Methods for Interpreting and Understanding Deep Neural Networks

This paper provides an entry point to the problem of interpreting a deep neural network model and explaining its predictions. It is based on a tutorial given at ICASSP 2017. It introduces some recently proposed techniques of interpretation, along with theory, tricks and recommendations, to make most efficient use of these techniques on real data. It also discusses a number of practical applications.Comment: 14 pages, 10 figure

Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups

Asymptotically Unbiased Estimation of A Nonsymmetric Dependence Measure Applied to Sensor Data Analytics and Financial Time Series

A fundamental concept frequently applied to statistical machine learning is the detection of dependencies between unknown random variables found from data samples. In previous work, we have introduced a nonparametric unilateral dependence measure based on Onicescu’s information energy and a kNN method for estimating this measure from an available sample set of discrete or continuous variables. This paper provides the formal proofs which show that the estimator is asymptotically unbiased and has asymptotic zero variance when the sample size increases. It implies that the estimator has good statistical qualities. We investigate the performance of the estimator for data analysis applications in sensor data analysis and financial time series

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field

Locality-Based Visual Outlier Detection Algorithm for Time Series

Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value. Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed. The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct. The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value

Discriminative dimensionality reduction: variations, applications, interpretations

Schulz A. Discriminative dimensionality reduction: variations, applications, interpretations. Bielefeld: Universität Bielefeld; 2017.The amount of digital data increases rapidly as a result of advances in information and sensor technology. Because the data sets grow with respect to their size, complexity and dimensionality, they are no longer easily accessible to a human user. The framework of dimensionality reduction addresses this problem by aiming to visualize complex data sets in two dimensions while preserving the relevant structure. While these methods can provide significant insights, the problem formulation of structure preservation is ill-posed in general and can lead to undesired effects. In this thesis, the concept of discriminative dimensionality reduction is investigated as a particular promising way to indicate relevant structure by specifying auxiliary data. The goal is to overcome challenges in data inspection and to investigate in how far discriminative dimensionality reduction methods can yield an improvement. The main scientific contributions are the following: (I) The most popular techniques for discriminative dimensionality reduction are based on the Fisher metric. However, they are restricted in their applicability as concerns complex settings: They can only be employed for fixed data sets, i.e. new data cannot be included in an existing embedding. Only data provided in vectorial representation can be processed. And they are designed for discrete-valued auxiliary data and cannot be applied to real-valued ones. We propose solutions to overcome these challenges. (II) Besides the problem that complex data are not accessible to humans, the same holds for trained machine learning models which often constitute black box models. In order to provide an intuitive interface to such models, we propose a general framework which allows to visualize high-dimensional functions, such as regression or classification functions, in two dimensions. (III) Although nonlinear dimensionality reduction techniques illustrate the structure of the data very well, they suffer from the fact that there is no explicit relationship between the original features and the obtained projection. We propose a methodology to create a connection, thus allowing to understand the importance of the features. (IV) Although linear mappings constitute a very popular tool, a direct interpretation of their weights as feature relevance can be misleading. We propose a methodology which enables a valid interpretation by providing relevance bounds for each feature. (V) The problem of transfer learning without given correspondence information between the source and target space and without labels is particularly challenging. Here, we utilize the structure preserving property of dimensionality reduction methods to transfer knowledge in a latent space given by dimensionality reduction

Survey of maps of dynamics for mobile robots

Robotic mapping provides spatial information for autonomous agents. Depending on the tasks they seek to enable, the maps created range from simple 2D representations of the environment geometry to complex, multilayered semantic maps. This survey article is about maps of dynamics (MoDs), which store semantic information about typical motion patterns in a given environment. Some MoDs use trajectories as input, and some can be built from short, disconnected observations of motion. Robots can use MoDs, for example, for global motion planning, improved localization, or human motion prediction. Accounting for the increasing importance of maps of dynamics, we present a comprehensive survey that organizes the knowledge accumulated in the field and identifies promising directions for future work. Specifically, we introduce field-specific vocabulary, summarize existing work according to a novel taxonomy, and describe possible applications and open research problems. We conclude that the field is mature enough, and we expect that maps of dynamics will be increasingly used to improve robot performance in real-world use cases. At the same time, the field is still in a phase of rapid development where novel contributions could significantly impact this research area

Täpne ja tõhus protsessimudelite automaatne koostamine sündmuslogidest

Töötajate igapäevatöö koosneb tegevustest, mille eesmärgiks on teenuste pakkumine või toodete valmistamine. Selliste tegevuste terviklikku jada nimetatakse protsessiks. Protsessi kvaliteet ja efektiivsus mõjutab otseselt kliendi kogemust – tema arvamust ja hinnangut teenusele või tootele. Kliendi kogemus on eduka ettevõtte arendamise oluline tegur, mis paneb ettevõtteid järjest rohkem pöörama tähelepanu oma protsesside kirjeldamisele, analüüsimisele ja parendamisele. Protsesside kirjeldamisel kasutatakse tavaliselt visuaalseid vahendeid, sellisel kujul koostatud kirjeldust nimetatakse protsessimudeliks. Kuna mudeli koostaja ei suuda panna kirja kõike erandeid, mis võivad reaalses protsessis esineda, siis ei ole need mudelid paljudel juhtudel terviklikud. Samuti on probleemiks suur töömaht - inimese ajakulu protsessimudeli koostamisel on suur. Protsessimudelite automaatne koostamine (protsessituvastus) võimaldab genereerida protsessimudeli toetudes tegevustega seotud andmetele. Protsessituvastus aitab meil vähendada protsessimudeli loomisele kuluvat aega ja samuti on tulemusena tekkiv mudel (võrreldes käsitsi tehtud mudeliga) kvaliteetsem. Protsessituvastuse tulemusel loodud mudeli kvaliteet sõltub nii algandmete kvaliteedist kui ka protsessituvastuse algoritmist. Antud doktoritöös anname ülevaate erinevatest protsessituvastuse algoritmidest. Toome välja puudused ja pakume välja uue algoritmi Split Miner. Võrreldes olemasolevate algoritmidega on Splint Miner kiirem ja annab tulemuseks kvaliteetsema protsessimudeli. Samuti pakume välja uue lähenemise automaatselt koostatud protsessimudeli korrektsuse hindamiseks, mis on võrreldes olemasolevate meetoditega usaldusväärsem. Doktoritöö näitab, kuidas kasutada optimiseerimise algoritme protsessimudeli korrektsuse suurendamiseks.Everyday, companies’ employees perform activities with the goal of providing services (or products) to their customers. A sequence of such activities is known as business process. The quality and the efficiency of a business process directly influence the customer experience. In a competitive business environment, achieving a great customer experience is fundamental to be a successful company. For this reason, companies are interested in identifying their business processes to analyse and improve them. To analyse and improve a business process, it is generally useful to first write it down in the form of a graphical representation, namely a business process model. Drawing such process models manually is time-consuming because of the time it takes to collect detailed information about the execution of the process. Also, manually drawn process models are often incomplete because it is difficult to uncover every possible execution path in the process via manual data collection. Automated process discovery allows business analysts to exploit process' execution data to automatically discover process models. Discovering high-quality process models is extremely important to reduce the time spent enhancing them and to avoid mistakes during process analysis. The quality of an automatically discovered process model depends on both the input data and the automated process discovery application that is used. In this thesis, we provide an overview of the available algorithms to perform automated process discovery. We identify deficiencies in existing algorithms, and we propose a new algorithm, called Split Miner, which is faster and consistently discovers more accurate process models than existing algorithms. We also propose a new approach to measure the accuracy of automatically discovered process models in a fine-grained manner, and we use this new measurement approach to optimize the accuracy of automatically discovered process models.https://www.ester.ee/record=b530061