OpenRaiser/Intern-Atlas huggingface dataset:
*-Continuous Kleene $ω$-Algebras for Energy Problems
Venue: arXiv cs.LO 2015
Year: 2015

Energy problems are important in the formal analysis of embedded or autonomous systems. Using recent results on star-continuous Kleene omega-algebras, we show here that energy problems can be solved by algebraic manipulations on the transition matrix of energy automata. To this end, we prove general results about certain classes of finitely additive functions on complete lattices which should be of a more general interest.
OMNeT++ and mosaik: Enabling Simulation of Smart Grid Communications
Venue: arXiv cs.NI 2015
Year: 2015

This paper presents a preliminary system architecture of integrating OMNeT++ into the mosaik co-simulation framework. This will enable realistic simulation of communication network protocols and services for smart grid scenarios and on the other side, further development of communication protocols for smart grid applications. Thus, by integrating OMNeT++ and mosaik, both communities will be able to leverage each others's sophisticated simulation models and expertise. The main challenges identified are the external management of the OMNeT++ simulation kernel and performance issues when federating various simulators, including OMNeT++ into the mosaik framework. The purpose of this paper is to bring these challenges up and to gather relevant experience and expertise from the OMNeT++ community. We especially encourage collaboration among all OMNeT++ developers and users.
OCR extensions - local identifiers, labeled GUIDs, file IO, and data block partitioning
Venue: arXiv cs.DC 2015
Year: 2015

We present several proposals for extending the Open Community Runtime (OCR) specification. The extension are identifiers with local validity, which use the concept of futures to provide OCR implementations more optimization opportunities, labeled GUIDs with creator functions, which are based on the local identifiers and allow the developer to create arrays of OCR objects that are safe from race conditions in case of concurrent creation of objects, a simple file IO interface, which builds on top of the existing data block concepts, and finally data block partitioning, which allows better control and flexibility in situations where multiple tasks want to access disjoint parts of a data block.
Discovery of good double and triple circulant codes using multiple impulse method
Venue: arXiv cs.IT 2015
Year: 2015

The construction of optimal linear block error-correcting codes is not an easy problem, for this, many studies describe methods for generating good error correcting codes in terms of minimum distance. In a previous work, we have presented the multiple impulse method (MIM) to evaluate the minimum distance of linear codes. In this paper we will present an optimization of the MIM method by genetic algorithms, and we found many new optimal Double and Triple Circulant Codes (DCC & TCC) with the highest known parameters using the MIM method as an evaluator of the minimum distance. Two approaches are used in the exploration of the space of generators; the first is based on genetic algorithms, however the second is on the random search algorithm.
A deep matrix factorization method for learning attribute representations
Venue: arXiv cs.CV 2015
Year: 2015

Semi-Non-negative Matrix Factorization is a technique that learns a low-dimensional representation of a dataset that lends itself to a clustering interpretation. It is possible that the mapping between this new representation and our original data matrix contains rather complex hierarchical information with implicit lower-level hidden attributes, that classical one level clustering methodologies can not interpret. In this work we propose a novel model, Deep Semi-NMF, that is able to learn such hidden representations that allow themselves to an interpretation of clustering according to different, unknown attributes of a given dataset. We also present a semi-supervised version of the algorithm, named Deep WSF, that allows the use of (partial) prior information for each of the known attributes of a dataset, that allows the model to be used on datasets with mixed attribute knowledge. Finally, we show that our models are able to learn low-dimensional representations that are better suited for clustering, but also classification, outperforming Semi-Non-negative Matrix Factorization, but also other state-of-the-art methodologies variants.
Efficient Convolutional Neural Networks for Pixelwise Classification on Heterogeneous Hardware Systems
Venue: arXiv cs.CV 2015
Year: 2015

This work presents and analyzes three convolutional neural network (CNN) models for efficient pixelwise classification of images. When using convolutional neural networks to classify single pixels in patches of a whole image, a lot of redundant computations are carried out when using sliding window networks. This set of new architectures solve this issue by either removing redundant computations or using fully convolutional architectures that inherently predict many pixels at once. The implementations of the three models are accessible through a new utility on top of the Caffe library. The utility provides support for a wide range of image input and output formats, pre-processing parameters and methods to equalize the label histogram during training. The Caffe library has been extended by new layers and a new backend for availability on a wider range of hardware such as CPUs and GPUs through OpenCL. On AMD GPUs, speedups of $54\times$ (SK-Net), $437\times$ (U-Net) and $320\times$ (USK-Net) have been observed, taking the SK equivalent SW (sliding window) network as the baseline. The label throughput is up to one megapixel per second. The analyzed neural networks have distinctive characteristics that apply during training or processing, and not every data set is suitable to every architecture. The quality of the predictions is assessed on two neural tissue data sets, of which one is the ISBI 2012 challenge data set. Two different loss functions, Malis loss and Softmax loss, were used during training. The whole pipeline, consisting of models, interface and modified Caffe library, is available as Open Source software under the working title Project Greentea.
Utility-Optimal Dynamic Rate Allocation under Average End-to-End Delay Requirements
Venue: arXiv cs.NI 2015
Year: 2015

QoS-aware networking applications such as real-time streaming and video surveillance systems require nearly fixed average end-to-end delay over long periods to communicate efficiently, although may tolerate some delay variations in short periods. This variability exhibits complex dynamics that makes rate control of such applications a formidable task. This paper addresses rate allocation for heterogeneous QoS-aware applications that preserves the long-term end-to-end delay constraint while, similar to Dynamic Network Utility Maximization (DNUM), strives to achieve the maximum network utility aggregated over a fixed time interval. Since capturing temporal dynamics in QoS requirements of sources is allowed in our system model, we incorporate a novel time-coupling constraint in which delay-sensitivity of sources is considered such that a certain end-to-end average delay for each source over a pre-specified time interval is satisfied. We propose DA-DNUM algorithm, as a dual-based solution, which allocates source rates for the next time interval in a distributed fashion, given the knowledge of network parameters in advance. Through numerical experiments, we show that DA-DNUM gains higher average link utilization and a wider range of feasible scenarios in comparison with the best, to our knowledge, rate control schemes that may guarantee such constraints on delay.
Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator
Venue: arXiv cs.RO 2015
Year: 2015

This paper describes the synthesis and evaluation of a novel state estimator for a Quadrotor Micro Aerial Vehicle. Dynamic equations which relate acceleration, attitude and the aero-dynamic propeller drag are encapsulated in an extended Kalman filter framework for estimating the velocity and the attitude of the quadrotor. It is demonstrated that exploiting the relationship between the body frame accelerations and velocities, due to blade flapping, enables drift free estimation of lateral and longitudinal components of body frame translational velocity along with improvements to roll and pitch components of body attitude estimations. Real world data sets gathered using a commercial off-the-shelf quadrotor platform, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm.
Improved Strength Four Covering Arrays with Three Symbols
Venue: arXiv cs.DM 2015
Year: 2015

A covering array $t$-$CA(n,k,g)$, of size $n$, strength $t$, degree $k$, and order $g$, is a $k\times n$ array on $g$ symbols such that every $t\times n$ sub-array contains every $t\times 1$ column on $g$ symbols at least once. Covering arrays have been studied for their applications to software testing, hardware testing, drug screening, and in areas where interactions of multiple parameters are to be tested. In this paper, we present an algebraic construction that improves many of the best known upper bounds on $n$ for covering arrays 4-$CA(n,k,g)$ with $g=3$. The $coverage$ $measure$ $μ_t(A)$ of a testing array $A$ is defined by the ratio between the number of distinct $t$-tuples contained in the column vectors of $A$ and the total number of $t$-tuples. A covering array is a testing array with full coverage. The $covering$ $arrays$ $with$ $budget$ $constraints$ $problem$ is the problem of constructing a testing array of size at most $n$ having largest possible coverage measure, given values of $k,g$ and $n$. This paper presents several strength four testing arrays with high coverage. The construction here is a generalisation of the construction methods used by Chateauneuf, Colbourn and Kreher, and Meagher and Stevens.
Optimization in the Loop: Implementing and Testing Scheduling Algorithms with SimuLTE
Venue: arXiv cs.NI 2015
Year: 2015

One of the main purposes of discrete event simulators such as OMNeT++ is to test new algorithms or protocols in realistic environments. These often need to be benchmarked against optimal/theoretical results obtained by running commercial optimization solvers. The usual way to do this is to have the simulator run in a standalone mode and generate (few) snapshots, which are then fed to the optimization solvers. This allows one to compare the optimal and suboptimal solutions in the snapshots, but does not allow to assess how the system being studied would evolve over time if the optimal solution was enforced every time. This requires optimization software to run directly in the loop of the simulation, exchanging information with the latter. The goal of this tutorial is to show how to integrate a commercial solver (CPLEX) into the simulation loop of the OMNeT++ environment. For this purpose, we propose two methods: a first one that uses a solver as an external program, and a second one that exploits a C-written API for CPLEX known as Callable Library. We then exemplify how to apply these two methods to SimuLTE, a simulation model for LTE cellular networks, implementing and testing a simple solution to a well-known resource-scheduling problem.
High Performance Computer Acoustic Data Accelerator: A New System for Exploring Marine Mammal Acoustics for Big Data Applications
Venue: arXiv cs.DC 2015
Year: 2015

This paper presents a new software model designed for distributed sonic signal detection runtime using machine learning algorithms called DeLMA. A new algorithm--Acoustic Data-mining Accelerator (ADA)--is also presented. ADA is a robust yet scalable solution for efficiently processing big sound archives using distributing computing technologies. Together, DeLMA and the ADA algorithm provide a powerful tool currently being used by the Bioacoustics Research Program (BRP) at the Cornell Lab of Ornithology, Cornell University. This paper provides a high level technical overview of the system, and discusses various aspects of the design. Basic runtime performance and project summary are presented. The DeLMA-ADA baseline performance comparing desktop serial configuration to a 64 core distributed HPC system shows as much as a 44 times faster increase in runtime execution. Performance tests using 48 cores on the HPC shows a 9x to 12x efficiency over a 4 core desktop solution. Project summary results for 19 east coast deployments show that the DeLMA-ADA solution has processed over three million channel hours of sound to date.
Oracle MCG: A first peek into COCO Detection Challenges
Venue: arXiv cs.CV 2015
Year: 2015

The recently presented COCO detection challenge will most probably be the reference benchmark in object detection in the next years. COCO is two orders of magnitude larger than Pascal and has four times the number of categories; so in all likelihood researchers will be faced with a number of new challenges. At this point, without any finished round of the competition, it is difficult for researchers to put their techniques in context, or in other words, to know how good their results are. In order to give a little context, this note evaluates a hypothetical object detector consisting in an oracle picking the best object proposal from a state-of-the-art technique. This oracle achieves a AP=0.292 in segmented objects and AP=0.317 in bounding boxes, showing that indeed the database is challenging, given that this value is the best one can expect if working on object proposals without refinement.
An LS-Decomposition Approach for Robust Data Recovery in Wireless Sensor Networks
Venue: arXiv cs.IT 2015
Year: 2015

Wireless sensor networks are widely adopted in military, civilian and commercial applications, which fuels an exponential explosion of sensory data. However, a major challenge to deploy effective sensing systems is the presence of {\em massive missing entries, measurement noise, and anomaly readings}. Existing works assume that sensory data matrices have low-rank structures. This does not hold in reality due to anomaly readings, causing serious performance degradation. In this paper, we introduce an {\em LS-Decomposition} approach for robust sensory data recovery, which decomposes a corrupted data matrix as the superposition of a low-rank matrix and a sparse anomaly matrix. First, we prove that LS-Decomposition solves a convex program with bounded approximation error. Second, using data sets from the IntelLab, GreenOrbs, and NBDC-CTD projects, we find that sensory data matrices contain anomaly readings. Third, we propose an accelerated proximal gradient algorithm and prove that it approximates the optimal solution with convergence rate $O(1/k^2)$ ($k$ is the number of iterations). Evaluations on real data sets show that our scheme achieves recovery error $\leq 5\%$ for sampling rate $\geq 50\%$ and almost exact recovery for sampling rate $\geq 60\%$, while state-of-the-art methods have error $10\% \sim 15\%$ at sampling rate $90\%$.
Hardware Implementation of Compressed Sensing based Low Complex Video Encoder
Venue: arXiv cs.MM 2015
Year: 2015

This paper presents a memory efficient VLSI architecture of low complex video encoder using three dimensional (3-D) wavelet and Compressed Sensing (CS) is proposed for space and low power video applications. Majority of the conventional video coding schemes are based on hybrid model, which requires complex operations like transform coding (DCT), motion estimation and deblocking filter at the encoder. Complexity of the proposed encoder is reduced by replacing those complex operations by 3-D DWT and CS at the encoder. The proposed architecture uses 3-D DWT to enable the scalability with levels of wavelet decomposition and also to exploit the spatial and the temporal redundancies. CS provides the good error resilience and coding efficiency. At the first stage of the proposed architecture for encoder, 3-D DWT has been applied (Lifting based 2-D DWT in spatial domain and Haar wavelet in temporal domain) on each frame of the group of frames (GOF), and in the second stage CS module exploits the sparsity of the wavelet coefficients. Small set of linear measurements are extracted by projecting the sparse 3-D wavelet coefficients onto random Bernoulli matrix at the encoder. Compared with the best existing 3-D DWT architectures, the proposed architecture for 3-D DWT requires less memory and provide high throughput. For an N?N image, the proposed 3-D DWT architecture consumes a total of only 2?(3N +40P) words of on-chip memory for the one level of decomposition. The proposed architecture for an encoder is first of its kind and to the best of my knowledge, no architecture is noted for comparison. The proposed VLSI architecture of the encoder has been synthesized on 90-nm CMOS process technology and results show that it consumes 90.08 mW power and occupies an area equivalent to 416.799 K equivalent gate at frequency of 158 MHz.
Vectors of Locally Aggregated Centers for Compact Video Representation
Venue: arXiv cs.MM 2015
Year: 2015

We propose a novel vector aggregation technique for compact video representation, with application in accurate similarity detection within large video datasets. The current state-of-the-art in visual search is formed by the vector of locally aggregated descriptors (VLAD) of Jegou et. al. VLAD generates compact video representations based on scale-invariant feature transform (SIFT) vectors (extracted per frame) and local feature centers computed over a training set. With the aim to increase robustness to visual distortions, we propose a new approach that operates at a coarser level in the feature representation. We create vectors of locally aggregated centers (VLAC) by first clustering SIFT features to obtain local feature centers (LFCs) and then encoding the latter with respect to given centers of local feature centers (CLFCs), extracted from a training set. The sum-of-differences between the LFCs and the CLFCs are aggregated to generate an extremely-compact video description used for accurate video segment similarity detection. Experimentation using a video dataset, comprising more than 1000 minutes of content from the Open Video Project, shows that VLAC obtains substantial gains in terms of mean Average Precision (mAP) against VLAD and the hyper-pooling method of Douze et. al., under the same compaction factor and the same set of distortions.
Raising The Bar For Vertex Cover: Fixed-parameter Tractability Above A Higher Guarantee
Venue: arXiv cs.DS 2015
Year: 2015

We investigate the following above-guarantee parameterization of the classical Vertex Cover problem: Given a graph $G$ and $k\in\mathbb{N}$ as input, does $G$ have a vertex cover of size at most $(2LP-MM)+k$? Here $MM$ is the size of a maximum matching of $G$, $LP$ is the value of an optimum solution to the relaxed (standard) LP for Vertex Cover on $G$, and $k$ is the parameter. Since $(2LP-MM)\geq{LP}\geq{MM}$, this is a stricter parameterization than those---namely, above-$MM$, and above-$LP$---which have been studied so far. We prove that Vertex Cover is fixed-parameter tractable for this stricter parameter $k$: We derive an algorithm which solves Vertex Cover in time $O^{*}(3^{k})$, pushing the envelope further on the parameterized tractability of Vertex Cover.
Secure Transmission on the Two-hop Relay Channel with Scaled Compute-and-Forward
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider communication on a two-hop channel, in which a source wants to send information reliably and securely to the destination via a relay. We consider both the untrusted relay case and the external eavesdropper case. In the untrusted relay case, the relay behaves as an eavesdropper and there is a cooperative node which sends a jamming signal to confuse the relay when the it is receiving from the source. We propose two secure transmission schemes using the scaled compute-and-forward technique. One of the schemes is based on a random binning code and the other one is based on a lattice chain code. It is proved that in either the high Signal-to-Noise-Ratio (SNR) scenario and/or the restricted relay power scenario, if the destination is used as the jammer, both schemes outperform all existing schemes and achieve the upper bound. In particular, if the SNR is large and the source, the relay, and the cooperative jammer have identical power and channels, both schemes achieve the upper bound for secrecy rate, which is merely $1/2$ bit per channel use lower than the channel capacity without secrecy constraints. We also prove that one of our schemes achieves a positive secrecy rate in the external eavesdropper case in which the relay is trusted and there exists an external eavesdropper.
Fame for sale: efficient detection of fake Twitter followers
Venue: arXiv cs.SI 2015
Year: 2015

$\textit{Fake followers}$ are those Twitter accounts specifically created to inflate the number of followers of a target account. Fake followers are dangerous for the social platform and beyond, since they may alter concepts like popularity and influence in the Twittersphere - hence impacting on economy, politics, and society. In this paper, we contribute along different dimensions. First, we review some of the most relevant existing features and rules (proposed by Academia and Media) for anomalous Twitter accounts detection. Second, we create a baseline dataset of verified human and fake follower accounts. Such baseline dataset is publicly available to the scientific community. Then, we exploit the baseline dataset to train a set of machine-learning classifiers built over the reviewed rules and features. Our results show that most of the rules proposed by Media provide unsatisfactory performance in revealing fake followers, while features proposed in the past by Academia for spam detection provide good results. Building on the most promising features, we revise the classifiers both in terms of reduction of overfitting and cost for gathering the data needed to compute the features. The final result is a novel $\textit{Class A}$ classifier, general enough to thwart overfitting, lightweight thanks to the usage of the less costly features, and still able to correctly classify more than 95% of the accounts of the original training set. We ultimately perform an information fusion-based sensitivity analysis, to assess the global sensitivity of each of the features employed by the classifier. The findings reported in this paper, other than being supported by a thorough experimental methodology and interesting on their own, also pave the way for further investigation on the novel issue of fake Twitter followers.
Dependency-Based Information Flow Analysis with Declassification in a Program Logic
Venue: arXiv cs.LO 2015
Year: 2015

We present a deductive approach for the analysis of secure information flows with support for fine-grained policies that include declassifications in the form of delimited information release. By explicitly tracking the dependencies of program locations as a computation history, we maintain high precision, while avoiding the need for comparing independent program runs. By considering an explicit heap model, we argue that the proposed analysis can straightforwardly be applied on object-oriented programs.