http://www.epa.gov/oppfead1/labeling/lrm/ individual chapters of the Pesticide Label Review Manual, 3rd Edition Pesticide Label Review Manual, 3rd Edition. Utilized by the EPA Office of Pesticide Programs (OPP) to improve the quality and consistency of pesticide labels. This document provides guidance on how the US EPA interprets the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) and its specific requirements for label language and format. Chapters include: • Precautionary labeling (including Signal Word, Personal Protective Equipment, and First Aid Statements) • Physical or Chemical Hazards • Worker Protection Labeling • Directions for Use • Labeling Claims • Storage and Disposal • Identification numbers (including EPA registration number)

Appendix A: informational sources on pesticides and health.

Program comprehension is an important activity in software maintenance, as software must be sufficiently understood before it can be properly modified. The study of a program's execution, known as dynamic analysis, has become a common technique in this respect and has received substantial attention from the research community, particularly over the last decade. These efforts have resulted in a large research body of which currently there exists no comprehensive overview. This paper reports on a systematic literature survey aimed at the identification and structuring of research on program comprehension through dynamic analysis. From a research body consisting of 4,795 articles published in 14 relevant venues between July 1999 and June 2008 and the references therein, we have systematically selected 176 articles and characterized them in terms of four main facets: activity, target, method, and evaluation. The resulting overview offers insight in what constitutes the main contributions of the field, supports the task of identifying gaps and opportunities, and has motivated our discussion of several important research directions that merit additional consideration in the near future.

/pdf/a-systematic-survey-of-program-comprehension-through-dynamic-2kqi3h5pbi.pdf

A Systematic Survey of Program Comprehension through Dynamic Analysis

Preprint of paper published in: IEEE Transactions on Software Engineering, 35 (5), 2009; doi:10.1109/TSE.2009.28 Program comprehension is an important activity in software maintenance, as software must be sufficiently understood before it can be properly modified. The study of a program's execution, known as dynamic analysis, has become a common technique in this respect and has received substantial attention from the research community, particularly over the last decade. These efforts have resulted in a large research body of which currently there exists no comprehensive overview. This paper reports on a systematic literature survey aimed at the identification and structuring of research on program comprehension through dynamic analysis. From a research body consisting of 4,795 articles published in 14 relevant venues between July 1999 and June 2008 and the references therein, we have systematically selected 176 articles and characterized them in terms of four main facets: activity, target, method, and evaluation. The resulting overview offers insight in what constitutes the main contributions of the field, supports the task of identifying gaps and opportunities, and has motivated our discussion of several important research directions that merit additional consideration in the near future.

/pdf/a-systematic-survey-of-program-comprehension-through-dynamic-mkait6016b.pdf

This book makes a clear presentation of the traditional topics included in a course of undergraduate parallel programming. As explained by the authors, it was developed from their own experience in classrooms, introducing their students to parallel programming. It can be used almost directly to teach basic parallel programming.

/pdf/parallel-programming-techniques-and-applications-using-1g2pyrr6g4.pdf

Parallel programming: techniques and applications using networked workstations and parallel computers. Barry Wilkinson, C. Michael Allen

Big data applications are consuming most of the space in industry and research area. Among the widespread examples of big data, the role of video streams from CCTV cameras is equally important as other sources like social media data, sensor data, agriculture data, medical data and data evolved from space research. Surveillance videos have a major contribution in unstructured big data. CCTV cameras are implemented in all places where security having much importance. Manual surveillance seems tedious and time consuming. Security can be defined in different terms in different contexts like theft identification, violence detection, chances of explosion etc. In crowded public places the term security covers almost all type of abnormal events. Among them violence detection is difficult to handle since it involves group activity. The anomalous or abnormal activity analysis in a crowd video scene is very difficult due to several real world constraints. The paper includes a deep rooted survey which starts from object recognition, action recognition, crowd analysis and finally violence detection in a crowd environment. Majority of the papers reviewed in this survey are based on deep learning technique. Various deep learning methods are compared in terms of their algorithms and models. The main focus of this survey is application of deep learning techniques in detecting the exact count, involved persons and the happened activity in a large crowd at all climate conditions. Paper discusses the underlying deep learning implementation technology involved in various crowd video analysis methods. Real time processing, an important issue which is yet to be explored more in this field is also considered. Not many methods are there in handling all these issues simultaneously. The issues recognized in existing methods are identified and summarized. Also future direction is given to reduce the obstacles identified. The survey provides a bibliographic summary of papers from ScienceDirect, IEEE Xplore and ACM digital library.

/pdf/intelligent-video-surveillance-a-review-through-deep-4u0qpijyy7.pdf

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

The emergence of inexpensive parallel computers powered by multicore chips combined with stagnating clock rates raises new challenges for software engineering. As future performance improvements will not come "for free" from increased clock rates, performance critical applications will need to be parallelized. However, little is known about the engineering principles for parallel general-purpose applications.This paper presents an experience report with four diverse case studies on multicore software development for general-purpose applications. They were programmed in different languages and benchmarked on several multicore computers. Empirical findings include: 1) Multicore computers deliver: Real speedups are achievable, albeit with significant programming effort and speedups that are typically lower than the number of cores employed; 2) Massive refactoring of sequential programs is required, sometimes at several levels. Special tools for parallelization refactorings appear to be an important area of research; 3) Autotuning is indispensable, as manually tuning thread assignment, number of pipeline stages, size of data partitions and other parameters is difficult and error prone; 4) Architectures that encompass several parallel components are poorly understood. Tuneable architectural patterns with parallelism at several levels need to be discovered.

/pdf/software-engineering-for-multicore-systems-an-experience-5fkkgowyqo.pdf

Software engineering for multicore systems: an experience report

Finding synchronization defects is difficult due to non-deterministic orderings of parallel threads. Current tools for detecting synchronization defects tend to miss many data races or produce an overwhelming number of false alarms. In this paper, we describe Helgrind+, a dynamic race detection tool that incorporates correct handling of condition variables and a combination of the lockset algorithm and happens-before relation. We compare our techniques with Intel Thread Checker and the original Helgrind tool on two substantial benchmark suites. Helgrind+ reduces the number of both false negatives (missed races) and false positives. The additional accuracy incurs almost no performance overhead.

Helgrind+: An efficient dynamic race detector

We conducted a case study of parallelizing a real program for multicore computers using currently available libraries and tools. We selected the sequential Bzip2 compression program for the study because it's a computing-intensive, widely used, and relevant application in everyday life. Its source code is available, and its algorithm is well documented. In addition, the algorithm is non-trivial, but, with 8,000 LOC, the application is small enough to manage in a course.

/pdf/parallelizing-bzip2-a-case-study-in-multicore-software-56j07fug1v.pdf

Parallelizing Bzip2: A Case Study in Multicore Software Engineering

Although multicore CPUs are dominating the market of desktops and servers, writing programs that utilize the available hardware parallelism on these architectures still remains a challenge. In this paper, we present a dynamic approach for automatically identifying potential parallelism in sequential programs. Our method is based on the notion of computational units, which are small sections of code following the read-compute-write pattern that can form the atoms of concurrent scheduling. In contrast to earlier approaches, our method can identify parallelism between code sections of arbitrary granularity and does not rely on a predefined notion of language constructs subject to parallelization. Experimental results show that reasonable speedups can be achieved by parallelizing sequential programs manually according to our findings. By comparing our findings to known parallel implementations of sequential programs, we demonstrate that we are able to detect the most important code locations to be parallelized.

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Discovery of Potential Parallelism in Sequential Programs

Parallel programs contain a surprising number of ad-hoc synchronization operations. Ad-hoc synchronization operations are loops that busy-wait on condition variables. Current race detectors produce unnecessary warnings (false positives) when ad-hoc synchronization is used. False positives are also generated when programmers use synchronization primitives that are unknown to race detectors, for instance when programmers switch libraries. These shortcomings may result in an overwhelming number of false positives, dissuading programmers from using race detectors. This paper shows that ad-hoc synchronization operations can be detected automatically. The method requires no user intervention such as annotations and has been implemented in the race detector Helgrind+. Evaluation results on various benchmarks confirm that Helgrind+ is aware of all synchronizations in programs, reliably reports true races, and produces few false alarms. A surprising result is that with the new technique, Helgrind+ can analyze synchronization libraries, so special knowledge about these libraries is not needed in the detector.

/pdf/identifying-ad-hoc-synchronization-for-enhanced-race-1rhrj4kwh5.pdf

Ali Jannesari

Papers

Software engineering for multicore systems: an experience report

Helgrind+: An efficient dynamic race detector

Parallelizing Bzip2: A Case Study in Multicore Software Engineering

Discovery of Potential Parallelism in Sequential Programs

Identifying ad-hoc synchronization for enhanced race detection