OpenRaiser/Intern-Atlas huggingface dataset:
Spatiotemporal Adaptive Gated Graph Convolution Network for Urban Traffic Flow Forecasting
Authors: Bin Lu, Xiaoying Gan, Haiming Jin, Luoyi Fu, Haisong Zhang
Venue: CIKM
Year: 2020

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. It is quite challenging due to the complicated spatiotemporal dependency and essential uncertainty brought about by the dynamic urban traffic conditions. In most of existing methods, the spatial correlation is captured by utilizing graph neural networks (GNNs) throughout a fixed graph based on local spatial proximity. However, urban road conditions are complex and changeable, which leads to the interactions between roads should also be dynamic over time. In addition, the global contextual information of roads are also crucial for accurate forecasting. In this paper, we exploit spatiotemporal correlation of urban traffic flow and construct a dynamic weighted graph by seeking both spatial neighbors and semantic neighbors of road nodes. Multi-head self-attention temporal convolution network is utilized to capture local and long-range temporal dependencies across historical observations. Besides, we propose an adaptive graph gating mechanism to extract selective spatial dependencies within multi-layer stacking and correct information deviations caused by artificially defined spatial correlation. Extensive experiments on real world urban traffic dataset from Didi Chuxing GAIA Initiative have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines. The source code of our model is publicly available at https://github.com/RobinLu1209/STAG-GCN.
Probabilistic Dynamic Non-negative Group Factor Model for Multi-source Text Mining
Authors: Chien Lu, Jaakko Peltonen, Jyrki Nummenmaa, Kalervo Järvelin
Venue: CIKM
Year: 2020

Nonnegative matrix factorization (NMF) is a popular approach to model data, however, most models are unable to flexibly take into account multiple matrices across sources and time or apply only to integer-valued data. We introduce a probabilistic, Gaussian Process-based, more inclusive NMF-based model which jointly analyzes nonnegative data such as text data word content from multiple sources in a temporal dynamic manner. The model collectively models observed matrix data, source-wise latent variables, and their dependencies and temporal evolution with a full-fledged hierarchical approach including flexible nonparametric temporal dynamics. Experiments on simulated data and real data show the model out-performs, comparable models. A case study on social media and news demonstrates the model discovers semantically meaningful topical factors and their evolution
User Taste-Aware Image Search
Authors: Jiyun Luo, Pak Ming Cheung, Wenyu Huo, Ying Huang, Rajat Raina
Venue: CIKM
Year: 2020

Pinterest as a popular image search platform has been widely adopted by users. Every day, people come to Pinterest searching for fashion- and home decor-related content. In these domains, users exhibit stable personal tastes. In this paper, we propose a novel search algorithm which can infer user tastes from their past engagement history and tailor the search results to fit their preferences. The online and offline experiments show that our method can efficiently improve user experience and increase user engagements.
Hierarchical Active Learning with Overlapping Regions
Authors: Zhipeng Luo, Milos Hauskrecht
Venue: CIKM
Year: 2020

Learning of classification models from real-world data often requires substantial human effort devoted to instance annotation. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To address this problem we explore a new type of human feedback - region-based feedback. Briefly, a region is defined as a hypercubic subspace of the input data space and represents a subpopulation of data instances; the region's label is a human assessment of the class proportion of the data subpopulation. By using learning from label proportions algorithms one can learn instance-based classifiers from such labeled regions. In general, the key challenge is that there can be infinite many regions one can define and query in a given data space. To minimize the number and complexity of region-based queries, we propose and develop a hierarchical active learning solution that aims at incrementally building a concise hierarchy of regions. Furthermore, to avoid building a possibly class-irrelevant region hierarchy, we further propose to grow multiple different hierarchies in parallel and expand those more informative hierarchies. Through experiments on numerous data sets, we demonstrate that methods using region-based feedback can learn very good classifiers from very few and simple queries, and hence are highly effective in reducing human annotation effort needed for building classification models.
RotaryDS: Fast Storage for Massive Data Streams via a Rotation Storage Model
Authors: Yanqi Lv, Peiquan Jin
Venue: CIKM
Year: 2020

In this paper, we propose RotaryDS to provide fast storage service for massive data streams. RotaryDS uses a rotation storage model, which employs distributed data buckets to accept highly-arriving data streams. All data buckets have a state, i.e., they can be in the state of data idle waiting, data filling, write waiting, and data dumping. The state of a data bucket is changed according to the data operations. With the rotation storage model, we distribute massive data streams among multiple data buckets, thereby improving the write throughput of the storage system. We implement RotaryDS based on the rotation storage model and conduct preliminary experiments to compare it with MongoDB. The results suggest the efficiency of our proposal.
Feature Selection on Data Stream via Multi-Cluster Structure Preservation
Authors: Rui Ma, Yijie Wang, Li Cheng
Venue: CIKM
Year: 2020

The modern data arrive continuously in a rapid and time-varying stream, which appears to generate unstable associations on the data structure. However, most of the existing methods focus on dealing with the static data, and they cannot fully take them into the structure construction. To address this issue, we propose an online unsupervised Feature Selection method via Multi-Cluster structure Preservation (FSMCP for short). FSMCP weighs all features by minimizing the differences between the Multi-Cluster structures in the original and the selected feature space. The structure integrates the three-level associations, i.e., the individual-level associations, the aggregation-level associations, and the streaming-level associations. To provide informative features in time, FSMCP check and update the associations as soon as new instances arrive. In comparison with the baseline methods, FSMCP holds better efficiency than offline methods, while still providing almost similar or even better quantitative feature subsets. It outperforms the existing online methods with average NMI improvement of 10.33%.
Examining the Additivity of Top-k Query Processing Innovations
Authors: Joel M. Mackenzie, Alistair Moffat
Venue: CIKM
Year: 2020

Research activity spanning more than five decades has led to index organizations, compression schemes, and traversal algorithms that allow extremely rapid response to ranked queries against very large text collections. However, little attention has been paid to the interactions between these many components, and the additivity of algorithmic improvements has not been explored. Here we examine the extent to which efficiency improvements add up. We employ four query processing algorithms, four compression codecs, and all possible combinations of four distinct further optimizations, and compare the performance of the 256 resulting systems to determine when and how different optimizations interact. Our results over two test collections show that efficiency enhancements are, for the most part, additive, and that there is little risk of negative interactions. In addition, our detailed profiling across this large pool of systems leads to key insights as to why the various individual enhancements work well, and indicates that optimizing "simpler" implementations can result in higher query throughput than is available from non-optimized versions of the more "complex" techniques, with clear implications for the choices needing to be made by practitioners.
CSNE: Conditional Signed Network Embedding
Authors: Alexandru Mara, Yoosof Mashayekhi, Jefrey Lijffijt, Tijl De Bie
Venue: CIKM
Year: 2020

Signed networks are mathematical structures that encode positive and negative relations between entities such as friend/foe or trust/distrust. Recently, several papers studied the construction of useful low-dimensional representations (embeddings) of these networks for the prediction of missing relations or signs. Existing network embedding methods for sign prediction, however, generally enforce different notions of status or balance theories in their optimization function. These theories, are often inaccurate or incomplete which negatively impacts method performance. In this context, we introduce conditional signed network embedding (CSNE). Our novel probabilistic approach models structural information about the signs in the network separately from fine-grained detail. Structural information is represented in the form of a prior, while the embedding itself is used for capturing fine-grained information. These components are then integrated in a rigorous manner. CSNE's accuracy depends on the existence of sufficiently powerful structural priors for modelling signed networks, currently unavailable in the literature. Thus, as a second main contribution, which we find to be highly valuable in its own right, we also introduce a novel approach to construct priors based on the Maximum Entropy (MaxEnt) principle. These priors can model the polarity of nodes (the degree to which their links are positive) as well as signed triangle counts (a measure for the degree structural balance holds to in a network). Experiments on a variety of real-world networks confirm that CSNE outperforms the state-of-the-art on the task of sign prediction. Moreover, the MaxEnt priors on their own, while less accurate than full CSNE, achieve accuracies competitive with the state-of-the-art at very limited computational cost, thus providing an excellent runtime-accuracy trade-off in resource-constrained situations.
Diversifying Top-k Point-of-Interest Queries via Collective Social Reach
Authors: Stella Maropaki, Sean Chester, Christos Doulkeridis, Kjetil Nørvåg
Venue: CIKM
Year: 2020

By "checking into'' various points-of-interest (POIs), users create a rich source of location-based social network data that can be used in expressive spatio-social queries. This paper studies the use of popularity as a means to diversify results of top-k nearby POI queries. In contrast to previous work, we evaluate social diversity as a group-based, rather than individual POI, metric. Algorithmically, evaluating this set-based notion of diversity is challenging, yet we present several effective algorithms based on (integer) linear programming, a greedy framework, and r-tree distance browsing. Experiments show scalability and interactive response times for up to 100 million unique check-ins across 25000 POIs.
ContentWise Impressions: An Industrial Dataset with Impressions Included
Authors: Fernando Benjamín Pérez Maurera, Maurizio Ferrari Dacrema, Lorenzo Saule, Mario Scriminaci, Paolo Cremonesi
Venue: CIKM
Year: 2020

In this article, we introduce the \dataset dataset, a collection of implicit interactions and impressions of movies and TV series from an Over-The-Top media service, which delivers its media contents over the Internet. The dataset is distinguished from other already available multimedia recommendation datasets by the availability of impressions, \idest the recommendations shown to the user, its size, and by being open-source. We describe the data collection process, the preprocessing applied, its characteristics, and statistics when compared to other commonly used datasets. We also highlight several possible use cases and research questions that can benefit from the availability of user impressions in an open-source dataset. Furthermore, we release software tools to load and split the data, as well as examples of how to use both user interactions and impressions in several common recommendation algorithms.
Learning to Distract: A Hierarchical Multi-Decoder Network for Automated Generation of Long Distractors for Multiple-Choice Questions for Reading Comprehension
Authors: Kaushal Kumar Maurya, Maunendra Sankar Desarkar
Venue: CIKM
Year: 2020

The task of generating incorrect options for multiple-choice questions is termed as distractor generation problem. The task requires high cognitive skills and is extremely challenging to automate. Existing neural approaches for the task leverage encoder-decoder architecture to generate long distractors. However, in this process two critical points are ignored - firstly, many methods use Jaccard similarity over a pool of candidate distractors to sample the distractors. This often makes the generated distractors too obvious or not relevant to the question context. Secondly, some approaches did not consider the answer in the model, which caused the generated distractors to be either answer-revealing or semantically equivalent to the answer. In this paper, we propose a novel Hierarchical Multi-Decoder Network (HMD-Net) consisting of one encoder and three decoders, where each decoder generates a single distractor. To overcome the first problem mentioned above, we include multiple decoders with a dis-similarity loss in the loss function. To address the second problem, we exploit richer interaction between the article, question, and answer with a SoftSel operation and a Gated Mechanism. This enables the generation of distractors that are in context with questions but semantically not equivalent to the answers. The proposed model outperformed all the previous approaches significantly in both automatic and manual evaluations. In addition, we also consider linguistic features and BERT contextual embedding with our base model which further push the model performance.
"Keep it Simple, Lazy" - MetaLazy: A New MetaStrategy for Lazy Text Classification
Authors: Luiz Felipe Mendes, Marcos André Gonçalves, Washington Cunha, Leonardo Rocha, Thierson Couto Rosa, Wellington Martins
Venue: CIKM
Year: 2020

Recent advances in text-related tasks on the Web, such as text (topic) classification and sentiment analysis, have been made possible by exploiting mostly the "rule of more": more data (massive amounts) more computing power, more complex solutions. We propose a shift in the paradigm to do "more with less" by focusing, at maximum extent, just on the task at hand (e.g., classify a single test instance). Accordingly, we propose MetaLazy, a new supervised lazy text classification meta-strategy that greatly extends the scope of lazy solutions. Lazy classifiers postpone the creation of a classification model until a given test instance for decision making is given. MetaLazy exploits new ideas and solutions, which have in common their lazy nature, producing altogether a solution for text classification, which is simpler, more efficient, and less data demanding than new alternatives. It extends and evolves the lazy creation of the model for the test instance by allowing: (i) to dynamically choose the best classifier for the task; (ii) the exploration of distances in the neighborhood of the test document when learning a classification model, thus diminishing the importance of irrelevant training instances; and (iii) a better representational space for training and test documents by augmenting them, in a lazy fashion, with new co-occurrence based features considering just those observed in the specific test instance. In a sizeable experimental evaluation, considering topics and sentiment analysis datasets and nine baselines, we show that our MetaLazy instantiations are among the top performers in most situations, even when compared to state-of-the-art deep learning classifiers such as Deep Network Transformer Architectures.
A Methodology Based on Deep Q-Learning/Genetic Algorithms for Optimizing COVID-19 Pandemic Government Actions
Authors: Luis Miralles-Pechuán, Fernando Jiménez, Hiram Ponce, Lourdes Martínez-Villaseñor
Venue: CIKM
Year: 2020

Whenever countries are threatened by a pandemic, as is the case with the COVID-19 virus, governments need help to take the right actions to safeguard public health as well as to mitigate the negative effects on the economy. A restrictive approach can seriously damage the economy. Conversely, a relaxed one may put at risk a high percentage of the population. Other investigations in this area are focused on modelling the spread of the virus or estimating the impact of the different measures on its propagation. However, in this paper, we propose a new methodology for helping governments in planning the phases to combat the pandemic based on their priorities. To this end, we implement the SEIR epidemiological model to represent the evolution of the COVID-19 virus on the population. To optimize the best sequences of actions governments can take, we propose a methodology with two approaches, one based on Deep Q-Learning and another one based on Genetic Algorithms. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system focused on meeting two objectives: firstly, getting few people infected so that hospitals are not overwhelmed, and secondly, avoiding taking drastic measures which could cause serious damage to the economy. The conducted experiments evaluate our methodology based on the accumulated rewards during the established period. The experiments also prove that it is a valid tool for governments to reduce the negative effects of a pandemic by optimizing the planning of the phases. According to our results, the approach based on Deep Q-Learning outperforms the one based on Genetic Algorithms.
Transformer Models for Recommending Related Questions in Web Search
Authors: Rajarshee Mitra, Manish Gupta, Sandipan Dandapat
Venue: CIKM
Year: 2020

People Also Ask (PAA) is an exciting feature in most of the leading search engines which recommends related questions for a given user query, thereby attempting to reduce the gap between user's information need. This helps users in diving deep into the topic of interest, and reduces task completion time. However, showing unrelated or irrelevant questions is highly detrimental to the user experience. While there has been significant work on query reformulation and related searches, there is hardly any published work on recommending related questions for a query. Question suggestion is challenging because the question needs to be interesting, structurally correct, not be a duplicate of other visible information, and must be reasonably related to the original query. In this paper, we present our system which is based on a Transformer-based neural representation, BERT (Bidirectional Encoder Representations from Transformers), for query, question and corresponding search result snippets. Our best model provides an accuracy of ~81%.
ALEX: Active Learning based Enhancement of a Classification Model's EXplainability
Authors: Ishani Mondal, Debasis Ganguly
Venue: CIKM
Year: 2020

An active learning (AL) algorithm seeks to construct an effective classifier with a minimal number of labeled examples in a bootstrapping manner. While standard AL heuristics, such as selecting those points for annotation for which a classification model yields least confident predictions, there has been no empirical investigation to see if these heuristics lead to models that are more interpretable to humans. In the era of data-driven learning, this is an important research direction to pursue. This paper describes our work-in-progress towards developing an AL selection function that in addition to model effectiveness also seeks to improve on the interpretability of a model during the bootstrapping steps. Concretely speaking, our proposed selection function trains an 'explainer' model in addition to the classifier model, and favours those instances where a different part of the data is used, on an average, to explain the predicted class. Initial experiments exhibited encouraging trends in showing that such a heuristic can lead to developing more effective and more explainable end-to-end data-driven classifiers.
SWE2: SubWord Enriched and Significant Word Emphasized Framework for Hate Speech Detection
Authors: Guanyi Mou, Pengyi Ye, Kyumin Lee
Venue: CIKM
Year: 2020

Hate speech detection on online social networks has become one of the emerging hot topics in recent years. With the broad spread and fast propagation speed across online social networks, hate speech makes significant impacts on society by increasing prejudice and hurting people. Therefore, there are aroused attention and concern from both industry and academia. In this paper, we address the hate speech problem and propose a novel hate speech detection framework called SWE2, which only relies on the content of messages and automatically identifies hate speech. In particular, our framework exploits both word-level semantic information and sub-word knowledge. It is intuitively persuasive and also practically performs well under a situation with/without character-level adversarial attack. Experimental results show that our proposed model achieves 0.975 accuracy and 0.953 macro F1, outperforming 7 state-of-the-art baselines under no adversarial attack. Our model robustly and significantly performed well under extreme adversarial attack (manipulation of 50% messages), achieving 0.967 accuracy and 0.934 macro F1.
PrivacyFL: A Simulator for Privacy-Preserving and Secure Federated Learning
Authors: Vaikkunth Mugunthan, Anton Peraire-Bueno, Lalana Kagal
Venue: CIKM
Year: 2020

Federated learning is a technique that enables distributed clients to collaboratively learn a shared machine learning model without sharing their training data. This reduces data privacy risks, however, privacy concerns still exist since it is possible to leak information about the training dataset from the trained model's weights or parameters. Therefore, it is important to develop federated learning algorithms that train highly accurate models in a privacy-preserving manner. Setting up a federated learning environment, especially with security and privacy guarantees, is a time-consuming process with numerous configurations and parameters that can be manipulated. In order to help clients ensure that collaboration is feasible and to check that it improves their model accuracy, a real-world simulator for privacy-preserving and secure federated learning is required. In this paper, we introduce PrivacyFL, which is an extensible, easily configurable, and scalable simulator for federated learning environments. Its key features include latency simulation, robustness to client departure/failure, support for both centralized (with one or more servers) and decentralized (serverless) learning, and configurable privacy and security mechanisms based on differential privacy and secure multiparty computation (MPC). In this paper, we motivate our research, describe the architecture of the simulator and associated protocols, and discuss its evaluation in numerous scenarios that highlight its wide range of functionality and its advantages. Our paper addresses a significant real-world problem: checking the feasibility of participating in a federated learning environment under a variety of circumstances. It also has a strong practical impact because organizations such as hospitals, banks, and research institutes, which have large amounts of sensitive data and would like to collaborate, would greatly benefit from having a system that enables them to do so in a privacy-preserving and secure manner.