OpenRaiser/Intern-Atlas huggingface dataset:
On the Security of Millimeter Wave Vehicular Communication Systems using Random Antenna Subsets
Venue: arXiv cs.IT 2016
Year: 2016

Millimeter wave (mmWave) vehicular communica tion systems have the potential to improve traffic efficiency and safety. Lack of secure communication links, however, may lead to a formidable set of abuses and attacks. To secure communication links, a physical layer precoding technique for mmWave vehicular communication systems is proposed in this paper. The proposed technique exploits the large dimensional antenna arrays available at mmWave systems to produce direction dependent transmission. This results in coherent transmission to the legitimate receiver and artificial noise that jams eavesdroppers with sensitive receivers. Theoretical and numerical results demonstrate the validity and effectiveness of the proposed technique and show that the proposed technique provides high secrecy throughput when compared to conventional array and switched array transmission techniques.
Function-Based Access Control (FBAC): From Access Control Matrix to Access Control Tensor
Venue: arXiv cs.CR 2016
Year: 2016

Security researchers have stated that the core concept behind current implementations of access control predates the Internet. These assertions are made to pinpoint that there is a foundational gap in this field, and one should consider revisiting the concepts from the ground up. Moreover, Insider threats, which are an increasing threat vector against organizations are also associated with the failure of access control. Access control models derived from access control matrix encompass three sets of entities, Subjects, Objects and Operations. Typically, objects are considered to be files and operations are regarded as Read, Write, and Execute. This implies an `open sesame' approach when granting access to data, i.e. once access is granted, there is no restriction on command executions. Inspired by Functional Encryption, we propose applying access authorizations at a much finer granularity, but instead of an ad-hoc or computationally hard cryptographic approach, we postulate a foundational transformation to access control. From an abstract viewpoint, we suggest storing access authorizations as a three-dimensional tensor, which we call Access Control Tensor (ACT). In Function-based Access Control (FBAC), applications do not give blind folded execution right and can only invoke commands that have been authorized for data segments. In other words, one might be authorized to use a certain command on one object, while being forbidden to use exactly the same command on another object. The theoretical foundations of FBAC are presented along with Policy, Enforcement and Implementation (PEI) requirements of it. A critical analysis of the advantages of deploying FBAC, how it will result in developing a new generation of applications, and compatibility with existing models and systems is also included. Finally, a proof of concept implementation of FBAC is presented.
Performance and Security Evaluation of SDN Networks in OMNeT++/INET
Venue: arXiv cs.NI 2016
Year: 2016

Software Defined Networking (SDN) has been recently introduced as a new communication paradigm in computer networks. By separating the control plane from the data plane and entrusting packet forwarding to straightforward switches, SDN makes it possible to deploy and run networks which are more flexible to manage and easier to configure. This paper describes a set of extensions for the INET framework, which allow researchers and network designers to simulate SDN architectures and evaluate their performance and security at design time. Together with performance evaluation and design optimization of SDN networks, our extensions enable the simulation of SDN-based anomaly detection and mitigation techniques, as well as the quantitative evaluation of cyber-physical attacks and their impact on the network and application. This work is an ongoing research activity, and we plan to propose it for an official contribution to the INET framework.
A parallel pattern for iterative stencil + reduce
Venue: arXiv cs.DC 2016
Year: 2016

We advocate the Loop-of-stencil-reduce pattern as a means of simplifying the implementation of data-parallel programs on heterogeneous multi-core platforms. Loop-of-stencil-reduce is general enough to subsume map, reduce, map-reduce, stencil, stencil-reduce, and, crucially, their usage in a loop in both data-parallel and streaming applications, or a combination of both. The pattern makes it possible to deploy a single stencil computation kernel on different GPUs. We discuss the implementation of Loop-of-stencil-reduce in FastFlow, a framework for the implementation of applications based on the parallel patterns. Experiments are presented to illustrate the use of Loop-of-stencil-reduce in developing data-parallel kernels running on heterogeneous systems.
Factored Neural Machine Translation
Venue: arXiv cs.CL 2016
Year: 2016

We present a new approach for neural machine translation (NMT) using the morphological and grammatical decomposition of the words (factors) in the output side of the neural network. This architecture addresses two main problems occurring in MT, namely dealing with a large target language vocabulary and the out of vocabulary (OOV) words. By the means of factors, we are able to handle larger vocabulary and reduce the training time (for systems with equivalent target language vocabulary size). In addition, we can produce new words that are not in the vocabulary. We use a morphological analyser to get a factored representation of each word (lemmas, Part of Speech tag, tense, person, gender and number). We have extended the NMT approach with attention mechanism in order to have two different outputs, one for the lemmas and the other for the rest of the factors. The final translation is built using some \textit{a priori} linguistic information. We compare our extension with a word-based NMT system. The experiments, performed on the IWSLT'15 dataset translating from English to French, show that while the performance do not always increase, the system can manage a much larger vocabulary and consistently reduce the OOV rate. We observe up to 2% BLEU point improvement in a simulated out of domain translation setup.
Conformity in virtual environments: a hybrid neurophysiological and psychosocial approach
Venue: arXiv cs.HC 2016
Year: 2016

The main aim of our study was to analyse the effects of a virtual environment on social conformity, with particular attention to the effects of different types of task and psychological variables on social influence, on one side, and to the neural correlates related to conformity, measured by means of an Emotiv EPOC device on the other. For our purpose, we replicated the famous Asch's visual task and created two new tasks of increasing ambiguity, assessed through the calculation of the item's entropy. We also administered five scales in order to assess different psychological traits. From the experiment, conducted on 181 university students, emerged that conformity grows according to the ambiguity of the task, but normative influence is significantly weaker in virtual environments, if compared to face-to-face experiments. The analysed psycho-logical traits, however, result not to be relatable to conformity, and they only affect the subjects' response times. From the ERP (Event-related potentials) analysis, we detected N200 and P300 components comparing the plots of conformist and non-conformist subjects, alongside with the detection of their Late Positive Potential, Readiness Potential, and Error-Related Negativity, which appear consistently different for the two typologies.
Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach
Venue: arXiv cs.CV 2016
Year: 2016

Current top-performing blind perceptual image quality prediction models are generally trained on legacy databases of human quality opinion scores on synthetically distorted images. Therefore they learn image features that effectively predict human visual quality judgments of inauthentic, and usually isolated (single) distortions. However, real-world images usually contain complex, composite mixtures of multiple distortions. We study the perceptually relevant natural scene statistics of such authentically distorted images, in different color spaces and transform domains. We propose a bag of feature-maps approach which avoids assumptions about the type of distortion(s) contained in an image, focusing instead on capturing consistencies, or departures therefrom, of the statistics of real world images. Using a large database of authentically distorted images, human opinions of them, and bags of features computed on them, we train a regressor to conduct image quality prediction. We demonstrate the competence of the features towards improving automatic perceptual quality prediction by testing a learned algorithm using them on a benchmark legacy database as well as on a newly introduced distortion-realistic resource called the LIVE In the Wild Image Quality Challenge Database. We extensively evaluate the perceptual quality prediction model and algorithm and show that it is able to achieve good quality prediction power that is better than other leading models.
Transport-based analysis, modeling, and learning from signal and data distributions
Venue: arXiv cs.CV 2016
Year: 2016

Transport-based techniques for signal and data analysis have received increased attention recently. Given their abilities to provide accurate generative models for signal intensities and other data distributions, they have been used in a variety of applications including content-based retrieval, cancer detection, image super-resolution, and statistical machine learning, to name a few, and shown to produce state of the art in several applications. Moreover, the geometric characteristics of transport-related metrics have inspired new kinds of algorithms for interpreting the meaning of data distributions. Here we provide an overview of the mathematical underpinnings of mass transport-related methods, including numerical implementation, as well as a review, with demonstrations, of several applications.
Confining Windows Inter-Process Communications for OS-Level Virtual Machine
Venue: arXiv cs.OS 2016
Year: 2016

As OS-level virtualization technology usually imposes little overhead on virtual machine start-up and running, it provides an excellent choice for building intrusion/fault tolerant applications that require redundancy and frequent invocation. When developing Windows OS-level virtual machine, however, people will inevitably face the challenge of confining Windows Inter-Process Communications (IPC). As IPC on Windows platform is more complex than UNIX style OS and most of the programs on Windows are not open-source, it is difficult to discover all of the performed IPCs and confine them. In this paper, we propose three general principles to confine IPC on Windows OS and a novel IPC confinement mechanism based on the principles. With the mechanism, for the first time from the literature, we successfully virtualized RPC System Service (RPCSS) and Internet Information Server (IIS) on Feather-weight Virtual Machine (FVM). Experimental results demonstrate that multiple IIS web server instances can simultaneously run on single Windows OS with much less performance overhead than other popular VM technology, offering a good basis for constructing dependable system.
Virtualizing System and Ordinary Services in Windows-based OS-Level Virtual Machines
Venue: arXiv cs.OS 2016
Year: 2016

OS-level virtualization incurs smaller start-up and run-time overhead than HAL-based virtualization and thus forms an important building block for developing fault-tolerant and intrusion-tolerant applications. A complete implementation of OS-level virtualization on the Windows platform requires virtualization of Windows services, such as system services like the Remote Procedure Call Server Service (RPCSS), because they are essentially extensions of the kernel. As Windows system services work very differently from their counterparts on UNIX-style OS, i.e., daemons, and many of their implementation details are proprietary, virtualizing Windows system services turned out to be the most challenging technical barrier for OS-level virtualization for the Windows platform. In this paper, we describe a general technique to virtualize Windows services, and demonstrate its effectiveness by applying it to successfully virtualize a set of important Windows system services and ordinary services on different versions of Windows OS, including RPCSS, DcomLaunch, IIS service group, Tlntsvr, MySQL, Apache2.2, CiSvc, ImapiService, etc.
Professional and Citizen Bibliometrics: Complementarities and ambivalences in the development and use of indicators
Venue: arXiv cs.DL 2016
Year: 2016

Bibliometric indicators such as journal impact factors, h-indices, and total citation counts are algorithmic artifacts that can be used in research evaluation and management. These artifacts have no meaning by themselves, but receive their meaning from attributions in institutional practices. We distinguish four main stakeholders in these practices: (1) producers of bibliometric data and indicators; (2) bibliometricians who develop and test indicators; (3) research managers who apply the indicators; and (4) the scientists being evaluated with potentially competing career interests. These different positions may lead to different and sometimes conflicting perspectives on the meaning and value of the indicators. The indicators can thus be considered as boundary objects which are socially constructed in translations among these perspectives. This paper proposes an analytical clarification by listing an informed set of (sometimes unsolved) problems in bibliometrics which can also shed light on the tension between simple but invalid indicators that are widely used (e.g., the h-index) and more sophisticated indicators that are not used or cannot be used in evaluation practices because they are not transparent for users, cannot be calculated, or are difficult to interpret.
From the Skin-Depth Equation to the Inverse RFEC Sensor Model
Venue: arXiv cs.CE 2016
Year: 2016

In this paper, we tackle the direct and inverse problems for the Remote-Field Eddy-Current (RFEC) technology. The direct problem is the sensor model, where given the geometry the measurements are obtained. Conversely, the inverse problem is where the geometry needs to be estimated given the field measurements. These problems are particularly important in the field of Non-Destructive Testing (NDT) because they allow assessing the quality of the structure monitored. We solve the direct problem in a parametric fashion using Least Absolute Shrinkage and Selection Operation (LASSO). The proposed inverse model uses the parameters from the direct model to recover the thickness using least squares producing the optimal solution given the direct model. This study is restricted to the 2D axisymmetric scenario. Both, direct and inverse models, are validated using a Finite Element Analysis (FEA) environment with realistic pipe profiles.
Design of an Optoelectronic State Machine with integrated BDD based Optical logic
Venue: arXiv cs.AR 2016
Year: 2016

In this paper I demonstrate a novel design for an optoelectronic State Machine which replaces input/output forming logic found in conventional state machines with BDD based optical logic while still using solid state memory in the form of flip-flops in order to store states. This type of logic makes use of waveguides and ring resonators to create binary switches. These switches in turn can be used to create combinational logic which can be used as input/output forming logic for a state machine. Replacing conventional combinational logic with BDD based optical logic allows for a faster range of state machines that can certainly outperform conventional state machines as propagation delays within the logic described are in the order of picoseconds as opposed to nanoseconds in digital logic.
Travel the World: Analyzing and Predicting Booking Behavior using E-mail Travel Receipts
Venue: arXiv cs.CY 2016
Year: 2016

Tourism industry has grown tremendously in the previous several decades. Despite its global impact, there still remain a number of open questions related to better understanding of tourists and their habits. In this work we analyze the largest data set of travel receipts considered thus far, and focus on exploring and modeling booking behavior of online customers. We extract useful, actionable insights into the booking behavior, and tackle the task of predicting the booking time. The presented results can be directly used to improve booking experience of customers and optimize targeting campaigns of travel operators.
Asymptotic Analysis of Equivalences and Core-Structures in Kronecker-Style Graph Models
Venue: arXiv cs.SI 2016
Year: 2016

Growing interest in modeling large, complex networks has spurred significant research into generative graph models. Kronecker-style models (SKG and R-MAT) are often used due to their scalability and ability to mimic key properties of real-world networks. Although a few papers theoretically establish these models' behavior for specific parameters, many claims used to justify their use are supported only empirically. In this work, we prove several results using asymptotic analysis which illustrate that empirical studies may not fully capture the true behavior of the models. Paramount to the widespread adoption of Kronecker-style models was the introduction of a linear-time edge-sampling variant (R-MAT), which existing literature typically treats as interchangeable with SKG. We prove that although several R-MAT formulations are asymptotically equivalent, their behavior diverges from that of SKG. Further, we show these results are observable even at relatively small graph sizes. Second, we consider a case where asymptotic analysis reveals unexpected behavior within a given model.
Style Imitation and Chord Invention in Polyphonic Music with Exponential Families
Venue: arXiv cs.AI 2016
Year: 2016

Modeling polyphonic music is a particularly challenging task because of the intricate interplay between melody and harmony. A good model should satisfy three requirements: statistical accuracy (capturing faithfully the statistics of correlations at various ranges, horizontally and vertically), flexibility (coping with arbitrary user constraints), and generalization capacity (inventing new material, while staying in the style of the training corpus). Models proposed so far fail on at least one of these requirements. We propose a statistical model of polyphonic music, based on the maximum entropy principle. This model is able to learn and reproduce pairwise statistics between neighboring note events in a given corpus. The model is also able to invent new chords and to harmonize unknown melodies. We evaluate the invention capacity of the model by assessing the amount of cited, re-discovered, and invented chords on a corpus of Bach chorales. We discuss how the model enables the user to specify and enforce user-defined constraints, which makes it useful for style-based, interactive music generation.
Information Theoretic Limits of Data Shuffling for Distributed Learning
Venue: arXiv cs.IT 2016
Year: 2016

Data shuffling is one of the fundamental building blocks for distributed learning algorithms, that increases the statistical gain for each step of the learning process. In each iteration, different shuffled data points are assigned by a central node to a distributed set of workers to perform local computations, which leads to communication bottlenecks. The focus of this paper is on formalizing and understanding the fundamental information-theoretic trade-off between storage (per worker) and the worst-case communication overhead for the data shuffling problem. We completely characterize the information theoretic trade-off for $K=2$, and $K=3$ workers, for any value of storage capacity, and show that increasing the storage across workers can reduce the communication overhead by leveraging coding. We propose a novel and systematic data delivery and storage update strategy for each data shuffle iteration, which preserves the structural properties of the storage across the workers, and aids in minimizing the communication overhead in subsequent data shuffling iterations.
CLOEE - Cross-Layer Optimization for Energy Efficiency of IEEE 802.15.6 IR-UWB WBANs
Venue: arXiv cs.NI 2016
Year: 2016

Advances in sensor networks enable pervasive health monitoring and patient-specific treatments that take into account the patients medical history, current state, genetic background, and personal habits. However, sensors are often battery power limited and vastly differ in their application related requirements. In this paper, we address both of these problems. Specifically, we study IEEE 802.15.6 ultra-wideband (UWB) wireless body area networks (WBANs) that use impulse radio (IR). We formulate the joint optimization of the payload size and number of pulses per symbol to optimize the energy efficiency under the minimum rate constraint. The proposed algorithm, cross-layer optimization for energy efficiency (CLOEE), enables us to carry out a cross-layer resource allocation that addresses the rate and reliability trade-off in the physical (PHY) layer as well as the packet size optimization and transmission efficiency for the medium access control (MAC) layer. Simulation results demonstrate that CLOEE can provide an order of magnitude improvement in to static strategies.
GeThR-Net: A Generalized Temporally Hybrid Recurrent Neural Network for Multimodal Information Fusion
Venue: arXiv cs.CV 2016
Year: 2016

Data generated from real world events are usually temporal and contain multimodal information such as audio, visual, depth, sensor etc. which are required to be intelligently combined for classification tasks. In this paper, we propose a novel generalized deep neural network architecture where temporal streams from multiple modalities are combined. There are total M+1 (M is the number of modalities) components in the proposed network. The first component is a novel temporally hybrid Recurrent Neural Network (RNN) that exploits the complimentary nature of the multimodal temporal information by allowing the network to learn both modality specific temporal dynamics as well as the dynamics in a multimodal feature space. M additional components are added to the network which extract discriminative but non-temporal cues from each modality. Finally, the predictions from all of these components are linearly combined using a set of automatically learned weights. We perform exhaustive experiments on three different datasets spanning four modalities. The proposed network is relatively 3.5%, 5.7% and 2% better than the best performing temporal multimodal baseline for UCF-101, CCV and Multimodal Gesture datasets respectively.
NPCs Vote! Changing Voter Reactions Over Time Using the Extreme AI Personality Engine
Venue: arXiv cs.AI 2016
Year: 2016

Can non-player characters have human-realistic personalities, changing over time depending on input from those around them? And can they have different reactions and thoughts about different people? Using Extreme AI, a psychology-based personality engine using the Five Factor model of personality, I answer these questions by creating personalities for 100 voters and allowing them to react to two politicians to see if the NPC voters' choice of candidate develops in a realistic-seeming way, based on initial and changing personality facets and on their differing feelings toward the politicians (in this case, across liking, trusting, and feeling affiliated with the candidates). After 16 test runs, the voters did indeed change their attitudes and feelings toward the candidates in different and yet generally realistic ways, and even changed their attitudes about other issues based on what a candidate extolled.