OpenRaiser/Intern-Atlas huggingface dataset:
Scale Estimation of Monocular SfM for a Multi-modal Stereo Camera
Venue: arXiv cs.CV 2018
Year: 2018

This paper proposes a novel method of estimating the absolute scale of monocular SfM for a multi-modal stereo camera. In the fields of computer vision and robotics, scale estimation for monocular SfM has been widely investigated in order to simplify systems. This paper addresses the scale estimation problem for a stereo camera system in which two cameras capture different spectral images (e.g., RGB and FIR), whose feature points are difficult to directly match using descriptors. Furthermore, the number of matching points between FIR images can be comparatively small, owing to the low resolution and lack of thermal scene texture. To cope with these difficulties, the proposed method estimates the scale parameter using batch optimization, based on the epipolar constraint of a small number of feature correspondences between the invisible light images. The accuracy and numerical stability of the proposed method are verified by synthetic and real image experiments.
Semi-Supervised Translation with MMD Networks
Venue: arXiv cs.LG 2018
Year: 2018

This work aims to improve semi-supervised learning in a neural network architecture by introducing a hybrid supervised and unsupervised cost function. The unsupervised component is trained using a differentiable estimator of the Maximum Mean Discrepancy (MMD) distance between the network output and the target dataset. We introduce the notion of an $n$-channel network and several methods to improve performance of these nets based on supervised pre-initialization, and multi-scale kernels. This work investigates the effectiveness of these methods on language translation where very few quality translations are known \textit{a priori}. We also present a thorough investigation of the hyper-parameter space of this method on both synthetic data.
Feature Bagging for Steganographer Identification
Venue: arXiv cs.MM 2018
Year: 2018

Traditional steganalysis algorithms focus on detecting the existence of steganography in a single object. In practice, one may face a complex scenario where one or some of multiple users also called actors are guilty of using steganography, which is defined as the steganographer identification problem (SIP). This requires steganalysis experts to design effective and robust detection algorithms to identify the guilty actor(s). The mainstream works use clustering, ensemble and anomaly detection, where distances in high dimensional space between features of actors are determined to find out the outlier(s) corresponding to steganographer(s). However, in high dimensional space, feature points could be sparse such that distances between feature points may become relatively similar to each other, which cannot benefit the detection. Moreover, it is well-known in machine learning that combining techniques such as boosting and bagging can be effective in improving detection performance. This motivates the authors in this paper to present a feature bagging approach to SIP. The proposed work merges results from multiple detection sub-models, each of which feature space is randomly sampled from the raw full dimensional space. We create a new dataset called ImgNetEase including 5108 images downloaded from a social website to mimic the real-world scenario. We extract PEV-274 features from images, and take nsF5 as the steganographic algorithm for evaluation. Experiments have shown that our work improves the detection accuracy significantly on created dataset in most cases, which has shown the superiority and applicability.
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
Venue: arXiv cs.CV 2018
Year: 2018

Hyperspectral imagery collected from airborne or satellite sources inevitably suffers from spectral variability, making it difficult for spectral unmixing to accurately estimate abundance maps. The classical unmixing model, the linear mixing model (LMM), generally fails to handle this sticky issue effectively. To this end, we propose a novel spectral mixture model, called the augmented linear mixing model (ALMM), to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing. The proposed approach models the main spectral variability (i.e., scaling factors) generated by variations in illumination or typography separately by means of the endmember dictionary. It then models other spectral variabilities caused by environmental conditions (e.g., local temperature and humidity, atmospheric effects) and instrumental configurations (e.g., sensor noise), as well as material nonlinear mixing effects, by introducing a spectral variability dictionary. To effectively run the data-driven learning strategy, we also propose a reasonable prior knowledge for the spectral variability dictionary, whose atoms are assumed to be low-coherent with spectral signatures of endmembers, which leads to a well-known low coherence dictionary learning problem. Thus, a dictionary learning technique is embedded in the framework of spectral unmixing so that the algorithm can learn the spectral variability dictionary and estimate the abundance maps simultaneously. Extensive experiments on synthetic and real datasets are performed to demonstrate the superiority and effectiveness of the proposed method in comparison with previous state-of-the-art methods.
Speaking style adaptation in Text-To-Speech synthesis using Sequence-to-sequence models with attention
Venue: arXiv cs.SD 2018
Year: 2018

Currently, there are increasing interests in text-to-speech (TTS) synthesis to use sequence-to-sequence models with attention. These models are end-to-end meaning that they learn both co-articulation and duration properties directly from text and speech. Since these models are entirely data-driven, they need large amounts of data to generate synthetic speech with good quality. However, in challenging speaking styles, such as Lombard speech, it is difficult to record sufficiently large speech corpora. Therefore, in this study we propose a transfer learning method to adapt a sequence-to-sequence based TTS system of normal speaking style to Lombard style. Moreover, we experiment with a WaveNet vocoder in synthesis of Lombard speech. We conducted subjective evaluations to assess the performance of the adapted TTS systems. The subjective evaluation results indicated that an adaptation system with the WaveNet vocoder clearly outperformed the conventional deep neural network based TTS system in synthesis of Lombard speech.
A study on load-balanced variants of the bin packing problem
Venue: arXiv cs.DS 2018
Year: 2018

We consider several extensions of the fractional bin packing problem, a relaxation of the traditional bin packing problem where the objects may be split across multiple bins. In these extensions, we introduce load-balancing constraints imposing that the share of each object which is assigned to a same bin must be equal. We propose a Mixed-Integer Programming (MIP) formulation and show that the problem becomes NP-hard if we limit to at most 3 the number of bins across which each object can be split. We then consider a variant where the balanced allocations of objects to bins may be done in successive rounds; this problem was inspired by telecommunication applications, and may be used to model simple Live Streaming networks. We show that two rounds are always sufficient to completely assign all objects to the bins and then provide an optimal polynomial-time allocation algorithm for this problem.
Causal Inference in Nonverbal Dyadic Communication with Relevant Interval Selection and Granger Causality
Venue: arXiv cs.CV 2018
Year: 2018

Human nonverbal emotional communication in dyadic dialogs is a process of mutual influence and adaptation. Identifying the direction of influence, or cause-effect relation between participants is a challenging task, due to two main obstacles. First, distinct emotions might not be clearly visible. Second, participants cause-effect relation is transient and variant over time. In this paper, we address these difficulties by using facial expressions that can be present even when strong distinct facial emotions are not visible. We also propose to apply a relevant interval selection approach prior to causal inference to identify those transient intervals where adaptation process occurs. To identify the direction of influence, we apply the concept of Granger causality to the time series of facial expressions on the set of relevant intervals. We tested our approach on synthetic data and then applied it to newly, experimentally obtained data. Here, we were able to show that a more sensitive facial expression detection algorithm and a relevant interval detection approach is most promising to reveal the cause-effect pattern for dyadic communication in various instructed interaction conditions.
ReviewQA: a relational aspect-based opinion reading dataset
Venue: arXiv cs.CL 2018
Year: 2018

Deep reading models for question-answering have demonstrated promising performance over the last couple of years. However current systems tend to learn how to cleverly extract a span of the source document, based on its similarity with the question, instead of seeking for the appropriate answer. Indeed, a reading machine should be able to detect relevant passages in a document regarding a question, but more importantly, it should be able to reason over the important pieces of the document in order to produce an answer when it is required. To motivate this purpose, we present ReviewQA, a question-answering dataset based on hotel reviews. The questions of this dataset are linked to a set of relational understanding competencies that we expect a model to master. Indeed, each question comes with an associated type that characterizes the required competency. With this framework, it is possible to benchmark the main families of models and to get an overview of what are the strengths and the weaknesses of a given model on the set of tasks evaluated in this dataset. Our corpus contains more than 500.000 questions in natural language over 100.000 hotel reviews. Our setup is projective, the answer of a question does not need to be extracted from a document, like in most of the recent datasets, but selected among a set of candidates that contains all the possible answers to the questions of the dataset. Finally, we present several baselines over this dataset.
The determinants of academic career advancement: evidence from Italy
Venue: arXiv cs.DL 2018
Year: 2018

In this work we investigate the determinants of professors' career advancement in Italian universities. From the analyses, it emerges that the fundamental determinant of an academic candidate's success is not scientific merit, but rather the number of years that the candidate has belonged to the same university as the selection committee president. Where applicants have participated in research work with the president, their probability of success also increases significantly. The factors of the years of service and occurrence of joint research for the other commission members also have an effect, however of lesser weight. The specific phenomenon of nepotism, although it exists, seems less important. The scientific quality of the commission members has negligible effect on the expected outcome of the competition, and even more so the geographic location of the university calling for the competition.
Quality-of-Service in Multihop Wireless Networks: Diffusion Approximation
Venue: arXiv cs.NI 2018
Year: 2018

We consider a multihop wireless system. There are multiple source-destination pairs. The data from a source may have to pass through multiple nodes. We obtain a channel scheduling policy which can guarantee end-to-end mean delay for the different traffic streams. We show the stability of the network for this policy by convergence to a fluid limit. It is intractable to obtain the stationary distribution of this network. Thus we also provide a diffusion approximation for this scheme under heavy traffic. We show that the stationary distribution of the scaled process of the network converges to that of the Brownian limit. This theoretically justifies the performance of the system. We provide simulations to verify our claims.
Wireless Terahertz System Architectures for Networks Beyond 5G
Venue: arXiv cs.NI 2018
Year: 2018

The present white paper focuses on the system requirements of TERRANOVA. Initially details the key use cases for the TERRANOVA technology and presents the description of the network architecture. In more detail, the use cases are classified into two categories, namely backhaul & fronthaul and access and small cell backhaul. The first category refers to fibre extender, point-to-point and redundancy applications, whereas the latter is designed to support backup connection for small and medium-sized enterprises (SMEs), internet of things (IoT) dense environments, data centres, indoor wireless access, ad hoc networks, and last mile access. Then, it provides the networks architecture for the TERRANOVA system as well as the network elements that need to be deployed. The use cases are matched to specific technical scenarios, namely outdoor fixed point-to-point (P2P), outdoor/indoor individual point-to-multipoint (P2MP), and outdoor/indoor "quasi"-omnidirection, and the key performance requirements of each scenario are identified. Likewise, we present the breakthrough novel technology concepts, including the joint design of baseband signal processing for the complete optical and wireless link, the development of broadband and spectrally efficient RF-frontends for frequencies >275 GHz, as well as channel modelling, waveforms, antenna array and multiple-access schemes design, which we are going to use in order to satisfy the presented requirements. Next, an overview of the required new functionalities in both physical (PHY) layer and medium access control (MAC) layers in the TERRANOVA system architecture will be given. Finally, the individual enablers of the TERRANOVA system are combined to develop particular candidate architectures for each of the three technical scenarios.
Analyzing Ideological Communities in Congressional Voting Networks
Venue: arXiv cs.SI 2018
Year: 2018

We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communities, their member polarization, and how such communities evolve over time, covering a 15-year period. Our results reveal very distinct patterns across the two case studies, in terms of both structural and dynamic properties.
Big Data Meet Cyber-Physical Systems: A Panoramic Survey
Venue: arXiv cs.LG 2018
Year: 2018

The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world {via} creating new services and applications in a variety of sectors such as environmental monitoring, mobile-health systems, intelligent transportation systems and so on. The {information and communication technology }(ICT) sector is experiencing a significant growth in { data} traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. {It} is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy {via} providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS {require} cybersecurity to protect {them} against malicious attacks and unauthorized intrusion, which {become} a challenge with the enormous amount of data that is continuously being generated in the network. {Thus, we also} provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.
TallyQA: Answering Complex Counting Questions
Venue: arXiv cs.CV 2018
Year: 2018

Most counting questions in visual question answering (VQA) datasets are simple and require no more than object detection. Here, we study algorithms for complex counting questions that involve relationships between objects, attribute identification, reasoning, and more. To do this, we created TallyQA, the world's largest dataset for open-ended counting. We propose a new algorithm for counting that uses relation networks with region proposals. Our method lets relation networks be efficiently used with high-resolution imagery. It yields state-of-the-art results compared to baseline and recent systems on both TallyQA and the HowMany-QA benchmark.
Distributed Convex Optimization With Limited Communications
Venue: arXiv cs.DC 2018
Year: 2018

In this paper, a distributed convex optimization algorithm, termed \emph{distributed coordinate dual averaging} (DCDA) algorithm, is proposed. The DCDA algorithm addresses the scenario of a large distributed optimization problem with limited communication among nodes in the network. Currently known distributed subgradient methods, such as the distributed dual averaging or the distributed alternating direction method of multipliers algorithms, assume that nodes can exchange messages of large cardinality. Such network communication capabilities are not valid in many scenarios of practical relevance. In the DCDA algorithm, on the other hand, communication of each coordinate of the optimization variable is restricted over time. For the proposed algorithm, we bound the rate of convergence under different communication protocols and network architectures. We also consider the extensions to the case of imperfect gradient knowledge and the case in which transmitted messages are corrupted by additive noise or are quantized. Relevant numerical simulations are also provided.
Dance Teaching by a Robot: Combining Cognitive and Physical Human-Robot Interaction for Supporting the Skill Learning Process
Venue: arXiv cs.RO 2018
Year: 2018

This letter presents a physical human-robot interaction scenario in which a robot guides and performs the role of a teacher within a defined dance training framework. A combined cognitive and physical feedback of performance is proposed for assisting the skill learning process. Direct contact cooperation has been designed through an adaptive impedance-based controller that adjusts according to the partner's performance in the task. In measuring performance, a scoring system has been designed using the concept of progressive teaching (PT). The system adjusts the difficulty based on the user's number of practices and performance history. Using the proposed method and a baseline constant controller, comparative experiments have shown that the PT presents better performance in the initial stage of skill learning. An analysis of the subjects' perception of comfort, peace of mind, and robot performance have shown a significant difference at the p < .01 level, favoring the PT algorithm.
Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration
Venue: arXiv cs.RO 2018
Year: 2018

Convolutional Neural Networks (CNNs) have recently been shown to excel at performing visual place recognition under changing appearance and viewpoint. Previously, place recognition has been improved by intelligently selecting relevant spatial keypoints within a convolutional layer and also by selecting the optimal layer to use. Rather than extracting features out of a particular layer, or a particular set of spatial keypoints within a layer, we propose the extraction of features using a subset of the channel dimensionality within a layer. Each feature map learns to encode a different set of weights that activate for different visual features within the set of training images. We propose a method of calibrating a CNN-based visual place recognition system, which selects the subset of feature maps that best encodes the visual features that are consistent between two different appearances of the same location. Using just 50 calibration images, all collected at the beginning of the current environment, we demonstrate a significant and consistent recognition improvement across multiple layers for two different neural networks. We evaluate our proposal on three datasets with different types of appearance changes - afternoon to morning, winter to summer and night to day. Additionally, the dimensionality reduction approach improves the computational processing speed of the recognition system.
Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms
Venue: arXiv cs.NE 2018
Year: 2018

As automatic optimization techniques find their way into industrial applications, the behavior of many complex systems is determined by some form of planner picking the right actions to optimize a given objective function. In many cases, the mapping of plans to objective reward may change due to unforeseen events or circumstances in the real world. In those cases, the planner usually needs some additional effort to adjust to the changed situation and reach its previous level of performance. Whenever we still need to continue polling the planner even during re-planning, it oftentimes exhibits severely lacking performance. In order to improve the planner's resilience to unforeseen change, we argue that maintaining a certain level of diversity amongst the considered plans at all times should be added to the planner's objective. Effectively, we encourage the planner to keep alternative plans to its currently best solution. As an example case, we implement a diversity-aware genetic algorithm using two different metrics for diversity (differing in their generality) and show that the blow in performance due to unexpected change can be severely lessened in the average case. We also analyze the parameter settings necessary for these techniques in order to gain an intuition how they can be incorporated into larger frameworks or process models for software and systems engineering.
SAFE-PDF: Robust Detection of JavaScript PDF Malware Using Abstract Interpretation
Venue: arXiv cs.CR 2018
Year: 2018

The popularity of the PDF format and the rich JavaScript environment that PDF viewers offer make PDF documents an attractive attack vector for malware developers. PDF documents present a serious threat to the security of organizations because most users are unsuspecting of them and thus likely to open documents from untrusted sources. We propose to identify malicious PDFs by using conservative abstract interpretation to statically reason about the behavior of the embedded JavaScript code. Currently, state-of-the-art tools either: (1) statically identify PDF malware based on structural similarity to known malicious samples; or (2) dynamically execute the code to detect malicious behavior. These two approaches are subject to evasion attacks that mimic the structure of benign documents or do not exhibit their malicious behavior when being analyzed dynamically. In contrast, abstract interpretation is oblivious to both types of evasions. A comparison with two state-of-the-art PDF malware detection tools shows that our conservative abstract interpretation approach achieves similar accuracy, while being more resilient to evasion attacks.