Result diversification is an important aspect in web-based search, document summarization, facility location, portfolio management, and other applications. Given a set of ranked results for a set of objects (e.g., web documents, facilities, etc.) with a distance between any pair, the goal is to select a subset S satisfying the following three criteria: (a) the subset S satisfies some constraint (e.g., bounded cardinality), (b) the subset contains results of high “quality,” and (c) the subset contains results that are “diverse” relative to the distance measure. The goal of result diversification is to produce a diversified subset while maintaining high quality as much as possible. We study a broad class of problems where the distances are a metric, where the constraint is given by independence in a matroid, where quality is determined by a monotone submodular function and diversity is defined as the sum of distances between objects in S . Our problem is a generalization of the max-sum diversification problem studied in Gollapudi and Sharma [2009], which in turn is a generalization of the max-sum p-dispersion problem studied extensively in location theory. It is NP-hard even with the triangle inequality. We propose two simple and natural algorithms: a greedy algorithm for a cardinality constraint and a local search algorithm for an arbitrary matroid constraint. We prove that both algorithms achieve constant approximation ratios.

In this paper, we propose Nutrilyzer, a novel mobile sensing system for characterizing the nutrients and detecting adulterants in liquid food with the photoacoustic effect. By listening to the sound of the intensity modulated light or electromagnetic wave with different wavelengths, our mobile photoacoustic sensing system captures unique spectra produced by the transmitted and scattered light while passing through various liquid food. As different liquid foods with different chemical compositions yield uniquely different spectral signatures, Nutrilyzer's signal processing and machine learning algorithm learn to map the photoacoustic signature to various liquid food characteristics including nutrients and adulterants. We evaluated Nutrilyzer for milk nutrient prediction (i.e., milk protein) and milk adulterant detection. We have also explored Nutrilyzer for alcohol concentration prediction. The Nutrilyzer mobile system consists of an array of 16 LEDs in ultraviolet, visible and near-infrared region, two piezoelectric sensors and an ARM microcontroller unit, which are designed and fabricated in a printed circuit board and a 3D printed photoacoustic housing.

Being able to detect and recognize human activities is essential for 3D collaborative applications for efficient quality of service provisioning and device management. A broad range of research has been devoted to analyze media data to identify human activity, which requires the knowledge of data format, application-specific coding technique and computationally expensive image analysis. In this paper, we propose a human activity detection technique based on application generated metadata and related system metadata. Our approach does not depend on specific data format or coding technique. We evaluate our algorithm with different cyber-physical setups, and show that we can achieve very high accuracy (above 97%) by using a good learning model.