OpenRaiser/Intern-Atlas huggingface dataset:
Subgraph-augmented Path Embedding for Semantic User Search on Heterogeneous Social Network
Authors: Zemin Liu, Vincent W. Zheng, Zhou Zhao, Hongxia Yang, Kevin Chen-Chuan Chang, Minghui Wu, Jing Ying
Venue: WWW
Year: 2018

Semantic user search is an important task on heterogeneous social networks. Its core problem is to measure the proximity between two user objects in the network w.r.t. certain semantic user relation. State-of-the-art solutions often take a path-based approach, which uses the sequences of objects connecting a query user and a target user to measure their proximity. Despite their success, we assert that path as a low-order structure is insufficient to capture the rich semantics between two users. Therefore, in this paper we introduce a new concept of subgraph-augmented path for semantic user search. Specifically, we consider sampling a set of object paths from a query user to a target user; then in each object path, we replace the linear object sequence between its every two neighboring users with their shared subgraph instances. Such subgraph-augmented paths are expected to leverage both path»s distance awareness and subgraph»s high-order structure. As it is non-trivial to model such subgraph-augmented paths, we develop a Subgraph-augmented Path Embedding (SPE) framework to accomplish the task. We evaluate our solution on six semantic user relations in three real-world public data sets, and show that it outperforms the baselines.
Exploring the Efficiency of Batch Active Learning for Human-in-the-Loop Relation Extraction
Authors: Ismini Lourentzou, Daniel Gruhl, Steve Welch
Venue: WWW
Year: 2018

Domain-specific relation extraction requires training data for supervised learning models, and thus, significant labeling effort. Distant supervision is often leveraged for creating large annotated corpora however these methods require handling the inherent noise. On the other hand, active learning approaches can reduce the annotation cost by selecting the most beneficial examples to label in order to learn a good model. The choice of examples can be performed sequentially, i.e. select one example in each iteration, or in batches, i.e. select a set of examples in each iteration. The optimization of the batch size is a practical problem faced in every real-world application of active learning, however it is often treated as a parameter decided in advance. In this work, we study the trade-off between model performance, the number of requested labels in a batch and the time spent in each round for real-time, domain specific relation extraction. Our results show that the use of an appropriate batch size produces competitive performance, even compared to a fully sequential strategy, while reducing the training time dramatically.
Uniform Random Sampling Not Recommended
Authors: Jianguo Lu, Hao Wang, Dingding Li
Venue: WWW
Year: 2018

We show that uniform random sampling is not as effective as PPS (probability proportional to size) sampling in many estimation tasks. In the setting of (graph) size estimation, this paper demonstrates that random edge sampling outperforms random node sampling, with a performance ratio proportional to the normalized graph degree variance. This result is particularly important in the era of big data, when data are typically large and scale-free, resulting in large degree variance. We derive the result by first giving the variances of random node and random edge estimators. A simpler and more intuitive result is obtained by assuming that the data is large and degree distribution follows a power law.
Arrays of (locality-sensitive) Count Estimators (ACE): Anomaly Detection on the Edge
Authors: Chen Luo, Anshumali Shrivastava
Venue: WWW
Year: 2018

Anomaly detection is one of the frequent and important subroutines deployed in large-scale data processing applications. Even being a well-studied topic, existing techniques for unsupervised anomaly detection require storing significant amounts of data, which is prohibitive from memory, latency and privacy perspectives, especially for small mobile devices which has ultra-low memory budget and limited computational power. In this paper, we propose ACE (Arrays of (locality-sensitive) Count Estimators) algorithm that can be 60x faster than most state-of-the-art unsupervised anomaly detection algorithms. In addition, ACE has appealing privacy properties. Our experiments show that ACE algorithm has significantly smaller memory footprints (∠ 4MB in our experiments) which can exploit Level 3 cache of any modern processor. At the core of the ACE algorithm, there is a novel statistical estimator which is derived from the sampling view of Locality Sensitive Hashing (LSH). This view is significantly different and efficient than the widely popular view of LSH for near-neighbor search. We show the superiority of ACE algorithm over 11 popular baselines on 3 benchmark datasets, including the KDD-Cup99 data which is the largest available public benchmark comprising of more than half a million entries with ground truth anomaly labels.
Scalable Supervised Discrete Hashing for Large-Scale Search
Authors: Xin Luo, Ye Wu, Xin-Shun Xu
Venue: WWW
Year: 2018

Supervised hashing methods have attracted much attention in these years. However, most existing supervised hashing algorithms have some of the following problems. First, most of them leverage the pairwise similarity matrix, whose size is quadratic to the number of training samples, to supervise the learning of hash codes. Thus, they are not scalable when dealing with large data. Second, most of them relax the discrete constraints for easy optimization and then quantize the learnt real-valued solution to binary hash codes. Therefore, the quantization error caused by the relaxation may lead to a decline of retrieval performance. To address these issues and make the supervised method scalable to large datasets, we present a novel hashing method, named Scalable Supervised Discrete Hashing (SSDH). Specifically, based on a new loss function, SSDH bypasses the direct optimization on the n by n pairwise similarity matrix. In addition, SSDH adopts no relaxation optimization scheme in the learning procedure and avoids the large quantization error problem. Moreover, during learning, it leverages both the pairwise similarity matrix and label matrix; thus, more semantic information can be embedded to the learning of hash codes. Extensive experiments are conducted on six benchmark datasets including two large-scale datasets, i.e., NUS-WIDE and ImageNet. The results show that SSDH can outperform state-of-the-art baselines on these datasets, demonstrating its effectiveness and efficiency.
Cognitive Computing Track Chairs' Welcome & Organization
Authors: Miltiadis D. Lytras, Naif Radi Aljohani, Amir Hussain, Jiebo Luo, Jacky Xi Zhang
Venue: WWW
Year: 2018

Cognitive Computing has received increasing attention from academia and industries as it brings cognitive science and computing together for the development of new computational platforms, infrastructures, systems and algorithms. Artificial intelligence and computational intelligence are key elements to succeed in cognitive computing. The Cognitive Computing track was successfully organized at WWW2017. This year, WWW2018 continues this track with a focus on all applications. Particularly, applications in healthcare, environment, education, sustainability, smart cities and food science are among the important global challenges in 21st Century.
Real-time Event-based News Suggestion for Wikipedia Pages from News Streams
Authors: Lijun Lyu, Besnik Fetahu
Venue: WWW
Year: 2018

Wikipedia is one of the top visited resources on the Web, furthermore, it is used extensively as the main source of information for applications like Web search, question & answering etc. This is mostly attributed to Wikipedia's coverage in terms of topics and real-world entities and the fact that Wikipedia articles are constantly updated with new and emerging facts. However, only a small fraction of articles are considered to be of good quality. The large majority of articles are incomplete and have other quality issues. A strong quality indicator is the presence of external references from third-party sources (e.g. news sources) as suggested by the verifiability principle in Wikipedia. Even for the existing references in Wikipedia there is an inherent lag in terms of the publication time of cited resources and the time they are cited in Wikipedia articles. We propose a near real-time suggestion of news references for Wikipedia from a daily news stream. We model daily news into specific events, spanning from a day up to year. Thus, we construct an event-chain from which we determine when the information in an event has converged and consequentially based on a learning-to-rank approach suggest the most authoritative and complete news article to Wikipedia articles involved in a specific event. We evaluate our news suggestion approach on a set of 41 events extracted from Wikipedia currents event portal, and on new corpus consisting of daily news between the period of 2016-2017 with more than 14 million news articles. We are able to suggest news articles to Wikipedia pages with an overall accuracy of MAP=0.77 and with a minimal lag w.r.t the publication time of the news article.
Web-Based VR Experiments Powered by the Crowd
Authors: Xiao Ma, Megan Cackett, Leslie Park, Eric Chien, Mor Naaman
Venue: WWW
Year: 2018

We build on the increasing availability of Virtual Reality (VR) devices and Web technologies to conduct behavioral experiments in VR using crowdsourcing techniques. A new recruiting and validation method allows us to create a panel of eligible experiment participants recruited from Amazon Mechanical Turk. Using this panel, we ran three different crowdsourced VR experiments, each reproducing one of three VR illusions: place illusion, embodiment illusion, and plausibility illusion. Our experience and worker feedback on these experiments show that conducting Web-based VR experiments using crowdsourcing is already feasible, though some challenges---including scale---remain. Such crowdsourced VR experiments on the Web have the potential to finally support replicable VR experiments with diverse populations at a low cost.
Aladdin: Automating Release of Deep-Link APIs on Android
Authors: Yun Ma, Ziniu Hu, Yunxin Liu, Tao Xie, Xuanzhe Liu
Venue: WWW
Year: 2018

Compared to the Web where each web page has a global URL for external access, a specific 'page' inside a mobile app cannot be easily accessed unless the user performs several steps from the landing page of this app. Recently, the concept of 'deep link' is expected to be a promising solution and has been advocated by major service providers to enable targeting and opening a specific page of an app externally with an accessible uniform resource identifier. In this paper, we present a large-scale empirical study to investigate how deep links are really adopted, over 25,000 Android apps. To our surprise, we find that deep links have quite low coverage, e.g., more than 70% and 90% of the apps do not have deep links on app stores Wandoujia and Google Play, respectively. One underlying reason is the mandatory and non-trivial manual efforts of app developers to provide APIs for deep links. We then propose the Aladdin approach along with its supporting tool to help developers practically automate the release of deep-link APIs to access locations inside their apps. Aladdin includes a novel cooperative framework by synthesizing the static analysis and the dynamic analysis while minimally engaging developers» inputs and configurations, without requiring any coding efforts or additional deployment efforts. We evaluate Aladdin with 579 popular apps and demonstrate its effectiveness and performance.
TIMES: Temporal Information Maximally Extracted from Structures
Authors: Abram N. Magner, Jithin K. Sreedharan, Ananth Y. Grama, Wojciech Szpankowski
Venue: WWW
Year: 2018

Inferring the node arrival sequence from a snapshot of a dynamic network is an important problem, with applications ranging from identifying sources of contagion to flow of capital in financial transaction networks. Variants of this problem have received significant recent research attention, including results on infeasibility of solution for prior formulations. We present a new formulation of the problem that admits probabilistic solutions for broad classes of dynamic network models. Instantiating our framework for a preferential attachment model, we present effectively computable and practically tight bounds on the tradeoff curve between optimal achievable precision and density/recall. We also present efficient algorithms for partial recovery of node arrival orders and derive theoretical and empirical performance bounds on the precision and density/recall of our methods in comparison to the best possible. We validate our methods through experiments on both synthetic and real networks to show that their performance is robust to model changes, and that they yield excellent results in practice. We also demonstrate their utility in the context of a novel application in analysis of the human brain connectome to draw new insights into the functional and structural organization and evolution of the human brain.
Automatic Hierarchical Table of Contents Generation for Educational Videos
Authors: Debabrata Mahapatra, Ragunathan Mariappan, Vaibhav Rajan
Venue: WWW
Year: 2018

The number of freely available online educational videos from universities and other organizations is growing rapidly. Accurate indexing and summarization are essential for efficient search, recommendation and effective consumption of videos. In this paper, we describe a new method of automatically creating a hierarchical table of contents for a video. It provides a summary of the video content along with a textbook--like facility for nonlinear navigation and search through the video. Our multimodal approach combines new methods for shot level video segmentation and for hierarchical summarization. Empirical results demonstrate the efficacy of our approach on many educational videos.
VideoKen: Automatic Video Summarization and Course Curation to Support Learning
Authors: Debabrata Mahapatra, Ragunathan Mariappan, Vaibhav Rajan, Kuldeep Yadav, Seby A., Sudeshna Roy
Venue: WWW
Year: 2018

The number of high quality online videos is increasing rapidly. Online courses as well as universities do not fully leverage the content due to several open challenges in video search, indexing, summarization and customization requirements for specific courses, instructors or learners. We present a new web-based social learning platform called Videoken. Using novel video summarization algorithms, Videoken automatically creates Table of Contents for videos. This allows a textbook-like facility for non-linear search and navigation through the video, enables extraction of semantically coherent clips from within a video and improves video search through better semantic indexing. The platform also allows new ways of course creation and sharing of learning modules; and can be both integrated with existing Learning Management Systems and used independently.
Matching Resumes to Jobs via Deep Siamese Network
Authors: Saket Maheshwary, Hemant Misra
Venue: WWW
Year: 2018

In this paper we investigate the important and challenging task of recommending appropriate jobs for job seeking candidates by matching semi structured resumes of candidates to job descriptions. To perform this task, we propose to use a siamese adaptation of convolutional neural network. The proposed approach effectively captures the underlying semantics thus enabling to project similar resumes and job descriptions closer to each other, and make dissimilar resumes and job descriptions distant from each other in the semantic space. Our experimental results on a set of 1314 resumes and a set of 3809 job descriptions (5,005,026 resume-job description pairs) demonstrate that our approach is better than the current state-of-the-art approaches.
WWW'18 Open Challenge: Financial Opinion Mining and Question Answering
Authors: Macedo Maia, Siegfried Handschuh, André Freitas, Brian Davis, Ross McDermott, Manel Zarrouk, Alexandra Balahur
Venue: WWW
Year: 2018

The growing maturity of Natural Language Processing (NLP) techniques and resources is dramatically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The finance domain, with its reliance on the interpretation of multiple unstructured and structured data sources and its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques for the automatic analysis of financial news and opinions online. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain.
Citizen Participation: Case Study on Participatory Apps in Germany
Authors: Agnes Mainka, Tobias Siebenlist, Lisa Beutelspacher
Venue: WWW
Year: 2018

Participatory smartphone apps empower citizens to interact with the city's administration. The purpose of this case study is to investigate the current state of participatory apps in Germany. The 29 apps that have been found can be categorized into four topics: Information Awareness, City Service, Transparency and Public Safety. Most citizen apps can be assigned to the category Travel & Local. None of the identified apps is based on open-source code, and the citizens' reports are not publicly visible, e.g., for other citizens. It is unclear to whom the data generated by citizens belong.
Spam2Vec: Learning Biased Embeddings for Spam Detection in Twitter
Authors: Suman Kalyan Maity, Santosh K. C., Arjun Mukherjee
Venue: WWW
Year: 2018

In this paper, we propose a semi-supervised framework Spam2Vec to identify spammers in Twitter. This algorithmic framework learns the spam representations of the node in the network by leveraging biased random walks. Our spammer detection method yields an AUC of 0.54 with precision@100 as 0.12 and performs significantly better with 7.77% increase in AUC and a 2.4 times improvement on precision over the best performing baseline.
Finding Tours for a Set of Interests
Authors: Mohamed Abdel Maksoud, Gaurav Pandey, Shuaiqiang Wang
Venue: WWW
Year: 2018

This paper addresses a novel tour discovery problem in the domain of travel search. We create a ranking of tours for a set of travel interests, where a tour is a group of city documents and a travel interest is a query. While generating and ranking tours, it is aimed that each interest (from the interest set) is satisfied by at least one city in a tour and the distance traveled to cover the tour is not too large. Firstly, we generate tours for the interest set, by utilizing the available ranking of cities for the individual interests and the distances between the cities. Then, in absence of existing methods directly related to our problem, we devise our novel techniques to calculate ranking scores for the tours and present a comparison of these techniques in our results. We demonstrate our web application Travición, that utilizes the best tour scoring technique.
Computationally Inferred Genealogical Networks Uncover Long-Term Trends in Assortative Mating
Authors: Eric Malmi, Aristides Gionis, Arno Solin
Venue: WWW
Year: 2018

Genealogical networks, also known as family trees or population pedigrees, are commonly studied by genealogists wanting to know about their ancestry, but they also provide a valuable resource for disciplines such as digital demography, genetics, and computational social science. These networks are typically constructed by hand through a very time-consuming process, which requires comparing large numbers of historical records manually. We develop computational methods for automatically inferring large-scale genealogical networks. A comparison with human-constructed networks attests to the accuracy of the proposed methods. To demonstrate the applicability of the inferred large-scale genealogical networks, we present a longitudinal analysis on the mating patterns observed in a network. This analysis shows a consistent tendency of people choosing a spouse with a similar socioeconomic status, a phenomenon known as assortative mating. Interestingly, we do not observe this tendency to consistently decrease (nor increase) over our study period of 150 years.
Pain Prediction in Humans using Human Brain Activity Data
Authors: Zara Mansoor, Mustansar Ali Ghazanfar, Syed Muhammad Anwar, Ahmed S. Alfakeeh, Khaled Hamed Alyoubi
Venue: WWW
Year: 2018

This research article focuses on the analysis of electroencephalography (EEG) signals of the brain during pain perception. The proposed system is based on the hypothesis that a noticeable change occurs in mental conditions while experiencing pain. When the human body is injured, sensory receptors in the brain enter a stimulated state. The injury may be the result of attention or an accident. Pain warnings are natural in humans and protect the body from further negative effects. In this article, an innovative and robust system based on prominent features extracted from the brain activity recorded using EEG, is proposed to predict the state of pain perception. The brain signals of subjects are observed using two low-cost EEG headsets including neurosky mindwave mobile and emotiv insight. Time and frequency domain features are selected to represent the observed signals. The results show that a combination of time and frequency domain features is the most informative approach for pain prediction using the observed brain activity.
Understanding User's Search Behavior towards Spiky Events
Authors: Behrooz Mansouri, Mohammad Sadegh Zahedi, Ricardo Campos, Mojgan Farhoodi, Maseud Rahgozar
Venue: WWW
Year: 2018

Web searches are done by users every day on a million-daily basis. Many of these web searches are related to events, social occasions that attracts society's attention. Events may happen multiple times on cyclic or non-periodic occasions. These are known as spiky events. When these events occur, multiple spikes can be observed in query logs triggered by a change in the user's behaviour and an increase in the frequency of the user's queries. In this paper, we aim to understand the user's search behaviour towards this kind of events. To this regard, we propose a new taxonomy of spiky events which categorizes queries into two groups: periodic (ongoing, historical, traditional) and aperiodic (predictable and unpredictable), and study how various features concerning the query and the clicked web pages describe the user's behaviour, before, during, and after the event. To conduct this research, we consider 100 spiky events and rely on a two-year Persian search engine query log to analyse their related queries and associated information. The results obtained show that users have a different behaviour regarding the query frequency, length and temporality, depending on the category of the spiky event and that query formulation and clicked pages are also different for each category before, during and after the event. Understanding these user's behaviours and their relationship with the different categories may play an important role for any search engine looking to provide better services for their users.