OpenRaiser/Intern-Atlas huggingface dataset:
CacheZoom: How SGX Amplifies The Power of Cache Attacks
Venue: arXiv cs.CR 2017
Year: 2017

In modern computing environments, hardware resources are commonly shared, and parallel computation is widely used. Parallel tasks can cause privacy and security problems if proper isolation is not enforced. Intel proposed SGX to create a trusted execution environment within the processor. SGX relies on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards side-channel attacks. We introduce a powerful cache side-channel attack that provides system adversaries a high resolution channel. Our attack tool named CacheZoom is able to virtually track all memory accesses of SGX enclaves with high spatial and temporal precision. As proof of concept, we demonstrate AES key recovery attacks on commonly used implementations including those that were believed to be resistant in previous scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous works which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover AES keys from T-Table based implementations with as few as ten measurements.
Low-Complexity Message Passing Based Massive MIMO Channel Estimation by Exploiting Unknown Sparse Common Support with Dirichlet Process
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates the problem of estimating sparse channels in massive MIMO systems. Most wireless channels are sparse with large delay spread, while some channels can be observed having sparse common support (SCS) within a certain area of the antenna array, i.e., the antenna array can be grouped into several clusters according to the sparse supports of channels. The SCS property is attractive when it comes to the estimation of large number of channels in massive MIMO systems. Using the SCS of channels, one expects better performance, but the number of clusters and the elements for each cluster are always unknown in the receiver. In this paper, {the Dirichlet process} is exploited to model such sparse channels where those in each cluster have SCS. We proposed a low complexity message passing based sparse Bayesian learning to perform channel estimation in massive MIMO systems by using combined BP with MF on a factor graph. Simulation results demonstrate that the proposed massive MIMO sparse channel estimation outperforms the state-of-the-art algorithms. Especially, it even shows better performance than the variational Bayesian method applied for massive MIMO channel estimation.
Verifying Patterns of Dynamic Architectures using Model Checking
Venue: arXiv cs.SE 2017
Year: 2017

Architecture patterns capture architectural design experience and provide abstract solutions to recurring architectural design problems. They consist of a description of component types and restrict component connection and activation. Therefore, they guarantee some desired properties for architectures employing the pattern. Unfortunately, most documented patterns do not provide a formal guarantee of whether their specification indeed leads to the desired guarantee. Failure in doing so, however, might lead to wrong architectures, i.e., architectures wrongly supposed to show certain desired properties. Since architectures, in general, have a high impact on the quality of the resulting system and architectural flaws are only difficult, if not to say impossible, to repair, this may lead to badly reparable quality issues in the resulting system. To address this problem, we propose an approach based on model checking to verify pattern specifications w.r.t. their guarantees. In the following we apply the approach to three well-known patterns for dynamic architectures: the Singleton, the Model-View-Controller, and the Broker pattern. Thereby, we discovered ambiguities and missing constraints for all three specifications. Thus, we conclude that verifying patterns of dynamic architectures using model checking is feasible and useful to discover ambiguities and flaws in pattern specifications.
Energy Efficient Joint Resource Allocation and Power Control for D2D Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, joint resource allocation and power control for energy efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem is removed by exploiting the penalty function approach. We further propose a novel two-layer approach which allows to find the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root-finding with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource allocation problem with single allocation format which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.
Optimal Causal Imputation for Control
Venue: arXiv cs.SY 2017
Year: 2017

The widespread applicability of analytics in cyber-physical systems has motivated research into causal inference methods. Predictive estimators are not sufficient when analytics are used for decision making; rather, the flow of causal effects must be determined. Generally speaking, these methods focus on estimation of a causal structure from experimental data. In this paper, we consider the dual problem: we fix the causal structure and optimize over causal imputations to achieve desirable system behaviors for a minimal imputation cost. First, we present the optimal causal imputation problem, and then we analyze the problem in two special cases: 1) when the causal imputations can only impute to a fixed value, 2) when the causal structure has linear dynamics with additive Gaussian noise. This optimal causal imputation framework serves to bridge the gap between causal structures and control.
A CMDP-based Approach for Energy Efficient Power Allocation in Massive MIMO Systems
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, energy efficient power allocation for the uplink of a multi-cell massive MIMO system is investigated. With the simplified power consumption model, the problem of power allocation is formulated as a constrained Markov decision process (CMDP) framework with infinite-horizon expected discounted total reward, which takes into account different quality of service (QoS) requirements for each user terminal (UT). We propose an offline solution containing the value iteration and Q-learning algorithms, which can obtain the global optimum power allocation policy. Simulation results show that our proposed policy performs very close to the ergodic optimal policy.
Interest-Driven Discovery of Local Process Models
Venue: arXiv cs.DB 2017
Year: 2017

Local Process Models (LPM) describe structured fragments of process behavior occurring in the context of less structured business processes. Traditional LPM discovery aims to generate a collection of process models that describe highly frequent behavior, but these models do not always provide useful answers for questions posed by process analysts aiming at business process improvement. We propose a framework for goal-driven LPM discovery, based on utility functions and constraints. We describe four scopes on which these utility functions and constrains can be defined, and show that utility functions and constraints on different scopes can be combined to form composite utility functions/constraints. Finally, we demonstrate the applicability of our approach by presenting several actionable business insights discovered with LPM discovery on two real life data sets.
Distributed System for Remote Monitoring and Control Greenhouse Environment
Venue: arXiv cs.CY 2017
Year: 2017

This article treats the issue of remote monitoring and control of greenhouse environment through a distributed system that interconnects several subsystems. Thus, a wireless data acquisition subsystem, a fully automated subsystem made of PLCs and industrial communication networks and an irrigation subsystem consists of two water tanks, sensors and actuators are interconnected. According to data provided by sensors, the automatic control subsystem decides whether the irrigation cycle of plants should be performed. In this case, it provides corresponding commands to irrigation subsystem. Moreover, two graphical interfaces (eKo-View and eKo-GreenHouse) are added of the process and presented in detail in this paper.
Optimal DoF region of the K-User MISO BC with Partial CSIT
Venue: arXiv cs.IT 2017
Year: 2017

We consider the $K$-User Multiple-Input-Single-Output (MISO) Broadcast Channel (BC) where the transmitter, equipped with $M$ antennas, serves $K$ users, with $K \leq M$. The transmitter has access to a partial channel state information of the users. This is modelled by letting the variance of the Channel State Information at the Transmitter (CSIT) error of user $i$ scale as $O(P^{-α_i}$) for the Signal-to-Noise Ratio (SNR) $P$ and some constant $α_i \geq 0$. In this work we derive the optimal Degrees-of-Freedom (DoF) region in such setting and we show that Rate-Splitting (RS) is the key scheme to achieve such a region.
ZM-Net: Real-time Zero-shot Image Manipulation Network
Venue: arXiv cs.CV 2017
Year: 2017

Many problems in image processing and computer vision (e.g. colorization, style transfer) can be posed as 'manipulating' an input image into a corresponding output image given a user-specified guiding signal. A holy-grail solution towards generic image manipulation should be able to efficiently alter an input image with any personalized signals (even signals unseen during training), such as diverse paintings and arbitrary descriptive attributes. However, existing methods are either inefficient to simultaneously process multiple signals (let alone generalize to unseen signals), or unable to handle signals from other modalities. In this paper, we make the first attempt to address the zero-shot image manipulation task. We cast this problem as manipulating an input image according to a parametric model whose key parameters can be conditionally generated from any guiding signal (even unseen ones). To this end, we propose the Zero-shot Manipulation Net (ZM-Net), a fully-differentiable architecture that jointly optimizes an image-transformation network (TNet) and a parameter network (PNet). The PNet learns to generate key transformation parameters for the TNet given any guiding signal while the TNet performs fast zero-shot image manipulation according to both signal-dependent parameters from the PNet and signal-invariant parameters from the TNet itself. Extensive experiments show that our ZM-Net can perform high-quality image manipulation conditioned on different forms of guiding signals (e.g. style images and attributes) in real-time (tens of milliseconds per image) even for unseen signals. Moreover, a large-scale style dataset with over 20,000 style images is also constructed to promote further research.
CNN-MERP: An FPGA-Based Memory-Efficient Reconfigurable Processor for Forward and Backward Propagation of Convolutional Neural Networks
Venue: arXiv cs.LG 2017
Year: 2017

Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. This paper presents CNN-MERP to address these issues. CNN-MERP incorporates an efficient memory hierarchy that significantly reduces the bandwidth requirements from multiple optimizations including on/off-chip data allocation, data flow optimization and data reuse. The proposed 2-level reconfigurability is utilized to enable fast and efficient reconfiguration, which is based on the control logic and the multiboot feature of FPGA. As a result, an external memory bandwidth requirement of 1.94MB/GFlop is achieved, which is 55% lower than prior arts. Under limited DRAM bandwidth, a system throughput of 1244GFlop/s is achieved at the Vertex UltraScale platform, which is 5.48 times higher than the state-of-the-art FPGA implementations.
Exploiting Hierarchy in the Abstraction-Based Verification of Statecharts Using SMT Solvers
Venue: arXiv cs.LO 2017
Year: 2017

Statecharts are frequently used as a modeling formalism in the design of state-based systems. Formal verification techniques are also often applied to prove certain properties about the behavior of the system. One of the most efficient techniques for formal verification is Counterexample-Guided Abstraction Refinement (CEGAR), which reduces the complexity of systems by automatically building and refining abstractions. In our paper we present a novel adaptation of the CEGAR approach to hierarchical statechart models. First we introduce an encoding of the statechart to logical formulas that preserves information about the state hierarchy. Based on this encoding we propose abstraction and refinement techniques that utilize the hierarchical structure of statecharts and also handle variables in the model. The encoding allows us to use SMT solvers for the systematic exploration and verification of the abstract model, including also bounded model checking. We demonstrate the applicability and efficiency of our abstraction techniques with measurements on an industry-motivated example.
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
Venue: arXiv cs.LG 2017
Year: 2017

Learning in models with discrete latent variables is challenging due to high variance gradient estimators. Generally, approaches have relied on control variates to reduce the variance of the REINFORCE estimator. Recent work (Jang et al. 2016, Maddison et al. 2016) has taken a different approach, introducing a continuous relaxation of discrete variables to produce low-variance, but biased, gradient estimates. In this work, we combine the two approaches through a novel control variate that produces low-variance, \emph{unbiased} gradient estimates. Then, we introduce a modification to the continuous relaxation and show that the tightness of the relaxation can be adapted online, removing it as a hyperparameter. We show state-of-the-art variance reduction on several benchmark generative modeling tasks, generally leading to faster convergence to a better final log-likelihood.
Ontology Based Pivoted normalization using Vector Based Approach for information Retrieval
Venue: arXiv cs.IR 2017
Year: 2017

The proposed methodology is procedural i.e. it follows finite number of steps that extracts relevant documents according to users query. It is based on principles of Data Mining for analyzing web data. Data Mining first adapts integration of data to generate warehouse. Then, it extracts useful information with the help of algorithm. The task of representing extracted documents is done by using Vector Based Statistical Approach that represents each document in set of Terms.
Hierarchical H2 Control of Large-Scale Network Dynamic Systems
Venue: arXiv cs.SY 2017
Year: 2017

Standard H2 optimal control of networked dynamic systems tend to become unscalable with network size. Structural constraints can be imposed on the design to counteract this problem albeit at the risk of making the solution non-convex. In this paper, we present a special class of structural constraints such that the H2 design satisfies a quadratic invariance condition, and therefore can be reformulated as a convex problem. This special class consists of structured and weighted projections of the input and output spaces. The choice of these projections can be optimized to match the closed-loop performance of the reformulated controller with that of the standard H2 controller. The advantage is that unlike the latter, the reformulated controller results in a hierarchical implementation which requires significantly lesser number of communication links, while also admitting model and controller reduction that helps the design to scale computationally. We illustrate our design with simulations of a 500-node network.
Deep Learning for Explicitly Modeling Optimization Landscapes
Venue: arXiv cs.NE 2017
Year: 2017

In all but the most trivial optimization problems, the structure of the solutions exhibit complex interdependencies between the input parameters. Decades of research with stochastic search techniques has shown the benefit of explicitly modeling the interactions between sets of parameters and the overall quality of the solutions discovered. We demonstrate a novel method, based on learning deep networks, to model the global landscapes of optimization problems. To represent the search space concisely and accurately, the deep networks must encode information about the underlying parameter interactions and their contributions to the quality of the solution. Once the networks are trained, the networks are probed to reveal parameter combinations with high expected performance with respect to the optimization task. These estimates are used to initialize fast, randomized, local search algorithms, which in turn expose more information about the search space that is subsequently used to refine the models. We demonstrate the technique on multiple optimization problems that have arisen in a variety of real-world domains, including: packing, graphics, job scheduling, layout and compression. The problems include combinatoric search spaces, discontinuous and highly non-linear spaces, and span binary, higher-cardinality discrete, as well as continuous parameters. Strengths, limitations, and extensions of the approach are extensively discussed and demonstrated.
Intrinsically Reliable and Lightweight Physical Obfuscated Keys
Venue: arXiv cs.CR 2017
Year: 2017

Physical Obfuscated Keys (POKs) allow tamper-resistant storage of random keys based on physical disorder. The output bits of current POK designs need to be first corrected due to measurement noise and next de-correlated since the original output bits may not be i.i.d. (independent and identically distributed) and also public helper information for error correction necessarily correlates the corrected output bits.For this reason, current designs include an interface for error correction and/or output reinforcement, and privacy amplification for compressing the corrected output to a uniform random bit string. We propose two intrinsically reliable POK designs with only XOR circuitry for privacy amplification (without need for reliability enhancement) by exploiting variability of lithographic process and variability of granularity in phase change memory (PCM) materials. The two designs are demonstrated through experiments and simulations.
Episode-Based Active Learning with Bayesian Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, such as commonly encountered in mobile robotics applications. An evaluation of different strategies for acquisition, updating, and final training on the CIFAR-10 dataset shows that incremental network updates with final training on the accumulated acquisition set are essential for best performance, while limiting the amount of required human labeling labor.
Snafu: Function-as-a-Service (FaaS) Runtime Design and Implementation
Venue: arXiv cs.DC 2017
Year: 2017

Snafu, or Snake Functions, is a modular system to host, execute and manage language-level functions offered as stateless (micro-)services to diverse external triggers. The system interfaces resemble those of commercial FaaS providers but its implementation provides distinct features which make it overall useful to research on FaaS and prototyping of FaaS-based applications. This paper argues about the system motivation in the presence of already existing alternatives, its design and architecture, the open source implementation and collected metrics which characterise the system.
Optimisation de la QoS dans un r{é}seau de radio cognitive en utilisant la m{é}taheuristique SFLA (Shuffled Frog Leaping Algorithm)
Venue: arXiv cs.NI 2017
Year: 2017

This work proposes a study of quality of service (QoS) in cognitive radio networks. This study is based on a stochastic optimization method called shuffled frog leaping algorithm (SFLA). The interest of the SFLA algorithm is to guarantee a better solution in a multi-carrier context in order to satisfy the requirements of the secondary user (SU).
A Generic Axiomatic Characterization of Centrality Measures in Social Network
Venue: arXiv cs.SI 2017
Year: 2017

Centrality is an important notion in complex networks; it could be used to characterize how influential a node or an edge is in the network. It plays an important role in several other network analysis tools including community detection. Even though there are a small number of axiomatic frameworks associated with this notion, the existing formalizations are not generic in nature. In this paper we propose a generic axiomatic framework to capture all the intrinsic properties of a centrality measure (a.k.a. centrality index). We analyze popular centrality measures along with other novel measures of centrality using this framework. We observed that none of the centrality measures considered satisfies all the axioms.
On the Selection of Calculable Residual Generators for UAV Fault Diagnosis
Venue: arXiv cs.SY 2017
Year: 2017

Structural Analysis is an established method for Fault Detection and Identification (FDI) in large-scale systems, enabling the discovery of Analytical Redundancy Relations (ARRs) which serve as residual generators. However, most techniques used to enumerate ARRs do not specify the matching used to calculate each of those ARRs. This can result in non-implementable or unusable residual generators, in the presence of non-invertibilities in the equations involved or in lack of computational tools. In this paper, we propose a methodology which combines a priori and a posteriori information in order to reduce the time required to find implementable, usable residual generators of minimum cost. The method is applied to a fixed-wing Unmanned Aerial Vehicle (UAV) model.