OpenRaiser/Intern-Atlas huggingface dataset:
Control What You Include! Server-Side Protection against Third Party Web Tracking
Venue: arXiv cs.CR 2017
Year: 2017

Third party tracking is the practice by which third parties recognize users accross different websites as they browse the web. Recent studies show that 90% of websites contain third party content that is tracking its users across the web. Website developers often need to include third party content in order to provide basic functionality. However, when a developer includes a third party content, she cannot know whether the third party contains tracking mechanisms. If a website developer wants to protect her users from being tracked, the only solution is to exclude any third-party content, thus trading functionality for privacy. We describe and implement a privacy-preserving web architecture that gives website developers a control over third party tracking: developers are able to include functionally useful third party content, the same time ensuring that the end users are not tracked by the third parties.
Quality-Driven Disorder Handling for M-way Sliding Window Stream Joins
Venue: arXiv cs.DB 2017
Year: 2017

Sliding window join is one of the most important operators for stream applications. To produce high quality join results, a stream processing system must deal with the ubiquitous disorder within input streams which is caused by network delay, asynchronous source clocks, etc. Disorder handling involves an inevitable tradeoff between the latency and the quality of produced join results. To meet different requirements of stream applications, it is desirable to provide a user-configurable result-latency vs. result-quality tradeoff. Existing disorder handling approaches either do not provide such configurability, or support only user-specified latency constraints. In this work, we advocate the idea of quality-driven disorder handling, and propose a buffer-based disorder handling approach for sliding window joins, which minimizes sizes of input-sorting buffers, thus the result latency, while respecting user-specified result-quality requirements. The core of our approach is an analytical model which directly captures the relationship between sizes of input buffers and the produced result quality. Our approach is generic. It supports m-way sliding window joins with arbitrary join conditions. Experiments on real-world and synthetic datasets show that, compared to the state of the art, our approach can reduce the result latency incurred by disorder handling by up to 95% while providing the same level of result quality.
Machine Learning Based Source Code Classification Using Syntax Oriented Features
Venue: arXiv cs.LG 2017
Year: 2017

As of today the programming language of the vast majority of the published source code is manually specified or programmatically assigned based on the sole file extension. In this paper we show that the source code programming language identification task can be fully automated using machine learning techniques. We first define the criteria that a production-level automatic programming language identification solution should meet. Our criteria include accuracy, programming language coverage, extensibility and performance. We then describe our approach: How training files are preprocessed for extracting features that mimic grammar productions, and then how these extracted grammar productions are used for the training and testing of our classifier. We achieve a 99 percent accuracy rate while classifying 29 of the most popular programming languages with a Maximum Entropy classifier.
A Counterexample to the "Majority is Least Stable" Conjecture
Venue: arXiv cs.CC 2017
Year: 2017

We exhibit a linear threshold function in 5 variables with strictly smaller noise stability (for small values of the correlation parameter) than the majority function on 5 variables, thereby providing a counterexample to the "Majority is Least Stable" Conjecture of Benjamini, Kalai, and Schramm.
Ozone: Efficient Execution with Zero Timing Leakage for Modern Microarchitectures
Venue: arXiv cs.CR 2017
Year: 2017

Time variation during program execution can leak sensitive information. Time variations due to program control flow and hardware resource contention have been used to steal encryption keys in cipher implementations such as AES and RSA. A number of approaches to mitigate timing-based side-channel attacks have been proposed including cache partitioning, control-flow obfuscation and injecting timing noise into the outputs of code. While these techniques make timing-based side-channel attacks more difficult, they do not eliminate the risks. Prior techniques are either too specific or too expensive, and all leave remnants of the original timing side channel for later attackers to attempt to exploit. In this work, we show that the state-of-the-art techniques in timing side-channel protection, which limit timing leakage but do not eliminate it, still have significant vulnerabilities to timing-based side-channel attacks. To provide a means for total protection from timing-based side-channel attacks, we develop Ozone, the first zero timing leakage execution resource for a modern microarchitecture. Code in Ozone execute under a special hardware thread that gains exclusive access to a single core's resources for a fixed (and limited) number of cycles during which it cannot be interrupted. Memory access under Ozone thread execution is limited to a fixed size uncached scratchpad memory, and all Ozone threads begin execution with a known fixed microarchitectural state. We evaluate Ozone using a number of security sensitive kernels that have previously been targets of timing side-channel attacks, and show that Ozone eliminates timing leakage with minimal performance overhead.
In Defense of the Triplet Loss for Person Re-Identification
Venue: arXiv cs.CV 2017
Year: 2017

In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person re-identification subfield is no exception to this. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms most other published methods by a large margin.
Ethical issues of ISPs in the modern web
Venue: arXiv cs.CY 2017
Year: 2017

In recent years, ethical issues in the networking field are getting moreimportant. In particular, there is a consistent debate about how Internet Service Providers (ISPs) should collect and treat network measurements. This kind of information, such as flow records, carry interesting knowledge from multiple points of view: research, traffic engineering and e-commerce can benefit from measurements retrievable through inspection of network traffic. Nevertheless, in some cases they can carry personal information about the users exposed to monitoring, and so generates several ethical issues. Modern web is very different from the one we could experience few years ago; web services converged to few protocols (i.e., HyperText Transfer Protocol (HTTP) and HTTPS) and always bigger share of encrypted traffic. The aim of this work is to provide an insight about which information is still visible to ISPs in the modern web and to what extent it carries personal information. We show ethical issues deriving by this new situation and provide general guidelines and best-practices to cope with the collection of network traffic measurements.
Randomized Kernel Methods for Least-Squares Support Vector Machines
Venue: arXiv cs.LG 2017
Year: 2017

The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification problems with relatively large data sets. Also, we present several numerical experiments that illustrate the practical applicability of the proposed methods.
Faster Reinforcement Learning Using Active Simulators
Venue: arXiv cs.LG 2017
Year: 2017

In this work, we propose several online methods to build a \emph{learning curriculum} from a given set of target-task-specific training tasks in order to speed up reinforcement learning (RL). These methods can decrease the total training time needed by an RL agent compared to training on the target task from scratch. Unlike traditional transfer learning, we consider creating a sequence from several training tasks in order to provide the most benefit in terms of reducing the total time to train. Our methods utilize the learning trajectory of the agent on the curriculum tasks seen so far to decide which tasks to train on next. An attractive feature of our methods is that they are weakly coupled to the choice of the RL algorithm as well as the transfer learning method. Further, when there is domain information available, our methods can incorporate such knowledge to further speed up the learning. We experimentally show that these methods can be used to obtain suitable learning curricula that speed up the overall training time on two different domains.
Adaptive User Perspective Rendering for Handheld Augmented Reality
Venue: arXiv cs.HC 2017
Year: 2017

Handheld Augmented Reality commonly implements some variant of magic lens rendering, which turns only a fraction of the user's real environment into AR while the rest of the environment remains unaffected. Since handheld AR devices are commonly equipped with video see-through capabilities, AR magic lens applications often suffer from spatial distortions, because the AR environment is presented from the perspective of the camera of the mobile device. Recent approaches counteract this distortion based on estimations of the user's head position, rendering the scene from the user's perspective. To this end, approaches usually apply face-tracking algorithms on the front camera of the mobile device. However, this demands high computational resources and therefore commonly affects the performance of the application beyond the already high computational load of AR applications. In this paper, we present a method to reduce the computational demands for user perspective rendering by applying lightweight optical flow tracking and an estimation of the user's motion before head tracking is started. We demonstrate the suitability of our approach for computationally limited mobile devices and we compare it to device perspective rendering, to head tracked user perspective rendering, as well as to fixed point of view user perspective rendering.
User Experience of the CoSTAR System for Instruction of Collaborative Robots
Venue: arXiv cs.RO 2017
Year: 2017

How can we enable novice users to create effective task plans for collaborative robots? Must there be a tradeoff between generalizability and ease of use? To answer these questions, we conducted a user study with the CoSTAR system, which integrates perception and reasoning into a Behavior Tree-based task plan editor. In our study, we ask novice users to perform simple pick-and-place assembly tasks under varying perception and planning capabilities. Our study shows that users found Behavior Trees to be an effective way of specifying task plans. Furthermore, users were also able to more quickly, effectively, and generally author task plans with the addition of CoSTAR's planning, perception, and reasoning capabilities. Despite these improvements, concepts associated with these capabilities were rated by users as less usable, and our results suggest a direction for further refinement.
Minimum Degree-Weighted Distance Decoding for Polynomial Residue Codes with Non-Pairwise Coprime Moduli
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents a new decoding for polynomial residue codes, called the minimum degree-weighted distance decoding. The newly proposed decoding is based on the degree-weighted distance and different from the traditional minimum Hamming distance decoding. It is shown that for the two types of minimum distance decoders, i.e., the minimum degree-weighted distance decoding and the minimum Hamming distance decoding, one is not absolutely stronger than the other, but they can complement each other from different points of view.
Self corrective Perturbations for Semantic Segmentation and Classification
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems. However, the behavior of deep networks is yet to be fully understood and is still an active area of research. In this work, we present an intriguing behavior: pre-trained CNNs can be made to improve their predictions by structurally perturbing the input. We observe that these perturbations - referred as Guided Perturbations - enable a trained network to improve its prediction performance without any learning or change in network weights. We perform various ablative experiments to understand how these perturbations affect the local context and feature representations. Furthermore, we demonstrate that this idea can improve performance of several existing approaches on semantic segmentation and scene labeling tasks on the PASCAL VOC dataset and supervised classification tasks on MNIST and CIFAR10 datasets.
Diversification-Based Learning in Computing and Optimization
Venue: arXiv cs.AI 2017
Year: 2017

Diversification-Based Learning (DBL) derives from a collection of principles and methods introduced in the field of metaheuristics that have broad applications in computing and optimization. We show that the DBL framework goes significantly beyond that of the more recent Opposition-based learning (OBL) framework introduced in Tizhoosh (2005), which has become the focus of numerous research initiatives in machine learning and metaheuristic optimization. We unify and extend earlier proposals in metaheuristic search (Glover, 1997, Glover and Laguna, 1997) to give a collection of approaches that are more flexible and comprehensive than OBL for creating intensification and diversification strategies in metaheuristic search. We also describe potential applications of DBL to various subfields of machine learning and optimization.
Robust SfM with Little Image Overlap
Venue: arXiv cs.CV 2017
Year: 2017

Usual Structure-from-Motion (SfM) techniques require at least trifocal overlaps to calibrate cameras and reconstruct a scene. We consider here scenarios of reduced image sets with little overlap, possibly as low as two images at most seeing the same part of the scene. We propose a new method, based on line coplanarity hypotheses, for estimating the relative scale of two independent bifocal calibrations sharing a camera, without the need of any trifocal information or Manhattan-world assumption. We use it to compute SfM in a chain of up-to-scale relative motions. For accuracy, we however also make use of trifocal information for line and/or point features, when present, relaxing usual trifocal constraints. For robustness to wrong assumptions and mismatches, we embed all constraints in a parameterless RANSAC-like approach. Experiments show that we can calibrate datasets that previously could not, and that this wider applicability does not come at the cost of inaccuracy.
Distributed algorithm for controlling scale-free polygonal formations
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a distributed algorithm for controlling the deployment of a team of mobile agents in formations whose shapes can be characterized by a broad class of polygons, including regular ones, where each agent occupies a corner of the polygon. The algorithm shares the appealing properties of the popular distance-based rigid formation control, but with the additional advantage of requiring the control of fewer pairs of neighboring agents. Furthermore, the scale of the polygon can be controlled by only one pair of neighboring agents. We also exploit the exponential stability of the controlled formation in order to steer the formation as a whole with translations and rotations in a prescribed way. We provide both theoretical analysis and illustrative simulations.
Improved Power Decoding of One-Point Hermitian Codes
Venue: arXiv cs.IT 2017
Year: 2017

We propose a new partial decoding algorithm for one-point Hermitian codes that can decode up to the same number of errors as the Guruswami--Sudan decoder. Simulations suggest that it has a similar failure probability as the latter one. The algorithm is based on a recent generalization of the power decoding algorithm for Reed--Solomon codes and does not require an expensive root-finding step. In addition, it promises improvements for decoding interleaved Hermitian codes.
Policy Enforcement with Proactive Libraries
Venue: arXiv cs.SE 2017
Year: 2017

Software libraries implement APIs that deliver reusable functionalities. To correctly use these functionalities, software applications must satisfy certain correctness policies, for instance policies about the order some API methods can be invoked and about the values that can be used for the parameters. If these policies are violated, applications may produce misbehaviors and failures at runtime. Although this problem is general, applications that incorrectly use API methods are more frequent in certain contexts. For instance, Android provides a rich and rapidly evolving set of APIs that might be used incorrectly by app developers who often implement and publish faulty apps in the marketplaces. To mitigate this problem, we introduce the novel notion of proactive library, which augments classic libraries with the capability of proactively detecting and healing misuses at run- time. Proactive libraries blend libraries with multiple proactive modules that collect data, check the correctness policies of the libraries, and heal executions as soon as the violation of a correctness policy is detected. The proactive modules can be activated or deactivated at runtime by the users and can be implemented without requiring any change to the original library and any knowledge about the applications that may use the library. We evaluated proactive libraries in the context of the Android ecosystem. Results show that proactive libraries can automati- cally overcome several problems related to bad resource usage at the cost of a small overhead.
Content-based similar document image retrieval using fusion of CNN features
Venue: arXiv cs.CV 2017
Year: 2017

Rapid increase of digitized document give birth to high demand of document image retrieval. While conventional document image retrieval approaches depend on complex OCR-based text recognition and text similarity detection, this paper proposes a new content-based approach, in which more attention is paid to features extraction and fusion. In the proposed approach, multiple features of document images are extracted by different CNN models. After that, the extracted CNN features are reduced and fused into weighted average feature. Finally, the document images are ranked based on feature similarity to a provided query image. Experimental procedure is performed on a group of document images that transformed from academic papers, which contain both English and Chinese document, the results show that the proposed approach has good ability to retrieve document images with similar text content, and the fusion of CNN features can effectively improve the retrieval accuracy.
A Flexible Wideband Millimeter-Wave Channel Sounder with Local Area and NLOS to LOS Transition Measurements
Venue: arXiv cs.IT 2017
Year: 2017

This paper presents a millimeter-wave (mmWave) wideband sliding correlator channel sounder with flexibility to operate at various transmission rates. The channel sounder can transmit and receive up to 1 GHz of RF null-to-null bandwidth while measuring a 2 nanosecond multipath time resolution. The system architecture takes advantage of field-programmable gate arrays (FPGAs), high-speed digital-to-analog converters (DACs), and low phase noise Rubidium (Rb) references for synchronization. Using steerable narrowbeam antennas, the system can measure up to 185 dB of path loss. The channel sounder is used to measure the directional and omnidirectional received power as a receiver transitions from line-of-sight to non-line-of-sight conditions down an urban canyon. A 25 dB drop in omnidirectional received power was observed as the receiver transitioned from line-of-sight (LOS) conditions to deeply shadowed non-LOS (NLOS) conditions. The channel sounder was also used to study signal variation and spatial consistency for a local set of receiver locations arranged in a cluster spanning a 5 m x 10 m local area, where the omnidirectional received power in LOS and NLOS environments is found to be relatively stable with standard deviations of received power of 2.2 dB and 4.3 dB, respectively. This work shows that when implementing beamforming at the transmitter at mmWave, the omnidirectional received power over a local area has little fluctuation among receiver locations separated by a few to several meters.
Sequential Recurrent Neural Networks for Language Modeling
Venue: arXiv cs.CL 2017
Year: 2017

Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) perform the same task based only on the last word and some context information that cycles in the network. This paper presents a novel approach, which bridges the gap between these two categories of networks. In particular, we propose an architecture which takes advantage of the explicit, sequential enumeration of the word history in FNN structure while enhancing each word representation at the projection layer through recurrent context information that evolves in the network. The context integration is performed using an additional word-dependent weight matrix that is also learned during the training. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art feedforward as well as recurrent neural network architectures.
Quality Resilient Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

We study deep neural networks for classification of images with quality distortions. We first show that networks fine-tuned on distorted data greatly outperform the original networks when tested on distorted data. However, fine-tuned networks perform poorly on quality distortions that they have not been trained for. We propose a mixture of experts ensemble method that is robust to different types of distortions. The "experts" in our model are trained on a particular type of distortion. The output of the model is a weighted sum of the expert models, where the weights are determined by a separate gating network. The gating network is trained to predict optimal weights for a particular distortion type and level. During testing, the network is blind to the distortion level and type, yet can still assign appropriate weights to the expert models. We additionally investigate weight sharing methods for the mixture model and show that improved performance can be achieved with a large reduction in the number of unique network parameters.