OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic Model-Based Approach for Heart Beat Detection
Venue: arXiv cs.AI 2015
Year: 2015

Nowadays, hospitals are ubiquitous and integral to modern society. Patients flow in and out of a veritable whirlwind of paperwork, consultations, and potential inpatient admissions, through an abstracted system that is not without flaws. One of the biggest flaws in the medical system is perhaps an unexpected one: the patient alarm system. One longitudinal study reported an 88.8% rate of false alarms, with other studies reporting numbers of similar magnitudes. These false alarm rates lead to a number of deleterious effects that manifest in a significantly lower standard of care across clinics. This paper discusses a model-based probabilistic inference approach to identifying variables at a detection level. We design a generative model that complies with an overview of human physiology and perform approximate Bayesian inference. One primary goal of this paper is to justify a Bayesian modeling approach to increasing robustness in a physiological domain. We use three data sets provided by Physionet, a research resource for complex physiological signals, in the form of the Physionet 2014 Challenge set-p1 and set-p2, as well as the MGH/MF Waveform Database. On the extended data set our algorithm is on par with the other top six submissions to the Physionet 2014 challenge.
An Experimental Evaluation of Computational Techniques for Planning and Assessment of International Interventions
Venue: arXiv cs.CY 2015
Year: 2015

We describe the experimental methodology developed and employed in a series of experiments within the Defense Advanced Research Projects Agency (DARPA) Conflict Modeling, Planning, and Outcomes Exploration (COMPOEX) Program. The primary purpose of the effort was development of tools and methods for analysis, planning and predictive assessment of plans for complex operations where integrated political-military-economic-social-infrastructure and information (PMESII) considerations play decisive roles. As part of the program, our team executed several broad-based experiments, involving dozens of experts from several agencies simultaneously. The methodology evolved from one experiment to another because of the lessons learned. The paper presents the motivation, objectives, and structure of this interagency experiment series; the methods we explored in the experiments; and the results, lessons learned and recommendations for future efforts of such nature.
Toward a Research Agenda in Adversarial Reasoning: Computational Approaches to Anticipating the Opponent's Intent and Actions
Venue: arXiv cs.AI 2015
Year: 2015

This paper defines adversarial reasoning as computational approaches to inferring and anticipating an enemy's perceptions, intents and actions. It argues that adversarial reasoning transcends the boundaries of game theory and must also leverage such disciplines as cognitive modeling, control theory, AI planning and others. To illustrate the challenges of applying adversarial reasoning to real-world problems, the paper explores the lessons learned in the CADET - a battle planning system that focuses on brigade-level ground operations and involves adversarial reasoning. From this example of current capabilities, the paper proceeds to describe RAID - a DARPA program that aims to build capabilities in adversarial reasoning, and how such capabilities would address practical requirements in Defense and other application areas.
Discovering topic structures of a temporally evolving document corpus
Venue: arXiv cs.IR 2015
Year: 2015

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting, and merging. The power of the proposed framework is demonstrated on two medical literature corpora concerned with the autism spectrum disorder (ASD) and the metabolic syndrome (MetS) -- both increasingly important research subjects with significant social and healthcare consequences. In addition to the collected ASD and metabolic syndrome literature corpora which we made freely available, our contribution also includes an extensive empirical analysis of the proposed framework. We describe a detailed and careful examination of the effects that our algorithms's free parameters have on its output, and discuss the significance of the findings both in the context of the practical application of our algorithm as well as in the context of the existing body of work on temporal topic analysis. Our quantitative analysis is followed by several qualitative case studies highly relevant to the current research on ASD and MetS, on which our algorithm is shown to capture well the actual developments in these fields.
The Improvement of Negative Sentences Translation in English-to-Korean Machine Translation
Venue: arXiv cs.CL 2015
Year: 2015

This paper describes the algorithm for translating English negative sentences into Korean in English-Korean Machine Translation (EKMT). The proposed algorithm is based on the comparative study of English and Korean negative sentences. The earlier translation software cannot translate English negative sentences into accurate Korean equivalents. We established a new algorithm for the negative sentence translation and evaluated it.
Multichannel audio signal source separation based on an Interchannel Loudness Vector Sum
Venue: arXiv cs.SD 2015
Year: 2015

In this paper, a Blind Source Separation (BSS) algorithm for multichannel audio contents is proposed. Unlike common BSS algorithms targeting stereo audio contents or microphone array signals, our technique is targeted at multichannel audio such as 5.1 and 7.1ch audio. Since most multichannel audio object sources are panned using the Inter-channel Loudness Difference (ILD), we employ the ILVS (Inter-channel Loudness Vector Sum) concept to cluster common signals (such as background music) from each channel. After separating the common signals from each channel, we employ an Expectation Maximization (EM) algorithm with a von-Mises distribution to successfully classify the clustering of sound source objects and separate the audio signals from the original mixture. Our proposed method can therefore separate common audio signals and object source signals from multiple channels with reasonable quality. Our multichannel audio content separation technique can be applied to an upmix system or a cinema audio system requiring multichannel audio source separation.
Rank error-correcting pairs
Venue: arXiv cs.IT 2015
Year: 2015

Error-correcting pairs were introduced independently by Pellikaan and Kötter as a general method of decoding linear codes with respect to the Hamming metric using coordinatewise products of vectors, and are used for many well-known families of codes. In this paper, we define new types of vector products, extending the coordinatewise product, some of which preserve symbolic products of linearized polynomials after evaluation and some of which coincide with usual products of matrices. Then we define rank error-correcting pairs for codes that are linear over the extension field and for codes that are linear over the base field, and relate both types. Bounds on the minimum rank distance of codes and MRD conditions are given. Finally we show that some well-known families of rank-metric codes admit rank error-correcting pairs, and show that the given algorithm generalizes the classical algorithm using error-correcting pairs for the Hamming metric.
Bibliothèques numériques et gamification : panorama et état de l'art
Venue: arXiv cs.DL 2015
Year: 2015

This article presents an overview of the main gamification projects for digital libraries, either for tagging or OCR correction. This overview is followed by a state of the art with functionalities, motivations, sociology of contributors and the scope of gamification compared to the serious games and explicit crowdsourcing. Finally a comparison of results between explicit crowdsourcing and gamification is proposed. [English Title: Digital libraries and gamification: overview and state of the art]
Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation
Venue: arXiv cs.NE 2015
Year: 2015

This paper describes a framework for modeling the interface between perception and memory on the algorithmic level of analysis. It is consistent with phenomena associated with many different brain regions. These include view-dependence (and invariance) effects in visual psychophysics and inferotemporal cortex physiology, as well as episodic memory recall interference effects associated with the medial temporal lobe. The perspective developed here relies on a novel interpretation of Hubel and Wiesel's conjecture for how receptive fields tuned to complex objects, and invariant to details, could be achieved. It complements existing accounts of two-speed learning systems in neocortex and hippocampus (e.g., McClelland et al. 1995) while significantly expanding their scope to encompass a unified view of the entire pathway from V1 to hippocampus.
Pairwise Secret Key Agreement based on Location-derived Common Randomness
Venue: arXiv cs.IT 2015
Year: 2015

A source model of key sharing between three users is considered in which each pair of them wishes to agree on a secret key hidden from the remaining user. There are rate-limited public channels for communications between the users. We give an inner bound on the secret key capacity region in this framework. Moreover, we investigate a practical setup in which localization information of the users as the correlated observations are exploited to share pairwise keys between the users. The inner and outer bounds of the key capacity region are analyzed in this setup for the case of i.i.d. Gaussian observations.
Combined statistical and model based texture features for improved image classification
Venue: arXiv cs.CV 2015
Year: 2015

This paper aims to improve the accuracy of texture classification based on extracting texture features using five different texture methods and classifying the patterns using a naive Bayesian classifier. Three statistical-based and two model-based methods are used to extract texture features from eight different texture images, then their accuracy is ranked after using each method individually and in pairs. The accuracy improved up to 97.01% when model based -Gaussian Markov random field (GMRF) and fractional Brownian motion (fBm) - were used together for classification as compared to the highest achieved using each of the five different methods alone; and proved to be better in classifying as compared to statistical methods. Also, using GMRF with statistical based methods, such as Gray level co-occurrence (GLCM) and run-length (RLM) matrices, improved the overall accuracy to 96.94% and 96.55%; respectively.
Robust Power Allocation and Outage Analysis for Secrecy in Independent Parallel Gaussian Channels
Venue: arXiv cs.IT 2015
Year: 2015

This letter studies parallel independent Gaussian channels with uncertain eavesdropper channel state information (CSI). Firstly, we evaluate the probability of zero secrecy rate in this system for (i) given instantaneous channel conditions and (ii) a Rayleigh fading scenario. Secondly, when non-zero secrecy is achievable in the low SNR regime, we aim to solve a robust power allocation problem which minimizes the outage probability at a target secrecy rate. We bound the outage probability and obtain a linear fractional program that takes into account the uncertainty in eavesdropper CSI while allocating power on the parallel channels. Problem structure is exploited to solve this optimization problem efficiently. We find the proposed scheme effective for uncertain eavesdropper CSI in comparison with conventional power allocation schemes.
Is Your Data Gone? Comparing Perceived Effectiveness of Thumb Drive Deletion Methods to Actual Effectiveness
Venue: arXiv cs.CR 2015
Year: 2015

Previous studies have shown that many users do not use effective data deletion techniques upon sale or surrender of storage devices. A logical assumption is that many users are still confused concerning proper sanitization techniques of devices upon surrender. This paper strives to measure this assumption through a buyback study with a survey component. We recorded participants' thoughts and beliefs concerning deletion, as well as general demographic information, in relation to actual deletion effectiveness on USB thumb drives. Thumb drives were chosen for this study due to their relative low cost, ease of use, and ubiquity. In addition, we also bought used thumb drives from eBay and Amazon Marketplace to use as a comparison to the wider world. We found that there is no statistically significant difference between buyback and market drives in terms of deletion methods nor presence of sensitive data, and thus our study may be predictive of the perceptions of the market sellers. In our combined data sets, we found over 60% of the drives tested still had recoverable sensitive data, and in the buyback group, we found no correlation between users' perceived versus actual effectiveness of deletion methods. Our results suggest the security community may need to take a different approach to increase the usability, availability, and/or necessity of strong deletion methods.
Dynamic Time-Dependent Route Planning in Road Networks with User Preferences
Venue: arXiv cs.DS 2015
Year: 2015

There has been tremendous progress in algorithmic methods for computing driving directions on road networks. Most of that work focuses on time-independent route planning, where it is assumed that the cost on each arc is constant per query. In practice, the current traffic situation significantly influences the travel time on large parts of the road network, and it changes over the day. One can distinguish between traffic congestion that can be predicted using historical traffic data, and congestion due to unpredictable events, e.g., accidents. In this work, we study the \emph{dynamic and time-dependent} route planning problem, which takes both prediction (based on historical data) and live traffic into account. To this end, we propose a practical algorithm that, while robust to user preferences, is able to integrate global changes of the time-dependent metric~(e.g., due to traffic updates or user restrictions) faster than previous approaches, while allowing subsequent queries that enable interactive applications.
Denoising and Completion of 3D Data via Multidimensional Dictionary Learning
Venue: arXiv cs.LG 2015
Year: 2015

In this paper a new dictionary learning algorithm for multidimensional data is proposed. Unlike most conventional dictionary learning methods which are derived for dealing with vectors or matrices, our algorithm, named KTSVD, learns a multidimensional dictionary directly via a novel algebraic approach for tensor factorization as proposed in [3, 12, 13]. Using this approach one can define a tensor-SVD and we propose to extend K-SVD algorithm used for 1-D data to a K-TSVD algorithm for handling 2-D and 3-D data. Our algorithm, based on the idea of sparse coding (using group-sparsity over multidimensional coefficient vectors), alternates between estimating a compact representation and dictionary learning. We analyze our KTSVD algorithm and demonstrate its result on video completion and multispectral image denoising.
Computational Pathology: Challenges and Promises for Tissue Analysis
Venue: arXiv cs.CV 2015
Year: 2015

The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.
Memristive fingerprints of electric arcs
Venue: arXiv cs.ET 2015
Year: 2015

We discuss the memristive fingerprints of the hybrid Cassie-Mayr model of electric arcs. In particular, it is shown that (i) the voltage-current characteristic of the model has the pinched hysteresis nature, (ii) the voltage and current zero crossings occur at the same instants, and, (iii) when the frequency $f$ of the power supply increases, the voltage-current pinched hysteresis characteristic tends closer to a single-valued one, meaning that the voltage-current graph becomes that of a resistor (with an increased linearity for $f\rightarrow \infty$). The conductance $g$ of the Cassie-Mayr model decreases when the frequency increases. The hybrid Cassie-Mayr model describes therefore an interesting case of a memristive phenomenon.
Using SVM to pre-classify government purchases
Venue: arXiv cs.LG 2015
Year: 2015

The Brazilian government often misclassifies the goods it buys. That makes it hard to audit government expenditures. We cannot know whether the price paid for a ballpoint pen (code #7510) was reasonable if the pen was misclassified as a technical drawing pen (code #6675) or as any other good. This paper shows how we can use machine learning to reduce misclassification. I trained a support vector machine (SVM) classifier that takes a product description as input and returns the most likely category codes as output. I trained the classifier using 20 million goods purchased by the Brazilian government between 1999-04-01 and 2015-04-02. In 83.3% of the cases the correct category code was one of the three most likely category codes identified by the classifier. I used the trained classifier to develop a web app that might help the government reduce misclassification. I open sourced the code on GitHub; anyone can use and modify it.
A Multivariate Biomarker for Parkinson's Disease
Venue: arXiv cs.LG 2015
Year: 2015

In this study, we executed a genomic analysis with the objective of selecting a set of genes (possibly small) that would help in the detection and classification of samples from patients affected by Parkinson Disease. We performed a complete data analysis and during the exploratory phase, we selected a list of differentially expressed genes. Despite their association with the diseased state, we could not use them as a biomarker tool. Therefore, our research was extended to include a multivariate analysis approach resulting in the identification and selection of a group of 20 genes that showed a clear potential in detecting and correctly classify Parkinson Disease samples even in the presence of other neurodegenerative disorders.
Features of formation of a distributive infrastructure of e-commerce in Russia
Venue: arXiv cs.CY 2015
Year: 2015

Article about objective laws of formation of a distributive infrastructure of e-commerce. The distributive infrastructure of e-commerce, according to the author, plays an important role in formation of network economy. The author opens strategic value of institutional regulation of distributive logistics for the decision problems of modernization of Russian economy.
Epidemiological geographic profiling for a meta-population network
Venue: arXiv cs.SI 2015
Year: 2015

Epidemiological geographic profiling is a statistical method for making inferences about likely areas of a source from the geographical distribution of patients. Epidemiological geographic profiling algorithms are developed to locate a source from the dataset on the number of new cases for a meta-population network model. It is found from the WHO dataset on the SARS outbreak that Hong Kong remains the most likely source throughout the period of observation. This reasoning is pertinent under the restricted circumstance that the number of reported probable cases in China was missing, unreliable, and incomprehensive. It may also imply that globally connected Hong Kong was more influential as a spreader than China. Singapore, Taiwan, Canada, and the United States follow Hong Kong in the likeliness ranking list.
A fuzzy based conceptual framework for career counselling
Venue: arXiv cs.CY 2015
Year: 2015

Career guidance for students, particularly in rural areas is a challenging issue in India. In the present era of digitalization, there is a need of an automated system that can analyze a student for his/her capabilities, suggest a career and provide related information. Keeping in mind the requirement, the present paper is an effort in this direction. In this paper, a fuzzy based conceptual framework has been suggested. It has two parts; in the first part a students will be analyzed for his/her capabilities and in the second part the available courses, job aspects related to their capabilities will be suggested. To analyze a student, marks in various subject in 10+2 standards and vocational interest in different fields have been considered and fuzzy sets have been formed. On example basis, fuzzy inference rules have been framed for analyzing the abilities in engineering, medical and hospitality fields only. In second part, concept of composition of relations has been used to suggest the related courses and jobs.
Analog Signal Processing Approach for Coarse and Fine Depth Estimation
Venue: arXiv cs.CV 2015
Year: 2015

Imaging and Image sensors is a field that is continuously evolving. There are new products coming into the market every day. Some of these have very severe Size, Weight and Power constraints whereas other devices have to handle very high computational loads. Some require both these conditions to be met simultaneously. Current imaging architectures and digital image processing solutions will not be able to meet these ever increasing demands. There is a need to develop novel imaging architectures and image processing solutions to address these requirements. In this work we propose analog signal processing as a solution to this problem. The analog processor is not suggested as a replacement to a digital processor but it will be used as an augmentation device which works in parallel with the digital processor, making the system faster and more efficient. In order to show the merits of analog processing two stereo correspondence algorithms are implemented. We propose novel modifications to the algorithms and new imaging architectures which, significantly reduces the computation time.