Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place.In this paper we present ML.NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML.NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML.NET compared to more recent entrants, and a discussion of some lessons learned.

ABSTRACT

Tannery wastewater in the East Calcutta Wetlands (a Ramsar site of West Bengal; number 1208) exerts adverse effects on commercial fish production and subsequently affects humans. The present study was conducted to investigate acute and chronic toxicity of tannery effluent on a fish biosystem by examining oxidative stress enzyme expression in different organs including liver, gills, and muscle following exposure. Phosphatases, both alkaline phosphatase and acid phosphatase, and antioxidant superoxide dismutase and catalase enzyme activities were determined in guppy fish (Poecilia reticulata) exposed to sublethal concentrations of composite tannery effluent. Data demonstrated that tannery effluent was capable of interfering with metabolic processes of fish by altering stress enzyme activities in fish organs, resulting in cellular injury. Data suggest that elevated activities of stress enzymes in fish upon exposure to environmental pollutants may serve as important biomarkers for oxidative stress.

No abstract

Tone mapping operators (TMO) are functions which map high dynamic range (HDR) images to limited dynamic media while aiming to preserve the perceptual cues of the scene that govern its aesthetic quality. Evaluating aesthetic quality of TMOs is non-trivial due to the high subjectivity of preference involved. Traditionally, TMO aesthetic quality has been evaluated via subjective experiments in a controlled laboratory environment. However, the last decade has brought a surge in popularity of crowdsourcing as an alternative methodology to conduct subjective experiments. However, uncontrolled experiment conditions and unreliability of participant behaviour puts doubts on the trustworthiness of the collected data. In this study, we explore the possibility of using crowdsourcing platforms for subjective quality evaluation of TMOs. We have conducted three experiments with systematic changes to investigate the effect of experiment conditions and participant recruitment methods on the collected subjective data. Our results show that subjective evaluation of TMO aesthetic quality can be conducted on Prolific crowdsourcing platform with negligible differences in comparison to laboratory experiments. Furthermore, we provide objective conclusions about the effect of number of observers on the certainty of the pairwise comparison results.