Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategy of Informational literacy for information workers of Villa Clara health system

I Máster en Ciencias de la Educación Superior de la UCLV. Profesor Instructor. Centro Provincial de Información, Villa Clara, Cuba. II Máster en Ciencias Pedagógicas del ISP: “Félix Varela”. Profesor Asistente. Director. Centro Provincial de Información. Villa Clara, Cuba. III Licenciada en Educación Especialidad Español y Literatura. Profesor Instructor. Centro Provincial de Información. Villa Clara, Cuba. IV Máster en Ciencias de la Educación del ISP: “Félix Varela”. Profesor Instructor. Centro Provincial de Información. Villa Clara, Cuba V Máster en Ciencias de la Educación del ISP: “Félix Varela”. Profesor Instructor. Centro Provincial de Información. Villa Clara, Cuba ___________________________________________________________
NVAlloc: rethinking heap metadata management in persistent memory allocators

Persistent memory allocation is a fundamental building block for developing high-performance and in-memory applications. Existing persistent memory allocators suffer from suboptimal heap organizations that introduce repeated cache line flushes and small random accesses in persistent memory. Worse, many allocators use static slab segregation resulting in a dramatic increase in memory consumption when allocation request size is changed. In this paper, we design a novel allocator, named NVAlloc, to solve the above issues simultaneously. First, NVAlloc eliminates cache line reflushes by mapping contiguous data blocks in slabs to interleaved metadata entries stored in different cache lines. Second, it writes small metadata units to a persistent bookkeeping log in a sequential pattern to remove random heap metadata accesses in persistent memory. Third, instead of using static slab segregation, it supports slab morphing, which allows slabs to be transformed between size classes to significantly improve slab usage. NVAlloc is complementary to the existing consistency models. Results on 6 benchmarks demonstrate that NVAlloc improves the performance of state-of-the-art persistent memory allocators by up to 6.4x and 57x for small and large allocations, respectively. Using NVAlloc reduces memory usage by up to 57.8%. Besides, we integrate NVAlloc in a persistent FPTree. Compared to the state-of-the-art allocators, NVAlloc improves the performance of this application by up to 3.1x.
Predicting Success in CS1 - An Open Access Data Project

PreSS# is an online Machine Learning prediction model that aims to identify students at risk of failing or dropping out in an introductory programming course (typically called CS1). PreSS# has been developed over the past 16 years, where the model is capable of predicting at-risk students with an accuracy of 71%. There is, however, a need to re-validate the model using a larger international multi-jurisdictional multi-university data set, as up until now the data sets have been predominantly from a single jurisdiction. The goal of this study is to not only re-validate the model using a multi-jurisdictional data set, but, in line with a 2015 ITiCSE working group report's Grand Challenges, to openly publish the data set itself. This work timely to the CSEd community as other researchers can use this data to further their research, re-validate PreSS# and will be able to then contribute, by submitting their local PreSS# data sets to this global online repository.
Student Motivations and Goals for CS1: Themes and Variations

Students come to CS1 with a wide variety of motivations and goals, which may differ across subpopulations and be indicative of their future engagement with CS. While there is a rich literature relating success in CS1 to specific constructs, such as belonging, goal-orientation, or self-efficacy, less work has examined what motivations and goals students volunteer as most important for their enrollment in CS1. Here, we use qualitative coding to identify themes from students' open-ended descriptions of why they're taking CS1 and what they hope to get out of it, collected across fifteen years. Using quantitative analysis of these coded descriptions, and word-frequency analysis, we identify and name three clusters of students that encompass the majority of students taking CS1: Explorers, Planners, and Utilitarians. We also identify motivations and goals that are more common for particular populations, such as students who have not yet declared a major or students without prior programming experience, as well as factors predicting students' later engagement with CS. This work demonstrates the potential of qualitative coding and computational analyses to enable us to better understand a population of students based on their own words.
Examples of Culturally Responsive Teaching in Computational Thinking Curriculum

Culturally Responsive Teaching (CRT) is a pedagogical strategy that aims to engage culturally and linguistically diverse students in the classroom, to validate and to empower them (Scott, Sheridan, & Clark, 2014; Gay, 2010). Five commonly described aspects of culturally responsive instruction include: (1) helping students to value the perspectives of others, (2) affirming a sense of belonging, (3) validating students' cultural identities, (4) educating students about the diversity of the world around them, (5) supporting students to develop an awareness the impactful social structures. Research has shown repeatedly that CRT increases the academic success of ethnically diverse communities (Gay, 2018; Ladson-Billings, 1995). This talk will provide examples of how CRT has been incorporated into a Scratch-based, upper-elementary computational thinking curriculum that was developed specifically for English language learners. Learning activities include cultural content, diverse role models in the computer science field, and student reflection.
Yashme: detecting persistency races

Persistent memory (PM) or Non-Volatile Random-Access Memory (NVRAM) hardware such as Intel’s Optane memory product promises to transform how programs store and manipulate information. Ensuring that persistent memory programs are crash consistent is a major challenge. We present a novel class of crash consistency bugs for persistent memory programs, which we call persistency races. Persistency races can cause non-atomic stores to be made partially persistent. Persistency races arise due to the interaction of standard compiler optimizations with persistent memory semantics. We present Yashme, the first detector for persistency races. A major challenge is that in order to detect persistency races, the execution must crash in a very narrow window between a store with a persistency race and its corresponding cache flush operation, making it challenging for naïve techniques to be effective. Yashme overcomes this challenge with a novel technique for detecting races in executions that are prefixes of the pre-crash execution. This technique enables Yashme to effectively find persistency races even if the injected crashes do not fall into that window. We have evaluated Yashme on a range of persistent memory benchmarks and have found 26 real persistency races that have never been reported before.
Debugging in the brave new world of reconfigurable hardware

Software and hardware development cycles have traditionally been quite distinct. Software allows post-deployment patches, which leads to a rapid development cycle. In contrast, hardware bugs that are found after fabrication are extremely costly to fix (and sometimes even unfixable), so the traditional hardware development cycle involves massive investment in extensive simulation and formal verification. Reconfigurable hardware, such as a Field Programmable Gate Array (FPGA), promises to propel hardware development towards an agile software-like development approach, since it enables a hardware developer to patch bugs that are detected during on-chip testing or in production. Unfortunately, FPGA programmers lack bug localization tools amenable to this rapid development cycle, since past tools mainly find bugs via simulation and verification. To develop hardware bug localization tools for a rapid development cycle, a thorough understanding of the symptoms, root causes, and fixes of hardware bugs is needed. In this paper, we first study bugs in existing FPGA designs and produce a testbed of reliably-reproducible bugs. We classify the bugs according to their intrinsic properties, symptoms, and root causes. We demonstrate that many hardware bugs are comparable to software bug counterparts, and would benefit from similar techniques for bug diagnosis and repair. Based upon our findings, we build a novel collection of hybrid static/dynamic program analysis and monitoring tools for debugging FPGA designs, showing that our tools enable a software-like development cycle by effectively reducing developers' manual efforts for bug localization.
Climate Science, Data Science and Distributed Computing to Build Teen Students' Positive Perceptions of CS

Providing learners with authentic interdisciplinary experiences is one strategy to foster positive perceptions of CS as a discipline that supports a breadth of applications. We designed a high school mini course using climate science as an interdisciplinary context, since it is of interest to today's youth and provides opportunities to engage with authentic applications of computing that leverage real data to examine issues. The course was designed to engage students in climate change, data visualization, distributed computing, and motivate the use of advanced data abstractions and practices. We used NetsBlox, an extension of Snap! that makes real datasets & Web services accessible through easy-to-use blocks. A paleoclimatologist led climate science discussions and research questions with students. Post-survey responses to questions probing insights students gained shed light on the positive impact on students' perceptions of CS through this interdisciplinary experience, and how it expanded their horizons for future STEM inquiry using data and computing. We share curricular details for use in high schools and takeaways to help promote rich programming experiences that improve students' perceptions of computing.
Enzian: an open, general, CPU/FPGA platform for systems software research

Hybrid computing platforms, comprising CPU cores and FPGA logic, are increasingly used for accelerating data-intensive workloads in cloud deployments, and are a growing topic of interest in systems research. However, from a research perspective, existing hardware platforms are limited: they are often optimized for concrete, narrow use-cases and, therefore lack the flexibility needed to explore other applications and configurations. We show that a research group can design and build a more general, open, and affordable hardware platform for hybrid systems research. The platform, Enzian, is capable of duplicating the functionality of existing CPU/FPGA systems with comparable performance but in an open, flexible system. It couples a large FPGA with a server-class CPU in an asymmetric cache-coherent NUMA system. Enzian also enables research not possible with existing hybrid platforms, through explicit access to coherence messages, extensive thermal and power instrumentation, and an open, programmable baseboard management processor. Enzian is already being used in multiple projects, is open source (both hardware and software), and available for remote use. We present the design principles of Enzian, the challenges in building it, and evaluate it with a range of existing research use-cases alongside other, more specialized platforms, as well as demonstrating research not possible on existing platforms.
Add Some Action to the Output: A Ready-to-Use, Customizable Asset for Easily Adding Animation to Python Programs

Compared to the colorful, animated interfaces that students use every day, the text-only output of a typical CS1 Python program is neither exciting, engaging nor motivating. But incorporating graphical output into a CS1 assignment can be tedious and time-consuming for an instructor and challenging for students. As a solution, this poster presents an Easy Graphical Asset (EGA) that provides a ready-to-use mechanism for defining and animating the movement of a graphical element along a linear path. The asset is composed of 3 customizable elements: a background image, a divided linear path
GenStore: a high-performance in-storage processing system for genome sequence analysis

Read mapping is a fundamental step in many genomics applications. It is used to identify potential matches and differences between fragments (called reads) of a sequenced genome and an already known genome (called a reference genome). Read mapping is costly because it needs to perform approximate string matching (ASM) on large amounts of data. To address the computational challenges in genome analysis, many prior works propose various approaches such as accurate filters that select the reads within a dataset of genomic reads (called a read set) that must undergo expensive computation, efficient heuristics, and hardware acceleration. While effective at reducing the amount of expensive computation, all such approaches still require the costly movement of a large amount of data from storage to the rest of the system, which can significantly lower the end-to-end performance of read mapping in conventional and emerging genomics systems. We propose GenStore, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. GenStore leverages hardware/software co-design to address the challenges of in-storage processing, supporting reads with 1) different properties such as read lengths and error rates, which highly depend on the sequencing technology, and 2) different degrees of genetic variation compared to the reference genome, which highly depends on the genomes that are being compared. Through rigorous analysis of read mapping processes of reads with different properties and degrees of genetic variation, we meticulously design low-cost hardware accelerators and data/computation flows inside a NAND flash-based solid-state drive (SSD). Our evaluation using a wide range of real genomic datasets shows that GenStore, when implemented in three modern NAND flash-based SSDs, significantly improves the read mapping performance of state-of-the-art software (hardware) baselines by 2.07-6.05× (1.52-3.32×) for read sets with high similarity to the reference genome and 1.45-33.63× (2.70-19.2×) for read sets with low similarity to the reference genome.
Self-care Diabetes: a diabetes self-management application based on the seven self-management areas (AADE7) of the American Association of Diabetes Educators

The constant increase in the number of patients with chronic diseases, such as diabetes, over the last few decades and the high cost of their medical care for health systems have led to the development of applications that address such widespread health service challenges. Diabetes, in particular, has undoubtedly reached epidemic proportions as its incidence is constantly increasing at a particularly high rate worldwide. The purpose of this paper is to present a twofold application that will be an easy-to-use tool to improve the patient's quality of life through the effective self-management of diabetes and at the same time will provide the treating physician with a complete picture of the patient's health. In order to achieve these goals, the proposed application was based on the seven self-management areas (AADE7) of the American Association of Diabetes Educators, which are healthy eating, physical activity, blood glucose monitoring, medication, treating complications, reducing the risk of complications and healthy treatment to reduce stress. The application was made available for testing to a number of users suffering from diabetes; following the researchers asked the patients who used the application for a whole week to participate in a structured evaluation. Main findings include inferences about the application usability, technical issues that should be solved and ideas for future extensions. Bluetooth integration, data synchronization with other applications or devices, data encryption and data sharing with the treating physician, are some of the most important extensions that could be implemented.
A Eficiência de Cross-hedge do Risco de Preço de Frangos com o Uso de Contratos Futuros de Milho da BM&F-BOVESPA The Efficiency of Poultry Price Risk Cross-hedge with the Usage of Corn Futures Contracts of BM&F-BOVESPA

The poultry industry bears price risk for which the analysis and mitigation are relevant steps to the efficient management of production units, allowing a more efficient allocation of resources. However a specific futures contract for the Brazilian poultry industry in the futures market does not exist, although there is a futures contract for corn is BM&F-BOVESPA. Given the significant cross participation among the poultry and corn industries in Brazil, it is worth asking if the poultry price risk can be neutralized with the domestic corn futures, analyzing the efficiency of the use of corn futures contracts traded on BM&F-BOVESPA in cross-hedge operations of the poultry price risk. The research question addressed is: corn futures contracts traded on the BM & F BOVESPA, provide efficient mitigation of price risk for the national poultry industry. The survey questions are: i. what are the statistical properties of poultry spot prices and corn futures time series of, ii. how to get different cross-hedging levels of poultry prices and corn using alternative models of hedging: minimum-variance, no hedge, total hedge (naive) and dynamic, and, iii. what are the cross-hedge effectiveness obtained through the use of the various models. The low effectiveness of these operations in different contexts and hedge portfolio: fine, full, dynamic is observed. The results can be attributed to different microeconomic structures, such as the low correlation between the price series due to the specificities in both industries, seasonality, marketing and pricing procedures in both markets. However, the strong market signals economic and financial potential for the introduction of a poultry future contract in Brazil.
Why Should Computer and Information Science Programs Require Service Learning?

Service learning-an educational experience in which students provide service to a community partner while learning content knowledge, professional skills, and critical thinking-can provide significant benefits to students and the community. We present survey results from 227 postsecondary students in computing to provide insights into their attitudes toward service learning, and how these relate to course-taking motivations and sense of civic duty. Based on the survey results, we argue that service learning should be required in an undergraduate computing major. However, we problematize this provocation based on three types of pitfalls: courses that do not prepare students to understand social contexts in which technical solutions are promoted, lack of resources for faculty teaching the courses, and the potential to harm both community partners and students.
Path planning for nonholonomic mobile robots using artificial potential field method

The path planning method based on the artificial potential field(APF) has attracted many researchers for more than twenty years. However, most of them focused their attention to theoretical aspects and considered the robot a particle or rigid body without constraints. Therefore many achievements can not be applied directly to wheeled mobile robots with nonholonomic constraints. To apply the APF method on a wheeled mobile robot, we theoretically analyze two existing approaches and reveal their hidden failures in reaching the target and the unbalanced performances of path planning in different environments. A new method based on fuzzy rules has been proposed to solve these problems by modifying the control pattern or parameters in different situations. Simulation studies show that the proposed method can acquire better overall performance in various environments on the premise of reaching the target.
Code Quality Defects across Introductory Programming Topics

Research on feedback in introductory programming focuses mostly on incomplete and incorrect programs. However, most of the functionally correct programs also contain defects that call for feedback. We analyzed 114,000 solutions to 161 short coding problems in Python and compiled a catalog of 32 defects in code quality. We found that most correct solutions contain some defects and that students do not stop making them if they do not receive targeted feedback. The catalog of defects, together with their prevalence across common topics like expressions, loops, and lists, informs educators which defects to address in which lectures and guides the development of exercises on code quality. Additionally, we describe defect detectors, which can be used to generate valuable feedback to students automatically.
FoRePlan: Supporting Digital Forensics Readiness Planning for Internet of Vehicles

The massive amount of data collected by the connected and autonomous vehicles can be used as digital evidence for investigating cyber-attacks, identifying vulnerabilities and providing response and recovery solutions to increase the security in the Internet-of-Vehicles ecosystems. In this respect, the investigation of digital incidents, (e.g., cyber-attacks) requires setting appropriate digital forensics readiness plans for screening data and acquiring those that are more relevant to the incident. Aiming to support the process of creating and executing digital forensics readiness plans, in this paper, we present FoRePlan, which provides the security experts with means to prepare, manage, and execute customized digital forensics readiness plans tailored to the detected attacks in Internet-of-Vehicles ecosystems.
Stochastic multi-objective scheduling of a combined cooling, heating and power microgrid containing a fuel cell

Combined cooling, heating and power (CCHP) microgrids that use fuel cells have a promising future, because they are energy efficient, have low emissions, and consume less energy. This study investigates the optimal energy management of a CCHP microgrid that includes a photovoltaic, a battery, a fuel cell, a gas boiler, a thermal storage tank, an absorption chiller, and an electric chiller, with a cooling, heating and power load. A multi-objective chance-constrained programming model is proposed, which aims to minimize the total running costs, and SO2 and NOx emissions. A Monte-Carlo stochastic simulation technique is applied to deal with the stochastic characteristics of the PV cells, and the cooling, heating and power loads. An improved multi-objective particle swarm optimization with Pareto set is proposed to solve the model. Simulation results of a hotel building show that the proposed approach can reduce the emissions of the pollutants, and effectively improve the system's efficiency.