OpenRaiser/Intern-Atlas huggingface dataset:
Aspect of Blame in Tweets: A Deep Recurrent Neural Network Approach
Authors: Herman Masindano Wandabwa, Muhammad Asif Naeem, Farhaan Mirza
Venue: WWW
Year: 2017

Twitter as an information dissemination tool has proved to be instrumental in generating user curated content in short spans of time. Tweeting usually occurs when reacting to events, speeches, about a service or product. This in some cases comes with its fair share of blame on varied aspects in reference to say an event. Our work in progress details how we plan to collect the informal texts, clean them and extract features for blame detection. We are interested in augmenting Recurrent Neural Networks (RNN) with self-developed association rules in getting the most out of the data for training and evaluation. We aim to test the performance of our approach using human-induced terror-related tweets corpus. It is possible tailoring the model to fit natural disaster scenarios.
Providing Research Graph Data in JSON-LD Using Schema.org
Authors: Jingbo Wang, Amir Aryani, Lesley Wyborn, Benjamin J. K. Evans
Venue: WWW
Year: 2017

In this position paper, we describe a pilot project that provides Research Graph records to external web services using JSON-LD. The Research Graph database contains a large-scale graph that links research datasets (i.e., data used to support research) to funding records (i.e. grants), publications and researcher records such as ORCID profiles. This database was derived from the work of the Research Data Alliance Working Group on Data Description Registry Interoperability (DDRI), and curated using the Research Data Switchboard open source software. By being available in Linked Data format, the Research Graph database is more accessible to third-party web services over the Internet, which thus opens the opportunity to connect to the rest of the world in the semantic format.
Is Scientific Collaboration Sustainability Predictable?
Authors: Wei Wang, Zixin Cui, Tong Gao, Shuo Yu, Xiangjie Kong, Feng Xia
Venue: WWW
Year: 2017

This work aims to explore whether the sustainability of scientific collaboration including collaboration duration and collaboration times can be predicted. For this purpose, we propose a series of features including structural similarity indices, authorship properties, and research interests. Experimental results on a real-world dataset show that our proposed model outperforms baseline model by 10% in MAE. Our study may shed light on investigating scientific collaboration from the perspective of sustainability.
Query Expansion Based on a Feedback Concept Model for Microblog Retrieval
Authors: Yashen Wang, Heyan Huang, Chong Feng
Venue: WWW
Year: 2017

We tackle the problem of improving microblog retrieval algorithms by proposing a Feedback Concept Model for query expansion. In particular, we expand the query using knowledge information derived from Probase so that the expanded one could better reflect users' search intent, which allows for microblog retrieval at a concept-level, rather than term-level. In the proposed feedback concept model: (i) we mine the concept information implicit in short-texts based on the external knowledge bases; (ii) with the relevant concepts associated with short-texts, a mixture model is generated to estimate a concept language model; (iii) finally, we utilize the concept language model for query expansion. Moreover, we incorporate temporal prior into the proposed query expansion method to satisfy real-time information need. Finally, we test the generalization power of the feedback concept model on the TREC Microblog corpora. The experimental results demonstrate that the proposed model outperforms the previous methods for microblog retrieval significantly.
An Explorative Study of the Mobile App Ecosystem from App Developers' Perspective
Authors: Haoyu Wang, Zhe Liu, Yao Guo, Xiangqun Chen, Miao Zhang, Guoai Xu, Jason I. Hong
Venue: WWW
Year: 2017

With the prevalence of smartphones, app markets such as Apple App Store and Google Play has become the center stage in the mobile app ecosystem, with millions of apps developed by tens of thousands of app developers in each major market. This paper presents a study of the mobile app ecosystem from the perspective of app developers. Based on over one million Android apps and 320,000 developers from Google Play, we analyzed the Android app ecosystem from different aspects. Our analysis shows that while over half of the developers have released only one app in the market, many of them have released hundreds of apps. We classified developers into different groups based on the number of apps they have released, and compared their characteristics. Specially, we have analyzed the group of aggressive developers who have released more than 50 apps, trying to understand how and why they create so many apps. We also investigated the privacy behaviors of app developers, showing that some developers have a habit of producing apps with low privacy ratings. Our study shows that understanding the behavior of mobile developers can be helpful to not only other app developers, but also to app markets and mobile users.
Shifu: Deep Learning Based Advisor-advisee Relationship Mining in Scholarly Big Data
Authors: Wei Wang, Jiaying Liu, Feng Xia, Irwin King, Hanghang Tong
Venue: WWW
Year: 2017

Scholars in academia are involved in various social relationships such as advisor-advisee relationships. The analysis of such relationship can provide invaluable information for understanding the interactions among scholars as well as providing many researcher-specific applications such as advisor recommendation and academic rising star identification. However, in most cases, high quality advisor-advisee relationship dataset is unavailable. To address this problem, we propose Shifu, a deep-learning-based advisor-advisee relationship identification method which takes into account both the local properties and network characteristics. In particular, we explore how to crawl advisor-advisee pairs from PhDtree project and extract their publication information by matching them with DBLP dataset as the experimental dataset. To the best of our knowledge, no prior effort has been made to address the scientific collaboration network features for relationship identification by exploiting deep learning. Our experiments demonstrate that the proposed method outperforms other state-of-the-art machine learning methods in precision (94%). Furthermore, we apply Shifu to the entire DBLP dataset and obtain a large-scale advisor-advisee relationship dataset.
Sharing Databases on the Web with Porter Proxy
Authors: Xin Wang, Aastha Madaan, Eugene Siow, Thanassis Tiropanis
Venue: WWW
Year: 2017

With large number of datasets now available through the Web, data-sharing ecosystems such as the Web Observatory have emerged. The Web Observatory provides an active decentralised ecosystem for datasets and applications based on a number Web Observatory sites, each of which can run in a different administrative domain. On a Web Observatory site users can publish and securely access datasets across domains via a harmonised API and reverse proxies for access control. However, that API provides a different interface to that of the databases on which datasets are stored and, consequently, existing applications that consume data from specific databases require major modification to be added to the Web Observatory ecosystem. In this paper we propose a lightweight architecture called Porter Proxy to address this concern. Porter Proxy exposes the same interfaces as databases as requested by the users while enforcing access control. Characteristics of the proposed Porter Proxy architecture are evaluated based on adversarial scenario-handling in Web Observatory eco-system.
Extending the Balance Theory by Measuring Bidirectional Opinions with Interactive Language
Authors: Bo Wang, Yingjun Sun, Bo Han, Yuexian Hou, Dawei Song
Venue: WWW
Year: 2017

The signs of social relationship in balance theory is objective and undirected which cannot characterize participants' bidirectional subjective opinions on social relationships. By redefining the signs with the bidirectional opinions according to homogeneity principle, we extend the balance theory by measuring the opinions with interactive language. Initial experimental results on Enron emails shows that the extended model supports the homogeneity principle of balance theory and make it possible to use balance theory to predict the directed opinions besides undirected signs.
Can Machines Intelligently Propose Novel and Reasonable Scientific Hypotheses?
Authors: Pengwei Wang, Zhongyuan Wang, Lei Ji, Jun Yan, Lianwen Jin
Venue: WWW
Year: 2017

Machine intelligence is attracting increasing attention from both industry and academia. However, the problem of how to make machines innovate novel hypothesis is underexplored. Automatic hypothesis generation can effectively shorten research process. In this work, we try to build an embedding based genetic algorithm to learn "experience" from past data, mine latent semantic information, and then propose the new scientific hypotheses. To our best knowledge, we are the first who propose to use an embedding based genetic algorithm for scientific hypothesis generation. Experiments show that our method outperforms the state of the art.
Location Privacy-Preserving Task Allocation for Mobile Crowdsensing with Differential Geo-Obfuscation
Authors: Leye Wang, Dingqi Yang, Xiao Han, Tianben Wang, Daqing Zhang, Xiaojuan Ma
Venue: WWW
Year: 2017

In traditional mobile crowdsensing applications, organizers need participants' precise locations for optimal task allocation, e.g., minimizing selected workers' travel distance to task locations. However, the exposure of their locations raises privacy concerns. Especially for those who are not eventually selected for any task, their location privacy is sacrificed in vain. Hence, in this paper, we propose a location privacy-preserving task allocation framework with geo-obfuscation to protect users' locations during task assignments. Specifically, we make participants obfuscate their reported locations under the guarantee of differential privacy, which can provide privacy protection regardless of adversaries' prior knowledge and without the involvement of any third-part entity. In order to achieve optimal task allocation with such differential geo-obfuscation, we formulate a mixed-integer non-linear programming problem to minimize the expected travel distance of the selected workers under the constraint of differential privacy. Evaluation results on both simulation and real-world user mobility traces show the effectiveness of our proposed framework. Particularly, our framework outperforms Laplace obfuscation, a state-of-the-art differential geo-obfuscation mechanism, by achieving 45% less average travel distance on the real-world data.
Digital Demography
Authors: Ingmar Weber, Bogdan State
Venue: WWW
Year: 2017

Demography is the science of human populations and, at its most basic, focuses on the processes of (i) fertility, (ii) mortality and (iii) mobility. Whereas modern states are typically in a reasonable position to keep records on both fertility and mortality, through birth and death registrations, as well as through censuses, measuring the mobility of populations represents a particular challenge due to reasons ranging from inconsistencies in official definitions across countries, to the difficulty of quantifying illegal migration. At the same time, mere numbers, whether on births, deaths or migration events, shed little light on the underlying causes, hence providing insufficient information to policy makers.
Why Do Cascade Sizes Follow a Power-Law?
Authors: Karol Wegrzycki, Piotr Sankowski, Andrzej Pacuk, Piotr Wygocki
Venue: WWW
Year: 2017

We introduce random directed acyclic graph and use it to model the information diffusion network. Subsequently, we analyze the cascade generation model (CGM) introduced by Leskovec et al. [19]. Until now only empirical studies of this model were done. In this paper, we present the first theoretical proof that the sizes of cascades generated by the CGM follow the power-law distribution, which is consistent with multiple empirical analysis of the large social networks. We compared the assumptions of our model with the Twitter social network and tested the goodness of approximation.
Cross View Link Prediction by Learning Noise-resilient Representation Consensus
Authors: Xiaokai Wei, Linchuan Xu, Bokai Cao, Philip S. Yu
Venue: WWW
Year: 2017

Link Prediction has been an important task for social and information networks. Existing approaches usually assume the completeness of network structure. However, in many real-world networks, the links and node attributes can usually be partially observable. In this paper, we study the problem of Cross View Link Prediction (CVLP) on partially observable networks, where the focus is to recommend nodes with only links to nodes with only attributes (or vice versa). We aim to bridge the information gap by learning a robust consensus for link-based and attribute-based representations so that nodes become comparable in the latent space. Also, the link-based and attribute-based representations can lend strength to each other via this consensus learning. Moreover, attribute selection is performed jointly with the representation learning to alleviate the effect of noisy high-dimensional attributes. We present two instantiations of this framework with different loss functions and develop an alternating optimization framework to solve the problem. Experimental results on four real-world datasets show the proposed algorithm outperforms the baseline methods significantly for cross-view link prediction.
What Computers Should Know, Shouldn't Know, and Shouldn't Believe
Authors: Gerhard Weikum
Venue: WWW
Year: 2017

Automatically constructed knowledge bases (KB's) are a powerful asset for search, analytics, recommendations and data integration, with intensive use at big industrial stake-holders. Examples are the knowledge graphs for search engines (e.g., Google, Bing, Baidu) and social networks (e.g., Facebook), as well as domain-specific KB's (e.g., Bloomberg, Walmart). These achievements are rooted in academic research and community projects. The largest general-purpose KB's with publicly accessible contents are BabelNet, DBpedia, Wikidata, and Yago. They contain millions of entities, organized in hundreds to hundred thousands of semantic classes, and billions of relational facts on entities. These and other knowledge and data resources are interlinked at the entity level, forming the Web of Linked Open Data.
Ethical and Social Challenges with developing Automated Methods to Detect and Warn potential victims of Mass-marketing Fraud (MMF)
Authors: Monica T. Whitty, Matthew Edwards, Michael Levi, Claudia Peersman, Awais Rashid, M. Angela Sasse, Tom Sorell, Gianluca Stringhini
Venue: WWW
Year: 2017

Mass-marketing frauds (MMFs) are on the increase. Given the amount of monies lost and the psychological impact of MMFs there is an urgent need to develop new and effective methods to prevent more of these crimes. This paper reports the early planning of automated methods our interdisciplinary team are developing to prevent and detect MMF. Importantly, the paper presents the ethical and social constraints involved in such a model and suggests concerns others might also consider when developing automated systems.
Journeys to Engagement: Ambient Activity Technologies for People Living with Dementia
Authors: Andrea Wilkinson, Vishuda Charoenkitkarn, Judy O&apos, Neill, Marc Kanik, Mark H. Chignell
Venue: WWW
Year: 2017

Caring for individuals with dementia living in long-term care environments requires the management of responsive behaviours (e.g., screaming, hitting, wandering), which affect 60-80% of residents with dementia. Responsive behaviours are commonly treated with antipsychotic medications, and they compromise well-being of residents as well as staff. Our multi-disciplinary team seeks to develop technological solutions to minimize agitated and aggressive behaviours by creating familiar and appropriate activities that allow residents with dementia opportunities to self-initiate (trigger), engage and interact with Ambient Activity Technologies (AATs) and Centivizers (reward-based learning). Once engaged, these technologies should provide the resident with individualized and stimulating experiences that are meaningful to the resident, facilitating the development of a calm and engaged state, thereby managing responsive behaviours (e.g., agitation and aggression) and improving quality of life. In this paper, we review the etiology of dementia and responsive behaviours to elucidate the rationale behind the design and development of AATs and Centivizers for individuals with dementia living in long-term care, using a person-centered technological approach. Our goal is to utilize technology to minimize caregiver burden and help to improve well-being related outcomes in residents, staff and family members.
Predicting Latent Structured Intents from Shopping Queries
Authors: Chao-Yuan Wu, Amr Ahmed, Gowtham Ramani Kumar, Ritendra Datta
Venue: WWW
Year: 2017

In online shopping, users usually express their intent through search queries. However, these queries are often ambiguous. For example, it is more likely (and easier) for users to write a query like "high-end bike" than "21 speed carbon frames jamis or giant road bike". It is challenging to interpret these ambiguous queries and thus search result accuracy suffers. A user oftentimes needs to go through the frustrating process of refining search queries or self-teaching from possibly unstructured information. However, shopping is indeed a structured domain, that is composed of category hierarchy, brands, product lines, features, etc. It would be much better if a shopping site could understand users' intent through this structure, present organized information, and then find the items with the right categories, brands or features.
Large-Scale Wi-Fi Hotspot Classification via Deep Learning
Authors: Chang Xu, Kuiyu Chang, Khee-Chin Chua, Meishan Hu, Zhenxiang Gao
Venue: WWW
Year: 2017

We describe the problem of classifying hundreds of millions of Wi-Fi hotspots using only connection and user count characteristics. We use a combination of deep learning and frequency analysis. Specifically, Convolution Neural Networks (CNN) capture the spatio-temporal relationship between adjacent connection/user counts across a 24 hour×7 day matrix, while FFT (Fast Fourier Transforms) extract user and connection frequencies. Our production system has been deployed to classify 239 million hotspots in 12 hours on a SPARK 2.0 cluster, achieving close to 80% F1-score for binary classification.
DL-BAC: Distributed Ledger Based Access Control for Web Applications
Authors: Lei Xu, Lin Chen, Nolan Shah, Zhimin Gao, Yang Lu, Weidong Shi
Venue: WWW
Year: 2017

Since Internet based applications have become the norm for most users, security has become a bigger concern than ever before, especially for applications like social networking and cloud based storage. Access control is one of the key techniques that can mitigate security concerns for web based applications. However, most existing access control mechanisms require a trusted party, which are vulnerable to many threats including malicious insiders and single point failure. In response to these challenges, we propose DL-BAC, a novel access control system based on the distributed ledger. DL-BAC robustly enforces access control policies without depending on a single trusted party. We also provide an extension of DL-BAC that is privacy respecting and evaluate the performance of DL-BAC to show its practicability.
On Learning Mixed Community-specific Similarity Metrics for Cold-start Link Prediction
Authors: Linchuan Xu, Xiaokai Wei, Jiannong Cao, Philip S. Yu
Venue: WWW
Year: 2017

We study the cold-start link prediction problem where edges between vertices is unavailable by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in many real-world social networks. Because different communities usually exhibit different intra-community homogeneities, learning a global similarity metric is not appropriate. In this paper, we thus propose to learn community-specific similarity metrics via joint community detection. Experiments on three real-world networks show that the intra-community homogeneities can be well preserved, and the mixed community-specific metrics perform better than a global similarity metric in terms of prediction accuracy.
Low-Cost Mobile Learning Solutions for Community Health Workers
Authors: Deepika Yadav
Venue: WWW
Year: 2017

Accredited Social Health Activists in India play a critical role in improving the access to healthcare services of rural populations. Despite their key contribution in Millennium Development Goals, they receive inadequate training and supervision. Traditional face to face training face challenges of infrastructure, management and cost. Existing research studies have highlighted the potential of alternative approaches e.g. mHealth for capacity building, however so far they mainly focus on providing job aids only.
Sangoshthi: Empowering Community Health Workers through Peer Learning in Rural India
Authors: Deepika Yadav, Pushpendra Singh, Kyle Montague, Vijay Kumar, Deepak Sood, Madeline Balaam, Drishti Sharma, Mona Duggal, Tom Bartindale, Delvin Varghese, Patrick Olivier
Venue: WWW
Year: 2017

The Healthcare system of India provides outreach services to the rural population with a key focus on the maternal and child health through its flagship program of Community Health Workers (CHWs). The program since its launch has reached a scale of over 900000 health workers across the country and observed significant benefits on the health indicators. However, traditional face to face training mechanisms face persistent challenge in providing adequate training and capacity building opportunities to CHWs which leads to their sub-optimal knowledge and skill sets. In this paper, we propose Sangoshthi, a low-cost mobile based training and learning platform that fits well into the environment of low-Internet access. Sangoshthi leverages the architecture that combines Internet and IVR technology to host real time training sessions with the CHWs having access to basic phones only. We present our findings of a four week long field deployment with 40 CHWs using both qualitative and quantitative methods. Sangoshthi offers a lively environment of peer learning that was well received by the CHW community and resulted into their knowledge gains (16%) and increased confidence levels to handle the cases. Our study highlights the potential of complementary training platforms that can empower CHWs in-situ without the need of additional infrastructure.
Advertising Keyword Recommendation based on Supervised Link Prediction in Multi-Relational Network
Authors: Xiao Yang, Tao Deng, Zhi Guo, Zongyao Ding
Venue: WWW
Year: 2017

In the sponsored search system, advertisers bid on keywords that are related to their products or services to participate in repeated auctions and display their ads on the search result pages. Since there is a huge inventory of possible terms, instructive keyword recommendation is an important component to help advertisers optimize their campaigns and improve ad monetization. In this paper, by constructing a heterogeneous network which contains four types of links between advertisers and keywords based on different data resources and mining complex representations of network structure and task-guided attributes of nodes, we propose an approach to keyword recommendation based on supervised link prediction in multi-relational network. This method can retrieve ample candidates and provide informative ranking scores for recommended list of keywords, experimental results with real sponsored search data validate the effectiveness of the proposed algorithm in producing valuable keywords for advertisers.