OpenRaiser/Intern-Atlas huggingface dataset:
Two-level Transmission Scheme for Cache-enabled Fog Radio Access Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we investigate the downlink transmission for cache-enabled fog radio access networks aiming at maximizing the delivery rate under the constraints of fronthaul capacity, maximum transmit power, and size of files. To reduce the delivery latency and the burden on fronthaul links and make full use of the local cache and baseband signal processing capabilities of enhanced remote radio heads (eRRHs), a two-level transmission scheme including cache-level and network-level transmission is proposed. In cache-level transmission, only requested files cached at the local cache are transmitted to the corresponding users. The duration of cache-level transmission is the delay caused by the transfer between the baseband unit (BBU) and eRRHs as well as the signal processing at the BBU. The remaining requested files are jointly transmitted to the corresponding users at network-level transmission. For cache-level transmission, a centralized optimization algorithm is firstly presented and then a decentralized optimization algorithm is provided to avoid the exchange of signaling among eRRHs. Meanwhile, another centralized optimization algorithm is presented to tackle the optimization problem for network-level transmission. All presented algorithms are proved to converge to the Karush-Kuhn-Tucker (KKT) solutions of the problems. Numerical results are provided to validate the effectiveness of the proposed transmission scheme as well as evaluating the system performance.
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

We consider the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable. Different from traditional super-resolution formulation, the low-resolution input is further degraded by noises and blurring. This complicated setting makes supervised learning and accurate kernel estimation impossible. To solve this problem, we resort to unsupervised learning without paired data, inspired by the recent successful image-to-image translation applications. With generative adversarial networks (GAN) as the basic component, we propose a Cycle-in-Cycle network structure to tackle the problem within three steps. First, the noisy and blurry input is mapped to a noise-free low-resolution space. Then the intermediate image is up-sampled with a pre-trained deep model. Finally, we fine-tune the two modules in an end-to-end manner to get the high-resolution output. Experiments on NTIRE2018 datasets demonstrate that the proposed unsupervised method achieves comparable results as the state-of-the-art supervised models.
Tensor Networks for Latent Variable Analysis: Higher Order Canonical Polyadic Decomposition
Venue: arXiv cs.NA 2018
Year: 2018

The Canonical Polyadic decomposition (CPD) is a convenient and intuitive tool for tensor factorization; however, for higher-order tensors, it often exhibits high computational cost and permutation of tensor entries, these undesirable effects grow exponentially with the tensor order. Prior compression of tensor in-hand can reduce the computational cost of CPD, but this is only applicable when the rank $R$ of the decomposition does not exceed the tensor dimensions. To resolve these issues, we present a novel method for CPD of higher-order tensors, which rests upon a simple tensor network of representative inter-connected core tensors of orders not higher than 3. For rigour, we develop an exact conversion scheme from the core tensors to the factor matrices in CPD, and an iterative algorithm with low complexity to estimate these factor matrices for the inexact case. Comprehensive simulations over a variety of scenarios support the approach.
Entanglement-assisted quantum codes from Galois LCD codes
Venue: arXiv cs.IT 2018
Year: 2018

Entanglement-assisted quantum error-correcting codes (EAQECCs) make use of preexisting entanglement between the sender and receiver to boost the rate of transmission. It is possible to construct an EAQECC from any classical linear code, unlike standard quantum error-correcting codes, which can only be constructed from dual-containing codes. However, the parameter of ebits $c$ is usually calculated by computer search. In this paper, we construct four classes of MDS entanglement-assisted quantum error-correcting codes (MDS EAQECCs) based on $k$-Galois LCD MDS codes for some certain code lengths, where the parameter of ebits $c$ can be easily generated algebraically and not by computational search. Moreover, the constructed four classes of EAQECCs are also maximal-entanglement EAQECCs.
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

The convolutional neural network model for optical flow estimation usually outputs a low-resolution(LR) optical flow field. To obtain the corresponding full image resolution,interpolation and variational approach are the most common options, which do not effectively improve the results. With the motivation of various convolutional neural network(CNN) structures succeeded in single image super-resolution(SISR) task, an end-to-end convolutional neural network is proposed to reconstruct the high resolution(HR) optical flow field from initial LR optical flow with the guidence of the first frame used in optical flow estimation. Our optical flow super-resolution(OFSR) problem differs from the general SISR problem in two main aspects. Firstly, the optical flow includes less texture information than image so that the SISR CNN structures can't be directly used in our OFSR problem. Secondly, the initial LR optical flow data contains estimation error, while the LR image data for SISR is generally a bicubic downsampled, blurred, and noisy version of HR ground truth. We evaluate the proposed approach on two different optical flow estimation mehods and show that it can not only obtain the full image resolution, but generate more accurate optical flow field (Accuracy improve 15% on FlyingChairs, 13% on MPI Sintel) with sharper edges than the estimation result of original method.
Learned Cardinalities: Estimating Correlated Joins with Deep Learning
Venue: arXiv cs.DB 2018
Year: 2018

We describe a new deep learning approach to cardinality estimation. MSCN is a multi-set convolutional network, tailored to representing relational query plans, that employs set semantics to capture query features and true cardinalities. MSCN builds on sampling-based estimation, addressing its weaknesses when no sampled tuples qualify a predicate, and in capturing join-crossing correlations. Our evaluation of MSCN using a real-world dataset shows that deep learning significantly enhances the quality of cardinality estimation, which is the core problem in query optimization.
Cobham's Theorem and Automaticity
Venue: arXiv cs.FL 2018
Year: 2018

We make certain bounds in Krebs' proof of Cobham's theorem explicit and obtain corresponding upper bounds on the length of a common prefix of an aperiodic $a$-automatic sequence and an aperiodic $b$-automatic sequence, where $a$ and $b$ are multiplicatively independent. We also show that an automatic sequence cannot have arbitrarily large factors in common with a Sturmian sequence.
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification
Venue: arXiv cs.CL 2018
Year: 2018

In this paper we present our approach to tackle the Implicit Emotion Shared Task (IEST) organized as part of WASSA 2018 at EMNLP 2018. Given a tweet, from which a certain word has been removed, we are asked to predict the emotion of the missing word. In this work, we experiment with neural Transfer Learning (TL) methods. Our models are based on LSTM networks, augmented with a self-attention mechanism. We use the weights of various pretrained models, for initializing specific layers of our networks. We leverage a big collection of unlabeled Twitter messages, for pretraining word2vec word embeddings and a set of diverse language models. Moreover, we utilize a sentiment analysis dataset for pretraining a model, which encodes emotion related information. The submitted model consists of an ensemble of the aforementioned TL models. Our team ranked 3rd out of 30 participants, achieving an F1 score of 0.703.
A characterization of the consistent Hoare powerdomains over dcpos
Venue: arXiv cs.LO 2018
Year: 2018

It has been shown that for a dcpo P, the Scott closure of Γ_c(P) in Γ(P) is a consistent Hoare powerdomain of P, where Γ_c(P) is the family of nonempty, consistent and Scott closed subsets of P, and Γ(P) is the collection of all nonempty Scott closed subsets of P. In this paper, by introducing the notion of a \vee-existing set, we present a direct characterization of the consistent Hoare powerdomain: the set of all \vee-existing Scott closed subsets of a dcpo P is exactly the consistent Hoare powerdomain of P. We also introduce the concept of an F-Scott closed set over each dcpo-\vee-semilattice. We prove that the Scott closed set lattice of a dcpo P is isomorphic to the family of all F-Scott closed sets of P's consistent Hoare powerdomain.
Plastic Waste is Exponentially Filling our Oceans, but where are the Robots?
Venue: arXiv cs.RO 2018
Year: 2018

Plastic waste is filling our oceans at an exponential rate. The situation is catastrophic and has now garnered worldwide attention. Despite the catastrophic conditions, little to no robotics research is conducted in the identification, collection, sorting, and removal of plastic waste from oceans and rivers and at the macro- and micro-scale. Only a scarce amount of individual efforts can be found from private sources. This paper presents a cursory view of the current plastic water waste catastrophe, associated robot research, and other efforts currently underway to address the issue. As well as the call that as a community, we must wait no longer to address the problem. Surely there is much potential for robots to help meet the challenges posed by the enormity of this problem.
On the Tasks and Characteristics of Product Owners: A Case Study in the Oil & Gas Industry
Venue: arXiv cs.SE 2018
Year: 2018

Product owners in the Scrum framework - respectively the on-site customer when applying eXtreme Programming - have an important role in the development process. They are responsible for the requirements and backlog deciding about the next steps within the development process. However, many companies face the difficulty of defining the tasks and the responsibilities of a product owner on their way towards an agile work environment. While literature addresses the tailoring of the product owner's role in general, research does not particularly consider the specifics of this role in the context of a systems development as we find for example in the oil and gas industry. Consequently, the question arises whether there are any differences between these two areas. In order to answer this question, we investigated on the current state of characteristics and tasks of product owners at Baker Hughes, a GE company (BHGE). In this position paper, we present initial results based on an online survey with answers of ten active product owners within the technical software department of BHGE. The results indicate that current product owners at BHGE primarily act as a nexus between all ends. While technical tasks are performed scarcely, communication skills seem even more important for product owners in a system development organization. However, to obtain more reliable results additional research in this area is required.
Metabolize Neural Network
Venue: arXiv cs.NE 2018
Year: 2018

The metabolism of cells is the most basic and important part of human function. Neural networks in deep learning stem from neuronal activity. It is self-evident that the significance of metabolize neuronal network(MetaNet) in model construction. In this study, we explore neuronal metabolism for shallow network from proliferation and autophagy two aspects. First, we propose different neuron proliferate methods that constructive the selfgrowing network in metabolism cycle. Proliferate neurons alleviate resources wasting and insufficient model learning problem when network initializes more or less parameters. Then combined with autophagy mechanism in the process of model self construction to ablate under-expressed neurons. The MetaNet can automatically determine the number of neurons during training, further, save more resource consumption. We verify the performance of the proposed methods on datasets: MNIST, Fashion-MNIST and CIFAR-10.
Assessment and support of emerging research groups
Venue: arXiv cs.DL 2018
Year: 2018

The starting point of this paper is a desktop research assessment model that does not take properly into account the complexities of research assessment, but rather bases itself on a series of highly simplifying, questionable assumptions related to the availability, validity and evaluative significance of research performance indicators, and to funding policy criteria. The paper presents a critique of this model, and proposes alternative assessment approaches, based on an explicit evaluative framework, focusing on preconditions to performance or communication effectiveness rather than on performance itself, combining metrics and expert knowledge, and using metrics primarily to set minimum standards. Giving special attention to early career scientists in emerging research groups, the paper discusses the limits of classical bibliometric indicators and altmetrics. It proposes alternative funding formula of research institutions aimed to support emerging research groups.
Modeling Surface Appearance from a Single Photograph using Self-augmented Convolutional Neural Networks
Venue: arXiv cs.GR 2018
Year: 2018

We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process.
User-centric Privacy Engineering for the Internet of Things
Venue: arXiv cs.NI 2018
Year: 2018

User privacy concerns are widely regarded as a key obstacle to the success of modern smart cyber-physical systems. In this paper, we analyse, through an example, some of the requirements that future data collection architectures of these systems should implement to provide effective privacy protection for users. Then, we give an example of how these requirements can be implemented in a smart home scenario. Our example architecture allows the user to balance the privacy risks with the potential benefits and take a practical decision determining the extent of the sharing. Based on this example architecture, we identify a number of challenges that must be addressed by future data processing systems in order to achieve effective privacy management for smart cyber-physical systems.
Improving generalization of vocal tract feature reconstruction: from augmented acoustic inversion to articulatory feature reconstruction without articulatory data
Venue: arXiv cs.CL 2018
Year: 2018

We address the problem of reconstructing articulatory movements, given audio and/or phonetic labels. The scarce availability of multi-speaker articulatory data makes it difficult to learn a reconstruction that generalizes to new speakers and across datasets. We first consider the XRMB dataset where audio, articulatory measurements and phonetic transcriptions are available. We show that phonetic labels, used as input to deep recurrent neural networks that reconstruct articulatory features, are in general more helpful than acoustic features in both matched and mismatched training-testing conditions. In a second experiment, we test a novel approach that attempts to build articulatory features from prior articulatory information extracted from phonetic labels. Such approach recovers vocal tract movements directly from an acoustic-only dataset without using any articulatory measurement. Results show that articulatory features generated by this approach can correlate up to 0.59 Pearson product-moment correlation with measured articulatory features.
Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Venue: arXiv cs.HC 2018
Year: 2018

Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of interactions is not only beneficial for individuals interested in their behavior in crowded situations, but also of interest to those who analyze crowd behavior. Currently, detecting social interactions is often performed using a variety of methods including ethnographic studies, computer vision techniques and manual annotation-based data analysis. However, mobile phones offer easier means for data collection that is easy to analyze and can preserve the user's privacy. In this work, we present a system for detecting stationary social interactions inside crowds, leveraging multi-modal mobile sensing data such as Bluetooth Smart (BLE), accelerometer and gyroscope. To inform the development of such system, we conducted a study with 24 participants, where we asked them to socialize with each other for 45 minutes. We built a machine learning system based on gradient-boosted trees that predicts both 1:1 and group interactions with 77.8% precision and 86.5% recall, a 30.2% performance increase compared to a proximity-based approach. By utilizing a community detection-based method, we further detected the various group formation that exist within the crowd. Using mobile phone sensors already carried by the majority of people in a crowd makes our approach particularly well suited to real-life analysis of crowd behavior and influence strategies.
Road User Abnormal Trajectory Detection using a Deep Autoencoder
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we focus on the development of a method that detects abnormal trajectories of road users at traffic intersections. The main difficulty with this is the fact that there are very few abnormal data and the normal ones are insufficient for the training of any kinds of machine learning model. To tackle these problems, we proposed the solution of using a deep autoencoder network trained solely through augmented data considered as normal. By generating artificial abnormal trajectories, our method is tested on four different outdoor urban users scenes and performs better compared to some classical outlier detection methods.
Energy-Efficient Mobile-Edge Computation Offloading for Applications with Shared Data
Venue: arXiv cs.IT 2018
Year: 2018

Mobile-edge computation offloading (MECO) has been recognized as a promising solution to alleviate the burden of resource-limited Internet of Thing (IoT) devices by offloading computation tasks to the edge of cellular networks (also known as {\em cloudlet}). Specifically, latency-critical applications such as virtual reality (VR) and augmented reality (AR) have inherent collaborative properties since part of the input/output data are shared by different users in proximity. In this paper, we consider a multi-user fog computing system, in which multiple single-antenna mobile users running applications featuring shared data can choose between (partially) offloading their individual tasks to a nearby single-antenna cloudlet for remote execution and performing pure local computation. The mobile users' energy minimization is formulated as a convex problem, subject to the total computing latency constraint, the total energy constraints for individual data downloading, and the computing frequency constraints for local computing, for which classical Lagrangian duality can be applied to find the optimal solution. Based upon the semi-closed form solution, the shared data proves to be transmitted by only one of the mobile users instead of multiple ones. Besides, compared to those baseline algorithms without considering the shared data property or the mobile users' local computing capabilities, the proposed joint computation offloading and communications resource allocation provides significant energy saving.
Performing energy modelling exercises in a transparent way the issue of data quality in power plant databases
Venue: arXiv cs.CY 2018
Year: 2018

In energy modelling, open data and open source code can help enhance traceability and reproducibility of model exercises which contribute to facilitate controversial debates and improve policy advice. While the availability of open power plant databases increased in recent years, they often differ considerably from each other and their data quality has not been systematically compared to proprietary sources yet. Here, we introduce the python-based "powerplantmatching" (PPM), an open source toolset for cleaning, standardizing and combining multiple power plant databases. We apply it once only with open databases and once with an additional proprietary database in order to discuss and elaborate the issue of data quality, by analysing capacities, countries, fuel types, geographic coordinates and commissioning years for conventional power plants. We find that a derived dataset purely based on open data is not yet on a par with one in which a proprietary database has been added to the matching, even though the statistical values for capacity matched to a large degree with both datasets. When commissioning years are needed for modelling purposes in the final dataset, the proprietary database helps crucially to increase the quality of the derived dataset.