OpenRaiser/Intern-Atlas huggingface dataset:
Automated Approach to Improve IoT Privacy Policies
Venue: arXiv cs.CR 2019
Year: 2019

The massive growth of the Internet of Things (IoT) as a network of interconnected entities [18], brings up new challenges in terms of privacy and security requirements to the traditional software engineering domain [4]. To protect the individuals' privacy, the FTC's Fair Information Practice Principles (FIPPs) [6] proposes to companies to give notice to the consumer about their data practices, provide them with choices and give them means to have control over their own data.. Using privacy policy is the most common way for this type of notices. However, privacy policies are not generally effective due to two main reasons: first, privacy policies are long and full of legal jargon which are not understandable by a normal user; second, it is not guaranteed that an IoT device behave as it is explained in its privacy policy. In this technical report, we propose and discuss our methodologies to analyze privacy policies. By the help of this analysis, we reduce the length of a privacy policy and make it organized based on privacy practices to improve understanding level for the user. We also come up with a method to find the inconsistencies between IoT devices and their privacy policies.
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models
Venue: arXiv cs.RO 2019
Year: 2019

Humans are masters at quickly learning many complex tasks, relying on an approximate understanding of the dynamics of their environments. In much the same way, we would like our learning agents to quickly adapt to new tasks. In this paper, we explore how model-based Reinforcement Learning (RL) can facilitate transfer to new tasks. We develop an algorithm that learns an action-conditional, predictive model of expected future observations, rewards and values from which a policy can be derived by following the gradient of the estimated value along imagined trajectories. We show how robust policy optimization can be achieved in robot manipulation tasks even with approximate models that are learned directly from vision and proprioception. We evaluate the efficacy of our approach in a transfer learning scenario, re-using previously learned models on tasks with different reward structures and visual distractors, and show a significant improvement in learning speed compared to strong off-policy baselines. Videos with results can be found at https://sites.google.com/view/ivg-corl19
WiFiCue: Public Wireless Access Security Assessment Tool
Venue: arXiv cs.CR 2019
Year: 2019

Public wireless access points are commonly provided by governments, businesses, schools and other organizations and provide access to the Internet for numerous use cases and can present varying degrees of risk to users. While there are steps that can be taken to mitigate public Wi-Fi risks, ranging from avoidance to the application of end-to-end encryption, application specific encryption, and other technologies and tools, these options are not always viable. This paper examines risks associated with Wi-Fi from on a network-by-network perspective. Recommender Systems are presented as part of a proposed mechanism for informing users of the risks of connecting to a specific access point. Implementing prototype architecture for this purpose is examined.
Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling
Venue: arXiv cs.LG 2019
Year: 2019

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize new data categories and assign most appropriate category labels to the data actually from known categories, existing models assume the number of unknown new categories is pre-specified, though it is difficult to determine in advance. In this paper, we propose a Bayesian nonparametric topic model to automatically infer this number, based on the hierarchical Dirichlet process and the notion of latent Dirichlet allocation. Exact inference in our model is intractable, so we provide an efficient collapsed Gibbs sampling algorithm for approximate posterior inference. Extensive experiments on various text data sets show that: (a) compared with parametric approaches that use pre-specified true number of new categories, the proposed nonparametric approach can yield comparable performance; and (b) when the exact number of new categories is unavailable, i.e. the parametric approaches only have a rough idea about the new categories, our approach has evident performance advantages.
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning
Venue: arXiv cs.RO 2019
Year: 2019

Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads. Our solution employs deep reinforcement learning (RL) in order to train a team of escorts to maintain payload safety while navigating alongside the payload. This is done in a distributed fashion, relying only on limited range positional information of other escorts, the payload, and the obstacles. When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31%. Additionally, escort teams increase success rate by up to 75% percent over escorts in static formations. We also show that this learned solution is general to several adaptations in the scenario including: a changing number of escorts in the team, changing obstacle density, and changes in payload conformation. Video: https://youtu.be/SoYesKti4VA.
On Existence of Equilibrium Under Social Coalition Structures
Venue: arXiv cs.GT 2019
Year: 2019

In a strategic form game a strategy profile is an equilibrium if no viable coalition of agents (or players) benefits (in the Pareto sense) from jointly changing their strategies. Weaker or stronger equilibrium notions can be defined by considering various restrictions on coalition formation. In a Nash equilibrium, for instance, the assumption is that viable coalitions are singletons, and in a super strong equilibrium, every coalition is viable. Restrictions on coalition formation can be justified by communication limitations, coordination problems or institutional constraints. In this paper, inspired by social structures in various real-life scenarios, we introduce certain restrictions on coalition formation, and on their basis we introduce a number of equilibrium notions. As an application we study our equilibrium notions in resource selection games (RSGs), and we present a complete set of existence and non-existence results for general RSGs and their important special cases.
Efficient Sketching Algorithm for Sparse Binary Data
Venue: arXiv cs.IR 2019
Year: 2019

Recent advancement of the WWW, IOT, social network, e-commerce, etc. have generated a large volume of data. These datasets are mostly represented by high dimensional and sparse datasets. Many fundamental subroutines of common data analytic tasks such as clustering, classification, ranking, nearest neighbour search, etc. scale poorly with the dimension of the dataset. In this work, we address this problem and propose a sketching (alternatively, dimensionality reduction) algorithm -- $\binsketch$ (Binary Data Sketch) -- for sparse binary datasets. $\binsketch$ preserves the binary version of the dataset after sketching and maintains estimates for multiple similarity measures such as Jaccard, Cosine, Inner-Product similarities, and Hamming distance, on the same sketch. We present a theoretical analysis of our algorithm and complement it with extensive experimentation on several real-world datasets. We compare the performance of our algorithm with the state-of-the-art algorithms on the task of mean-square-error and ranking. Our proposed algorithm offers a comparable accuracy while suggesting a significant speedup in the dimensionality reduction time, with respect to the other candidate algorithms. Our proposal is simple, easy to implement, and therefore can be adopted in practice.
Interactive Light Field Tilt-Shift Refocus with Generalized Shift-and-Sum
Venue: arXiv cs.GR 2019
Year: 2019

Since their introduction more than two decades ago, light fields have gained considerable interest in graphics and vision communities due to their ability to provide the user with interactive visual content. One of the earliest and most common light field operations is digital refocus, enabling the user to choose the focus and depth-of-field for the image after capture. A common interactive method for such an operation utilizes disparity estimations, readily available from the light field, to allow the user to point-and-click on the image to chose the location of the refocus plane. In this paper, we address the interactivity of a lesser-known light field operation: refocus to a non-frontoparallel plane, simulating the result of traditional tilt-shift photography. For this purpose we introduce a generalized shift-and-sum framework. Further, we show that the inclusion of depth information allows for intuitive interactive methods for placement of the refocus plane. In addition to refocusing, light fields also enable the user to interact with the viewpoint, which can be easily included in the proposed generalized shift-and-sum framework.
Predictive Simulation: Using Regression and Artificial Neural Networks to Negate Latency in Networked Interactive Virtual Reality
Venue: arXiv cs.HC 2019
Year: 2019

Current virtual reality systems are typically limited by performance/cost, usability (size), or a combination of both. By using a networked client/server environment, we have solved these limitations for the client. However, in doing so we have introduced a new problem, namely increased latency. Interactive networked virtual environments such as games and simulations have existed for nearly as long as the Internet and have consistently faced latency issues. We propose a solution for negating the effects of latency for interactive networked virtual environments with lightweight clients, with respect to the server being used. The proposed method extrapolates future client states to be incorporated in the server's updates, which helps to synchronize actions on the client-side and the results coming from the server. We refer to this approach as predictive simulation. In addition to describing our method, in this paper, we look at extrapolation methods because the success of our predictive simulation method is dependent on strong predictions. We focus on regression methods and briefly examine the use of artificial neural networks.
LISA: Towards Learned DNA Sequence Search
Venue: arXiv cs.DB 2019
Year: 2019

Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale requires overcoming performance bottlenecks, such as searching for short DNA sequences over long reference sequences. In this paper, we introduce LISA (Learned Indexes for Sequence Analysis), a novel learning-based approach to DNA sequence search. As a first proof of concept, we focus on accelerating one of the most essential flavors of the problem, called exact search. LISA builds on and extends FM-index, which is the state-of-the-art technique widely deployed in genomics tool-chains. Initial experiments with human genome datasets indicate that LISA achieves up to a factor of 4X performance speedup against its traditional counterpart.
Efficient Intrinsically Motivated Robotic Grasping with Learning-Adaptive Imagination in Latent Space
Venue: arXiv cs.LG 2019
Year: 2019

Combining model-based and model-free deep reinforcement learning has shown great promise for improving sample efficiency on complex control tasks while still retaining high performance. Incorporating imagination is a recent effort in this direction inspired by human mental simulation of motor behavior. We propose a learning-adaptive imagination approach which, unlike previous approaches, takes into account the reliability of the learned dynamics model used for imagining the future. Our approach learns an ensemble of disjoint local dynamics models in latent space and derives an intrinsic reward based on learning progress, motivating the controller to take actions leading to data that improves the models. The learned models are used to generate imagined experiences, augmenting the training set of real experiences. We evaluate our approach on learning vision-based robotic grasping and show that it significantly improves sample efficiency and achieves near-optimal performance in a sparse reward environment.
Studying Software Engineering Patterns for Designing Machine Learning Systems
Venue: arXiv cs.SE 2019
Year: 2019

Machine-learning (ML) techniques have become popular in the recent years. ML techniques rely on mathematics and on software engineering. Researchers and practitioners studying best practices for designing ML application systems and software to address the software complexity and quality of ML techniques. Such design practices are often formalized as architecture patterns and design patterns by encapsulating reusable solutions to commonly occurring problems within given contexts. However, to the best of our knowledge, there has been no work collecting, classifying, and discussing these software-engineering (SE) design patterns for ML techniques systematically. Thus, we set out to collect good/bad SE design patterns for ML techniques to provide developers with a comprehensive and ordered classification of such patterns. We report here preliminary results of a systematic-literature review (SLR) of good/bad design patterns for ML.
Referring Expression Object Segmentation with Caption-Aware Consistency
Venue: arXiv cs.CV 2019
Year: 2019

Referring expressions are natural language descriptions that identify a particular object within a scene and are widely used in our daily conversations. In this work, we focus on segmenting the object in an image specified by a referring expression. To this end, we propose an end-to-end trainable comprehension network that consists of the language and visual encoders to extract feature representations from both domains. We introduce the spatial-aware dynamic filters to transfer knowledge from text to image, and effectively capture the spatial information of the specified object. To better communicate between the language and visual modules, we employ a caption generation network that takes features shared across both domains as input, and improves both representations via a consistency that enforces the generated sentence to be similar to the given referring expression. We evaluate the proposed framework on two referring expression datasets and show that our method performs favorably against the state-of-the-art algorithms.
Efficient Path Routing Over Road Networks in the Presence of Ad-Hoc Obstacles (Technical Report)
Venue: arXiv cs.DB 2019
Year: 2019

Nowadays, the path routing over road networks has become increasingly important, yet challenging, in many real-world applications such as location-based services (LBS), logistics and supply chain management, transportation systems, map utilities, and so on. While many prior works aimed to find a path between a source and a destination with the smallest traveling distance/time, they do not take into account the quality constraints (e.g., obstacles) of the returned paths, such as uneven roads, roads under construction, and weather conditions on roads. Inspired by this, in this paper, we consider two types of ad-hoc obstacles, keyword-based and weather-based obstacles, on road networks, which can be used for modeling roads that the returned paths should not pass through. In the presence of such ad-hoc obstacles on roads, we formulate a path routing query over road networks with ad-hoc obstacles (PRAO), which retrieves paths from source to destination on road networks that do not pass ad-hoc keyword and weather obstacles and have the smallest traveling time. In order to efficiently answer PRAO queries, we design effective pruning methods and indexing mechanism to facilitate efficient PRAO query answering. Extensive experiments have demonstrated the efficiency and effectiveness of our approaches over real/synthetic data sets.
CompareNet: Anatomical Segmentation Network with Deep Non-local Label Fusion
Venue: arXiv cs.CV 2019
Year: 2019

Label propagation is a popular technique for anatomical segmentation. In this work, we propose a novel deep framework for label propagation based on non-local label fusion. Our framework, named CompareNet, incorporates subnets for both extracting discriminating features, and learning the similarity measure, which lead to accurate segmentation. We also introduce the voxel-wise classification as an unary potential to the label fusion function, for alleviating the search failure issue of the existing non-local fusion strategies. Moreover, CompareNet is end-to-end trainable, and all the parameters are learnt together for the optimal performance. By evaluating CompareNet on two public datasets IBSRv2 and MICCAI 2012 for brain segmentation, we show it outperforms state-of-the-art methods in accuracy, while being robust to pathologies.
Brain-inspired automated visual object discovery and detection
Venue: arXiv cs.CV 2019
Year: 2019

Despite significant recent progress, machine vision systems lag considerably behind their biological counterparts in performance, scalability, and robustness. A distinctive hallmark of the brain is its ability to automatically discover and model objects, at multiscale resolutions, from repeated exposures to unlabeled contextual data and then to be able to robustly detect the learned objects under various nonideal circumstances, such as partial occlusion and different view angles. Replication of such capabilities in a machine would require three key ingredients: (i) access to large-scale perceptual data of the kind that humans experience, (ii) flexible representations of objects, and (iii) an efficient unsupervised learning algorithm. The Internet fortunately provides unprecedented access to vast amounts of visual data. This paper leverages the availability of such data to develop a scalable framework for unsupervised learning of object prototypes--brain-inspired flexible, scale, and shift invariant representations of deformable objects (e.g., humans, motorcycles, cars, airplanes) comprised of parts, their different configurations and views, and their spatial relationships. Computationally, the object prototypes are represented as geometric associative networks using probabilistic constructs such as Markov random fields. We apply our framework to various datasets and show that our approach is computationally scalable and can construct accurate and operational part-aware object models much more efficiently than in much of the recent computer vision literature. We also present efficient algorithms for detection and localization in new scenes of objects and their partial views.
Implementing evaluation strategies for continuous real functions
Venue: arXiv cs.LO 2019
Year: 2019

We give a technical overview of our exact-real implementation of various representations of the space of continuous unary real functions over the unit domain and a family of associated (partial) operations, including integration, range computation, as well as pointwise addition, multiplication, division, sine, cosine, square root and maximisation. We use several representations close to the usual theoretical model, based on an oracle that evaluates the function at a point or over an interval. We also include several representations based on an oracle that computes a converging sequence of rigorous (piecewise or one-piece) polynomial and rational approximations over the whole unit domain. Finally, we describe "local" representations that combine both approaches, i.e. oracle-like representations that return a rigorous symbolic approximation of the function over a requested interval sub-domain with a requested effort. See also our paper "Representations and evaluation strategies for feasibly approximable functions" which compares the efficiency of these representations and algorithms and also formally describes and analyses one of the key algorithms, namely a polynomial-time division of functions in a piecewise-polynomial representation. We do not reproduce this division algorithm here.
Coloring the Black Box: Visualizing neural network behavior with a self-introspective model
Venue: arXiv cs.LG 2019
Year: 2019

The following work presents how autoencoding all the possible hidden activations of a network for a given problem can provide insight about its structure, behavior, and vulnerabilities. The method, termed self-introspection, can show that a trained model showcases similar activation patterns (albeit randomly distributed due to initialization) when shown data belonging to the same category, and classification errors occur in fringe areas where the activations are not as clearly defined, suggesting some form of random, slowly varying, implicit encoding occurring within deep networks, that can be observed with this representation. Additionally, obtaining a low-dimensional representation of all the activations allows for (1) real-time model evaluation in the context of a multiclass classification problem, (2) the rearrangement of all hidden layers by their relevance in obtaining a specific output, and (3) the obtainment of a framework where studying possible counter-measures to noise and adversarial attacks is possible. Self-introspection can show how damaged input data can modify the hidden activations, producing an erroneous response. A few illustrative are implemented for feedforward and convolutional models and the MNIST and CIFAR-10 datasets, showcasing its capabilities as a model evaluation framework.
Metastability-Resilient Synchronization FIFO for SFQ Logic
Venue: arXiv cs.ET 2019
Year: 2019

Digital single-flux quantum (SFQ) technology promises to meet the demands of ultra low power and high speed computing needed for future exascale supercomputing systems. The combination of ultra high clock frequencies, gate-level pipelines, and numerous sources of variability in SFQ circuits, however, make low-skew global clock distribution a challenge. This motivates the support of multiple independent clock domains and related clock domain crossing circuits that enable reliable communication across domains. Existing J-SIM simulation models indicate that setup violations can cause clock-to-Q increases of up to 100%. This paper first shows that naive SFQ clock domain crossing (CDC) first-in-first-out buffers (FIFOs) are vulnerable to these delay increases, motivating the need for more robust CDC FIFOs. Inspired by CMOS multi-flip-flop asynchronous FIFO synchronizers, we then propose a novel 1-bit metastability-resilient SFQ CDC FIFO that simulations show delivers over a 1000 reduction in logical error rate at 30 GHz. Moreover, for a 10-stage FIFO, the Josephson junction (JJ) area of our proposed design is only 7.5% larger than the non-resilient counterpart. Finally, we propose design guidelines that define the minimal FIFO depth subject to both throughput and burstiness constraints.