Reviewerly/exHarmony huggingface dataset:
Predicting long-time contributors for GitHub projects using machine learning

Many organizations develop software systems using open source software (OSS), which is risky due to the high possibility of losing support. Contributors are critical for the survival of OSS projects, but very few new contributors remain with OSS projects to become long-time contributors (LTCs). Identification of factors that contribute to become an LTC can help OSS project owners utilize limited resources to retain new contributors. In this paper, we investigate whether we can effectively predict new contributors to OSS repositories becoming long time contributors based on repository and contributor meta-data collected from GitHub. We construct a dataset containing 70,899 observations from 888 most popular repositories with 56,766 contributors. Each observation represents a contributor who joined the repository and is categorized as either an LTC or a non-LTC, depending on whether their project tenure is longer than 3 years. Each observation has 31 features that are calculated using the information of the new contributor and the repository when a new contributor joins the project. We build several machine learning models, including naive Bayes, k-nearest neighbor, logistic regression, decision tree, and random forest to predict LTC validated using 10-fold cross-validation. We compare our best model with state of the art model in terms of precision, recall, F1-score, Matthews correlation coefficient (MCC), and area under the curve (AUC). In 10-fold cross-validation, the precision, recall, F1-score, MCC, and AUC of our best model (random forest) are 0.695, 0.079, 0.140, 0.226, and 0.913, respectively. These values are 27.29%, 92.68%, 86.67%, 56.94%, and 0.55%, respectively better than the best baseline state of the art model (random forest). Compared to state of the art models, the models built using our approach use less than 50% features (31 vs 63), have no wait time of one month after the contributor joins to predict future LTC status, and produce better results.
Implementation of support-vector machine algorithm to develop a model for electronic commerce energy regulatory system

In the current stage, China has paid special focuses to the data and information utilization of the Electronic-commerce (e-commerce) energy regulatory system. However, it is difficult to guarantee the scientific data information management of the energy regulatory system at present. It was considering how to use the scientific framework for data mining has been the recent hot spot. Under this condition, this manuscript analysis the e-commerce energy regulatory system model based on data mining. This paper begins with the discussions on the current models of the energy regulatory system at home and abroad, enhances the methodology combined with the shortage of data mining, and then applies support-vector machine (SVM) algorithm in the e-commerce energy regulatory model. The experimental analysis shows that the revised SVM algorithm can realize the objective evaluation of regulatory efficiency based on data mining and can lead to the optimal strategy according to the scenarios in the actual application steps of the energy supervision system. The performance is satisfactory, which means the energy supervision system can reach more than 97%, this performance is better than most of the latest approaches.
Exquisite Fabrication

Digital fabrication and craftsmanship is entering into a new phase with increasing levels of complexity and a renewed desire for composites and cross-material experimentation. However, allowing work to travel from machine to machine, remains a challenge in terms of workflow, communication, orientation and material. Based on an exploration to combine embroidery and 3D printing in the pursuit of inflatable solutions, we propose the metaphor of the drawing game Exquisite Corpse to outline the three emerging concerns: turn taking, orientation and trade-offs. We propose a set of guidelines that suggest ways in which, we may allow different digital fabrication machines to be used in sequence, as a method for adding complexity to the things we make and the ways our machines may talk to one another.
A Gamified Simulator and Physical Platform for Self-Driving Algorithm Training and Validation

We identify the need for an easy-to-use self-driving simulator where game mechanics implicitly encourage high-quality data capture and an associated low-cost physical test platform. We design such a simulator incorporating environmental domain randomization to enhance data generalizability and a low-cost physical test platform running the Robotic Operating System. A toolchain comprising a gamified driving simulator and low-cost vehicle platform is novel and facilitates behavior cloning and domain adaptation without specialized knowledge, supporting crowdsourced data generation. This enables small organizations to develop certain robust and resilient self-driving systems. As proof-of-concept, the simulator is used to capture lane-following data from AI-driven and human-operated agents, with these data training line following Convolutional Neural Networks that transfer without domain adaptation to work on the physical platform.
Floral Tribute Ritual in Virtual Reality: Design and Validation of SenseVase with Virtual Memorial

While floral tributes are commonly used for the public commemoration of victims of disasters, war, and other accidents, flowers in vases color everyday life. In this research, these features of flowers are intertwined with the recent phenomenon of online memorials to develop a virtual floral tribute concept that includes physical rituals. We designed SenseVase, a smart vase to detect flowers placed in it, and a 3DCG Virtual Memorial that illustrates floral tributes given by people using SenseVases at home. This paper describes how we developed our design concept by reviewing previous literature and social aspects, and presents a video illustrating the concept. To validate the current concept, we interviewed several experts knowledgeable in public commemorations, virtual and online communities, and the floral business. Through a discussion of our findings from the design process and interviews, we propose a new direction for how HCI technology can contribute to public commemoration in addition to personal memorialization.
FloorPlanCAD: A Large-Scale CAD Drawing Dataset for Panoptic Symbol Spotting

Access to large and diverse computer-aided design (CAD) drawings is critical for developing symbol spotting algorithms. In this paper, we present FloorPlan-CAD, a large-scale real-world CAD drawing dataset containing over 10,000 floor plans, ranging from residential to commercial buildings. CAD drawings in the dataset are all represented as vector graphics, which enable us to provide line-grained annotations of 30 object categories. Equipped by such annotations, we introduce the task of panoptic symbol spotting, which requires to spot not only instances of countable things, but also the semantic of uncountable stuff. Aiming to solve this task, we propose a novel method by combining Graph Convolutional Networks (GCNs) with Convolutional Neural Networks (CNNs), which captures both non-Euclidean and Euclidean features and can be trained end-to-end. The proposed CNN-GCN method achieved state-of-the-art (SOTA) performance on the task of semantic symbol spotting, and help us build a baseline network for the panoptic symbol spotting task. Our contributions are three-fold: 1) to the best of our knowledge, the presented CAD drawing dataset is the first of its kind; 2) the panoptic symbol spotting task considers the spotting of both thing instances and stuff semantic as one recognition problem; and 3) we presented a baseline solution to the panoptic symbol spotting task based on a novel CNN-GCN method, which achieved SOTA performance on semantic symbol spotting. We believe that these contributions will boost research in related areas. The dataset and code is publicly available at https://floorplancad.github.io/.
Design and Analysis of Intelligent Text Entry Systems with Function Structure Models and Envelope Analysis

Designing intelligent interactive text entry systems often relies on factors that are difficult to estimate or assess using traditional HCI design and evaluation methods. We introduce a complementary approach by adapting function structure models from engineering design. We extend their use by extracting controllable and uncontrollable parameters from function structure models and visualizing their impact using envelope analysis. Function structure models allow designers to understand a system in terms of its functions and flows between functions and decouple functions from function carriers. Envelope analysis allows the designer to further study how parameters affect variables of interest, for example, accuracy, keystroke savings and other dependent variables. We provide examples of function structure models and illustrate a complete envelope analysis by investigating a parameterized function structure model of predictive text entry. We discuss the implications of this design approach for both text entry system design and for critique of system contributions.
Blockchain technology development and implementation for global logistics operations: a reference model perspective

Purpose Blockchain technology offers a lot of potential benefits in supply chain management. However, there is a need of a reference model which addresses the gaps in existing frameworks. This paper aims to propose a blockchain technology-based reference model which can be applied to global logistics operations. Design/methodology/approach The researchers have integrated the fit-for-purpose theoretical framework and prototyping methodology to design the reference model, a blockchain-based logistics, tracking and traceability system (BLTTS). The researchers demonstrated the application of the reference model through a health-care supply chain case study. The proposed BLTTS can be implemented across global logistics operations for business performance improvement. Findings The research provides a framework and recommendations for global companies to consider when adopting the blockchain technology for implementation. The researchers found that the Ethereum blockchain technology improves security of the data shared within the block through the secure hashing algorithm 1. The hash algorithm ensures anonymity of the involved parties. The model integrates blockchain with supply chain thus creating transparent process, efficiency and real-time communication. Research limitations/implications The reference model will offer a better solution to global logistics operations challenges. It provides recommendations to key stakeholders involved in logistics operations segment of the logistics industry while adopting blockchain technology. Apart from the methodological limitation of the study, the system compatibility and the layer configuration aspects might be posing potential challenges while upscaling the implementation. Originality/value The proposed reference model overcomes the drawbacks of existing models as it integrates Ethereum technology. In addition, the researchers have applied the model to demonstrate its functioning in real-time environment, which could guide for future research.
Metrics for improving the management of Cloud environments — Load balancing using measures of Quality of Service, Service Level Agreement Violations and energy consumption

Cloud service providers use load balancing algorithms in order to avoid Service Level Agreement Violations (SLAVs) and wasted energy consumption due to host over- and under-utilization, respectively. Load balancing algorithms migrate VMs between hosts in order to balance host loads. Any Virtual Machines (VMs) that are migrated experience performance degradation which results in lower Quality of Service (QoS) and can possibly result in SLAVs. Hence, an optimal load balancing method should reduce the number of over- and under-utilized hosts with a minimal number of VM migrations. One of the metrics used previously in the literature for evaluating load balancing stated that it equally considered SLAVs caused by both over-utilized hosts and migrations. However, in this paper, we show that, in fact, this metric values keeping the number of migrations low at the expense of an increased number of over-utilized hosts. This disparity is demonstrated by simulation of Google, PlanetLab and Azure data sets in CloudSim. This metric may suit public cloud providers which are focused on minimizing SLAVs and keeping energy costs low, but does not consider the QoS of customer VMs. We propose an alternative metric that considers QoS for the VMs. This alternative metric considers not only performance loss during migration, but also performance degradation due to host over-utilization. Private cloud providers, e.g., IT services within large organizations, often value the performance of their "customer" VMs, i.e., the QoS their organization receives, as well as traditional cloud provider costs, i.e., energy and SLAV costs. Hence, our alternative metric would be more appropriate in these scenarios. We compare and contrast load balancing methods using both the existing, biased metric and our new alternative metric.
SecondSight: A Framework for Cross-Device Augmented Reality Interfaces

This paper describes a modular framework developed to facilitate the design space exploration of cross-device Augmented Reality (AR) interfaces that combine an AR head-mounted display (HMD) with a smartphone. Currently, there is a growing interest in how AR HMDs can be used with smartphones to improve the user's AR experience. In this work, we describe a framework that enables rapid prototyping and evaluation of an interface. Our system enables different modes of interaction, content placement, and simulated AR HMD field of view to assess which combination is best suited to inform future researchers on design recommendations. We provide examples of how the framework could be used to create sample applications, the types of the studies which could be supported, and example results from a simple pilot study.
SQLRepair: Identifying and Repairing Mistakes in Student-Authored SQL Queries

Computer science educators seek to understand the types of mistakes that students make when learning a new (programming) language so that they can help students avoid those mistakes in the future. While educators know what mistakes students regularly make in languages such as C and Python, students struggle with SQL and regularly make mistakes when working with it. We present an analysis of mistakes that students made when first working with SQL, classify the types of errors introduced, and provide suggestions on how to avoid them going forward. In addition, we present an automated tool, SQLRepair, that is capable of repairing errors introduced by undergraduate programmers when writing SQL queries. Our results show that students find repairs produced by our tool comparable in understandability to queries written by themselves or by other students, suggesting that SQL repair tools may be useful in an educational context. We also provide to the community a benchmark of SQL queries written by the students in our study that we used for evaluation of SQLRepair.
Using multiple attribute-based explanations of multidimensional projections to explore high-dimensional data

Multidimensional projections (MPs) are effective methods for visualizing high-dimensional datasets to find structures in the data like groups of similar points and outliers. The insights obtained from MPs can be amplified by complementing these techniques by several so-called explanatory mechanisms. We present and discuss a set of six such mechanisms that explain MPs in terms of similar dimensions, local dimensionality, and dimension correlations. We implement our explanatory tools using an image-based approach, which is efficient to compute, scales well visually for large and dense MP scatterplots, and can handle any projection technique. We demonstrate how the provided explanatory views can be combined to augment each other’s value and thereby lead to refined insights in the data for several high-dimensional datasets, and how these insights correlate with known facts about the data under study.
Performance Comparison of Jpeg, Jpeg XT, Jpeg LS, Jpeg 2000, Jpeg XR, HEVC, EVC and VVC for Images

Online videos and image blogging are some of the major sources for internet traffic and are estimated to have exponential growth in the future. Efforts are made to introduce advanced compression formats for images and included in the future codecs. This paper focuses on analyzing and comparing the existing codecs with the potential codecs to obtain very good quality images with substantially high compression rate. Using the same test conditions and same test content-set for both image and video codecs, like JPEG, JPEG LS, JPEG XR, JPEG 2000, JPEG XT, HEVC, VVC and EVC, the output quality of the image is analyzed using quality metrics like PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity Index Metric). Fitting properties of each codec, conditions of testing and performance comparison between the codecs are analyzed. The test results indicate that JPEG XR is best suited for low resolution and HD image compressions while JPEG 2000 and JPEG XT are best suited for grey-scale and 4K image compressions respectively. The paper concludes with detailed analysis of codec performance including suggestion over its applications and scope for future work.
The Method and Devices for Research the Parameters of the Human Visual System to Video Quality Assessment

The susceptibility of distortions introduced by compression from the point of view of human perception is an important area of research. The ultimate purpose of video quality assessment is to emulate the performance of the biological visual system. Perceptual distortion models have proven successful in describing spatial coding in all human visual systems. However, the question of how the visual regions of the human brain process temporal information have received significantly less attention. Based on the researches of limit to human vision, here we offer a method, software, and test equipment for research and measure the characteristics of human visual systems. This set forms and evaluates stimuli with a specified combination of spatial, temporal, and color characteristics, which differ in a narrowband enough spectrum for measurements.
Radio Modulation Classification Using Deep Learning Architectures

Over the past five years, there has been a focus on creating deep learning architectures for radio modulation recognition. Many of these architectures are either convolutional neural networks, recurrent neural networks, or the combination of those and contain over a million trainable parameters. This paper focuses on a reduction of the parameters in the networks while maintaining comparable accuracy. We introduce two architectures, a convolutional neural network (CNN) and a convolutional long-short-term deep neural network (CLDNN). The results presented in this paper are comparable to or even outperform architectures with up to 20 times more parameters. Significant parameter reduction presented in this paper is more suitable for later model deployment on an embedded platform with limited memory size and computational power. We further discuss other needed steps before implementing the architectures on an embedded platform other than the model size reduction at the end of the paper.
Design and development of ground station for UAV/UGV heterogeneous collaborative system

Unmanned Aerial Vehicle/Unmanned Ground Vehicle heterogeneous collaborative system can complete complex tasks. In order to monitor and control the two agents simultaneously, a comprehensive ground station is designed. First, the preliminary work is introduced. Then, the architecture of the ground station of the system is proposed. In data communication module, since the amount of data from the system is large, the data of structure and multi-threaded processing are designed in detail. In data display module, the ground station provides various ways for displaying data. In image display module, in order to balance the accuracy and real-time performance of the system, an Efficient Convolution Operators tracking algorithm is improved for tracking moving target. In digital map module, a three-dimensional path planning method based on improved Ant Colony is proposed. The experiment results show that the ground station works well in real-time performance and accuracy.
RNN-T Based Open-Vocabulary Keyword Spotting in Mandarin with Multi-Level Detection

Despite the recent prevalence of keyword spotting (KWS) in smart-home, open-vocabulary KWS remains a keen but unmet need among the users. In this paper, we propose an RNN Transducer (RNN-T) based keyword spotting system with a constrained attention mechanism biasing module that biases the RNN-T model towards a specific keyword of interest. The atonal syllables are adopted as the modeling units, which addresses the out-of-vocabulary (OOV) problem. A multi-level detection is applied to the posterior probabilities for the judgement. Evaluating on the AISHELL-2 dataset shows our proposed method outperforms the RNN-T-based approach by 2.70% in false reject rate (FRR) at 1 false alarm (FA) per hour. We further provide insights into the role of each stage of the detection cascade, where most negative samples are filtered out by the first stage with high computational efficiency.
Real-time virtual reality co-creation: collective intelligence and consciousness for student engagement and focused attention within online communities

This study investigated student engagement and focused attention in three co-creation environments (a paper-based environment, the 2D Jamboard platform, and the CoSpaces virtual reality (VR) platform). The study utilized a quasi-experimental research design with a total of 66 tenth-grade students including 34 males (52%) and 32 females (48%) from two English classes at a public senior high school in northern Taiwan. To assess the effects of the environments, the classes were divided into a control group, experimental group A, and experimental group B, with a valid sample of N = 22 in each group. The results showed that the three co-creation environments were not significant causal factors of either the learners' behavioral and cognitive engagement or their focused attention. However, co-creation in VR structure visualization indeed resulted in significant effects on emotional engagement due to its novel and immersive environment. For improved focused attention and engagement, especially in the behavioral and cognitive domains, the present study suggests that co-creation be implemented as a semester-long project to allow thorough discussion and idea synthesis and to avoid underestimation of the value and strength of co-creation tools.