Reviewerly/exHarmony huggingface dataset:
Uniform and scalable SAT-sampling for configurable systems

Several relevant analyses on configurable software systems remain intractable because they require examining vast and highly-constrained configuration spaces. Those analyses could be addressed through statistical inference, i.e., working with a much more tractable sample that later supports generalizing the results obtained to the entire configuration space. To make this possible, the laws of statistical inference impose an indispensable requirement: each member of the population must be equally likely to be included in the sample, i.e., the sampling process needs to be "uniform". Various SAT-samplers have been developed for generating uniform random samples at a reasonable computational cost. Unfortunately, there is a lack of experimental validation over large configuration models to show whether the samplers indeed produce genuine uniform samples or not. This paper (i) presents a new statistical test to verify to what extent samplers accomplish uniformity and (ii) reports the evaluation of four state-of-the-art samplers: Spur, QuickSampler, Unigen2, and Smarch. According to our experimental results, only Spur satisfies both scalability and uniformity.
Sneak Pique

Natural language interaction has evolved as a useful modality to help users explore and interact with their data during visual analysis. Little work has been done to explore how autocompletion can help with data discovery while helping users formulate analytical questions. We developed a system called \system as a design probe to better understand the usefulness of autocompletion for visual analysis. We ran three Mechanical Turk studies to evaluate user preferences for various text- and visualization widget-based autocompletion design variants for helping with partial search queries. Our findings indicate that users found data previews to be useful in the suggestions. Widgets were preferred for previewing temporal, geospatial, and numerical data while text autocompletion was preferred for categorical and hierarchical data. We conducted an exploratory analysis of our system implementing this specific subset of preferred autocompletion variants. Our insights regarding the efficacy of these autocompletion suggestions can inform the future design of natural language interfaces supporting visual analysis.
Readiness model for DevOps implementation in software organizations

Abstract DevOps is a new software engineering paradigm adopted by various software organizations to develop the quality software within time and budget. The implementation of DevOps practices is critical, and there are no guidelines to assess and improve the DevOps activities in software organizations. Hence, there is a need to develop a readiness model for DevOps (RMDevOps) with an aim to assist the practitioners for implementation of DevOps practices in software firms. To achieve the study objective, we conducted a systematic literature review (SLR) study to identify the critical challenges and associated best practices of DevOps. A total of 18 challenges and 73 best practices were identified from the 69 primary studies. The identified challenges and best practices were further evaluated by conducting a survey with industry practitioners. The RMDevOps was developed based on other well‐established models in software engineering domain, for example, software process improvement readiness model (SPIRM) and software outsourcing vendor readiness model (SOVRM). Finally, case studies were conducted with three different organizations with an aim to validate the developed model. The results show that the RMDevOps is effective to assess and improve the DevOps practices in software organizations.
Adiabatic Quantum Graph Matching with Permutation Matrix Constraints

Matching problems on 3D shapes and images are challenging as they are frequently formulated as combinatorial quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard. In this work, we address such problems with emerging quantum computing technology and propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware. We investigate several ways to inject permutation matrix constraints in a quadratic unconstrained binary optimization problem which can be mapped to quantum hardware. We focus on obtaining a sufficient spectral gap, which further increases the probability to measure optimal solutions and valid permutation matrices in a single run. We perform our experiments on the quantum computer D-Wave 2000Q(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> qubits, adiabatic). Despite the observed discrepancy between simulated adiabatic quantum computing and execution on real quantum hardware, our reformulation of permutation matrix constraints increases the robustness of the numerical computations over other penalty approaches in our experiments. The proposed algorithm has the potential to scale to higher dimensions on future quantum computing architectures, which opens up multiple new directions for solving matching problems in 3D computer vision and graphics.
DeepEC: Adversarial attacks against graph structure prediction models

Inspired by the practical importance of graph structured data, link prediction, one of the most frequently applied tasks on graph data, has garnered considerable attention in recent years, and they have been widely applied in item recommendation, privacy inference attack, knowledge graph completion, fraud detection, and other fields. However, recent studies show that machine learning-based intelligent systems are vulnerable to adversarial attacks, which has recently inspired much research on the security problems of machine learning in the context of computer vision, natural language processing, physical world, etc. Nonetheless, there is a lack of understanding of the vulnerability of link prediction methods in face of adversarial attacks. To unveil the weaknesses and aid in the development of robust link prediction methods, we propose a deep architecture-based adversarial attack method, called Deep Ensemble Coding, against link prediction. In particular, based on the assumption that links play different structural roles in structure organization, we propose a deep linear coding-based structure enhancement mechanism to generate adversarial examples. We also empirically investigate other adversarial attack methods for graph data, including heuristic and evolutionary perturbation methods. Based on the comprehensive experiments conducted on various real-world networks, we can conclude that the proposed adversarial attack method has satisfactory performance for link prediction. Moreover, we can observe that state-of-the-art link prediction algorithms are vulnerable to adversarial attacks and, for adversarial defense, the attack can be viewed as a robustness evaluation for the construction of robust link prediction methods.
Refactoring, Bug Fixing, and New Development Effect on Technical Debt: An Industrial Case Study

Code evolution, whether related to the development of new features, bug fixing, or refactoring, inevitably changes the quality of the code. One particular type of such change is the accumulation of Technical Debt (TD) resulting from sub-optimal design decisions. Traditionally, refactoring is one of the means that has been acknowledged to help to keep TD under control. Developers refactor their code to improve its maintainability and to repay TD (e.g., by removing existing code smells and anti-patterns in the source code). While the accumulation of the TD and the effect of refactoring on TD have been studied before, there is a lack of empirical evidence from industrial projects on how the different types of code changes affect the TD and whether specific refactoring operations are more effective for repaying TD. To fill this gap, we conducted an empirical study on an industrial project and investigated how Refactoring, Bug Fixing, and New Development affect the TD. We have analyzed 2, 286 commits in total to identify which activities reduced, kept the same, or even increased the TD, further delving into specific refactoring operations to assess their impact. Our results suggest that TD in the studied project is mainly introduced in the development of new features (estimated in 72.8 hours). Counterintuitively, from the commits tagged as refactoring, only 22.90% repay TD (estimated to repay 8.30 hours of the TD). Moreover, while some types of refactoring operations (e.g., Extract Method), help repaying TD, other refactoring operations (e.g., Move Class) are highly prone to introduce more TD.
Hiding in Plain Site

In this paper, we perform a large-scale measurement study of JavaScript obfuscation of browser APIs in the wild. We rely on a simple, but powerful observation: if dynamic analysis of a script's behavior (specifically, how it interacts with browser APIs) reveals browser API feature usage that cannot be reconciled with static analysis of the script's source code, then that behavior is obfuscated. To quantify and test this observation, we create a hybrid analysis platform using instrumented Chromium to log all browser API accesses by the scripts executed when a user visits a page. We filter the API access traces from our dynamic analysis through a static analysis tool that we developed in order to quantify how much and what kind of functionality is hidden on the web. When applying this methodology across the Alexa top 100k domains, we discover that 95.90% of the domains we successfully visited contain at least one script which invokes APIs that cannot be resolved from static analysis. We observe that eval is no longer the prominent obfuscation method on the web and we uncover families of novel obfuscation techniques that no longer rely on the use of eval.
Towards Game Design via Creative Machine Learning (GDCML)

In recent years, machine learning (ML) systems have been increasingly applied for performing creative tasks. Such creative ML approaches have seen wide use in the domains of visual art and music for applications such as image and music generation and style transfer. However, similar creative ML techniques have not been as widely adopted in the domain of game design despite the emergence of ML-based methods for generating game content. In this paper, we argue for leveraging and repurposing such creative techniques for designing content for games, referring to these as approaches for Game Design via Creative ML (GDCML). We highlight existing systems that enable GDCML and illustrate how creative ML can inform new systems via example applications and a proposed system.
View-graph construction framework for robust and efficient structure-from-motion

A view-graph is vital for both the accuracy and robustness of structure-from-motion (SfM). Conventional matrix decomposition techniques treat all edges of view-graph equally; hence, many edge outliers are produced in matching pairs with fewer feature matches. To address this problem, we propose an incremental framework for view-graph construction, where the robustness of matched pairs that have a larger number of feature matches is propagated to their connected images. Given pairwise feature matches, a verified maximum spanning tree (VMST) is first constructed; for each edge in the VMST, we perform a local reconstruction and register its visible cameras. Based on the local reconstruction, pairwise relative geometries are computed and some new epipolar edges are produced. In this way, these newly computed edges inherit the robustness and accuracy of VMST, and by embedding them into VMST, our view-graph is constructed. We feed our view-graph into a standard SfM pipeline and compare this newly formed system with many of state-of-the-art SfM methods. The experimental results demonstrate that our view-graph provides a better foundation for conventional SfM systems, and enables them to reconstruct both general and ambiguous images.
Neural graph personalized ranking for Top-N Recommendation

Personalized recommendation has been widely applied to many real-world services. Many of recent studies focus on collaborative filtering (CF) by deep neural networks, which pursue to predict users' preference on items based on the past user–item interactions (e.g., a user rates an item). A general CF approach consists of two key modules, embedding representation learning and interaction modeling. In most existing methods, the embedding module is followed by the interaction modeling module, and the user–item interaction information is only emploited in interaction modeling directly. Existing methods, however, defectively overlook the correlation between users and items, as well as the inherent connection between embedding learning and the interaction information. To fill this gap, we propose neural graph personalized ranking (NGPR) which directly makes use of the user–item interaction information in embedding learning by incorporating the user–item interaction graph in embedding learning. Specifically, we construct the user–item interaction graph using de facto interaction between a user and an item. Correlation between users and items can also be reserved by concatenating representations of users and items in the entire procedure of embedding learning. Moreover, more complicated structures like multilayer perceptron (MLP) can be used in interaction modeling to make the most use of the representations, rather than simple linear transformation. We conduct extensive experiments on three public benchmarks and demonstrate the superior performance of the proposed NGPR model on personalized ranking task. In addition, our ablation studies verify that our novel design to incorporate the user–item interaction graph in embedding learning is effective.
A Large-Scale Frontal Vehicle Image Dataset for Fine-Grained Vehicle Categorization

Fine-grained vehicle categorization has evolved into a significant subject of study due to its importance in the Intelligent Transportation System. A highly accurate and real-time vehicle categorization system will help to support many applications not only in the security aspect but also many walks of life. In this paper, facing the growing importance of this study, we present an image dataset named Frontal-103 to promote the development of the vision-based research on the vehicle, and particularly for the task of fine-grained vehicle categorization. This paper provides a detailed analysis of Frontal-103 in its current state: 1,759 fine-grained vehicle models in 103 vehicle makes and 65,433 web-nature images in total. Apart from the specific viewpoint and vehicle hierarchy, Frontal-103 is superior to the other state-of-the-art vehicle image datasets not only in the scale and diversity but also the accuracy and fine-grained level. We further discuss the peculiar challenges and issues lies in the task of fine-grained vehicle categorization and illustrate the usefulness of our dataset in addressing those problems. We hope Frontal-103 will be beneficial to the vision-based vehicle analysis and contribute to the computer vision community.
A Multi-modal System to Assess Cognition in Children from their Physical Movements

In recent years, computer and game-based cognitive tests have become popular with the advancement in mobile technology. However, these tests require very little body movements and do not consider the influence that physical motion has on cognitive development. Our work mainly focus on assessing cognition in children through their physical movements. Hence, an assessment test "Ball-Drop-to-the-Beat" that is both physically and cognitively demanding has been used where the child is expected to perform certain actions based on the commands. The task is specifically designed to measure attention, response inhibition, and coordination in children. A dataset has been created with 25 children performing this test. To automate the scoring, a computer vision-based assessment system has been developed. The vision system employs an attention-based fusion mechanism to combine multiple modalities such as optical flow, human poses, and objects in the scene to predict a child's action. The proposed method outperforms other state-of-the-art approaches by achieving an average accuracy of 89.8 percent on predicting the actions and an average accuracy of 88.5 percent on predicting the rhythm on the Ball-Drop-to-the-Beat dataset.
COINSTAC: Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation

Central to the field of neuroimaging is the development of techniques for making sense of complex brain data.However, rapid technological advancements are pushing the spatial and temporal resolution of imaging in different modalities to an unprecedented level, leading to large datasets which cannot be analyzed in the traditional desktop computing paradigm.This has led to a paradigm shift in scientific research with an increasing emphasis on collaborative data sharing.However, current approaches to data sharing, such as negotiating multiple data sharing agreements, can be cumbersome.In addition, there are also significant data transfer, organizational, and computational challenges, the result being that collaborative group research requires a great deal of coordination.Human and business factors can hamper research from happening at a constructive pace, maybe even forbidding group research to occur at all.
A Virtual Reality Platform for Safety Training in Coal Mines with AI and Cloud Computing

Coal mining, regarded as a high-risk industry, has a strong demand for virtual reality (VR) to fulfill safety and emergency rescue training. In the past ten years, VR technology has significantly improved miner training on both the hardware and software side. However, it still has some drawbacks, such as expensive and unsuitable hardware, lack of satisfactory user experience, without direct browser access, and lack of humanized and intelligent design. To solve these problems, a cloud-based VR system is designed for the training of coal miners in this paper. The system, with browser/client architecture, includes eight modules demonstrating the full procedure of an underground coal mine. The online cloud-rendered video streaming is adopted to provide enough computing and rendering power and hence a better browser-based user experience. Furthermore, game artificial intelligence (AI) is also introduced into the system to increase the emotional exchange between the system and users. Unlike traditional VR training software, this system designs two virtual miners to enhance the experience of trainees. The first virtual miner is a task-oriented non-player-character (NPC) which conveys general knowledge about the mine and guides the users in visiting the underground work sites. The second virtual miner is a disaster-oriented character which prepares the users for typical disasters. The system has been successfully implemented in a laboratory environment, and its performance has been validated. Yet, further practices are needed to stimulate more innovative applications of VR-based miner training and disaster drilling.
Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

Methods for dynamic difficulty adjustment allow games to be tailored to particular players to maximize their engagement. However, current methods often only modify a limited set of game features such as the difficulty of the opponents, or the availability of resources. Other approaches, such as experience-driven Procedural Content Generation (PCG), can generate complete levels with desired properties such as levels that are neither too hard nor too easy, but require many iterations. This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials. This advance is enabled by through an Intelligent Trial-and-Error algorithm, originally developed to allow robots to adapt quickly. Our algorithm first creates a large variety of different levels that vary across predefined dimensions such as leniency or map coverage. The performance of an AI playing agent on these maps gives a proxy for how difficult the level would be for another AI agent (e.g. one that employs Monte Carlo Tree Search instead of Greedy Tree Search); using this information, a Bayesian Optimization procedure is deployed, updating the difficulty of the prior map to reflect the ability of the agent. The approach can reliably find levels with a specific target difficulty for a variety of planning agents in only a few trials, while maintaining an understanding of their skill landscape.
pedestrian detection for advanced driving assisting system: a transfer learning approach

Pedestrian detection is an important task that must be integrated into an advanced driving assisting system (ADAS). For a pedestrian detection task many rules must be respected like high performance, real-time processing, and lightweight size to fit into the embedded device of the ADAS. In this paper, we propose a pedestrian detection system based on a convolutional neural network (CNN). CNN is a deep learning model generally used for computer vision tasks like classification and detection because of its power in image processing and decision making. The proposed CNN model is named Yolov3 tiny. It was firstly used for general object detection. In this work, we applied the transfer learning technique on the proposed CNN model to make it suitable for pedestrian detection. The pedestrian detection dataset Caltech US was used to train and evaluate the proposed model. The model achieves an average precision of 76.7% and an inference time of 202 FPS.
Fast Attributed Multiplex Heterogeneous Network Embedding

In recent years, heterogeneous network representation learning has attracted considerable attentions with the consideration of multiple node types. However, most of them ignore the rich set of network attributes (attributed network) and different types of relations (multiplex network), which can hardly recognize the multi-modal contextual signals across different interactions. While a handful of network embedding techniques are developed for attributed multiplex heterogeneous networks, they are significantly limited to the scalability issue on large-scale network data, due to their heavy cost both in computation and memory. In this work, we propose a Fast Attributed Multiplex heterogeneous network Embedding framework (FAME) for large-scale network data, by mapping the units from different modalities (i.e., network topological structures, various node features and relations) into the same latent space in a very efficient way. Our FAME is an integrative architecture with the scalable spectral transformation and sparse random projection, to automatically preserve both attribute semantics and multi-type interactions in the learned embeddings. Extensive experiments on four real-world datasets with various network analytical tasks, demonstrate that FAME achieves both effectiveness and significant efficiency over state-of-the-art baselines. The source code is available at: https://github.com/ZhijunLiu95/FAME.