OpenRaiser/Intern-Atlas huggingface dataset:
An Architecture for Embedded Systems Supporting Assisted Living
Venue: arXiv cs.SE 2018
Year: 2018

The rise in life expectancy is one of the great achievements of the twentieth century. This phenomenon originates a still increasing interest in Ambient Assisted Living (AAL) technological solutions that may support people in their daily routines allowing an independent and safe lifestyle as long as possible. AAL systems generally acquire data from the field and reason on them and the context to accomplish their tasks. Very often, AAL systems are vertical solutions, thus making hard their reuse and adaptation to different domains with respect to the ones for which they have been developed. In this paper we propose an architectural solution that allows the acquisition level of an ALL system to be easily built, configured, and extended without affecting the reasoning level of the system. We experienced our proposal in a fall detection system.
Relative Age of Information: Maintaining Freshness while Considering the Most Recently Generated Information
Venue: arXiv cs.PF 2018
Year: 2018

A queueing system handling a sequence of message arrivals is considered where each message obsoletes all previous messages. The objective is to assess the freshness of the latest message/information that has been successfully transmitted, i.e., "age of information" (AoI). We study a variation of traditional AoI, the "Relative AoI", here defined so as to account for the presence of newly arrived messages/information to the queue to be transmitted.
SlimNets: An Exploration of Deep Model Compression and Acceleration
Venue: arXiv cs.LG 2018
Year: 2018

Deep neural networks have achieved increasingly accurate results on a wide variety of complex tasks. However, much of this improvement is due to the growing use and availability of computational resources (e.g use of GPUs, more layers, more parameters, etc). Most state-of-the-art deep networks, despite performing well, over-parameterize approximate functions and take a significant amount of time to train. With increased focus on deploying deep neural networks on resource constrained devices like smart phones, there has been a push to evaluate why these models are so resource hungry and how they can be made more efficient. This work evaluates and compares three distinct methods for deep model compression and acceleration: weight pruning, low rank factorization, and knowledge distillation. Comparisons on VGG nets trained on CIFAR10 show that each of the models on their own are effective, but that the true power lies in combining them. We show that by combining pruning and knowledge distillation methods we can create a compressed network 85 times smaller than the original, all while retaining 96% of the original model's accuracy.
Data Augmentation for Robust Keyword Spotting under Playback Interference
Venue: arXiv cs.CL 2018
Year: 2018

Accurate on-device keyword spotting (KWS) with low false accept and false reject rate is crucial to customer experience for far-field voice control of conversational agents. It is particularly challenging to maintain low false reject rate in real world conditions where there is (a) ambient noise from external sources such as TV, household appliances, or other speech that is not directed at the device (b) imperfect cancellation of the audio playback from the device, resulting in residual echo, after being processed by the Acoustic Echo Cancellation (AEC) system. In this paper, we propose a data augmentation strategy to improve keyword spotting performance under these challenging conditions. The training set audio is artificially corrupted by mixing in music and TV/movie audio, at different signal to interference ratios. Our results show that we get around 30-45% relative reduction in false reject rates, at a range of false alarm rates, under audio playback from such devices.
Forest Learning from Data and its Universal Coding
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers structure learning from data with $n$ samples of $p$ variables, assuming that the structure is a forest, using the Chow-Liu algorithm. Specifically, for incomplete data, we construct two model selection algorithms that complete in $O(p^2)$ steps: one obtains a forest with the maximum posterior probability given the data, and the other obtains a forest that converges to the true one as $n$ increases. We show that the two forests are generally different when some values are missing. Additionally, we present estimations for benchmark data sets to demonstrate that both algorithms work in realistic situations. Moreover, we derive the conditional entropy provided that no value is missing, and we evaluate the per-sample expected redundancy for the universal coding of incomplete data in terms of the number of non-missing samples.
Impact of Beam Misalignment on Hybrid Beamforming NOMA for mmWave Communications
Venue: arXiv cs.IT 2018
Year: 2018

This paper studies hybrid beamforming (HB)-based non-orthogonal multiple access (NOMA) in multiuser millimeter wave (mmWave) communications. HB offers power-efficient and low-complexity precoding for downlink multiuser mmWave systems which increases multiplexing gain and spectral efficiency of the system. Applying NOMA to HB-based systems, called HB-NOMA, can scale the number of users while offering a high spectral efficiency. However, an imperfect correlation between the effective channels of users in each NOMA cluster seriously degrades the achievable rate of HB-NOMA. In this paper, first a sum-rate maximization problem is formulated for HB-NOMA, and an algorithm is proposed to solve it effectively. It is then shown that the relationship between the effective channels of the users in each NOMA cluster can be approximated by a correlation factor. Next, the effect of imperfect correlation is analyzed, and a lower bound on the achievable rate of the users is derived for both perfect and imperfect correlation. Finally, the rate gap resulting from an imperfect correlation is evaluated and a tight upper bound is derived for that. Simulation results show that low correlation degrades the achievable rate of users. The lower bounds are tight in the large dimensional regime and in single-path channels.
Recursive Geman-McClure method for implementing second-order Volterra filter
Venue: arXiv cs.SY 2018
Year: 2018

The second-order Volterra (SOV) filter is a powerful tool for modeling the nonlinear system. The Geman-McClure estimator, whose loss function is non-convex and has been proven to be a robust and efficient optimization criterion for learning system. In this paper, we present a SOV filter, named SOV recursive Geman-McClure, which is an adaptive recursive Volterra algorithm based on the Geman-McClure estimator. The mean stability and mean-square stability (steady-state excess mean square error (EMSE)) of the proposed algorithm is analyzed in detail. Simulation results support the analytical findings and show the improved performance of the proposed new SOV filter as compared with existing algorithms in both Gaussian and impulsive noise environments.
Mixture Matrix Completion
Venue: arXiv cs.LG 2018
Year: 2018

Completing a data matrix X has become an ubiquitous problem in modern data science, with applications in recommender systems, computer vision, and networks inference, to name a few. One typical assumption is that X is low-rank. A more general model assumes that each column of X corresponds to one of several low-rank matrices. This paper generalizes these models to what we call mixture matrix completion (MMC): the case where each entry of X corresponds to one of several low-rank matrices. MMC is a more accurate model for recommender systems, and brings more flexibility to other completion and clustering problems. We make four fundamental contributions about this new model. First, we show that MMC is theoretically possible (well-posed). Second, we give its precise information-theoretic identifiability conditions. Third, we derive the sample complexity of MMC. Finally, we give a practical algorithm for MMC with performance comparable to the state-of-the-art for simpler related problems, both on synthetic and real data.
A Class of Weighted TSPs with Applications
Venue: arXiv cs.DS 2018
Year: 2018

Motivated by applications to poaching and burglary prevention, we define a class of weighted Traveling Salesman Problems on metric spaces. The goal is to output an infinite (though typically periodic) tour that visits the n points repeatedly, such that no point goes unvisited for "too long." More specifically, we consider two objective functions for each point x. The maximum objective is simply the maximum duration of any absence from x, while the quadratic objective is the normalized sum of squares of absence lengths from x. For periodic tours, the quadratic objective captures the expected duration of absence from x at a uniformly random point in time during the tour. The overall objective is then the weighted maximum of the individual points' objectives. When a point has weight w_x, the absences under an optimal tour should be roughly a 1/w_x fraction of the absences from points of weight 1. Thus, the objective naturally encourages visiting high-weight points more frequently, and at roughly evenly spaced intervals. We give a polynomial-time combinatorial algorithm whose output is simultaneously an O(log n) approximation under both objectives. We prove that up to constant factors, approximation guarantees for the quadratic objective directly imply the same guarantees for a natural security patrol game defined in recent work.
A Practical Searchable Symmetric Encryption Scheme for Smart Grid Data
Venue: arXiv cs.CR 2018
Year: 2018

Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility company may choose to encrypt the data before uploading them to the cloud. However, while encryption provides confidentiality to data, it also sacrifices the data owners' ability to query a special segment in their data. Searchable symmetric encryption is a technology that enables users to store documents in ciphertext form while keeping the functionality to search keywords in the documents. However, most state-of-the-art SSE algorithms are only focusing on general document storage, which may become unsuitable for smart grid applications. In this paper, we propose a simple, practical SSE scheme that aims to protect the privacy of data generated in the smart grid. Our scheme achieves high space complexity with small information disclosure that was acceptable for practical smart grid application. We also implement a prototype over the statistical data of advanced meter infrastructure to show the effectiveness of our approach.
Chaff Bugs: Deterring Attackers by Making Software Buggier
Venue: arXiv cs.CR 2018
Year: 2018

Sophisticated attackers find bugs in software, evaluate their exploitability, and then create and launch exploits for bugs found to be exploitable. Most efforts to secure software attempt either to eliminate bugs or to add mitigations that make exploitation more difficult. In this paper, we introduce a new defensive technique called chaff bugs, which instead target the bug discovery and exploit creation stages of this process. Rather than eliminating bugs, we instead add large numbers of bugs that are provably (but not obviously) non-exploitable. Attackers who attempt to find and exploit bugs in software will, with high probability, find an intentionally placed non-exploitable bug and waste precious resources in trying to build a working exploit. We develop two strategies for ensuring non-exploitability and use them to automatically add thousands of non-exploitable bugs to real-world software such as nginx and libFLAC; we show that the functionality of the software is not harmed and demonstrate that our bugs look exploitable to current triage tools. We believe that chaff bugs can serve as an effective deterrent against both human attackers and automated Cyber Reasoning Systems (CRSes).
OntoSenseNet: A Verb-Centric Ontological Resource for Indian Languages
Venue: arXiv cs.CL 2018
Year: 2018

Following approaches for understanding lexical meaning developed by Yaska, Patanjali and Bhartrihari from Indian linguistic traditions and extending approaches developed by Leibniz and Brentano in the modern times, a framework of formal ontology of language was developed. This framework proposes that meaning of words are in-formed by intrinsic and extrinsic ontological structures. The paper aims to capture such intrinsic and extrinsic meanings of words for two major Indian languages, namely, Hindi and Telugu. Parts-of-speech have been rendered into sense-types and sense-classes. Using them we have developed a gold- standard annotated lexical resource to support semantic understanding of a language. The resource has collection of Hindi and Telugu lexicons, which has been manually annotated by native speakers of the languages following our annotation guidelines. Further, the resource was utilised to derive adverbial sense-class distribution of verbs and karaka-verb sense- type distribution. Different corpora (news, novels) were compared using verb sense-types distribution. Word Embedding was used as an aid for the enrichment of the resource. This is a work in progress that aims at lexical coverage of language extensively.
Dynamic Adaptation on Non-Stationary Visual Domains
Venue: arXiv cs.CV 2018
Year: 2018

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results.
DCASE 2018 Challenge Surrey Cross-Task convolutional neural network baseline
Venue: arXiv cs.SD 2018
Year: 2018

The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five audio classification and sound event detection tasks: 1) Acoustic scene classification, 2) General-purpose audio tagging of Freesound, 3) Bird audio detection, 4) Weakly-labeled semi-supervised sound event detection and 5) Multi-channel audio classification. In this paper, we create a cross-task baseline system for all five tasks based on a convlutional neural network (CNN): a "CNN Baseline" system. We implemented CNNs with 4 layers and 8 layers originating from AlexNet and VGG from computer vision. We investigated how the performance varies from task to task with the same configuration of neural networks. Experiments show that deeper CNN with 8 layers performs better than CNN with 4 layers on all tasks except Task 1. Using CNN with 8 layers, we achieve an accuracy of 0.680 on Task 1, an accuracy of 0.895 and a mean average precision (MAP) of 0.928 on Task 2, an accuracy of 0.751 and an area under the curve (AUC) of 0.854 on Task 3, a sound event detection F1 score of 20.8% on Task 4, and an F1 score of 87.75% on Task 5. We released the Python source code of the baseline systems under the MIT license for further research.
Fleet Sizing in Vehicle Sharing Systems with Service Quality Guarantees
Venue: arXiv cs.SY 2018
Year: 2018

Vehicle sharing system consists of a fleet of vehicles (usually bikes or cars) that can be rented at one station and returned at another station. We study how to achieve guaranteed service availability in such systems. Specifically, we are interested in determining a) the fleet size and initial allocation of vehicles to stations and b) the minimum capacity of each station needed to guarantee that a) every customer will find an available vehicle at the origin station and b) the customer will find a free parking spot at the destination station. We model the evolution of number of vehicles at each station as a stochastic process and prove that the relevant probabilities in the system can be approximated from above using a computationally-tractable decoupled model. This property can be exploited to efficiently determine the size of fleet, initial distribution of vehicles to stations, and station capacities that are sufficient to achieve the desired service level. The applicability of the method is demonstrated by computing the initial vehicle stock and the capacity of each station that would be needed to avoid service failures in Boston's bike sharing system "The Hubway". Our simulation shows that the proposed method is able to find more efficient design parameters than the naive approach and consequently it can achieve the equivalent quality-of-service level with half of the vehicle fleet and half of the parking capacity.
Digging into Browser-based Crypto Mining
Venue: arXiv cs.CR 2018
Year: 2018

Mining is the foundation of blockchain-based cryptocurrencies such as Bitcoin rewarding the miner for finding blocks for new transactions. The Monero currency enables mining with standard hardware in contrast to special hardware (ASICs) as often used in Bitcoin, paving the way for in-browser mining as a new revenue model for website operators. In this work, we study the prevalence of this new phenomenon. We identify and classify mining websites in 138M domains and present a new fingerprinting method which finds up to a factor of 5.7 more miners than publicly available block lists. Our work identifies and dissects Coinhive as the major browser-mining stakeholder. Further, we present a new method to associate mined blocks in the Monero blockchain to mining pools and uncover that Coinhive currently contributes 1.18% of mined blocks having turned over 1293 Moneros in June 2018.
Benefit of Self-Stabilizing Protocols in Eventually Consistent Key-Value Stores: A Case Study
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, we focus on the implementation of distributed programs in using a key-value store where the state of the nodes is stored in a replicated and partitioned data store to improve performance and reliability. Applications of such algorithms occur in weather monitoring, social media, etc. We argue that these applications should be designed to be stabilizing so that they recover from an arbitrary state to a legitimate state. Specifically, if we use a stabilizing algorithm then we can work with more efficient implementations that provide eventual consistency rather than sequential consistency where the data store behaves as if there is just one copy of the data. We find that, although the use of eventual consistency results in consistency violation faults (cvf) where some node executes its action incorrectly because it relies on an older version of the data, the overall performance of the resulting protocol is better. We use experimental analysis to evaluate the expected improvement. We also identify other variations of stabilization that can provide additional guarantees in the presence of eventual consistency.Finally, we note that if the underlying algorithm is not stabilizing, even a single \cvf may cause the algorithm to fail completely thereby making it impossible to benefit from this approach.
An Adaptive Partial Sensitivity Updating Scheme for Fast Nonlinear Model Predictive Control
Venue: arXiv cs.SY 2018
Year: 2018

In recent years, efficient optimization algorithms for Nonlinear Model Predictive Control (NMPC) have been proposed, that significantly reduce the on-line computational time. In particular, direct multiple shooting and Sequential Quadratic Programming (SQP) are used to efficiently solve Nonlinear Programming (NLP) problems arising from continuous-time NMPC applications. One of the computationally demanding steps for on-line optimization is the computation of sensitivities of the nonlinear dynamics at every sampling instant, especially for systems of large dimensions, strong stiffness, and when using long prediction horizons. In this paper, within the algorithmic framework of the Real-Time Iteration (RTI) scheme based on multiple shooting, an inexact sensitivity updating scheme is proposed, that performs a partial update of the Jacobian of the constraints in the NLP. Such update is triggered by using a Curvature-like Measure of Nonlinearity (CMoN), so that only sensitivities exhibiting highly nonlinear behaviour are updated, thus adapting to system operating conditions and possibly reducing the computational burden. An advanced tuning strategy for the updating scheme is provided to automatically determine the number of sensitivities being updated, with a guaranteed bounded error on the Quadratic Programming (QP) solution. Numerical and control performance of the scheme is evaluated by means of two simulation examples performed on a dedicated implementation. Local convergence analysis is also presented and a tunable convergence rate is proven, when applied to the SQP method.
Estimation and Control Using Sampling-Based Bayesian Reinforcement Learning
Venue: arXiv cs.SY 2018
Year: 2018

Real-world autonomous systems operate under uncertainty about both their pose and dynamics. Autonomous control systems must simultaneously perform estimation and control tasks to maintain robustness to changing dynamics or modeling errors. However, information gathering actions often conflict with optimal actions for reaching control objectives, requiring a trade-off between exploration and exploitation. The specific problem setting considered here is for discrete-time nonlinear systems, with process noise, input-constraints, and parameter uncertainty. This article frames this problem as a Bayes-adaptive Markov decision process and solves it online using Monte Carlo tree search with an unscented Kalman filter to account for process noise and parameter uncertainty. This method is compared with certainty equivalent model predictive control and a tree search method that approximates the QMDP solution, providing insight into when information gathering is useful. Discrete time simulations characterize performance over a range of process noise and bounds on unknown parameters. An offline optimization method is used to select the Monte Carlo tree search parameters without hand-tuning. In lieu of recursive feasibility guarantees, a probabilistic bounding heuristic is offered that increases the probability of keeping the state within a desired region.