OpenRaiser/Intern-Atlas huggingface dataset:
Certificate Enhanced Data-Flow Analysis
Venue: arXiv cs.PL 2018
Year: 2018

Proof-carrying-code was proposed as a solution to ensure a trust relationship between two parties: a (heavyweight) analyzer and a (lightweight) checker. The analyzer verifies the conformance of a given application to a specified property and generates a certificate attesting the validity of the analysis result. It suffices then for the checker just to test the consistency of the proof instead of constructing it. We set out to study the applicability of this technique in the context of data- flow analysis. In particular, we want to know if there is a significant performance difference between the analyzer and the checker. Therefore, we developed a tool, called DCert, implementing an inter-procedural context and flow-sensitive data-flow analyzer and checker for Android. Applying our tool to real-world large applications, we found out that checking can be up to 8 times faster than verification. This important gain in time suggests a potential for equipping applications on app stores with certificates that can be checked on mobile devices which are limited in computation and storage resources. We describe our implementation and report on experimental results.
EGBTER: Capturing degree distribution, clustering coefficients, and community structure in a single random graph model
Venue: arXiv cs.SI 2018
Year: 2018

Random graph models are important constructs for data analytic applications as well as pure mathematical developments, as they provide capabilities for network synthesis and principled analysis. Several models have been developed with the aim of faithfully preserving important graph metrics and substructures. With the goal of capturing degree distribution, clustering coefficient, and communities in a single random graph model, we propose a new model to address shortcomings in a progression of network modeling capabilities. The Block Two-Level Erd{ő}s-R{é}nyi (BTER) model of Seshadhri et al., designed to allow prescription of expected degree and clustering coefficient distributions, neglects community modeling, while the Generalized BTER (GBTER) model of Bridges et al., designed to add community modeling capabilities to BTER, struggles to faithfully represent all three characteristics simultaneously. In this work, we fit BTER and two GBTER configurations to several real-world networks and compare the results with that of our new model, the Extended GBTER (EGBTER) model. Our results support that EBGTER adds a community-modeling flexibility to BTER, while retaining a satisfactory level of accuracy in terms of degree and clustering coefficient. Our insights and empirical testing of previous models as well as the new model are novel contributions to the literature.
Edge Based Data-Driven Pipelines (Technical Report)
Venue: arXiv cs.DC 2018
Year: 2018

This research reports investigates an edge on-device stream processing platform, which extends the serverless com- puting model to the edge to help facilitate real-time data analytics across the cloud and edge in a uniform manner. We investigate associated use cases and architectural design. We deployed and tested our system on edge devices (Raspberry Pi and Android Phone), which proves that stream processing analytics can be performed at the edge of the network with single board computers in a real-time fashion.
Use of "Web Map Image" and copyright act
Venue: arXiv cs.DL 2018
Year: 2018

In this paper, we reviewed the notes on using Web map image provided by Web map service, from the viewpoint of copyright act. The copyright act aims to contribute to creation of culture by protecting the rights of authors and others, and promoting fair exploitation of cultural products. Therefore, everyone can use copyrighted materials to the extent of the copyright limitation based on copyright act. The Web map image, including maps, aerial photo and satellite image, are one of copyrighted materials, so it can be used within the limits of copyright. However, the available range of Web map image under the copyright act is not wide. In addition, it is pointed out that the copyright act has not been able to follow the progress of digitalization of copyrighted materials. It is expected to revise the copyright act corresponding to digitalization of copyrighted work.
NetO-App: A Network Orchestration Application for Centralized Network Management in Small Business Networks
Venue: arXiv cs.NI 2018
Year: 2018

Software-defined networking (SDN) is reshaping the networking paradigm. Previous research shows that SDN has advantages over traditional networks because it separates the control and data plane, leading to greater flexibility through network automation and programmability. Small business networks require flexibility, like service provider networks, to scale, deploy, and self-heal network infrastructure that comprises of cloud operating systems, virtual machines, containers, vendor networking equipment, and virtual network functions (VNFs); however, as SDN evolves in industry, there has been limited research to develop an SDN architecture to fulfill the requirements of small business networks. This research proposes a network architecture that can abstract, orchestrate, and scale configurations based on small business network requirements. Our results show that the proposed architecture provides enhanced network management and operations when combined with the network orchestration application (NetO-App) developed in this research. The NetO-App orchestrates network policies, automates configuration changes, and manages internal and external communication between the campus networking infrastructure.
Rapido: A Layer2 Payment System for Decentralized Currencies
Venue: arXiv cs.DC 2018
Year: 2018

Bitcoin blockchain faces the bitcoin scalability problem, for which bitcoin's blocks contain the transactions on the bitcoin network. The on-chain transaction processing capacity of the bitcoin network is limited by the average block creation time of 10 minutes and the block size limit. These jointly constrain the network's throughput. The transaction processing capacity maximum is estimated between 3.3 and 7 transactions per second (TPS). A Layer2 Network, named Lightning Network, is proposed and activated solutions to address this problem. LN operates on top of the bitcoin network as a cache to allow payments to be affected that are not immediately put on the blockchain. However, it also brings some drawbacks. In this paper, we observe a specific payment issue among current LN, which requires additional claims to blockchain and is time-consuming. We call the issue as shares issue. Therefore, we propose Rapido to explicitly address the shares issue. Furthermore, a new smart contract, D-HTLC, is equipped with Rapido as the payment protocol. We finally provide a proof of concept implementation and simulation for both Rapido and LN, in which Rapdio not only mitigates the shares issue but also mitigates the skewness issue thus is proved to be more applicable for various transactions than LN.
On the Distributability of Mobile Ambients (Technical Report)
Venue: arXiv cs.LO 2018
Year: 2018

Modern society is dependent on distributed software systems and to verify them different modelling languages such as mobile ambients were developed. To analyse the quality of mobile ambients as a good foundational model for distributed computation, we analyse the level of synchronisation between distributed components that they can express. Therefore, we rely on earlier established synchronisation patterns. It turns out that mobile ambients are not fully distributed, because they can express enough synchronisation to express a synchronisation pattern called M. However, they can express strictly less synchronisation than the standard pi-calculus. For this reason, we can show that there is no good and distributability-preserving encoding from the standard pi-calculus into mobile ambients and also no such encoding from mobile ambients into the join-calculus, i.e., the expressive power of mobile ambients is in between these languages. Finally, we discuss how these results can be used to obtain a fully distributed variant of mobile ambients.
Multi-Scale Supervised Network for Human Pose Estimation
Venue: arXiv cs.CV 2018
Year: 2018

Human pose estimation is an important topic in computer vision with many applications including gesture and activity recognition. However, pose estimation from image is challenging due to appearance variations, occlusions, clutter background, and complex activities. To alleviate these problems, we develop a robust pose estimation method based on the recent deep conv-deconv modules with two improvements: (1) multi-scale supervision of body keypoints, and (2) a global regression to improve structural consistency of keypoints. We refine keypoint detection heatmaps using layer-wise multi-scale supervision to better capture local contexts. Pose inference via keypoint association is optimized globally using a regression network at the end. Our method can effectively disambiguate keypoint matches in close proximity including the mismatch of left-right body parts, and better infer occluded parts. Experimental results show that our method achieves competitive performance among state-of-the-art methods on the MPII and FLIC datasets.
Towards Efficient Maximum Likelihood Estimation of LPV-SS Models
Venue: arXiv cs.SY 2018
Year: 2018

How to efficiently identify multiple-input multiple-output (MIMO) linear parameter-varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling variable still remains an open question, as identification methods proposed in the literature suffer heavily from the curse of dimensionality and/or depend on over-restrictive approximations of the measured signal behaviors. However, obtaining an SS model of the targeted system is crucial for many LPV control synthesis methods, as these synthesis tools are almost exclusively formulated for the aforementioned representation of the system dynamics. Therefore, in this paper, we tackle the problem by combining state-of-the-art LPV input-output (IO) identification methods with an LPV-IO to LPV-SS realization scheme and a maximum likelihood refinement step. The resulting modular LPV-SS identification approach achieves statical efficiency with a relatively low computational load. The method contains the following three steps: 1) estimation of the Markov coefficient sequence of the underlying system using correlation analysis or Bayesian impulse response estimation, then 2) LPV-SS realization of the estimated coefficients by using a basis reduced Ho-Kalman method, and 3) refinement of the LPV-SS model estimate from a maximum-likelihood point of view by a gradient-based or an expectation-maximization optimization methodology. The effectiveness of the full identification scheme is demonstrated by a Monte Carlo study where our proposed method is compared to existing schemes for identifying a MIMO LPV system.
Instantiation
Venue: arXiv cs.CL 2018
Year: 2018

In computational linguistics, a large body of work exists on distributed modeling of lexical relations, focussing largely on lexical relations such as hypernymy (scientist -- person) that hold between two categories, as expressed by common nouns. In contrast, computational linguistics has paid little attention to entities denoted by proper nouns (Marie Curie, Mumbai, ...). These have investigated in detail by the Knowledge Representation and Semantic Web communities, but generally not with regard to their linguistic properties. Our paper closes this gap by investigating and modeling the lexical relation of instantiation, which holds between an entity-denoting and a category-denoting expression (Marie Curie -- scientist or Mumbai -- city). We present a new, principled dataset for the task of instantiation detection as well as experiments and analyses on this dataset. We obtain the following results: (a), entities belonging to one category form a region in distributional space, but the embedding for the category word is typically located outside this subspace; (b) it is easy to learn to distinguish entities from categories from distributional evidence, but due to (a), instantiation proper is much harder to learn when using common nouns as representations of categories; (c) this problem can be alleviated by using category representations based on entity rather than category word embeddings.
Revisiting the simulation of quantum Turing machines by quantum circuits
Venue: arXiv cs.CC 2018
Year: 2018

Yao (1993) proved that quantum Turing machines and uniformly generated quantum circuits are polynomially equivalent computational models: $t \geq n$ steps of a quantum Turing machine running on an input of length $n$ can be simulated by a uniformly generated family of quantum circuits with size quadratic in $t$, and a polynomial-time uniformly generated family of quantum circuits can be simulated by a quantum Turing machine running in polynomial time. We revisit the simulation of quantum Turing machines with uniformly generated quantum circuits, which is the more challenging of the two simulation tasks, and present a variation on the simulation method employed by Yao together with an analysis of it. This analysis reveals that the simulation of quantum Turing machines can be performed by quantum circuits having depth linear in $t$, rather than quadratic depth, and can be extended to variants of quantum Turing machines, such as ones having multi-dimensional tapes. Our analysis is based on an extension of a method of Arrighi, Nesme, and Werner (2011) that allows for the localization of causal unitary evolutions.
Signal Jamming Attacks Against Communication-Based Train Control: Attack Impact and Countermeasure
Venue: arXiv cs.CR 2018
Year: 2018

We study the impact of signal jamming attacks against the communication based train control (CBTC) systems and develop the countermeasures to limit the attacks' impact. CBTC supports the train operation automation and moving-block signaling, which improves the transport efficiency. We consider an attacker jamming the wireless communication between the trains or the train to wayside access point, which can disable CBTC and the corresponding benefits. In contrast to prior work studying jamming only at the physical or link layer, we study the real impact of such attacks on end users, namely train journey time and passenger congestion. Our analysis employs a detailed model of leaky medium-based communication system (leaky waveguide or leaky feeder/coaxial cable) popularly used in CBTC systems. To counteract the jamming attacks, we develop a mitigation approach based on frequency hopping spread spectrum taking into account domain-specific structure of the leaky-medium CBTC systems. Specifically, compared with existing implementations of FHSS, we apply FHSS not only between the transmitter-receiver pair but also at the track-side repeaters. To demonstrate the feasibility of implementing this technology in CBTC systems, we develop a FHSS repeater prototype using software-defined radios on both leaky-medium and open-air (free-wave) channels. We perform extensive simulations driven by realistic running profiles of trains and real-world passenger data to provide insights into the jamming attack's impact and the effectiveness of the proposed countermeasure.
From traffic conflict simulation to traffic crash simulation: introducing traffic safety indicators based on the explicit simulation of potential driver errors
Venue: arXiv cs.MA 2018
Year: 2018

This paper introduces a general simulation framework that can allow the simulation of crashes and the evaluation of consequences on existing microsimulation packages. A specific family of simple and reproducible conflict indicators is proposed and applied to many case studies. In this approach driver failures are simulated by assuming that a driver stops reacting to an external stimulus and keeps driving at the current speed for a given time. The trajectory of the distracted driver vehicle is thus evaluated and projected, for the given time steps, for the established distraction time, over the actual trajectories of other vehicles. Every occurring crash is then evaluated in terms of energy involved in the crash, or with any other severity index (which can be easily calculated since the accident dynamics can be accurately simulated). The simulation of a driver error allows not only the typology of crashes to be included, normally accounted for with surrogate safety measures, but also many other type of typical crashes that it is impossible to simulate with microsimulation and traditional methodologies being caused by vehicles who are driving on non-conflicting trajectories such as drivers speeding at a red light, drivers taking the wrong lane or side of the street or just driving off the road in isolated accidents against external obstacles or traffic barriers. The total crash energy of all crashes is proposed as an indicator of risk and adopted in the case studies. Moreover, the concepts introduced in this paper allow scientists to define other relevant variables that can be used as surrogate safety indicators that consider driving errors. Preliminary results on different case studies have shown a great accordance of safety evaluations with statistical data and empirical expectations and also with other traditional safety indicators that are commonly used in microsimulation.
The Fluid Mechanics of Liquid Democracy
Venue: arXiv cs.GT 2018
Year: 2018

Liquid democracy is the principle of making collective decisions by letting agents transitively delegate their votes. Despite its significant appeal, it has become apparent that a weakness of liquid democracy is that a small subset of agents may gain massive influence. To address this, we propose to change the current practice by allowing agents to specify multiple delegation options instead of just one. Much like in nature, where --- fluid mechanics teaches us --- liquid maintains an equal level in connected vessels, so do we seek to control the flow of votes in a way that balances influence as much as possible. Specifically, we analyze the problem of choosing delegations to approximately minimize the maximum number of votes entrusted to any agent, by drawing connections to the literature on confluent flow. We also introduce a random graph model for liquid democracy, and use it to demonstrate the benefits of our approach both theoretically and empirically.
On the Duality and File Size Hierarchy of Fractional Repetition Codes
Venue: arXiv cs.IT 2018
Year: 2018

Distributed storage systems that deploy erasure codes can provide better features such as lower storage overhead and higher data reliability. In this paper, we focus on fractional repetition (FR) codes, which are a class of storage codes characterized by the features of uncoded exact repair and minimum repair bandwidth. We study the duality of FR codes, and investigate the relationship between the supported file size of an FR code and its dual code. Based on the established relationship, we derive an improved dual bound on the supported file size of FR codes. We further show that FR codes constructed from $t$-designs are optimal when the size of the stored file is sufficiently large. Moreover, we present the tensor product technique for combining FR codes, and elaborate on the file size hierarchy of resulting codes.
Super Resolution Phase Retrieval for Sparse Signals
Venue: arXiv cs.IT 2018
Year: 2018

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts to recover the phase information of a signal from the magnitude of its Fourier transform to enable the reconstruction of the original signal. Solving the phase retrieval problem is equivalent to recovering a signal from its auto-correlation function. In this paper, we assume the original signal to be sparse; this is a natural assumption in many applications, such as X-ray crystallography, speckle imaging and blind channel estimation. We propose an algorithm that resolves the phase retrieval problem in three stages: i) we leverage the finite rate of innovation sampling theory to super-resolve the auto-correlation function from a limited number of samples, ii) we design a greedy algorithm that identifies the locations of a sparse solution given the super-resolved auto-correlation function, iii) we recover the amplitudes of the atoms given their locations and the measured auto-correlation function. Unlike traditional approaches that recover a discrete approximation of the underlying signal, our algorithm estimates the signal on a continuous domain, which makes it the first of its kind. Along with the algorithm, we derive its performance bound with a theoretical analysis and propose a set of enhancements to improve its computational complexity and noise resilience. Finally, we demonstrate the benefits of the proposed method via a comparison against Charge Flipping, a notable algorithm in crystallography.
Robust Secrecy Energy Efficient Beamforming in MISOME-SWIPT Systems With Proportional Fairness
Venue: arXiv cs.IT 2018
Year: 2018

The joint design of beamforming vector and artificial noise covariance matrix is investigated for multiple-input-single-output-multiple-eavesdropper simultaneous wireless information and power transferring (MISOME-SWIPT) systems. A secrecy energy efficiency (SEE) maximization problem is formulated in the MISOME-SWIPT system with imperfect channel state information and proportional secrecy rate constraints. Since the formulated SEE maximization problem is non-convex, it is first recast into a series of convex problems in order to obtain the optimal solution with a reasonable computational complexity. Numerical results are used to verify the performance of the proposed algorithm and to reveal practical insights.
Metric Temporal Extensions of DL-Lite and Interval-Rigid Names
Venue: arXiv cs.LO 2018
Year: 2018

The DL-Lite description logics allow for modeling domain knowledge on top of databases and for efficient reasoning. We focus on metric temporal extensions of DL-Lite_bool and its fragments, and study the complexity of satisfiability. In particular, we investigate the influence of rigid and interval-rigid symbols, which allow for modeling knowledge that remains valid over (some) time. We show that especially the latter add considerable expressive power in many logics, but they do not always increase complexity.
Deep Generative Modeling for Scene Synthesis via Hybrid Representations
Venue: arXiv cs.CV 2018
Year: 2018

We present a deep generative scene modeling technique for indoor environments. Our goal is to train a generative model using a feed-forward neural network that maps a prior distribution (e.g., a normal distribution) to the distribution of primary objects in indoor scenes. We introduce a 3D object arrangement representation that models the locations and orientations of objects, based on their size and shape attributes. Moreover, our scene representation is applicable for 3D objects with different multiplicities (repetition counts), selected from a database. We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation. We demonstrate the effectiveness of our scene representation and the deep learning method on benchmark datasets. We also show the applications of this generative model in scene interpolation and scene completion.