Open-set Experiment Research Articles

Adaptively weighted learning for twin support vector machines via Bregman divergences

Some versions of weighted (twin) support vector machines have been developed to handle the contaminated data. However, the weights of samples are generally obtained from the prior knowledge of data in advance. This article develops an adaptively weighted twin support vector machine via Bregman divergences. To better handle the contaminated data, we employ an insensitive loss function to control the fitting error of the samples in one class and introduce the weight (fuzzy membership) of each sample into the proposed model. The alternating optimization technique is utilized to solve the proposed model due to the characteristics of the model. The accelerated version of first-order methods is used to solve a quadratic programming problem, and the fuzzy membership of each sample is achieved analytically in the case of Bregman divergences. Experiments on some data sets have been conducted to show that our method gains better classification performance than previous methods, especially for the open set experiment.

A continuous combination of security & forensics for mobile devices

In this research, we introduce the concept of adversary identification in combination with continuous authentication. To protect the system from session hijacking, it is important to not only use the traditional access control at the beginning of a session but also continuously monitor the entire session whether the present user is still the legitimate user or not. In case an impostor is detected, the system should lock to avoid loss or disclosure of personal or confidential information. In many cases, it will be important to not only secure the system but also establish the identity of the impostor which could be seen as a deterrence measure or could be used as a shred of evidence in the court. This concept has not been introduced in this manner before, and it combines security and forensics continuously.We have performed a closed-set and an open-set experiment to validate our proof of concept with two different publicly available mobile biometrics datasets. Depending upon the dataset and used settings, we show that the adversary can be correctly identified with 82.2% to 97.9% of the attack cases for closed-set experiment and for the open-set experiment this performance ranges from 73.8% to 77.6%.

I-벡터 기반 오픈세트 언어 인식을 위한 다중 판별 DNN

본 논문에서는 여러 개의 이원 support vector machine (binary SVM)을 사용하여 세 개 이상의 클래스를 분류하는 multi-class SVM과 유사하게 다중의 판별 deep neural network (DNN) 모델을 사용하는 i-벡터 기반의 언어 인식 시스템을 제안한다. 제안하는 시스템은 NIST 2015 i-vector Machine Learning Challenge 데이터베이스에 포함된 i-벡터들을 이용하여 학습 및 테스트 되었으며, 오픈 세트에서 기존의 cosine distance, multi-class SVM 및 단일 neural network (NN) 기반의 언어 인식 시스템에 비하여 높은 성능을 보임이 확인되었다. In this paper, we propose an i-vector based language recognition system to identify the spoken language of the speaker, which uses multiple discriminative deep neural network (DNN) models analogous to the multi-class support vector machine (SVM) classification system. The proposed model was trained and tested using the i-vectors included in the NIST 2015 i-vector Machine Learning Challenge database, and shown to outperform the conventional language recognition methods such as cosine distance, SVM and softmax NN classifier in open-set experiments.

HRR Automatic Target Recognition from Superresolution Scattering Center Features

The work presented here introduces a procedure for the automatic recognition of ground-based targets from high range resolution (HRR) profile sequences that may be obtained from a synthetic aperture radar (SAR) platform. The procedure incorporates an adaptive target mask and uses a superresolution algorithm to identify the cross-range positions of target scattering centers. These are used to generate a pseudoimage of the target whose low-order discrete cosine transform coefficients form the recognizer feature vector. Within the recognizer, the states of a hidden Markov model (HMM) are used to represent the target orientation and a Gaussian mixture model is used for the feature vector distribution. In a closed-set identification experiment, the misclassification rate for ten MSTAR targets was 2.8%. Also presented are results from open-set experiments and investigates the effect on recognizer performance of variations in feature vector dimension, azimuth aperture, and target variants.

PathMiner: a Web-based tool for computer-assisted diagnostics in pathology.

Large-scale, multisite collaboration has become indispensable for a wide range of research and clinical activities that rely on the capacity of individuals to dynamically acquire, share, and assess images and correlated data. In this paper, we report the development of a Web-based system, PathMiner , for interactive telemedicine, intelligent archiving, and automated decision support in pathology. The PathMiner system supports network-based submission of queries and can automatically locate and retrieve digitized pathology specimens along with correlated molecular studies of cases from "ground-truth" databases that exhibit spectral and spatial profiles consistent with a given query image. The statistically most probable diagnosis is provided to the individual who is seeking decision support. To test the system under real-case scenarios, a pipeline infrastructure was developed and a network-based test laboratory was established at strategic sites at the University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, Robert Wood Johnson University Hospital, the University of Pennsylvania School of Medicine, Hospital of the University of Pennsylvania, The Cancer Institute of New Jersey, and Rutgers University. The average five-class classification accuracy of the system was 93.18% based on a tenfold cross validation on a close dataset containing 3691 imaged specimens. We also conducted prospective performance studies with the PathMiner system in real applications in which the specimens exhibited large variations in staining characters compared with the training data. The average five-class classification accuracy in this open-set experiment was 87.22%. We also provide the comparative results with the previous literature and the PathMiner system shows superior performance.