Cache-Aware and Roofline-Ideal Automatic Differentiation

Automatic differentiation software libraries augment arithmetic operations with their derivatives, thereby relieving the programmer of deriving, implementing, debugging, and maintaining derivative code. With this encapsulation however, the responsibility of code optimization relies more heavily on the AD system itself (as opposed to the programmer and the compiler). Moreover, given that there are multiple contexts in reservoir simulation software for which derivatives are required (e.g. property package and discrete operator evaluations), the AD infrastructure must also be adaptable. An Operator Overloading AD design is proposed and tested to provide scalability and computational efficiency seemlessly across memory- and compute-bound applications. This is achieved by 1) use of portable and standard programming language constructs (C++17 and OpenMP 4.5 standards), 2) adopting a vectorized programming interface, 3) lazy evaluation via expression templates, and 4) multiple memory alignment and layout policies. Empirical analysis is conducted on various kernels spanning various arithmetic intensity and working set sizes. Cache- aware roofline analysis results show that the performance and scalability attained are reliably ideal. In terms of floapting point operations executed per second, the performance of the AD system matches optimized hand-code. Finally, the implementation is benchmarked using the Automatically Differentiable Expression Templates Library (ADETL).

Securing the wireless emergency alerts system

Modern cell phones are required to receive and display alerts via the Wireless Emergency Alert (WEA) program, under the mandate of the Warning, Alert, and Response Act of 2006. These alerts include AMBER alerts, severe weather alerts, and (unblockable) Presidential Alerts, intended to inform the public of imminent threats. Recently, a test Presidential Alert was sent to all capable phones in the U.S., prompting concerns about how the underlying WEA protocol could be misused or attacked. In this paper, we investigate the details of this system and develop and demonstrate the first practical spoofing attack on Presidential Alerts, using commercially available hardware and modified open source software. Our attack can be performed using a commercially available software-defined radio, and our modifications to the open source software libraries. We find that with only four malicious portable base stations of a single Watt of transmit power each, almost all of a 50,000-seat stadium can be attacked with a 90% success rate. The real impact of such an attack would, of course, depend on the density of cellphones in range; fake alerts in crowded cities or stadiums could potentially result in cascades of panic. Fixing this problem will require a large collaborative effort between carriers, government stakeholders, and cellphone manufacturers. To seed this effort, we also propose three mitigation solutions to address this threat.

API Recommendation Based on WII-WMD#

Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.

API Recommendation Based on WII-WMD

Improving software development efficiency based on existing APIs is one of the hot researches in software engineering. Understanding and learning so many APIs in large software libraries is not easy and software developers prefer to provide only requirements descriptions to get the right API. In order to solve this problem, this paper proposes an API recommendation method based on WII-WMD, an improved similarity calculation algorithm. This method firstly structures the text, and then fully mines the semantic information in the text. Finally, it calculates the similarity between the user's query problem and the information described in the API document. The experiment results show that the API recommendation based on WII-WMD can improve the efficiency of the API recommendation system.

Pan-neuro: interactive computing at scale with BRAIN datasets

New technical and scientific breakthroughs are enabling neuroscientific measurements that are both wider in scope and denser in their sampling, providing views of the brain that have not been possible before. At the same time, funding initiatives, as well as scientific institutions and communities are promoting sharing of neuroscientific data. These factors are creating a deluge of neuroscience data that promises to provide new and meaningful insights into brain function. However, the size, complexity, and identifiability of the data also present challenges that arise from the difficulties in storing, accessing, processing, analyzing, visualizing and understanding data at large scale. Based on their successful adoption in the earth sciences, we have started adopting and adapting a set of tools for interactive scalable computing in neuroscience. We are building an approach that is based on a combination of a vibrant ecosystem of open-source software libraries and standards, coupled with the massive computational power of the public cloud, and served through interactive browser-based Jupyter interfaces. Together, these could provide uniform universal access to datasets for flexible and scalable exploration and analysis. We present a few prototype use-cases of this approach. We identify barriers and technical challenges that still need to be addressed to facilitate wider deployment of this approach and full exploitation of its advantages.

Monitoring Soil and Ambient Parameters in the IoT Precision Agriculture Scenario: An Original Modeling Approach Dedicated to Low-Cost Soil Water Content Sensors

A low power wireless sensor network based on LoRaWAN protocol was designed with a focus on the IoT low-cost Precision Agriculture applications, such as greenhouse sensing and actuation. All subsystems used in this research are designed by using commercial components and free or open-source software libraries. The whole system was implemented to demonstrate the feasibility of a modular system built with cheap off-the-shelf components, including sensors. The experimental outputs were collected and stored in a database managed by a virtual machine running in a cloud service. The collected data can be visualized in real time by the user with a graphical interface. The reliability of the whole system was proven during a continued experiment with two natural soils, Loamy Sand and Silty Loam. Regarding soil parameters, the system performance has been compared with that of a reference sensor from Sentek. Measurements highlighted a good agreement for the temperature within the supposed accuracy of the adopted sensors and a non-constant sensitivity for the low-cost volumetric water contents (VWC) sensor. Finally, for the low-cost VWC sensor we implemented a novel procedure to optimize the parameters of the non-linear fitting equation correlating its analog voltage output with the reference VWC.

Retrieval from software libraries for bug localization

This retrospective on our 2011 MSR publication starts with the research milieu that led to the work reported in our paper. We brie y review the competing ideas of a decade ago that could be applied to solving the problem of identifying the les in a software library related to a query. We were especially interested in nding out if the more complex text retrieval methods of that time would be e ective in the software context. A surprising conclusion of our paper was that the reality was exactly the opposite: the more traditional simpler methods outperformed the complex methods. In addition to this surprising result, our paper was also the rst to report what was considered at that time a large-scale quantitative evaluation of the IR-based approaches to automatic bug localization. Over the years, such quantitative evaluations have become the norm. We believe that these contributions were largely responsible for the popularity of this paper in the research literature.

graphed: A Web-Based Concept Mapping Application for Instruction and Research

The ongoing worldwide pandemic has forced educational establishments to accelerate full-scale adoption of online learning at an accelerated pace, while the development of tools appropriate for remote instruction assessment is yet to catch up. Most of the time traditional assessment methods are still employed, but they are not always optimal for use in online environments; better tools are needed to help gain deeper insights into how students think and learn. graphed is a web application developed to support the assessment of learners’ understanding and knowledge acquisition and, simultaneously, provide researchers with data that can help in the development of dedicated processes for the automatic evaluation and comparison of concept maps. Our goal is to take a more practical approach by studying the capabilities offered by existing software, libraries, and computational avenues to advance the use of concept maps as assessment tools. Preliminary findings suggest that the concept mapping activity has achieved its purpose of promoting deep thinking, that the application is relatively usable, and clarified the path for future development and enhancement. Examples on the use of graphed in the classroom are provided.