Florida Polytechnic University

About: Florida Polytechnic University is a education organization based out in Lakeland, Florida, United States. It is known for research contribution in the topics: Computer science & Catalysis. The organization has 302 authors who have published 538 publications receiving 6549 citations. The organization is also known as: Florida Poly.

Advanced green analytical chemistry for environmental pesticide detection

Abstract: By understanding the adverse effects of pesticide residues on human and aquatic health, the 21st century has awareness to the importance of adopting advanced agriculture based on minimum use of pesticide residues. Such advancements advocate the development of novel and sensitive analytical methods, which can detect ultra-low levels of pesticide residues with minimum complexity and requirement of expensive traditional analytical techniques. The objective of the present work is to bring forth the concept of green analytical chemistry and to assess the recent progress in pesticide detection techniques within the framework of green chemistry and sustainability. Herein, recent advances are outlined in analytical techniques based on nanosystems for convenient, fast, green, and ultrasensitive detection of pesticide residues in food and environmental samples, including deliberations on newer and future cost-effective, analytical approaches in the field of pesticide detection.

MR-Droid: A Scalable and Prioritized Analysis of Inter-App Communication Risks

Abstract: Inter-Component Communication (ICC) enables useful interactions between mobile apps. However, misuse of ICC exposes users to serious threats, allowing malicious apps to access privileged user data via another app. Unfortunately, existing ICC analyses are largely insufficient in both accuracy and scalability. Most approaches rely on single-app ICC analysis which results in inaccurate and excessive alerts. A few recent works use pairwise app analysis, but are limited by small data sizes and scalability. In this paper, we present MR-Droid, a MapReduce-based computing framework for accurate and scalable inter-app ICC analysis in Android. MR-Droid extracts data-flow features between multiple communicating apps to build a large-scale ICC graph. We leverage the ICC graph to provide contexts for inter-app communications to produce precise alerts and prioritize risk assessments. This scheme requires quickly processing a large number of app-pairs, which is enabled by our MapReduce-based program analysis. Extensive experiments on 11,996 apps from 24 app categories (13 million pairs) demonstrate the effectiveness of our risk prioritization scheme. Our analyses also reveal new real-world hijacking attacks and collusive app pairs. Based on our findings, we provide practical recommendations for reducing inter-app communication risks.

Automated extraction of signs from continuous sign language sentences using Iterated Conditional Modes

Abstract: Recognition of signs in sentences requires a training set constructed out of signs found in continuous sentences. Currently, this is done manually, which is a tedious process. In this work, we consider a framework where the modeler just provides multiple video sequences of sign language sentences, constructed to contain the vocabulary of interest. We learn the models of the recurring signs, automatically. Specifically, we automatically extract the parts of the signs that are present in most occurrences of the sign in context. These parts of the signs that is stable with respect to adjacent signs, are referred to as signemes. Each video is first transformed into a multidimensional time series representation, capturing the motion and shape aspects of the sign. We then extract signemes from multiple sentences, concurrently, using Iterated Conditional Modes (ICM). We show results by learning multiple instances of 10 different signs from a set of 136 sign language sentences. We classify the extracted signemes as correct, partially correct or incorrect depending on whether both the start and end locations are correct, only one of them is correct or both are incorrect, respectively. Out of the 136 extracted video signemes, 98 were correct, 20 were partially correct and 18 were incorrect. To demonstrate the generality of the unsupervised modeling idea, we also show the ability to automatically extract common spoken words in audio. We consider the English glosses (spoken) corresponding to the sign language sentences and extract the audio counterparts of the signs. Of the 136 such instances, we recovered 127 correct, 8 partially correct, and 1 incorrect representation of the words.

Comments on Shulman, Golde, Bueschel, and Garabedian: Existing Practice Is Not the Template

Abstract: In the April 2006 issue of Educational Researcher, Shulman, Golde, Bueschel, and Garabedian offered their response to the recent outpouring of criticism calling for reform of doctoral education degrees in the United States. The centerpiece of their proposal was the development of a new practitioner-oriented doctoral degree to replace the Ed.D. This article critiques the conceptual validity of the proposal—especially the idea that existing practice can be the driving force for the proposed curriculum reforms. The author argues for a fuller and more complex form of practice as praxis, in contrast with Shulman et al.’s implied preference for concrete existing practice—what might be called the actuality of practice—as the template for future practice.