OpenRaiser/Intern-Atlas huggingface dataset:
MONICA in Hamburg: Towards Large-Scale IoT Deployments in a Smart City
Venue: arXiv cs.NI 2018
Year: 2018

Modern cities and metropolitan areas all over the world face new management challenges in the 21st century primarily due to increasing demands on living standards by the urban population. These challenges range from climate change, pollution, transportation, and citizen engagement, to urban planning, and security threats. The primary goal of a Smart City is to counteract these problems and mitigate their effects by means of modern ICT to improve urban administration and infrastructure. Key ideas are to utilise network communication to inter-connect public authorities; but also to deploy and integrate numerous sensors and actuators throughout the city infrastructure - which is also widely known as the Internet of Things (IoT). Thus, IoT technologies will be an integral part and key enabler to achieve many objectives of the Smart City vision. The contributions of this paper are as follows. We first examine a number of IoT platforms, technologies and network standards that can help to foster a Smart City environment. Second, we introduce the EU project MONICA which aims for demonstration of large-scale IoT deployments at public, inner-city events and give an overview on its IoT platform architecture. And third, we provide a case-study report on SmartCity activities by the City of Hamburg and provide insights on recent (on-going) field tests of a vertically integrated, end-to-end IoT sensor application.
Symbol-Level Precoding Design for Max-Min SINR in Multiuser MISO Broadcast Channels
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we address the symbol level precoding (SLP) design problem under max-min SINR criterion in the downlink of multiuser multiple-input single-output (MISO) channels. First, we show that the distance preserving constructive interference regions (DPCIR) are always polyhedral angles (shifted pointed cones) for any given constellation point with unbounded decision region. Then we prove that any signal in a given unbounded DPCIR has a norm larger than the norm of the corresponding vertex if and only if the convex hull of the constellation contains the origin. Using these properties, we show that the power of the noiseless received signal lying on an unbounded DPCIR is an strictly increasing function of two parameters. This allows us to reformulate the originally non-convex SLP max-min SINR as a convex optimization problem. We discuss the loss due to our proposed convex reformulation and provide some simulation results.
A Mixture of Views Network with Applications to the Classification of Breast Microcalcifications
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. This data fusion concept, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. It is demonstrated on the task of classifying breast microcalcifications as benign or malignant based on CC and MLO mammography views. The single view decisions are combined by a data-driven decision, according to the relevance of each view in a given case, into a global decision. The method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). The experimental results show that our method outperforms previously suggested fusion methods.
Cloud Workload Prediction based on Workflow Execution Time Discrepancies
Venue: arXiv cs.DC 2018
Year: 2018

Infrastructure as a service clouds hide the complexity of maintaining the physical infrastructure with a slight disadvantage: they also hide their internal working details. Should users need knowledge about these details e.g., to increase the reliability or performance of their applications, they would need solutions to detect behavioural changes in the underlying system. Existing runtime solutions for such purposes offer limited capabilities as they are mostly restricted to revealing weekly or yearly behavioural periodicity in the infrastructure. This article proposes a technique for predicting generic background workload by means of simulations that are capable of providing additional knowledge of the underlying private cloud systems in order to support activities like cloud orchestration or workflow enactment. Our technique uses long-running scientific workflows and their behaviour discrepancies and tries to replicate these in a simulated cloud with known (trace-based) workloads. We argue that the better we can mimic the current discrepancies the better we can tell expected workloads in the near future on the real life cloud. We evaluated the proposed prediction approach with a biochemical application on both real and simulated cloud infrastructures. The proposed algorithm has shown to produce significantly (~20%) better workload predictions for the future of simulated clouds than random workload selection.
AISC: Approximate Instruction Set Computer
Venue: arXiv cs.AR 2018
Year: 2018

This paper makes the case for a single-ISA heterogeneous computing platform, AISC, where each compute engine (be it a core or an accelerator) supports a different subset of the very same ISA. An ISA subset may not be functionally complete, but the union of the (per compute engine) subsets renders a functionally complete, platform-wide single ISA. Tailoring the microarchitecture of each compute engine to the subset of the ISA that it supports can easily reduce hardware complexity. At the same time, the energy efficiency of computing can improve by exploiting algorithmic noise tolerance: by mapping code sequences that can tolerate (any potential inaccuracy induced by) the incomplete ISA-subsets to the corresponding compute engines.
Featureless: Bypassing feature extraction in action categorization
Venue: arXiv cs.CV 2018
Year: 2018

This method introduces an efficient manner of learning action categories without the need of feature estimation. The approach starts from low-level values, in a similar style to the successful CNN methods. However, rather than extracting general image features, we learn to predict specific video representations from raw video data. The benefit of such an approach is that at the same computational expense it can predict 2 D video representations as well as 3 D ones, based on motion. The proposed model relies on discriminative Waldboost, which we enhance to a multiclass formulation for the purpose of learning video representations. The suitability of the proposed approach as well as its time efficiency are tested on the UCF11 action recognition dataset.
Radio bearing of sources with directional antennas in urban environment
Venue: arXiv cs.IT 2018
Year: 2018

This paper focuses on assessing the limitations in the direction-finding process of radio sources with directional antennas in an urbanized environment, demonstrating how signal source antenna parameters, such as beamwidth and maximum radiation direction affect bearing accuracy in non-line-of-sight (NLOS) conditions. These evaluations are based on simulation studies, which use measurement-tested signal processing procedures. These procedures are based on a multi-elliptical propagation model, the geometry of which is related to the environment by the power delay profile or spectrum. The probability density function of the angle of arrival for different parameters of the transmitting antenna is the simulation result. This characteristic allows assessing the effect of the signal source antenna parameters on bearing error. The obtained results are the basis for practical correction bearing error and these show the possibility of improving the efficiency of the radio source location in the urbanized environment.
A black market for upvotes and likes
Venue: arXiv cs.CY 2018
Year: 2018

Purpose: This research investigates controversial online marketing techniques that involve buying hundreds or even thousands of upvotes, likes, comments, etc. Methodology: Observation and categorization of 7,426 campaigns posted on the crowdsourcing platform microworkers.com over a 365 day (i.e., yearlong) period were conducted. Hypotheses about the mechanics and effectiveness of these campaigns were established and evaluated. Findings: The campaigns contained a combined 1,856,316 microtasks, 89.7% of which were connected to online promotion. Techniques for search engine manipulation, comment-generating in the scale of tens of thousands, online vote manipulation, mass account creation, methods for covering tracks were discovered. The article presents an assessment of the effectiveness of such campaigns as well as various security challenges created by these campaigns. Research limitations: The observed campaigns form only a small portion of the overall "black market". This is due to invite-only campaigns and the presence of alternative, unobservable platforms. Practical implications: The findings of this article could be input for detecting and avoiding such online campaigns. Social implications: The findings show that in some conditions tremendous levels of manipulation of an online discourse can be achieved with a limited budget. Originality: While there is related work on "follower factories" and "click/troll farms", those entities offer complete "solutions" and their techniques are rather opaque. By investigating a crowdsourcing platform, this research unveils the underlying mechanics and organization of such campaigns. The research is based on a uniquely large number of observations. Small, cheap campaigns, the manipulation of less significant platforms is also included, while the related work tends to focus on mass, politically motivated efforts.
Embedding graphs into two-dimensional simplicial complexes
Venue: arXiv cs.CG 2018
Year: 2018

We consider the problem of deciding whether an input graph G admits a topological embedding into a two-dimensional simplicial complex C. This problem includes, among others, the embeddability problem of a graph on a surface and the topological crossing number of a graph, but is more general. The problem is NP-complete when C is part of the input, and we give a polynomial-time algorithm if the complex C is fixed. Our strategy is to reduce the problem to an embedding extension problem on a surface, which has the following form: Given a subgraph H' of a graph G', and an embedding of H' on a surface S, can that embedding be extended to an embedding of G' on S? Such problems can be solved, in turn, using a key component in Mohar's algorithm to decide the embeddability of a graph on a fixed surface (STOC 1996, SIAM J. Discr. Math. 1999).
High Speed and Low Power Sensing Schemes for STT-MRAM with IPMTJs
Venue: arXiv cs.ET 2018
Year: 2018

STT-MRAM with interfacial-anisotropy-type perpendicular MTJ (IPMTJ) is a powerful candidate for the low switching energy design of STT-MRAM. In the literature, the reading operation of STT-MRAM structured with IPMTJs have been not studied until this time, in our knowledge. We investigated the reading operation of STT-MRAM structured with IPMTJs. To enumerate the read operations of the NVSenseAmp have successfully been performed a 2.5X reduction in average low power and a 13X increase in average high speed compared with the previous works.
On recovery of sequences from subsequences: the case of non-periodic spectrum gaps
Venue: arXiv cs.IT 2018
Year: 2018

The paper investigates recoverability of sequences from their periodic subsequences and offers some modification of the approach suggested in papers arXiv:1605.00414 and arXiv:1803.02233. It is shown that there exists a class of sequences that is everywhere dense in the class of all square-summable sequences and such that its members can be recovered from their periodic subsequences. This recoverability is associated with certain spectrum degeneracy of a new kind.
The nested materiality of environmental monitoring
Venue: arXiv cs.CY 2018
Year: 2018

Present knowledge about the marine ecosystem on the Norwegian Continental Shelf towards the Arctic is sparse. These areas are vast, remote and subject to harsh weather conditions. We report from a three-year case study of an ongoing effort for real-time, subsea environmental monitoring by an oil and gas operator. The facts about the subsea environment are anything but neutral; they are intrinsically caught up with the material means by which they are known. The marine ecosystem is monitored through a network of sensors, communication links, visualisation and analysis tools. Our concept of nested materiality draws heavily on perspectives in sociomateriality but highlights (i) the distributed and interconnected infrastructure of the material means (as opposed to artefact-centric) and (ii) in-the-making (as opposed to black-boxed) technology.
Probabilistic Occupancy Function and Sets Using Forward Stochastic Reachability for Rigid-Body Dynamic Obstacles
Venue: arXiv cs.SY 2018
Year: 2018

We present theory and algorithms for the computation of probability-weighted "keep-out" sets to assure probabilistically safe navigation in the presence of multiple rigid body obstacles with stochastic dynamics. Our forward stochastic reachability-based approach characterizes the stochasticity of the future obstacle states in a grid-free and recursion-free manner, using Fourier transforms and computational geometry. We consider discrete-time Markovian switched systems with affine parameter-varying stochastic subsystems (DMSP) as the obstacle dynamics, which includes Markov jump affine systems and discrete-time affine parameter-varying stochastic systems (DPV). We define a probabilistic occupancy function, to describe the probability that a given state is occupied by a rigid body obstacle with stochastic dynamics at a given time; keep-out sets are the super-level sets of this occupancy function. We provide sufficient conditions that ensure convexity and compactness of these keep-out sets for DPV obstacle dynamics. We also propose two computationally efficient algorithms to overapproximate the keep-out sets --- a tight polytopic approximation using projections, and an overapproximation using Minkowski sum. For DMSP obstacle dynamics, we compute a union of convex and compact sets that covers the potentially non-convex keep-out set. Numerical simulations show the efficacy of the proposed algorithms for a modified version of the classical unicycle dynamics, modeled as a DMSP.
Training Recurrent Neural Networks as a Constraint Satisfaction Problem
Venue: arXiv cs.LG 2018
Year: 2018

This paper presents a new approach for training artificial neural networks using techniques for solving the constraint satisfaction problem (CSP). The quotient gradient system (QGS) is a trajectory-based method for solving the CSP. This study converts the training set of a neural network into a CSP and uses the QGS to find its solutions. The QGS finds the global minimum of the optimization problem by tracking trajectories of a nonlinear dynamical system and does not stop at a local minimum of the optimization problem. Lyapunov theory is used to prove the asymptotic stability of the solutions with and without the presence of measurement errors. Numerical examples illustrate the effectiveness of the proposed methodology and compare it to a genetic algorithm and error backpropagation.
Why not be Versatile? Applications of the SGNMT Decoder for Machine Translation
Venue: arXiv cs.CL 2018
Year: 2018

SGNMT is a decoding platform for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic models. In this paper, we describe three use cases in which SGNMT is currently playing an active role: (1) teaching as SGNMT is being used for course work and student theses in the MPhil in Machine Learning, Speech and Language Technology at the University of Cambridge, (2) research as most of the research work of the Cambridge MT group is based on SGNMT, and (3) technology transfer as we show how SGNMT is helping to transfer research findings from the laboratory to the industry, eg. into a product of SDL plc.
Closing the AI Knowledge Gap
Venue: arXiv cs.CY 2018
Year: 2018

AI researchers employ not only the scientific method, but also methodology from mathematics and engineering. However, the use of the scientific method - specifically hypothesis testing - in AI is typically conducted in service of engineering objectives. Growing interest in topics such as fairness and algorithmic bias show that engineering-focused questions only comprise a subset of the important questions about AI systems. This results in the AI Knowledge Gap: the number of unique AI systems grows faster than the number of studies that characterize these systems' behavior. To close this gap, we argue that the study of AI could benefit from the greater inclusion of researchers who are well positioned to formulate and test hypotheses about the behavior of AI systems. We examine the barriers preventing social and behavioral scientists from conducting such studies. Our diagnosis suggests that accelerating the scientific study of AI systems requires new incentives for academia and industry, mediated by new tools and institutions. To address these needs, we propose a two-sided marketplace called TuringBox. On one side, AI contributors upload existing and novel algorithms to be studied scientifically by others. On the other side, AI examiners develop and post machine intelligence tasks designed to evaluate and characterize algorithmic behavior. We discuss this market's potential to democratize the scientific study of AI behavior, and thus narrow the AI Knowledge Gap.
SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

This work tackles the automatic fine-grained slide quality assessment problem for digitized direct smears test using the Gram staining protocol. Automatic quality assessment can provide useful information for the pathologists and the whole digital pathology workflow. For instance, if the system found a slide to have a low staining quality, it could send a request to the automatic slide preparation system to remake the slide. If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required. In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way. Specifically, the first step of our framework is to perform dense fine-grained region classification on the whole slide and calculate the region distribution histogram. Next, our framework will generate assessments of the slide quality from various perspectives: staining quality, information density, damage level and which regions are more valuable for subsequent high-magnification analysis. To make the information more accessible, we present our results in the form of a heat map and text summaries. Additionally, in order to stimulate research in this direction, we propose a novel dataset for slide quality assessment. Experiments show that the proposed framework outperforms recent related works.
Cluster-based Wireless Energy Transfer for Low Complex Energy Receivers
Venue: arXiv cs.IT 2018
Year: 2018

This paper proposes a novel channel estimation method and a cluster-based opportunistic scheduling policy, for a wireless energy transfer (WET) system consisting of multiple low-complex energy receivers (ERs) with limited processing capabilities. Firstly, in the training stage, the energy transmitter (ET) obtains a set of Received Signal Strength Indicator (RSSI) feedback values from all ERs, and these values are used to estimate the channels between the ET and all ERs. Next, based on the channel estimates, the ERs are grouped into clusters, and the cluster that has its members closest to its centroid in phase is selected for dedicated WET. The beamformer that maximizes the minimum harvested energy among all ERs in the selected cluster is found by solving a convex optimization problem. All ERs have the same chance of being selected regardless of their distances from the ET, and hence, this scheduling policy can be considered to be opportunistic as well as fair. It is shown that the proposed method achieves significant performance gains over benchmark schemes.
Statistical evaluation of the azimuth and elevation angles seen at the output of the receiving antenna
Venue: arXiv cs.IT 2018
Year: 2018

A method to evaluate the statistical properties of the reception angle seen at the input receiver that considers the receiving antenna pattern is presented. In particular, the impact of the direction and beamwidth of the antenna pattern on distribution of the reception angle is shown on the basis of 3D simulation studies. The obtained results show significant differences between distributions of angle of arrival and angle of reception. This means that the presented new method allows assessing the impact of the receiving antenna pattern on the correlation and spectral characteristics at the receiver input in simulation studies of wireless channel. The use of this method also provides an opportunity for analysis of a co-existence between small cells and wireless backhaul, what is currently a significant problem in designing 5G networks.
Tensor2Tensor for Neural Machine Translation
Venue: arXiv cs.LG 2018
Year: 2018

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.
Ocean Eddy Identification and Tracking using Neural Networks
Venue: arXiv cs.CV 2018
Year: 2018

Global climate change plays an essential role in our daily life. Mesoscale ocean eddies have a significant impact on global warming, since they affect the ocean dynamics, the energy as well as the mass transports of ocean circulation. From satellite altimetry we can derive high-resolution, global maps containing ocean signals with dominating coherent eddy structures. The aim of this study is the development and evaluation of a deep-learning based approach for the analysis of eddies. In detail, we develop an eddy identification and tracking framework with two different approaches that are mainly based on feature learning with convolutional neural networks. Furthermore, state-of-the-art image processing tools and object tracking methods are used to support the eddy tracking. In contrast to previous methods, our framework is able to learn a representation of the data in which eddies can be detected and tracked in more objective and robust way. We show the detection and tracking results on sea level anomalies (SLA) data from the area of Australia and the East Australia current, and compare our two eddy detection and tracking approaches to identify the most robust and objective method.