OpenRaiser/Intern-Atlas huggingface dataset:
An Intent-Based Automation Framework for Securing Dynamic Consumer IoT Infrastructures
Authors: Vasudevan Nagendra, Arani Bhattacharya, Vinod Yegneswaran, Amir Rahmati, Samir Ranjan Das
Venue: WWW
Year: 2020

Consumer IoT networks are characterized by heterogeneous devices with diverse functionality and programming interfaces. This lack of homogeneity makes the integration and secure management of IoT infrastructures a daunting task for users and administrators. In this paper, we introduce VISCR, a Vendor-Independent policy Specification and Conflict Resolution engine that enables intent-based conflict-free policy specification and enforcement in IoT environments. VISCR converts the topology of the IoT infrastructure into a tree-based abstraction and translates existing policies from heterogeneous vendor-specific programming languages, such as Groovy-based SmartThings, OpenHAB, IFTTT-based templates, and MUD-based profiles, into a vendor-independent graph-based specification. These are then used to automatically detect rogue policies, policy conflicts, and automation bugs. We evaluated VISCR using a dataset of 907 IoT apps, programmed using heterogeneous automation specifications, in a simulated smart-building IoT infrastructure. In our experiments, among 907 IoT apps, VISCR exposed 342 of IoT apps as exhibiting one or more violations, while also running 14.2x faster than the state-of-the-art tool (Soteria). VISCR detected 100% of violations reported by Soteria while also detecting new types of violations in 266 additional apps.
Using Cliques with Higher-order Spectral Embeddings Improves Graph Visualizations
Authors: Huda Nassar, Caitlin Kennedy, Shweta Jain, Austin R. Benson, David F. Gleich
Venue: WWW
Year: 2020

In the simplest setting, graph visualization is the problem of producing a set of two-dimensional coordinates for each node that meaningfully shows connections and latent structure in a graph. Among other uses, having a meaningful layout is often useful to help interpret the results from network science tasks such as community detection and link prediction. There are several existing graph visualization techniques in the literature that are based on spectral methods, graph embeddings, or optimizing graph distances. Despite the large number of methods, it is still often challenging or extremely time consuming to produce meaningful layouts of graphs with hundreds of thousands of vertices. Existing methods often either fail to produce a visualization in a meaningful time window, or produce a layout colorfully called a “hairball”, which does not illustrate any internal structure in the graph. Here, we show that adding higher-order information based on cliques to a classic eigenvector based graph visualization technique enables it to produce meaningful plots of large graphs. We further evaluate these visualizations along a number of graph visualization metrics and we find that it outperforms existing techniques on a metric that uses random walks to measure the local structure. Finally, we show many examples of how our algorithm successfully produces layouts of large networks. Code to reproduce our results is available.
SETLBI: An Integrated Platform for Semantic Business Intelligence
Authors: Rudra Pratap Deb Nath, Katja Hose, Torben Bach Pedersen, Oscar Romero, Amrit Bhattacharjee
Venue: WWW
Year: 2020

With the growing popularity of Semantic Web technologies, more and more organizations natively manage data using Semantic Web standards, in particular RDF. This development gives rise to new requirements for Business Intelligence tools to enable analyses in the style of On-Line Analytical Processing (OLAP) over RDF data. In this demonstration, we therefore present the SETLBI (Semantic Extract-Transform-Load and Business Intelligence) integration platform that brings together the Semantic Web and Business Intelligence technologies. SETLBI covers all phases of integration: target definition, source to target mappings generation, semantic and non-semantic source extraction, data transformation, and target population and update. It facilitates Data Warehouse designers to build a semantic Data Warehouse, either from scratch or by defining a multi-dimensional view over existing RDF data sources, and further enables OLAP-style analyses.
Mining Points-of-Interest for Explaining Urban Phenomena: A Scalable Variational Inference Approach
Authors: Christof Naumzik, Patrick Zoechbauer, Stefan Feuerriegel
Venue: WWW
Year: 2020

Points-of-interest (POIs; i.e., restaurants, bars, landmarks, and other entities) are common in web-mined data: they greatly explain the spatial distributions of urban phenomena. The conventional modeling approach relies upon feature engineering, yet it ignores the spatial structure among POIs. In order to overcome this shortcoming, the present paper proposes a novel spatial model for explaining spatial distributions based on web-mined POIs. Our key contributions are: (1) We present a rigorous yet highly interpretable formalization in order to model the influence of POIs on a given outcome variable. Specifically, we accommodate the spatial distributions of both the outcome and POIs. In our case, this modeled by the sum of latent Gaussian processes. (2) In contrast to previous literature, our model infers the influence of POIs without feature engineering, instead we model the influence of POIs via distance-weighted kernel functions with fully learnable parameterizations. (3) We propose a scalable learning algorithm based on sparse variational approximation. For this purpose, we derive a tailored evidence lower bound (ELBO) and, for appropriate likelihoods, we even show that an analytical expression can be obtained. This allows fast and accurate computation of the ELBO. Finally, the value of our approach for web mining is demonstrated in two real-world case studies. Our findings provide substantial improvements over state-of-the-art baselines with regard to both predictive and, in particular, explanatory performance. Altogether, this yields a novel spatial model for leveraging web-mined POIs. Within the context of location-based social networks, it promises an extensive range of new insights and use cases.
Multilingual Deception Detection by Autonomous Agents
Authors: Evgeny Hershkovitch Neiterman, Moshe Bitan, Amos Azaria
Venue: WWW
Year: 2020

In this work we present the development of a multilingual deception detection model based on speech. In addition, we also develop a model that detects whether a statement will be perceived as a lie or not by human subjects. To this end, we developed a game for collecting a large scale and high quality labeled data-set in a controlled environments in English and Hebrew. We developed a model that can detect deception based only on a vocal statement from the participants of the experiment. The data-set will be released to the community.
Hybrid Reciprocal Recommender Systems: Integrating Item-to-User Principles in Reciprocal Recommendation
Authors: James Neve, Iván Palomares
Venue: WWW
Year: 2020

Reciprocal Recommender Systems (RRS) recommend users to other users in a personalised manner, in scenarios where both sides of the preference relation must be considered. Existing RRS approaches based on collaborative filtering or content-based filtering, have been used for enhancing user experience in online dating and other online services aimed at connecting users with each other. However, some of these services e.g. skill sharing platforms, are still pervaded by content published, shared and consumed by users, consequently there is a valuable source of item-to-user preferential information not captured by existing RRS models. We present a novel hybrid RRS framework that integrates user preferences towards content in reciprocal recommendation, and we instantiate and evaluate it using data from Cookpad, a recipe sharing social media platform. As part of our model, we also implement a novel content-based extension of Jaccard similarity measure that operates on word embeddings.
FIMS: Identifying, Predicting and Visualising Food Insecurity
Authors: Georgiana Nica-Avram, John Harvey, James Goulding, Benjamin Lucas, Andrew Smith, Gavin Smith, Bertrand Perrat
Venue: WWW
Year: 2020

Food insecurity is a persistent and pernicious problem in the UK. Due to logistical challenges, national food insecurity statistics are unmeasured by government bodies - and this lack of data leads to any local estimates that do exist being routinely questioned by policymakers. We demonstrate a data-driven approach to address this issue, deriving national estimates of food insecurity via combination of supervised machine learning with network analysis of user behaviour, extracted from the world’s most popular peer-to-peer food sharing application (OLIO). Despite long-standing theoretical links between social graph topologies and physical neighbourhoods, prior research has not considered dimensions of geography, network interactions and behaviours in the digital/analogue space simultaneously. In addressing this oversight, we produce a browser-based, interactive and rapidly updateable visualisation, which can be used to analyse the spatial distribution of food insecurity across the UK, and provide new perspective for policy research.
Unbiased Learning to Rank: Counterfactual and Online Approaches
Authors: Harrie Oosterhuis, Rolf Jagerman, Maarten de Rijke
Venue: WWW
Year: 2020

This tutorial is about Unbiased Learning to Rank, a recent research field that aims to learn unbiased user preferences from biased user interactions. We will provide an overview of the two main families of methods in Unbiased Learning to Rank: Counterfactual Learning to Rank (CLTR) and Online Learning to Rank (OLTR) and their underlying theory. First, the tutorial will start with a brief introduction to the general Learning to Rank (LTR) field and the difficulties user interactions pose for traditional supervised LTR methods. The second part will cover Counterfactual Learning to Rank (CLTR), a LTR field that sprung out of click models. Using an explicit model of user biases, CLTR methods correct for them in their learning process and can learn from historical data. Besides these methods, we will also cover practical considerations, such as how certain biases can be estimated. In the third part of the tutorial we focus on Online Learning to Rank (OLTR), methods that learn by directly interacting with users and dealing with biases by adding stochasticity to displayed results. We will cover cascading bandits, dueling bandit techniques and the most recent pairwise differentiable approach. Finally, in the concluding part of the tutorial, both approaches are contrasted, highlighting their relative strengths and weaknesses, and presenting future directions of research. For LTR practitioners our comparison gives guidance on how the choice between methods should be made. For the field of Information Retrieval (IR) we aim to provide an essential guide on unbiased LTR to understanding and choosing between methodologies.
Toward Characterizing Cities with Social Media Images Using Activity Recognition, Topic Modeling and Visualization
Authors: Daniela Opitz, Eduardo Graells-Garrido, Ignacio Pérez-Messina
Venue: WWW
Year: 2020

Images are at the heart of the digital life, registering what, where, and when people do activities. This rich visual information may allow to characterize cities at spatial granularities unseen with traditional methods. To advance in that direction, here we propose a methodology to explore the activities held in cities, by means of activity recognition (what happens in images), topic modeling (what activities are relevant), and visualization (how to choose the number of topics, interpret them, and where do they appear in the city). We apply this methodology to Santiago, Chile, using images from Flickr, and find promising results for planners and urbanites.
Finding large balanced subgraphs in signed networks
Authors: Bruno Ordozgoiti, Antonis Matakos, Aristides Gionis
Venue: WWW
Year: 2020

Signed networks are graphs whose edges are labelled with either a positive or a negative sign, and can be used to capture nuances in interactions that are missed by their unsigned counterparts. The concept of balance in signed graph theory determines whether a network can be partitioned into two perfectly opposing subsets, and is therefore useful for modelling phenomena such as the existence of polarized communities in social networks. While determining whether a graph is balanced is easy, finding a large balanced subgraph is hard. The few heuristics available in the literature for this purpose are either ineffective or non-scalable. In this paper we propose an efficient algorithm for finding large balanced subgraphs in signed networks. The algorithm relies on signed spectral theory and a novel bound for perturbations of the graph Laplacian. In a wide variety of experiments on real-world data we show that our algorithm can find balanced subgraphs much larger than those detected by existing methods, and in addition, it is faster. We test its scalability on graphs of up to 34 million edges.
Towards Incomplete SPARQL Query in RDF Question Answering - A Semantic Completion Approach
Authors: Jinhui Pang, Jie Jiao, Guangxi Ji, Yunjie Wu, Ding Zhang, Shujun Wang
Venue: WWW
Year: 2020

RDF question/answering(Q/A) system allows users to ask questions in natural language on a knowledge base represented by RDF and retrieve answers. A common problem in RDF Q/A is that existing works tend to translate a natural language question into an incomplete SPARQL query, which means that SPARQL queries may not fully understand user’s ideas. For example, some triple patterns may be missing in the question translation stage. In this poster, we first present a siamese adaptation of the Long Short-Term Memory(LSTM) network to detect whether the SPARQL query generated by the RDF Q/A system is complete. Then, for incomplete queries, we propose a Markov-based method to supplement SPARQL queries. Finally, we compare our approach with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method improves the precision significantly.
Domain-specific Noisy Query Correction using Linguistic Network Community Detection
Authors: Sangameshwar Patil
Venue: WWW
Year: 2020

Noisy queries pose an important challenge for retrieving relevant search results. The importance for query correction increases with increasing use of hand-held devices and technologies such as SMS, tweets to search and access information. The task is further complicated for domain-specific search engines as the amount of query logs may be significantly smaller than general purpose search engines. In this paper, we propose to use the community detection technique from social network analysis for spelling correction of a set of noisy queries such as SMS messages. We focus on the task of identifying relevant questions from a set of Frequently Asked Questions (FAQ) for different domains for a set of incoming noisy queries. Experimental validation shows that the proposed CD-Speller method performs significantly better than Hunspell, the popular and industry-strength spelling correction tool.
#Outage: Detecting Power and Communication Outages from Social Networks
Authors: Udit Paul, Alexander Ermakov, Michael Nekrasov, Vivek Adarsh, Elizabeth M. Belding
Venue: WWW
Year: 2020

Natural disasters are increasing worldwide at an alarming rate. To aid relief operations during and post disaster, humanitarian organizations rely on various types of situational information such as missing, trapped or injured people and damaged infrastructure in an area. Crucial and timely identification of infrastructure and utility damage is critical to properly plan and execute search and rescue operations. However, in the wake of natural disasters, real-time identification of this information becomes challenging. In this research, we investigate the use of tweets posted on the Twitter social media platform to detect power and communication outages during natural disasters. We first curate a data set of 18,097 tweets based on domain-specific keywords obtained using Latent Dirichlet Allocation. We annotate the gathered data set to separate the tweets into different types of outage-related events: power outage, communication outage and both power-communication outage. We analyze the tweets to identify information such as popular words, length of words and hashtags as well as sentiments that are associated with tweets in these outage-related categories. Furthermore, we apply machine learning algorithms to classify these tweets into their respective categories. Our results show that simple classifiers such as the boosting algorithm are able to classify outage related tweets from unrelated tweets with close to 100% f1-score. Additionally, we observe that the transfer learning model, BERT, is able to classify different categories of outage-related tweets with close to 90% accuracy in less than 90 seconds of training and testing time, demonstrating that tweets can be mined in real-time to assist first responders during natural disasters.
Attention Please: Your Attention Check Questions in Survey Studies Can Be Automatically Answered
Authors: Weiping Pei, Arthur Mayer, Kaylynn Tu, Chuan Yue
Venue: WWW
Year: 2020

Attention check questions have become commonly used in online surveys published on popular crowdsourcing platforms as a key mechanism to filter out inattentive respondents and improve data quality. However, little research considers the vulnerabilities of this important quality control mechanism that can allow attackers including irresponsible and malicious respondents to automatically answer attention check questions for efficiently achieving their goals. In this paper, we perform the first study to investigate such vulnerabilities, and demonstrate that attackers can leverage deep learning techniques to pass attention check questions automatically. We propose AC-EasyPass, an attack framework with a concrete model, that combines convolutional neural network and weighted feature reconstruction to easily pass attention check questions. We construct the first attention check question dataset that consists of both original and augmented questions, and demonstrate the effectiveness of AC-EasyPass. We explore two simple defense methods, adding adversarial sentences and adding typos, for survey designers to mitigate the risks posed by AC-EasyPass; however, these methods are fragile due to their limitations from both technical and usability perspectives, underlining the challenging nature of defense. We hope our work will raise sufficient attention of the research community towards developing more robust attention check mechanisms. More broadly, our work intends to prompt the research community to seriously consider the emerging risks posed by the malicious use of machine learning techniques to the quality, validity, and trustworthiness of crowdsourcing and social computing.
Enhanced-RCNN: An Efficient Method for Learning Sentence Similarity
Authors: Shuang Peng, Hengbin Cui, Niantao Xie, Sujian Li, Jiaxing Zhang, Xiaolong Li
Venue: WWW
Year: 2020

Learning sentence similarity is a fundamental research topic and has been explored using various deep learning methods recently. In this paper, we further propose an enhanced recurrent convolutional neural network (Enhanced-RCNN) model for learning sentence similarity. Compared to the state-of-the-art BERT model, the architecture of our proposed model is far less complex. Experimental results show that our similarity learning method outperforms the baselines and achieves the competitive performance on two real-world paraphrase identification datasets.
The Diffusion of Mainstream and Disinformation News on Twitter: The Case of Italy and France
Authors: Francesco Pierri
Venue: WWW
Year: 2020

In this work we provide preliminary results from an ongoing investigation on the Twitter diffusion of news pertaining to two classes of sources, namely websites which notably produce disinformation, i.e. misleading and harmful information, opposed to more traditional and mainstream websites which instead publish credible information. We used the Twitter Streaming API to collect a large-scale dataset of thousands of tweets containing links to news articles in two different countries, Italy and France. We show that mainstream news outlets generate a much larger engagement in both settings, with a larger discrepancy between the two news domains in France. We also show that only a handful of Italian outlets actively engage with Twitter users, whereas in France there is a larger number of outlets sharing misleading information which exhibit a non-negligible volume of shares. We observe a strong tendency towards sharing mainstream news in those users who also share non-credible information in both countries. Analyzing the diffusion networks of distinct news domains and countries, we observed that disinformation networks are more clustered and connected, but much smaller than the mainstream ones (with the largest discrepancy in the French scenario).
AirCalypse: Can Twitter Help in Urban Air Quality Measurement and Who are the Influential Users?
Authors: Prithviraj Pramanik, Tamal Mondal, Subrata Nandi, Mousumi Saha
Venue: WWW
Year: 2020

In this digital age, Online Social Media’s ubiquity has led it to it’s role as a ”Sensor”. Starting from disaster response to political predictions, online social media like Twitter, have been instrumental and are actively researched areas. In this work, we have focused on something quite insidious in the current context, i.e., air pollution in developing regions. Starting as an empirical study on using Twitter as a ”Sensor” to measure air quality, the focal point of this work is to identify the users who have been actively tweeting in the air pollution events in Delhi, the capital of India. From these users, we try to identify the influential ones, who play a significant role in creating the initial awareness and hence act as ”Sensors”. We have utilized a tailored ”TRank” algorithm for finding out the influential users by considering Retweet, Favorite, and Follower influence of the users. After ranking the users based on their social influence, we further study the behavior, i.e., perception of pollution from those users’ posts with respect to the actual air pollution levels using the physical sensors. The tracking of influential users in air quality monitoring assists in developing a crowd sensed air quality measurement framework, which can augment the physical air quality sensors for raising awareness against air pollution.