OpenRaiser/Intern-Atlas huggingface dataset:
Model Based Desgin Environment for Launcher Upper Stage GNC Development
Venue: arXiv cs.SY 2018
Year: 2018

The cost associated with developing flight control forms a significant part of the overall development cost. The increased demand for greater functionality and for extending the domain of applicability of future launchers leads to higher complexity of the GNC algorithms. Furthermore, there is also a need for a responsive design methodology, which can quickly adapt to changes in the requirements during the development process. These demands are stretching the current,largely manual, development process, which is fragmented in the different disciplines and activities concerning modelling, algorithm design, software coding, implementation on the avionics platform and the associated testing at the different stages. The model-based-design (MBD) philosophy presents an attractive solution in addressing the multi-disciplinary nature of the flight algorithm design task. In terms of design process, this materializes in a tight coupling between the modelling, the design and the analysis activities.An integrated design framework for the GNC development for future launcher upper stages has been realized.
Characterisation of (Sub)sequential Rational Functions over a General Class Monoids
Venue: arXiv cs.FL 2018
Year: 2018

In this technical report we describe a general class of monoids for which (sub)sequential rational can be characterised in terms of a congruence relation in the flavour of Myhill-Nerode relation. The class of monoids that we consider can be described in terms of natural algebraic axioms, contains the free monoids, groups, the tropical monoid, and is closed under Cartesian.
Asymptotic Analysis for Low-Resolution Massive MIMO Systems with MMSE Receiver
Venue: arXiv cs.IT 2018
Year: 2018

The uplink achievable rate of massive multiple- input-multiple-output (MIMO) systems, where the low-resolution analog-to-digital converters (ADCs) are assumed to equip at the base station (BS), is investigated in this paper. We assume that only imperfect channel station information is known at the BS. Then a new MMSE receiver is designed by taking not only the Gaussian noise, but also the channel estimation error and quantizer noise into account. By using the Stieltjes transform of random matrix, we further derive a tight asymptotic equivalent for the uplink achievable rate with proposed MMSE receiver. We present a detailed analysis for the number of BS antennas through the expression of the achievable rates and validate the results using numerical simulations. It is also shown that we can compensate the performance loss due to the low-resolution quantization by increasing the number of antennas at the BS.
Image Captioning at Will: A Versatile Scheme for Effectively Injecting Sentiments into Image Descriptions
Venue: arXiv cs.CV 2018
Year: 2018

Automatic image captioning has recently approached human-level performance due to the latest advances in computer vision and natural language understanding. However, most of the current models can only generate plain factual descriptions about the content of a given image. However, for human beings, image caption writing is quite flexible and diverse, where additional language dimensions, such as emotion, humor and language styles, are often incorporated to produce diverse, emotional, or appealing captions. In particular, we are interested in generating sentiment-conveying image descriptions, which has received little attention. The main challenge is how to effectively inject sentiments into the generated captions without altering the semantic matching between the visual content and the generated descriptions. In this work, we propose two different models, which employ different schemes for injecting sentiments into image captions. Compared with the few existing approaches, the proposed models are much simpler and yet more effective. The experimental results show that our model outperform the state-of-the-art models in generating sentimental (i.e., sentiment-bearing) image captions. In addition, we can also easily manipulate the model by assigning different sentiments to the testing image to generate captions with the corresponding sentiments.
Cost- and Energy-Aware Multi-Flow Mobile Data Offloading Using Markov Decision Process
Venue: arXiv cs.NI 2018
Year: 2018

With the rapid increase in demand for mobile data, mobile network operators are trying to expand wireless network capacity by deploying wireless local area network (LAN) hotspots on which they can offload their mobile traffic. However, these network-centric methods usually do not fulfill the interests of mobile users (MUs). Taking into consideration many issues, MUs should be able to decide whether to offload their traffic to a complementary wireless LAN. Our previous work studied single-flow wireless LAN offloading from a MU's perspective by considering delay-tolerance of traffic, monetary cost and energy consumption. In this paper, we study the multi-flow mobile data offloading problem from a MU's perspective in which a MU has multiple applications to download data simultaneously from remote servers, and different applications' data have different deadlines. We formulate the wireless LAN offloading problem as a finite-horizon discrete-time Markov decision process (MDP) and establish an optimal policy by a dynamic programming based algorithm. Since the time complexity of the dynamic programming based offloading algorithm is still high, we propose a low time complexity heuristic offloading algorithm with performance sacrifice. Extensive simulations are conducted to validate our proposed offloading algorithms.
Cell-Free Massive MIMO with Limited Backhaul
Venue: arXiv cs.IT 2018
Year: 2018

We consider a cell-free Massive multiple-input multiple-output (MIMO) system and investigate the system performance for the case when the quantized version of the estimated channel and the quantized received signal are available at the central processing unit (CPU), and the case when only the quantized version of the combined signal with maximum ratio combining (MRC) detector is available at the CPU. Next, we study the max-min optimization problem, where the minimum user uplink rate is maximized with backhaul capacity constraints. To deal with the max-min non-convex problem, we propose to decompose the original problem into two sub-problems. Based on these sub-problems, we develop an iterative scheme which solves the original max-min user uplink rate. Moreover, we present a user assignment algorithm to further improve the performance of cell-free Massive MIMO with limited backhaul links.
The Impact of Correlated Metrics on Defect Models
Venue: arXiv cs.SE 2018
Year: 2018

Defect models are analytical models that are used to build empirical theories that are related to software quality. Prior studies often derive knowledge from such models using interpretation techniques, such as ANOVA Type-I. Recent work raises concerns that prior studies rarely remove correlated metrics when constructing such models. Such correlated metrics may impact the interpretation of models. Yet, the impact of correlated metrics in such models has not been investigated. In this paper, we set out to investigate the impact of correlated metrics, and the benefits and costs of removing correlated metrics on defect models. Through a case study of 15 publicly-available defect datasets, we find that (1) correlated metrics impact the ranking of the highest ranked metric for all of the 9 studied model interpretation techniques. On the other hand, removing correlated metrics (2) improves the consistency of the highest ranked metric regardless of how a model is specified for all of the studied interpretation techniques (except for ANOVA Type-I); and (3) negligibly impacts the performance and stability of defect models. Thus, researchers must (1) mitigate (e.g., remove) correlated metrics prior to constructing a defect model; and (2) avoid using ANOVA Type-I even if all correlated metrics are removed.
Visually Explainable Recommendation
Venue: arXiv cs.IR 2018
Year: 2018

Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.
Paraphrase-Supervised Models of Compositionality
Venue: arXiv cs.CL 2018
Year: 2018

Compositional vector space models of meaning promise new solutions to stubborn language understanding problems. This paper makes two contributions toward this end: (i) it uses automatically-extracted paraphrase examples as a source of supervision for training compositional models, replacing previous work which relied on manual annotations used for the same purpose, and (ii) develops a context-aware model for scoring phrasal compositionality. Experimental results indicate that these multiple sources of information can be used to learn partial semantic supervision that matches previous techniques in intrinsic evaluation tasks. Our approaches are also evaluated for their impact on a machine translation system where we show improvements in translation quality, demonstrating that compositionality in interpretation correlates with compositionality in translation.
Protecting Privacy in VANETs Using Mix Zones With Virtual Pseudonym Change
Venue: arXiv cs.NI 2018
Year: 2018

Vehicular ad hoc networks VANETs use pseudonyms to communicate among them and with road side units, these pseudonyms are used to authenticate these vehicles and to hide real identities behind these pseudonyms, to better enhance privacy, these pseudonyms are changed frequently so that it will not be that easy to link these pseudonyms together and hence reveal real identities. However, changing pseudonyms will not be that useful if previous and current pseudonyms are easily linked together. Therefore different techniques have been proposed to hide the pseudonym changes and make it difficult to link pseudonyms together. Most of these techniques do not fully quarantine privacy when changing a pseudonym under some situations such as low traffic. In this paper, we provide a technique for changing pseudonyms that has the same privacy level under all traffic conditions. The technique relies on fixed mix zones that are planted and distributed over the roads. By this technique, a vehicle guarantees a high level of security when changing its pseudonym at that mix zone which will make it very difficult for an adversary to link particular pseudonyms together and hence reveal real identity. Performance analysis showed that our model works efficiently with very few computational costs
Could scientists use Altmetric.com scores to predict longer term citation counts?
Venue: arXiv cs.DL 2018
Year: 2018

Altmetrics from Altmetric.com are widely used by publishers and researchers to give earlier evidence of attention than citation counts. This article assesses whether Altmetric.com scores are reliable early indicators of likely future impact and whether they may also reflect non-scholarly impacts. A preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is Mendeley reader counts, with weaker news, informational and social network discussion/promotion dimensions in some fields. Based on a regression analysis of Altmetric.com data from November 2015 and Scopus citation counts from October 2017 for articles in 30 narrow fields, only Mendeley reader counts are consistent predictors of future citation impact. Most other Altmetric.com scores can help predict future impact in some fields. Overall, the results confirm that early Altmetric.com scores can predict later citation counts, although less well than journal impact factors, and the optimal strategy is to consider both Altmetric.com scores and journal impact factors. Altmetric.com scores can also reflect dimensions of non-scholarly impact in some fields.
Probability of detection of an extraneous mobile object by autonomous unmanned underwater vehicles as a solution of the Buffon problem
Venue: arXiv cs.RO 2018
Year: 2018

Underwater robotics addresses the problem of object detection apparatus. Offers a probabilistic formulation of the problem, which uses the reduction of the detection task to a classical task of Buffon. This formulation arises naturally in the formulation of the problem in the coordinate system associated with the apparatus. It is shown that the problem allows analysis in the presence of an asymptotic parameter, determined by the ratio of the local scan size of the apparatus to the global size of the problem under consideration.
Fast and Accurate Reconstruction of Compressed Color Light Field
Venue: arXiv cs.CV 2018
Year: 2018

Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field photography has been proposed to tackle the trade-offs between the spatial and angular resolutions. It obtains by only one lens, a compressed version of the regular multi-lens system. The acquisition system consists of a dedicated hardware followed by a decompression algorithm, which usually suffers from high computational time. In this work, we propose a computationally efficient neural network that recovers a high-quality color light field from a single coded image. Unlike previous works, we compress the color channels as well, removing the need for a CFA in the imaging system. Our approach outperforms existing solutions in terms of recovery quality and computational complexity. We propose also a neural network for depth map extraction based on the decompressed light field, which is trained in an unsupervised manner without the ground truth depth map.
Hardness, Approximability, and Fixed-Parameter Tractability of the Clustered Shortest-Path Tree Problem
Venue: arXiv cs.DS 2018
Year: 2018

Given an $n$-vertex non-negatively real-weighted graph $G$, whose vertices are partitioned into a set of $k$ clusters, a \emph{clustered network design problem} on $G$ consists of solving a given network design optimization problem on $G$, subject to some additional constraint on its clusters. In particular, we focus on the classic problem of designing a \emph{single-source shortest-path tree}, and we analyze its computational hardness when in a feasible solution each cluster is required to form a subtree. We first study the \emph{unweighted} case, and prove that the problem is \np-hard. However, on the positive side, we show the existence of an approximation algorithm whose quality essentially depends on few parameters, but which remarkably is an $O(1)$-approximation when the largest out of all the \emph{diameters} of the clusters is either $O(1)$ or $Θ(n)$. Furthermore, we also show that the problem is \emph{fixed-parameter tractable} with respect to $k$ or to the number of vertices that belong to clusters of size at least 2. Then, we focus on the \emph{weighted} case, and show that the problem can be approximated within a tight factor of $O(n)$, and that it is fixed-parameter tractable as well. Finally, we analyze the unweighted \emph{single-pair shortest path problem}, and we show it is hard to approximate within a (tight) factor of $n^{1-ε}$, for any $ε>0$.
Hierarchical restricted isometry property for Kronecker product measurements
Venue: arXiv cs.IT 2018
Year: 2018

Hierarchically sparse signals and Kronecker product structured measurements arise naturally in a variety of applications. The simplest example of a hierarchical sparsity structure is two-level $(s,σ)$-hierarchical sparsity which features $s$-block-sparse signals with $σ$-sparse blocks. For a large class of algorithms recovery guarantees can be derived based on the restricted isometry property (RIP) of the measurement matrix and model-based variants thereof. We show that given two matrices $\mathbf{A}$ and $\mathbf{B}$ having the standard $s$-sparse and $σ$-sparse RIP their Kronecker product $\mathbf{A}\otimes\mathbf{B}$ has two-level $(s,σ)$-hierarchically sparse RIP (HiRIP). This result can be recursively generalized to signals with multiple hierarchical sparsity levels and measurements with multiple Kronecker product factors. As a corollary we establish the efficient reconstruction of hierarchical sparse signals from Kronecker product measurements using the HiHTP algorithm. We argue that Kronecker product measurement matrices allow to design large practical compressed sensing systems that are deterministically certified to reliably recover signals in a stable fashion. We elaborate on their motivation from the perspective of applications.
Robust 3D Human Motion Reconstruction Via Dynamic Template Construction
Venue: arXiv cs.CV 2018
Year: 2018

In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited field of- view, etc. Direct estimation of 3D pose, body shape or motion on these low-quality data has been traditionally challenging.In this paper, we present a graph-based non-rigid shape registration framework that can simultaneously recover 3D human body geometry and estimate pose/motion at high fidelity.Our approach first generates a global full-body template by registering all poses in the acquired motion sequence.We then construct a deformable graph by utilizing the rigid components in the global template. We directly warp the global template graph back to each motion frame in order to fill in missing geometry. Specifically, we combine local rigidity and temporal coherence constraints to maintain geometry and motion consistencies. Comprehensive experiments on various scenes show that our method is accurate and robust even in the presence of drastic motions.
Analysing Collective Behaviour in Temporal Networks Using Event Graphs and Temporal Motifs
Venue: arXiv cs.SI 2018
Year: 2018

Historically studies of behaviour on networks have focused on the behaviour of individuals (node-based) or on the aggregate behaviour of the entire network. We propose a new method to decompose a temporal network into macroscale components and to analyse the behaviour of these components, or collectives of nodes, across time. This method utilises all available information in the temporal network (i.e. no temporal aggregation), combining both topological and temporal structure using temporal motifs and inter-event times. This allows us create an embedding of a temporal network in order to describe behaviour over time and at different timescales. We illustrate this method using an example of digital communication data collected from an online social network.
Message Transmission over Classical Quantum Channels with a Jammer with Side Information: Message Transmission Capacity and Resources
Venue: arXiv cs.IT 2018
Year: 2018

In this paper we propose a new model for arbitrarily varying classical-quantum channels. In this model a jammer has side information. We consider two scenarios. In the first scenario the jammer knows the channel input, while in the second scenario the jammer knows both the channel input and the message. The transmitter and receiver share a secret random key with a vanishing key rate. We determine the capacity for both average and maximum error criteria for both scenarios. We also establish the strong converse. We show that all these corresponding capacities are equal, which means that additionally revealing the message to the jammer does not change the capacity.
Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture
Venue: arXiv cs.LG 2018
Year: 2018

The present work shows the application of transfer learning for a pre-trained deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a single workstation with enabled NVIDIA GPU card that takes up to 1 hour to complete the training task and archive an overall average accuracy of $94.7\%$. The DNN presents a $20\%$ score of misclassification for an external test dataset. The accuracy of the proposed methodology is equivalent to ones using HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of being independent on special equipment to classify wheat kernel for FHB symptoms.
Deceptive Games
Venue: arXiv cs.AI 2018
Year: 2018

Deceptive games are games where the reward structure or other aspects of the game are designed to lead the agent away from a globally optimal policy. While many games are already deceptive to some extent, we designed a series of games in the Video Game Description Language (VGDL) implementing specific types of deception, classified by the cognitive biases they exploit. VGDL games can be run in the General Video Game Artificial Intelligence (GVGAI) Framework, making it possible to test a variety of existing AI agents that have been submitted to the GVGAI Competition on these deceptive games. Our results show that all tested agents are vulnerable to several kinds of deception, but that different agents have different weaknesses. This suggests that we can use deception to understand the capabilities of a game-playing algorithm, and game-playing algorithms to characterize the deception displayed by a game.
Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction
Venue: arXiv cs.CV 2018
Year: 2018

The study and modeling of driver's gaze dynamics is important because, if and how the driver is monitoring the driving environment is vital for driver assistance in manual mode, for take-over requests in highly automated mode and for semantic perception of the surround in fully autonomous mode. We developed a machine vision based framework to classify driver's gaze into context rich zones of interest and model driver's gaze behavior by representing gaze dynamics over a time period using gaze accumulation, glance duration and glance frequencies. As a use case, we explore the driver's gaze dynamic patterns during maneuvers executed in freeway driving, namely, left lane change maneuver, right lane change maneuver and lane keeping. It is shown that condensing gaze dynamics into durations and frequencies leads to recurring patterns based on driver activities. Furthermore, modeling these patterns show predictive powers in maneuver detection up to a few hundred milliseconds a priori.