Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing the Effect of Device-Based Test Scheduling on Heterogeneous Test Suite Execution

As software takes on more responsibility, it gets increasingly complex, requiring an extremely large number of tests for effective validation. This problem is more serious in heterogeneous system testing, where an application is required to be tested against a diverse collection of processors before is accepted for release. Test scheduling methodologies are concerned with scheduling the execution of tests in an order which enhances performance. In this paper we assess the effect of device-based test scheduling on the execution time of heterogeneous test suites. We utilize a test scheduling algorithm which improves load balance between the multiple devices of a heterogeneous system during test suite execution in an attempt to reduce its execution time. We conduct an empirical evaluation on a large-scaled test suite targeting implementations of the SYCL standard which has been developed by Codeplay Software. We found that a maximum of 25.42% speed-up is achieved by our test scheduling algorithm when compared to parallel test execution without test scheduling.
Sistemas de Quóruns Bizantinos Pró-Ativos

Byzantine Quorum Systems is a replication technique used to ensure availability and consistency of replicates data even in presence of arbitrary faults. This paper presents a Byzantine Quorum Systems protocol that provides atomic semantics despite the existence of Byzantine clients and servers. Moreover, this protocol is integrated with a protocol for proactive recovery of servers. In that way, the system tolerates any number of failures during its lifetime, since no more than f out of n servers fail during a small interval of time between recoveries. All solutions proposed in this paper can be used on asynchronous systems, which requires no time assumptions. The proposed quorum system read and write protocols have been implemented and their efficiency is demonstrated through some experiments carried out in the Emulab platform.
ShapelineGuide: Teaching Mid-Air Gestures for Large Interactive Displays

We present ShapelineGuide, a dynamic visual guide that supports users of large interactive displays while performing mid-air gestures. Today, we find many examples of large displays supporting interaction through gestures performed in Mid-air. Yet, approaches that support users in learning and executing these gestures are still scarce. Prior approaches require complex setups, are targeted towards the use of 2D gestures, or focus on the initial gestures only. Our work extends state-of-the-art by presenting a feedforward system that provides users constant updates on their gestures. We report on the design and implementation of the approach and present findings from an evaluation of the system in a lab study (N=44), focusing on learning performance, accuracy, and errors. We found that ShapelineGuide helps users with regard to learning the gestures as well as decreases execution times and cognitive load.
Challenges of blockchain technology adoption for e-government: a systematic literature review

The ability of blockchain technology to record transactions on distributed ledgers offers new opportunities for governments to improve transparency, prevent fraud, and establish trust in the public sector. However, blockchain adoption and use in the context of e-Government is rather unexplored in academic literature. In this paper, we systematically review relevant research to understand the current research topics, challenges and future directions regarding blockchain adoption for e-Government. The results show that the adoption of blockchain-based applications in e-Government is still very limited and there is a lack of empirical evidence. The main challenges faced in blockchain adoption are predominantly presented as technological aspects such as security, scalability and flexibility. From an organizational point of view, the issues of acceptability and the need of new governance models are presented as the main barriers to adoption. Moreover, the lack of legal and regulatory support is identified as the main environmental barrier of adoption. Based on the challenges presented in the literature, we propose future research questions that need to be addressed to inform how the public sector should approach the blockchain technology adoption.
Navigating the Unexpected Realities of Big Data Transfers in a Cloud-based World

The emergence of big data has created new challenges for researchers transmitting big data sets across campus networks to local (HPC) cloud resources, or over wide area networks to public cloud services. Unlike conventional HPC systems where the network is carefully architected (e.g., a high speed local interconnect, or a wide area connection between Data Transfer Nodes), today's big data communication often occurs over shared network infrastructures with many external and uncontrolled factors influencing performance. This paper describes our efforts to understand and characterize the performance of various big data transfer tools such as rclone, cyberduck, and other provider-specific CLI tools when moving data to/from public and private cloud resources. We analyze the various parameter settings available on each of these tools and their impact on performance. Our experimental results give insights into the performance of cloud providers and transfer tools, and provide guidance for parameter settings when using cloud transfer tools. We also explore performance when coming from HPC DTN nodes as well as researcher machines located deep in the campus network, and show that emerging SDN approaches such as the VIP Lanes system can deliver excellent performance even from researchers' machines.
Click-Based Asynchronous Mesh Network with Bounded Bundled Data

We have implemented an asynchronous mesh network. This paper describes our innovative design using a Click controller. Compared to designs that use other asynchronous circuit families with C-elements and four-phase bundled data, our two-phase Click-based Bounded Bundled Data design is faster, but introduces phase skews when handling concurrent traffic at a single node. Instead of eliminating the phase skews, we use them as computation slots. Our network uses a novel asynchronous arbiter with a queue that can accept data from both the four cardinal directions as well as from a local source, five directions in all. We have implemented our network design in 1 × 1, 2 × 2 and 4 × 4 sizes, larger network could be implemented easier since the isomorphism and modularity of the routing nodes. Our experiments show that an initial data item passes through a node in 157ns v.s. 81ns for non-delay-branch and delay-branch designs separately. Following items take about 65% as long. But for a network, the average latency of a node keeps almost same for different paths. We believe that with the non-delay-branch designs, our asynchronous mesh network could offer 10.1M routes per second for a 1 × 1 network and 5.33M routes per second for 2 × 2 or 5.06M for 4 × 4 networks, and work at the rate of 17.3M, 10.1M and 11.7M with the enhanced delay-branch way. For both cases, its latency is approximately linear with scale.
SPECTR: Scalable Parallel Short Read Error Correction on Multi-core and Many-core Architectures

Modern high throughput sequencing platforms can produce large amounts of short read DNA data at low cost. Error correction is an important but time-consuming initial step when processing this data in order to improve the quality of downstream analyses. In this paper, we present a Scalable Parallel Error CorrecToR designed to improve the throughput of DNA error correction for Illumina reads on various parallel platforms. Our design is based on a k-spectrum approach where a Bloom filter is frequently probed as a key operation and is optimized towards AVX-512-based multi-core CPUs, Xeon Phi many-cores (both KNC and KNL), and heterogeneous compute clusters. A number of architecture-specific optimizations are employed to achieve high performance such as memory alignment, vectorized Bloom filter probing, and a stack-based iteration to eliminate recursion. Our experiments show that our optimizations result in speedups of up to 2.8, 5.2, and 9.3 on a CPU (Xeon W-2123), a KNC-based Xeon Phi (31S1P), and a KNL-based Xeon Phi (7210), respectively, compared to a multi-threaded CPU reference implementation for the error correction stage. Furthermore, when executed on the same hardware, SPECTR achieves a speedup of up to 1.7, 2.1, 2.4, and 6.4, compared to the state-of-the-art tools Lighter, BLESS2, RECKONER, and Musket, respectively. In addition, our MPI implementation exhibits an efficiency of around 86% when executed on 32 nodes of the Tianhe-2 supercomputer. SPECTR is available at https://github.com/Xu-Kai/SPECTR.
The application of WeChat official accounts for provincial archives in China

WeChat Official Account is one of the most popular government social media in China. This study explores the application of WeChat Official Accounts for provincial archives in China, focusing on two major functions of the Official Account: custom menu and broadcast function. Statistical analyze menu items of 20 provincial archive WeChat Official Accounts and use WCI to evaluate broadcast messages. The result shows that the current menu design of each Official Account is different, and the resource type of broadcast messages are more attractive for users. Discussion offers a framework to improve the menu design and advocates the promotion of archive compilation, in order to provide high quality services and broadcast messages for users.
Evaluating the Fault Tolerance Performance of HDFS and Ceph

Large-scale distributed systems are a collection of loosely coupled computers interconnected by a communication network. They are now an integral part of everyday life with the development of large web applications, social networks, peer-to-peer systems, wireless sensor networks and many more. Because each disk by itself is prone to failure, one key challenge in designing such systems is their ability to tolerate faults. Hence, fault tolerance mechanisms such as replication are widely used to provide data availability at all times. On the other hand, many systems now are increasingly supporting new mechanism called erasure coding (EC), claiming that using EC provides high reliability at lower storage cost than replication. However, this comes at the cost of performance. Our goal in this paper is to compare the performance and storage requirements of these two data reliability techniques for two open source systems: HDFS and Ceph especially that the Apache Software Foundation had released a new version of Hadoop, Apache Hadoop 3.0. 0, which now supports EC. In addition, with the Firefly release (May 2014) Ceph added support for EC as well. We tested replication vs. EC in both systems using several benchmarks shipped with these systems. Results show that there are trade-offs between replication and EC in terms of performance and storage requirements.
Estimating Story Points from Issue Reports

Estimating the effort of software engineering tasks is notoriously hard but essential for project planning. The agile community often adopts issue reports to describe tasks, and story points to estimate task effort. In this paper, we propose a machine learning classifier for estimating the story points required to address an issue. Through empirical evaluation on one industrial project and eight open source projects, we demonstrate that such classifier is feasible. We show that ---after an initial training on over 300 issue reports--- the classifier estimates a new issue in less than 15 seconds with a mean magnitude of relative error between 0.16 and 0.61. In addition, issue type, summary, description, and related components prove to be project dependent features pivotal for story point estimation.
Fast texture segmentation by Fourier optic implementation of Gaussian smoothed fan filters

Image segmentation by texture is achieved by using Gaussian smoothed fan (GSF) filters in the Fourier domain. These filters have intrinsically more defined bandwidths than Gabor filters and were developed earlier by the authors for multichannel filtering on digitized images. This paper reports a Fourier optical implementation of GSF filters on images constructed from Brodatz album textures. The back-transformed images are compared with corresponding output images obtained from digital processing and demonstrate that the approach is well suited to real-time fast segmentation of images containing texture fields.
An Empirical Evaluation of Distribution-based Thresholds for Internal Software Measures

Background Setting thresholds is important for the practical use of internal software measures, so software modules can be classified as having either acceptable or unacceptable quality, and software practitioners can take appropriate quality improvement actions. Quite a few methods have been proposed for setting thresholds and several of them are based on the distribution of an internal measure's values (and, possibly, other internal measures), without any explicit relationship with any external software quality of interest. Objective In this paper, we empirically investigate the consequences of defining thresholds on internal measures without taking into account the external measures that quantify qualities of practical interest. We focus on fault-proneness as the specific quality of practical interest. Method We analyzed datasets from the PROMISE repository. First, we computed the thresholds of code measures according to three distribution-based methods. Then, we derived statistically significant models of fault-proneness that use internal measures as independent variables. We then evaluated the indications provided by the distribution-based thresholds when used along with the fault-proneness models. Results Some methods for defining distribution-based thresholds requires that code measures be normally distributed. However, we found that this is hardly ever the case with the PROMISE datasets, making that entire class of methods inapplicable. We adapted these methods for non-normal distributions and obtained thresholds that appear reasonable, but are characterized by a large variation in the fault-proneness risk level they entail. Given a dataset, the thresholds for different internal measures---when used as independent variables of statistically significant models---provide fairly different values of fault-proneness. This is quite dangerous for practitioners, since they get thresholds that are presented as equally important, but practically can correspond to very different levels of user-perceivable quality. For other distribution-based methods, we found that the proposed thresholds are practically useless, as many modules with values of internal measures deemed acceptable according to the thresholds actually have high fault-proneness. Also, the accuracy of all of these methods appears to be lower than the accuracy obtained by simply estimating modules at random. Conclusions Our results indicate that distribution-based thresholds appear to be unreliable in providing sensible indications about the quality of software modules. Practitioners should instead use different kinds of threshold-setting methods, such as the ones that take into account data about the presence of faults in software modules, in addition to the values of internal software measures.
Census big data analytics use: international cross case analysis

Despite the growing practices in big data and big data analytics use, there is still the paucity of research on links between government big data analytics use and public value creation. This multi-case study of Australia, Ireland, Mexico, and U.S.A. examines the state of big data and big data analytics use in the national census context. The census agencies are at varying stages in digitally transforming their national census process, products and services through assimilating and using big data and big data analytics. The cross-case analysis of government websites and documents identified emerging agency challenges in creating public value in the national census context: (1) big data analytics capability development, (2) cross agency data access and data integration, and (3) data security, privacy & trust. Based on the insights gained, a research model aims to postulate the possible links among challenges, big data/big data analytics use, and public value creation.
Integrating Low-latency Analysis into HPC System Monitoring

The growth of High Performance Computer (HPC) systems increases the complexity with respect to understanding resource utilization, system management, and performance issues. While raw performance data is increasingly exposed at the component level, the usefulness of the data is dependent on the ability to do meaningful analysis on actionable timescales. However, current system monitoring infrastructures largely focus on data collection, with analysis performed off-system in post-processing mode. This increases the time required to provide analysis and feedback to a variety of consumers. In this work, we enhance the architecture of a monitoring system used on large-scale computational platforms, to integrate streaming analysis capabilities at arbitrary locations within its data collection, transport, and aggregation facilities. We leverage the flexible communication topology of the monitoring system to enable placement of transformations based on overhead concerns, while still enabling low-latency exposure on node. Our design internally supports and exposes the raw and transformed data uniformly for both node level and off-system consumers. We show the viability of our implementation for a case with production-relevance: run-time determination of the relative per-node files system demands.
Weblogs y Wikis en la educación

La educación debe preparar a los alumnos para desenvolverse adecuadamente en la sociedad, esto es, aprender a cooperar, discutir, negociar, resolver problemas, fundamentar, comunicarse. Las tendencias más modernas en educación postulan al trabajo colaborativo entre grupo de alumnos como una metodología importante, en la cuál los alumnos son los protagonistas del trabajo dentro como fuera del aula. El uso cada vez más masivo de tecnología digital y fundamentalmente de Internet por parte de los alumnos puede facilitar esta tarea. La Web sigue siendo un ámbito propicio llevar a la práctica los principios del trabajo colaborativo, en particular a partir de la relevancia que han cobrado en los últimos tiempos son los Weblogs, un sitio web con entradas o apuntes fechados en orden cronológico, y las Wikis, una colección libremente ampliable de páginas Web vinculadas entre sí. Los alumnos que participan activamente en estos espacios viven una experiencia educativa en la cual realizan actividades de aprendizaje. Los Weblogs y las Wikis nutren a Internet de nuevas posibilidades para apoyar la enseñanza, debido a que son promotores de la generación de ideas, de la socialización y la colaboración, posibilidades nada despreciables para experiencias de aprendizaje más ricas y significativas.
Checkpoint Scheduling for Shared Usage of Burst-Buffers in Supercomputers

User-defined and system-level checkpointing have contrary properties. While user-defined checkpoints are smaller and simpler to recover, system-level checkpointing better knows the global system's state and parameters like the expected mean time to failure (MTTF) per node. Both approaches lead to non-optimal checkpoint time, intervals, sizes, or I/O bandwidth when concurrent checkpoints conflict and compete for it. We combine user-defined and system-level checkpointing to exploit the benefits and avoid the drawbacks of each other. Thus, applications frequently offer to create checkpoints. The system accepts such offers according to the current status and implied costs to recalculate from the last checkpoint or denies them, i.e., immediately lets continue the application without checkpoint creation. To support this approach, we develop economic models for multi-application checkpointing on shared I/O resources that are dedicated for checkpointing (e.g. burst-buffers) by defining an appropriate goal function and solving a global optimization problem. Using our models, the checkpoints of applications on a supercomputer are scheduled to effectively use the available I/O bandwidth and minimize the failure overhead (checkpoint creations plus recalculations). Our simulations show an overall reduction in failure overhead of all nodes of up to 30% for a typical supercomputer workload (HLRN). We can also derive the most cost effective burst-buffer bandwidth for a given node's MTTF and application workload.
Linking User Requests, Developer Responses and Code Changes: Android OS Case Study

Since software systems are designed to satisfy customers' needs, developers have an obligation to address users' requirements and demands logged via issue trackers and other forums. Having to respond to a large number of requests while developing and perfecting systems presents prioritization challenges, however. Android Operating System (OS) developers have largely overcome this obstacle by responding to specific user requests, which may be traced back to actual software code changes, providing lessons for the software engineering community. This study applies text and data mining techniques to investigate the Android community as an ecosystem, exploring how developers responded to issues raised by the community over several versions of the OS. Results show a strong relationship between issues raised by the community and developer responses to these issues. This relationship also extended to actual source code changes made by developers. Furthermore, the findings show a correlation between user requests and developer responses enacted via code changes across specific Android versions and important functionalities. This evidence suggests that developers have invested in the Android platform to guarantee its survival and overall success, largely through addressing user demands. We outline implications for software engineering professionals and software systems success.
Contention-Aware Resource Scheduling for Burst Buffer Systems

Many scientific applications in critical areas are becoming more and more data-intensive. As the data volume continues to grow, the data movement between storage and compute nodes has turned into a crucial performance bottleneck for many data-intensive applications. Burst buffer provides a promising solution for these applications by absorbing bursty I/O traffic to let applications return to computing phase quickly. However, the resource allocation policy for burst buffer is understudied, and the existing strategies may cause severe I/O contention when a large number of I/O-intensive jobs access the burst buffer system concurrently. In this study, based on the theoretic analysis of I/O model, we present a contention-aware resource scheduling (CARS) strategy that manages the burst buffer resource to coordinate concurrent jobs. Extensive experiments have been conducted and the results have demonstrated that the proposed CARS design outperforms the existing allocation strategies and improves both job performance and system utilization.