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Speaker verification is a challenging problem in speaker recognition where the objective is to determine whether a segment of speech in fact comes from a specific individual. In supervised machine learning terms this is a challenging problem as, while examples belonging to the target class are easy to gather, the set of counter-examples is completely open. In this paper we cast this as a one-class classification problem and evaluate a variety of state-of-the-art one-class classification techniques on a benchmark speech recognition dataset. We show that of the one-class classification techniques, Gaussian Mixture Models shows the best performance on this task.

Evaluation of subjective answers submitted in an exam is an essential but one of the most resource consuming educational activity. This paper details experiments conducted under our project to build a software that evaluates the subjective answers of informative nature in a given knowledge domain. The paper first summarizes the techniques such as Generalized Latent Semantic Analysis (GLSA) and Cosine Similarity that provide basis for the proposed model. The further sections point out the areas of
improvement in the previous work and describe our approach towards the solutions of the same. We then discuss the implementation details of the project followed by the findings that show the improvements achieved. Our approach focuses on comprehending the various forms of expressing same entity and thereby capturing the subjectivity of text into objective parameters. The model is tested by evaluating answers submitted by 61 students of Third Year B. Tech. CSE class of Walchand College of Engineering
Sangli in a test on Database Engineering.

The two mast cameras (Mastcam) onboard the Mars rover, Curiosity, are multispectral imagers with nine bands in each camera. Currently, the images are compressed losslessly using JPEG, which can achieve
only two to three times compression. We present a two-step approach to compressing multispectral Mastcam images. First, we propose to apply principal component analysis (PCA) to compress the nine bands into three or six bands. This step optimally compresses the 9-band images through spectral correlation between the bands. Second, several well-known image compression codecs, such as JPEG, JPEG-2000 (J2K), X264, and X265, in the literature are applied to compress the 3-band or 6-band images coming out of PCA. The performance of dif erent algorithms was assessed using four well-known performance metrics. Extensive experiments using actual Mastcam images have been performed to demonstrate the proposed framework. We observed that perceptually lossless compression can be achieved at a 10:1 compression ratio. In particular, the performance gain of an approach using a combination of PCA and X265 is at least 5 dBs in terms peak signal-to-noise ratio (PSNR) at a 10:1 compression ratio over that of JPEG when using our proposed approach.

Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is " cancerous " or " healthy " from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.