pwc-archive/papers-with-abstracts huggingface dataset:
ChangeNet: A Deep Learning Architecture for Visual Change Detection
Authors: Ashley Varghese, Jayavardhana Gubbi, and Akshaya Ramaswamy Balamuralidhar P Embedding Systems and Robotics, TCS Research and Innovation, Bengaluru, India
Date: 2019-01-29 00:00:00
Proceeding: Conference 2019 1
Tasks: Change Detection, Deep Learning

The increasing urban population in cities necessitates the need for the development of smart cities that can offer better services to its citizens. Drone technology plays a crucial role in the smart city environment and is already involved in a number of functions in smart cities such as traffic control and construction monitoring. A major challenge in fast growing cities is the encroachment of public spaces. A robotic solution using visual change detection can be used for such purposes. For the detection of encroachment, a drone can monitor outdoor urban areas over a period of time to infer the visual changes. Visual change detection is a higher level inference task that aims at accurately identifying variations between a reference image (historical) and a new test image
depicting the current scenario. In case of images, the challenges are complex considering the variations caused by environmental conditions that are actually unchanged events. Human mind interprets the change by comparing the current status with historical data at intelligence level rather than using only visual information. In this paper, we present a deep architecture called ChangeNet for detecting changes between pairs of images and express the same semantically (label the change). A parallel deep convolutional neural network (CNN) architecture for localizing and identifying the changes between image pair has been proposed in this paper. The architecture is evaluated with VL-CMU-CD street view change detection, TSUNAMI and Google Street View (GSV) datasets that resemble drone captured images. The performance of the model for different lighting and seasonal conditions are experimented quantitatively and qualitatively. The result shows that ChangeNet outperforms the state of the art by achieving 98.3% pixel accuracy, 77.35% object based Intersection over Union (IoU) and 88.9% area under Receiver Operating Characteristics (RoC) curve.
Multiclass Queueing Network Modeling and Traffic Flow Analysis for SDN-enabled Mobile Core Networks with Network Slicing
Authors: Santhosha Kamath, Sanjay Singh, M Sathish Kumar
Date: 2019-12-13 00:00:00
Proceeding: IEEE Access 2019 12
Tasks: Management

The back-haul networks of 5G are formed by heterogeneous links which need to handle massive traffic.  The service providers are not able to provide good QoS for their users. The technology like Software Defined Networks(SDN) and Network Slicing helps a little for a service provider to providing QoS for multiple links.  The service providers face a challenge in the efficient utilization of resources to fulfill the QoS requirement of users to comply with the growth and thereby increasing the revenue.  These problems require an accurate traffic model to determine the steady-state of the system. The proposed model uses an architecture that has the combination of two technologies: SDN and network slicing, which empowers an administrator a flexible, programmable network, and the best management of network resources. Heterogeneous application is well managed by creating multiple logical networks called slicing. The slicing can be modeled using multi-class queuing networks. These technologies encourage service providers to fulfill QoS and revenue growth. To leverage the benefits of these technologies in allocating QoS is to identify the performance of the system, which requires a precise model of traffic to decide the steady-state condition of the framework. In this paper, we focus on SDN and slicing in mobile networks and quantify the performance measure considering an in-band  OpenFlow architecture for a single node and homogeneous traffic class, which is further extended to the multi-class heterogeneous class queuing model and analyzed. The results obtained help a service provider to monitor the utilization of resources in every node by every class of core network, which in turn helps to allocate the resources precisely to fulfill QoS requirements.
Brain-inspired Robust Vision using Convolutional Neural Networks with Feedback
Authors: Yujia Huang, Sihui Dai, Tan Nguyen, Pinglei Bao, Doris Y. Tsao, Richard G. Baraniuk, Anima Anandkumar
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS 2019 Workshop Neuro AI 2019 9
Tasks: Disentanglement

Primates have a remarkable ability to correctly classify images even in the presence
of significant noise and degradation. In contrast, even the state-of-art CNNs are
extremely vulnerable to imperceptible level of noise. Many neuroscience studies
have suggested that robustness in human vision arises from the interaction between
the feedforward signals from bottom-up pathways of the visual cortex and the
feedback signals from the top-down pathways. Motivated by this, we propose a
new neuro-inspired model, namely Convolutional Neural Networks with Feedback
(CNN-F). CNN-F augments CNN with a feedback generative network that shares
the same set of weights along with an additional set of latent variables. CNN-F
combines bottom-up and top-down inference through approximate loopy belief
propagation to obtain the MAP-estimates of the latent variables. We show that
CNN-F’s iterative inference allows for disentanglement of latent variables across
layers. We validate the advantages of CNN-F over the baseline CNN in multiple
ways. Our experimental results suggest that the CNN-F is more robust to image
degradation such as pixel noise, occlusion, and blur than the corresponding CNN.
Furthermore, we show that the CNN-F is capable of restoring original images from
the degraded ones with high reconstruction accuracy while introducing negligible
artifacts.
Latent-variable Private Information Retrieval
Authors: Islam Samy, Mohamed A. Attia, Ravi Tandon, Loukas Lazos
Date: 2020-01-16 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This solution, while private, can be too costly, particularly, when perfect (information-theoretic) privacy constraints are imposed. For instance, for a single database holding $K$ messages, privately retrieving one message is possible if and only if the user downloads the entire database of $K$ messages. Retrieving content privately, however, may not be necessary to perfectly hide the latent attributes. Motivated by the above, we formulate and study the problem of latent-variable private information retrieval (LV-PIR), which aims at allowing the user efficiently retrieve one out of $K$ messages (indexed by $\theta$) without revealing any information about the latent variable (modeled by $S$). We focus on the practically relevant setting of a single database and show that one can significantly reduce the download cost of LV-PIR (compared to the classical PIR) based on the correlation between $\theta$ and $S$. We present a general scheme for LV-PIR as a function of the statistical relationship between $\theta$ and $S$, and also provide new results on the capacity/download cost of LV-PIR. Several open problems and new directions are also discussed.
Knowledge Discovery from Social Media using Big Data provided Sentiment Analysis (SoMABiT)
Authors: Mahdi Bohlouli, Jens Dalter, Mareike Dornhöfer, Johannes Zenkert, Madjid Fathi
Date: 2020-01-16 00:00:00
Tasks: Sentiment Analysis

In todays competitive business world, being aware of customer needs and market-oriented production is a key success factor for industries. To this aim, the use of efficient analytic algorithms ensures a better understanding of customer feedback and improves the next generation of products. Accordingly, the dramatic increase in using social media in daily life provides beneficial sources for market analytics. But how traditional analytic algorithms and methods can scale up for such disparate and multi-structured data sources is the main challenge in this regard. This paper presents and discusses the technological and scientific focus of the SoMABiT as a social media analysis platform using big data technology. Sentiment analysis has been employed in order to discover knowledge from social media. The use of MapReduce and developing a distributed algorithm towards an integrated platform that can scale for any data volume and provide a social media-driven knowledge is the main novelty of the proposed concept in comparison to the state-of-the-art technologies.
Adversarially Guided Self-Play for Adopting Social Conventions
Authors: Mycal Tucker, Yilun Zhou, Julie Shah
Date: 2020-01-16 00:00:00

Robotic agents must adopt existing social conventions in order to be effective teammates. These social conventions, such as driving on the right or left side of the road, are arbitrary choices among optimal policies, but all agents on a successful team must use the same convention. Prior work has identified a method of combining self-play with paired input-output data gathered from existing agents in order to learn their social convention without interacting with them. We build upon this work by introducing a technique called Adversarial Self-Play (ASP) that uses adversarial training to shape the space of possible learned policies and substantially improves learning efficiency. ASP only requires the addition of unpaired data: a dataset of outputs produced by the social convention without associated inputs. Theoretical analysis reveals how ASP shapes the policy space and the circumstances (when behaviors are clustered or exhibit some other structure) under which it offers the greatest benefits. Empirical results across three domains confirm ASP's advantages: it produces models that more closely match the desired social convention when given as few as two paired datapoints.
Inferring Individual Level Causal Models from Graph-based Relational Time Series
Authors: Ryan Rossi, Somdeb Sarkhel, Nesreen Ahmed
Date: 2020-01-16 00:00:00
Tasks: Causal Inference, Time Series, Time Series Analysis

In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes. Furthermore, the relational time-series causal inference models are able to estimate local effects for individual nodes by exploiting local node-centric temporal dependencies and topological/structural dependencies. We show that simpler causal models that do not consider the graph topology are recovered as special cases of the proposed relational time-series causal inference model. We describe the conditions under which the resulting estimate can be used to estimate a causal effect, and describe how the Durbin-Wu-Hausman test of specification can be used to test for the consistency of the proposed estimator from data. Empirically, we demonstrate the effectiveness of the causal inference models on both synthetic data with known ground-truth and a large-scale observational relational time-series data set collected from Wikipedia.
Provable Benefit of Orthogonal Initialization in Optimizing Deep Linear Networks
Authors: Wei Hu, Lechao Xiao, Jeffrey Pennington
Date: 2020-01-16 00:00:00
Proceeding: ICLR 2020 1

The selection of initial parameter values for gradient-based optimization of deep neural networks is one of the most impactful hyperparameter choices in deep learning systems, affecting both convergence times and model performance. Yet despite significant empirical and theoretical analysis, relatively little has been proved about the concrete effects of different initialization schemes. In this work, we analyze the effect of initialization in deep linear networks, and provide for the first time a rigorous proof that drawing the initial weights from the orthogonal group speeds up convergence relative to the standard Gaussian initialization with iid weights. We show that for deep networks, the width needed for efficient convergence to a global minimum with orthogonal initializations is independent of the depth, whereas the width needed for efficient convergence with Gaussian initializations scales linearly in the depth. Our results demonstrate how the benefits of a good initialization can persist throughout learning, suggesting an explanation for the recent empirical successes found by initializing very deep non-linear networks according to the principle of dynamical isometry.
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$-Divergences
Authors: Shahab Asoodeh, Jiachun Liao, Flavio P. Calmon, Oliver Kosut, Lalitha Sankar
Date: 2020-01-16 00:00:00

We derive the optimal differential privacy (DP) parameters of a mechanism that satisfies a given level of R\'enyi differential privacy (RDP). Our result is based on the joint range of two $f$-divergences that underlie the approximate and the R\'enyi variations of differential privacy. We apply our result to the moments accountant framework for characterizing privacy guarantees of stochastic gradient descent. When compared to the state-of-the-art, our bounds may lead to about 100 more stochastic gradient descent iterations for training deep learning models for the same privacy budget.
Conformal e-prediction
Authors: Vladimir Vovk
Date: 2020-01-16 00:00:00
Tasks: Conformal Prediction, Prediction

This paper discusses a counterpart of conformal prediction for e-values, conformal e-prediction. Conformal e-prediction is conceptually simpler and had been developed in the 1990s as precursor of conformal prediction. When conformal prediction emerged as result of replacing e-values by p-values, it seemed to have important advantages over conformal e-prediction without obvious disadvantages. This paper re-examines relations between conformal prediction and conformal e-prediction systematically from a modern perspective. Conformal e-prediction has advantages of its own, such as the ease of designing conditional conformal e-predictors and the guaranteed validity of cross-conformal e-predictors (whereas for cross-conformal predictors validity is only an empirical fact and can be broken with excessive randomization). Even where conformal prediction has clear advantages, conformal e-prediction can often emulate those advantages, more or less successfully.
A Common Operating Picture Framework Leveraging Data Fusion and Deep Learning
Authors: Benjamin Ortiz, David Lindenbaum, Joseph Nassar, Brendan Lammers, John Wahl, Robert Mangum, Margaret Smith, Marc Bosch
Date: 2020-01-16 00:00:00

Organizations are starting to realize of the combined power of data and data-driven algorithmic models to gain insights, situational awareness, and advance their mission. A common challenge to gaining insights is connecting inherently different datasets. These datasets (e.g. geocoded features, video streams, raw text, social network data, etc.) per separate they provide very narrow answers; however collectively they can provide new capabilities. In this work, we present a data fusion framework for accelerating solutions for Processing, Exploitation, and Dissemination (PED). Our platform is a collection of services that extract information from several data sources (per separate) by leveraging deep learning and other means of processing. This information is fused by a set of analytical engines that perform data correlations, searches, and other modeling operations to combine information from the disparate data sources. As a result, events of interest are detected, geolocated, logged, and presented into a common operating picture. This common operating picture allows the user to visualize in real time all the data sources, per separate and their collective cooperation. In addition, forensic activities have been implemented and made available through the framework. Users can review archived results and compare them to the most recent snapshot of the operational environment. In our first iteration we have focused on visual data (FMV, WAMI, CCTV/PTZ-Cameras, open source video, etc.) and AIS data streams (satellite and terrestrial sources). As a proof-of-concept, in our experiments we show how FMV detections can be combined with vessel tracking signals from AIS sources to confirm identity, tip-and-cue aerial reconnaissance, and monitor vessel activity in an area.
Contextual Sense Making by Fusing Scene Classification, Detections, and Events in Full Motion Video
Authors: Marc Bosch, Joseph Nassar, Benjamin Ortiz, Brendan Lammers, David Lindenbaum, John Wahl, Robert Mangum, Margaret Smith
Date: 2020-01-16 00:00:00
Tasks: Event Detection, General Classification, Object, Scene Classification

With the proliferation of imaging sensors, the volume of multi-modal imagery far exceeds the ability of human analysts to adequately consume and exploit it. Full motion video (FMV) possesses the extra challenge of containing large amounts of redundant temporal data. We aim to address the needs of human analysts to consume and exploit data given aerial FMV. We have investigated and designed a system capable of detecting events and activities of interest that deviate from the baseline patterns of observation given FMV feeds. We have divided the problem into three tasks: (1) Context awareness, (2) object cataloging, and (3) event detection. The goal of context awareness is to constraint the problem of visual search and detection in video data. A custom image classifier categorizes the scene with one or multiple labels to identify the operating context and environment. This step helps reducing the semantic search space of downstream tasks in order to increase their accuracy. The second step is object cataloging, where an ensemble of object detectors locates and labels any known objects found in the scene (people, vehicles, boats, planes, buildings, etc.). Finally, context information and detections are sent to the event detection engine to monitor for certain behaviors. A series of analytics monitor the scene by tracking object counts, and object interactions. If these object interactions are not declared to be commonly observed in the current scene, the system will report, geolocate, and log the event. Events of interest include identifying a gathering of people as a meeting and/or a crowd, alerting when there are boats on a beach unloading cargo, increased count of people entering a building, people getting in and/or out of vehicles of interest, etc. We have applied our methods on data from different sensors at different resolutions in a variety of geographical areas.
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives
Authors: E. M. Hahn, M. Perez, S. Schewe, F. Somenzi, A. Trivedi, D. Wojtczak
Date: 2020-01-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Recently, successful approaches have been made to exploit good-for-MDPs automata (B\"uchi automata with a restricted form of nondeterminism) for model free reinforcement learning, a class of automata that subsumes good for games automata and the most widespread class of limit deterministic automata. The foundation of using these B\"uchi automata is that the B\"uchi condition can, for good-for-MDP automata, be translated to reachability. The drawback of this translation is that the rewards are, on average, reaped very late, which requires long episodes during the learning process. We devise a new reward shaping approach that overcomes this issue. We show that the resulting model is equivalent to a discounted payoff objective with a biased discount that simplifies and improves on prior work in this direction.
Topological Descriptors Help Predict Guest Adsorption in Nanoporous Materials
Authors: Aditi S. Krishnapriyan, Maciej Haranczyk, Dmitriy Morozov
Date: 2020-01-16 00:00:00
Tasks: Feature Importance, Topological Data Analysis

Machine learning has emerged as an attractive alternative to experiments and simulations for predicting material properties. Usually, such an approach relies on specific domain knowledge for feature design: each learning target requires careful selection of features that an expert recognizes as important for the specific task. The major drawback of this approach is that computation of only a few structural features has been implemented so far, and it is difficult to tell a priori which features are important for a particular application. The latter problem has been empirically observed for predictors of guest uptake in nanoporous materials: local and global porosity features become dominant descriptors at low and high pressures, respectively. We investigate a feature representation of materials using tools from topological data analysis. Specifically, we use persistent homology to describe the geometry of nanoporous materials at various scales. We combine our topological descriptor with traditional structural features and investigate the relative importance of each to the prediction tasks. We demonstrate an application of this feature representation by predicting methane adsorption in zeolites, for pressures in the range of 1-200 bar. Our results not only show a considerable improvement compared to the baseline, but they also highlight that topological features capture information complementary to the structural features: this is especially important for the adsorption at low pressure, a task particularly difficult for the traditional features. Furthermore, by investigation of the importance of individual topological features in the adsorption model, we are able to pinpoint the location of the pores that correlate best to adsorption at different pressure, contributing to our atom-level understanding of structure-property relationships.
#MeToo on Campus: Studying College Sexual Assault at Scale Using Data Reported on Social Media
Authors: Viet Duong, Phu Pham, Ritwik Bose, Jiebo Luo
Date: 2020-01-16 00:00:00

Recently, the emergence of the #MeToo trend on social media has empowered thousands of people to share their own sexual harassment experiences. This viral trend, in conjunction with the massive personal information and content available on Twitter, presents a promising opportunity to extract data driven insights to complement the ongoing survey based studies about sexual harassment in college. In this paper, we analyze the influence of the #MeToo trend on a pool of college followers. The results show that the majority of topics embedded in those #MeToo tweets detail sexual harassment stories, and there exists a significant correlation between the prevalence of this trend and official reports on several major geographical regions. Furthermore, we discover the outstanding sentiments of the #MeToo tweets using deep semantic meaning representations and their implications on the affected users experiencing different types of sexual harassment. We hope this study can raise further awareness regarding sexual misconduct in academia.