OpenRaiser/Intern-Atlas huggingface dataset:
Cryptanalysis of a Chaos-Based Fast Image Encryption Algorithm for Embedded Systems
Venue: arXiv cs.CR 2019
Year: 2019

Fairly recently, a new encryption scheme for embedded systems based on continuous third-order hyperbolic sine chaotic system was proposed by Z. Lin et al. The cryptosystem's main objective is to provide a faster algorithm with lowest computational time in order to be qualified for use in embedded systems especially on a program of UAV (unmanned aerial vehicle). In this paper, we scrutinize the design architecture of this recently proposed scheme against conventional attacks e.g., chosen plaintext attack, differential attack, known plaintext attack. We prove in this paper that, negatively, the studied system is vulnerable. For differential attack, only two chosen plain images are required to recover the full equivalent key. Moreover, only one 3x400 size image is sufficient to break the cryptosystem under chosen plaintext attack considering stability of sort algorithm. Therefore, the proposed scheme is not recommended for security purposes.
Task-Motion Planning for Navigation in Belief Space
Venue: arXiv cs.RO 2019
Year: 2019

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environment. Autonomous robots operating in real world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge intensive domains, on the one hand, a robot has to reason at the highest-level, for example the regions to navigate to; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. We discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in indoor domains, returning a plan that is optimal at the task-level. Furthermore, our framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated with a simulated office environment in Gazebo. In addition, we discuss the limitations and provide suggestions for improvements and future work.
Multi-scale Deep Neural Networks for Solving High Dimensional PDEs
Venue: arXiv cs.LG 2019
Year: 2019

In this paper, we propose the idea of radial scaling in frequency domain and activation functions with compact support to produce a multi-scale DNN (MscaleDNN), which will have the multi-scale capability in approximating high frequency and high dimensional functions and speeding up the solution of high dimensional PDEs. Numerical results on high dimensional function fitting and solutions of high dimensional PDEs, using loss functions with either Ritz energy or least squared PDE residuals, have validated the increased power of multi-scale resolution and high frequency capturing of the proposed MscaleDNN.
Embracing a mechanized formalization gap
Venue: arXiv cs.PL 2019
Year: 2019

If a code base is so big and complicated that complete mechanical verification is intractable, can we still apply and benefit from verification methods? We show that by allowing a deliberate mechanized formalization gap we can shrink and simplify the model until it is manageable, while still retaining a meaningful, declaratively documented connection to the original, unmodified source code. Concretely, we translate core parts of the Haskell compiler GHC into Coq, using hs-to-coq, and verify invariants related to the use of term variables.
Optimal Orthogonal Drawings of Planar 3-Graphs in Linear Time
Venue: arXiv cs.DS 2019
Year: 2019

A planar orthogonal drawing $Γ$ of a planar graph $G$ is a geometric representation of $G$ such that the vertices are drawn as distinct points of the plane, the edges are drawn as chains of horizontal and vertical segments, and no two edges intersect except at their common end-points. A bend of $Γ$ is a point of an edge where a horizontal and a vertical segment meet. $Γ$ is bend-minimum if it has the minimum number of bends over all possible planar orthogonal drawings of $G$. This paper addresses a long standing, widely studied, open question: Given a planar 3-graph $G$ (i.e., a planar graph with vertex degree at most three), what is the best computational upper bound to compute a bend-minimum planar orthogonal drawing of $G$ in the variable embedding setting? In this setting the algorithm can choose among the exponentially many planar embeddings of $G$ the one that leads to an orthogonal drawing with the minimum number of bends. We answer the question by describing an $O(n)$-time algorithm that computes a bend-minimum planar orthogonal drawing of $G$ with at most one bend per edge, where $n$ is the number of vertices of $G$. The existence of an orthogonal drawing algorithm that simultaneously minimizes the total number of bends and the number of bends per edge was previously unknown.
Real-time Memory Efficient Large-pose Face Alignment via Deep Evolutionary Network
Venue: arXiv cs.CV 2019
Year: 2019

There is an urgent need to apply face alignment in a memory-efficient and real-time manner due to the recent explosion of face recognition applications. However, impact factors such as large pose variation and computational inefficiency, still hinder its broad implementation. To this end, we propose a computationally efficient deep evolutionary model integrated with 3D Diffusion Heap Maps (DHM). First, we introduce a sparse 3D DHM to assist the initial modeling process under extreme pose conditions. Afterward, a simple and effective CNN feature is extracted and fed to Recurrent Neural Network (RNN) for evolutionary learning. To accelerate the model, we propose an efficient network structure to accelerate the evolutionary learning process through a factorization strategy. Extensive experiments on three popular alignment databases demonstrate the advantage of the proposed models over the state-of-the-art, especially under large-pose conditions. Notably, the computational speed of our model is 6 times faster than the state-of-the-art on CPU and 14 times on GPU. We also discuss and analyze the limitations of our models and future research work.
A Calculus for Language Transformations
Venue: arXiv cs.PL 2019
Year: 2019

In this paper we propose a calculus for expressing algorithms for programming languages transformations. We present the type system and operational semantics of the calculus, and we prove that it is type sound. We have implemented our calculus, and we demonstrate its applicability with common examples in programming languages. As our calculus manipulates inference systems, our work can, in principle, be applied to logical systems.
On the Storage Cost of Private Information Retrieval
Venue: arXiv cs.IT 2019
Year: 2019

We consider the fundamental tradeoff between the storage cost and the download cost in private information retrieval systems, without any explicit structural restrictions on the storage codes, such as maximum distance separable codes or uncoded storage. Two novel outer bounds are provided, which have the following implications. When the messages are stored without any redundancy across the databases, the optimal PIR strategy is to download all the messages; on the other hand, for PIR capacity-achieving codes, each database can reduce the storage cost, from storing all the messages, by no more than one message on average. We then focus on the two-message two-database case, and show that a stronger outer bound can be derived through a novel pseudo-message technique. This stronger outer bound suggests that a precise characterization of the storage-download tradeoff may require non-Shannon type inequalities, or at least more sophisticated bounding techniques.
SlotSwapper: A Schedule Randomization protocol for Real-Time WirelessHART Networks
Venue: arXiv cs.NI 2019
Year: 2019

Industrial process control systems are time-critical systems where reliable communications between sensors and actuators need to be guaranteed within strict deadlines to maintain safe operation of all the components of the system. WirelessHART is the most widely adopted standard which serve as the medium of communication in industrial setups due to its support for Time Division Multiple Access (TDMA)based communication, multiple channels, channel hopping, centralized architecture, redundant routes and avoidance of spatial re-use of channels. However, the communication schedule in WirelessHART network is decided by a centralized network manager at the time of network initialization and the same communication schedule repeats every hyper-period. Due to predictability in the time slots of the communication schedule, these systems are vulnerable to timing attacks which eventually can disrupt the safety of the system. In this work, we present a moving target defense mechanism, the SlotSwapper, which uses schedule randomization techniques to randomize the time slots over a hyper-period schedule, while still preserving all the feasibility constraints of a real-time WirelessHART network and makes the schedule uncertain every hyper-period. We tested the feasibility of the generated schedules on random topologies with 100 simulated motes in Cooja simulator. We use schedule entropy to measure the confidentiality of our algorithm in terms of randomness in the time slots of the generated schedules.
DDM: A Demand-based Dynamic Mitigation for SMT Transient Channels
Venue: arXiv cs.CR 2019
Year: 2019

Different from the traditional software vulnerability, the microarchitecture side channel has three characteristics: extensive influence, potent threat, and tough defense. The main reason for the micro-architecture side channel is resource sharing. There are many reasons for resource sharing, one of which is SMT (Simultaneous Multi-Threading) technology. In this paper, we define the SMT Transient Channel, which uses the transient state of shared resources between threads to steal information. To mitigate it, we designed a security demand-based dynamic mitigation (DDM) to Mitigate the SMT transient channels. The DDM writes the processes' security requirements to the CPU register sets, and the operating system calls the HLT instruction to dynamically turn on and off the hyper-threading according to the register values to avoid the side channels caused by execution resource sharing. During the implementation of the scheme, we modified the Linux kernel and used the MSR register groups of Intel processor. The evaluation results show that DDM can effectively protect against the transient side-channel attacks such as PortsMash that rely on SMT, and the performance loss of DDM is less than 8%.
A class of functions with low-valued Walsh spectrum
Venue: arXiv cs.IT 2019
Year: 2019

Let $l\equiv 3\pmod 4$, $l\ne 3$, be a prime, $N=l^2$, $f=\frac{l(l-1)}2$ the multiplicative order of a prime $p$ modulo $N$, and $q=p^f$. In this paper, we investigate the Walsh spectrum of the monomial functions $f(x)={\rm Tr}_{q/p}(x^{\frac{q-1}{l^2}})$ in index two case. In special, we explicitly present the value distribution of the Walsh transform of $f(x)$ if $1+l=4p^h$, where $h$ is a class number of $\Bbb Q(\sqrt{-l})$.
A Deep Learning Approach to Universal Binary Visible Light Communication Transceiver
Venue: arXiv cs.IT 2019
Year: 2019

This paper studies a deep learning (DL) framework for the design of binary modulated visible light communication (VLC) transceiver with universal dimming support. The dimming control for the optical binary signal boils down to a combinatorial codebook design so that the average Hamming weight of binary codewords matches with arbitrary dimming target. An unsupervised DL technique is employed for obtaining a neural network to replace the encoder-decoder pair that recovers the message from the optically transmitted signal. In such a task, a novel stochastic binarization method is developed to generate the set of binary codewords from continuous-valued neural network outputs. For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods. We develop a new training algorithm that addresses the dimming constraints through a dual formulation of the optimization. Based on the developed algorithm, the resulting VLC transceiver can be optimized via the end-to-end training procedure. Numerical results verify that the proposed codebook outperforms theoretically best constant weight codebooks under various VLC setups.
Detection of Adversarial Attacks and Characterization of Adversarial Subspace
Venue: arXiv cs.LG 2019
Year: 2019

Adversarial attacks have always been a serious threat for any data-driven model. In this paper, we explore subspaces of adversarial examples in unitary vector domain, and we propose a novel detector for defending our models trained for environmental sound classification. We measure chordal distance between legitimate and malicious representation of sounds in unitary space of generalized Schur decomposition and show that their manifolds lie far from each other. Our front-end detector is a regularized logistic regression which discriminates eigenvalues of legitimate and adversarial spectrograms. The experimental results on three benchmarking datasets of environmental sounds represented by spectrograms reveal high detection rate of the proposed detector for eight types of adversarial attacks and outperforms other detection approaches.
Understanding Isomorphism Bias in Graph Data Sets
Venue: arXiv cs.LG 2019
Year: 2019

In recent years there has been a rapid increase in classification methods on graph structured data. Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance. However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e. artificially increasing the accuracy of the models by memorizing target information from the training set. This prevents fair competition of the algorithms and raises a question of the validity of the obtained results. We analyze 54 data sets, previously extensively used for graph-related tasks, on the existence of isomorphism bias, give a set of recommendations to machine learning practitioners to properly set up their models, and open source new data sets for the future experiments.
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Venue: arXiv cs.LG 2019
Year: 2019

Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, and learning of node embeddings. Despite their impressive performance, the techniques have a limited capability to incorporate the uncertainty in the underlined graph structure. In order to address this issue, a Bayesian GCNN (BGCN) framework was recently proposed. In this framework, the observed graph is considered to be a random realization from a parametric random graph model and the joint Bayesian inference of the graph and GCNN weights is performed. In this paper, we propose a non-parametric generative model for graphs and incorporate it within the BGCN framework. In addition to the observed graph, our approach effectively uses the node features and training labels in the posterior inference of graphs and attains superior or comparable performance in benchmark node classification tasks.
Computing a Geodesic Two-Center of Points in a Simple Polygon
Venue: arXiv cs.CG 2019
Year: 2019

Given a simple polygon $P$ and a set $Q$ of points contained in $P$, we consider the geodesic $k$-center problem where we want to find $k$ points, called \emph{centers}, in $P$ to minimize the maximum geodesic distance of any point of $Q$ to its closest center. In this paper, we focus on the case for $k=2$ and present the first exact algorithm that efficiently computes an optimal $2$-center of $Q$ with respect to the geodesic distance in $P$.
Secret Key Generation via Pulse-Coupled Synchronization
Venue: arXiv cs.IT 2019
Year: 2019

A novel framework for sharing common randomness and generating secret keys in wireless networks is considered. In particular, a network of users equipped with pulse oscillators (POs) and coupling mechanisms in between is considered. Such mechanisms exist in synchronized biological and natural systems, and have been exploited to provide synchronization in distributed networks. We show that naturally-existing initial random phase differences between the POs in the network can be utilized to provide almost identical common randomness to the users. This randomness is extracted from the synchronization time in the network. Bounds on the entropy of such randomness are derived for a two-user system and a conjecture is made for a general $n$-user system. Then, a three-terminal scenario is considered including two legitimate users and a passive eavesdropper, referred to as Eve. Since in a practical setting Eve receives pulses with propagation delays, she can not identify the exact synchronization time. A simplified model is then considered for Eve's receiver and then a bound on the rate of secret key generation is derived. Also, it is shown, under certain conditions, that the proposed protocol is resilient to an active jammer equipped with a similar pulse generation mechanism.
Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
Venue: arXiv cs.LG 2019
Year: 2019

Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.
Declarative Semantics of the Hybrid Constraint Language HydLa
Venue: arXiv cs.PL 2019
Year: 2019

Hybrid systems are dynamical systems with continuous evolution of states and discrete evolution of states and governing equations. We have worked on the design and implementation of HydLa, a constraint-based modeling language for hybrid systems, with a view to the proper handling of uncertainties and the integration of simulation and verification. HydLa's constraint hierarchies facilitate the description of constraints with adequate strength, but its semantical foundations are not obvious due to the interaction of various language constructs. This paper gives the declarative semantics of HydLa and discusses its properties and consequences by means of examples.
Robots as Actors in a Film: No War, A Robot Story
Venue: arXiv cs.RO 2019
Year: 2019

Will the Third World War be fought by robots? This short film is a light-hearted comedy that aims to trigger an interesting discussion and reflexion on the terrifying killer-robot stories that increasingly fill us with dread when we read the news headlines. The fictional scenario takes inspiration from current scientific research and describes a future where robots are asked by humans to join the war. Robots are divided, sparking protests in robot society... will robots join the conflict or will they refuse to be employed in human warfare? Food for thought for engineers, roboticists and anyone imagining what the upcoming robot revolution could look like. We let robots pop on camera to tell a story, taking on the role of actors playing in the film, instructed through code on how to "act" for each scene.
Multi-Resolution Overlapping Stripes Network for Person Re-Identification
Venue: arXiv cs.CV 2019
Year: 2019

This paper addresses the person re-identification (PReID) problem by combining global and local information at multiple feature resolutions with different loss functions. Many previous studies address this problem using either part-based features or global features. In case of part-based representation, the spatial correlation between these parts is not considered, while global-based representation are not sensitive to spatial variations. This paper presents a part-based model with a multi-resolution network that uses different level of features. The output of the last two conv blocks is then partitioned horizontally and processed in pairs with overlapping stripes to cover the important information that might lie between parts. We use different loss functions to combine local and global information for classification. Experimental results on a benchmark dataset demonstrate that the presented method outperforms the state-of-the-art methods.
Leveraging Auxiliary Text for Deep Recognition of Unseen Visual Relationships
Venue: arXiv cs.CV 2019
Year: 2019

One of the most difficult tasks in scene understanding is recognizing interactions between objects in an image. This task is often called visual relationship detection (VRD). We consider the question of whether, given auxiliary textual data in addition to the standard visual data used for training VRD models, VRD performance can be improved. We present a new deep model that can leverage additional textual data. Our model relies on a shared text--image representation of subject-verb-object relationships appearing in the text, and object interactions in images. Our method is the first to enable recognition of visual relationships missing in the visual training data and appearing only in the auxiliary text. We test our approach on two different text sources: text originating in images and text originating in books. We test and validate our approach using two large-scale recognition tasks: VRD and Scene Graph Generation. We show a surprising result: Our approach works better with text originating in books, and outperforms the text originating in images on the task of unseen relationship recognition. It is comparable to the model which utilizes text originating in images on the task of seen relationship recognition.
Zero-Error Capacity of Multiple Access Channels via Nonstochastic Information
Venue: arXiv cs.IT 2019
Year: 2019

The problem of characterising the zero-error capacity region for multiple access channels even in the noiseless case has remained an open problem for over three decades. Motivated by this challenging question, a recently developed theory of nonstochastic information is applied to characterise the zero-error capacity region for the case of two correlated transmitters. Unlike previous contributions, this analysis does not assume that the blocklength is asymptotically large. Finally, a new notion of nonstochastic information is proposed for a noncooperative problem involving three agents. These results are preliminary steps towards understanding information flows in worst-case distributed estimation and control problems.