Discourse Analysis (DA)
Discourse analysis (DA), or discourse studies, is a general term for a number of approaches to analyzing written, vocal, or sign language use or any significant semiotic event. The objects of discourse analysis – discourse, writing, conversation, communicative event – are variously defined in terms of coherent sequences of sentences, propositions, speech, or turns-at-talk. Contrary to much of traditional linguistics, discourse analysts not only study language use ‘beyond the sentence boundary’, but also prefer to analyze ‘naturally occurring’ language use, and not invented examples. Text linguistics is related. The essential difference between discourse analysis and text linguistics is that it aims at revealing socio-psychological characteristics of a person/persons rather than text structure. Discourse analysis has been taken up in a variety of social science disciplines, including linguistics, education, sociology, anthropology, social work, cognitive psychology, social psychology, area studies, cultural studies, international relations, human geography, communication studies, and translation studies, each of which is subject to its own assumptions, dimensions of analysis, and methodologies. …

Isolation Forest
Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our best knowledge, the concept of isolation has not been explored in current literature. The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement. Our empirical evaluation shows that iForest performs favourably to ORCA, a near-linear time complexity distance-based method, LOF and Random Forests in terms of AUC and processing time, and especially in large data sets. iForest also works well in high dimensional problems which have a large number of irrelevant attributes, and in situations where training set does not contain any anomalies.
➚ “Extended Isolation Forest” …

Generated-Artificial-Noise to Generated-Artificial-Noise (GAN2GAN)
We tackle a challenging blind image denoising problem, in which only single noisy images are available for training a denoiser and no information about noise is known, except for it being zero-mean, additive, and independent of the clean image. In such a setting, which often occurs in practice, it is not possible to train a denoiser with the standard discriminative training or with the recently developed Noise2Noise (N2N) training; the former requires the underlying clean image for the given noisy image, and the latter requires two independently realized noisy image pair for a clean image. To that end, we propose GAN2GAN (Generated-Artificial-Noise to Generated-Artificial-Noise) method that can first learn to generate synthetic noisy image pairs that simulate independent realizations of the noise in the given images, then carry out the N2N training of a denoiser with those synthetically generated noisy image pairs. Our method consists of three parts: extracting smooth noisy patches to learn the noise distribution in the given images, training a generative model to synthesize the noisy image pairs, and devising an iterative N2N training of a denoiser. In results, we show the denoiser trained with our GAN2GAN, solely based on single noisy images, achieves an impressive denoising performance, almost approaching the performance of the standard discriminatively-trained or N2N-trained models that have more information than ours, and significantly outperforming the recent baselines for the same setting. …

k-Same-Siamese-GAN (kSS-GAN)
In recent years, advances in camera and computing hardware have made it easy to capture and store amounts of image and video data. Consider a data holder, such as a hospital or a government entity, who has a privately held collection of personal data. Then, how can we ensure that the data holder does conceal the identity of each individual in the imagery of personal data while still preserving certain useful aspects of the data after de-identification? In this work, we proposed a novel approach towards high-resolution facial image de-identification, called k-Same-Siamese-GAN (kSS-GAN), which leverages k-Same-Anonymity mechanism, Generative Adversarial Network (GAN), and hyperparameter tuning. To speed up training and reduce memory consumption, the mixed precision training (MPT) technique is also applied to make kSS-GAN provide guarantees regarding privacy protection on close-form identities and be trained much more efficiently as well. Finally, we dedicated our system to an actual dataset: RafD dataset for performance testing. Besides protecting privacy of high resolution of facial images, the proposed system is also justified for its ability in automating parameter tuning and breaking through the limitation of the number of adjustable parameters. …