Towards Explainability of UAV-Based Convolutional Neural Networks for Object Classification

f autonomous systems using trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautical Solutions (CAS) Project. One critical research element of ATTRACTOR is explainability of the decision-making across relevant subsystems of an autonomous system. The ability to explain why an autonomous system makes a decision is needed to establish a basis of trustworthiness to safely complete a mission. Convolutional Neural Networks (CNNs) are popular visual object classifiers that have achieved high levels of classification performances without clear insight into the mechanisms of the internal layers and features. To explore the explainability of the internal components of CNNs, we reviewed three feature visualization methods in a layer-by-layer approach using aviation related images as inputs. Our approach to this is to analyze the key components of a classification event in order to generate component labels for features of the classified image at different layers of depths. For example, an airplane has wings, engines, and landing gear. These could possibly be identified somewhere in the hidden layers from the classification and these descriptive labels could be provided to a human or machine teammate while conducting a shared mission and to engender trust. Each descriptive feature may also be decomposed to a combination of primitives such as shapes and lines. We expect that knowing the combination of shapes and parts that create a classification will enable trust in the system and insight into creating better structures for the CNN

Automating Metadata Extraction: Genre Classification

A problem that frequently arises in the management and integration of scientific data is the lack of context and semantics that would link data encoded in disparate ways. To bridge the discrepancy, it often helps to mine scientific texts to aid the understanding of the database. Mining relevant text can be significantly aided by the availability of descriptive and semantic metadata. The Digital Curation Centre (DCC) has undertaken research to automate the extraction of metadata from documents in PDF([22]). Documents may include scientific journal papers, lab notes or even emails. We suggest genre classification as a first step toward automating metadata extraction. The classification method will be built on looking at the documents from five directions; as an object of specific visual format, a layout of strings with characteristic grammar, an object with stylo-metric signatures, an object with meaning and purpose, and an object linked to previously classified objects and external sources. Some results of experiments in relation to the first two directions are described here; they are meant to be indicative of the promise underlying this multi-faceted approach.

The classification of Harris: Influences of Bacon and Hegel in the universe of library classification

The studies of library classifications generally interact with a historical approach that contextualizes the research and with the ideas related to classification that are typical of Philosophy. In the 19th century, the North-American philosopher and educator William Torrey Harris developed a book classification at the St. Louis Public School, based on Francis Bacon and Georg Wilhelm Friedrich Hegel. The objective of the present study is to analyze Harris’s classification, reflecting upon his theoretical and philosophical backgrounds in order to understand Harris’s contribution to Knowledge Organization (KO). To achieve such objective, this study adopts a critical-descriptive approach for the analysis. The results show some influences of Bacon and Hegel in Harris’s classification

Study on Naive Bayesian Classifier and its relation to Information Gain

Classification and clustering techniques in d ata mining are useful for a wide variety of real time applications dealing with large amount o f data. Some of the application areas of data mining are text classification, medical diagnosis, intrusion detection systems etc . The Naive Bayes Classifier techn ique is based on the Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods. The approach is called "naïve" because it assumes the independence between the various attribute values. Naïve Bayes classification can be viewed as both a descriptive and a predictive type of algorithm. The probabilities are descriptive and are then used to predict the class membership for a untrained data

The Prevalence of Children with Special needs In Inclusive Elementary Schools in Iodine Deficiency Area

ABSTRACT Purpose:In general, this research aims to recognize the prevalence of CWSN in inclusive elementary school in the iodine deficiency area. The findings of this research are expected to be proceeded to the intervention programs that enables teachers to help CWSN education in inclusive elementary schools in iodine deficiency area. Method: The research approach uses descriptive study which is conducted in 57 inclusive elementary schools with school requirements: (a) the school has a Decree of the local Education Authority as a school for inclusive education, (b) there is a child with special needs (CWSN) in this certain school, and (c) the school has had a special teacher for CWSN.Collected data includes: (a) the number of CWSN according to class, gender and classification, (b) inclusive school teacher profiles and required teachers for inclusive education implementation for CWSN. The method of collecting data for CWSN and their classification using a screening method with screening instruments needed from Directorate of Special Education and Special Services Ministry of National Education in 2010. While the teachers data profiles are collected by questionnaire method developed by researchers. The validity of this data collection instruments is using content validity with expert judgment. Data analysis method is using percentage quantitative-descriptive technique. Findings: The results can be concluded that: (a) the type of slow-learning CWSN most followed by child learning disabilities, mild mental retardation, behavior disturbances, autistic, physically disabled, visually impaired and deaf-mute, (b) most of the teachers need guidebooks and inclusive education technical guidance, compensatory services and curriculum modifications, learning and assessment of learning outcomes in inclusive schools of CWSN

Universal countable Borel quasi-orders

In recent years, much work in descriptive set theory has been focused on the Borel complexity of naturally occurring classification problems, in particular, the study of countable Borel equivalence relations and their structure under the quasi-order of Borel reducibility. Following the approach of Louveau and Rosendal for the study of analytic equivalence relations, we study countable Borel quasi-orders. In this paper we are concerned with universal countable Borel quasi-orders, i.e. countable Borel quasi-orders above all other countable Borel quasi-orders with regard to Borel reducibility. We first establish that there is a universal countable Borel quasi-order, and then establish that several countable Borel quasi-orders are universal. An important example is an embeddability relation on descriptive set theoretic trees. Our main result states that embeddability of finitely generated groups is a universal countable Borel quasi-order, answering a question of Louveau and Rosendal. This immediately implies that biembeddability of finitely generated groups is a universal countable Borel equivalence relation. The same techniques are also used to show that embeddability of countable groups is a universal analytic quasi-order. Finally, we show that, up to Borel bireducibility, there are continuum-many distinct countable Borel quasi-orders which symmetrize to a universal countable Borel equivalence relation

Robust Feature Selection by Mutual Information Distributions

Mutual information is widely used in artificial intelligence, in a descriptive way, to measure the stochastic dependence of discrete random variables. In order to address questions such as the reliability of the empirical value, one must consider sample-to-population inferential approaches. This paper deals with the distribution of mutual information, as obtained in a Bayesian framework by a second-order Dirichlet prior distribution. The exact analytical expression for the mean and an analytical approximation of the variance are reported. Asymptotic approximations of the distribution are proposed. The results are applied to the problem of selecting features for incremental learning and classification of the naive Bayes classifier. A fast, newly defined method is shown to outperform the traditional approach based on empirical mutual information on a number of real data sets. Finally, a theoretical development is reported that allows one to efficiently extend the above methods to incomplete samples in an easy and effective way.Comment: 8 two-column page