OpenRaiser/Intern-Atlas huggingface dataset:
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Venue: arXiv cs.CR 2018
Year: 2018

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.
Towards ultra-high resolution 3D reconstruction of a whole rat brain from 3D-PLI data
Venue: arXiv cs.CV 2018
Year: 2018

3D reconstruction of the fiber connectivity of the rat brain at microscopic scale enables gaining detailed insight about the complex structural organization of the brain. We introduce a new method for registration and 3D reconstruction of high- and ultra-high resolution (64 $μ$m and 1.3 $μ$m pixel size) histological images of a Wistar rat brain acquired by 3D polarized light imaging (3D-PLI). Our method exploits multi-scale and multi-modal 3D-PLI data up to cellular resolution. We propose a new feature transform-based similarity measure and a weighted regularization scheme for accurate and robust non-rigid registration. To transform the 1.3 $μ$m ultra-high resolution data to the reference blockface images a feature-based registration method followed by a non-rigid registration is proposed. Our approach has been successfully applied to 278 histological sections of a rat brain and the performance has been quantitatively evaluated using manually placed landmarks by an expert.
Convolutional Gated Recurrent Units for Medical Relation Classification
Venue: arXiv cs.CL 2018
Year: 2018

Convolutional neural network (CNN) and recurrent neural network (RNN) models have become the mainstream methods for relation classification. We propose a unified architecture, which exploits the advantages of CNN and RNN simultaneously, to identify medical relations in clinical records, with only word embedding features. Our model learns phrase-level features through a CNN layer, and these feature representations are directly fed into a bidirectional gated recurrent unit (GRU) layer to capture long-term feature dependencies. We evaluate our model on two clinical datasets, and experiments demonstrate that our model performs significantly better than previous single-model methods on both datasets.
An Atemporal Model of Physical Complexity
Venue: arXiv cs.LO 2018
Year: 2018

We present the finite first-order theory (FFOT) machine, which provides an atemporal description of computation. We then develop a concept of complexity for the FFOT machine, and prove that the class of problems decidable by a FFOT machine with polynomial resources is NP intersect co-NP.
Causal Modeling with Probabilistic Simulation Models
Venue: arXiv cs.AI 2018
Year: 2018

Recent authors have proposed analyzing conditional reasoning through a notion of intervention on a simulation program, and have found a sound and complete axiomatization of the logic of conditionals in this setting. Here we extend this setting to the case of probabilistic simulation models. We give a natural definition of probability on formulas of the conditional language, allowing for the expression of counterfactuals, and prove foundational results about this definition. We also find an axiomatization for reasoning about linear inequalities involving probabilities in this setting. We prove soundness, completeness, and NP-completeness of the satisfiability problem for this logic.
Arbitration Among Vertical Services
Venue: arXiv cs.NI 2018
Year: 2018

A 5G network provides several service types, tailored to specific needs such as high bandwidth or low latency. On top of these communication services, verticals are enabled to deploy their own vertical services. These vertical service instances compete for the resources of the underlying common infrastructure. We present a resource arbitration approach that allows to handle such resource conflicts on a high level and to provide guidance to lowerlevel orchestration components.
Uncertainty Quantification in CNN-Based Surface Prediction Using Shape Priors
Venue: arXiv cs.CV 2018
Year: 2018

Surface reconstruction is a vital tool in a wide range of areas of medical image analysis and clinical research. Despite the fact that many methods have proposed solutions to the reconstruction problem, most, due to their deterministic nature, do not directly address the issue of quantifying uncertainty associated with their predictions. We remedy this by proposing a novel probabilistic deep learning approach capable of simultaneous surface reconstruction and associated uncertainty prediction. The method incorporates prior shape information in the form of a principal component analysis (PCA) model. Experiments using the UK Biobank data show that our probabilistic approach outperforms an analogous deterministic PCA-based method in the task of 2D organ delineation and quantifies uncertainty by formulating distributions over predicted surface vertex positions.
Stochastic Policy Gradient Ascent in Reproducing Kernel Hilbert Spaces
Venue: arXiv cs.SY 2018
Year: 2018

Reinforcement learning consists of finding policies that maximize an expected cumulative long-term reward in a Markov decision process with unknown transition probabilities and instantaneous rewards. In this paper, we consider the problem of finding such optimal policies while assuming they are continuous functions belonging to a reproducing kernel Hilbert space (RKHS). To learn the optimal policy we introduce a stochastic policy gradient ascent algorithm with three unique novel features: (i) The stochastic estimates of policy gradients are unbiased. (ii) The variance of stochastic gradients is reduced by drawing on ideas from numerical differentiation. (iii) Policy complexity is controlled using sparse RKHS representations. Novel feature (i) is instrumental in proving convergence to a stationary point of the expected cumulative reward. Novel feature (ii) facilitates reasonable convergence times. Novel feature (iii) is a necessity in practical implementations which we show can be done in a way that does not eliminate convergence guarantees. Numerical examples in standard problems illustrate successful learning of policies with low complexity representations which are close to stationary points of the expected cumulative reward.
Kernel Density Estimation-Based Markov Models with Hidden State
Venue: arXiv cs.LG 2018
Year: 2018

We consider Markov models of stochastic processes where the next-step conditional distribution is defined by a kernel density estimator (KDE), similar to Markov forecast densities and certain time-series bootstrap schemes. The KDE Markov models (KDE-MMs) we discuss are nonlinear, nonparametric, fully probabilistic representations of stationary processes, based on techniques with strong asymptotic consistency properties. The models generate new data by concatenating points from the training data sequences in a context-sensitive manner, together with some additive driving noise. We present novel EM-type maximum-likelihood algorithms for data-driven bandwidth selection in KDE-MMs. Additionally, we augment the KDE-MMs with a hidden state, yielding a new model class, KDE-HMMs. The added state variable captures non-Markovian long memory and signal structure (e.g., slow oscillations), complementing the short-range dependences described by the Markov process. The resulting joint Markov and hidden-Markov structure is appealing for modelling complex real-world processes such as speech signals. We present guaranteed-ascent EM-update equations for model parameters in the case of Gaussian kernels, as well as relaxed update formulas that greatly accelerate training in practice. Experiments demonstrate increased held-out set probability for KDE-HMMs on several challenging natural and synthetic data series, compared to traditional techniques such as autoregressive models, HMMs, and their combinations.
Action Detection from a Robot-Car Perspective
Venue: arXiv cs.CV 2018
Year: 2018

We present the new Road Event and Activity Detection (READ) dataset, designed and created from an autonomous vehicle perspective to take action detection challenges to autonomous driving. READ will give scholars in computer vision, smart cars and machine learning at large the opportunity to conduct research into exciting new problems such as understanding complex (road) activities, discerning the behaviour of sentient agents, and predicting both the label and the location of future actions and events, with the final goal of supporting autonomous decision making.
Parsec: A State Channel for the Internet of Value
Venue: arXiv cs.CR 2018
Year: 2018

We propose Parsec, a web-scale State channel for the Internet of Value to exterminate the consensus bottleneck in Blockchain by leveraging a network of state channels which enable to robustly transfer value off-chain. It acts as an infrastructure layer developed on top of Ethereum Blockchain, as a network protocol which allows coherent routing and interlocking channel transfers for trade-off between parties. A web-scale solution for state channels is implemented to enable a layer of value transfer to the internet. Existing network protocol on State Channels include Raiden for Ethereum and Lightning Network for Bitcoin. However, we intend to leverage existing web-scale technologies used by large Internet companies such as Uber, LinkedIn or Netflix. We use Apache Kafka to scale the global payment operation to trillions of operations per day enabling near-instant, low-fee, scalable, and privacy-sustainable payments. Our architecture follows Event Sourcing pattern which solves current issues of payment solutions such as scaling, transfer, interoperability, low-fees, micropayments and to name a few. To the best of knowledge, our proposed model achieve better performance than state-of-the-art lightning network on the Ethereum based (fork) cryptocoins.
Security Solutions for Local Wireless Networks in Control Applications based on Physical Layer Security
Venue: arXiv cs.NI 2018
Year: 2018

The Design of new wireless communication systems for industrial applications, e.g. control applications, is currently a hot research topic, as they deal as a key enabler for more flexible solutions at a lower cost compared to systems based on wired communication. However, one of their main drawbacks is, that they provide a huge potential for miscellaneous cyber attacks due to the open nature of the wireless channel in combination with the huge economic potential they are able to provide. Therefore, security measures need to be taken into account for the design of such systems. Within this work, an approach for the security architecture of local wireless systems with respect to the needs of control applications is presented and discussed. Further, new security solutions based on Physical Layer Security are introduced in order to overcome the drawbacks of state of the art security technologies within that scope.
Reach-Avoid Problems via Sum-of-Squares Optimization and Dynamic Programming
Venue: arXiv cs.SY 2018
Year: 2018

Reach-avoid problems involve driving a system to a set of desirable configurations while keeping it away from undesirable ones. Providing mathematical guarantees for such scenarios is challenging but have numerous potential practical applications. Due to the challenges, analysis of reach-avoid problems involves making trade-offs between generality of system dynamics, generality of problem setups, optimality of solutions, and computational complexity. In this paper, we combine sum-of-squares optimization and dynamic programming to address the reach-avoid problem, and provide a conservative solution that maintains reaching and avoidance guarantees. Our method is applicable to polynomial system dynamics and to general problem setups, and is more computationally scalable than previous related methods. Through a numerical example involving two single integrators, we validate our proposed theory and compare our method to Hamilton-Jacobi reachability. Having validated our theory, we demonstrate the computational scalability of our method by computing the reach-avoid set of a system involving two kinematic cars.
Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection
Venue: arXiv cs.CV 2018
Year: 2018

Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by $\sim$5\% compared to our best performing fully-supervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64.
Peripheral Authentication for Parked Vehicles over Wireless Radio Communication
Venue: arXiv cs.CR 2018
Year: 2018

Peripheral authentication is an important aspect in the vehicle networks to provide services to only authenticated peripherals and a security to internal vehicle modules such as anti-lock braking system, power-train control module, engine control unit, transmission control unit, and tire pressure monitoring. In this paper a three-way handshake scheme is proposed for a vehicle to a keyfob authentication. A keyfob is a key with a secure hardware that communicates and authenticates the vehicle over the wireless channel. Conventionally, a vehicle to keyfob authentication is realized through a challenge-response verification protocol. An authentic coupling between the vehicle identity and the keyfob avoids any illegal access to the vehicle. However, these authentication messages can be relayed by an active adversary, thereby, can amplify the actual distance between an authentic vehicle and a keyfob. Eventually, an adversary can possibly gain access to the vehicle by relaying wireless signals and without any effort to generate or decode the secret credentials. Hence, the vehicle to keyfob authentication scheme must contain an additional attribute verification such as physical movement of a keyfob holder. Our solution is a two-party and three-way handshake scheme with proactive and reactive commitment verification. The proposed solution also uses a time interval verification such that both vehicle and keyfob would yield a similar locomotion pattern of a dynamic keyfob within a similar observational time interval. Hence, the solution is different from the distance bounding protocols that require multiple iterations for the round-trip delay measurement. The proposed scheme is shown to be adaptable with the existing commitment scheme such as Schnorr identification scheme and Pedersen commitment scheme.
Rank and Rate: Multi-task Learning for Recommender Systems
Venue: arXiv cs.IR 2018
Year: 2018

The two main tasks in the Recommender Systems domain are the ranking and rating prediction tasks. The rating prediction task aims at predicting to what extent a user would like any given item, which would enable to recommend the items with the highest predicted scores. The ranking task on the other hand directly aims at recommending the most valuable items for the user. Several previous approaches proposed learning user and item representations to optimize both tasks simultaneously in a multi-task framework. In this work we propose a novel multi-task framework that exploits the fact that a user does a two-phase decision process - first decides to interact with an item (ranking task) and only afterward to rate it (rating prediction task). We evaluated our framework on two benchmark datasets, on two different configurations and showed its superiority over state-of-the-art methods.
Computing Integer Sequences: Filtering vs Generation (Functional Pearl)
Venue: arXiv cs.PL 2018
Year: 2018

As a report of a teaching experience, we analyse Haskell programs computing two integer sequences: the Hamming sequence and the Ulam sequence. For both of them, we investigate two strategies of computation: the first is based on filtering out those natural numbers that do not belong to the sequence, whereas the second is based on the direct generation of num- bers that belong to the sequence. Advocating cross-fertilisation among ideas emerging when programming in different programming paradigms, in the background, we sketch out some considerations about corresponding C programs solving the same two problems.
Attention is All We Need: Nailing Down Object-centric Attention for Egocentric Activity Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we propose an end-to-end trainable deep neural network model for egocentric activity recognition. Our model is built on the observation that egocentric activities are highly characterized by the objects and their locations in the video. Based on this, we develop a spatial attention mechanism that enables the network to attend to regions containing objects that are correlated with the activity under consideration. We learn highly specialized attention maps for each frame using class-specific activations from a CNN pre-trained for generic image recognition, and use them for spatio-temporal encoding of the video with a convolutional LSTM. Our model is trained in a weakly supervised setting using raw video-level activity-class labels. Nonetheless, on standard egocentric activity benchmarks our model surpasses by up to +6% points recognition accuracy the currently best performing method that leverages hand segmentation and object location strong supervision for training. We visually analyze attention maps generated by the network, revealing that the network successfully identifies the relevant objects present in the video frames which may explain the strong recognition performance. We also discuss an extensive ablation analysis regarding the design choices.
Resource Allocation in Full-Duplex Mobile-Edge Computing Systems with NOMA and Energy Harvesting
Venue: arXiv cs.IT 2018
Year: 2018

This paper considers a full-duplex (FD) mobile-edge computing (MEC) system with non-orthogonal multiple access (NOMA) and energy harvesting (EH), where one group of users simultaneously offload task data to the base station (BS) via NOMA and the BS simultaneously receive data and broadcast energy to other group of users with FD. We aim at minimizing the total energy consumption of the system via power control, time scheduling and computation capacity allocation. To solve this nonconvex problem, we first transform it into an equivalent problem with less variables. The equivalent problem is shown to be convex in each vector with the other two vectors fixed, which allows us to design an iterative algorithm with low complexity. Simulation results show that the proposed algorithm achieves better performance than the conventional methods.
Data Center Interconnects at 400G and Beyond
Venue: arXiv cs.NI 2018
Year: 2018

Current trends in Data Center Interconnectivity are considered in the light of increasing traffic and under the constraint of limited cost and power consumption.
Web-STAR: A Visual Web-Based IDE for a Story Comprehension System
Venue: arXiv cs.CY 2018
Year: 2018

We present Web-STAR, an online platform for story understanding built on top of the STAR reasoning engine for STory comprehension through ARgumentation. The platform includes a web-based IDE, integration with the STAR system, and a web service infrastructure to support integration with other systems that rely on story understanding functionality to complete their tasks. The platform also delivers a number of "social" features, including a community repository for public story sharing with a built-in commenting system, and tools for collaborative story editing that can be used for team development projects and for educational purposes.
How to sample connected $K$-partitions of a graph
Venue: arXiv cs.DM 2018
Year: 2018

A connected undirected graph $G=(V,E)$ is given. This paper presents an algorithm that samples (non-uniformly) a $K$ partition $U_1,\ldots U_K$ of the graph nodes $V$, such that the subgraph induced by each $U_k$, with $k=1:K$, is connected. Moreover, the probability induced by the algorithm over the set ${\mathcal C}_K$ of all such partitions is obtained in closed form.