OpenRaiser/Intern-Atlas huggingface dataset:
Quantized State Hybrid Automata for Cyber-Physical Systems
Venue: arXiv cs.SY 2018
Year: 2018

Cyber-physical systems involve a network of discrete controllers that control physical processes. Examples range from autonomous cars to implantable medical devices, which are highly safety critical. Hybrid Automata (HA) based formal approach is gaining momentum for the specification and validation of CPS. HA combines the model of the plant along with its discrete controller resulting in a piece-wise continuous system with discontinuities. Accurate detection of these discontinuities, using appropriate level crossing detectors, is a key challenge to simulation of CPS based on HA. Existing techniques employ time discrete numerical integration with bracketing for level crossing detection. These techniques involve back-tracking and are highly non-deterministic and hence error prone. As level crossings happen based on the values of continuous variables, Quantized State System (QSS)- integration may be more suitable. Existing QSS integrators, based on fixed quanta, are also unsuitable for simulating HAs. This is since the quantum selected is not dependent on the HA guard conditions, which are the main cause of discontinuities. Considering this, we propose a new dynamic quanta based formal model called Quantized State Hybrid Automata (QSHA). The developed formal model and the associated simulation framework guarantees that (1) all level crossings are accurately detected and (2) the time of the level crossing is also accurate within floating point error bounds. Interestingly, benchmarking results reveal that the proposed simulation technique takes 720, 1.33 and 4.41 times fewer simulation steps compared to standard Quantized State System (QSS)-1, Runge-Kutta (RK)-45, and Differential Algebraic System Solver (DASSL) integration based techniques respectively.
PADS: Practical Attestation for Highly Dynamic Swarm Topologies
Venue: arXiv cs.CR 2018
Year: 2018

Remote attestation protocols are widely used to detect device configuration (e.g., software and/or data) compromise in Internet of Things (IoT) scenarios. Unfortunately, the performances of such protocols are unsatisfactory when dealing with thousands of smart devices. Recently, researchers are focusing on addressing this limitation. The approach is to run attestation in a collective way, with the goal of reducing computation and communication. Despite these advances, current solutions for attestation are still unsatisfactory because of their complex management and strict assumptions concerning the topology (e.g., being time invariant or maintaining a fixed topology). In this paper, we propose PADS, a secure, efficient, and practical protocol for attesting potentially large networks of smart devices with unstructured or dynamic topologies. PADS builds upon the recent concept of non-interactive attestation, by reducing the collective attestation problem into a minimum consensus one. We compare PADS with a state-of-the art collective attestation protocol and validate it by using realistic simulations that show practicality and efficiency. The results confirm the suitability of PADS for low-end devices, and highly unstructured networks.
Monaural source enhancement maximizing source-to-distortion ratio via automatic differentiation
Venue: arXiv cs.SD 2018
Year: 2018

Recently, deep neural network (DNN) has made a breakthrough in monaural source enhancement. Through a training step by using a large amount of data, DNN estimates a mapping between mixed signals and clean signals. At this time, we use an objective function that numerically expresses the quality of a mapping by DNN. In the conventional methods, L1 norm, L2 norm, and Itakura-Saito divergence are often used as objective functions. Recently, an objective function based on short-time objective intelligibility (STOI) has also been proposed. However, these functions only indicate similarity between the clean signal and the estimated signal by DNN. In other words, they do not show the quality of noise reduction or source enhancement. Motivated by the fact, this paper adopts signal-to-distortion ratio (SDR) as the objective function. Since SDR virtually shows signal-to-noise ratio (SNR), maximizing SDR solves the above problem. The experimental results revealed that the proposed method achieved better performance than the conventional methods.
Design Patterns which Facilitate Message Digest Collision Attacks on Blockchains
Venue: arXiv cs.CR 2018
Year: 2018

Message digest algorithms are one of the underlying building blocks of blockchain platforms such as Ethereum. This paper analyses situations in which the message digest collision resistance property can be exploited by attackers. Two mitigations for possible attacks are described: longer message digest sizes make attacks more difficult; and, including timeliness properties limits the amount of time an attacker has to determine a hash collision.
Oreo: Detection of Clones in the Twilight Zone
Venue: arXiv cs.SE 2018
Year: 2018

Source code clones are categorized into four types of increasing difficulty of detection, ranging from purely textual (Type-1) to purely semantic (Type-4). Most clone detectors reported in the literature work well up to Type-3, which accounts for syntactic differences. In between Type-3 and Type-4, however, there lies a spectrum of clones that, although still exhibiting some syntactic similarities, are extremely hard to detect -- the Twilight Zone. Most clone detectors reported in the literature fail to operate in this zone. We present Oreo, a novel approach to source code clone detection that not only detects Type-1 to Type-3 clones accurately, but is also capable of detecting harder-to-detect clones in the Twilight Zone. Oreo is built using a combination of machine learning, information retrieval, and software metrics. We evaluate the recall of Oreo on BigCloneBench, and perform manual evaluation for precision. Oreo has both high recall and precision. More importantly, it pushes the boundary in detection of clones with moderate to weak syntactic similarity in a scalable manner.
A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints
Venue: arXiv cs.NE 2018
Year: 2018

This paper addresses the development of a covariance matrix self-adaptation evolution strategy (CMSA-ES) for solving optimization problems with linear constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES (lcCMSA-ES). It uses a specially built mutation operator together with repair by projection to satisfy the constraints. The lcCMSA-ES evolves itself on a linear manifold defined by the constraints. The objective function is only evaluated at feasible search points (interior point method). This is a property often required in application domains such as simulation optimization and finite element methods. The algorithm is tested on a variety of different test problems revealing considerable results.
Semantic Variation in Online Communities of Practice
Venue: arXiv cs.CL 2018
Year: 2018

We introduce a framework for quantifying semantic variation of common words in Communities of Practice and in sets of topic-related communities. We show that while some meaning shifts are shared across related communities, others are community-specific, and therefore independent from the discussed topic. We propose such findings as evidence in favour of sociolinguistic theories of socially-driven semantic variation. Results are evaluated using an independent language modelling task. Furthermore, we investigate extralinguistic features and show that factors such as prominence and dissemination of words are related to semantic variation.
Landscape Boolean Functions
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we define a class of Boolean and generalized Boolean functions defined on $\mathbb{F}_2^n$ with values in $\mathbb{Z}_q$ (mostly, we consider $q=2^k$), which we call landscape functions (whose class containing generalized bent, semibent, and plateaued) and find their complete characterization in terms of their components. In particular, we show that the previously published characterizations of generalized bent and plateaued Boolean functions are in fact particular cases of this more general setting. Furthermore, we provide an inductive construction of landscape functions, having any number of nonzero Walsh-Hadamard coefficients. We also completely characterize generalized plateaued functions in terms of the second derivatives and fourth moments.
A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina
Venue: arXiv cs.CV 2018
Year: 2018

Photoreceptors in the retina are coupled by electrical synapses called "gap junctions". It has long been established that gap junctions increase the signal-to-noise ratio of photoreceptors. Inspired by electrically coupled photoreceptors, we introduced a simple filter, the PR-filter, with only one variable. On BSD68 dataset, PR-filter showed outstanding performance in SSIM during blind denoising tasks. It also significantly improved the performance of state-of-the-art convolutional neural network blind denosing on non-Gaussian noise. The performance of keeping more details might be attributed to small receptive field of the photoreceptors.
Improving width-based planning with compact policies
Venue: arXiv cs.AI 2018
Year: 2018

Optimal action selection in decision problems characterized by sparse, delayed rewards is still an open challenge. For these problems, current deep reinforcement learning methods require enormous amounts of data to learn controllers that reach human-level performance. In this work, we propose a method that interleaves planning and learning to address this issue. The planning step hinges on the Iterated-Width (IW) planner, a state of the art planner that makes explicit use of the state representation to perform structured exploration. IW is able to scale up to problems independently of the size of the state space. From the state-actions visited by IW, the learning step estimates a compact policy, which in turn is used to guide the planning step. The type of exploration used by our method is radically different than the standard random exploration used in RL. We evaluate our method in simple problems where we show it to have superior performance than the state-of-the-art reinforcement learning algorithms A2C and Alpha Zero. Finally, we present preliminary results in a subset of the Atari games suite.
Survey and Performance Evaluation of the Upcoming Next Generation WLAN Standard - IEEE 802.11ax
Venue: arXiv cs.NI 2018
Year: 2018

With the ever-increasing demand for wireless traffic and quality of serives (QoS), wireless local area networks (WLANs) have developed into one of the most dominant wireless networks that fully influence human life. As the most widely used WLANs standard, Institute of Electrical and Electronics Engineers (IEEE) 802.11 will release the upcoming next generation WLANs standard amendment: IEEE 802.11ax. This article comprehensively surveys and analyzes the application scenarios, technical requirements, standardization process, key technologies, and performance evaluations of IEEE 802.11ax. Starting from the technical objectives and requirements of IEEE 802.11ax, this article pays special attention to high-dense deployment scenarios. After that, the key technologies of IEEE 802.11ax, including the physical layer (PHY) enhancements, multi-user (MU) medium access control (MU-MAC), spatial reuse (SR), and power efficiency are discussed in detail, covering both standardization technologies as well as the latest academic studies. Furthermore, performance requirements of IEEE 802.11ax are evaluated via a newly proposed systems and link-level integrated simulation platform (SLISP). Simulations results confirm that IEEE 802.11ax significantly improves the user experience in high-density deployment, while successfully achieves the average per user throughput requirement in project authorization request (PAR) by four times compared to the legacy IEEE 802.11. Finally, potential advancement beyond IEEE 802.11ax are discussed to complete this holistic study on the latest IEEE 802.11ax. To the best of our knowledge, this article is the first study to directly investigate and analyze the latest stable version of IEEE 802.11ax, and the first work to thoroughly and deeply evaluate the compliance of the performance requirements of IEEE 802.11ax.
Anonymous Identity-Based Encryption with Identity Recovery
Venue: arXiv cs.CR 2018
Year: 2018

Anonymous Identity-Based Encryption can protect privacy of the receiver. However, there are some situations that we need to recover the identity of the receiver, for example a dispute occurs or the privacy mechanism is abused. In this paper, we propose a new concept, referred to as Anonymous Identity-Based Encryption with Identity Recovery(AIBEIR), which is an anonymous IBE with identity recovery property. There is a party called the Identity Recovery Manager(IRM) who has a secret key to recover the identity from the ciphertext in our scheme. We construct it with an anonymous IBE and a special IBE which we call it testable IBE. In order to ensure the semantic security in the case where the identity recovery manager is an adversary, we define a stronger semantic security model in which the adversary is given the secret key of the identity recovery manager. To our knowledge, we propose the first AIBEIR scheme and prove the security in our defined model.
VIoLET: A Large-scale Virtual Environment for Internet of Things
Venue: arXiv cs.DC 2018
Year: 2018

IoT deployments have been growing manifold, encompassing sensors, networks, edge, fog and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose VIoLET, a virtual environment for defining and launching large-scale IoT deployments within cloud VMs. It offers a declarative model to specify container-based compute resources that match the performance of the native edge, fog and cloud devices using Docker. These can be inter-connected by complex topologies on which private/public networks, and bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation on these devices as well. We validate VIoLET for deployments with > 400 devices and > 1500 device-cores, and show that the virtual IoT environment closely matches the expected compute and network performance at modest costs. This fills an important gap between IoT simulators and real deployments.
The Toybox Dataset of Egocentric Visual Object Transformations
Venue: arXiv cs.CV 2018
Year: 2018

In object recognition research, many commonly used datasets (e.g., ImageNet and similar) contain relatively sparse distributions of object instances and views, e.g., one might see a thousand different pictures of a thousand different giraffes, mostly taken from a few conventionally photographed angles. These distributional properties constrain the types of computational experiments that are able to be conducted with such datasets, and also do not reflect naturalistic patterns of embodied visual experience. As a contribution to the small (but growing) number of multi-view object datasets that have been created to bridge this gap, we introduce a new video dataset called Toybox that contains egocentric (i.e., first-person perspective) videos of common household objects and toys being manually manipulated to undergo structured transformations, such as rotation, translation, and zooming. To illustrate potential uses of Toybox, we also present initial neural network experiments that examine 1) how training on different distributions of object instances and views affects recognition performance, and 2) how viewpoint-dependent object concepts are represented within the hidden layers of a trained network.
SPNets: Differentiable Fluid Dynamics for Deep Neural Networks
Venue: arXiv cs.RO 2018
Year: 2018

In this paper we introduce Smooth Particle Networks (SPNets), a framework for integrating fluid dynamics with deep networks. SPNets adds two new layers to the neural network toolbox: ConvSP and ConvSDF, which enable computing physical interactions with unordered particle sets. We use these lay- ers in combination with standard neural network layers to directly implement fluid dynamics inside a deep network, where the parameters of the network are the fluid parameters themselves (e.g., viscosity, cohesion, etc.). Because SPNets are imple- mented as a neural network, the resulting fluid dynamics are fully differentiable. We then show how this can be successfully used to learn fluid parameters from data, perform liquid control tasks, and learn policies to manipulate liquids.
Arithmetic Circuits with Locally Low Algebraic Rank
Venue: arXiv cs.CC 2018
Year: 2018

In recent years, there has been a flurry of activity towards proving lower bounds for homogeneous depth-4 arithmetic circuits, which has brought us very close to statements that are known to imply $\textsf{VP} \neq \textsf{VNP}$. It is open if these techniques can go beyond homogeneity, and in this paper we make some progress in this direction by considering depth-4 circuits of low algebraic rank, which are a natural extension of homogeneous depth-4 circuits. A depth-4 circuit is a representation of an $N$-variate, degree-$n$ polynomial $P$ as \[ P = \sum_{i = 1}^T Q_{i1}\cdot Q_{i2}\cdot \cdots \cdot Q_{it} \; , \] where the $Q_{ij}$ are given by their monomial expansion. Homogeneity adds the constraint that for every $i \in [T]$, $\sum_{j} \operatorname{deg}(Q_{ij}) = n$. We study an extension, where, for every $i \in [T]$, the algebraic rank of the set $\{Q_{i1}, Q_{i2}, \ldots ,Q_{it}\}$ of polynomials is at most some parameter $k$. Already for $k = n$, these circuits are a generalization of the class of homogeneous depth-4 circuits, where in particular $t \leq n$ (and hence $k \leq n$). We study lower bounds and polynomial identity tests for such circuits and prove the following results. We show an $\exp{(Ω(\sqrt{n}\log N))}$ lower bound for such circuits for an explicit $N$ variate degree $n$ polynomial family when $k \leq n$. We also show quasipolynomial hitting sets when the degree of each $Q_{ij}$ and the $k$ are at most $\operatorname{poly}(\log n)$. A key technical ingredient of the proofs, which may be of independent interest, is a result which states that over any field of characteristic zero, up to a translation, every polynomial in a set of polynomials can be written as a function of the polynomials in a transcendence basis of the set. We combine this with methods based on shifted partial derivatives to obtain our final results.
Straggler-Resilient and Communication-Efficient Distributed Iterative Linear Solver
Venue: arXiv cs.IT 2018
Year: 2018

We propose a novel distributed iterative linear inverse solver method. Our method, PolyLin, has significantly lower communication cost, both in terms of number of rounds as well as number of bits, in comparison with the state of the art at the cost of higher computational complexity and storage. Our algorithm also has a built-in resilience to straggling and faulty computation nodes. We develop a natural variant of our main algorithm that trades off communication cost for computational complexity. Our method is inspired by ideas in error correcting codes.
B-FICA: BlockChain based Framework for Auto-insurance Claim and Adjudication
Venue: arXiv cs.CY 2018
Year: 2018

In this paper, we propose a partitioned BlockChain based Framework for Auto-insurance Claims and Adjudication (B-FICA) for CAVs that tracks both sensor data and entity interactions with two-sided verification. B-FICA uses permissioned BC with two partitions to share information on a need to know basis. It also uses multi-signed transactions for proof of execution of instructions, for reliability and auditability and also uses a dynamic lightweight consensus and validation protocol to prevent evidence alteration. Qualitative evaluation shows that B-FICA is resilient to several security attacks from potential liable entities. Finally, simulations show that compared to the state of the art, B-FICA reduces processing time and its delay overhead is negligible for practical scenarios and at marginal security cost.
Fast Distance Sensitivity Oracle for Multiple Failures
Venue: arXiv cs.DS 2018
Year: 2018

When a network is prone to failures, it is very expensive to compute the shortest paths every time from the scratch. Distance sensitivity oracle provides this privilege to find the new shortest paths faster and with lower cost by once pre-computing an oracle in advance. Although several efficient solutions are proposed in the literature to support the single failure, few efforts are done to devise an efficient method regarding the case of multiple failures. In this paper, we present a novel distance sensitivity oracle based on Markov Tensor Theory \cite{golnari2017markov} to support replacement path queries $(*,t,\mathcal{F})$ in general directed and weighted networks facing the set of failures $\mathcal{F}$. In contrast to the existing work, there is no limitation on maximum failure size supported by our oracle and there is no need to know the size of failure for constructing the oracle. The specifications of our oracle are: space size of $O(n^2)$, pre-process time of $O(n^ω)$, where $ω$ is the exponent of fast matrix multiplication, and query time of $O(m)$ for answering to replacement path query of $(*,t,\mathcal{F})$ which computes the replacement (shortest) paths from all nodes to target $t$ at once. While the computation time for regular shortest path methods, such as Dijkstra's, is $O(m+nlogn)$ for each query after a failure, our algorithm can save a considerable computational time when the size of failure set $|\mathcal{F}|$ is $O(m^{1/ω})$ or less and the network is sparse $O(m)<O(nlogn)$.
Key Enabling Technologies for Secure and Scalable Future Fog-IoT Architecture: A Survey
Venue: arXiv cs.NI 2018
Year: 2018

Fog or Edge computing has recently attracted broad attention from both industry and academia. It is deemed as a paradigm shift from the current centralized cloud computing model and could potentially bring a "Fog-IoT" architecture that would significantly benefit the future ubiquitous Internet of Things (IoT) systems and applications. However, it takes a series of key enabling technologies including emerging technologies to realize such a vision. In this article, we will survey these key enabling technologies with specific focuses on security and scalability, which are two very important and much-needed characteristics for future large-scale deployment. We aim to draw an overall big picture of the future for the research and development in these areas.