OpenRaiser/Intern-Atlas huggingface dataset:
An Exploratory Study of Applying a Scrum Development Process for Safety-Critical Systems
Venue: arXiv cs.SE 2018
Year: 2018

Agile techniques recently have received attention in developing safety-critical systems. However, a lack of empirical knowledge of performing safety assurance techniques in practice, especially safety analysis into agile development processes prevents further steps. In this article, we aim at investigating the feasibility and the effects of our S-Scrum development process, and stepwise improving and proposing an Optimized S-Scrum development process for safety-critical systems in a real environment. We conducted an exploratory case study in a one-year student project "Smart Home" at the University of Stuttgart, Germany. We participated in the project and collected quantitative and qualitative data from questionnaire, interviews, participant observation, physical artifacts, and documentation review. Furthermore, we evaluated the Optimized S-Scrum in industry by conducting interviews. The first-stage results showed that by integrating STPA (System-Theoretic Process Analysis) can ensure the safety during each sprint and enhance the safety of delivered products, while the agility of S-Scrum is slightly worse than the original Scrum. Six challenges have been explored: Management changes the team's priorities during an iteration; Disturbed safety-related communication; Non-functional requirements are determined too late; Insufficient upfront planning; Insufficient well-defined completion criteria; Excessive time to perform upfront planning. We investigated further the causalities and optimizations. The second-stage results revealed that the safety and agility have been improved after the optimizations. We have gained a positive assessment and suggestions from industry. The optimized S-Scrum is feasible for developing safety-critical systems concerning the capability to ensure safety and the acceptable agility in a student project. Further attempt is still needed in industrial projects.
Compact Neighborhood Index for Subgraph Queries in Massive Graphs
Venue: arXiv cs.DB 2018
Year: 2018

Subgraph queries also known as subgraph isomorphism search is a fundamental problem in querying graph-like structured data. It consists to enumerate the subgraphs of a data graph that match a query graph. This problem arises in many real-world applications related to query processing or pattern recognition such as computer vision, social network analysis, bioinformatic and big data analytic. Subgraph isomorphism search knows a lot of investigations and solutions mainly because of its importance and use but also because of its NP-completeness. Existing solutions use filtering mechanisms and optimise the order within witch the query vertices are matched on the data vertices to obtain acceptable processing times. However, existing approaches are iterative and generate several intermediate results. They also require that the data graph is loaded in main memory and consequently are not adapted to large graphs that do not fit into memory or are accessed by streams. To tackle this problem, we propose a new approach based on concepts widely different from existing works. Our approach distills the semantic and topological information that surround a vertex into a simple integer. This simple vertex encoding that can be computed and updated incrementally reduces considerably intermediate results and avoid to load the entire data graph into main memory. We evaluate our approach on several real-word datasets. The experimental results show that our approach is efficient and scalable.
Fast Sequential Decoding of Polar Codes
Venue: arXiv cs.IT 2018
Year: 2018

A new score function is proposed for stack decoding of polar codes, which enables one to accurately compare paths of different lengths. The proposed score function includes bias, which reflects the average behaviour of the correct path. This enables significant complexity reduction with respect to the original stack algorithm at the expense of a negligible performance loss.
On the conversion of multivalued to Boolean dynamics
Venue: arXiv cs.DM 2018
Year: 2018

Results and tools on discrete interaction networks are often concerned with Boolean variables, whereas considering more than two levels is sometimes useful. Multivalued networks can be converted to partial Boolean maps, in a way that preserves the asynchronous dynamics. We investigate the problem of extending these maps to non-admissible states, i.e. states that do not have a multivalued counterpart. We observe that attractors are preserved if a stepwise version of the original function is considered for conversion. Different extensions of the Boolean conversion affect the structure of the interaction graphs in different ways. A particular technique for extending the partial Boolean conversion is identified, that ensures that feedback cycles are preserved. This property, combined with the conservation of the asymptotic behaviour, can prove useful for the application of results and analyses defined in the Boolean setting to multivalued networks, and vice versa. As a first application, by considering the conversion of a known example for the discrete multivalued case, we create a Boolean map showing that the existence of a cyclic attractor and the absence of fixed points are compatible with the absence of local negative cycles. We then state a multivalued version of a result connecting mirror states and local feedback cycles.
On the Tree Augmentation Problem
Venue: arXiv cs.DS 2018
Year: 2018

In the Tree Augmentation problem we are given a tree $T=(V,F)$ and a set $E \subseteq V \times V$ of edges with positive integer costs $\{c_e:e \in E\}$. The goal is to augment $T$ by a minimum cost edge set $J \subseteq E$ such that $T \cup J$ is $2$-edge-connected. We obtain the following results. Recently, Adjiashvili [SODA 17] introduced a novel LP for the problem and used it to break the $2$-approximation barrier for instances when the maximum cost $M$ of an edge in $E$ is bounded by a constant; his algorithm computes a $1.96418+ε$ approximate solution in time $n^{{(M/ε^2)}^{O(1)}}$. Using a simpler LP, we achieve ratio $\frac{12}{7}+ε$ in time $2^{O(M/ε^2)} poly(n)$.This gives ratio better than $2$ for logarithmic costs, and not only for constant costs. One of the oldest open questions for the problem is whether for unit costs (when $M=1$) the standard LP-relaxation, so called Cut-LP, has integrality gap less than $2$. We resolve this open question by proving that for unit costs the integrality gap of the Cut-LP is at most $28/15=2-2/15$. In addition, we will prove that another natural LP-relaxation, that is much simpler than the ones in previous work, has integrality gap at most $7/4$.
Can Attackers with Limited Information Exploit Historical Data to Mount Successful False Data Injection Attacks on Power Systems?
Venue: arXiv cs.SY 2018
Year: 2018

This paper studies physical consequences of unobservable false data injection (FDI) attacks designed only with information inside a sub-network of the power system. The goal of this attack is to overload a chosen target line without being detected via measurements. To overcome the limited information, a multiple linear regression model is developed to learn the relationship between the external network and the attack sub-network from historical data. The worst possible consequences of such FDI attacks are evaluated by solving a bi-level optimization problem wherein the first level models the limited attack resources, while the second level formulates the system response to such attacks via DC optimal power flow (OPF). The attack model with limited information is reflected in the DC OPF formulation that only takes into account the system information for the attack sub-network. The vulnerability of this attack model is illustrated on the IEEE 24-bus RTS and IEEE 118-bus systems.
A Las Vegas algorithm to solve the elliptic curve discrete logarithm problem
Venue: arXiv cs.CR 2018
Year: 2018

In this paper, we describe a new Las Vegas algorithm to solve the elliptic curve discrete logarithm problem. The algorithm depends on a property of the group of rational points of an elliptic curve and is thus not a generic algorithm. The algorithm that we describe has some similarities with the most powerful index-calculus algorithm for the discrete logarithm problem over a finite field.
Sparse geometries handling in lattice-Boltzmann method implementation for graphic processors
Venue: arXiv cs.DC 2018
Year: 2018

We describe a high-performance implementation of the lattice-Boltzmann method (LBM) for sparse geometries on graphic processors. In our implementation we cover the whole geometry with a uniform mesh of small tiles and carry out calculations for each tile independently with a proper data synchronization at tile edges. For this method we provide both the theoretical analysis of complexity and the results for real implementations for 2D and 3D geometries. Based on the theoretical model, we show that tiles offer significantly smaller bandwidth overhead than solutions based on indirect addressing. For 2-dimensional lattice arrangements a reduction of memory usage is also possible, though at the cost of diminished performance. We reached the performance of 682 MLUPS on GTX Titan (72\% of peak theoretical memory bandwidth) for D3Q19 lattice arrangement and double precision data.
Is Second-order Information Helpful for Large-scale Visual Recognition?
Venue: arXiv cs.CV 2018
Year: 2018

By stacking layers of convolution and nonlinearity, convolutional networks (ConvNets) effectively learn from low-level to high-level features and discriminative representations. Since the end goal of large-scale recognition is to delineate complex boundaries of thousands of classes, adequate exploration of feature distributions is important for realizing full potentials of ConvNets. However, state-of-the-art works concentrate only on deeper or wider architecture design, while rarely exploring feature statistics higher than first-order. We take a step towards addressing this problem. Our method consists in covariance pooling, instead of the most commonly used first-order pooling, of high-level convolutional features. The main challenges involved are robust covariance estimation given a small sample of large-dimensional features and usage of the manifold structure of covariance matrices. To address these challenges, we present a Matrix Power Normalized Covariance (MPN-COV) method. We develop forward and backward propagation formulas regarding the nonlinear matrix functions such that MPN-COV can be trained end-to-end. In addition, we analyze both qualitatively and quantitatively its advantage over the well-known Log-Euclidean metric. On the ImageNet 2012 validation set, by combining MPN-COV we achieve over 4%, 3% and 2.5% gains for AlexNet, VGG-M and VGG-16, respectively; integration of MPN-COV into 50-layer ResNet outperforms ResNet-101 and is comparable to ResNet-152. The source code will be available on the project page: http://www.peihuali.org/MPN-COV
Smart Meter Privacy with Renewable Energy and an Energy Storage Device
Venue: arXiv cs.IT 2018
Year: 2018

A smart meter (SM) measures a consumer's electricity consumption and reports it automatically to a utility provider (UP) in almost real time. Despite many advantages of SMs, their use also leads to serious concerns about consumer privacy. In this paper, SM privacy is studied by considering the presence of a renewable energy source (RES) and a rechargeable battery (RB), which can be used to partially hide the consumer's energy consumption behavior. Privacy is measured by the information leakage rate, which denotes the average mutual information between the user's real energy consumption and the energy requested from the grid, which the SM reads and reports to the UP. The impact of the knowledge of the amount of energy generated by the RES at the UP is also considered. The minimum information leakage rate is characterized as a computable information theoretic single-letter expression in the two extreme cases, that is, when the battery capacity is infinite or zero. Numerical results are presented for the finite battery capacity case to illustrate the potential privacy gains from the existence of an RB. It is shown that, while the information leakage rate decreases with increasing availability of an RES, larger storage capacity is needed to fully exploit the available energy to improve the privacy.
Routing in Polygonal Domains
Venue: arXiv cs.CG 2018
Year: 2018

We consider the problem of routing a data packet through the visibility graph of a polygonal domain $P$ with $n$ vertices and $h$ holes. We may preprocess $P$ to obtain a label and a routing table for each vertex of $P$. Then, we must be able to route a data packet between any two vertices $p$ and $q$ of $P$, where each step must use only the label of the target node $q$ and the routing table of the current node. For any fixed $\varepsilon > 0$, we present a routing scheme that always achieves a routing path whose length exceeds the shortest path by a factor of at most $1 + \varepsilon$. The labels have $O(\log n)$ bits, and the routing tables are of size $O((\varepsilon^{-1}+h)\log n)$. The preprocessing time is $O(n^2\log n)$. It can be improved to $O(n^2)$ for simple polygons.
An Epipolar Line from a Single Pixel
Venue: arXiv cs.CV 2018
Year: 2018

Computing the epipolar geometry from feature points between cameras with very different viewpoints is often error prone, as an object's appearance can vary greatly between images. For such cases, it has been shown that using motion extracted from video can achieve much better results than using a static image. This paper extends these earlier works based on the scene dynamics. In this paper we propose a new method to compute the epipolar geometry from a video stream, by exploiting the following observation: For a pixel p in Image A, all pixels corresponding to p in Image B are on the same epipolar line. Equivalently, the image of the line going through camera A's center and p is an epipolar line in B. Therefore, when cameras A and B are synchronized, the momentary images of two objects projecting to the same pixel, p, in camera A at times t1 and t2, lie on an epipolar line in camera B. Based on this observation we achieve fast and precise computation of epipolar lines. Calibrating cameras based on our method of finding epipolar lines is much faster and more robust than previous methods.
Experience-based Optimization: A Coevolutionary Approach
Venue: arXiv cs.NE 2018
Year: 2018

This paper studies improving solvers based on their past solving experiences, and focuses on improving solvers by offline training. Specifically, the key issues of offline training methods are discussed, and research belonging to this category but from different areas are reviewed in a unified framework. Existing training methods generally adopt a two-stage strategy in which selecting the training instances and training instances are treated in two independent phases. This paper proposes a new training method, dubbed LiangYi, which addresses these two issues simultaneously. LiangYi includes a training module for a population-based solver and an instance sampling module for updating the training instances. The idea behind LiangYi is to promote the population-based solver by training it (with the training module) to improve its performance on those instances (discovered by the sampling module) on which it performs badly, while keeping the good performances obtained by it on previous instances. An instantiation of LiangYi on the Travelling Salesman Problem is also proposed. Empirical results on a huge testing set containing 10000 instances showed LiangYi could train solvers that perform significantly better than the solvers trained by other state-of-the-art training method. Moreover, empirical investigation of the behaviours of LiangYi confirmed it was able to continuously improve the solver through training.
Electrosense: Open and Big Spectrum Data
Venue: arXiv cs.NI 2018
Year: 2018

While the radio spectrum allocation is well regulated, there is little knowledge about its actual utilization over time and space. This limitation hinders taking effective actions in various applications including cognitive radios, electrosmog monitoring, and law enforcement. We introduce Electrosense, an initiative that seeks a more efficient, safe and reliable monitoring of the electromagnetic space by improving the accessibility of spectrum data for the general public. A collaborative spectrum monitoring network is designed that monitors the spectrum at large scale with low-cost spectrum sensing nodes. The large set of data is stored and processed in a big data architecture and provided back to the community with an open spectrum data as a service model, that allows users to build diverse and novel applications with different requirements. We illustrate useful usage scenarios of the Electrosense data.
Gelly-Scheduling: Distributed Graph Processing for Network Service Placement in Community Networks
Venue: arXiv cs.DC 2018
Year: 2018

Community networks (CNs) have seen an increase in the last fifteen years. Their members contact nodes which operate Internet proxies, web servers, user file storage and video streaming services, to name a few. Detecting communities of nodes with properties (such as co-location) and assessing node eligibility for service placement is thus a key-factor in optimizing the experience of users. We present a novel solution for the problem of service placement as a two-phase approach, based on: 1) community finding using a scalable graph label propagation technique and 2) a decentralized election procedure to address the multi-objective challenge of optimizing service placement in CNs. Herein we: i) highlight the applicability of leader election heuristics which are important for service placement in community networks and scheduler-dependent scenarios; ii) present a parallel and distributed solution designed as a scalable alternative for the problem of service placement, which has mostly seen computational approaches based on centralization and sequential execution.
Factorization tricks for LSTM networks
Venue: arXiv cs.CL 2018
Year: 2018

We present two simple ways of reducing the number of parameters and accelerating the training of large Long Short-Term Memory (LSTM) networks: the first one is "matrix factorization by design" of LSTM matrix into the product of two smaller matrices, and the second one is partitioning of LSTM matrix, its inputs and states into the independent groups. Both approaches allow us to train large LSTM networks significantly faster to the near state-of the art perplexity while using significantly less RNN parameters.
Intersection Types for the lambda-mu Calculus
Venue: arXiv cs.LO 2018
Year: 2018

We introduce an intersection type system for the lambda-mu calculus that is invariant under subject reduction and expansion. The system is obtained by describing Streicher and Reus's denotational model of continuations in the category of omega-algebraic lattices via Abramsky's domain-logic approach. This provides at the same time an interpretation of the type system and a proof of the completeness of the system with respect to the continuation models by means of a filter model construction. We then define a restriction of our system, such that a lambda-mu term is typeable if and only if it is strongly normalising. We also show that Parigot's typing of lambda-mu terms with classically valid propositional formulas can be translated into the restricted system, which then provides an alternative proof of strong normalisability for the typed lambda-mu calculus.
Approximately certifying the restricted isometry property is hard
Venue: arXiv cs.CC 2018
Year: 2018

A matrix is said to possess the Restricted Isometry Property (RIP) if it acts as an approximate isometry when restricted to sparse vectors. Previous work has shown it to be NP-hard to determine whether a matrix possess this property, but only in a narrow range of parameters. In this work, we show that it is NP-hard to make this determination for any accuracy parameter, even when we restrict ourselves to instances which are either RIP or far from being RIP. This result implies that it is NP-hard to approximate the range of parameters for which a matrix possesses the Restricted Isometry Property with accuracy better than some constant. Ours is the first work to prove such a claim without any additional assumptions.
Modeling and Routing for Predictable Dynamic Networks: This paper is only for copyright protection, and unpublished to the top-level version
Venue: arXiv cs.NI 2018
Year: 2018

The topologies of predictable dynamic networks are continuously dynamic in terms of node position, network connectivity and link metric. However, their dynamics are almost predictable compared with the ad-hoc network. The existing routing protocols specific to static or ad-hoc network do not consider this predictability and thus are not very efficient for some cases. We present a topology model based on Divide-and-Merge methodology to formulate the dynamic topology into the series of static topologies, which can reflect the topology dynamics correctly with the least number of static topologies. Then we design a dynamic programing algorithm to solve that model and determine the timing of routing update and the topology to be used. Besides, for the classic predictable dynamic network---space Internet, the links at some region have shorter delay, which leads to most traffic converge at these links. Meanwhile, the connectivity and metric of these links continuously vary, which results in a great end-to-end path variations and routing updates. In this paper, we propose a stable routing scheme which adds link life-time into its metric to eliminate these dynamics. And then we take use of the Dijkstra's greedy feature to release some paths from the dynamic link, achieving the goal of routing stability. Experimental results show that our method can significantly decrease the number of changed paths and affected network nodes, and then greatly improve the network stability. Interestingly, our method can also achieve better network performance, including the less number of loss packets, smoother variation of end-to-end delay and higher throughput.
Ontology based Scene Creation for the Development of Automated Vehicles
Venue: arXiv cs.AI 2018
Year: 2018

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.