OpenRaiser/Intern-Atlas huggingface dataset:
Genre Differences of Song Lyrics and Artist Wikis: An Analysis of Popularity, Length, Repetitiveness, and Readability
Authors: Markus Schedl
Venue: WWW
Year: 2019

Music is known to exhibit different characteristics, depending on genre and style. While most research that studies such differences takes a musicological perspective and analyzes acoustic properties of individual pieces or artists, we conduct a large-scale analysis using various web resources. Exploiting content information from song lyrics, contextual information reflected in music artists' Wikipedia articles, and listening information, we particularly study the aspects of popularity, length, repetitiveness, and readability of lyrics and Wikipedia articles. We measure popularity in terms of song play count (PC) and listener count (LC), length in terms of character and word count, repetitiveness in terms of text compression ratio, and readability in terms of the Simple Measure of Gobbledygook (SMOG). Extending datasets of music listening histories and genre annotations from Last.fm, we extract and analyze 424,476 song lyrics by 18,724 artists from LyricWiki. We set out to answer whether there exist significant genre differences in song lyrics (RQ1) and artist Wikipedia articles (RQ2) in terms of repetitiveness and readability. We also assess whether we can find evidence to support the cliche´ that lyrics of very popular artists are particularly simple and repetitive (RQ3). We further investigate whether the characteristics of popularity, length, repetitiveness, and readability correlate within and between lyrics and Wikipedia articles (RQ4). We identify substantial differences in repetitiveness and readability of lyrics between music genres. In contrast, no significant differences between genres are found for artists' Wikipedia pages. Also, we find that lyrics of highly popular artists are repetitive but not necessarily simple in terms of readability. Furthermore, we uncover weak correlations between length of lyrics and of Wikipedia pages of the same artist, weak correlations between lyrics' reading difficulty and their length, and moderate correlations between artists' popularity and length of their lyrics.
Externalities and Fairness
Authors: Masoud Seddighin, Hamed Saleh, Mohammad Ghodsi
Venue: WWW
Year: 2019

One of the important yet insufficiently studied subjects in fair allocation is the externality effect among agents. For a resource allocation problem, externalities imply that the share allocated to an agent may affect the utilities of other agents. In this paper, we conduct a study of fair allocation of indivisible goods when the externalities are not negligible. Inspired by the models in the context of network diffusion, we present a simple and natural model, namely network externalities, to capture the externalities. To evaluate fairness in the network externalities model, we generalize the idea behind the notion of maximin-share () to achieve a new criterion, namely, extended-maximin-share (). Next, we consider two problems concerning our model. First, we discuss the computational aspects of finding the value of for every agent. For this, we introduce a generalized form of partitioning problem that includes many famous partitioning problems such as maximin, minimax, and leximin. We further show that a 1/2-approximation algorithm exists for this partitioning problem. Next, we investigate on finding approximately optimal allocations, i.e., allocations that guarantee each agent a utility of at least a fraction of his extended-maximin-share. We show that under a natural assumption that the agents are a-self-reliant, an a/2- allocation always exists. The combination of this with the former result yields a polynomial-time a/4- allocation algorithm.
Exploiting Diversity in Android TLS Implementations for Mobile App Traffic Classification
Authors: Satadal Sengupta, Niloy Ganguly, Pradipta De, Sandip Chakraborty
Venue: WWW
Year: 2019

Network traffic classification is an important tool for network administrators in enabling monitoring and service provisioning. Traditional techniques employed in classifying traffic do not work well for mobile app traffic due to lack of unique signatures. Encryption renders this task even more difficult since packet content is no longer available to parse. More recent techniques based on statistical analysis of parameters such as packet-size and arrival time of packets have shown promise; such techniques have been shown to classify traffic from a small number of applications with a high degree of accuracy. However, we show that when employed to a large number of applications, the performance falls short of satisfactory. In this paper, we propose a novel set of bit-sequence based features which exploit differences in randomness of data generated by different applications. These differences originating due to dissimilarities in encryption implementations by different applications leave footprints on the data generated by them. We validate that these features can differentiate data encrypted with various ciphers (89% accuracy) and key-sizes (83% accuracy). Our evaluation shows that such features can not only differentiate traffic originating from different categories of mobile apps (90% accuracy), but can also classify 175 individual applications with 95% accuracy.
Travel Time Estimation Accuracy in Developing Regions: An Empirical Case Study with Uber Data in Delhi-NCR✱
Authors: Daksh Shah, Aravinda Kumaran, Rijurekha Sen, Ponnurangam Kumaraguru
Venue: WWW
Year: 2019

Travel time estimates are highly useful in planning urban mobility events. This paper investigates the quality of travel time estimates in the Indian capital city of Delhi and the National Capital Region (NCR). Using Uber mobile and web applications, we collect data about 610 trips from 34 Uber users. We empirically show the unpredictability of travel time estimates for Uber cabs. We also discuss the adverse effects of such unpredictability on passengers waiting for the cabs, leading to a whopping 28.4% of the requested trips being cancelled. Our empirical observations differ significantly from the high accuracies reported in travel time estimation literature. These pessimistic results will hopefully trigger useful investigations in future on why the travel time estimates are mismatching the high accuracy levels reported in literature - (a) is it a lack of training data issue for developing countries or (b) an algorithmic shortcoming that cannot capture the (lack of) historical patterns in developing region travel times or (c) a conscious policy decision by Uber platform or Uber drivers, to mismatch the correctly predicted travel time estimates and increase cab cancellation fees? In the context of smartphone apps extensively generating and utilizing travel time information for urban commute, this paper identifies and discusses the important problem of travel time estimation inaccuracies in developing countries.
What is in Your Password? Analyzing Memorable and Secure Passwords using a Tensor Decomposition
Authors: Youjin Shin, Simon S. Woo
Venue: WWW
Year: 2019

In the past, there have been several studies in analyzing password strength and structures. However, there are still many unknown questions to understand what really makes passwords both memorable and strong. In this work, we aim to answer some of these questions by analyzing password dataset through the lenses of data science and machine learning perspectives. We use memorable 3,260 password dataset collected from prior IRB-approved user studies over 3 years and classify passwords into three strength groups using online and offline attack limits. Then, we apply a tensor decomposition to analyze password dataset by constructing a 3rd-order tensor with passwords' syntactic and semantic features. In particular, we used PARAFAC2 tensor decomposition to uncover the main characteristics and features that affect password strength. We quantitatively identified the underlying factors that are more frequently observed in strong and memorable passwords. We hope that our finding can validate widely accepted advice for creating strong passwords and provide useful insights to design a better password suggestion system.
Panel: Knowledge Graph Industry Applications
Authors: Joshua Shinavier, Kim Branson, Wei Zhang, Shima Dastgheib, Yuqing Gao, Bogdan G. Arsintescu, Fatma Özcan, Edgar Meij
Venue: WWW
Year: 2019

This panel will focus on industry applications related to knowledge graph and showcase how knowledge graph transforms the conventional and unconventional industries to the new era of AI, ranging from innovations in medicine and healthcare, literature search, e-commerce, professional connections, to getting a ride. Panelists are: Senior Software Engineer/Research Scientist at Uber, Co-founder of Tinkerpop, specialized on real-time semantics, RDF streams and graph databases Head of AI at Genentech, passionate about modeling, and currently developing a general medical inference engine that can be applied to a wide variety of areas, from point of care decision support, triage, insurance risk managment to name a few A senior staff engineer/director at Business Platform Unit, Alibaba, leading product knowledge graph (PKG) team and Business Platform AI team. He and his team have built a huge PKG with 10 billion of entities. Data Scientist at Numedii, previously postdoctoral research fellow at Stanford University School of Medicine, developing novel methods to integrate and explore a broad set of biological and clinical data for scientific reproducibility and biomedical discovery. An accomplished technical scientist, innovator and R&D leader in cutting-edge technology research and product development. Proven track record of success (20+ years of successful professional career in leading R&D organizations) in leading rapid technological advancement, innovation, and highly competitive environments. Broad range of skills from initiating research breakthroughs to achieving marketable product development. Renowned expert and technological visionary in the fields of enterprise middleware, cloud computing, data centric computing, workload optimized systems and appliances, business analytics, big data, social media and multimedia, speech and natural language processing. Extended technical leadership in systems design for LinkedIn Economic Graph, Google Search and Google Research. Breadth and depth of expertise in building data systems and platforms. Technical leadership and management of software development in both start-up and large companies. A principal research staff member, and a senior manager at IBM Almaden Research Center. I manage the information management department, working on HTAP (hybrid transactional and analytical processing) systems, large scale machine learning, and natural language querying of data. A team lead and senior scientist at Bloomberg. He holds a PhD in computer science from the University of Amsterdam and has an extensive track record in artificial intelligence, information retrieval, knowledge graphs, natural language processing, and machine learning. Before joining Bloomberg he worked at Yahoo Labs on semantic search at web scale using the Yahoo Knowledge Graph. At Bloomberg he leads the team that is responsible for leveraging knowledge graph technology to drive advanced financial insights.
Generative Graph Models based on Laplacian Spectra?
Authors: Alana Shine, David Kempe
Venue: WWW
Year: 2019

We present techniques for generating random graphs whose Laplacian spectrum approximately matches that of a given input graph. The motivation for matching the Laplacian spectrum is that it naturally encodes high-level connectivity information about the input graph; most existing models (e.g., variants of the Configuration Model, Stochastic Block Model, or Kronecker Graphs) focus on local structure or limited high-level partitions. Our techniques succeed in matching the spectrum of the input graph more closely than the benchmark models. We also evaluate our generative model using other global and local properties, including shortest path distances, betweenness centrality, degree distribution, and clustering coefficients. The graphs produced by our model almost always match the input graph better than those produced by the benchmark models with respect to shortest path distance and clustering coefficient distributions. The performance on betweenness centrality is comparable to the benchmarks, while a worse match on the degree distribution is a price our method pays for more global similarity. Our results suggest that focusing on spectral properties may lead to good performance for other global properties, at a modest loss in local similarity. Since global connectivity patterns are usually more important than local features for processes such as information flow, spread of epidemics, routing, etc., our main goal is to advocate for a shift in focus from graph generative models matching local properties to those matching global connectivity patterns.
VACCINE: Using Contextual Integrity For Data Leakage Detection
Authors: Yan Shvartzshnaider, Zvonimir Pavlinovic, Ananth Balashankar, Thomas Wies, Lakshminarayanan Subramanian, Helen Nissenbaum, Prateek Mittal
Venue: WWW
Year: 2019

Modern enterprises rely on Data Leakage Prevention (DLP) systems to enforce privacy policies that prevent unintentional flow of sensitive information to unauthorized entities. However, these systems operate based on rule sets that are limited to syntactic analysis and therefore completely ignore the semantic relationships between participants involved in the information exchanges. For similar reasons, these systems cannot enforce complex privacy policies that require temporal reasoning about events that have previously occurred. To address these limitations, we advocate a new design methodology for DLP systems centered on the notion of Contextual Integrity (CI). We use the CI framework to abstract real-world communication exchanges into formally defined information flows where privacy policies describe sequences of admissible flows. CI allows us to decouple (1) the syntactic extraction of flows from information exchanges, and (2) the enforcement of privacy policies on these flows. We applied this approach to built VACCINE, a DLP auditing system for emails. VACCINE uses state-of-the-art techniques in natural language processing to extract flows from email text. It also provides a declarative language for describing privacy policies. These policies are automatically compiled to operational rules that the system uses for detecting data leakages. We evaluated VACCINE on the Enron email corpus and show that it improves over the state of the art both in terms of the expressivity of the policies that DLP systems can enforce as well as its precision in detecting data leakages.
After Brazil's General Data Protection Law: Authorization in Decentralized Web Applications
Authors: Jefferson Silva, Newton Calegari, Eduardo Gomes
Venue: WWW
Year: 2019

Decentralized web applications do not offer fine-grained access controls to users’ data, which potentially creates openings for data breaches. For software companies that need to comply with Brazil’s General Data Protection Law (LGPD), data breaches not only might harm application users but also could expose the companies to hefty fines. In this context, engineering fine-grained authorization controls (that comply with the LGPD) to decentralized web application requires creating audit trails, possibly in the source code. Although the literature offers some solutions, they are scattered. We present Esfinge Guardian, an authorization framework that completely separates authorization from other concerns, which increases compliance with the LGPD. We conclude the work with a brief discussion.
Anything to Hide? Studying Minified and Obfuscated Code in the Web
Authors: Philippe Skolka, Cristian-Alexandru Staicu, Michael Pradel
Venue: WWW
Year: 2019

JavaScript has been used for various attacks on client-side web applications. To hinder both manual and automated analysis from detecting malicious scripts, code minification and code obfuscation may hide the behavior of a script. Unfortunately, little is currently known about how real-world websites use such code transformations. This paper presents an empirical study of obfuscation and minification in 967,149 scripts (424,023 unique) from the top 100,000 websites. The core of our study is a highly accurate (95%-100%) neural network-based classifier that we train to identify whether obfuscation or minification have been applied and if yes, using what tools. We find that code transformations are very widespread, affecting 38% of all scripts. Most of the transformed code has been minified, whereas advanced obfuscation techniques, such as encoding parts of the code or fetching all strings from a global array, affect less than 1% of all scripts (2,842 unique scripts in total). Studying which code gets obfuscated, we find that obfuscation is particularly common in certain website categories, e.g., adult content. Further analysis of the obfuscated code shows that most of it is similar to the output produced by a single obfuscation tool and that some obfuscated scripts trigger suspicious behavior, such as likely fingerprinting and timing attacks. Finally, we show that obfuscation comes at a cost, because it slows down execution and risks to produce code that changes the intended behavior. Overall, our study shows that the security community must consider minified and obfuscated JavaScript code, and it provides insights into what kinds of transformations to focus on. Our learned classifiers provide an automated and accurate way to identify obfuscated code, and we release a set of real-world obfuscated scripts for future research.
Understanding Reader Backtracking Behavior in Online News Articles
Authors: Uzi Smadja, Max Grusky, Yoav Artzi, Mor Naaman
Venue: WWW
Year: 2019

Rich engagement data can shed light on how people interact with online content and how such interactions may be determined by the content of the page. In this work, we investigate a specific type of interaction, backtracking, which refers to the action of scrolling back in a browser while reading an online news article. We leverage a dataset of close to 700K instances of more than 15K readers interacting with online news articles, in order to characterize and predict backtracking behavior. We first define different types of backtracking actions. We then show that “full” backtracks, where the readers eventually return to the spot at which they left the text, can be predicted by using features that were previously shown to relate to text readability. This finding highlights the relationship between backtracking and readability and suggests that backtracking could help assess readability of content at scale.
Concept to Code: Deep Learning for Fashion Recommendation
Authors: Omprakash Sonie, Muthusamy Chelliah, Shamik Sural
Venue: WWW
Year: 2019

Deep Learning has shown significant results in various domains. In this tutorial, we provide conceptual understanding of embedding methods, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNNs). We present fashion use case and apply these techniques for modeling image, text as well as sequence data to figure out user profiles, give personalized recommendations tailored to changing user taste and interest. Given the image of a fashion item, recommending complementary matches is a challenge. Users’ taste evolves over time and depends on persona. Humans relate objects based on their appearance and non-visual factors of lifestyle merchandise which further complicate recommendation task. Composing outfits in addition necessitates constituent items to be compatible - similar in some but different in other aspects.
Towards Enabling Internet-Scale Context-as-a-Service: A Position Paper
Authors: Alexandru Sorici, Andrei Olaru, Adina Magda Florea
Venue: WWW
Year: 2019

Deploying context management systems at a global scale comes with a number of challenges and requirements. We argue that the hypermedia model and the agent-oriented paradigm help achieve the vision of Context-as-a-Service. We categorize challenges according to context processing concerns and use a scenario to exemplify how the proposed architectural principles help overcome the challenges.
Unnecessarily Identifiable: Quantifying the fingerprintability of browser extensions due to bloat
Authors: Oleksii Starov, Pierre Laperdrix, Alexandros Kapravelos, Nick Nikiforakis
Venue: WWW
Year: 2019

In this paper, we investigate to what extent the page modifications that make browser extensions fingerprintable are necessary for their operation. We characterize page modifications that are completely unnecessary for the extension's functionality as extension bloat. By analyzing 58,034 extensions from the Google Chrome store, we discovered that 5.7% of them were unnecessarily identifiable because of extension bloat. To protect users against unnecessary extension fingerprinting due to bloat, we describe the design and implementation of an in-browser mechanism that provides coarse-grained access control for extensions on all websites. The proposed mechanism and its built-in policies, does not only protect users from fingerprinting, but also offers additional protection against malicious extensions exfiltrating user data from sensitive websites.
Geolocation in the Browser
Authors: Thomas Steiner, Anssi Kostiainen, Marijn Kruisselbrink
Venue: WWW
Year: 2019

Geolocation is arguably one of the most powerful capabilities of smartphones and a lot of attention has been paid to native applications that make use of it. The discontinued Google Gears plugin was one of the first approaches to access exact location data on the Web as well, apart from server-side coarse location lookups based on Internet Protocol (ip) addresses; and the plugin led directly to the now widely implemented Geolocation api. The World Wide Web Consortium (w3c) Geolocation api specification defines a standard for accessing location services in the browser via JavaScript. For a long time, developers have also demanded more advanced features like background geolocation tracking and geofencing. The w3c Geolocation and the Devices and Sensors Working Groups, as well as the Web Incubator Community Group (wicg), have addressed these demands with the no longer maintained Geofencing api specification for the former, and—with now (early 2019) resumed efforts—the in-flight Geolocation Sensors specification for the latter two groups. This paper first provides an in-depth overview of the historical development of geolocation in the browser and motivates privacy decisions that were made at the time, and then gives an outlook on current and future efforts, challenges, and use cases from both a technology as well as from a privacy angle.
Fairness in Social Influence Maximization
Authors: Ana-Andreea Stoica, Augustin Chaintreau
Venue: WWW
Year: 2019

Algorithms for social influence maximization have been extensively studied for the purpose of strategically choosing an initial set of individuals in a social network from which information gets propagated. With many applications in advertisement, news spread, vaccination, and online trend-setting, this problem is a central one in understanding how information flows in a network of individuals. As human networks may encode historical biases, algorithms performing on them might capture and reproduce such biases when automating outcomes. In this work, we study the social influence maximization problem for the purpose of designing fair algorithms for diffusion, aiming to understand the effect of communities in the creation of disparate impact among network participants based on demographic attributes (gender, race etc). We propose a set of definitions and models for assessing the fairness-utility tradeoff in designing algorithms that maximize influence through a mathematical model of diffusion and an empirical analysis of a collected dataset from Instagram. Our work shows that being feature-aware can lead to more diverse outcomes in outreach and seed selection, as well as better efficiency, than being feature-blind.