictionaries in main memory. Our main technique is a combination of hierarchical Front-coding with ideas from longest-common-prefix computation in suffix arrays. Our data structures yield relevant space-time tradeoffs in real-world dictionaries. We focus on two domains where string dictionaries are extensively used and efficient compression is required: URL collections, a key element in Web graphs and applications such as Web mining; and collections of URIs and literals, the basic components of RDF datasets. Our experiments show that our data structures achieve better compression than the state-of-the-art alternatives while providing very competitive query times.
Adversarial Structured Neural Network Pruning
Authors: Xingyu Cai, Jinfeng Yi, Fan Zhang, Sanguthevar Rajasekaran
Venue: CIKM
Year: 2019

In recent years, convolutional neural networks (CNN) have been successfully employed for performing various tasks due to their high capacity. However, just like a double-edged sword, high capacity results from millions of parameters, which also brings a huge amount of redundancy and dramatically increases the computational complexity. The task of pruning a pretrained network to make it thinner and easier to deploy on resource-limited devices is still challenging. In this paper, we employ the idea of adversarial examples to sparsify a CNN. Adversarial examples were originally designed to fool a network. Rather than adjusting the input image, we view any layer as an input to the layers afterwards. By performing an adversarial attack algorithm, the sensitivity information of the network components could be observed. With this information, we perform pruning in a structured manner to retain only the most critical channels. Empirical evaluations show that our proposed approach obtains the state-of-the-art structured pruning performance.
Adversarial Training of Gradient-Boosted Decision Trees
Authors: Stefano Calzavara, Claudio Lucchese, Gabriele Tolomei
Venue: CIKM
Year: 2019

Adversarial training is a prominent approach to make machine learning (ML) models resilient to adversarial examples. Unfortunately, such approach assumes the use of differentiable learning models, hence it cannot be applied to relevant ML techniques, such as ensembles of decision trees. In this paper, we generalize adversarial training to gradient-boosted decision trees (GBDTs). Our experiments show that the performance of classifiers based on existing learning techniques either sharply decreases upon attack or is unsatisfactory in absence of attacks, while adversarial training provides a very good trade-off between resiliency to attacks and accuracy in the unattacked setting.
Nested Relation Extraction with Iterative Neural Network
Authors: Yixuan Cao, Dian Chen, Hongwei Li, Ping Luo
Venue: CIKM
Year: 2019

Natural language is used to describe objective facts, including simple relations like ""Jobs was the CEO of Apple"", and complex relations like ""the GDP of the United States in 2018 grew 2.9% compared with 2017". For the latter example, the growth rate relation is between two other relations. Due to the complex nature of language, this kind of nested relations is expressed frequently, especially in professional documents in fields like economics, finance, and biomedicine. But extracting nested relations is challenging, and research on this problem is almost vacant. In this paper, we formally formulate the nested relation extraction problem, and come up with a solution using Iterative Neural Network. Specifically, we observe that the nested relation structures can be expressed as a Directed Acyclic Graph (DAG), and propose the model to simultaneously consider the word sequence of natural language in the horizontal direction and the DAG structure in the vertical direction. Based on two nested relation extraction tasks, namely semantic causality relation extraction and formula extraction, we show that the proposed model works well on them. Moreover, we speed up the DAG-LSTM training significantly by a simple parallelization solution.
HiCAN: Hierarchical Convolutional Attention Network for Sequence Modeling
Authors: Yi Cao, Weifeng Zhang, Bo Song, Congfu Xu
Venue: CIKM
Year: 2019

Convolutional neural networks (CNN) are widely used on sequential data since it can capture local context dependencies and temporal order information inside sequences. Attention (ATT) mechanisms have also attracted enormous interests due to its capability of capturing the important parts of a sequence. These two neural networks can extract different features from sequences. In order to combine the advantages of CNN and ATT, we propose a convolutional attention network (CAN), which merges the structure of CNN and ATT into a single neural network and can serve as a new basic module in complex neural networks. Based on CAN, we then build a sequence encoding model with hierarchical structure, "hierarchical convolutional attention network (HiCAN)", to tackle sequence modeling problems. It can explicitly capture both the local and global context dependencies and temporal order information in sequences. Extensive experiments conducted on session-based recommendation (Recommender Systems) demonstrate that HiCAN is able to outperform state-of-the-art methods and show higher computational efficiency. Furthermore, we conduct extended experiments on text classification (Natural Language Processing). The results show that our model can also achieve competitive performance on NLP tasks.
Adversarial Domain Adaptation with Semantic Consistency for Cross-Domain Image Classification
Authors: Manliang Cao, Xiangdong Zhou, Yiming Xu, Yue Pang, Bo Yao
Venue: CIKM
Year: 2019

In the cross-domain image classification scenario, domain adaption aims to address the challenge of transferring the knowledge obtained from the source domain to the target domain that is regarded as similar but different from the source domain. To get more reliable domain invariant representations, recent methods start to consider class-level distribution alignment across the source and target domains by adaptively assigning pseudo target labels. However, these approaches are vulnerable to the error accumulation and hence unable to preserve cross-domain category consistency. Because the accuracy of pseudo labels cannot be guaranteed explicitly. In this paper, we propose Adversarial Domain Adaptation with Semantic Consistency (ADASC) model to align the discriminative features across domains progressively and effectively, via exploiting the class-level relations between domains. Specifically, to simultaneously alleviate the negative influence of the false pseudo-target labels and get the discriminative domain invariant features, we introduce an Adaptive Centroid Alignment (ACA) strategy and a Class Discriminative Constraint (CDC) step to complement each other iteratively and alternatively in an end-to-end framework. Extensive experiments are conducted on several unsupervised domain adaptation datasets, and the results show that ADASC outperforms the state-of-the-art methods.
Adaptive Algorithms for Estimating Betweenness and k-path Centralities
Authors: Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem
Venue: CIKM
Year: 2019

Betweenness centrality and k-path centrality are two important indices that are widely used to analyze social, technological and information networks. In the current paper, first given a directed network G and a vertex $r\in V(G)$, we present a novel adaptive algorithm for estimating betweenness score of r. Our algorithm first computes two subsets of the vertex set of G, called $\mathcalRF (r)$ and $\mathcalRT (r)$. They define the sample spaces of the start-points and the end-points of the samples. Then, it adaptively samples from $\mathcalRF (r)$ and $\mathcalRT (r)$ and stops as soon as some condition is satisfied. The stopping condition depends on the samples met so far, $|\mathcalRF (r)|$ and $|\mathcalRT (r)|$. We show that compared to the well-known existing algorithms, our algorithm gives a better $(łambda,δ)$-approximation. Then, we propose a novel algorithm for estimating k-path centrality of r. Our algorithm is based on computing two sets $\mathcalRF (r)$ and $\mathcalD (r)$. While $\mathcalRF (r)$ defines the sample space of the source vertices of the sampled paths, $\mathcalD (r)$ defines the sample space of the other vertices of the paths. We show that in order to give a $(łambda,δ)$-approximation of the k-path score of r, our algorithm requires considerably less samples. Moreover, it processes each sample faster and with less memory. Finally, we empirically evaluate our proposed algorithms and show their superior performance. Also, we show that they can be used to efficiently compute centrality scores of a set of vertices.
Leveraging Graph Neighborhoods for Efficient Inference
Authors: Melisachew Wudage Chekol, Heiner Stuckenschmidt
Venue: CIKM
Year: 2019

Several probabilistic extensions of description logic languages have been proposed and thoroughly studied. However, their practical use has been hampered by intractability of various reasoning tasks. While present-day knowledge bases (KBs) contain millions of instances and thousands of axioms, most state-of-the-art reasoners are capable of handling small scale KBs with thousands of instances. Thus, recent research has focused on leveraging the structure of KBs and queries in order to speed up inference runtime. However, these efforts have not been satisfactory in providing reasoners that are suitable for practical use in large scale KBs. In this study, we aim to tackle this challenging problem. In doing so, we use a probabilistic extension of OWL RL (called PRORL) as a modeling language and exploit graph neighborhoods (of undirected graphical models) for efficient approximate probabilistic inference. We show that subgraph extraction based inference is much faster and has comparable accuracy to full graph inference. We perform several experiments, in order to support our claim, over a NELL KB containing millions of instances and thousands of axioms. Furthermore, we propose a novel graph-based algorithm to automatically partition inferences rules based on their structure for efficient parallel inference.
Recommendation for Multi-stakeholders and through Neural Review Mining
Authors: Muthusamy Chelliah, Yong Zheng, Sudeshna Sarkar
Venue: CIKM
Year: 2019

Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only stakeholder in these traditional system. However, there could be multiple stakeholders in several applications domains (e.g., e-commerce, movies, music). Recommendations are necessary to be produced by balancing the needs of different stakeholders. First session of this tutorial introduces multi-stakeholder recommender systems (MSRS) with several case studies, and discusses the corresponding methods and challenges in MSRS. Reviews in an e-commerce platform may be mined to address cold-start problem and to generate explanations. Our earlier tutorial covered aspect-based sentiment analysis of products and topic models/distributed representations that bridge vocabulary gap between user reviews and product descriptions. Focus in the second session of this tutorial instead is on recent neural methods for review text mining - covering hands-on code for its use to enhance product recommendation. Each section will introduce topics from various mechanism (e.g., attention) and task (e.g., review ranking) perspectives, present cutting-edge research and a walk-through of programs executed on Jupyter notebook using real-world data sets.
A Dynamic Co-attention Network for Session-based Recommendation
Authors: Wanyu Chen, Fei Cai, Honghui Chen, Maarten de Rijke
Venue: CIKM
Year: 2019

Session-based recommendation is the task of recommending the next item a user might be interested in given partially known session information, e.g., part of a session or recent historical sessions. An effective session-based recommender should be able to exploit a user's evolving preferences, which we assume to be a mixture of her short- and long-term interests. Existing session-based recommendation methods often embed a user's long-term preference into a static representation, which plays a fixed role when dealing with her current short-term interests. This is problematic because long-term preferences may be more or less important for predicting the next conversion depending on the user's short-term interests. We propose a DCN-SR. DCN-SR applies a co-attention network to capture the dynamic interactions between the user's long- and short-term interaction behavior and generates co-dependent representations of the user's long- and short-term interests. For modeling a user's short-term interaction behavior, we design a CGRU network to take actions like "click'', "collect'' and "buy'' into account. Experiments on e-commerce datasets show significant improvements of DCN-SR over state-of-the-art session-based recommendation methods, with improvements of up to 2.58% on the Tmall dataset and 3.08% on the Tianchi dataset in terms of Recall@10. MRR@10 improvements are 3.78% and 4.05%, respectively. We also investigate the scalability and sensitivity of DCN-SR. The improvements of DCN-SR over state-of-the-art baselines are especially noticeable for short sessions and active users with many historical interactions.
Cost-effective Resource Provisioning for Spark Workloads
Authors: Yuxing Chen, Jiaheng Lu, Chen Chen, Mohammad Ashraful Hoque, Sasu Tarkoma
Venue: CIKM
Year: 2019

Spark is one of the prevalent big data analytical platforms. Configuring proper resource provision for Spark jobs is challenging but essential for organizations to save time, achieve high resource utilization, and remain cost-effective. In this paper, we study the challenge of determining the proper parameter values that meet the performance requirements of workloads while minimizing both resource cost and resource utilization time. We propose a simulation-based cost model to predict the performance of jobs accurately. We achieve low-cost training by taking advantage of simulation framework, i.e., Monte Carlo (MC) simulation, which uses a small amount of data and resources to make a reliable prediction for larger datasets and clusters. The salient feature of our method is that it allows us to invest low training cost while obtaining an accurate prediction. Through experiments with six benchmark workloads, we demonstrate that the cost model yields less than 7% error on average prediction accuracy and the recommendation achieves up to 5x resource cost saving.
Sentiment Commonsense Induced Sequential Neural Networks for Sentiment Classification
Authors: Shiyun Chen, Xin Lin, Yanghua Xiao, Liang He
Venue: CIKM
Year: 2019

Although neural networks achieve promising performance in sentence level sentiment classification, most of them are not aware of sentiment commonsense, such as sentiment polarity tags (Positive or Negative) for words, which explicitly determine the sentiment of the sentence in most cases. In this paper, we propose an auxiliary tagging task to integrate sentiment commonsense into sequential neural networks (such as LSTM). We employ the advantage of multitask learning to achieve two goals simultaneously: 1) the sequential learning task accounts for incorporating the semantic information of the surrounding words; 2) the word tagging task ensures the sequential representation still retains the corresponding word tagging information. Besides, considering the most direct way to introduce sentiment information into models as additional knowledge, we further incorporate the additional knowledge enhancing tagging task model to strengthen the effect of sentiment commonsense. We prove the effectiveness of the sentiment commonsense by extensive experiments. The results show that our models exhibit consistent superiority over competitors on three real-word datasets. Specifically, we obtain an accuracy of 55.2%, which is a new state-of-the-art for SST-fine dataset.
Knowledge-aware Textual Entailment with Graph Attention Network
Authors: Daoyuan Chen, Yaliang Li, Min Yang, Hai-Tao Zheng, Ying Shen
Venue: CIKM
Year: 2019

Textual entailment is a central problem of language variability, which has been attracting a lot of interest and it poses significant issues in front of systems aimed at natural language understanding. Recently, various frameworks have been proposed for textual entailment recognition, ranging from traditional computational linguistics techniques to deep learning model based methods. However, recent deep neural networks that achieve the state of the art on textual entailment task only consider the context information of the given sentences rather than the real-world background information and knowledge beyond the context. In the paper, we propose a Knowledge-Context Interactive Textual Entailment Network (KCI-TEN) that learns graph level sentence representations by harnessing external knowledge graph with graph attention network. We further propose a text-graph interaction mechanism for neural based entailment matching learning, which endows the redundancy and noise with less importance and put emphasis on the informative representations. Experiments on the SciTail dataset demonstrate that KCI-TEN outperforms the state-of-the-art methods.
Attention-Residual Network with CNN for Rumor Detection
Authors: Yixuan Chen, Jie Sui, Liang Hu, Wei Gong
Venue: CIKM
Year: 2019

Wide dissemination of unverified claims has negative influence on social lives. Rumors are easy to emerge and spread in the crowds especially in Online Social Network (OSN), due to its openness and extensive amount of users. Therefore, rumor detection in OSN is a very challenging and urgent issue. In this paper, we propose an Attention-Residual network combined with CNN (ARC), which is based on the content features for rumor detection. First, we build a data encoding model based on word-level data for contextual feature representation. Second, we propose a residual framework based on fine-tuned attention mechanism to capture long-range dependency. Third, we apply convolution neural network with varying window size to select important components and local features. Experiments on two twitter datasets demonstrate that the proposed model has better performance than other content-based methods both in rumor detection and early rumor verification. To the best of our knowledge, we are the first work that utilize attention model in conjunction with residual network on rumor detection.
Fast and Accurate Network Embeddings via Very Sparse Random Projection
Authors: Haochen Chen, Syed Fahad Sultan, Yingtao Tian, Muhao Chen, Steven Skiena
Venue: CIKM
Year: 2019

We present FastRP, a scalable and performant algorithm for learning distributed node representations in a graph. FastRP is over 4,000 times faster than state-of-the-art methods such as DeepWalk and node2vec, while achieving comparable or even better performance as evaluated on several real-world networks on various downstream tasks. We observe that most network embedding methods consist of two components: construct a node similarity matrix and then apply dimension reduction techniques to this matrix. We show that the success of these methods should be attributed to the proper construction of this similarity matrix, rather than the dimension reduction method employed. FastRP is proposed as a scalable algorithm for network embeddings. Two key features of FastRP are: 1) it explicitly constructs a node similarity matrix that captures transitive relationships in a graph and normalizes matrix entries based on node degrees; 2) it utilizes very sparse random projection, which is a scalable optimization-free method for dimension reduction. An extra benefit from combining these two design choices is that it allows the iterative computation of node embeddings so that the similarity matrix need not be explicitly constructed, which further speeds up FastRP. FastRP is also advantageous for its ease of implementation, parallelization and hyperparameter tuning. The source code is available at https://github.com/GTmac/FastRP.
Query Embedding Learning for Context-based Social Search
Authors: Yi-Chun Chen, Yu-Che Tsai, Cheng-Te Li
Venue: CIKM
Year: 2019

Recommending individuals through keywords is an essential and common search task in online social platforms such as Facebook and LinkedIn. However, it is often that one has only the impression about the desired targets, depicted by labels of social contexts (e.g. gender, interests, skills, visited locations, employment, etc). Assume each user is associated a set of labels, we propose a novel task, Search by Social Contexts (SSC), in online social networks. SSC is a kind of query-based people recommendation, recommending the desired target based on a set of user-specified query labels. We develop the method Social Query Embedding Learning (SQEL) to deal with SSC. SQEL aims to learn the feature representation (i.e., embedding vector) of the query, along with user feature vectors derived from graph embedding, and use the learned query vectors to find the targets via similarity. Experiments conducted on Facebook and Twitter datasets exhibit satisfying accuracy and encourage more advanced efforts on search by social contexts.