OpenRaiser/Intern-Atlas huggingface dataset:
Partition-based Unscented Kalman Filter for Reconfigurable Battery Pack State Estimation using an Electrochemical Model
Venue: arXiv cs.SY 2017
Year: 2017

Accurate state estimation of large-scale lithium-ion battery packs is necessary for the advanced control of batteries, which could potentially increase their lifetime through e.g. reconfiguration. To tackle this problem, an enhanced reduced-order electrochemical model is used here. This model allows considering a wider operating range and thermal coupling between cells, the latter turning out to be significant. The resulting nonlinear model is exploited for state estimation through unscented Kalman filters (UKF). A sensor network composed of one sensor node per battery cell is deployed. Each sensor node is equipped with a local UKF, which uses available local measurements together with additional information coming from neighboring sensor nodes. Such state estimation scheme gives rise to a partition-based unscented Kalman filter (PUKF). The method is validated on data from a detailed simulator for a battery pack comprised of six cells, with reconfiguration capabilities. The results show that the distributed approach outperforms the centralized one in terms of computation time at the expense of a very low increase of mean-square estimation error.
On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search
Venue: arXiv cs.LG 2017
Year: 2017

Active Learning (AL) methods have proven cost-saving against passive supervised methods in many application domains. An active learner, aiming to find some target hypothesis, formulates sequential queries to some oracle. The set of hypotheses consistent with the already answered queries is called version space. Several query selection measures (QSMs) for determining the best query to ask next have been proposed. Assuming binaryoutcome queries, we analyze various QSMs wrt. to the discrimination power of their selected queries within the current version space. As a result, we derive superiority and equivalence relations between these QSMs and introduce improved versions of existing QSMs to overcome identified issues. The obtained picture gives a hint about which QSMs should preferably be used in pool-based AL scenarios. Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on these, we demonstrate how efficient heuristic search methods for optimal queries in query synthesis AL scenarios can be devised.
Optical computing by injection-locked lasers
Venue: arXiv cs.ET 2017
Year: 2017

A programmable optical computer has remained an elusive concept. To construct a practical computing primitive equivalent to an electronic Boolean logic, one should find a nonlinear phenomenon that overcomes weaknesses present in many optical processing schemes. Ideally, the nonlinearity should provide a functionally complete set of logic operations, enable ultrafast all-optical programmability, and allow cascaded operations without a change in the operating wavelength or in the signal encoding format. Here we demonstrate a programmable logic gate using an injection-locked Vertical-Cavity Surface-Emitting Laser (VCSEL). The gate program is switched between the AND and the OR operations at the rate of 1 GHz with Bit Error Ratio (BER) of 10e-6 without changes in the wavelength or in the signal encoding format. The scheme is based on nonlinearity of normalization operations, which can be used to construct any continuous complex function or operation, Boolean or otherwise.
Beam Tracking for UAV Mounted SatCom on-the-Move with Massive Antenna Array
Venue: arXiv cs.SY 2017
Year: 2017

Unmanned aerial vehicle (UAV)-satellite communication has drawn dramatic attention for its potential to build the integrated space-air-ground network and the seamless wide-area coverage. The key challenge to UAV-satellite communication is its unstable beam pointing due to the UAV navigation, which is a typical SatCom on-the-move scenario. In this paper, we propose a blind beam tracking approach for Ka-band UAVsatellite communication system, where UAV is equipped with a large-scale antenna array. The effects of UAV navigation are firstly released through the mechanical adjustment, which could approximately point the beam towards the target satellite through beam stabilization and dynamic isolation. Specially, the attitude information can be realtimely derived from data fusion of lowcost sensors. Then, the precision of the beam pointing is blindly refined through electrically adjusting the weight of the massive antennas, where an array structure based simultaneous perturbation algorithm is designed. Simulation results are provided to demonstrate the superiority of the proposed method over the existing ones.
A semi-automated segmentation method for detection of pulmonary embolism in True-FISP MRI sequences
Venue: arXiv cs.CV 2017
Year: 2017

Pulmonary embolism (PE) is a highly mortal disease, currently assessed by pulmonary CT angiography. True-FISP MRI has emerged as an innocuous alternative that does not hold many of the limitations of x-ray imaging. However, True-FISP MRI is very sensitive to turbulent blood flow, generating artifacts that may resemble fake clots in the pulmonary vasculature. These misinterpretations reduce its overall diagnostic accuracy to 94%, limiting a wider use in clinical environments. A new segmentation algorithm is proposed to confirm the presence of real pulmonary clots in True-FISP MR images by quantitative means, measuring the shape, intensity, and solidity of the formation. The algorithm was evaluated in 37 patients. The developed method increased the diagnostic accuracy of expert observers assessing Pulmonary True-FISP MRI sequences by 6% without the use of ionizing radiation, achieving a diagnostic accuracy comparable to standard CT angiography.
Emerging Topics in Assistive Reading Technology: From Presentation to Content Accessibility
Venue: arXiv cs.CY 2017
Year: 2017

With the recent focus in the accessibility field, researchers from academia and industry have been very active in developing innovative techniques and tools for assistive technology. Especially with handheld devices getting ever powerful and being able to recognize the user's voice, screen magnification for individuals with low-vision, and eye tracking devices used in studies with individuals with physical and intellectual disabilities, the science field is quickly adapting and creating conclusions as well as products to help. In this paper, we will focus on new technology and tools to help make reading easier--including reformatting document presentation (for people with physical vision impairments) and text simplification to make information itself easier to interpret (for people with intellectual disabilities). A real-world case study is reported based on our experience to make documents more accessible.
A Serious Game Design: Nudging Users' Memorability of Security Questions
Venue: arXiv cs.CR 2017
Year: 2017

Security questions are one of the techniques used to recover passwords. The main limitation of security questions is that users find strong answers difficult to remember. This leads users to trade-off security for the convenience of an improved memorability. Previous research found that increased fun and enjoyment can lead to an enhanced memorability, which provides a better learning experience. Hence, we empirically investigate whether a serious game has the potential of improving the memorability of strong answers to security questions. For our serious game, we adapted the popular "4 Pics 1 word" mobile game because of its use of pictures and cues, which psychology research found to be important to help with memorability. Our findings indicate that the proposed serious game could potentially improve the memorability of answers to security questions. This potential improvement in memorability, could eventually help reduce the trade-off between usability and security in fall-back authentication.
Memristive System Design for Variable Pixel G-Neighbor Denoising Filter
Venue: arXiv cs.ET 2017
Year: 2017

Image blurring artifact is the main challenge to any spatial, denoising filters. This artifact is contributed by the heterogeneous intensities within the given neighborhood or window of fixed size. Selection of most similar intensities (G-Neighbors) helps to adapt the window shape which is of edge-aware nature and subsequently reduce this blurring artifact. The paper presents a memristive circuit design to implement this variable pixel G-Neighbor filter. The memristive circuits exhibits parallel processing capabilities (near real-time) and neuromorphic architectures. The proposed design is demonstrated as simulations of both algorithm (MATLAB) and circuit (SPICE). Circuit design is evaluated for various parameters such as processing time, fabrication area used, and power consumption. Denoising performance is demonstrated using image quality metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), and structural similarity index measure (SSIM). Combining adaptive filtering method with mean filter resulted in average improvement of MSE to about 65\% reduction, increase of PSNR and SSIM to nearly 18\% and 12\% correspondingly.
An Optimal Online Method of Selecting Source Policies for Reinforcement Learning
Venue: arXiv cs.AI 2017
Year: 2017

Transfer learning significantly accelerates the reinforcement learning process by exploiting relevant knowledge from previous experiences. The problem of optimally selecting source policies during the learning process is of great importance yet challenging. There has been little theoretical analysis of this problem. In this paper, we develop an optimal online method to select source policies for reinforcement learning. This method formulates online source policy selection as a multi-armed bandit problem and augments Q-learning with policy reuse. We provide theoretical guarantees of the optimal selection process and convergence to the optimal policy. In addition, we conduct experiments on a grid-based robot navigation domain to demonstrate its efficiency and robustness by comparing to the state-of-the-art transfer learning method.
Achievable Rate Analysis of Cooperative Relay Assisted Opportunistic-NOMA under Rician Fading Channels with Channel State Information
Venue: arXiv cs.IT 2017
Year: 2017

The average achievable rate of an Opportunistic Non-Orthogonal Multiple Access (O-NOMA) based Cooperative Relaying System (CRS) with Channel State Information (CSI) known at the transmitter end is analyzed under Rician fading channels. For opportunistic transmission of data signals, CSI is used for the source-to-relay and source-to-destination links, which helps the transmitter to select the best out of the cooperative NOMA transmission and the direct transmission of signals, instantaneously. The average achievable rate of the O-NOMA and conventional NOMA (C-NOMA) based CRSs is considered for the performance comparison and the exact and asymptotic analytical expressions of the achievable rates has been provided. Asymptotic results are verified through Monte Carlo simulations for various channel powers and power allocation coefficients. Numerical results verify that the derived analytical results are matched well with the Monte Carlo simulations and shows that O-NOMA-based CRS achieves better rate performance than C-NOMA-based CRS with the increasing power allocation coefficients, transmit Signal-to-Noise Ratios (SNRs) and channel powers.
3D Camouflaging Object using RGB-D Sensors
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a new optical camouflage system that uses RGB-D cameras, for acquiring point cloud of background scene, and tracking observers eyes. This system enables a user to conceal an object located behind a display that surrounded by 3D objects. If we considered here the tracked point of observer s eyes is a light source, the system will work on estimating shadow shape of the display device that falls on the objects in background. The system uses the 3d observer s eyes and the locations of display corners to predict their shadow points which have nearest neighbors in the constructed point cloud of background scene.
Continuous Monitoring of Pareto Frontiers on Partially Ordered Attributes for Many Users
Venue: arXiv cs.DB 2017
Year: 2017

We study the problem of continuous object dissemination---given a large number of users and continuously arriving new objects, deliver an object to all users who prefer the object. Many real world applications analyze users' preferences for effective object dissemination. For continuously arriving objects, timely finding users who prefer a new object is challenging. In this paper, we consider an append-only table of objects with multiple attributes and users' preferences on individual attributes are modeled as strict partial orders. An object is preferred by a user if it belongs to the Pareto frontier with respect to the user's partial orders. Users' preferences can be similar. Exploiting shared computation across similar preferences of different users, we design algorithms to find target users of a new object. In order to find users of similar preferences, we study the novel problem of clustering users' preferences that are represented as partial orders. We also present an approximate solution of the problem of finding target users which is more efficient than the exact one while ensuring sufficient accuracy. Furthermore, we extend the algorithms to operate under the semantics of sliding window. We present the results from comprehensive experiments for evaluating the efficiency and effectiveness of the proposed techniques.
Confidence Interval of Probability Estimator of Laplace Smoothing
Venue: arXiv cs.IT 2017
Year: 2017

Sometimes, we do not use a maximum likelihood estimator of a probability but it's a smoothed estimator in order to cope with the zero frequency problem. This is often the case when we use the Naive Bayes classifier. Laplace smoothing is a popular choice with the value of Laplace smoothing estimator being the expected value of posterior distribution of the probability where we assume that the prior is uniform distribution. In this paper, we investigate the confidence intervals of the estimator of Laplace smoothing. We show that the likelihood function for this confidence interval is the same as the likelihood of a maximum likelihood estimated value of a probability of Bernoulli trials. Although the confidence interval of the maximum likelihood estimator of the Bernoulli trial probability has been studied well, and although the approximate formulas for the confidence interval are well known, we cannot use the interval of maximum likelihood estimator since the interval contains the value 0, which is not suitable for the Naive Bayes classifier. We are also interested in the accuracy of existing approximation methods since these approximation methods are frequently used but their accuracy is not well discussed. Thus, we obtain the confidence interval by numerically integrating the likelihood function. In this paper, we report the difference between the confidence interval that we computed and the confidence interval by approximate formulas. Finally, we include a URL, where all of the intervals that we computed are available.
Realizing Half-Diminished Reality from Video Stream of Manipulating Objects
Venue: arXiv cs.CV 2017
Year: 2017

When we watch a video, in which human hands manipulate objects, these hands may obscure some parts of those objects. We are willing to make clear how the objects are manipulated by making the image of hands semi-transparent, and showing the complete images of the hands and the object. By carefully choosing a Half-Diminished Reality method, this paper proposes a method that can process the video in real time and verifies that the proposed method works well.
Predicting interviewee attitude and body language from speech descriptors
Venue: arXiv cs.SD 2017
Year: 2017

This present research investigated the relationship between personal impressions and the acoustic nonverbal communication conveyed by employees being interviewed. First, we investigated the extent to which different conversation topics addressed during the interview induced changes in the interviewees' acoustic parameters. Next, we attempted to predict the observed and self-assessed attitudes and body language of the interviewees based on the acoustic data. The results showed that topicality caused significant deviations in the acoustic parameters statistics, but the ability to predict the personal perceptions of the interviewees based on their acoustic non-verbal communication was relatively independent of topicality, due to the natural redundancy inherent in acoustic attributes. Our findings suggest that joint modeling of speech and visual cues may improve the assessment of interviewee profiles.
Deep Learning Based Cryptographic Primitive Classification
Venue: arXiv cs.CR 2017
Year: 2017

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a DCNN, is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of contemporary data compendiums, hence feeding the model cognition, a methodology for the procedural generation of synthetic cryptographic binaries is defined, utilising core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesis combinable variants which are automatically fed in its core model. Converging at 91% accuracy, CryptoKnight is successfully able to classify the sample algorithms with minimal loss.
Morphable Face Models - An Open Framework
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration and model-building, demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.
A Linear Algorithm for Finding the Sink of Unique Sink Orientations on Grids
Venue: arXiv cs.DS 2017
Year: 2017

An orientation of a grid is called unique sink orientation (USO) if each of its nonempty subgrids has a unique sink. Particularly, the original grid itself has a unique global sink. In this work we investigate the problem of how to find the global sink using minimum number of queries to an oracle. There are two different oracle models: the vertex query model where the orientation of all edges incident to the queried vertex are provided, and the edge query model where the orientation of the queried edge is provided. In the 2-dimensional case, we design an optimal linear deterministic algorithm for the vertex query model and an almost linear deterministic algorithm for the edge query model, previously the best known algorithms run in O(N logN) time for the vertex query model and O(N^1.404) time for the edge query model.
Extracting Ontological Knowledge from Textual Descriptions
Venue: arXiv cs.AI 2017
Year: 2017

Authoring of OWL-DL ontologies is intellectually challenging and to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach can be successful only when it is combined with an effective method for extracting ontological axioms from text. Extracting axioms from unrestricted English input is a substantially challenging task due to the richness of the language. Controlled natural languages (CNLs) have been proposed in this context and these tend to be highly restrictive. In this paper, we propose a new CNL called TEDEI (TExtual DEscription Identifier) whose grammar is inspired by the different ways OWL-DL constructs are expressed in English. We built a system that transforms TEDEI sentences into corresponding OWL-DL axioms. Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context. We find that the best way to handle these challenges is to construct axioms corresponding to alternative formalizations of the sentence so that the end-user can make an appropriate choice. The output is compared against human-authored axioms and in substantial number of cases, human-authored axiom is indeed one of the alternatives given by the system. The proposed system substantially enhances the types of sentence structures that can be used for ontology authoring.
Bayesian Filtering for ODEs with Bounded Derivatives
Venue: arXiv cs.NA 2017
Year: 2017

Recently there has been increasing interest in probabilistic solvers for ordinary differential equations (ODEs) that return full probability measures, instead of point estimates, over the solution and can incorporate uncertainty over the ODE at hand, e.g. if the vector field or the initial value is only approximately known or evaluable. The ODE filter proposed in recent work models the solution of the ODE by a Gauss-Markov process which serves as a prior in the sense of Bayesian statistics. While previous work employed a Wiener process prior on the (possibly multiple times) differentiated solution of the ODE and established equivalence of the corresponding solver with classical numerical methods, this paper raises the question whether other priors also yield practically useful solvers. To this end, we discuss a range of possible priors which enable fast filtering and propose a new prior--the Integrated Ornstein Uhlenbeck Process (IOUP)--that complements the existing Integrated Wiener process (IWP) filter by encoding the property that a derivative in time of the solution is bounded in the sense that it tends to drift back to zero. We provide experiments comparing IWP and IOUP filters which support the belief that IWP approximates better divergent ODE's solutions whereas IOUP is a better prior for trajectories with bounded derivatives.