OpenRaiser/Intern-Atlas huggingface dataset:
FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation
Authors: Xi Chen, Hang Li, Chenyi Zhou, Xue Liu, Di Wu, Gregory Dudek
Venue: WWW
Year: 2020

To fully support the emerging location-aware applications, location information with meter-level resolution (or even higher) is required anytime and anywhere. Unfortunately, most of the current location sources (e.g., GPS and check-in data) either are unavailable indoor or provide only house-level resolutions. To fill the gap, this paper utilizes the ubiquitous WiFi signals to establish a (sub)meter-level localization system, which employs WiFi propagation characteristics as location fingerprints. However, an unsolved issue of these WiFi fingerprints lies in their inconsistency across different users. In other words, WiFi fingerprints collected from one user may not be used to localize another user. To address this issue, we propose a WiFi-based Domain-adaptive system FiDo, which is able to localize many different users with labelled data from only one or two example users. FiDo contains two modules: 1) a data augmenter that introduces data diversity using a Variational Autoencoder (VAE); and 2) a domain-adaptive classifier that adjusts itself to newly collected unlabelled data using a joint classification-reconstruction structure. Compared to the state of the art, FiDo increases average F1 score by 11.8% and improves the worst-case accuracy by 20.2%.
Contour Accentuation for Transfer Learning-Based Ship Recognition Method
Authors: Chi-Hua Chen, Yizhuo Zhang, Wenzhong Guo, Mingyang Pan, Lingjuan Lyu, Chia-Yu Lin
Venue: WWW
Year: 2020

This study proposes a ship recognition system which includes intelligent bridge piers and a ship recognition server. The ship recognition server can analyse the contour features of ship images from intelligent bridge piers by the proposed contour accentuation method; the ship image with contour accentuation can be adopted as the inputs of transfer learning-based neural network for ship classification by the proposed transfer learning-based ship recognition method. In practical experiments, the results showed that the proposed transfer learning-based ship recognition method with contour accentuation can obtain higher accuracy, and the accuracy of the proposed method was 97.79%.
Efficient Non-Sampling Factorization Machines for Optimal Context-Aware Recommendation
Authors: Chong Chen, Min Zhang, Weizhi Ma, Yiqun Liu, Shaoping Ma
Venue: WWW
Year: 2020

To provide more accurate recommendation, it is a trending topic to go beyond modeling user-item interactions and take context features into account. Factorization Machines (FM) with negative sampling is a popular solution for context-aware recommendation. However, it is not robust as sampling may lost important information and usually leads to non-optimal performances in practical. Several recent efforts have enhanced FM with deep learning architectures for modelling high-order feature interactions. While they either focus on rating prediction task only, or typically adopt the negative sampling strategy for optimizing the ranking performance. Due to the dramatic fluctuation of sampling, it is reasonable to argue that these sampling-based FM methods are still suboptimal for context-aware recommendation. In this paper, we propose to learn FM without sampling for ranking tasks that helps context-aware recommendation particularly. Despite effectiveness, such a non-sampling strategy presents strong challenge in learning efficiency of the model. Accordingly, we further design a new ideal framework named Efficient Non-Sampling Factorization Machines (ENSFM). ENSFM not only seamlessly connects the relationship between FM and Matrix Factorization (MF), but also resolves the challenging efficiency issue via novel memorization strategies. Through extensive experiments on three real-world public datasets, we show that 1) the proposed ENSFM consistently and significantly outperforms the state-of-the-art methods on context-aware Top-K recommendation, and 2) ENSFM achieves significant advantages in training efficiency, which makes it more applicable to real-world large-scale systems. Moreover, the empirical results indicate that a proper learning method is even more important than advanced neural network structures for Top-K recommendation task. Our implementation has been released 1 to facilitate further developments on efficient non-sampling methods.
Automatic Knowledge Fusion in Transferrable Networks for Semantic Text Matching
Authors: Cen Chen, Ya-Lin Zhang, Minghui Qiu, Bingzhe Wu, Li Wang, Longfei Li, Jun Zhou
Venue: WWW
Year: 2020

Transfer learning has been widely studied in many real-world applications to help the model performance of data-deficient domains. However, the design choice, such as what, where and how to share, can greatly influence the model performance. To save the effort in the transfer learning model design, we propose a novel method to automatically fuse transferable network architecture. Extensive experiments on public datasets of semantic text matching tasks show that our proposed method has better performance than the state-of-the-art transfer learning architectures.
Towards Web-based Etymological Hanzi Learning
Authors: Yong Cheng, Sijia Ma, Jun Chen, Yingying Zhao, Yaolin Chen, Jingtao Wang, Jia Xing, Wenhui Guo, Yushi Jing, Genze Wu, Wenli Zhu
Venue: WWW
Year: 2020

Modern-day Chinese characters, or Hanzi, originate from the ancient oracle-bone scripts (甲骨文). Such etymological relationship creates unique opportunities for Hanzi learning. This work proposes to use Web-based tools and the latest machine learning techniques to scale-up and enhance etymological Hanzi learning. By sharing our implementation details from launching an interactive sketch-based learning exhibition, we hope education-AI becomes more widely incorporated into today’s commercial Web applications. Our demo video can be found at: https://youtube/1met8Uk5pA0
Activity-selection Behavior of Users in StackExchange Websites
Authors: Anamika Chhabra, S. R. S. Iyengar
Venue: WWW
Year: 2020

In this work, we examine the disparity in contribution behavior of users with respect to the activities that they choose to perform on Q&A websites. We collect the data of 156 websites of StackExchange and analyze the contribution made by over 5.3 million users. We find that most of the users tend to contribute predominantly to one of the primary activities. Such a behavior yields a high-level distribution of users across the activities, which provides useful insights into the user base composition of these websites. In this work, we show how this distribution varies for StackExchange websites and discuss how it can be a valuable maintenance parameter for these websites.
Asymptotic Behavior of Sequence Models
Authors: Flavio Chierichetti, Ravi Kumar, Andrew Tomkins
Venue: WWW
Year: 2020

In this paper we study the limiting dynamics of a sequential process that generalizes Pólya’s urn. This process has been studied also in the context of language generation, discrete choice, repeat consumption, and models for the web graph. The process we study generates future items by copying from past items. It is parameterized by a sequence of weights describing how much to prefer copying from recent versus more distant locations. We show that, if the weight sequence follows a power law with exponent α ∈ [0, 1), then the sequences generated by the model tend toward a limiting behavior in which the eventual frequency of each token in the alphabet attains a limit. Moreover, in the case α > 2, we show that the sequence converges to a token being chosen infinitely often, and each other token being chosen only constantly many times.
Games for Fairness and Interpretability
Authors: Eric Chu, Nabeel Gillani, Sneha Priscilla Makini
Venue: WWW
Year: 2020

As Machine Learning (ML) systems becomes more ubiquitous, ensuring the fair and equitable application of their underlying algorithms is of paramount importance. We argue that one way to achieve this is to proactively cultivate public pressure for ML developers to design and develop fairer algorithms — and that one way to cultivate public pressure while simultaneously serving the interests and objectives of algorithm developers is through gameplay. We propose a new class of games — “games for fairness and interpretability” — as one example of an incentive-aligned approach for producing fairer and more equitable algorithms. Games for fairness and interpretability are carefully-designed games with mass appeal. They are inherently engaging, provide insights into how machine learning models work, and ultimately produce data that helps researchers and developers improve their algorithms. We highlight several possible examples of games, their implications for fairness and interpretability, how their proliferation could creative positive public pressure by narrowing the gap between algorithm developers and the general public, and why the machine learning community could benefit from them.
Domain-Specific Automatic Scholar ProfilingBased on Wikipedia
Authors: Ziang Chuai, Qian Geng, Jian Jin
Venue: WWW
Year: 2020

Scholar profiling is usually invited to provide junior researchers with domain-specific concepts systematically, but the organization of domain literatures is usually time-consuming. Then, an automatic scholar profiling method is preferred. Firstly, to extract some properties of a given scholar, structured data, like infobox in Wikipedia, are often used as training datasets. But it may lead to serious mis-labeling problems, such as institutions and alma maters, and a Fine-Grained Entity Typing method is expected. Thus, a novel Relation Embedding method based on local context is proposed to enhance the typing performance. Also, to highlight critical concepts in selective bibliographies of scholars, a novel Keyword Extraction method based on Learning to Rank is proposed to bridge the gap that conventional supervised methods fail to provide junior scholars with relative importance of keywords. Categories of experiments were conducted to evaluate the Relation Embedding method and the Keyword Extraction method, which show that the proposed approaches outperform existing baseline methods notably.
Valve: Securing Function Workflows on Serverless Computing Platforms
Authors: Pubali Datta, Prabuddha Kumar, Tristan Morris, Michael Grace, Amir Rahmati, Adam Bates
Venue: WWW
Year: 2020

Serverless Computing has quickly emerged as a dominant cloud computing paradigm, allowing developers to rapidly prototype event-driven applications using a composition of small functions that each perform a single logical task. However, many such application workflows are based in part on publicly-available functions developed by third-parties, creating the potential for functions to behave in unexpected, or even malicious, ways. At present, developers are not in total control of where and how their data is flowing, creating significant security and privacy risks in growth markets that have embraced serverless (e.g., IoT). As a practical means of addressing this problem, we present Valve, a serverless platform that enables developers to exert complete fine-grained control of information flows in their applications. Valve enables workflow developers to reason about function behaviors, and specify restrictions, through auditing of network-layer information flows. By proxying network requests and propagating taint labels across network flows, Valve is able to restrict function behavior without code modification. We demonstrate that Valve is able defend against known serverless attack behaviors including container reuse-based persistence and data exfiltration over cloud platform APIs with less than 2.8% runtime overhead, 6.25% deployment overhead and 2.35% teardown overhead.
Semantic Textual Similarity of Sentences with Emojis
Authors: Alok Debnath, Nikhil Pinnaparaju, Manish Shrivastava, Vasudeva Varma, Isabelle Augenstein
Venue: WWW
Year: 2020

In this paper, we extend the task of semantic textual similarity to include sentences which contain emojis. Emojis are ubiquitous on social media today, but are often removed in the pre-processing stage of curating datasets for NLP tasks. In this paper, we qualitatively ascertain the amount of semantic information lost by discounting emojis, as well as show a mechanism of accounting for emojis in a semantic task. We create a sentence similarity dataset of 4000 pairs of tweets with emojis, which have been annotated for relatedness. The corpus contains tweets curated based on common topic as well as by replacement of emojis. The latter was done to analyze the difference in semantics associated with different emojis. We aim to provide an understanding of the information lost by removing emojis by providing a qualitative analysis of the dataset. We also aim to present a method of using both emojis and words for downstream NLP tasks beyond sentiment analysis.
Open Data User Needs: A Preliminary Synthesis
Authors: Auriol Degbelo
Venue: WWW
Year: 2020

Open city data is critical for smart cities and is becoming increasingly available, thanks to open government initiatives. Yet, we still know little about the needs of users concerning open data reuse. Taking fragmented and scattered issues reported by users in several works as a starting point, this article formulates user needs statements for research at the intersection of the topics of web, city, and data reuse. The 27 user needs statements proposed can inform the design and evaluation of tools that facilitate open data reuse.
Open Geodata Reuse: Towards Natural Language Interfaces to Web APIs
Authors: Auriol Degbelo, Ang Sherpa
Venue: WWW
Year: 2020

Open Government datasets have been flooding the Web recently, and Application Programming Interfaces (APIs) are key to the development of services on top of these datasets. An issue of current APIs worldwide is that their learnability is limited. This work has explored the potential of querying APIs using natural language terms to mitigate that issue. A user study with 20 participants has demonstrated that a natural-language-based, along with an order-agnostic approach to API design can produce easily learnable APIs for both novice and experienced API users. These insights can pave the way for a paradigm change on Web-API design for open geodata retrieval and beyond.
Differentially Private Stream Processing for the Semantic Web
Authors: Daniele Dell&apos, Aglio, Abraham Bernstein
Venue: WWW
Year: 2020

Data often contains sensitive information, which poses a major obstacle to publishing it. Some suggest to obfuscate the data or only releasing some data statistics. These approaches have, however, been shown to provide insufficient safeguards against de-anonymisation. Recently, differential privacy (DP), an approach that injects noise into the query answers to provide statistical privacy guarantees, has emerged as a solution to release sensitive data. This study investigates how to continuously release privacy-preserving histograms (or distributions) from online streams of sensitive data by combining DP and semantic web technologies. We focus on distributions, as they are the basis for many analytic applications. Specifically, we propose SihlQL, a query language that processes RDF streams in a privacy-preserving fashion. SihlQL builds on top of SPARQL and the w-event DP framework. We show how some peculiarities of w-event privacy constrain the expressiveness of SihlQL queries. Addressing these constraints, we propose an extension of w-event privacy that provides answers to a larger class of queries while preserving their privacy. To evaluate SihlQL, we implemented a prototype engine that compiles queries to Apache Flink topologies and studied its privacy properties using real-world data from an IPTV provider and an online e-commerce web site.
Private Data Manipulation in Optimal Sponsored Search Auction
Authors: Xiaotie Deng, Tao Lin, Tao Xiao
Venue: WWW
Year: 2020

In this paper, We revisit the sponsored search auction as a repeated auction. We view it as a learning and exploiting task of the seller against the private data distribution of the buyers. We model such a game between the seller and buyers by a Private Data Manipulation (PDM) game: the auction seller first announces an auction for which allocation and payment rules are based on the value distributions submitted by buyers. The seller’s expected revenue depends on the design of the protocol and the game played among the buyers in their choice on the submitted (fake) value distributions. Under the PDM game, we re-evaluate the theory, methodology, and techniques in the sponsored search auctions that have been the most intensively studied in Internet economics.
P-Simrank: Extending Simrank to Scale-Free Bipartite Networks
Authors: Prasenjit Dey, Kunal Goel, Rahul Agrawal
Venue: WWW
Year: 2020

The measure of similarity between nodes in a graph is a useful tool in many areas of computer science. SimRank, proposed by Jeh and Widom [7], is a classic measure of similarities of nodes in graph that has both theoretical and intuitive properties and has been extensively studied and used in many applications such as Query-Rewriting, link prediction, collaborative filtering and so on. Existing works based on Simrank primarily focus on preserving the microscopic structure, such as the second and third order proximity of the vertices, while the macroscopic scale-free property is largely ignored. Scale-free property is a critical property of any real-world web graphs where the vertex degrees follow a heavy-tailed distribution. In this paper, we introduce P-Simrank which extends the idea of Simrank to Scale-free bipartite networks. To study the efficacy of the proposed solution on a real world problem, we tested the same on the well known query-rewriting problem in sponsored search domain using bipartite click graph, similar to Simrank++ [1], which acts as our baseline. We show that Simrank++ produces sub-optimal similarity scores in case of bipartite graphs where degree distribution of vertices follow power-law. We also show how P-Simrank can be optimized for real-world large graphs. Finally, we experimentally evaluate P-Simrank algorithm against Simrank++, using actual click graphs obtained from Bing, and show that P-Simrank outperforms Simrank++ in variety of metrics.
Higher-Order Label Homogeneity and Spreading in Graphs
Authors: Dhivya Eswaran, Srijan Kumar, Christos Faloutsos
Venue: WWW
Year: 2020

Do higher-order network structures aid graph semi-supervised learning? Given a graph and a few labeled vertices, labeling the remaining vertices is a high-impact problem with applications in several tasks, such as recommender systems, fraud detection and protein identification. However, traditional methods rely on edges for spreading labels, which is limited as all edges are not equal. Vertices with stronger connections participate in higher-order structures in graphs, which calls for methods that can leverage these structures in the semi-supervised learning tasks. To this end, we propose Higher-Order Label Spreading (HOLS) to spread labels using higher-order structures. HOLS has strong theoretical guarantees and reduces to standard label spreading in the base case. Via extensive experiments, we show that higher-order label spreading using triangles in addition to edges is up to 4.7% better than label spreading using edges alone. Compared to prior traditional and state-of-the-art methods, the proposed method leads to statistically significant accuracy gains in all-but-one cases, while remaining fast and scalable to large graphs.
Forecasting Big Time Series: Theory and Practice
Authors: Christos Faloutsos, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Yuyang Wang
Venue: WWW
Year: 2020

Time series forecasting is a key ingredient in the automation and optimization of business processes: in retail, deciding which products to order and where to store them depends on the forecasts of future demand in different regions; in cloud computing, the estimated future usage of services and infrastructure components guides capacity planning; and workforce scheduling in warehouses and factories requires forecasts of the future workload. Recent years have witnessed a paradigm shift in forecasting techniques and applications, from computer-assisted model- and assumption-based to data-driven and fully-automated. This shift can be attributed to the availability of large, rich, and diverse time series data sources and result in a set of challenges that need to be addressed such as the following. How can we build statistical models to efficiently and effectively learn to forecast from large and diverse data sources? How can we leverage the statistical power of “similar” time series to improve forecasts in the case of limited observations? What are the implications for building forecasting systems that can handle large data volumes? The objective of this tutorial is to provide a concise and intuitive overview of the most important methods and tools available for solving large-scale forecasting problems. We review the state of the art in both: (1) classical modeling of time series, (2) deep learning for forecasting. We also discuss the practical aspects of building a large scale forecasting system, including data integration, feature generation, backtesting framework, error tracking and analysis, etc. Accompanied with the practice side is a hands-on session, where we would engage the audience with Jupyter notebooks that demonstrates the key concepts in the theory part. Furthermore, we provides interactive demos, showing various avenues to solve business problems with AWS Forecasting offerings such as GluonTS, DeepAR (SageMaker), and Amazon Forecast.
One2Multi Graph Autoencoder for Multi-view Graph Clustering
Authors: Shaohua Fan, Xiao Wang, Chuan Shi, Emiao Lu, Ken Lin, Bai Wang
Venue: WWW
Year: 2020

Multi-view graph clustering, which seeks a partition of the graph with multiple views that often provide more comprehensive yet complex information, has received considerable attention in recent years. Although some efforts have been made for multi-view graph clustering and achieve decent performances, most of them employ shallow model to deal with the complex relation within multi-view graph, which may seriously restrict the capacity for modeling multi-view graph information. In this paper, we make the first attempt to employ deep learning technique for attributed multi-view graph clustering, and propose a novel task-guided One2Multi graph autoencoder clustering framework. The One2Multi graph autoencoder is able to learn node embeddings by employing one informative graph view and content data to reconstruct multiple graph views. Hence, the shared feature representation of multiple graphs can be well captured. Furthermore, a self-training clustering objective is proposed to iteratively improve the clustering results. By integrating the self-training and autoencoder’s reconstruction into a unified framework, our model can jointly optimize the cluster label assignments and embeddings suitable for graph clustering. Experiments on real-world attributed multi-view graph datasets well validate the effectiveness of our model.