OpenRaiser/Intern-Atlas huggingface dataset:
Wearable affect and stress recognition: A review
Venue: arXiv cs.HC 2018
Year: 2018

Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. provide reasoning for decision making or support mental wellbeing. Recently, besides approaches based on audio, visual or text information, solutions relying on wearable sensors as observables (recording mainly physiological and inertial parameters) have received increasing attention. Wearable systems offer an ideal platform for long-term affect recognition applications due to their rich functionality and form factor. However, existing literature lacks a comprehensive overview of state-of-the-art research in wearable-based affect recognition. Therefore, the aim of this paper is to provide a broad overview and in-depth understanding of the theoretical background, methods, and best practices of wearable affect and stress recognition. We summarise psychological models, and detail affect-related physiological changes and their measurement with wearables. We outline lab protocols eliciting affective states, and provide guidelines for ground truth generation in field studies. We also describe the standard data processing chain, and review common approaches to preprocessing, feature extraction, and classification. By providing a comprehensive summary of the state-of-the-art and guidelines to various aspects, we would like to enable other researchers in the field of affect recognition to conduct and evaluate user studies and develop wearable systems.
The Price of Uncertain Priors in Source Coding
Venue: arXiv cs.IT 2018
Year: 2018

We consider the problem of one-way communication when the recipient does not know exactly the distribution that the messages are drawn from, but has a "prior" distribution that is known to be close to the source distribution, a problem first considered by Juba et al. We consider the question of how much longer the messages need to be in order to cope with the uncertainty about the receiver's prior and the source distribution, respectively, as compared to the standard source coding problem. We consider two variants of this uncertain priors problem: the original setting of Juba et al. in which the receiver is required to correctly recover the message with probability 1, and a setting introduced by Haramaty and Sudan, in which the receiver is permitted to fail with some probability $ε$. In both settings, we obtain lower bounds that are tight up to logarithmically smaller terms. In the latter setting, we furthermore present a variant of the coding scheme of Juba et al. with an overhead of $\logα+\log 1/ε+1$ bits, thus also establishing the nearly tight upper bound.
Robust Myopic Control for Systems with Imperfect Observations
Venue: arXiv cs.SY 2018
Year: 2018

Control of systems operating in unexplored environments is challenging due to lack of complete model knowledge. Additionally, under measurement noises, data collected from onboard sensors are of limited accuracy. This paper considers imperfect state observations in developing a control strategy for systems moving in unknown environments. First, we include hard constraints in the problem for safety concerns. Given the observed states, the robust myopic control approach learns local dynamics, explores all possible trajectories within the observation error bound, and computes the optimal control action using robust optimization. Finally, we validate the method in an OSIRIS-REx-based asteroid landing scenario.
On the use of supervised clustering in stochastic NMPC design
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, a supervised clustering based-heuristic is proposed for the real-time implementation of approximate solutions to stochastic nonlinear model predictive control frameworks. The key idea is to update on-line a low cardinality set of uncertainty vectors to be used in the expression of the stochastic cost and constraints. These vectors are the centers of uncertainty clusters that are built using the optimal control sequences, cost and constraints indicators as supervision labels. The use of a moving clustering data buffer which accumulates recent past computations enables to reduce the computational burden per sampling period while making available at each period a relevant amount of samples for the clustering task. A relevant example is given to illustrate the contribution and the associated algorithms.
Feature-based groupwise registration of historical aerial images to present-day ortho-photo maps
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we address the registration of historical WWII images to present-day ortho-photo maps for the purpose of geolocalization. Due to the challenging nature of this problem, we propose to register the images jointly as a group rather than in a step-by-step manner. To this end, we exploit Hough Voting spaces as pairwise registration estimators and show how they can be integrated into a probabilistic groupwise registration framework that can be efficiently optimized. The feature-based nature of our registration framework allows to register images with a-priori unknown translational and rotational relations, and is also able to handle scale changes of up to 30% in our test data due to a final geometrically guided matching step. The superiority of the proposed method over existing pairwise and groupwise registration methods is demonstrated on eight highly challenging sets of historical images with corresponding ortho-photo maps.
IEGAN: Multi-purpose Perceptual Quality Image Enhancement Using Generative Adversarial Network
Venue: arXiv cs.CV 2018
Year: 2018

Despite the breakthroughs in quality of image enhancement, an end-to-end solution for simultaneous recovery of the finer texture details and sharpness for degraded images with low resolution is still unsolved. Some existing approaches focus on minimizing the pixel-wise reconstruction error which results in a high peak signal-to-noise ratio. The enhanced images fail to provide high-frequency details and are perceptually unsatisfying, i.e., they fail to match the quality expected in a photo-realistic image. In this paper, we present Image Enhancement Generative Adversarial Network (IEGAN), a versatile framework capable of inferring photo-realistic natural images for both artifact removal and super-resolution simultaneously. Moreover, we propose a new loss function consisting of a combination of reconstruction loss, feature loss and an edge loss counterpart. The feature loss helps to push the output image to the natural image manifold and the edge loss preserves the sharpness of the output image. The reconstruction loss provides low-level semantic information to the generator regarding the quality of the generated images compared to the original. Our approach has been experimentally proven to recover photo-realistic textures from heavily compressed low-resolution images on public benchmarks and our proposed high-resolution World100 dataset.
PE-AONT: Partial Encryption combined with an All-or-Nothing Transform
Venue: arXiv cs.CR 2018
Year: 2018

In this report, we introduce PE-AONT: a novel algorithm for fast and secure data fragmentation. Initial data are fragmented and only a selected subset of the fragments is encrypted. Further, fragments are transformed using a variation of an all-or-nothing transform that blends encrypted and non-encrypted fragments. By encrypting data only partially, we achieve better performance than relevant techniques including data encryption and straightforward fragmentation. Moreover, when the ratio between the number of encrypted and non-encrypted fragments is wisely chosen, data inside fragments are protected against exposure of the encryption key unless all fragments are gathered by an attacker.
The typical cell in anisotropic tessellations
Venue: arXiv cs.NI 2018
Year: 2018

The typical cell is a key concept for stochastic-geometry based modeling in communication networks, as it provides a rigorous framework for describing properties of a serving zone associated with a component selected at random in a large network. We consider a setting where network components are located on a large street network. While earlier investigations were restricted to street systems without preferred directions, in this paper we derive the distribution of the typical cell in Manhattan-type systems characterized by a pattern of horizontal and vertical streets. We explain how the mathematical description can be turned into a simulation algorithm and provide numerical results uncovering novel effects when compared to classical isotropic networks.
Digital Forensics for IoT and WSNs
Venue: arXiv cs.CR 2018
Year: 2018

In the last decade, wireless sensor networks (WSNs) and Internet-of-Things (IoT) devices are proliferated in many domains including critical infrastructures such as energy, transportation and manufacturing. Consequently, most of the daily operations now rely on the data coming from wireless sensors or IoT devices and their actions. In addition, personal IoT devices are heavily used for social media applications, which connect people as well as all critical infrastructures to each other under the cyber domain. However, this connectedness also comes with the risk of increasing number of cyber attacks through WSNs and/or IoT. While a significant research has been dedicated to secure WSN/IoT, this still indicates that there needs to be forensics mechanisms to be able to conduct investigations and analysis. In particular, understanding what has happened after a failure is crucial to many businesses, which rely on WSN/IoT applications. Therefore, there is a great interest and need for understanding digital forensics applications in WSN and IoT realms. This chapter fills this gap by providing an overview and classification of digital forensics research and applications in these emerging domains in a comprehensive manner. In addition to analyzing the technical challenges, the chapter provides a survey of the existing efforts from the device level to network level while also pointing out future research opportunities.
AutoSense Model for Word Sense Induction
Venue: arXiv cs.CL 2018
Year: 2018

Word sense induction (WSI), or the task of automatically discovering multiple senses or meanings of a word, has three main challenges: domain adaptability, novel sense detection, and sense granularity flexibility. While current latent variable models are known to solve the first two challenges, they are not flexible to different word sense granularities, which differ very much among words, from aardvark with one sense, to play with over 50 senses. Current models either require hyperparameter tuning or nonparametric induction of the number of senses, which we find both to be ineffective. Thus, we aim to eliminate these requirements and solve the sense granularity problem by proposing AutoSense, a latent variable model based on two observations: (1) senses are represented as a distribution over topics, and (2) senses generate pairings between the target word and its neighboring word. These observations alleviate the problem by (a) throwing garbage senses and (b) additionally inducing fine-grained word senses. Results show great improvements over the state-of-the-art models on popular WSI datasets. We also show that AutoSense is able to learn the appropriate sense granularity of a word. Finally, we apply AutoSense to the unsupervised author name disambiguation task where the sense granularity problem is more evident and show that AutoSense is evidently better than competing models. We share our data and code here: https://github.com/rktamplayo/AutoSense.
A Blended Human-Robot Shared Control Framework to Handle Drift and Latency
Venue: arXiv cs.RO 2018
Year: 2018

Maximizing the utility of human-robot teams in disaster response and search and rescue (SAR) missions remains to be a challenging problem. This is due to the dynamic, uncertain nature of the environment and the variability in cognitive performance of the human operators. By having an autonomous agent share control with the operator, we can achieve near-optimal performance by augmenting the operator's input and compensate for the factors resulting in degraded performance. What this solution does not consider though is the human input latency and errors caused by potential hardware failures that can occur during task completion when operating in disaster response and SAR scenarios. In this paper, we propose the use of blended shared control (BSC) architecture to address these issues and investigate the architecture's performance in constrained, dynamic environments with a differential drive robot that has input latency and erroneous odometry feedback. We conduct a validation study (n=12) for our control architecture and then a user study (n=14) in 2 different environments that are unknown to both the human operator and the autonomous agent. The results demonstrate that the BSC architecture can prevent collisions and enhance operator performance without the need of a complete transfer of control between the human operator and autonomous agent.
The Child Prodigies of IEEE 802.11 Family
Venue: arXiv cs.NI 2018
Year: 2018

WiFi was designed just to replace the wired connections, but it has changed the Internet as a whole. WiFi users are increasing day by day and the traffic from wireless devices is dominating the Internet. With an increasing number of users and their expectations on QoS(Quality of Service) and data rates, there is a need for better WiFi techniques which can support the above-mentioned challenges. The IEEE LAN/MAN standards committee (LMSC) has launched new protocols like IEEE 802.11ac, 802.11ad, and 802.11ax. Understanding them is very important to use them efficiently and effectively to fulfill our expectations. In this paper, we summarize few of the works which talk about the above the mentioned protocol standards of WiFi that brought a significant improvement in WiFi's performance. Moreover we discuss the differences in the protocols and their applications. We also try to compare these three protocols with respect to their supported data rates, range of transmission and Robustness.
Propositional modal logic with implicit modal quantification
Venue: arXiv cs.LO 2018
Year: 2018

Propositional term modal logic is interpreted over Kripke structures with unboundedly many accessibility relations and hence the syntax admits variables indexing modalities and quantification over them. This logic is undecidable, and we consider a variable-free propositional bi-modal logic with implicit quantification. Thus $[\forall] α$ asserts necessity over all accessibility relations and $[\exists] α$ is classical necessity over some accessibility relation. The logic is associated with a natural bisimulation relation over models and we show that the logic is exactly the bisimulation invariant fragment of a two sorted first order logic. The logic is easily seen to be decidable and admits a complete axiomatization of valid formulas. Moreover the decision procedure extends naturally to the `bundled fragment' of full term modal logic.
Defect Detection from UAV Images based on Region-Based CNNs
Venue: arXiv cs.CV 2018
Year: 2018

With the wide applications of Unmanned Aerial Vehicle (UAV) in engineering such as the inspection of the electrical equipment from distance, the demands of efficient object detection algorithms for abundant images acquired by UAV have also been significantly increased in recent years. In this work, we study the performance of the region-based CNN for the electrical equipment defect detection by using the UAV images. In order to train the detection model, we collect a UAV images dataset composes of four classes of electrical equipment defects with thousands of annotated labels. Then, based on the region-based faster R-CNN model, we present a multi-class defects detection model for electrical equipment which is more efficient and accurate than traditional single class detection methods. Technically, we have replaced the RoI pooling layer with a similar operation in Tensorflow and promoted the mini-batch to 128 per image in the training procedure. These improvements have slightly increased the speed of detection without any accuracy loss. Therefore, the modified region-based CNN could simultaneously detect multi-class of defects of the electrical devices in nearly real time. Experimental results on the real word electrical equipment images demonstrate that the proposed method achieves better performance than the traditional object detection algorithms in defect detection.
Contributions to Biclustering of Microarray Data Using Formal Concept Analysis
Venue: arXiv cs.LG 2018
Year: 2018

Biclustering is an unsupervised data mining technique that aims to unveil patterns (biclusters) from gene expression data matrices. In the framework of this thesis, we propose new biclustering algorithms for microarray data. The latter is done using data mining techniques. The objective is to identify positively and negatively correlated biclusters. This thesis is divided into two part: In the first part, we present an overview of the pattern-mining techniques and the biclustering of microarray data. In the second part, we present our proposed biclustering algorithms where we rely on two axes. In the first axis, we initially focus on extracting biclusters of positive correlations. For this, we use both Formal Concept Analysis and Association Rules. In the second axis, we focus on the extraction of negatively correlated biclusters. The performed experimental studies highlight the very promising results offered by the proposed algorithms. Our biclustering algorithms are evaluated and compared statistically and biologically.
Kernel-Based Training of Generative Networks
Venue: arXiv cs.LG 2018
Year: 2018

Generative adversarial networks (GANs) are designed with the help of min-max optimization problems that are solved with stochastic gradient-type algorithms which are known to be non-robust. In this work we revisit a non-adversarial method based on kernels which relies on a pure minimization problem and propose a simple stochastic gradient algorithm for the computation of its solution. Using simplified tools from Stochastic Approximation theory we demonstrate that batch versions of the algorithm or smoothing of the gradient do not improve convergence. These observations allow for the development of a training algorithm that enjoys reduced computational complexity and increased robustness while exhibiting similar synthesis characteristics as classical GANs.
A Survey on Synchrophasor Data Quality and Cybersecurity Challenges, and Evaluation of their Interdependencies
Venue: arXiv cs.CR 2018
Year: 2018

Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of the smart grid. With their widespread implementation, significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems, when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyber-attacks. In doing so, this paper serves as a starting point for researchers entering the fields of synchrophasor data analytics and security.
Spectral Multigraph Networks for Discovering and Fusing Relationships in Molecules
Venue: arXiv cs.LG 2018
Year: 2018

Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Chebyshev GCNs restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We model learned edges with abstract meaning and experiment with different ways to fuse the representations extracted from annotated and learned edges, achieving competitive results on a variety of chemical classification benchmarks.
Model-Based Reinforcement Learning for Sepsis Treatment
Venue: arXiv cs.LG 2018
Year: 2018

Sepsis is a dangerous condition that is a leading cause of patient mortality. Treating sepsis is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. In this work, we explore the use of continuous state-space model-based reinforcement learning (RL) to discover high-quality treatment policies for sepsis patients. Our quantitative evaluation reveals that by blending the treatment strategy discovered with RL with what clinicians follow, we can obtain improved policies, potentially allowing for better medical treatment for sepsis.
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study
Venue: arXiv cs.CV 2018
Year: 2018

Research on content-based image retrieval (CBIR) has been under development for decades, and numerous methods have been competing to extract the most discriminative features for improved representation of the image content. Recently, deep learning methods have gained attention in computer vision, including CBIR. In this paper, we present a comparative investigation of different features, including low-level and high-level features, for CBIR. We compare the performance of CBIR systems using different deep features with state-of-the-art low-level features such as SIFT, SURF, HOG, LBP, and LTP, using different dictionaries and coefficient learning techniques. Furthermore, we conduct comparisons with a set of primitive and popular features that have been used in this field, including colour histograms and Gabor features. We also investigate the discriminative power of deep features using certain similarity measures under different validation approaches. Furthermore, we investigate the effects of the dimensionality reduction of deep features on the performance of CBIR systems using principal component analysis, discrete wavelet transform, and discrete cosine transform. Unprecedentedly, the experimental results demonstrate high (95\% and 93\%) mean average precisions when using the VGG-16 FC7 deep features of Corel-1000 and Coil-20 datasets with 10-D and 20-D K-SVD, respectively.
Homogeneity-Based Transmissive Process to Model True and False News in Social Networks
Venue: arXiv cs.CY 2018
Year: 2018

An overwhelming number of true and false news stories are posted and shared in social networks, and users diffuse the stories based on multiple factors. Diffusion of news stories from one user to another depends not only on the stories' content and the genuineness but also on the alignment of the topical interests between the users. In this paper, we propose a novel Bayesian nonparametric model that incorporates homogeneity of news stories as the key component that regulates the topical similarity between the posting and sharing users' topical interests. Our model extends hierarchical Dirichlet process to model the topics of the news stories and incorporates Bayesian Gaussian process latent variable model to discover the homogeneity values. We train our model on a real-world social network dataset and find homogeneity values of news stories that strongly relate to their labels of genuineness and their contents. Finally, we show that the supervised version of our model predicts the labels of news stories better than the state-of-the-art neural network and Bayesian models.