OpenRaiser/Intern-Atlas huggingface dataset:
Effective and Real-time In-App Activity Analysis in Encrypted Internet Traffic Streams
Authors: Junming Liu, Yanjie Fu, Jingci Ming, Yong Ren, Leilei Sun, Hui Xiong
Venue: KDD
Year: 2017

The mobile in-App service analysis, aiming at classifying mobile internet traffic into different types of service usages, has become a challenging and emergent task for mobile service providers due to the increasing adoption of secure protocols for in-App services. While some efforts have been made for the classification of mobile internet traffic, existing methods rely on complex feature construction and large storage cache, which lead to low processing speed, and thus not practical for online real-time scenarios. To this end, we develop an iterative analyzer for classifying encrypted mobile traffic in a real-time way. Specifically, we first select an optimal set of most discriminative features from raw features extracted from traffic packet sequences by a novel Maximizing Inner activity similarity and Minimizing Different activity similarity (MIMD) measurement. To develop the online analyzer, we first represent a traffic flow with a series of time windows, which are described by the optimal feature vector and are updated iteratively at the packet level. Instead of extracting feature elements from a series of raw traffic packets, our feature elements are updated when a new traffic packet is observed and the storage of raw traffic packets is not required. The time windows generated from the same service usage activity are grouped by our proposed method, namely, recursive time continuity constrained KMeans clustering (rCKC). The feature vectors of cluster centers are then fed into a random forest classifier to identify corresponding service usages. Finally, we provide extensive experiments on real-world Internet traffic data from Wechat, Whatsapp, and Facebook to demonstrate the effectiveness and efficiency of our approach. The results show that the proposed analyzer provides high accuracy in real-world scenarios, and has low storage cache requirement as well as fast processing speed.
Supporting Employer Name Normalization at both Entity and Cluster Level
Authors: Qiaoling Liu, Faizan Javed, Vachik S. Dave, Ankita Joshi
Venue: KDD
Year: 2017

In the recruitment domain, the employer name normalization task, which links employer names in job postings or resumes to entities in an employer knowledge base (KB), is important to many business applications. In previous work, we proposed the CompanyDepot system, which used machine learning techniques to address the problem. After applying it to several applications at CareerBuilder, we faced several new challenges: 1) how to avoid duplicate normalization results when the KB is noisy and contains many duplicate entities; 2) how to address the vocabulary gap between query names and entity names in the KB; and 3) how to use the context available in jobs and resumes to improve normalization quality.
Functional Annotation of Human Protein Coding Isoforms via Non-convex Multi-Instance Learning
Authors: Tingjin Luo, Weizhong Zhang, Shang Qiu, Yang Yang, Dongyun Yi, Guangtao Wang, Jieping Ye, Jie Wang
Venue: KDD
Year: 2017

Functional annotation of human genes is fundamentally important for understanding the molecular basis of various genetic diseases. A major challenge in determining the functions of human genes lies in the functional diversity of proteins, that is, a gene can perform different functions as it may consist of multiple protein coding isoforms (PCIs). Therefore, differentiating functions of PCIs can significantly deepen our understanding of the functions of genes. However, due to the lack of isoform-level gold-standards (ground-truth annotation), many existing functional annotation approaches are developed at gene-level. In this paper, we propose a novel approach to differentiate the functions of PCIs by integrating sparse simplex projection---that is, a nonconvex sparsity-inducing regularizer---with the framework of multi-instance learning (MIL). Specifically, we label the genes that are annotated to the function under consideration as positive bags and the genes without the function as negative bags. Then, by sparse projections onto simplex, we learn a mapping that embeds the original bag space to a discriminative feature space. Our framework is flexible to incorporate various smooth and non-smooth loss functions such as logistic loss and hinge loss. To solve the resulting highly nontrivial non-convex and non-smooth optimization problem, we further develop an efficient block coordinate descent algorithm. Extensive experiments on human genome data demonstrate that the proposed approaches significantly outperform the state-of-the-art methods in terms of functional annotation accuracy of human PCIs and efficiency.
Internet Device Graphs
Authors: Matthew Malloy, Paul Barford, Enis Ceyhun Alp, Jonathan Koller, Adria Jewell
Venue: KDD
Year: 2017

Internet device graphs identify relationships between user-centric internet connected devices such as desktops, laptops, smartphones, tablets, gaming consoles, TV's, etc. The ability to create such graphs is compelling for online advertising, content customization, recommendation systems, security, and operations. We begin by describing an algorithm for generating a device graph based on IP-colocation, and then apply the algorithm to a corpus of over 2.5 trillion internet events collected over the period of six weeks in the United States. The resulting graph exhibits immense scale with greater than 7.3 billion edges (pair-wise relationships) between more than 1.2 billion nodes (devices), accounting for the vast majority of internet connected devices in the US. Next, we apply community detection algorithms to the graph resulting in a partitioning of internet devices into 100 million small communities representing physical households. We validate this partition with a unique ground truth dataset. We report on the characteristics of the graph and the communities. Lastly, we discuss the important issues of ethics and privacy that must be considered when creating and studying device graphs, and suggest further opportunities for device graph enrichment and application.
RUSH!: Targeted Time-limited Coupons via Purchase Forecasts
Authors: Emaad A. Manzoor, Leman Akoglu
Venue: KDD
Year: 2017

Time-limited promotions that exploit consumers' sense of urgency to boost sales account for billions of dollars in consumer spending each year. However, it is challenging to discover the right timing and duration of a promotion to increase its chances of being redeemed. In this work, we consider the problem of delivering time-limited discount coupons, where we partner with a large national bank functioning as a commission-based third-party coupon provider. Specifically, we use large-scale anonymized transaction records to model consumer spending and forecast future purchases, based on which we generate data-driven, personalized coupons. Our proposed model RUSH! (1) predicts {both the time and category} of the next event; (2) captures correlations between purchases in different categories (such as shopping triggering dining purchases); (3) incorporates temporal dynamics of purchase behavior (such as increased spending on weekends); (4) is composed of additive factors that are easily interpretable; and finally (5) scales linearly to millions of transactions. We design a cost-benefit framework that facilitates systematic evaluation in terms of our application, and show that RUSH! provides higher expected value than various baselines that do not jointly model time and category information.
Let's See Your Digits: Anomalous-State Detection using Benford's Law
Authors: Samuel Maurus, Claudia Plant
Venue: KDD
Year: 2017

Benford's Law explains a curious phenomenon in which the leading digits of "naturally-occurring" numerical data are distributed in a precise fashion. In this paper we begin by showing that system metrics generated by many modern information systems like Twitter, Wikipedia, YouTube and GitHub obey this law. We then propose a novel unsupervised approach called BenFound that exploits this property to detect anomalous system events. BenFound tracks the "Benfordness" of key system metrics, like the follower counts of tweeting Twitter users or the change deltas in Wikipedia page edits. It then applies a novel Benford-conformity test in real-time to identify "non-Benford events". We investigate a variety of such events, showing that they correspond to unnatural and often undesirable system interactions like spamming, hashtag-hijacking and denial-of-service attacks. The result is a technically-uncomplicated and effective "red flagging" technique that can be used to complement existing anomaly-detection approaches. Although not without its limitations, it is highly efficient and requires neither obscure parameters, nor text streams, nor natural-language processing.
Addressing Challenges with Big Data for Media Measurement
Authors: Mainak Mazumdar
Venue: KDD
Year: 2017

The digital media and TV - which is increasingly digitized, have amassed and generating enormous amount of data. While extremely useful, the big data generated by these platforms poses unique challenges for Data Scientists working on developing measurement framework and metrics. Most practitioners optimize speed and scale at the expense of accuracy, which is critical for any measurement. And, the trade-off between bias and variance is not in consideration. In this paper, we will demonstrate how Nielsen is combining proprietary ground truth data and methodologies with Big Data to address the accuracy and bias/variance challenges. We argue that high quality ground truth or training set is pre-requisite to deploying Big Data for high quality media measurement. To illustrate the point, we will share how Nielsen is combining its proprietary high quality panels with Set Top Box for TV measurement in the U.S.
Developing a Comprehensive Framework for Multimodal Feature Extraction
Authors: Quinten McNamara, Alejandro de la Vega, Tal Yarkoni
Venue: KDD
Year: 2017

Feature extraction is a critical component of many applied data science workflows. In recent years, rapid advances in artificial intelligence and machine learning have led to an explosion of feature extraction tools and services that allow data scientists to cheaply and effectively annotate their data along a vast array of dimensions--ranging from detecting faces in images to analyzing the sentiment expressed in coherent text. Unfortunately, the proliferation of powerful feature extraction services has been mirrored by a corresponding expansion in the number of distinct interfaces to feature extraction services. In a world where nearly every new service has its own API, documentation, and/or client library, data scientists who need to combine diverse features obtained from multiple sources are often forced to write and maintain ever more elaborate feature extraction pipelines. To address this challenge, we introduce a new open-source framework for comprehensive multimodal feature extraction. Pliers is an open-source Python package that supports standardized annotation of diverse data types (videos, images, audio, and text), and is expressly implemented with both ease-of-use and extensibility in mind. Users can apply a wide range of pre-existing feature extraction tools to their data in just a few lines of Python code, and can also easily add their own custom extractors by writing modular classes. A graph-based API enables rapid development of feature extraction pipelines that output results in a single, standardized format. We describe the package's architecture, detail its advantages over previous feature extraction toolboxes, and use a sample application to a large functional MRI dataset to illustrate how pliers can significantly reduce the time and effort required to construct simple feature extraction workflows while increasing code clarity and maintainability.
The Future of Data Integration
Authors: Renée J. Miller
Venue: KDD
Year: 2017

The value of data explodes when it is integrated. In this talk, I present some important innovations in data integration over the last two decades. These include data exchange [1], which provides a foundation for reasoning about the correctness of transformed data, and the use of declarative mappings in integration [2]. I discuss how data mining has been used to facilitate data integration, including constraint discovery [3], mapping discovery [4], and in schema discovery to combat database decay and facilitate integration [5,6]. I present some important new data integration challenges that arise in data science. These include the use of mining for query and visualization recommendation over massive data lakes [7] and data set search, finding datasets of interest at interactive speeds [8].
Learning to Count Mosquitoes for the Sterile Insect Technique
Authors: Yaniv Ovadia, Yoni Halpern, Dilip Krishnan, Josh Livni, Daniel E. Newburger, Ryan Poplin, Tiantian Zha, D. Sculley
Venue: KDD
Year: 2017

Mosquito-borne illnesses such as dengue, chikungunya, and Zika are major global health problems, which are not yet addressable with vaccines and must be countered by reducing mosquito populations. The Sterile Insect Technique (SIT) is a promising alternative to pesticides; however, effective SIT relies on minimal releases of female insects. This paper describes a multi-objective convolutional neural net to significantly streamline the process of counting male and female mosquitoes released from a SIT factory and provides a statistical basis for verifying strict contamination rate limits from these counts despite measurement noise. These results are a promising indication that such methods may dramatically reduce the cost of effective SIT methods in practice.
Machine Learning Software in Practice: Quo Vadis?
Authors: Szilárd Pafka
Venue: KDD
Year: 2017

Due to the hype in our industry in the last couple of years, there is a growing mismatch between software tools machine learning practitioners wish for, what they would truly need for their work, what's available (either commercially or open source) and what tool developers and researchers focus on. In this talk we will give a couple of examples of this mismatch. Several surveys and anecdotal evidence show that most practitioners work most of the time (at least in the modeling phase) with datasets that t in the RAM of a single server, therefore distributed computing tools are very of- ten overkill. Our benchmarks (available on github [1]) of the most widely used open source tools for binary classification (various implementations of algorithms such as linear methods, random forests, gradient boosted trees and neural networks) on such data show over 10x speed and over 10x RAM usage difference between various tools, with "big data" tools being the most inefficient. Significant performance gains have been obtained by those tools that incorporate various low-level (close to CPU and memory architecture) optimizations. Nevertheless, we will show that even the best tools show degrading performance on the multi-socket servers featuring a high number of cores, systems that have become widely accessible more recently. Finally, while most of this talk is about performance, we will also argue that machine learning tools that feature high-level easy-to-use APIs provide increasing productivity for practitioners and therefore are preferable.
Deep Design: Product Aesthetics for Heterogeneous Markets
Authors: Yanxin Pan, Alexander Burnap, Jeffrey Hartley, Richard Gonzalez, Panos Y. Papalambros
Venue: KDD
Year: 2017

Aesthetic appeal is a primary driver of customer consideration for products such as automobiles. Product designers must accordingly convey design attributes (e.g., 'Sportiness'), a challenging proposition given the subjective nature of aesthetics and heterogeneous market segments with potentially different aesthetic preferences. We introduce a scalable deep learning approach that predicts how customers across different market segments perceive aesthetic designs and provides a visualization that can aid in product design. We tested this approach using a large-scale product design and crowdsourced customer data set with a Siamese neural network architecture containing a pair of conditional generative adversarial networks. The results show that the model predicts aesthetic design attributes of customers in heterogeneous market segments and provides a visualization of these aesthetic perceptions. This suggests that the proposed deep learning approach provides a scalable method for understanding customer aesthetic perceptions.
An Intelligent Customer Care Assistant System for Large-Scale Cellular Network Diagnosis
Authors: Lujia Pan, Jianfeng Zhang, Patrick P. C. Lee, Hong Cheng, Cheng He, Caifeng He, Keli Zhang
Venue: KDD
Year: 2017

With the advent of cellular network technologies, mobile Internet access becomes the norm in everyday life. In the meantime, the complaints made by subscribers about unsatisfactory cellular network access also become increasingly frequent. From a network operator's perspective, achieving accurate and timely cellular network diagnosis about the causes of the complaints is critical for both improving subscriber-perceived experience and maintaining network robustness. We present the Intelligent Customer Care Assistant (ICCA), a distributed fault classification system that exploits a data-driven approach to perform large-scale cellular network diagnosis. ICCA takes massive network data as input, and realizes both offline model training and online feature computation to distinguish between user and network faults in real time. ICCA is currently deployed in a metropolitan LTE network in China that is serving around 50 million subscribers. We show via evaluation that ICCA achieves high classification accuracy (85.3%) and fast query response time (less than 2.3 seconds). We also report our experiences learned from the deployment.
Designing AI at Scale to Power Everyday Life
Authors: Rajesh Parekh
Venue: KDD
Year: 2017

The majority of the experiences and interactions people have on Facebook today are made possible with AI. Well over 1 billion people enjoy unique, personalized experiences on Facebook that are powered by a wealth of AI and machine learning algorithms. AI is an incredibly fast-moving field: engineers and researchers across the company are turning the latest research breakthroughs into tools, platforms, and infrastructure that make it possible for anyone at Facebook to use AI in the experiences and products they build. This talk will look at how Facebook is conducting and applying industry-leading research to help drive advancements in AI disciplines like computer vision, language understanding, speech and video. We will also talk about building an infrastructure that anyone at Facebook can use to easily reuse algorithms in different products, scale to run thousands of simultaneous custom experiments, and give concrete examples of how employees across the company are able to leverage these platforms to build new AI products and services.
Compass: Spatio Temporal Sentiment Analysis of US Election What Twitter Says!
Authors: Debjyoti Paul, Feifei Li, Murali Krishna Teja, Xin Yu, Richie Frost
Venue: KDD
Year: 2017

With the widespread growth of various social network tools and platforms, analyzing and understanding societal response and crowd reaction to important and emerging social issues and events through social media data is increasingly an important problem. However, there are numerous challenges towards realizing this goal effectively and efficiently, due to the unstructured and noisy nature of social media data. The large volume of the underlying data also presents a fundamental challenge. Furthermore, in many application scenarios, it is often interesting, and in some cases critical, to discover patterns and trends based on geographical and/or temporal partitions, and keep track of how they will change overtime.
SPARTan: Scalable PARAFAC2 for Large & Sparse Data
Authors: Ioakeim Perros, Evangelos E. Papalexakis, Fei Wang, Richard W. Vuduc, Elizabeth Searles, Michael Thompson, Jimeng Sun
Venue: KDD
Year: 2017

In exploratory tensor mining, a common problem is how to analyze a set of variables across a set of subjects whose observations do not align naturally. For example, when modeling medical features across a set of patients, the number and duration of treatments may vary widely in time, meaning there is no meaningful way to align their clinical records across time points for analysis purposes. To handle such data, the state-of-the-art tensor model is the so-called PARAFAC2, which yields interpretable and robust output and can naturally handle sparse data. However, its main limitation up to now has been the lack of efficient algorithms that can handle large-scale datasets. In this work, we fill this gap by developing a scalable method to compute the PARAFAC2 decomposition of large and sparse datasets, called SPARTan. Our method exploits special structure within PARAFAC2, leading to a novel algorithmic reformulation that is both faster (in absolute time) and more memory-efficient than prior work. We evaluate SPARTan on both synthetic and real datasets, showing 22X performance gains over the best previous implementation and also handling larger problem instances for which the baseline fails. Furthermore, we are able to apply SPARTan to the mining of temporally-evolving phenotypes on data taken from real and medically complex pediatric patients. The clinical meaningfulness of the phenotypes identified in this process, as well as their temporal evolution over time for several patients, have been endorsed by clinical experts.
Backpage and Bitcoin: Uncovering Human Traffickers
Authors: Rebecca S. Portnoff, Danny Yuxing Huang, Periwinkle Doerfler, Sadia Afroz, Damon McCoy
Venue: KDD
Year: 2017

Sites for online classified ads selling sex are widely used by human traffickers to support their pernicious business. The sheer quantity of ads makes manual exploration and analysis unscalable. In addition, discerning whether an ad is advertising a trafficked victim or an independent sex worker is a very difficult task. Very little concrete ground truth (i.e., ads definitively known to be posted by a trafficker) exists in this space. In this work, we develop tools and techniques that can be used separately and in conjunction to group sex ads by their true owner (and not the claimed author in the ad). Specifically, we develop a machine learning classifier that uses stylometry to distinguish between ads posted by the same vs. different authors with 90% TPR and 1% FPR. We also design a linking technique that takes advantage of leakages from the Bitcoin mempool, blockchain and sex ad site, to link a subset of sex ads to Bitcoin public wallets and transactions. Finally, we demonstrate via a 4-week proof of concept using Backpage as the sex ad site, how an analyst can use these automated approaches to potentially find human traffickers.
Spaceborne Data Enters the Mainstream
Authors: David Potere
Venue: KDD
Year: 2017

Thanks to a diverse constellation of Earth observing satellites, humanity is effectively looking everywhere, at everything, all the time. And we've been quietly doing so for a long time. The challenge is that for many years these massive data archives have been stranded from operational, cloud-based, modern data science. That is changing fast. In this session we'll do a rapid primer on satellite imagery as a source of novel data about our Earth and discuss how machine learning is a key force for translating all of this Earth data into real insights. We'll use the global agriculture system as a case in point, highlighting some of potential that a spaceborne perspective brings to this vital sector of the economy. Along the way, we will explore some of the beautiful imagery of our home planet that fuels this new class of insights.