OpenRaiser/Intern-Atlas huggingface dataset:
Modelling and Verification of a Cluster-tree Formation Protocol Implementation for the IEEE 802.15.4 TSCH MAC Operation Mode
Venue: arXiv cs.NI 2017
Year: 2017

Correct and efficient initialization of wireless sensor networks can be challenging in the face of many uncertainties present in ad hoc wireless networks. In this paper we examine an implementation for the formation of a cluster-tree topology in a network which operates on top of the TSCH MAC operation mode of the IEEE 802.15.4 standard, and investigate it using formal methods. We show how both the mCRL2 language and toolset help us in identifying scenarios where the implementation does not form a proper topology. More importantly, our analysis leads to the conclusion that the cluster-tree formation algorithm has a super linear time complexity. So, it does not scale to large networks.
Economic Analysis of Ransomware
Venue: arXiv cs.CR 2017
Year: 2017

We present in this work an economic analysis of ransomware, with relevant data from Cryptolocker, CryptoWall, TeslaCrypt and other major strands. We include a detailed study of the impact that different price discrimination strategies can have on the success of a ransomware family, examining uniform pricing, optimal price discrimination and bargaining strategies and analysing their advantages and limitations. In addition, we present results of a preliminary survey that can helps in estimating an optimal ransom value. We discuss at each stage whether the different schemes we analyse have been encountered already in existing malware, and the likelihood of them being implemented and becoming successful. We hope this work will help to gain some useful insights for predicting how ransomware may evolve in the future and be better prepared to counter its current and future threat.
On the effect of pooling on the geometry of representations
Venue: arXiv cs.LG 2017
Year: 2017

In machine learning and neuroscience, certain computational structures and algorithms are known to yield disentangled representations without us understanding why, the most striking examples being perhaps convolutional neural networks and the ventral stream of the visual cortex in humans and primates. As for the latter, it was conjectured that representations may be disentangled by being flattened progressively and at a local scale. An attempt at a formalization of the role of invariance in learning representations was made recently, being referred to as I-theory. In this framework and using the language of differential geometry, we show that pooling over a group of transformations of the input contracts the metric and reduces its curvature, and provide quantitative bounds, in the aim of moving towards a theoretical understanding on how to disentangle representations.
Deriving Generic Bounds for Time-Series Constraints Based on Regular Expressions Characteristics
Venue: arXiv cs.FL 2017
Year: 2017

We introduce the concept of regular expression characteristics as a unified way to concisely express bounds on time-series constraints. This allows us not only to define time-series constraints in a compositional way, but also to deal with their combinatorial aspect in a compositional way, without developing ad-hoc bounds for each time-series constraint separately.
Second-order Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Networks (CNNs) have been successfully applied to many computer vision tasks, such as image classification. By performing linear combinations and element-wise nonlinear operations, these networks can be thought of as extracting solely first-order information from an input image. In the past, however, second-order statistics computed from handcrafted features, e.g., covariances, have proven highly effective in diverse recognition tasks. In this paper, we introduce a novel class of CNNs that exploit second-order statistics. To this end, we design a series of new layers that (i) extract a covariance matrix from convolutional activations, (ii) compute a parametric, second-order transformation of a matrix, and (iii) perform a parametric vectorization of a matrix. These operations can be assembled to form a Covariance Descriptor Unit (CDU), which replaces the fully-connected layers of standard CNNs. Our experiments demonstrate the benefits of our new architecture, which outperform the first-order CNNs, while relying on up to 90% fewer parameters.
Dance Dance Convolution
Venue: arXiv cs.LG 2017
Year: 2017

Dance Dance Revolution (DDR) is a popular rhythm-based video game. Players perform steps on a dance platform in synchronization with music as directed by on-screen step charts. While many step charts are available in standardized packs, players may grow tired of existing charts, or wish to dance to a song for which no chart exists. We introduce the task of learning to choreograph. Given a raw audio track, the goal is to produce a new step chart. This task decomposes naturally into two subtasks: deciding when to place steps and deciding which steps to select. For the step placement task, we combine recurrent and convolutional neural networks to ingest spectrograms of low-level audio features to predict steps, conditioned on chart difficulty. For step selection, we present a conditional LSTM generative model that substantially outperforms n-gram and fixed-window approaches.
The Fake News Spreading Plague: Was it Preventable?
Venue: arXiv cs.SI 2017
Year: 2017

In 2010, a paper entitled "From Obscurity to Prominence in Minutes: Political Speech and Real-time search" won the Best Paper Prize of the Web Science 2010 Conference. Among its findings were the discovery and documentation of what was termed a "Twitter-bomb", an organized effort to spread misinformation about the democratic candidate Martha Coakley through anonymous Twitter accounts. In this paper, after summarizing the details of that event, we outline the recipe of how social networks are used to spread misinformation. One of the most important steps in such a recipe is the "infiltration" of a community of users who are already engaged in conversations about a topic, to use them as organic spreaders of misinformation in their extended subnetworks. Then, we take this misinformation spreading recipe and indicate how it was successfully used to spread fake news during the 2016 U.S. Presidential Election. The main differences between the scenarios are the use of Facebook instead of Twitter, and the respective motivations (in 2010: political influence; in 2016: financial benefit through online advertising). After situating these events in the broader context of exploiting the Web, we seize this opportunity to address limitations of the reach of research findings and to start a conversation about how communities of researchers can increase their impact on real-world societal issues.
Metalearning for Feature Selection
Venue: arXiv cs.LG 2017
Year: 2017

A general formulation of optimization problems in which various candidate solutions may use different feature-sets is presented, encompassing supervised classification, automated program learning and other cases. A novel characterization of the concept of a "good quality feature" for such an optimization problem is provided; and a proposal regarding the integration of quality based feature selection into metalearning is suggested, wherein the quality of a feature for a problem is estimated using knowledge about related features in the context of related problems. Results are presented regarding extensive testing of this "feature metalearning" approach on supervised text classification problems; it is demonstrated that, in this context, feature metalearning can provide significant and sometimes dramatic speedup over standard feature selection heuristics.
Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices
Venue: arXiv cs.SE 2017
Year: 2017

Context: Continuous practices, i.e., continuous integration, delivery, and deployment, are the software development industry practices that enable organizations to frequently and reliably release new features and products. With the increasing interest in and literature on continuous practices, it is important to systematically review and synthesize the approaches, tools, challenges, and practices reported for adopting and implementing continuous practices. Objective: This research aimed at systematically reviewing the state of the art of continuous practices to classify approaches and tools, identify challenges and practices in this regard, and identify the gaps for future research. Method: We used systematic literature review (SLR) method for reviewing the peer-reviewed papers on continuous practices published between 2004 and 1st June 2016. We applied thematic analysis method for analysing the data extracted from reviewing 69 papers selected using predefined criteria. Results: We have identified thirty approaches and associated tools, which facilitate the implementation of continuous practices in the following ways: (1) "reducing build and test time in continuous integration (CI)"; (2) "increasing visibility and awareness on build and test results in CI"; (3) "supporting (semi-) automated continuous testing"; (4) "detecting violations, flaws and faults in CI"; (5) "addressing security and scalability issues in deployment pipeline", and (6) "improving dependability and reliability of deployment process". We have also determined a list of critical factors such as "testing (effort and time)", "team awareness and transparency", "good design principles", "customer", "highly skilled and motivated team", "application domain", and "appropriate infrastructure" that should be carefully considered when introducing continuous practices in a given organization.
Model-based Testing of the Java Network API
Venue: arXiv cs.SE 2017
Year: 2017

Testing networked systems is challenging. The client or server side cannot be tested by itself. We present a solution using tool "Modbat" that generates test cases for Java's network library java.nio, where we test both blocking and non-blocking network functions. Our test model can dynamically simulate actions in multiple worker and client threads, thanks to a carefully orchestrated design that covers non-determinism while ensuring progress.
Modelling System of Systems Interface Contract Behaviour
Venue: arXiv cs.SE 2017
Year: 2017

A key challenge in System of Systems (SoS) engineering is the analysis and maintenance of global properties under SoS evolution, and the integration of new constituent elements. There is a need to model the constituent systems composing a SoS in order to allow the analysis of emergent behaviours at the SoS boundary. The Contract pattern allows the engineer to specify constrained behaviours to which constituent systems are required to conform in order to be a part of the SoS. However, the Contract pattern faces some limitations in terms of its accessibility and suitability for verifying contract compatibility. To address these deficiencies, we propose the enrichment of the Contract pattern, which hitherto has been defined using SysML and the COMPASS Modelling Language (CML), by utilising SysML and Object Constraint Language (OCL). In addition, we examine the potential of interface automata, a notation for improving loose coupling between interfaces of constituent systems defined according to the contract, as a means of enabling the verification of contract compatibility. The approach is demonstrated using a case study in audio/video content streaming.
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems
Venue: arXiv cs.CL 2017
Year: 2017

Language understanding is a key component in a spoken dialogue system. In this paper, we investigate how the language understanding module influences the dialogue system performance by conducting a series of systematic experiments on a task-oriented neural dialogue system in a reinforcement learning based setting. The empirical study shows that among different types of language understanding errors, slot-level errors can have more impact on the overall performance of a dialogue system compared to intent-level errors. In addition, our experiments demonstrate that the reinforcement learning based dialogue system is able to learn when and what to confirm in order to achieve better performance and greater robustness.
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
Venue: arXiv cs.LG 2017
Year: 2017

Simplified Molecular Input Line Entry System (SMILES) is a single line text representation of a unique molecule. One molecule can however have multiple SMILES strings, which is a reason that canonical SMILES have been defined, which ensures a one to one correspondence between SMILES string and molecule. Here the fact that multiple SMILES represent the same molecule is explored as a technique for data augmentation of a molecular QSAR dataset modeled by a long short term memory (LSTM) cell based neural network. The augmented dataset was 130 times bigger than the original. The network trained with the augmented dataset shows better performance on a test set when compared to a model built with only one canonical SMILES string per molecule. The correlation coefficient R2 on the test set was improved from 0.56 to 0.66 when using SMILES enumeration, and the root mean square error (RMS) likewise fell from 0.62 to 0.55. The technique also works in the prediction phase. By taking the average per molecule of the predictions for the enumerated SMILES a further improvement to a correlation coefficient of 0.68 and a RMS of 0.52 was found.
Mendeley readership as a filtering tool to identify highly cited publications
Venue: arXiv cs.DL 2017
Year: 2017

This study presents a large scale analysis of the distribution and presence of Mendeley readership scores over time and across disciplines. We study whether Mendeley readership scores (RS) can identify highly cited publications more effectively than journal citation scores (JCS). Web of Science (WoS) publications with DOIs published during the period 2004-2013 and across 5 major scientific fields have been analyzed. The main result of this study shows that readership scores are more effective (in terms of precision/recall values) than journal citation scores to identify highly cited publications across all fields of science and publication years. The findings also show that 86.5% of all the publications are covered by Mendeley and have at least one reader. Also the share of publications with Mendeley readership scores is increasing from 84% in 2004 to 89% in 2009, and decreasing from 88% in 2010 to 82% in 2013. However, it is noted that publications from 2010 onwards exhibit on average a higher density of readership vs. citation scores. This indicates that compared to citation scores, readership scores are more prevalent for recent publications and hence they could work as an early indicator of research impact. These findings highlight the potential and value of Mendeley as a tool for scientometric purposes and particularly as a relevant tool to identify highly cited publications.
On the Interplay between Strong Regularity and Graph Densification
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we analyze the practical implications of Szemerédi's regularity lemma in the preservation of metric information contained in large graphs. To this end, we present a heuristic algorithm to find regular partitions. Our experiments show that this method is quite robust to the natural sparsification of proximity graphs. In addition, this robustness can be enforced by graph densification.
Volumetric parametrization from a level set boundary representation with PHT Splines
Venue: arXiv cs.GR 2017
Year: 2017

A challenge in isogeometric analysis is constructing analysis-suitable volumetric meshes which can accurately represent the geometry of a given physical domain. In this paper, we propose a method to derive a spline-based representation of a domain of interest from voxel-based data. We show an efficient way to obtain a boundary representation of the domain by a level-set function. Then, we use the geometric information from the boundary (the normal vectors and curvature) to construct a matching C1 representation with hierarchical cubic splines. The approximation is done by a single template and linear transformations (scaling, translations and rotations) without the need for solving an optimization problem. We illustrate our method with several examples in two and three dimensions, and show good performance on some standard benchmark test problems.
A GPU-based Multi-level Algorithm for Boundary Value Problems
Venue: arXiv cs.MS 2017
Year: 2017

A novel and scalable geometric multi-level algorithm is presented for the numerical solution of elliptic partial differential equations, specially designed to run with high occupancy of streaming processors inside Graphics Processing Units(GPUs). The algorithm consists of iterative, superposed operations on a single grid, and it is composed of two simple full-grid routines: a restriction and a coarsened interpolation-relaxation. The restriction is used to collect sources using recursive coarsened averages, and the interpolation-relaxation simultaneously applies coarsened finite-difference operators and interpolations. The routines are scheduled in a saw-like refining cycle. Convergence to machine precision is achieved repeating the full cycle using accumulated residuals and successively collecting the solution. Its total number of operations scale linearly with the number of nodes. It provides an attractive fast solver for Boundary Value Problems (BVPs), specially for simulations running entirely in the GPU. Applications shown in this work include the deformation of two-dimensional grids, the computation of three-dimensional streamlines for a singular trifoil-knot vortex and the calculation of three-dimensional electric potentials in heterogeneous dielectric media.
Energy Efficient Precoding Design for SWIPT in MIMO Two-Way Relay Networks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we study the energy efficiency (EE) maximization problem in multiple-input multiple-output (MIMO) two-way relay networks with simultaneous wireless information and power transfer (SWIPT). The network consists of a multiple-antenna amplify-and-forward relay node which provides bidirectional communications between two multiple-antenna transceiver nodes
Hardware Trojan Detection Game: A Prospect-Theoretic Approach
Venue: arXiv cs.IT 2017
Year: 2017

Outsourcing integrated circuit (IC) manufacturing to offshore foundries has grown exponentially in recent years. Given the critical role of ICs in the control and operation of vehicular systems and other modern engineering designs, such offshore outsourcing has led to serious security threats due to the potential of insertion of hardware trojans - malicious designs that, when activated, can lead to highly detrimental consequences. In this paper, a novel game-theoretic framework is proposed to analyze the interactions between a hardware manufacturer, acting as attacker, and an IC testing facility, acting as defender. The problem is formulated as a noncooperative game in which the attacker must decide on the type of trojan that it inserts while taking into account the detection penalty as well as the damage caused by the trojan. Meanwhile, the resource-constrained defender must decide on the best testing strategy that allows optimizing its overall utility which accounts for both damages and the fines. The proposed game is based on the robust behavioral framework of prospect theory (PT) which allows capturing the potential uncertainty, risk, and irrational behavior in the decision making of both the attacker and defender. For both, the standard rational expected utility (EUT) case and the PT case, a novel algorithm based on fictitious play is proposed and shown to converge to a mixed-strategy Nash equilibrium. For an illustrative case study, thorough analytical results are derived for both EUT and PT to study the properties of the reached equilibrium as well as the impact of key system parameters such as the defender-set fine. Simulation results assess the performance of the proposed framework under both EUT and PT and show that the use of PT will provide invaluable insights on the outcomes of the proposed hardware trojan game, in particular, and system security, in general.
Networked Systems under Denial-of-Service: Co-located vs. Remote Control Architectures
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we study networked systems in the presence of Denial-of-Service (DoS) attacks, namely attacks that prevent transmissions over the communication network. Previous studies have shown that co-located architectures (control unit co-located with the actuators and networked sensor channel) can ensure a high level of robustness against DoS. However, co-location requires a wired or dedicated actuator channel, which could not meet flexibility and cost requirements. In this paper we consider a control architecture that approximates co-location while enabling remote implementation (networked sensor and actuator channels). We analyze closed-loop stability and quantify the robustness "gap" between this architecture and the co-located one.
Comments on "CRB-RPL: A Receiver-based Routing Protocol for Communications in Cognitive Radio Enabled Smart Grid"
Venue: arXiv cs.NI 2017
Year: 2017

A recent paper by Yang et al. [1] proposed CRB-RPL as a new RPL-based routing protocol for communications in cognitive radio (CR) enabled smart grid. Essentially, CRB-RPL adopts the operation of CRB-MAC, which is a medium access control (MAC) protocol for cognitive machine-to-machine (M2M) communications, and depicts it as a network layer enhancement. CRB-RPL suffers from a number of technical flaws in terms of protocol operation and analytical aspects. The main objective of this paper is to highlight these technical flaws of CRB-RPL and provide corrections to analytical modeling.
Memos: Revisiting Hybrid Memory Management in Modern Operating System
Venue: arXiv cs.OS 2017
Year: 2017

The emerging hybrid DRAM-NVM architecture is challenging the existing memory management mechanism in operating system. In this paper, we introduce memos, which can schedule memory resources over the entire memory hierarchy including cache, channels, main memory comprising DRAM and NVM simultaneously. Powered by our newly designed kernel-level monitoring module and page migration engine, memos can dynamically optimize the data placement at the memory hierarchy in terms of the on-line memory patterns, current resource utilization and feature of memory medium. Our experimental results show that memos can achieve high memory utilization, contributing to system throughput by 19.1% and QoS by 23.6% on average. Moreover, memos can reduce the NVM side memory latency by 3~83.3%, energy consumption by 25.1~99%, and benefit the NVM lifetime significantly (40X improvement on average).