OpenRaiser/Intern-Atlas huggingface dataset:
On Overcoming the Impact of Doppler Spectrum in Millimeter-Wave V2I Communications
Venue: arXiv cs.IT 2018
Year: 2018

With the advent of 5G cellular systems there is an increased interest in exploring higher frequency bands above 6 GHz. At these frequencies, beamforming appears as a straightforward solution to overcome higher path loss thereby altering the Doppler characteristics of the received waves. Higher frequencies can suffer from strong Doppler impairments because of the linear dependency of Doppler shift with carrier frequency, which makes them challenging to use in high-mobility scenarios, particularly Vehicular-to-Infrastructure (V2I) communications. Therefore, the impact of beamforming on the Doppler characteristics of the received signals is of utter importance for future V2I systems. This paper presents a theoretical analysis of the Doppler power spectrum in the presence of beamforming at the transmit and/or the receive sides. Further approximations are made for the resulting Doppler spread and Doppler shift when the receive beam width is sufficiently small, and a possible design solution is presented to control the Doppler spread in V2I systems. The results can be of key importance in waveform and air interface design for V2I systems.
Spatial networks with wireless applications
Venue: arXiv cs.NI 2018
Year: 2018

Many networks have nodes located in physical space, with links more common between closely spaced pairs of nodes. For example, the nodes could be wireless devices and links communication channels in a wireless mesh network. We describe recent work involving such networks, considering effects due to the geometry (convex,non-convex, and fractal), node distribution, distance-dependent link probability, mobility, directivity and interference.
The organizing vision of integrated health information systems
Venue: arXiv cs.CY 2018
Year: 2018

The notion of integration in the context of health information systems is ill-defined yet in wide-spread use. We identify a variety of meanings spanning from purely technical integration of information systems to integration of services. This ambiguity (or interpretative flexibility), we argue, is inherent rather accidental: it a necessary prerequisite for mobilising political and ideological support among stakeholders for integrated health information systems. Building on this, our aim is to trace out the career dynamics of the vision of integration/ integrated. The career dynamics is the transformation of both the imagery and material (technological) realisations of the unfolding implementation of the vision of integrated care. Empirically we draw on a large, ongoing project at the University hospital of North Norway (UNN) to establish an integrated health information system
The family resemblance of technologically mediated work practices
Venue: arXiv cs.CY 2018
Year: 2018

Practice-based perspectives in information systems have established how, in every instance of use i.e., work practices, the user exercises considerable discretion in their appropriation of the technology with local workarounds and situated improvisations. We analyse the relationship between technologically mediated work practices separated in time and space. Specifically, we analyse how similarity in work practices is achieved. Achieving absolutely similar or best practices is unattainable. Drawing on a longitudinal 2007 to 2011 case of ambulatory maintenance work in the oil and gas sector, we identify and discuss three constituting strategies called differentiation, assembling and punctuation through which a family resemblance of similar but not the same work practices is crafted. We discuss how, in the absence of an essentialist criterion, similarity is subject to pragmatic but also political negotiations.
Data Science Methodology for Cybersecurity Projects
Venue: arXiv cs.CY 2018
Year: 2018

Cyber-security solutions are traditionally static and signature-based. The traditional solutions along with the use of analytic models, machine learning and big data could be improved by automatically trigger mitigation or provide relevant awareness to control or limit consequences of threats. This kind of intelligent solutions is covered in the context of Data Science for Cyber-security. Data Science provides a significant role in cyber-security by utilising the power of data (and big data), high-performance computing and data mining (and machine learning) to protect users against cyber-crimes. For this purpose, a successful data science project requires an effective methodology to cover all issues and provide adequate resources. In this paper, we are introducing popular data science methodologies and will compare them in accordance with cyber-security challenges. A comparison discussion has also delivered to explain methodologies strengths and weaknesses in case of cyber-security projects.
New Algorithms for Weighted $k$-Domination and Total $k$-Domination Problems in Proper Interval Graphs
Venue: arXiv cs.DS 2018
Year: 2018

Given a positive integer $k$, a $k$-dominating set in a graph $G$ is a set of vertices such that every vertex not in the set has at least $k$ neighbors in the set. A total $k$-dominating set, also known as a $k$-tuple total dominating set, is a set of vertices such that every vertex of the graph has at least $k$ neighbors in the set. The problems of finding the minimum size of a $k$-dominating, respectively total $k$-dominating set, in a given graph, are referred to as $k$-domination, respectively total $k$-domination. These generalizations of the classical domination and total domination problems are known to be NP-hard in the class of chordal graphs, and, more specifically, even in the classes of split graphs (both problems) and undirected path graphs (in the case of total $k$-domination). On the other hand, it follows from recent work of Kang et al.~(2017) that these two families of problems are solvable in time $\mathcal{O}(|V(G)|^{6k+4})$ in the class of interval graphs. We develop faster algorithms for $k$-domination and total $k$-domination in the class of proper interval graphs, by means of reduction to a single shortest path computation in a derived directed acyclic graph with $\mathcal{O}(|V(G)|^{2k})$ nodes and $\mathcal{O}(|V(G)|^{4k})$ arcs. We show that a suitable implementation, which avoids constructing all arcs of the digraph, leads to a running time of $\mathcal{O}(|V(G)|^{3k})$. The algorithms are also applicable to the weighted case.
A state of the art of urban reconstruction: street, street network, vegetation, urban feature
Venue: arXiv cs.OH 2018
Year: 2018

World population is raising, especially the part of people living in cities. With increased population and complex roles regarding their inhabitants and their surroundings, cities concentrate difficulties for design, planning and analysis. These tasks require a way to reconstruct/model a city. Traditionally, much attention has been given to buildings reconstruction, yet an essential part of city were neglected: streets. Streets reconstruction has been seldom researched. Streets are also complex compositions of urban features, and have a unique role for transportation (as they comprise roads). We aim at completing the recent state of the art for building reconstruction (Musialski2012) by considering all other aspect of urban reconstruction. We introduce the need for city models. Because reconstruction always necessitates data, we first analyse which data are available. We then expose a state of the art of street reconstruction, street network reconstruction, urban features reconstruction/modelling, vegetation , and urban objects reconstruction/modelling. Although reconstruction strategies vary widely, we can order them by the role the model plays, from data driven approach, to model-based approach, to inverse procedural modelling and model catalogue matching. The main challenges seems to come from the complex nature of urban environment and from the limitations of the available data. Urban features have strong relationships, between them, and to their surrounding, as well as in hierarchical relations. Procedural modelling has the power to express these relations, and could be applied to the reconstruction of urban features via the Inverse Procedural Modelling paradigm.
idtracker.ai: Tracking all individuals in large collectives of unmarked animals
Venue: arXiv cs.CV 2018
Year: 2018

Our understanding of collective animal behavior is limited by our ability to track each of the individuals. We describe an algorithm and software, idtracker.ai, that extracts from video all trajectories with correct identities at a high accuracy for collectives of up to 100 individuals. It uses two deep networks, one detecting when animals touch or cross and another one for animal identification, trained adaptively to conditions and difficulty of the video.
Efficient Communication over Cellular Networks with Network Coding in Emergency Scenarios
Venue: arXiv cs.NI 2018
Year: 2018

Emergency communications requires reliability and flexibility for disaster recovery and relief operation. Based upon existing commercial portable devices (e.g., smartphones, tablets, laptops), we propose a network architecture that uses cellular networks and WiFi connections to deliver large files in emergency scenarios under the impairments of wireless channel such as packet losses and intermittent connection issues. Network coding (NC) is exploited to improve the delivery probability. We first review the state-of-the-art of NC for emergency communications. Then, we present the proposed network architecture which utilizes multiple radio interfaces of portable devices to support data delivery. A random linear NC scheme is exploited at source to enhance the reliability for content delivery against packet losses. Besides, an analytical model for the successful decoding probability in linear NC is derived. Finally, we evaluate the effectiveness of the proposed architecture with NC in terms of the delivery ratio of content for intermittent connectivity scenarios.
Memory Models for C/C++ Programmers
Venue: arXiv cs.DC 2018
Year: 2018

The memory model is the crux of the concurrency semantics of shared-memory systems. It defines the possible values that a read operation is allowed to return for any given set of write operations performed by a concurrent program, thereby defining the basic semantics of shared variables. It is therefore impossible to meaningfully reason about a program or any part of the programming language implementation without an unambiguous memory model. This note provides a brief introduction into the topic of memory models, explaining why it is essential for concurrent programs and covering well known memory models from sequential consistency to those of the x86 and ARM/POWER CPUs. Section 4 is fully dedicated to the C++11 memory model, explaining how it can be used to write concurrent code that is not only correct and portable, but also efficient by utilizing the relaxed memory models of modern architectures.
Network Coding Function Virtualization
Venue: arXiv cs.NI 2018
Year: 2018

Network Functions Virtualization (NFV) and Network Coding (NC) have attracted much attention in recent years as key concepts for providing 5G networks with flexibility and differentiated reliability, respectively. In this paper, we present the integration of NC architectural design and NFV. In order to do so we first describe what we call a virtualization process upon our proposed architectural design of NC that should help to offer the reliability functionality to a network. The process consists of identifying the required functional entities of NC and analyzing when the functionality should be activated towards complexity/energy efficiency. The relevance of our proposed NC function virtualization is its applicability to any underlying physical network, satellite or hybrid thus enabling softwarization, and rapid innovative deployment. Finally, we validate our framework to a study case of geo-control of network reliability that is based on device's geographical location-based signal/network information.
Interval-based Resource Usage Verification by Translation into Horn Clauses and an Application to Energy Consumption
Venue: arXiv cs.PL 2018
Year: 2018

Many applications require conformance with specifications that constrain the use of resources, such as execution time, energy, bandwidth, etc. We have presented a configurable framework for static resource usage verification where specifications can include lower and upper bound, data size-dependent resource usage functions. To statically check such specifications, our framework infers the same type of resource usage functions, which safely approximate the actual resource usage of the program, and compares them against the specification. We review how this framework supports several languages and compilation output formats by translating them to an intermediate representation based on Horn clauses and using the configurability of the framework to describe the resource semantics of the input language. We provide a more detailed formalization and extend the framework so that both resource usage specification and analysis/verification output can include preconditions expressing intervals for the input data sizes for which assertions are applicable, proved, or disproved. Most importantly, we also extend the classes of functions that can be checked. We provide results from an implementation within the Ciao/CiaoPP framework, and report on a tool built by instantiating this framework for the verification of energy consumption specifications for imperative/embedded programs. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).
PotentialNet for Molecular Property Prediction
Venue: arXiv cs.LG 2018
Year: 2018

The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. They key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning---instead of feature engineering---deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein-ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor $EF_χ^{(R)}$, to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.
Correction by Projection: Denoising Images with Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Generative adversarial networks (GANs) transform low-dimensional latent vectors into visually plausible images. If the real dataset contains only clean images, then ostensibly, the manifold learned by the GAN should contain only clean images. In this paper, we propose to denoise corrupted images by finding the nearest point on the GAN manifold, recovering latent vectors by minimizing distances in image space. We first demonstrate that given a corrupted version of an image that truly lies on the GAN manifold, we can approximately recover the latent vector and denoise the image, obtaining significantly higher quality, comparing with BM3D. Next, we demonstrate that latent vectors recovered from noisy images exhibit a consistent bias. By subtracting this bias before projecting back to image space, we improve denoising results even further. Finally, even for unseen images, our method performs better at denoising better than BM3D. Notably, the basic version of our method (without bias correction) requires no prior knowledge on the noise variance. To achieve the highest possible denoising quality, the best performing signal processing based methods, such as BM3D, require an estimate of the blur kernel.
Effects of Topology Knowledge and Relay Depth on Asynchronous Consensus
Venue: arXiv cs.DC 2018
Year: 2018

Consider a point-to-point message-passing network. We are interested in the asynchronous crash-tolerant consensus problem in incomplete networks. We study the feasibility and efficiency of approximate consensus under different restrictions on topology knowledge and the relay depth, i.e., the maximum number of hops any message can be relayed. These two constraints are common in large-scale networks, and are used to avoid memory overload and network congestion respectively. Specifically, for different values of integers k, k , we consider that each node knows all its neighbors of at most k-hop distance (k-hop topology knowledge), and the relay depth is k . We consider both directed and undirected graphs. More concretely, we answer the following main question in asynchronous systems: What is a tight condition on the underlying communication graphs for achieving approximate consensus if each node has only a k-hop topology knowledge and relay depth k? To prove that the necessary conditions presented in the paper are also sufficient, we have developed algorithms that achieve consensus in graphs satisfying those conditions: -The first class of algorithms requires k-hop topology knowledge and relay depth k. Unlike prior algorithms, these algorithms do not flood the network, and each node does not need the full topology knowledge. We show how the convergence time and the message complexity of those algorithms is affected by k, providing the respective upper bounds. -The second set of algorithms requires only one-hop neighborhood knowledge, i.e., immediate incoming and outgoing neighbors, but needs to flood the network (i.e., relay depth is n, where n is the number of nodes). One result that may be of independent interest is a topology discovery mechanism to learn and "estimate" the topology in asynchronous directed networks with crash faults.
Text Data Mining from the Author's Perspective: Whose Text, Whose Mining, and to Whose Benefit?
Venue: arXiv cs.DL 2018
Year: 2018

Given the many technical, social, and policy shifts in access to scholarly content since the early days of text data mining, it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcher-authors about how their data are mined, by whom, for what purposes, and to whose benefits.
Luby--Veličković--Wigderson revisited: Improved correlation bounds and pseudorandom generators for depth-two circuits
Venue: arXiv cs.CC 2018
Year: 2018

We study correlation bounds and pseudorandom generators for depth-two circuits that consist of a $\mathsf{SYM}$-gate (computing an arbitrary symmetric function) or $\mathsf{THR}$-gate (computing an arbitrary linear threshold function) that is fed by $S$ $\mathsf{AND}$ gates. Such circuits were considered in early influential work on unconditional derandomization of Luby, Veličković, and Wigderson [LVW93], who gave the first non-trivial PRG with seed length $2^{O(\sqrt{\log(S/\varepsilon)})}$ that $\varepsilon$-fools these circuits. In this work we obtain the first strict improvement of [LVW93]'s seed length: we construct a PRG that $\varepsilon$-fools size-$S$ $\{\mathsf{SYM},\mathsf{THR}\} \circ\mathsf{AND}$ circuits over $\{0,1\}^n$ with seed length \[ 2^{O(\sqrt{\log S })} + \mathrm{polylog}(1/\varepsilon), \] an exponential (and near-optimal) improvement of the $\varepsilon$-dependence of [LVW93]. The above PRG is actually a special case of a more general PRG which we establish for constant-depth circuits containing multiple $\mathsf{SYM}$ or $\mathsf{THR}$ gates, including as a special case $\{\mathsf{SYM},\mathsf{THR}\} \circ \mathsf{AC^0}$ circuits. These more general results strengthen previous results of Viola [Vio06] and essentially strengthen more recent results of Lovett and Srinivasan [LS11]. Our improved PRGs follow from improved correlation bounds, which are transformed into PRGs via the Nisan--Wigderson "hardness versus randomness" paradigm [NW94]. The key to our improved correlation bounds is the use of a recent powerful \emph{multi-switching} lemma due to Håstad [Hås14].
Convolutional Neural Networks and Language Embeddings for End-to-End Dialect Recognition
Venue: arXiv cs.SD 2018
Year: 2018

Dialect identification (DID) is a special case of general language identification (LID), but a more challenging problem due to the linguistic similarity between dialects. In this paper, we propose an end-to-end DID system and a Siamese neural network to extract language embeddings. We use both acoustic and linguistic features for the DID task on the Arabic dialectal speech dataset: Multi-Genre Broadcast 3 (MGB-3). The end-to-end DID system was trained using three kinds of acoustic features: Mel-Frequency Cepstral Coefficients (MFCCs), log Mel-scale Filter Bank energies (FBANK) and spectrogram energies. We also investigated a dataset augmentation approach to achieve robust performance with limited data resources. Our linguistic feature research focused on learning similarities and dissimilarities between dialects using the Siamese network, so that we can reduce feature dimensionality as well as improve DID performance. The best system using a single feature set achieves 73% accuracy, while a fusion system using multiple features yields 78% on the MGB-3 dialect test set consisting of 5 dialects. The experimental results indicate that FBANK features achieve slightly better results than MFCCs. Dataset augmentation via speed perturbation appears to add significant robustness to the system. Although the Siamese network with language embeddings did not achieve as good a result as the end-to-end DID system, the two approaches had good synergy when combined together in a fused system.
Using Grouped Linear Prediction and Accelerated Reinforcement Learning for Online Content Caching
Venue: arXiv cs.NI 2018
Year: 2018

Proactive caching is an effective way to alleviate peak-hour traffic congestion by prefetching popular contents at the wireless network edge. To maximize the caching efficiency requires the knowledge of content popularity profile, which however is often unavailable in advance. In this paper, we first propose a new linear prediction model, named grouped linear model (GLM) to estimate the future content requests based on historical data. Unlike many existing works that assumed the static content popularity profile, our model can adapt to the temporal variation of the content popularity in practical systems due to the arrival of new contents and dynamics of user preference. Based on the predicted content requests, we then propose a reinforcement learning approach with model-free acceleration (RLMA) for online cache replacement by taking into account both the cache hits and replacement cost. This approach accelerates the learning process in non-stationary environment by generating imaginary samples for Q-value updates. Numerical results based on real-world traces show that the proposed prediction and learning based online caching policy outperform all considered existing schemes.