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Dorina Thanou, Philip A. Chou, and Pascal Frossard

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes. As temporally successive point cloud frames share some similarities, motion estimation is key to effective compression of these sequences. It however remains a challenging problem as the point cloud frames have varying numbers of points without explicit correspondence information. We represent the time-varying geometry of these sequences with a set of graphs,...

Publication details
Date: 1 December 2016
Type: Article
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Philip A. Chou and Ricardo L. de Queiroz

We introduce the Gaussian Process Transform (GPT), an orthogonal transform for signals defined on a finite but otherwise arbitrary set of points in a Euclidean domain. The GPT is obtained as the e Transform (KLT) of the marginalization of a Gaussian Process defined on the domain. Compared to the Graph Transform (GT), which is the KLT of a Gauss Markov Random Field over the same set of points whose neighborhood structure is inherited from the Euclidean domain, the GPT retains up to 6 dB higher energy in...

Publication details
Date: 1 September 2016
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Samuel Haney, Ashwin Machanavajjhala, and Bolin Ding

The problem of designing error optimal differentially private algorithms is well studied. Recent work applying differential privacy to real world settings have used variants of differential privacy that appropriately modify the notion of neighboring databases. The problem of designing error optimal algorithms for such variants of differential privacy is open. In this paper, we show a novel transformational equivalence result that can turn the problem of query answering under differential privacy with a...

Publication details
Date: 1 September 2016
Type: Article
Publisher: VLDB – Very Large Data Bases
Rishabh Singh

The recent Programming By Example (PBE) techniques such as FlashFill have shown great promise for enabling end-users to perform data transformation tasks using input-output examples. Since examples are inherently an under-specification, there are typically a large number of hypotheses conforming to the examples, and the PBE techniques suffer from scalability issues for finding the intended program amongst the large space.

We present a semi-supervised learning technique to significantly reduce...

Publication details
Date: 1 September 2016
Type: Inproceeding
Publisher: VLDB – Very Large Data Bases
Zhongyuan Wang and Haixun Wang

Billions of short texts are produced everyday, in the form of search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Unlike documents, short texts have some unique characteristics which make them difficult to handle. First, short texts, especially search queries, do not always observe the syntax of a written language. This means traditional NLP techniques, such as syntactic parsing, do not always apply to short texts. Second, short texts contain limited context....

Publication details
Date: 1 August 2016
Type: Inproceeding
Wen-tau Yih, Matthew Richardson, Christopher Meek, Ming-Wei Chang, and Jina Suh
Publication details
Date: 1 August 2016
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Ganesh Jawahar, Manish Gupta, and Vasudeva Varma

Doc2Sent2Vec is an unsupervised approach to learn low-dimensional feature vector (or embedding) for a document. This embedding captures the semantics of the document and can be fed as input to machine learning algorithms to solve a myriad number of applications in the field of data mining and information retrieval. Some of these applications include document classification, retrieval, and ranking.

The proposed approach is two-phased. In the first phase, the model learns a vector for each sentence...

Publication details
Date: 17 July 2016
Type: Inproceeding
Aleksandar Zeljic, Christoph M. Wintersteiger, and Philipp Rummer

The Model-Constructing Satisfiability Calculus (mcSAT) is a recently proposed generalisation of propositional DPLL/CDCL for reasoning modulo theories. In contrast to most DPLL(T)-based SMT solvers, which carry out con ict-driven learning only on the propositional level, mcSAT calculi can also synthesise new theory literals during learning, resulting in a simple yet very exible framework for designing efficient decision procedures. We present an mcSAT calculus for the theory of bounded-length...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: Springer
Loris D'Antoni, Roopsha Samanta, and Rishabh Singh

The goal of automatic program repair is to identify a set of syntactic changes
that can turn a program that is incorrect with respect to a given specification into a correct one.
Existing program repair techniques typically
aim to find any program that meets the given specification.
Such ``best-effort'' strategies can end up generating a program that is quite different
from the original one.
Novel techniques have been proposed to compute syntactically minimal program...

Publication details
Date: 1 July 2016
Type: Inproceeding
Jorge Proença, Dirce Celorico, Carla Lopes, Miguel Sales Dias, Michael Tjalve, Andreas Stolcke, Sara Candeias, and Fernando Perdigão

To evaluate the reading performance of children, human assessment is usually involved, where a teacher or tutor has to take time to individually estimate the performance in terms of fluency (speed, accuracy and expression). Automatic estimation of reading ability can be an important alternative or complement to the usual methods, and can improve other applications such as elearning. Techniques must be developed to analyse audio recordings of read utterances by children and detect the deviations from the...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: ACL – Association for Computational Linguistics
Publication details
Date: 1 July 2016
Type: Inproceeding
Ye Liu, Yu Zheng, Yuxuan Liang, Shuming Liu, and David S. Rosenblum

Urban water quality is of great importance to our daily lives. Prediction of urban water quality help
control water pollution and protect human health. In this work, we forecast the water quality of a
station over the next few hours, using a multitask multi-view learning method to fuse multiple
datasets from different domains. In particular, our learning model comprises two alignments. The
first alignment is the spaio-temporal view alignment, which combines local spatial and...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Moshe Babaioff, Liad Blumrosen, and Noam Nisan

We consider a network of sellers, each selling a single product, where the graph structure represents pair-wise complementarities between products. We study how the network structure affects revenue and social welfare of equilibria of the pricing game between the sellers. We prove positive and negative results, both of "Price of Anarchy" and of "Price of Stability" type, for special families of graphs (paths, cycles) as well as more general ones (trees, graphs). We describe best-reply dynamics that...

Publication details
Date: 1 July 2016
Type: Inproceeding
Loris D'Antoni and Margus Veanes

Symbolic tree automata allow transitions to carry predicates over rich alphabet theories, such as linear arithmetic, and therefore extend finite tree automata to operate over infinite alphabets, such as the set of rational numbers. Existing tree automata algorithms rely on the alphabet being finite, and generalizing them to the sym bolic setting is not a trivial task.

In this paper we study the problem of minimizing symbolic tree automata. First, we formally define and prove the basic prope rties...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: IEEE – Institute of Electrical and Electronics Engineers
Masrour Zoghi, Tomáš Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, and Maarten de Rijke
Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Yang Song, Ali Elkahky, and Xiaodong He

Modeling temporal behavior in recommendation systems is an important and challenging problem. Its challenges come from the fact that temporal modeling increases the cost of parameter estimation and inference, while requires large amount of data to reliably learn the model with additional time dimensions. Therefore, it is hard to model temporal behavior in large scale real-world recommendation applications.

In this work, we propose a new deep neural network based architecture that models the...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Apurv Bhartia, Deeparnab Chakrabarty, Krishna Kant Chintalapudi, Lili Qiu, Bozidar Radunovic, and Ramachandran Ramjee

Interference in WiFi deployments is a growing problem
due to the increasing popularity of WiFi. Therefore it is important
that APs find the right channel to operate upon. Through a large
scale measurement study involving over 10,000 WiFi APs we show that
channel measurements and selection are most effective when
performed frequently (every few minutes). This is because of the
highly dynamic nature of WiFi traffic congestion.

Our key contribution in this paper is a...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, Pushmeet Kohli, Vladimir Tankovich, and Shahram Izadi
Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: SIGGRAPH
Xiuwen Yi, Yu Zheng, Junbo Zhang, and Tianrui Li

Many sensors have been deployed in the physical world, generating massive geo-tagged time series data. In reality, we usually lose readings of sensors at some unexpected moments because of sensor or communication errors. Those missing rea­dings do not only affect real-time monitoring but also com­promise the performance of further data analysis. In this paper, we propose a spatio-temporal multi-view-based learning (ST-MVL) method to collec­tively fill missing readings in a collection of...

Publication details
Date: 1 July 2016
Type: Inproceeding
Publisher: AAAI - Association for the Advancement of Artificial Intelligence
Anshumali Srivastava, Arnd Christian König, and Misha Bilenko

Obtaining frequency information of data streams, in limited space, is a well-recognized problem in literature. A number of recent practical applications (such as those in computational advertising) require temporally-aware solutions: obtaining historical count statistics for both time-points as well as time-ranges. In these scenarios, accuracy of estimates is typically more important for recent instances than for older ones; we call this desirable property as ``Time Adaptiveness". With this...

Publication details
Date: 26 June 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Kukjin Lee, Arnd Christian Konig, Vivek Narasayya, Bolin Ding, Surajit Chaudhuri, Brent Ellwein, Alexey Eksarevskiy, Manbeen Kohli, Jacob Wyant, Praneeta Prakash, Rimma Nehme, Jiexing Li, and Jeff Naughton

We describe the design and implementation of the new Live Query Statistics (LQS) feature in Microsoft SQL Server 2016. The functionality includes the display of overall query progress as well as progress of individual operators in the query execution plan.
We describe the overall functionality of LQS, give usage examples and detail all areas where we had to extend the current state-of-the-art to build the complete LQS feature. Finally, we evaluate the effect these extensions have on progress...

Publication details
Date: 26 June 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Meghana Marathe, Jacki O’Neill, Paromita Pain, and William Thies

Helping citizens to resolve grievances is an important part of many e-governance initiatives. In this paper, we examine two contemporary initiatives that use ICTs to help citizens resolve grievances in central India. One system is a state-run call center (the CM Helpline), while the other is an independent citizen journalism service (CGNet Swara). Despite similarities in their high-level goals, approach, and geographies served, the systems have key differences in their use of technology, their level of...

Publication details
Date: 3 June 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Jacki O’Neill, Kentaro Toyama, Jay Chen, Berthel Tate, and Aysha Siddique

During the first decade of the 21 st century, the rise of mobile feature phones in India saw the development of both an economy of informal media exchange and a culture of active media sharing for entertainment. Mobile phone owners paid for pirated movies and music on the grey market, and they traded them with one another, even using poorly designed mechanisms such as Bluetooth file exchange.

In this paper, we update what is known about the dynamic mobile media sharing culture through qualitative...

Publication details
Date: 3 June 2016
Type: Inproceeding
Publisher: ACM – Association for Computing Machinery
Milan Vojnovic and Seyoung Yun

We consider the maximum likelihood parameter estimation problem for a generalized Thurstone choice model, where choices are from comparison sets of two or more items. We provide tight characterizations of the mean square error, as well as necessary and sufficient conditions for correct classification when each item belongs to one of two classes. These results provide insights into how the estimation accuracy depends on the choice of a generalized Thurstone choice model and the structure of comparison...

Publication details
Date: 1 June 2016
Type: Inproceeding
Tanzirul Azim, Oriana Riva, and Suman Nath
Publication details
Date: 1 June 2016
Type: Proceedings
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