OpenRaiser/Intern-Atlas huggingface dataset:
CIPARSim: Cache Intersection Property Assisted Rapid Single-pass FIFO Cache Simulation Technique
Venue: arXiv cs.AR 2015
Year: 2015

In this paper, for the first time, we introduce a cache property called the Intersection Property that helps to reduce singlepass simulation time in a manner similar to inclusion property. An intersection property defines conditions that if met, prove a particular element exists in larger caches, thus avoiding further search time. We have discussed three such intersection properties for caches using the FIFO replacement policy in this paper. A rapid singlepass FIFO cache simulator CIPARSim has also been proposed. CIPARSim is the first singlepass simulator dependent on the FIFO cache properties to reduce simulation time significantly. CIPARSim simulation time was up to 5 times faster compared to the state of the art singlepass FIFO cache simulator for the cache configurations tested. CIPARSim produces the cache hit and miss rates of an application accurately on various cache configurations. During simulation, CIPARSim intersection properties alone predict up to 90% of the total hits, reducing simulationtime immensely
Memory and information processing in neuromorphic systems
Venue: arXiv cs.NE 2015
Year: 2015

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.
The Online Coupon-Collector Problem and Its Application to Lifelong Reinforcement Learning
Venue: arXiv cs.LG 2015
Year: 2015

Transferring knowledge across a sequence of related tasks is an important challenge in reinforcement learning (RL). Despite much encouraging empirical evidence, there has been little theoretical analysis. In this paper, we study a class of lifelong RL problems: the agent solves a sequence of tasks modeled as finite Markov decision processes (MDPs), each of which is from a finite set of MDPs with the same state/action sets and different transition/reward functions. Motivated by the need for cross-task exploration in lifelong learning, we formulate a novel online coupon-collector problem and give an optimal algorithm. This allows us to develop a new lifelong RL algorithm, whose overall sample complexity in a sequence of tasks is much smaller than single-task learning, even if the sequence of tasks is generated by an adversary. Benefits of the algorithm are demonstrated in simulated problems, including a recently introduced human-robot interaction problem.
Model Driven Reactive Applications
Venue: arXiv cs.SE 2015
Year: 2015

Reactive applications (rapps) are of interest because of the explosion of mobile, tablet and web-based platforms. The complexity and proliferation of implementation technologies makes it attractive to use model-driven techniques to develop rapp systems. This article proposes a domain specific language for rapps consisting of stereotyped class models for the structure of the application and state machine models for the application behaviour. The models are given a semantics in terms of a transformation to a calculus called Widget. The languages are introduced using an example application for mobile phones.
Formal Verification of Real-Time Function Blocks Using PVS
Venue: arXiv cs.SE 2015
Year: 2015

A critical step towards certifying safety-critical systems is to check their conformance to hard real-time requirements. A promising way to achieve this is by building the systems from pre-verified components and verifying their correctness in a compositional manner. We previously reported a formal approach to verifying function blocks (FBs) using tabular expressions and the PVS proof assistant. By applying our approach to the IEC 61131-3 standard of Programmable Logic Controllers (PLCs), we constructed a repository of precise specification and reusable (proven) theorems of feasibility and correctness for FBs. However, we previously did not apply our approach to verify FBs against timing requirements, since IEC 61131-3 does not define composite FBs built from timers. In this paper, based on our experience in the nuclear domain, we conduct two realistic case studies, consisting of the software requirements and the proposed FB implementations for two subsystems of an industrial control system. The implementations are built from IEC 61131-3 FBs, including the on-delay timer. We find issues during the verification process and suggest solutions.
Yang-Baxter Equations, Computational Methods and Applications
Venue: arXiv cs.CE 2015
Year: 2015

Computational methods are an important tool for solving the Yang-Baxter equations(in small dimensions), for classifying (unifying) structures, and for solving related problems. This paper is an account of some of the latest developments on the Yang-Baxter equation, its set-theoretical version, and its applications. We construct new set-theoretical solutions for the Yang-Baxter equation. Unification theories and other results are proposed or proved.
A correction to the enhanced bottom drag parameterisation of tidal turbines
Venue: arXiv cs.CE 2015
Year: 2015

Hydrodynamic modelling is an important tool for the development of tidal stream energy projects. Many hydrodynamic models incorporate the effect of tidal turbines through an enhanced bottom drag. In this paper we show that although for coarse grid resolutions (kilometre scale) the resulting force exerted on the flow agrees well with the theoretical value, the force starts decreasing with decreasing grid sizes when these become smaller than the length scale of the wake recovery. This is because the assumption that the upstream velocity can be approximated by the local model velocity, is no longer valid. Using linear momentum actuator disc theory however, we derive a relationship between these two velocities and formulate a correction to the enhanced bottom drag formulation that consistently applies a force that remains closed to the theoretical value, for all grid sizes down to the turbine scale. In addition, a better understanding of the relation between the model, upstream, and actual turbine velocity, as predicted by actuator disc theory, leads to an improved estimate of the usefully extractable energy. We show how the corrections can be applied (demonstrated here for the models MIKE 21 and Fluidity) by a simple modification of the drag coefficient.
Margin-Based Feed-Forward Neural Network Classifiers
Venue: arXiv cs.LG 2015
Year: 2015

Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labelled samples and flexible network. We have conducted experiments on four UCI open datasets and achieved good results as expected. In conclusion, our model could handle more sparse labelled and more high-dimension dataset in a high accuracy while modification from old ANN method to our method is easy and almost free of work.
An Exclusion zone for Massive MIMO With Underlay D2D Communication
Venue: arXiv cs.IT 2015
Year: 2015

Fifth generation networks will incorporate a variety of new features in wireless networks such as data offloading, D2D communication, and Massive MIMO. Massive MIMO is specially appealing since it achieves huge gains while enabling simple processing like MRC receivers. It suffers, though, from a major shortcoming refereed to as pilot contamination. In this paper we propose a frame-work in which, a D2D underlaid Massive MIMO system is implemented and we will prove that this scheme can reduce the pilot contamination problem while enabling an optimization of the system spectral efficiency. The D2D communication will help maintain the network coverage while allowing a better channel estimation to be performed.
Impact of the spatial context on human communication activity
Venue: arXiv cs.SI 2015
Year: 2015

Technology development produces terabytes of data generated by hu- man activity in space and time. This enormous amount of data often called big data becomes crucial for delivering new insights to decision makers. It contains behavioral information on different types of human activity influenced by many external factors such as geographic infor- mation and weather forecast. Early recognition and prediction of those human behaviors are of great importance in many societal applications like health-care, risk management and urban planning, etc. In this pa- per, we investigate relevant geographical areas based on their categories of human activities (i.e., working and shopping) which identified from ge- ographic information (i.e., Openstreetmap). We use spectral clustering followed by k-means clustering algorithm based on TF/IDF cosine simi- larity metric. We evaluate the quality of those observed clusters with the use of silhouette coefficients which are estimated based on the similari- ties of the mobile communication activity temporal patterns. The area clusters are further used to explain typical or exceptional communication activities. We demonstrate the study using a real dataset containing 1 million Call Detailed Records. This type of analysis and its application are important for analyzing the dependency of human behaviors from the external factors and hidden relationships and unknown correlations and other useful information that can support decision-making.
The Pseudo-Dimension of Near-Optimal Auctions
Venue: arXiv cs.GT 2015
Year: 2015

This paper develops a general approach, rooted in statistical learning theory, to learning an approximately revenue-maximizing auction from data. We introduce $t$-level auctions to interpolate between simple auctions, such as welfare maximization with reserve prices, and optimal auctions, thereby balancing the competing demands of expressivity and simplicity. We prove that such auctions have small representation error, in the sense that for every product distribution $F$ over bidders' valuations, there exists a $t$-level auction with small $t$ and expected revenue close to optimal. We show that the set of $t$-level auctions has modest pseudo-dimension (for polynomial $t$) and therefore leads to small learning error. One consequence of our results is that, in arbitrary single-parameter settings, one can learn a mechanism with expected revenue arbitrarily close to optimal from a polynomial number of samples.
Product Construction of Affine Codes
Venue: arXiv cs.IT 2015
Year: 2015

Binary matrix codes with restricted row and column weights are a desirable method of coded modulation for power line communication. In this work, we construct such matrix codes that are obtained as products of affine codes - cosets of binary linear codes. Additionally, the constructions have the property that they are systematic. Subsequently, we generalize our construction to irregular product of affine codes, where the component codes are affine codes of different rates.
Fast Methods for Eikonal Equations: an Experimental Survey
Venue: arXiv cs.NA 2015
Year: 2015

The Fast Marching Method is a very popular algorithm to compute times-of-arrival maps (distances map measured in time units). Since their proposal in 1995, it has been applied to many different applications such as robotics, medical computer vision, fluid simulation, etc. Many alternatives have been proposed with two main objectives: to reduce its computational time and to improve its accuracy. In this paper, we collect the main approaches which improve the computational time of the standard Fast Marching Method, focusing on single-threaded methods and isotropic environments. 9 different methods are studied under a common mathematical framework and experimentally in representative environments: Fast Marching Method with binary heap, Fast Marching Method with Fibonacci Heap, Simplified Fast Marching Method, Untidy Fast Marching Method, Fast Iterative Method, Group Marching Method, Fast Sweeping Method, Lock Sweeping Method and Double Dynamic Queue Method.
Techniques for effective and efficient fire detection from social media images
Venue: arXiv cs.CV 2015
Year: 2015

Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFDnR), which combines feature extractor and evaluation functions to support instance-based learning, (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -- the FlickrFire dataset, and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFDnR was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.
Games with Delays. A Frankenstein Approach
Venue: arXiv cs.GT 2015
Year: 2015

We investigate infinite games on finite graphs where the information flow is perturbed by nondeterministic signalling delays. It is known that such perturbations make synthesis problems virtually unsolvable, in the general case. On the classical model where signals are attached to states, tractable cases are rare and difficult to identify. Here, we propose a model where signals are detached from control states, and we identify a subclass on which equilibrium outcomes can be preserved, even if signals are delivered with a delay that is finitely bounded. To offset the perturbation, our solution procedure combines responses from a collection of virtual plays following an equilibrium strategy in the instant- signalling game to synthesise, in a Frankenstein manner, an equivalent equilibrium strategy for the delayed-signalling game.
The Isogeometric Nyström Method
Venue: arXiv cs.NA 2015
Year: 2015

In this paper the isogeometric Nyström method is presented. It's outstanding features are: it allows the analysis of domains described by many different geometrical mapping methods in computer aided geometric design and it requires only pointwise function evaluations just like isogeometric collocation methods. The analysis of the computational domain is carried out by means of boundary integral equations, therefor only the boundary representation is required. The method is thoroughly integrated into the isogeometric framework. For example, the regularization of the arising singular integrals performed with local correction as well as the interpolation of the pointwise existing results are carried out by means of Bezier elements. The presented isogeometric Nyström method is applied to practical problems solved by the Laplace and the Lame-Navier equation. Numerical tests show higher order convergence in two and three dimensions. It is concluded that the presented approach provides a simple and flexible alternative to currently used methods for solving boundary integral equations, but has some limitations.
Optimal $γ$ and $C$ for $ε$-Support Vector Regression with RBF Kernels
Venue: arXiv cs.LG 2015
Year: 2015

The objective of this study is to investigate the efficient determination of $C$ and $γ$ for Support Vector Regression with RBF or mahalanobis kernel based on numerical and statistician considerations, which indicates the connection between $C$ and kernels and demonstrates that the deviation of geometric distance of neighbour observation in mapped space effects the predict accuracy of $ε$-SVR. We determinate the arrange of $γ$ & $C$ and propose our method to choose their best values.
Mapping Large Scale Research Metadata to Linked Data: A Performance Comparison of HBase, CSV and XML
Venue: arXiv cs.DB 2015
Year: 2015

OpenAIRE, the Open Access Infrastructure for Research in Europe, comprises a database of all EC FP7 and H2020 funded research projects, including metadata of their results (publications and datasets). These data are stored in an HBase NoSQL database, post-processed, and exposed as HTML for human consumption, and as XML through a web service interface. As an intermediate format to facilitate statistical computations, CSV is generated internally. To interlink the OpenAIRE data with related data on the Web, we aim at exporting them as Linked Open Data (LOD). The LOD export is required to integrate into the overall data processing workflow, where derived data are regenerated from the base data every day. We thus faced the challenge of identifying the best-performing conversion approach.We evaluated the performances of creating LOD by a MapReduce job on top of HBase, by mapping the intermediate CSV files, and by mapping the XML output.
Because we care: Privacy Dashboard on Firefox OS
Venue: arXiv cs.CR 2015
Year: 2015

In this paper we present the Privacy Dashboard -- a tool designed to inform and empower the people using mobile devices, by introducing features such as Remote Privacy Protection, Backup, Adjustable Location Accuracy, Permission Control and Secondary-User Mode. We have implemented our solution on FirefoxOS and conducted user studies to verify the usefulness and usability of our tool. The paper starts with a discussion of different aspects of mobile privacy, how users perceive it and how much they are willing to give up for better usability. Then we describe the tool in detail, presenting what incentives drove us to certain design decisions. During our studies we tried to understand how users interact with the system and what are their priorities. We have verified our hypothesis, and the impact of the educational aspects on the decisions about the privacy settings. We show that by taking a user-centric development of privacy extensions we can reduce the gap between protection and usability.
Breaking Bad: Detecting malicious domains using word segmentation
Venue: arXiv cs.CR 2015
Year: 2015

In recent years, vulnerable hosts and maliciously registered domains have been frequently involved in mobile attacks. In this paper, we explore the feasibility of detecting malicious domains visited on a cellular network based solely on lexical characteristics of the domain names. In addition to using traditional quantitative features of domain names, we also use a word segmentation algorithm to segment the domain names into individual words to greatly expand the size of the feature set. Experiments on a sample of real-world data from a large cellular network show that using word segmentation improves our ability to detect malicious domains relative to approaches without segmentation, as measured by misclassification rates and areas under the ROC curve. Furthermore, the results are interpretable, allowing one to discover (with little supervision or tuning required) which words are used most often to attract users to malicious domains. Such a lightweight approach could be performed in near-real time when a device attempts to visit a domain. This approach can complement (rather than substitute) other more expensive and time-consuming approaches to similar problems that use richer feature sets.
Practical Solutions For Format-Preserving Encryption
Venue: arXiv cs.CR 2015
Year: 2015

Format Preserving Encryption (FPE) schemes encrypt a plaintext into a ciphertext while preserving its format (e.g., a valid social-security number is encrypted into a valid social-security number), thus allowing encrypted data to be stored and used in the same manner as unencrypted data. Motivated by the always-increasing use of cloud-computing and memory delegation, which require preserving both plaintext format and privacy, several FPE schemes for general formats have been previously suggested. However, current solutions are both insecure and inefficient in practice. We propose an efficient FPE scheme with optimal security. Our scheme includes an efficient method of representing general (complex) formats, and provides efficient encryption and decryption algorithms that do not require an expensive set-up. During encryption, only format-specific properties are preserved, while all message-specific properties remain hidden, thus guaranteeing data privacy. As experimental results show that in many cases large formats domains cannot be encrypted efficiently, we extend our scheme to support large formats, by imposing a user-defined bound on the maximal format size, thus obtaining a flexible security-efficiency tradeoff and the best possible security (under the size limitation).
Two Challenges of Stealthy Hypervisors Detection: Time Cheating and Data Fluctuations
Venue: arXiv cs.CR 2015
Year: 2015

Hardware virtualization technologies play a significant role in cyber security. On the one hand these technologies enhance security levels, by designing a trusted operating system. On the other hand these technologies can be taken up into modern malware which is rather hard to detect. None of the existing methods is able to efficiently detect a hypervisor in the face of countermeasures such as time cheating, temporary self uninstalling, memory hiding etc. New hypervisor detection methods which will be described in this paper can detect a hypervisor under these countermeasures and even count several nested ones. These novel approaches rely on the new statistical analysis of time discrepancies by examination of a set of instructions, which are unconditionally intercepted by a hypervisor. Reliability was achieved through the comprehensive analysis of the collected data despite its fluctuation. These offered methods were comprehensively assessed in both Intel and AMD CPUs.
Reducing offline evaluation bias of collaborative filtering algorithms
Venue: arXiv cs.IR 2015
Year: 2015

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequence, bias the evaluation of the performance of a recommendation algorithm computed using historical data (via offline evaluation). This paper presents a new application of a weighted offline evaluation to reduce this bias for collaborative filtering algorithms.