OpenRaiser/Intern-Atlas huggingface dataset:
An Illuminating Algorithm for the Light Bulb Problem
Venue: arXiv cs.DS 2018
Year: 2018

The Light Bulb Problem is one of the most basic problems in data analysis. One is given as input $n$ vectors in $\{-1,1\}^d$, which are all independently and uniformly random, except for a planted pair of vectors with inner product at least $ρ\cdot d$ for some constant $ρ> 0$. The task is to find the planted pair. The most straightforward algorithm leads to a runtime of $Ω(n^2)$. Algorithms based on techniques like Locality-Sensitive Hashing achieve runtimes of $n^{2 - O(ρ)}$; as $ρ$ gets small, these approach quadratic. Building on prior work, we give a new algorithm for this problem which runs in time $O(n^{1.582} + nd),$ regardless of how small $ρ$ is. This matches the best known runtime due to Karppa et al. Our algorithm combines techniques from previous work on the Light Bulb Problem with the so-called `polynomial method in algorithm design,' and has a simpler analysis than previous work. Our algorithm is also easily derandomized, leading to a deterministic algorithm for the Light Bulb Problem with the same runtime of $O(n^{1.582} + nd),$ improving previous results.
Achieving Covert Wireless Communications Using a Full-Duplex Receiver
Venue: arXiv cs.IT 2018
Year: 2018

Covert communications hide the transmission of a message from a watchful adversary while ensuring a certain decoding performance at the receiver. In this work, a wireless communication system under fading channels is considered where covertness is achieved by using a full-duplex (FD) receiver. More precisely, the receiver of covert information generates artificial noise with a varying power causing uncertainty at the adversary, Willie, regarding the statistics of the received signals. Given that Willie's optimal detector is a threshold test on the received power, we derive a closed-form expression for the optimal detection performance of Willie averaged over the fading channel realizations. Furthermore, we provide guidelines for the optimal choice of artificial noise power range, and the optimal transmission probability of covert information to maximize the detection errors at Willie. Our analysis shows that the transmission of artificial noise, although causes self-interference, provides the opportunity of achieving covertness but its transmit power levels need to be managed carefully. We also demonstrate that the prior transmission probability of 0.5 is not always the best choice for achieving the maximum possible covertness, when the covert transmission probability and artificial noise power can be jointly optimized.
Can Euroscepticism Contribute to a European Public Sphere? The Europeanization of Media Discourses about Euroscepticism across Six Countries
Venue: arXiv cs.CL 2018
Year: 2018

This study compares the media discourses about Euroscepticism in 2014 across six countries (United Kingdom, Ireland, France, Spain, Sweden, and Denmark). We assess the extent to which the mass media's reporting of Euroscepticism indicates the Europeanization of public spheres. Using a mixed-methods approach combining LDA topic modeling and qualitative coding, we find that approximately 70 per cent of print articles mentioning "Euroscepticism" or "Eurosceptic" are framed in a non-domestic (i.e. European) context. In five of the six cases studied, articles exhibiting a European context are strikingly similar in content, with the British case as the exception. However, coverage of British Euroscepticism drives Europeanization in other Member States. Bivariate logistic regressions further reveal three macro-level structural variables that significantly correlate with a Europeanized media discourse: newspaper type (tabloid or broadsheet), presence of a strong Eurosceptic party, and relationship to the EU budget (net contributor or receiver of EU funds).
A survey of automatic de-identification of longitudinal clinical narratives
Venue: arXiv cs.AI 2018
Year: 2018

Use of medical data, also known as electronic health records, in research helps develop and advance medical science. However, protecting patient confidentiality and identity while using medical data for analysis is crucial. Medical data can be in the form of tabular structures (i.e. tables), free-form narratives, and images. This study focuses on medical data in the free form longitudinal text. De-identification of electronic health records provides the opportunity to use such data for research without it affecting patient privacy, and avoids the need for individual patient consent. In recent years there is increasing interest in developing an accurate, robust and adaptable automatic de-identification system for electronic health records. This is mainly due to the dilemma between the availability of an abundance of health data, and the inability to use such data in research due to legal and ethical restrictions. De-identification tracks in competitions such as the 2014 i2b2 UTHealth and the 2016 CEGS N-GRID shared tasks have provided a great platform to advance this area. The primary reasons for this include the open source nature of the dataset and the fact that raw psychiatric data were used for 2016 competitions. This study focuses on noticeable trend changes in the techniques used in the development of automatic de-identification for longitudinal clinical narratives. More specifically, the shift from using conditional random fields (CRF) based systems only or rules (regular expressions, dictionary or combinations) based systems only, to hybrid models (combining CRF and rules), and more recently to deep learning based systems. We review the literature and results that arose from the 2014 and the 2016 competitions and discuss the outcomes of these systems. We also provide a list of research questions that emerged from this survey.
SpiNNTools: The Execution Engine for the SpiNNaker Platform
Venue: arXiv cs.DC 2018
Year: 2018

Distributed systems are becoming more common place, as computers typically contain multiple computation processors. The SpiNNaker architecture is such a distributed architecture, containing millions of cores connected with a unique communication network, making it one of the largest neuromorphic computing platforms in the world. Utilising these processors efficiently usually requires expert knowledge of the architecture to generate executable code. This work introduces a set of tools (SpiNNTools) that can map computational work described as a graph in to executable code that runs on this novel machine. The SpiNNaker architecture is highly scalable which in turn produces unique challenges in loading data, executing the mapped problem and the retrieval of data. In this paper we describe these challenges in detail and the solutions implemented.
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
Venue: arXiv cs.ET 2018
Year: 2018

The Internet of Things (IoT) is a ubiquitous system connecting many different devices - the things - which can be accessed from the distance. The cyber-physical systems (CPS) monitor and control the things from the distance. As a result, the concepts of dependability and security get deeply intertwined. The increasing level of dynamicity, heterogeneity, and complexity adds to the system's vulnerability, and challenges its ability to react to faults. This paper summarizes state-of-the-art of existing work on anomaly detection, fault-tolerance and self-healing, and adds a number of other methods applicable to achieve resilience in an IoT. We particularly focus on non-intrusive methods ensuring data integrity in the network. Furthermore, this paper presents the main challenges in building a resilient IoT for CPS which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles). It further summarizes our solutions, work-in-progress and future work to this topic to enable "Trustworthy IoT for CPS". Finally, this framework is illustrated on a selected use case: A smart sensor infrastructure in the transport domain.
The LORACs prior for VAEs: Letting the Trees Speak for the Data
Venue: arXiv cs.LG 2018
Year: 2018

In variational autoencoders, the prior on the latent codes $z$ is often treated as an afterthought, but the prior shapes the kind of latent representation that the model learns. If the goal is to learn a representation that is interpretable and useful, then the prior should reflect the ways in which the high-level factors that describe the data vary. The "default" prior is an isotropic normal, but if the natural factors of variation in the dataset exhibit discrete structure or are not independent, then the isotropic-normal prior will actually encourage learning representations that mask this structure. To alleviate this problem, we propose using a flexible Bayesian nonparametric hierarchical clustering prior based on the time-marginalized coalescent (TMC). To scale learning to large datasets, we develop a new inducing-point approximation and inference algorithm. We then apply the method without supervision to several datasets and examine the interpretability and practical performance of the inferred hierarchies and learned latent space.
Sound event detection using weakly-labeled semi-supervised data with GCRNNS, VAT and Self-Adaptive Label Refinement
Venue: arXiv cs.SD 2018
Year: 2018

In this paper, we present a gated convolutional recurrent neural network based approach to solve task 4, large-scale weakly labelled semi-supervised sound event detection in domestic environments, of the DCASE 2018 challenge. Gated linear units and a temporal attention layer are used to predict the onset and offset of sound events in 10s long audio clips. Whereby for training only weakly-labelled data is used. Virtual adversarial training is used for regularization, utilizing both labelled and unlabeled data. Furthermore, we introduce self-adaptive label refinement, a method which allows unsupervised adaption of our trained system to refine the accuracy of frame-level class predictions. The proposed system reaches an overall macro averaged event-based F-score of 34.6%, resulting in a relative improvement of 20.5% over the baseline system.
Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC)
Venue: arXiv cs.IT 2018
Year: 2018

The future connectivity landscape and, notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. The paper also touches upon the important question of the proper statistical methodology for designing and assessing extremely high reliability levels.
Wi-Fi Direct Based Mobile Ad hoc Network
Venue: arXiv cs.DC 2018
Year: 2018

A mobile ad hoc network is a wireless network of mobile devices that communicate with one another without any fixed network infrastructure. Existing mobile ad hoc networks are either based on IEEE 802.11 a or b standard. Both standards provide limited bandwidth and therefore are not suitable for data-intensive applications such as automated video surveillance. Wi-Fi Direct is a new technology that enables mobile devices to directly communicate with each other without a wireless access point. Compare to existing technologies it provides data rates of up to 250 Mbps which is sufficient for several mobile data-intensive applications. Wi-Fi Direct technology, however, has two main limitations. It does not support communication between two client devices in a group and it also does not provide support for multi hop communication. This paper describes a Wi-Fi Direct based multi hop mobile ad hoc network. More specifically a routing layer has been developed to support communication between Wi-Fi Direct devices in a group and multi-hop communication between devices across a group. The proposed system has been implemented on a group of four Wi-Fi Direct enabled Samsung mobile devices.
Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities
Venue: arXiv cs.CV 2018
Year: 2018

Delineating infarcted tissue in ischemic stroke lesions is crucial to determine the extend of damage and optimal treatment for this life-threatening condition. However, this problem remains challenging due to high variability of ischemic strokes' location and shape. Recently, fully-convolutional neural networks (CNN), in particular those based on U-Net, have led to improved performances for this task. In this work, we propose a novel architecture that improves standard U-Net based methods in three important ways. First, instead of combining the available image modalities at the input, each of them is processed in a different path to better exploit their unique information. Moreover, the network is densely-connected (i.e., each layer is connected to all following layers), both within each path and across different paths, similar to HyperDenseNet. This gives our model the freedom to learn the scale at which modalities should be processed and combined. Finally, inspired by the Inception architecture, we improve standard U-Net modules by extending inception modules with two convolutional blocks with dilated convolutions of different scale. This helps handling the variability in lesion sizes. We split the 93 stroke datasets into training and validation sets containing 83 and 9 examples respectively. Our network was trained on a NVidia TITAN XP GPU with 16 GBs RAM, using ADAM as optimizer and a learning rate of 1$\times$10$^{-5}$ during 200 epochs. Training took around 5 hours and segmentation of a whole volume took between 0.2 and 2 seconds, as average. The performance on the test set obtained by our method is compared to several baselines, to demonstrate the effectiveness of our architecture, and to a state-of-art architecture that employs factorized dilated convolutions, i.e., ERFNet.
IRA assisted MMC-based topology optimization method
Venue: arXiv cs.NA 2018
Year: 2018

An Iterative Reanalysis Approximation (IRA) is integrated with the Moving Morphable Components (MMCs) based topology optimization (IRA-MMC) in this study. Compared with other classical topology optimization methods, the Finite Element (FE) based solver is replaced with the suggested IRA method. In this way, the expensive computational cost can be significantly saved by several nested iterations. The optimization of linearly elastic planar structures is constructed by the MMC, the specifically geometric parameters of which are taken as design variables to acquire explicitly geometric boundary. In the suggested algorithm, a hybrid optimizer based on the Method of Moving Asymptotes (MMA) approach and the Globally Convergent version of the Method of Moving Asymptotes (GCMMA) is suggested to improve convergence ratio and avoid local optimum. The proposed approach is evaluated by some classical benchmark problems in topology optimization, where the results show significant time saving without compromising accuracy.
On the Off-chip Memory Latency of Real-Time Systems: Is DDR DRAM Really the Best Option?
Venue: arXiv cs.AR 2018
Year: 2018

Predictable execution time upon accessing shared memories in multi-core real-time systems is a stringent requirement. A plethora of existing works focus on the analysis of Double Data Rate Dynamic Random Access Memories (DDR DRAMs), or redesigning its memory to provide predictable memory behavior. In this paper, we show that DDR DRAMs by construction suffer inherent limitations associated with achieving such predictability. These limitations lead to 1) highly variable access latencies that fluctuate based on various factors such as access patterns and memory state from previous accesses, and 2) overly pessimistic latency bounds. As a result, DDR DRAMs can be ill-suited for some real-time systems that mandate a strict predictable performance with tight timing constraints. Targeting these systems, we promote an alternative off-chip memory solution that is based on the emerging Reduced Latency DRAM (RLDRAM) protocol, and propose a predictable memory controller (RLDC) managing accesses to this memory. Comparing with the state-of-the-art predictable DDR controllers, the proposed solution provides up to 11x less timing variability and 6.4x reduction in the worst case memory latency.
Incremental learning abstract discrete planning domains and mappings to continuous perceptions
Venue: arXiv cs.AI 2018
Year: 2018

Most of the works on planning and learning, e.g., planning by (model based) reinforcement learning, are based on two main assumptions: (i) the set of states of the planning domain is fixed; (ii) the mapping between the observations from the real word and the states is implicitly assumed or learned offline, and it is not part of the planning domain. Consequently, the focus is on learning the transitions between states. In this paper, we drop such assumptions. We provide a formal framework in which (i) the agent can learn dynamically new states of the planning domain; (ii) the mapping between abstract states and the perception from the real world, represented by continuous variables, is part of the planning domain; (iii) such mapping is learned and updated along the "life" of the agent. We define an algorithm that interleaves planning, acting, and learning, and allows the agent to update the planning domain depending on how much it trusts the model w.r.t. the new experiences learned by executing actions. We define a measure of coherence between the planning domain and the real world as perceived by the agent. We test our approach showing that the agent learns increasingly coherent models, and that the system can scale to deal with models with an order of $10^6$ states.
Motifs corrélés rares : Caractérisation et nouvelles représentations concises
Venue: arXiv cs.DB 2018
Year: 2018

Recently, rare pattern mining proves to be of added-value in different data mining applications since these patterns allow conveying knowledge on rare and unexpected events. However, the extraction of rare patterns suffers from two main limits, namely the large number of mined patterns in real-life applications, as well as the low informativeness quality of several rare patterns. In this situation, we propose to use the correlation measure, bond, in the mining process in order to only retain those rare patterns having a certain degree of correlation between their respective items. A characterization of the resulting set, of rare correlated patterns, is then carried out based on the study of constraints of distinct types induced by the rarity and the correlation. In addition, based on the equivalence classes associated to a closure operator dedicated to the bond measure, we propose concise representations of rare correlated patterns. We then design a new algorithm CRP_Miner dedicated to the extraction of the whole set of rare correlated patterns. We also introduce the CRPR_Miner algorithm allowing an efficient extraction of the proposed concise representations. In addition, we design two other algorithms which allow to us the query and the regeneration of the whole set of rare correlated patterns. The carried out experimental studies show the effectiveness of the algorithm CRPR_Miner and prove the compactness rate offered by the proposed concise representations.
Adversarial Attacks on Cognitive Self-Organizing Networks: The Challenge and the Way Forward
Venue: arXiv cs.CR 2018
Year: 2018

Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive self-organization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work.
PUF-AES-PUF: a novel PUF architecture against non-invasive attacks
Venue: arXiv cs.CR 2018
Year: 2018

In this letter, a physical unclonable function (PUF)-advanced encryption standard (AES)-PUF is proposed as a new PUF architecture by embedding an AES cryptographic circuit between two conventional PUF circuits to conceal their challenge-to-response pairs (CRPs) against machine learning attacks. Moreover, an internal confidential data is added to the secret key of the AES cryptographic circuit in the new PUF architecture to update the secret key in real-time against side-channel attacks. As shown in the results, even if 1 million number of data are enabled by the adversary to implement machine learning or side-channel attacks, the proposed PUF can not be cracked. By contrast, only 5,000 (1,000) number of data are sufficient to leak the confidential information of a conventional PUF via machine learning (side-channel) attacks.
Classification of malware based on file content and characteristics
Venue: arXiv cs.CY 2018
Year: 2018

In general, the industry of malware has come to be a market which brings on loads of money by investing and implementing high end technology to escape traditional detection while vendors of anti-malware spend thousands if not millions of dollars to stop the malware breach since it not only causes financial losses but also emotional ones. This paper study the classification of malware based on file content and characteristics, this was done through use of Clamp Integrated dataset that includes 5210 instances. There are different algorithms were applied using Weka software, which are; ZeroR, bayesNet, SMO, KNN, J48, as well as Random Forest. The obtained results showed that Random Forest that achieved the highest overall accuracy of (99.0979%). This means that Random Forest algorithm is efficient to be used in malware classification based on file content and characteristics.
Nearly Optimal Space Efficient Algorithm for Depth First Search
Venue: arXiv cs.DS 2018
Year: 2018

We design a space-efficient algorithm for performing depth-first search traversal(DFS) of a graph in $O(m+n\log^* n)$ time using $O(n)$ bits of space. While a normal DFS algorithm results in a DFS-tree (in case the graph is connected), our space bounds do not permit us even to store such a tree. However, our algorithm correctly outputs all edges of the DFS-tree. The previous best algorithm (which used $O(n)$ working space) took $O(m \log n)$ time (Asano, Izumi, Kiyomi, Konagaya, Ono, Otachi, Schweitzer, Tarui, Uehara (ISAAC 2014) and Elmasry, Hagerup, Krammer (STACS 2015)). The main open question left behind in this area was to design faster algorithm for DFS using $O(n)$ bits of space. Our algorithm answers this open question as it has a nearly optimal running time (as the DFS takes $O(m+n)$ time even if there is no space restriction).