OpenRaiser/Intern-Atlas huggingface dataset:
Verification of Hypermedia Characteristic of RESTful Finite-State Machines
Authors: Henry Vu, Tobias Fertig, Peter Braun
Venue: WWW
Year: 2018

Being an architectural style rather than a specification or a standard, the proper design of REpresentational State Transfer (REST) APIs is not trivial, since developers have to deal with a flood of recommendations and best practices, especially the proper application of the hypermedia constraint requires some decent experience. Furthermore, testing RESTful APIs is a missing topic within literature and especially, hypermedia testing is not mentioned at all. To deal with this state of affairs, we have elaborated a Model-Driven Software Development (MDSD) approach for creating RESTful APIs. As this project matured, we also explored the possibility of Model-Driven Testing (MDT). This work addresses the challenges of hypermedia testing and proposes approaches to overcome them with MDT techniques. We present the results of hypermedia testing for RESTful APIs using a model verification approach that were discovered within our research. MDT enables the verification of the underlying model of a RESTful API and ensuring its correctness before initiating any code generation. Therefore, we can prevent a poorly designed model from being transformed into a poorly designed RESTful API.
Joint User- and Event- Driven Stable Social Event Organization
Authors: Xin Wang, Wenwu Zhu, Chun Chen, Martin Ester
Venue: WWW
Year: 2018

The problem of social event organization (SEO) rises with the advent of online web services and plays an important role in helping users discover new offline events. Existing work on SEO only assumes that different users have different preferences towards different events, ignoring the fact that each event (its organizer) may have a separate preference towards every user. In this paper, we investigate joint user- and event- driven SEO by simultaneously considering user preferences (towards events) and event preferences (towards users). A risen challenging problem is that this joint consideration may suffer instabilities between users and events which are NP-hard to handle in SEO. Stability is a desired property that needs to be maintained in SEO, otherwise participants will incline towards changing to other events and trust less the organizer.
Attention Network for Information Diffusion Prediction
Authors: Zhitao Wang, Chengyao Chen, Wenjie Li
Venue: WWW
Year: 2018

In this paper, we propose an attention network for diffusion prediction problem. The developed diffusion attention module can effectively explore the implicit user-to-user diffusion dependency among information cascade users. Besides, the user-to-cascade importance and the time-decay effect are captured and utilized by the model. The superiority of the proposed model over state-of-the-art methods is demonstrated by experiments on real diffusion data.
SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints
Authors: Yanyan Wang, Qun Chen, Xin Liu, Murtadha H. M. Ahmed, Zhanhuai Li, Wei Pan, Hailong Liu
Venue: WWW
Year: 2018

The state-of-the-art techniques for aspect-level sentiment analysis focus on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their practical performance may fall short of expectations due to semantic complexity of natural languages. Motivated by the observation that linguistic hints (e.g. explicit sentiment words and shift words) can be strong indicators of sentiment, we present a joint framework, SenHint, which integrates the output of deep neural networks and the implication of linguistic hints into a coherent reasoning model based on Markov Logic Network (MLN). In SenHint, linguistic hints are used in two ways: (1) to identify easy instances, whose sentiment can be automatically determined by machine with high accuracy; (2) to capture implicit relations between aspect polarities. We also empirically evaluate the performance of SenHint on both English and Chinese benchmark datasets. Our experimental results show that SenHint can effectively improve accuracy compared with the state-of-the-art alternatives.
DKN: Deep Knowledge-Aware Network for News Recommendation
Authors: Hongwei Wang, Fuzheng Zhang, Xing Xie, Minyi Guo
Venue: WWW
Year: 2018

Online news recommender systems aim to address the information explosion of news and make personalized recommendation for users. In general, news language is highly condensed, full of knowledge entities and common sense. However, existing methods are unaware of such external knowledge and cannot fully discover latent knowledge-level connections among news. The recommended results for a user are consequently limited to simple patterns and cannot be extended reasonably. To solve the above problem, in this paper, we propose a deep knowledge-aware network (DKN) that incorporates knowledge graph representation into news recommendation. DKN is a content-based deep recommendation framework for click-through rate prediction. The key component of DKN is a multi-channel and word-entity-aligned knowledge-aware convolutional neural network (KCNN) that fuses semantic-level and knowledge-level representations of news. KCNN treats words and entities as multiple channels, and explicitly keeps their alignment relationship during convolution. In addition, to address users» diverse interests, we also design an attention module in DKN to dynamically aggregate a user»s history with respect to current candidate news. Through extensive experiments on a real online news platform, we demonstrate that DKN achieves substantial gains over state-of-the-art deep recommendation models. We also validate the efficacy of the usage of knowledge in DKN.
Human-level Multiple Choice Question Guessing Without Domain Knowledge: Machine-Learning of Framing Effects
Authors: Patrick Watson, Tengfei Ma, Ravi Tejwani, Maria Chang, Jae-wook Ahn, Sharad Sundararajan
Venue: WWW
Year: 2018

The availability of open educational resources (OER) has enabled educators and researchers to access a variety of learning assessments online. OER communities are particularly useful for gathering multiple choice questions (MCQs), which are easy to grade, but difficult to design well. To account for this, OERs often rely on crowd-sourced data to validate the quality of MCQs. However, because crowds contain many non-experts, and are susceptible to question framing effects, they may produce ratings driven by guessing on the basis of surface-level linguistic features, rather than deep topic knowledge. Consumers of OER multiple choice questions (and authors of original multiple choice questions) would benefit from a tool that automatically provided feedback on assessment quality, and assessed the degree to which OER MCQs are susceptible to framing effects. This paper describes a model that is trained to use domain-naive strategies to guess which multiple choice answer is correct. The extent to which this model can predict the correct answer to an MCQ is an indicator that the MCQ is a poor measure of domain-specific knowledge. We describe an integration of this model with a front-end visualizer and MCQ authoring tool.
Network Embedding Based Recommendation Method in Social Networks
Authors: Yufei Wen, Lei Guo, Zhumin Chen, Jun Ma
Venue: WWW
Year: 2018

With the advent of online social networks, the use of information hidden in social networks for recommendation has been extensively studied. Unlike previous work regarded social influence as regularization terms, we take advantage of network embedding techniques and propose an embedding based recommendation method. Specifically, we first pre-train a network embedding model on the users' social network to map each user into a low dimensional space, and then incorporate them into a matrix factorization model, which combines both latent and pre-learned features for recommendation. The experimental results on two real-world datasets indicate that our proposed model is more effective and can reach better performance than other related methods.
HTTP/2 Prioritization and its Impact on Web Performance
Authors: Maarten Wijnants, Robin Marx, Peter Quax, Wim Lamotte
Venue: WWW
Year: 2018

Web performance is a hot topic, as many studies have shown a strong correlation between slow webpages and loss of revenue due to user dissatisfaction. Front and center in Page Load Time (PLT) optimization is the order in which resources are downloaded and processed. The new HTTP/2 specification includes dedicated resource prioritization provisions, to be used in tandem with resource multiplexing over a single, well-filled TCP connection. However, little is yet known about its application by browsers and its impact on page load performance. This article details an extensive survey of modern User Agent implementations, with the conclusion that the major vendors all approach HTTP/2 prioritization in widely different ways, from naive (Safari, IE, Edge) to complex (Chrome, Firefox). We investigate the performance effect of these discrepancies with a full-factorial experimental evaluation involving eight prioritization algorithms, two off-the-shelf User Agents, 40 realistic webpages, and five heterogeneous (emulated) network conditions. We find that in general the complex approaches yield the best results, while naive schemes can lead to over 25% slower median visual load times. Also, prioritization is found to matter most for heavy-weight pages. Finally, it is ascertained that achieving PLT optimizations via generic server-side HTTP/2 re-prioritization schemes is a non-trivial task and that their performance is influenced by the implementation intricacies of individual browsers.
Surfing the API Web: Web Concepts
Authors: Erik Wilde
Venue: WWW
Year: 2018

The Web is based on numerous standards that together make up the surface of the Web: By knowing and supporting those standards, problems can be solved in well-known ways. This general design pattern on the Web applies to APIs in the very same way as it does to the human Web: By using an (evolving) set of standards, API developers benefit by not having to reinvent the wheel, and developers benefit by the same problem being solved in the same way across a variety of APIs. The evolving set of standards for Web APIs can be regarded as a set of building blocks or vocabularies for API design. Web Concepts is a site (webconcepts.info) and a repository (github.com/dret/webconcepts) that can be used to manage how within organizations these building blocks are used, thus helping to establish a Web API design culture. The main idea of Web Concepts is to promote reuse of existing standards and technologies, and to therefore make it easier for teams to understand which options are available generally speaking, and maybe which ones are popular choices within their organization.
Workshop on Web APIs and Service Architecture (WS-REST) Chairs' Welcome & Organization
Authors: Erik Wilde, Mike Amundsen, Mehdi Medjaoui
Venue: WWW
Year: 2018

Welcome to the 9th International Workshop on Web APIs and Service Architecture (WS-REST). First held in 2010 at WWW in Raleigh, North Carolina, USA, this 2018 edition of the WS-REST Workshop Series is proud to be a part of the renowned WWW conference series in Lyon, France. WS-REST 2018 brings together a community of researcher and practitioners interested in Web APIs and service architecture. Bringing Research and Industry Together In keeping with the history of WS-REST events, the 2018 edition strives to bring together vital content from both the Web Services and REST communities. This year we are hosting two individual tracks (Research and Industry) as a way to continue and strengthen this collaboration between academic and applied experience. The research track submissions received careful peer-review and will be published in the Web Conference proceedings. Industry track submissions focus on field-tested examples and use-cases in the form of extended abstracts or position papers and were selected by the organizers with advice from select program committee members. APIs, Services, and REST APIs have become the connective fabric of the Web and any application area that uses Internet or Web technologies. The goal of WS-REST 2018 is to provide a forum for researchers and practitioners where they can openly and freely exchange ideas about how they are using Web technologies in their APIs, what works and what does not work for them, and what challenges they see in the current landscape of standards and technologies. Our goal is to capture both the state of the art when it comes to Web APIs and service architecture, but to also provide a forum that identifies some of the most pressing issues in that space, and can help solving them.
Automatic Translation of Competency Questions into SPARQL-OWL Queries
Authors: Dawid Wisniewski
Venue: WWW
Year: 2018

The process of ontology authoring is inseparably connected with the quality assurance phase. One can verify the maturity and correctness of a given ontology by evaluating how many competency questions give correct answers. Competency questions are defined as a set of questions expressed in natural language that the finished ontology should be able to answer to correctly. Although this method can easily indicate what is the development status of an ontology, one has to translate competency questions from natural language into an ontology query language. This task is very hard and time consuming. To overcome this problem, my PhD thesis focuses on methods for automatically checking answerability of competency questions for a given ontology and proposing SPARQL-OWL query (OWL-aware SPARQL query) for each question where it is possible to create the query. Because the task of automatic translation from competency questions to SPARQL-OWL queries is a novel one, besides a method, we have proposed a new benchmark to evaluate such translation.
Your Secrets Are Safe: How Browsers' Explanations Impact Misconceptions About Private Browsing Mode
Authors: Yuxi Wu, Panya Gupta, Miranda Wei, Yasemin Acar, Sascha Fahl, Blase Ur
Venue: WWW
Year: 2018

All major web browsers include a private browsing mode that does not store browsing history, cookies, or temporary files across browsing sessions. Unfortunately, users have misconceptions about what this mode does. Many factors likely contribute to these misconceptions. In this paper, we focus on browsers» disclosures, or their in-browser explanations of private browsing mode. In a 460-participant online study, each participant saw one of 13 different disclosures (the desktop and mobile disclosures of six popular browsers, plus a control). Based on the disclosure they saw, participants answered questions about what would happen in twenty browsing scenarios capturing previously documented misconceptions. We found that browsers» disclosures fail to correct the majority of the misconceptions we tested. These misconceptions included beliefs that private browsing mode would prevent geolocation, advertisements, viruses, and tracking by both the websites visited and the network provider. Furthermore, participants who saw certain disclosures were more likely to have misconceptions about private browsing»s impact on targeted advertising, the persistence of lists of downloaded files, and tracking by ISPs, employers, and governments.
User Type Affinity Estimation Using Gamma-Poisson Model
Authors: Fei Wu, Yanen Li, Ning Xu
Venue: WWW
Year: 2018

The affinity of a user to a type of items (e.g., stories from the same publisher, and movies of the same genre) is an important signal reflecting the user's interests. Accurately estimating of the user type affinity has various applications in ranking and recommendation systems. For frequent users, simply dividing the number of interactions with content (e.g., clicks) by the number of impressions (e.g., the number of times the content is presented to each user) would be a good estimate. However, such estimates are erroneous for users who have sparse interaction history, (e.g., new users). To alleviate the problem, feature-based approaches aim to learn functions predicting the affinity score using only none-click features, such as user demographics, locations, and interests. Likewise, such approaches do not take full advantage of the interaction history of frequent users. Motivated by the limitations of the two approaches, we propose a Gamma-Poisson model that aims at utilizing the interaction history of frequent users, as well as leveraging a feature-based model for infrequent users. Our intuition is that we should rely more on the interaction history when estimating affinity for frequent users, and weigh more on feature-based model for infrequent users. We present experimental results on large-scale real-world data in a publisher content clicks prediction task to demonstrate the effectiveness of the proposed method in estimating user type affinity scores.
Treating Each Intent Equally: The Equilibrium of IA-Select
Authors: Yingying Wu, Yiqun Liu, Ke Zhou, Xiaochuan Wang, Min Zhang, Shaoping Ma
Venue: WWW
Year: 2018

Diversifying search results to satisfy as many users' intentions as possible is NP-hard. Some research employs a pruned exhaustive search, and some uses a greedy approach. However, the objective function of the result diversification problem adopts the cascade assumption which assumes users' information needs will drop once their subtopic search intents are satisfied. As a result, the intent distribution of diversified results deviates from the actual distribution of user intentions, and each subtopic tends to be chosen equally. This phenomenon is unreasonable, especially when the original distribution of user intent is unbalanced. In this paper, we present empirical evidence of the diversification equilibrium by showing that the standard deviations of subtopic distribution approach zero.
Sharing Deep Neural Network Models with Interpretation
Authors: Huijun Wu, Chen Wang, Jie Yin, Kai Lu, Liming Zhu
Venue: WWW
Year: 2018

Despite outperforming humans in many tasks, deep neural network models are also criticized for the lack of transparency and interpretability in decision making. The opaqueness results in uncertainty and low confidence when deploying such a model in model sharing scenarios, where the model is developed by a third party. For a supervised machine learning model, sharing training process including training data is a way to gain trust and to better understand model predictions. However, it is not always possible to share all training data due to privacy and policy constraints. In this paper, we propose a method to disclose a small set of training data that is just sufficient for users to get the insight into a complicated model. The method constructs a boundary tree using selected training data and the tree is able to approximate the complicated deep neural network models with high fidelity. We show that data point pairs in the tree give users significantly better understanding of the model decision boundaries and paves the way for trustworthy model sharing.
Query Suggestion with Feedback Memory Network
Authors: Bin Wu, Chenyan Xiong, Maosong Sun, Zhiyuan Liu
Venue: WWW
Year: 2018

This paper presents Feedback Memory Network (\textttFMN) which models user interactions with the search engine for query suggestion. Besides modeling the queries issued by the user, \textttFMN also considers user feedback on the search results. It converts user browsing and click actions to the attention over the top-ranked documents and combines them into the feedback memories of the query, thus better models the underlying information needs. The feedback memories and the query sequence are then combined to suggest queries by the sequence-to-sequence neural network. Modeling user feedback makes it possible to suggest diverse queries for the same query sequence, if users have preferred different search results that indicate different information needs. Our experiments on the search log from a Chinese commercial search engine showed the stable and robust advantages of \textttFMN. Especially when the feedback is richer or more informative, \textttFMN provides more diverse and accurate suggestions, which is exceptionally helpful for ambiguous sessions where more information is required to infer the search intents.
P3RPQ: Pregel-Based Parallel Provenance-Aware Regular Path Query Processing on Large RDF Graphs
Authors: Yueqi Xin, Bingyi Zhang, Xin Wang, Qiang Xu, Zhiyong Feng
Venue: WWW
Year: 2018

This paper proposes a novel method for answering Pregel-based Parallel Provenance-aware Regular Path Queries (P3RPQ) on large RDF graphs. Our method is developed using the Pregel framework, which utilizes Glushkov automata to keep track of the matching process of RPQs in parallel. Meanwhile, four optimization strategies are devised, which can reduce the response time of the basic algorithm dramatically and overcome the counting paths problem to some extent. The experiments are conducted to verify the performance of our algorithms on both synthetic and real-world datasets.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
Authors: Haowen Xu, Wenxiao Chen, Nengwen Zhao, Zeyan Li, Jiahao Bu, Zhihan Li, Ying Liu, Youjian Zhao, Dan Pei, Yang Feng, Jie Chen, Zhaogang Wang, Honglin Qiao
Venue: WWW
Year: 2018

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.
PDSM: Pregel-Based Distributed Subgraph Matching on Large Scale RDF Graphs
Authors: Qiang Xu, Xin Wang, Yueqi Xin, Zhiyong Feng, Renhai Chen
Venue: WWW
Year: 2018

This paper presents a novel Pregel-based Distributed Subgraph Matching method PDSM to answer subgraph matching queries on big RDF graphs. In our method, the query graph is transformed to a spanning tree based on the breadth-first search (BFS). Two optimization techniques are proposed to filter out part of the unpromising intermediate results and postpone the Cartesian product operations in the Pregel iterative computation. The extensive experiments on both synthetic and real-world datasets show that PDSM outperforms the state-of-the-art methods by an order of magnitude.