OpenRaiser/Intern-Atlas huggingface dataset:
Sensor-based Linked Open Rules (S-LOR): An Automated Rule Discovery Approach for IoT Applications and its use in Smart Cities
Authors: Amelie Gyrard, Martin Serrano, Joao Bosco Jares, Soumya Kanti Datta, Muhammad Intizar Ali
Venue: WWW
Year: 2017

This paper introduces an automated rule discovery approach for IoT device data (S-LOR: Sensor-based Linked Open Rules) and its use in smart cities. S-LOR is built following Linked Open Data (LOD) standards and provides support for semantics-based mechanisms to share, reuse and execute logical rules for interpreting data produced by IoT systems. S-LOR follows LOD principles for data re-usability, semantics-based reasoning and interoperability. In this paper, S-LOR main capability is demonstrated in the context of enabling semantics-based reasoning mechanisms and tools according to application-demand and user requirements. S-LOR (i) supports an automated interpretation of IoT data by executing rules, and (ii) allows an automated rule discovery interface. The implemented S-LOR mechanism can automatically process and interpret data from IoT devices by using rule-based discovery paradigm. Its extension called Linked Open Reasoning (LOR) enables and encourages re-usability of reasoning mechanisms and tools for different IoT smart city applications. The use cases described in this paper fits in the domain of smart city applications within Internet of Things deployed systems.
Post-edit Analysis of Collective Biography Generation
Authors: Bo Han, Will Radford, Anaïs Cadilhac, Art Harol, Andrew Chisholm, Ben Hachey
Venue: WWW
Year: 2017

Text generation is increasingly common but often requires manual post-editing where high precision is critical to end users. However, manual editing is expensive so we want to ensure this effort is focused on high-value tasks. And we want to maintain stylistic consistency, a particular challenge in crowd settings. We present a case study, analysing human post-editing in the context of a template-based biography generation system. An edit flow visualisation combined with manual characterisation of edits helps identify and prioritise work for improving end-to-end efficiency and accuracy.
Measuring and Improving the Reliability of Wide-Area Cloud Paths
Authors: Osama Haq, Mamoon Raja, Fahad R. Dogar
Venue: WWW
Year: 2017

Many popular cloud applications use inter-data center paths; yet, little is known about the characteristics of these ``cloud paths''. Over an eighteen month period, we measure the inter-continental cloud paths of three providers (Amazon, Google, and Microsoft) using client side (VM-to-VM) measurements. We find that cloud paths are more predictable compared to public Internet paths, with an order of magnitude lower loss rate and jitter at the tail (95th percentile and beyond) compared to public Internet paths. We also investigate the nature of packet losses on these paths (e.g., random vs. bursty) and potential reasons why these paths may be better in quality. Based on our insights, we consider how we can further improve the quality of these paths with the help of existing loss mitigation techniques. We demonstrate that using the cloud path in conjunction with a detour path can mask most of the cloud losses, resulting in up to five 9's of network availability for applications.
Wikipedia Verification Check: A Chrome Browser Extension
Authors: Reed H. Harder, Alfredo J. Velasco, Michael S. Evans, Chuankai An, Daniel N. Rockmore
Venue: WWW
Year: 2017

In this paper we present Version 1.0 of an implementation of a Wiki reference parser with a light-weight plugin in the form of a Google Chrome Extension with Javascript. The output of the parser is a "verification score" for any Wikipedia page, constructed from a combination of scores derived from reference accessibility and quality. The extension presented herein works from a pre-stored database of DOI and reference checks. Future versions working in real-time are also discussed. This work suggests generalizations to real-time verification checkers for arbitrary webpages and possibly even a real-time fact-checker for news platforms.
Worker Viewpoints: Valuable Feedback for Microtask Designers in Crowdsourcing
Authors: Ryota Hayashi, Nobuyuki Shimizu, Atsuyuki Morishima
Venue: WWW
Year: 2017

One of the problems a requester faces when crowdsourcing a microtask is that, due to the underspecified or ambiguous task description, workers may misinterpret the microtask at hand. We call a set of such interpretations worker viewpoints. In this paper, we argue that assisting requesters to gather a worker's interpretation of the microtask can help in providing useful feedback to designers, who may restate the task description if necessary. In our method, we create a corpus of viewpoints annotated with the types of viewpoints that reflect the logical structure embedded in them. Our experimental results suggest that the logic-oriented annotation is effective in choosing useful viewpoints from a possibly huge set of collected viewpoints, in the sense that removing viewpoints of particular types did not affect the quality of revised task instructions. We also show that the logic-oriented annotation can perform comparably with an entropy-based method, without several workers performing the same task in parallel.
Sustainable Strategy for Recycling Edge Devices in Internet of Everything Networks
Authors: Weigang Hou, Zhaolong Ning, Lei Guo
Venue: WWW
Year: 2017

Internet of Everything (IoE) devices have different operation principles, which weakens the network scalability and data interoperability. Virtualization is an economic way of solving this problem. The data-collected by different vendors' sensors-can share the same computing program encapsulated by the Virtual Machine (VM), thus neglecting the physical-layer difference. To eliminate the extreme cost and long delay of transferring VMs to the remote cloud, the Edge Device (ED) preliminary processes its running VMs. Currently, the recycling of IoE devices become a major dilemma for individuals, since it is not simply a matter of concern for environmental damage or a solution to an environmental problem. Therefore, the sustainable strategy for recycling EDs is an important way to safeguard the network sustainability. To improve the recycling efficiency, most of the EDs should be upgraded simultaneously during one batch by migrating their running VMs to others for the service continuity. We investigate the least upgrade batch for recycling EDs in IoE networks. A two-step algorithm called MSBP (Minimized upgrade batch VM Scheduling and Bandwidth Planning) is designed to minimize the number of upgrade batches. Because migrating VM brings the bandwidth consumption along trajectories, MSBP has two strategies-Shortest Trajectory First (STF) and Least Bandwidth Utilization First (LBUF)-of allocating bandwidth and trajectories. The simulation results show that: 1) MSBP has the optimal recycling efficiency (least number of upgrade batches) for EDs; 2) LBUF more effectively mitigates the phenomenon where VM migration trajectories compete for the common link bandwidth, thus achieving a lower negative impact of path contention level on the recycling efficiency; 3) the battery power is not exhausted for the ED functioned as the sensor head of data transferring, thus prolonging the network lifetime. In summary, our solution well improves the network, social, economic and ecological sustainability.
MeDJ: Multidimensional Emotion-aware Music Delivery for Adolescent
Authors: Xiping Hu, Kun Bai, Jun Cheng, Jun-qi Deng, Yanxiang Guo, Bin Hu, Arun Sai Krishnan, Fei Wang
Venue: WWW
Year: 2017

Music listening is an integral part of many adolescents? everyday lives, but it is also a time when adolescents are uniquely vulnerable. Emotional-oriented and avoidance through listening to unsuitable music may bring negative emotion to adolescents and increase the level of their depression. We propose MeDJ, a multidimensional emotion-aware music delivery application, which turns adolescents' music listening into a health and pleasure way. MeDJ aims at helping adolescents to improve emotional management and prevent depression. It is built on a cloud-based platform that enables adolescents and their peers collaboratively recommend suitable music to each other through smartphones. Prototype implementation and initial results of MeDJ have demonstrated its practicability for real-world deployment.
Deep Graph Clustering in Social Network
Authors: Pengwei Hu, Keith C. C. Chan, Tiantian He
Venue: WWW
Year: 2017

In this paper, we present deep attributes residue graph algorithm (DARG), a novel model for learning deep representations of graph. The algorithm can discover clusters by taking into consideration node relevance. DARG does so by first learns attributes relevance and cluster deep representations of vertices appearing in a graph, unlike existing work, integrates content interactions of the nodes into the graph learning process. First, the relevance of contents between each node pair within the network is abstracted. Then we turn the problem of computing the first k eigenvectors in spectral clustering into a computing deep representations task. This model just need learns content information to represent vertices appearing in a graph and without the need for considering topological information. Such content information is much easier to obtain than topological links in the real world. We conduct an experiment on SNAP Facebook dataset, empirical results demonstrate that proposed approach significantly outperforms other state-of-the-art methods in such task.
Source Locating of Spreading Dynamics in Temporal Networks
Authors: Qiangjuan Huang
Venue: WWW
Year: 2017

The topological structure of many real networks changes with time. Thus, locating the sources of a temporal network is a creative and challenging problem, as the enormous size of many real networks makes it unfeasible to observe the state of all nodes. In this paper, we propose an algorithm to solve this problem, named the backward temporal diffusion process. The proposed algorithm calculates the shortest temporal distance to locate the transmission source. We assume that the spreading process can be modeled as a simple diffusion process and by consensus dynamics. To improve the location accuracy, we also adopt four strategies to select which nodes should be observed by ranking their importance in the temporal network. Our paper proposes a highly accurate method for locating the source in temporal networks and is, to the best of our knowledge, a frontier work in this field. Moreover, our framework has important significance for controlling the transmission of diseases or rumors and formulating immediate immunization strategies.
Towards a Better Learning of Near-Synonyms: Automatically Suggesting Example Sentences via Fill in the Blank
Authors: Chieh-Yang Huang, Mei-Hua Chen, Lun-Wei Ku
Venue: WWW
Year: 2017

Language learners are confused by near-synonyms and often look for answers from the Web. However, there is little to aid them in sorting through the overwhelming load of information that is offered. In this paper, we propose a new research problem: suggesting example sentences for learning word distinctions. We focus on near-synonyms as the first step. Two kinds of one-class classifiers, the GMM and BiLSTM models, are used to solve fill-in-the-blank (FITB) questions and further to select example sentences which best differentiate groups of near-synonyms. Experiments are conducted on both an open benchmark and a private dataset for the FITB task. Experiments show that the proposed approach yields an accuracy of 73.05% and 83.59% respectively, comparable to state-of-the-art multi-class classifiers. Learner study further shows the results of the example sentence suggestion by the learning effectiveness and demonstrates the proposed model indeed is more effective in learning near-synonyms compared to the resource-based models.
Pinning Down Abuse on Google Maps
Authors: Danny Yuxing Huang, Doug Grundman, Kurt Thomas, Abhishek Kumar, Elie Bursztein, Kirill Levchenko, Alex C. Snoeren
Venue: WWW
Year: 2017

In this paper, we investigate a new form of blackhat search engine optimization that targets local listing services like Google Maps. Miscreants register abusive business listings in an attempt to siphon search traffic away from legitimate businesses and funnel it to deceptive service industries---such as unaccredited locksmiths---or to traffic-referral scams, often for the restaurant and hotel industry. In order to understand the prevalence and scope of this threat, we obtain access to over a hundred-thousand business listings on Google Maps that were suspended for abuse. We categorize the types of abuse affecting Google Maps; analyze how miscreants circumvented the protections against fraudulent business registration such as postcard mail verification; identify the volume of search queries affected; and ultimately explore how miscreants generated a profit from traffic that necessitates physical proximity to the victim. This physical requirement leads to unique abusive behaviors that are distinct from other online fraud such as pharmaceutical and luxury product scams.
Identification of Homographic Pun Location for Pun Understanding
Authors: Yu-Hsiang Huang, Hen-Hsen Huang, Hsin-Hsi Chen
Venue: WWW
Year: 2017

This paper introduces a novel framework for homographic pun location identification. Two observations, the existence of the support term and the preferred positions of the pun in context, are considered as crucial hints for pun identification. We first nominate the pun candidates, and then select the most probable one based on various strategies. Experimental results show the effectiveness of our method.
Detection of False Online Advertisements with DCNN
Authors: Hen-Hsen Huang, Yu-Wei Wen, Hsin-Hsi Chen
Venue: WWW
Year: 2017

In addition to opinion spam, the overstated or unproven information in false advertisements could also mislead customers while making purchasing decisions. A false-advertisement judgement system aims at recognizing and explaining the illegal false advertisements. In this paper, we incorporate the convolutional neural network (CNN) with word embeddings and syntactic features in the system. The recognition experiments show that Dependency-based CNN (DCNN) achieves F-scores of 86.77%, 93.18%, and 87.46% in the cosmetics, food, and drug datasets, respectively. Moreover, the explanation of illegality experiments shows the F-scores of 56.19%, 50.36%, and 62.06% in the three datasets. Our judgement system can contribute to different roles in the online advertising.
Bias in Wikipedia
Authors: Christoph Hube
Venue: WWW
Year: 2017

While studies have shown that Wikipedia articles exhibit quality that is comparable to conventional encyclopedias, research still proves that Wikipedia, overall, is prone to many different types of Neutral Point of View (NPOV) violations that are explicitly or implicitly caused by bias from its editors. Related work focuses on political, cultural and gender bias. We are developing an approach for detecting both explicit and implicit bias in Wikipedia articles and observing its evolution over time. Our approach is based on different factors of bias, with the most important ones being language style, editors, and citations. In this paper we present the approach, methodology and a first analysis.
Visual Analytics for High Dimensional Data: Very Late Added Paper
Authors: Alfred Inselberg, Leonidas G. Anthopoulos
Venue: WWW
Year: 2017

A dataset with M items has 2M subsets anyone of which may be the one satisfying our objective. With a good data display and interactivity our fantastic pattern-recognition defeats the combinatorial explosion by extracting insights from the visual patterns. This is the core reason for data visualization. With parallel coordinates, as illustrated here, the search for relations in multivariate data is transformed into a 2-D pattern recognition problem. A geometric classification algorithm yields the classification rule explicitly and visually. The minimal set of variables, features, are found and ordered by their predictive value. A model of a country's economy reveals sensitivities, impact of constraints, trade-offs and economic sectors unknowingly competing for the same resources. A glimpse into this beautiful and powerful multidimensional geometry is shown at the end.
Digital City, Smart City and Beyond
Authors: Toru Ishida
Venue: WWW
Year: 2017

This article revisited past digital cities, and discussed smart cities and the future. If we understand digital cities as exploration of cyber space and smart cities as exploitation of physical space, the next stage is to evolve networked society based on cyber physical systems. The current movements suggest two different directions, socialization of commerce and commercialization of society. We can predict the convergence of the two directions in the future.
A Fast and Provable Method for Estimating Clique Counts Using Turán's Theorem
Authors: Shweta Jain, C. Seshadhri
Venue: WWW
Year: 2017

Clique counts reveal important properties about the structure of massive graphs, especially social networks. The simple setting of just 3-cliques (triangles) has received much attention from the research community. For larger cliques (even, say 6-cliques) the problem quickly becomes intractable because of combinatorial explosion. Most methods used for triangle counting do not scale for large cliques, and existing algorithms require massive parallelism to be feasible. We present a new randomized algorithm that provably approximates the number of k-cliques, for any constant k. The key insight is the use of (strengthenings of) the classic Turán's theorem: this claims that if the edge density of a graph is sufficiently high, the k-clique density must be non-trivial. We define a combinatorial structure called a Turàn shadow, the construction of which leads to fast algorithms for clique counting. We design a practical heuristic, called TURÀN-SHADOW, based on this theoretical algorithm, and test it on a large class of test graphs. In all cases, TURÀN-SHADOW has less than 2% error, in a fraction of the time used by well-tuned exact algorithms. We do detailed comparisons with a range of other sampling algorithms, and find that TURÀN-SHADOW is generally much faster and more accurate. For example, TURÀN-SHADOW estimates all cliques numbers up to size 10 in social network with over a hundred million edges. This is done in less than three hours on a single commodity machine.
Performance Monitoring and Root Cause Analysis for Cloud-hosted Web Applications
Authors: Hiranya Jayathilaka, Chandra Krintz, Rich Wolski
Venue: WWW
Year: 2017

In this paper, we describe Roots - a system for automatically identifying the "root cause" of performance anomalies in web applications deployed in Platform-as-a-Service (PaaS) clouds. Roots does not require application-level instrumentation. Instead, it tracks events within the PaaS cloud that are triggered by application requests using a combination of metadata injection and platform-level instrumentation.
Interval Signature: Persistence and Distinctiveness of Inter-event Time Distributions in Online Human Behavior
Authors: Jiwan Jeong, Sue B. Moon
Venue: WWW
Year: 2017

Interval patterns, or inter-event time distributions that occur in human activity, have long been an interest of many researchers studying human dynamics. While previous studies have mostly focused on characterizing the aggregated inter-arrival patterns or finding universal patterns across all individuals, we focus on the diversity among the patterns of different individuals; the goal of this paper is to understand how persistent an individual's interval pattern is and how distinctive it is from those of the others. We use Wikipedia, me2DAY, Twitter, and Enron email data to study the interval patterns of online human behavior. Our analysis reveals that individuals have robust and unique interval signatures. The interval pattern of a user tends to persist over years, even after coming back from a long hiatus of inactivity, despite considerable change in circadian rhythms. Furthermore, the interval patterns of individuals are highly distinct from that of others. We put our new findings in practical use of identifying users.
An Analysis on a YouTube-like UGC site with Enhanced Social Features
Authors: Adele Lu Jia, Siqi Shen, Shengling Chen, Dongsheng Li, Alexandru Iosup
Venue: WWW
Year: 2017

YouTube-like User Generated Content (UGC) sites are nowadays entertaining over a billion people. Resource provision is essential for these giant UGC sites as they allow users to request videos from a potentially unlimited selection in an asynchronous fashion. Still, the UGC sites are seeking to create new viewing patterns and social interactions that would engage and attract more users and complicate the already rigorous resource provision problem. In this paper, we seek to combine these two tasks by leveraging social features to provide the reference for resource provision.
Detection and Characterization of Influential Cross-lingual Information Diffusion on Social Networks
Authors: Hongshan Jin
Venue: WWW
Year: 2017

Social network services (SNSs) have become new global and multilingual information platforms due to their popularity. In SNSs with content-sharing functionality, such as "retweet" in Twitter and "share" in Facebook, posts are easily and quickly shared among users, and some of which can spread over different regions and languages. In this work, we first define the concept of cross-lingual information cascade on the basis of the main language of users and then try to characterize and detect those information cascades which can widely spread over different regions and languages on social networks. Understanding the cross-lingual characteristics of information cascades is not only valuable for sociological research, but also beneficial in the practical sense for those who want to know globally-influential events (e.g. ALS Ice Bucket Challenge and Terrorism in Europe) and estimate the impact of global advertisements on products (e.g. Samsung galaxy phone and a movie, Your Name). On the first attempt, we conducted statistical analysis of cascade growth and language distribution of information cascades with a large Twitter dataset. Based on the results, we propose a feature-based model, by which we successfully detected influential cross-lingual information cascades.