Miracle-dz/UniFAR-FTUs huggingface dataset:
Unraveling relevant factors for effective on farm demonstration: the crucial role of relevance for participants and structural set up

ABSTRACT Purpose The paper focuses on exploring the influence of structural and functional characteristics of demonstrations on their effectiveness. Design/Methodology In the framework of AgriDemo-F2F project, we analysed the responses to 345 post-demonstration questionnaires filled out by the attendees of 31 demo events held in 12 EU countries. Factor analysis was employed and on a subsequent step a linear regression to predict general effectiveness. Findings Results indicate that the very first steps in the organisation of on-farm demonstration are of critical importance for the successful delivery of their objectives, particularly decisions relating to relevance to farmers’ needs and the structure of the event. Moreover, the paper offers first evidence that when/if on-farm demonstrations are appropriately structured and delivered they can meet their objectives regardless of the status and strength of the corresponding AKIS. Practical implications Demonstration organisers should take special care with regard to the relevance of the topic and the group (demonstrator and attendees) as well as of the structure of the event. Theoretical implications The various elements influencing the success of on-farm demonstrations are examined together and ranked. Originality/Value The study generates important (theoretical and practical) insights concerning the success of a widely used technique of advisory/extension services, that is on-farm demonstrations, based on a large data-set from demonstration events through out Europe.
Performance of DNA methylation‐based biomarkers in the cervical cancer screening program of northern Portugal: A feasibility study

Cervical cancer remains a health concern. Effective screening programs are critical to reduce the incidence and mortality. High‐risk HPV (hr‐HPV) testing as primary screening tool discloses high sensitivity but suboptimal specificity. Adequate triage tests to reduce unnecessary colposcopy referrals and overdiagnosis/overtreatment are crucial. Hence, we aimed to validate a panel of DNA methylation‐based markers as triage test for women hr‐HPV+ in the population‐based Regional Cervical Cancer Screening Program of Northern Portugal. Firstly, CADM1, MAL, FAM19A4 and hsa‐miR124‐2 promoter methylation levels were assessed by multiplex QMSP in a testing set of 402 FFPE tissue samples (159 normal samples and 243 cervical lesions, including 39 low‐grade intraepithelial squamous lesions [LSIL], 59 high‐grade intraepithelial squamous lesions [HSIL] and 145 cancerous lesions). Then, preliminary validation was performed in 125 hr‐HPV+ cervical scrapes (including 59 normal samples, 30 LSIL, 34 HSIL and 2 cancerous lesions). Higher MALme, FAM19A4me and hsa‐miR124‐2me methylation levels were disclosed in histological HSIL or worse (HSIL+) in testing set. Individually, markers depicted over 86% specificity for HSIL+ detection. In validation set, all these genes significantly differed between histological HSIL+ and low‐grade squamous intraepithelial lesions or less. In combination, these markers reached 74% specificity and 61% sensitivity for identification of histological HSIL+. We concluded that host gene methylation might constitute a useful referral triage tool of hr‐HPV+ women enrolled in the Cervical Cancer Screening Program of Northern Portugal.
Measurement Challenges for Cyber Cyber Digital Twins: Experiences from the Deployment of Facebook's WW Simulation System

A cyber cyber digital twin is a deployed software model that executes in tandem with the system it simulates, contributing to, and drawing from, the system's behaviour. This paper outlines Facebook's cyber cyber digital twin, dubbed WW, a twin of Facebook's WWW platform, built using web-enabled simulation. The paper focuses on the current research challenges and opportunities in the area of measurement. Measurement challenges lie at the heart of modern simulation. They directly impact how we use simulation outcomes for automated online and semi-automated offline decision making. Measurements also encompas how we verify and validate those outcomes. Modern simulation systems are increasingly becoming more like cyber cyber digital twins, effectively moving from manual to automated decision making, hence, these measurement challenges acquire ever greater significance.
Detecting and Finding the True Meaning of Jargons

While looking at the online bulletin boards, some obscure description could be found in the threads. In most cases, it is usually because that the authors are not willing to mention the true name of somebody or something directly, and in hence take the place of it with a substitute expression which has the same attributes, such as homophonic word or objects which appear similarly. These words and phrases, which are used irregularly in the context, could be called jargons. In this study, we propose a method to detect and understand jargons by considering the relevance of context words in word-embedding expressions. Specifically, we first calculate the topic correlation-coefficient of each word pair occurring in the text to find the jargon. Then, we combine a set of contextual information to find out a list of candidate words which probably deliver the true meaning of the jargon. By comparing the candidate words and the jargon word from 3 aspects (meaning, pronunciation and shape), the true meaning of the jargon could be determined based on the aggregative similarity. Finally, we have conducted an experiment to examine the usefulness of our approach. Additional Keywords and Phrases: Jargon, Word-embedding expression, Word-vectors, Similarity, Natural processing language.
Testing Smart Contracts: Which Technique Performs Best?

Background: Executing, verifying and enforcing credible transactions on permissionless blockchains is done using smart contracts. A key challenge with smart contracts is ensuring their correctness and security. Several test input generation techniques for detecting vulnerabilities in smart contracts have been proposed in the last few years. However, a comparison of proposed techniques to gauge their effectiveness is missing. Aim: This paper conducts an empirical evaluation of testing techniques for smart contracts. The testing techniques we evaluated are: (1) Blackbox fuzzing, (2) Adaptive fuzzing, (3) Coverage-guided fuzzing with an SMT solver and (4) Genetic algorithm. We do not consider static analysis tools, as several recent studies have assessed and compared effectiveness of these tools. Method: We evaluate effectiveness of the test generation techniques using (1) Coverage achieved - we use four code coverage metrics targeting smart contracts, (2) Fault finding ability - using artificially seeded and real security vulnerabilities of different types. We used two datasets in our evaluation - one with 1665 real smart contracts from Etherscan, and another with 90 real contracts with known vulnerabilities to assess fault finding ability. Result: We find Adaptive fuzzing performs best in terms of coverage and fault finding over contracts in both datasets. Conclusion: However, we believe considering dependencies between functions and handling Solidity specific features will help improve the performance of all techniques considerably.
Research on the Influence of Mobile E-commerce Webcast on Users' Online Shopping Intentions

With the development of the Internet, the function of mobile e-commerce webcast on e-commerce platforms has begun to develop. At the beginning of this year, because of COVID-19, people were more shopping at home by watching webcast, which promoted the further development of mobile e-commerce webcast. Mobile e-commerce webcast refers to the display of abundant products on mobile e-commerce platform merchants through live streaming media. In this paper, combined with the environmental characteristics of mobile e-commerce webcast, based on the Stimulus-Organism-Response (S-O-R) theoretical model, a model of the influencing factors of users’ online shopping behavior intentions is constructed. Taking users of China's major mobile e-commerce webcast platforms as the research object, through structural equation model analysis, the factors affecting users' willingness to use online shopping under mobile e-commerce webcast are explored.
A Method to Identify Respiratory Phase in Needle Biopsy on Thorax Using Point Clouds

In needle biopsy on thorax, the patient needs to hold breath both in CT scan stage and needle advance stage and tries to make the two breath-hold states identical to improve the accuracy of the needle advance. However, whether the two breath-hold states are identical is judged by the doctor with naked eyes, which influences the success rate significantly. In this paper, we propose to use structured light system and registration of point clouds to evaluate the consistency of the two breath-hold states. During both breath-hold stages, point clouds of the patient's chest and abdomen skin are reconstructed. Then, the registration of the two point clouds is implemented using a fast and high-precision registration method, and the registration result is evaluated by Surface Interpenetration Measure. Whether the consistency of the two breath-hold states meets the requirement of needle biopsy can be determined by comparing the SIM score and a threshold that can be obtained by a series of experiments. Preliminary experiments on a dummy show that an accuracy of 1.25 mm for identification of respiration phase is achieved.
ModelGame: A Quality Model for Gamified Software Modeling Learning

Gamification has been adopted in software development tasks in recent years. This adoption seeks, for example, to improve the engagement of developers while creating UML models or writing code. Empirical studies report that UML models suffer from incompleteness and inconsistency problems. This study conjectures that gamification mechanics can improve learner engagement while learning software modeling, mitigating such problems concerning UML models. The current literature lacks studies that explore gamification and UML model quality in the context of software modeling learning. This article, therefore, proposes ModelGame, which is a quality model to support software modeling learning in a gamified way. It serves as a reference framework so that instructors can obtain a parameterized way to evaluate UML models created by learners. The quality of UML models can be improved by applying gamified activities and providing guidelines aware of quality issues. A qualitative questionnaire was answered by 19 instructors who teach software modeling at higher education institutions. The results show that (1) 94.7% recognize that the proposed model can improve the quality of UML models, indicating that they would adopt the ModelGame in their learning practices; and (2) 47.4% do not use any gamification mechanics in their classes. The results are encouraging, showing the potential for applying and improving the teaching and learning of software modeling.
Optimizing Winograd-Based Convolution with Tensor Cores

Convolution computing is one of the primary time consuming part of convolutional neural networks (CNNs). State of the art convolutional neural networks use samll, 3 × 3 filters. Recent work on Winograd convolution can reduce the computational complexity a lot, making the convolution computing fast. But existing implementations of Winograd convolution is limited to small tiles, i.e. F(4 × 4, 3 × 3) and F(2 × 2, 3 × 3) where 4 × 4 and 2 × 2 are tile sizes of output channels and 3 × 3 is the filter size, and single precision data. In this paper, we propose an optimized mixed precision F(6 × 6, 3 × 3) Winograd convolution implementation on NVIDIA Ampere GPUs using Tensor Cores. Our experiments show that the accuracy of mixed precision F(6 × 6, 3 × 3) Winograd convolution is sufficient to infer the convolutional neural networks. Besides, our method achieves up to 15.71x and 2.41x speedup on NVIDIA Ampere A100, compared with the state of the art Winograd based convolution and GEMM based convolution in cuDNN 8.1. 0, respectively. Moreover, we integrate our F(6 × 6, 3 × 3) Winograd convolution implementation into NVIDIA TensorRT, which is a C++ inference library on GPUs provided by NVIDIA, as custom layer plugins. And we build the whole VGG network model using our custom Winograd convolution layers and other layers supported by TensorRT. The experiments show that the accuracy of the whole VGG network using our F(6 × 6, 3 × 3) Winograd convolution is 71.24%, while the accuracy of using FP32 computing for the VGG network is 71.22%.
Multi Scale UNet Encoder-Decoder Network for Building Extraction

Buildings in remote sensing images have large scale differences and complex shapes. And there are often distractors with visual features similar to buildings in complex scenes. The traditional methods used to extract buildings are limited by the ability of feature representation, resulting in low accuracy and low universality. The semantic segmentation network based on the Encoder-Decoder structure can automatically learn multi-level building feature representation from the data set, and achieve end-to-end building extraction. UNet is a typical semantic segmentation Encoder-Decoder network, but UNet cannot explore enough building information. Small buildings are easy to be missed, large buildings with complex colors and shapes are incompletely extracted, boundary segmentation is inaccurate. And the network is easily affected by roads, trees, shadows and other distractors. Therefore, this article improves UNet and proposes a multi-scale Encoder-Decoder network to learn multi-scale and distinguishable features to better identify buildings and backgrounds. We experiment with the improved network and the classic U-Net on two data sets, and show that the multi-scale Encoder-Decoder network can effectively improve the accuracy of building extraction.
Efficiently Parallelizable Strassen-Based Multiplication of a Matrix by its Transpose

The multiplication of a matrix by its transpose, ATA, appears as an intermediate operation in the solution of a wide set of problems. In this paper, we propose a new cache-oblivious algorithm (AtA) for computing this product, based upon the classical Strassen algorithm as a sub-routine. In particular, we decrease the computational cost to the time required by Strassen’s algorithm, amounting to floating point operations. AtA works for generic rectangular matrices, and exploits the peculiar symmetry of the resulting product matrix for saving memory. In addition, we provide an extensive implementation study of AtA in a shared memory system, and extend its applicability to a distributed environment. To support our findings, we compare our algorithm with state-of-the-art solutions specialized in the computation of ATA. Our experiments highlight good scalability with respect to both the matrix size and the number of involved processes, as well as favorable performance for both the parallel paradigms and the sequential implementation, when compared with other methods in the literature.
Synthesis of Random Real-World Java Programs from Preexisting Libraries

With the great advances in the software development area, where systems are becoming larger and more complex, the testing area is suffering to keep up with. In this paper, we describe a formal procedure to perform the synthesis of random programs from preexisting code, considering all the constraints imposed by the Java compiler. We prove that the code produced by our method is well-typed with regard to a simplified type-system for Java. Furthermore, we present an implementation of this procedure using Java and some extra tools. The results of this paper can be used as input for testing libraries and compilers.
Receiver-Driven Congestion Control for InfiniBand

InfiniBand (IB) has become one of the most popular high-speed interconnects in High Performance Computing (HPC). The backpressure effect of credit-based link-layer flow control in IB introduces congestion spreading, which increases queueing delay and hurts application completion time. IB congestion control (IB CC) has been defined in IB specification to address the congestion spreading problem. Nowadays, HPC clusters are increasingly being used to run diverse workloads with a shared network infrastructure. The coexistence of messages transfers of different applications imposes great challenges to IB CC. In this paper, we re-exam IB CC through fine-grained experimental observations and reveal several fundamental problems. Inspired by our understanding and insights, we present a new receiver-driven congestion control for InfiniBand (RR CC). RR CC includes two key mechanisms: receiver-driven congestion identification and receiver-driven rate regulation, which empower eliminating both in-network congestion and endpoint congestion in one control loop. RR CC has much fewer parameters and requires no modifications to InfiniBand switches. Evaluations show that RR CC achieves better average/tail message latency and link utilization than IB CC under various scenarios.
Regu2D: Accelerating Vectorization of SpMV on Intel Processors through 2D-partitioning and Regular Arrangement

Sparse matrix-vector multiplication (SpMV) is an elementary kernel of many high-performance computing (HPC) applications, and it is often one of the performance bottlenecks of them. Accelerating SpMV on vector processors usually faces several issues including irregular data accesses, memory bandwidth limitation, and the short vector problem. Based on a detailed analysis of the effects and interactions of various technologies introduced by state-of-the-art studies (ALBUS, CVR, CSR5, SELL-C-σ etc.), we propose Regu2D, a comprehensive solution to accelerate vectorization of SpMV through three methods: adaptive 2D-partitioning, the regular arrangement of matrix elements, and indices compression. Dynamic programming algorithms are used to optimize the first two methods. We conduct experiments on Intel Xeon processors (Skylake architecture) which support AVX-512 SIMD instructions and use sparse matrices from the University of Florida Sparse Matrix Collection. Experiments show that Regu2D achieves an average speedup of 1.69X, 1.93X, 1.40X, and 1.20X over ALBUS, CVR, CSR5, and SELL-C-σ for 30 scale-free sparse matrices, respectively. For 16 HPC sparse matrices, Regu2D achieves an average speedup of 1.34X, 1.89X, 1.34X, and 1.50X over them, respectively.
CERES: Container-Based Elastic Resource Management System for Mixed Workloads

It is common to deploy multiple workloads in one cluster to achieve high resource utilization, which tends to bring more resource contentions and performance interferences. If the allocable resources cannot satisfy the resource requirements of a task, the task should wait for resources, significantly increasing its scheduling latency. The inappropriate resource requirements may make a running task become a swollen task or a straggler task, which makes many allocated resources underutilized or the task be processed slowly. Therefore, how to guarantee the QoS of various services in the mixed workload deployment cluster is a challenge. Existing solutions preempt the resources from batch jobs to guarantee the resource requirements of latency-sensitive tasks without taking into account the underutilized resources in swollen tasks, which inevitably compromises the performance of batch jobs. Thus, we try to meet the resource requirements of newly incoming latency-sensitive tasks and straggler tasks with the underutilized resources instead of directly preempting the resources of batch jobs. This paper presents CERES, which tries to ensure the QoS of latency-sensitive services and reduce the performance impact on batch jobs. Firstly, CERES periodically screens out swollen tasks from batch jobs and straggler tasks from latency-sensitive services. Secondly, CERES reclaims resources from the swollen tasks and even preempts resources from common batch tasks when the idle resources in the cluster cannot meet the resource requirements of newly incoming latency-sensitive tasks and the straggler tasks. If there are sufficient allocable resources in the cluster, CERES expands the resources of the straggler tasks. We have implemented CERES based on Hadoop YARN and conducted comprehensive experiments. The results show that compared with the state-of-the-art approach, CERES can decrease the task completion time of latency-sensitive services by 20.77%, reduce performance losses to batch jobs by 15.46%, and improve the cluster resource utilization by 27.06%.
TIMEG: Development and Evaluation of an Inspection Checklist Tool for Mobile Educational Games

The software inspection process allows identifying defects during the development cycle. In the context of mobile serious games, specific inspection checklists can be employed to guarantee their quality. However, there are few tools to support the use of these checklists and the existing ones are generic and not specific for the evaluation of digital educational games for mobile devices. To assist software engineers in the evaluation of this type of games, an open-source desktop tool was developed in C#, called TIMEG (Tool for Checklist Inspection of Mobile Educational Games). TIMEG allows to: (1) embed inspection checklists; (2) point out defects based on the items in the checklists; (3) generate inspection reports; and if the game is already available in the Google Play store, (4) present information about the users’ opinions. The tool was evaluated from the point of view of inexperienced software engineers when applied for evaluating a mobile serious game; and through the answers to a questionnaire. The results indicate that the tool was perceived as useful and easy to use, in addition to identifying opportunities for improvement in the tool’s interface and usability, as well as improvements in the presentation of relevant information in the defect reports. Link para Vídeo: Clique Aqui
Towards Improving Experimentation in Software Engineering

[Background:] Experimentation in Software Engineering plays a central role on sharing and verifying scientific findings. As experiments have increased significantly in Software Engineering area, we observe that most of them fail to provide a way to be repeated, replicated or reproduced, thus jeopardizing or delaying the evolution of the Software Engineering area. [Aims:] In this vision paper, we present and discuss techniques and infrastructure to continuously improve experiments towards repeatability, replicability, and reproducibility. [Method:] We define these techniques and infrastructure based on experiences of our research groups and existing literature. Furthermore, we follow Open Science principles. [Results:] We provide incipient results and foresee a central infrastructure composed of two repositories and two recommendation systems to support techniques for: reporting experiments; developing ontologies for experiments and open educational resources; mining and recommending experiments; specifying data management plans, identifying families of experiments; and teaching and learning experimentation. [Conclusions:] Our techniques and infrastructure will prospectively motivate and benefit Software Engineering evolution by improving the conduction and further reproducibility of experiments.