An OpenSHMEM Implementation for the Adapteva Epiphany Coprocessor

This paper reports the implementation and performance evaluation of the OpenSHMEM 1.3 specification for the Adapteva Epiphany architecture within the Parallella single-board computer. The Epiphany architecture exhibits massive many-core scalability with a physically compact 2D array of RISC CPU cores and a fast network-on-chip (NoC). While fully capable of MPMD execution, the physical topology and memory-mapped capabilities of the core and network translate well to Partitioned Global Address Space (PGAS) programming models and SPMD execution with SHMEM.Comment: 14 pages, 9 figures, OpenSHMEM 2016: Third workshop on OpenSHMEM and Related Technologie

Building a Sustainable Agricultural Career Pipeline: Effective Recruitment and Retention Practices Used by Colleges of Agriculture in the United States

This national study examined effective student recruitment and retention practices used by colleges of agriculture in the United States among 1862 land-grant, 1890 land-grant, and non-land-grant institutions. Respondents reported that faculty at colleges of agriculture were primarily white. Through the analysis of sub-group percentages, the researchers found that the ethnic makeup of faculty was not reflective of the general population. The researchers found that administrators from 1862 land-grant institutions reported statistically significant differences (p \u3c .05) regarding the use of specific strategies to target underrepresented populations in student recruitment as compared to other institutional types. Further, 1862 land-grant institutions reported statistically significant differences in student retention strategies (p \u3c .05) as compared to other institutional types regarding the delivery of programs that aimed to retain first-year students. Based on key findings from this investigation, the authors developed the agricultural student retention model (ASRM) to help guide colleges of agriculture in improving their holistic retention program as they navigate inclusive and diverse institutional contexts. Additionally, key recruitment strategies were identified as well, that could facilitate holistic student recruitment efforts. Perhaps more significant progress can be made toward creating a sustainable agricultural workforce that is more reflective of U.S. population demographics using this model

The Knoxville World\u27s Fair: What Have We Learned?

This is a summary of previous presentations with a focus on future implications. He emphasizes the importance of cooperation in the development of special events

Statistical testing of random number generators and their improvement using randomness extraction

Random number generators (RNGs) are notoriously hard to build and test, especially in a cryptographic setting. Although one cannot conclusively determine the quality of an RNG by testing the statistical properties of its output alone, running numerical tests is both a powerful verification tool and the only universally applicable method. In this work, we present and make available a comprehensive statistical testing environment (STE) that is based on existing statistical test suites. The STE can be parameterised to run lightweight (i.e. fast) all the way to intensive testing, which goes far beyond what is required by certification bodies. With it, we benchmark the statistical properties of several RNGs, comparing them against each other. We then present and implement a variety of post-processing methods, in the form of randomness extractors, which improve the RNG's output quality under different sets of assumptions and analyse their impact through numerical testing with the STE.Comment: 20+10 pages, 8 figures and 28 tables. Comments are welcome

Toward a Holistic Agricultural Student Recruitment Model: A National Analysis of the Factors Affecting Students’ Decision to Pursue an Agricultural Related Degree

Currently, the agricultural industry struggles to fill positions with qualified agricultural workers. Therefore, it is critical to attract high caliber individuals to agricultural degree programs that are prepared to enter the workforce with the skills needed to navigate complex issues and problems. The purpose of this national study was to identify key factors that influence the recruitment of agriculture students at land-grant and non-land-grant universities. Using Chapman’s model of student success as our conceptual lens, we tested 66 factors identified in the literature as successful recruitment strategies for colleges of agriculture based on students’ personal characteristics as well as key external influences. We discovered statistically significant (p \u3c .05) differences existed based on students’ gender and race/ethnicity. To better operationalize the findings from this study for U.S. colleges of agriculture, we developed the agricultural student recruitment model (ASRM). The model visually represents the distinct but intersecting factors that most profoundly influence students’ academic degree decisions. Moving forward, we recommend colleges of agriculture use the ASRM as a tool to better resonate with populations that may lack representation in their degree programs and the state’s agricultural industry

New grayscale hit-miss operator

The morphological binary hit-miss operator has been used extensively to locate features within a binary image. We propose a grayscale hit-miss operator that detects signal shapes and is applicable to scalar-valued functions on one, two, or more dimensions. The hit and miss structuring elements define the lower and upper bounds of the signal: If a signal lies between the hit and miss templates, then the hit-miss operator will produce a one output; otherwise, it will respond with zero. We incorporate a fuzzy logic element to the hit-miss operator to indicate how strongly the signal matches the hit-miss templates

Cryptomite: A versatile and user-friendly library of randomness extractors

We present Cryptomite, a Python library of randomness extractor implementations. The library offers a range of two-source, seeded and deterministic randomness extractors, together with parameter calculation modules, making it easy to use and suitable for a variety of applications. We also present theoretical results, including new extractor constructions and improvements to existing extractor parameters. The extractor implementations are efficient in practice and tolerate input sizes of up to $2^{40} > 10^{12}$ bits. They are also numerically precise (implementing convolutions using the Number Theoretic Transform to avoid floating point arithmetic), making them well suited to cryptography. The algorithms and parameter calculation are described in detail, including illustrative code examples and performance benchmarking.Comment: 24 + 10 pages, including figures and examples with cod

Control Led Through Internet Based on Nodemcu with Blynk Application

   Internet (interconnected-networking) is a series of computers that are connected globally in several circuits and use TCP / IP as packet exchange communication protocol. The internet as part of the technological development that is very rapidly developing in people\u27s lives today has been able to be used as a medium of communication and control of devices from a distance as long as they are still connected to each other. The internet is like virtual threads that connect with one another, forward data and convey data from one point to another. However, along with the development of increasingly advanced science and technology, the internet is no longer just to connect between humans but also to control between any object that can be connected. In this study there were 3 (three) problems and 3 (three) problem solving methods Electronic device control in the form of LED lights using an IoT platform that is open source. This study uses the NodeMCU module as a station, which will be controlled by the Blynk application with an internet connection. In this study using the NodeMCU module as a station, which will be controlled by the Blynk application by connecting to the internet