OpenRaiser/Intern-Atlas huggingface dataset:
ScaRR: Scalable Runtime Remote Attestation for Complex Systems
Venue: arXiv cs.CR 2019
Year: 2019

The introduction of remote attestation (RA) schemes has allowed academia and industry to enhance the security of their systems. The commercial products currently available enable only the validation of static properties, such as applications fingerprint, and do not handle runtime properties, such as control-flow correctness. This limitation pushed researchers towards the identification of new approaches, called runtime RA. However, those mainly work on embedded devices, which share very few common features with complex systems, such as virtual machines in a cloud. A naive deployment of runtime RA schemes for embedded devices on complex systems faces scalability problems, such as the representation of complex control-flows or slow verification phase. In this work, we present ScaRR: the first Scalable Runtime Remote attestation schema for complex systems. Thanks to its novel control-flow model, ScaRR enables the deployment of runtime RA on any application regardless of its complexity, by also achieving good performance. We implemented ScaRR and tested it on the benchmark suite SPEC CPU 2017. We show that ScaRR can validate on average 2M control-flow events per second, definitely outperforming existing solutions.
An Adaptive Groundtrack Maintenance Scheme for Spacecraft with Electric Propulsion
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, the repeat-groundtrack orbit maintenance problem is addressed for spacecraft driven by electric propulsion. An adaptive solution is proposed, which combines an hysteresis controller and a recursive least squares filter. The controller provides a pulse-width modulated command to the thruster, in compliance with the peculiarities of the electric propulsion technology. The filter takes care of estimating a set of environmental disturbance parameters, from inertial position and velocity measurements. The resulting control scheme is able to compensate for the groundtrack drift due to atmospheric drag, in a fully autonomous manner. A numerical study of a low Earth orbit mission confirms the effectiveness of the proposed method.
Hardware-Limited Task-Based Quantization
Venue: arXiv cs.IT 2019
Year: 2019

Quantization plays a critical role in digital signal processing systems. Quantizers are typically designed to obtain an accurate digital representation of the input signal, operating independently of the system task, and are commonly implemented using serial scalar analog-to-digital converters (ADCs). In this work, we study hardware-limited task-based quantization, where a system utilizing a serial scalar ADC is designed to provide a suitable representation in order to allow the recovery of a parameter vector underlying the input signal. We propose hardware-limited task-based quantization systems for a fixed and finite quantization resolution, and characterize their achievable distortion. We then apply the analysis to the practical setups of channel estimation and eigen-spectrum recovery from quantized measurements. Our results illustrate that properly designed hardware-limited systems can approach the optimal performance achievable with vector quantizers, and that by taking the underlying task into account, the quantization error can be made negligible with a relatively small number of bits.
Performance Analysis of Cooperative Communications at Road Intersections Using Stochastic Geometry Tools
Venue: arXiv cs.IT 2019
Year: 2019

Vehicular safety communications (VSCs) are known to provide relevant contributions to avoid congestions and prevent road accidents, and more particularly at road intersections since these areas are more prone to accidents. In this context, one of the main impairments that affect the performance of VSCs are interference. In this paper, we develop a tractable framework to model cooperative transmissions in presence of interference for VSCs at intersections. We use tools from stochastic geometry, and model interferer vehicles locations as a Poisson point process. First, we calculate the outage probability (OP) for a direct transmission when the received node can be anywhere on the plan. Then, we analyze the OP performance of a cooperative transmission scheme. The analysis takes into account two dimensions: the decoding strategy at the receiver, and the vehicles mobility. We derive the optimal relay position, from analytical and simulation results, for different traffic densities and for different vehicles mobility models. We also show that the OP does not improve after the number of infrastructure relays reached a threshold value. Finally, we show that the OP performance of VSCs is higher at intersections than on highways. We validated our analytical results by Monte-Carlo simulations.
Explainable Neural Computation via Stack Neural Module Networks
Venue: arXiv cs.CV 2019
Year: 2019

In complex inferential tasks like question answering, machine learning models must confront two challenges: the need to implement a compositional reasoning process, and, in many applications, the need for this reasoning process to be interpretable to assist users in both development and prediction. Existing models designed to produce interpretable traces of their decision-making process typically require these traces to be supervised at training time. In this paper, we present a novel neural modular approach that performs compositional reasoning by automatically inducing a desired sub-task decomposition without relying on strong supervision. Our model allows linking different reasoning tasks though shared modules that handle common routines across tasks. Experiments show that the model is more interpretable to human evaluators compared to other state-of-the-art models: users can better understand the model's underlying reasoning procedure and predict when it will succeed or fail based on observing its intermediate outputs.
A Survey of Energy Efficiency in SDN Software Based Methods and Optimization Models
Venue: arXiv cs.NI 2019
Year: 2019

Software Defined Networking (SDN) paradigm has the benefits of programmable network elements by separating the control and the forwarding planes, efficiency through optimized routing and flexibility in network management. As the energy costs contribute largely to the overall costs in networks, energy efficiency has become a significant design requirement for modern networking mechanisms. However, designing energy efficient solutions is non-trivial since they need to tackle the trade-off between energy efficiency and network performance. In this article, we address the energy efficiency capabilities that can be utilized in the emerging SDN. We provide a comprehensive and novel classification of software-based energy efficient solutions into subcategories of traffic aware, end system aware and rule placement. We propose general optimization models for each subcategory, and present the objective function, the parameters and constraints to be considered in each model. Detailed information on the characteristics of state-of-the-art methods, their advantages, drawbacks are provided. Hardware-based solutions used to enhance the efficiency of switches are also described. Furthermore, we discuss the open issues and future research directions in the area of energy efficiency in SDN.
Supporting Very Large Models using Automatic Dataflow Graph Partitioning
Venue: arXiv cs.DC 2019
Year: 2019

This paper presents Tofu, a system that partitions very large DNN models across multiple GPU devices to reduce per-GPU memory footprint. Tofu is designed to partition a dataflow graph of fine-grained tensor operators in order to work transparently with a general-purpose deep learning platform like MXNet. In order to automatically partition each operator, we propose to describe the semantics of an operator in a simple language which represents tensors as lambda functions mapping from tensor coordinates to values. To optimally partition different operators in a dataflow graph, Tofu uses a recursive search algorithm that minimizes the total communication cost. Our experiments on an 8-GPU machine show that Tofu enables the training of very large CNN and RNN models. It also achieves 25% - 400% speedup over alternative approaches to train very large models.
SAAGs: Biased Stochastic Variance Reduction Methods for Large-scale Learning
Venue: arXiv cs.LG 2019
Year: 2019

Stochastic approximation is one of the effective approach to deal with the large-scale machine learning problems and the recent research has focused on reduction of variance, caused by the noisy approximations of the gradients. In this paper, we have proposed novel variants of SAAG-I and II (Stochastic Average Adjusted Gradient) (Chauhan et al. 2017), called SAAG-III and IV, respectively. Unlike SAAG-I, starting point is set to average of previous epoch in SAAG-III, and unlike SAAG-II, the snap point and starting point are set to average and last iterate of previous epoch in SAAG-IV, respectively. To determine the step size, we have used Stochastic Backtracking-Armijo line Search (SBAS) which performs line search only on selected mini-batch of data points. Since backtracking line search is not suitable for large-scale problems and the constants used to find the step size, like Lipschitz constant, are not always available so SBAS could be very effective in such cases. We have extended SAAGs (I, II, III and IV) to solve non-smooth problems and designed two update rules for smooth and non-smooth problems. Moreover, our theoretical results have proved linear convergence of SAAG-IV for all the four combinations of smoothness and strong-convexity, in expectation. Finally, our experimental studies have proved the efficacy of proposed methods against the state-of-art techniques.
Revisiting the Challenges of MaxClique
Venue: arXiv cs.DS 2019
Year: 2019

The MaxClique problem, finding the largest complete subgraph in an Erd{ö}s-R{é}nyi $G(N,p)$ random graph in the large $N$ limit, is a well-known example of a simple problem for which finding any approximate solution within a factor of $2$ of the known, probabilistically determined limit, appears to require P$=$NP. This type of search has practical importance in very large graphs. Algorithmic approaches run into phase boundaries long before they reach the size of the largest likely solutions. And, most intriguing, there is an extensive literature of \textit{challenges} posed for concrete methods of finding maximum naturally occurring as well as artificially hidden cliques, with computational costs that are at most polynomial in the size of the problem. We use the probabilistic approach in a novel way to provide a more insightful test of constructive algorithms for this problem. We show that extensions of existing methods of greedy local search will be able to meet the \textit{challenges} for practical problems of size $N$ as large as $10^{10}$ and perhaps more. Experiments with spectral methods that treat a single large clique of size $αN^{1/2}$ \textit{planted} in the graph as an impurity level in a tight binding energy band show that such a clique can be detected when $α\geq \approx1.0$. Belief propagation using a recent \textit{approximate message passing} (\textbf{AMP}) scheme of inference pushes this limit down to $α\sim \sqrt{1/e}$. Exhaustive local search (with early stopping when the planted clique is found) does even better on problems of practical size, and proves to be the fastest solution method for this problem.
Architectures for Detecting Interleaved Multi-stage Network Attacks Using Hidden Markov Models
Venue: arXiv cs.CR 2019
Year: 2019

With the growing amount of cyber threats, the need for development of high-assurance cyber systems is becoming increasingly important. The objective of this paper is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved attacks. We present the interleaving concept and investigate how interleaving multiple attacks can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop two architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a database of HMM templates of known attacks and exhibit varying performance and complexity. For performance evaluation, in the presence of multiple multi-stage attack scenarios, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are used to demonstrate the efficacy of the proposed architectures.
Loosely-Coupled Semi-Direct Monocular SLAM
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods. The proposed pipeline loosely couples direct odometry and feature-based SLAM to perform three levels of parallel optimizations: (1) photometric bundle adjustment (BA) that jointly optimizes the local structure and motion, (2) geometric BA that refines keyframe poses and associated feature map points, and (3) pose graph optimization to achieve global map consistency in the presence of loop closures. This is achieved in real-time by limiting the feature-based operations to marginalized keyframes from the direct odometry module. Exhaustive evaluation on two benchmark datasets demonstrates that our system outperforms the state-of-the-art monocular odometry and SLAM systems in terms of overall accuracy and robustness.
IDTxl: The Information Dynamics Toolkit xl: a Python package for the efficient analysis of multivariate information dynamics in networks
Venue: arXiv cs.IT 2019
Year: 2019

The Information Dynamics Toolkit xl (IDTxl) is a comprehensive software package for efficient inference of networks and their node dynamics from multivariate time series data using information theory. IDTxl provides functionality to estimate the following measures: 1) For network inference: multivariate transfer entropy (TE)/Granger causality (GC), multivariate mutual information (MI), bivariate TE/GC, bivariate MI 2) For analysis of node dynamics: active information storage (AIS), partial information decomposition (PID) IDTxl implements estimators for discrete and continuous data with parallel computing engines for both GPU and CPU platforms. Written for Python3.4.3+.
Markets for Public Decision-making
Venue: arXiv cs.GT 2019
Year: 2019

A public decision-making problem consists of a set of issues, each with multiple possible alternatives, and a set of competing agents, each with a preferred alternative for each issue. We study adaptations of market economies to this setting, focusing on binary issues. Issues have prices, and each agent is endowed with artificial currency that she can use to purchase probability for her preferred alternatives (we allow randomized outcomes). We first show that when each issue has a single price that is common to all agents, market equilibria can be arbitrarily bad. This negative result motivates a different approach. We present a novel technique called "pairwise issue expansion", which transforms any public decision-making instance into an equivalent Fisher market, the simplest type of private goods market. This is done by expanding each issue into many goods: one for each pair of agents who disagree on that issue. We show that the equilibrium prices in the constructed Fisher market yield a "pairwise pricing equilibrium" in the original public decision-making problem which maximizes Nash welfare. More broadly, pairwise issue expansion uncovers a powerful connection between the public decision-making and private goods settings; this immediately yields several interesting results about public decisions markets, and furthers the hope that we will be able to find a simple iterative voting protocol that leads to near-optimum decisions.
Deep Recurrent Neural Networks for ECG Signal Denoising
Venue: arXiv cs.NE 2019
Year: 2019

Electrocardiographic signal is a subject to multiple noises, caused by various factors. It is therefore a standard practice to denoise such signal before further analysis. With advances of new branch of machine learning, called deep learning, new methods are available that promises state-of-the-art performance for this task. We present a novel approach to denoise electrocardiographic signals with deep recurrent denoising neural networks. We utilize a transfer learning technique by pretraining the network using synthetic data, generated by a dynamic ECG model, and fine-tuning it with a real data. We also investigate the impact of the synthetic training data on the network performance on real signals. The proposed method was tested on a real dataset with varying amount of noise. The results indicate that four-layer deep recurrent neural network can outperform reference methods for heavily noised signal. Moreover, networks pretrained with synthetic data seem to have better results than network trained with real data only. We show that it is possible to create state-of-the art denoising neural network that, pretrained on artificial data, can perform exceptionally well on real ECG signals after proper fine-tuning.
Techniques for Interpretable Machine Learning
Venue: arXiv cs.LG 2019
Year: 2019

Interpretable machine learning tackles the important problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. Although many approaches have been proposed, a comprehensive understanding of the achievements and challenges is still lacking. We provide a survey covering existing techniques to increase the interpretability of machine learning models. We also discuss crucial issues that the community should consider in future work such as designing user-friendly explanations and developing comprehensive evaluation metrics to further push forward the area of interpretable machine learning.
Towards Composable Bias Rating of AI Services
Venue: arXiv cs.AI 2019
Year: 2019

A new wave of decision-support systems are being built today using AI services that draw insights from data (like text and video) and incorporate them in human-in-the-loop assistance. However, just as we expect humans to be ethical, the same expectation needs to be met by automated systems that increasingly get delegated to act on their behalf. A very important aspect of an ethical behavior is to avoid (intended, perceived, or accidental) bias. Bias occurs when the data distribution is not representative enough of the natural phenomenon one wants to model and reason about. The possibly biased behavior of a service is hard to detect and handle if the AI service is merely being used and not developed from scratch, since the training data set is not available. In this situation, we envisage a 3rd party rating agency that is independent of the API producer or consumer and has its own set of biased and unbiased data, with customizable distributions. We propose a 2-step rating approach that generates bias ratings signifying whether the AI service is unbiased compensating, data-sensitive biased, or biased. The approach also works on composite services. We implement it in the context of text translation and report interesting results.
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Venue: arXiv cs.IR 2019
Year: 2019

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
Venue: arXiv cs.CR 2019
Year: 2019

With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query it with their data via an API. However, if the user's input is sensitive, sending it to the server is undesirable and sometimes even legally not possible. Equally, the service provider does not want to share the model by sending it to the client for protecting its intellectual property and pay-per-query business model. In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service. MLCapsule executes the model locally on the user's side and therefore the data never leaves the client. Meanwhile, MLCapsule offers the service provider the same level of control and security of its model as the commonly used server-side execution. In addition, MLCapsule is applicable to offline applications that require local execution. Beyond protecting against direct model access, we couple the secure offline deployment with defenses against advanced attacks on machine learning models such as model stealing, reverse engineering, and membership inference.
Double Supervised Network with Attention Mechanism for Scene Text Recognition
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose Double Supervised Network with Attention Mechanism (DSAN), a novel end-to-end trainable framework for scene text recognition. It incorporates one text attention module during feature extraction which enforces the model to focus on text regions and the whole framework is supervised by two branches. One supervision branch comes from context-level modelling and another comes from one extra supervision enhancement branch which aims at tackling inexplicit semantic information at character level. These two supervisions can benefit each other and yield better performance. The proposed approach can recognize text in arbitrary length and does not need any predefined lexicon. Our method outperforms the current state-of-the-art methods on three text recognition benchmarks: IIIT5K, ICDAR2013 and SVT reaching accuracy 88.6%, 92.3% and 84.1% respectively which suggests the effectiveness of the proposed method.