OpenRaiser/Intern-Atlas huggingface dataset:
On the Naming of Methods: A Survey of Professional Developers
Venue: arXiv cs.SE 2021
Year: 2021

This paper describes the results of a large (+1100 responses) survey of professional software developers concerning standards for naming source code methods. The various standards for source code method names are derived from and supported in the software engineering literature. The goal of the survey is to determine if there is a general consensus among developers that the standards are accepted and used in practice. Additionally, the paper examines factors such as years of experience and programming language knowledge in the context of survey responses. The survey results show that participants very much agree about the importance of various standards and how they apply to names. Additionally, the survey shows that years of experience and the programming language the participants use has almost no effect on their responses.
Beyond Convolutions: A Novel Deep Learning Approach for Raw Seismic Data Ingestion
Venue: arXiv cs.LG 2021
Year: 2021

Traditional seismic processing workflows (SPW) are expensive, requiring over a year of human and computational effort. Deep learning (DL) based data-driven seismic workflows (DSPW) hold the potential to reduce these timelines to a few minutes. Raw seismic data (terabytes) and required subsurface prediction (gigabytes) are enormous. This large-scale, spatially irregular time-series data poses seismic data ingestion (SDI) as an unconventional yet fundamental problem in DSPW. Current DL research is limited to small-scale simplified synthetic datasets as they treat seismic data like images and process them with convolution networks. Real seismic data, however, is at least 5D. Applying 5D convolutions to this scale is computationally prohibitive. Moreover, raw seismic data is highly unstructured and hence inherently non-image like. We propose a fundamental shift to move away from convolutions and introduce SESDI: Set Embedding based SDI approach. SESDI first breaks down the mammoth task of large-scale prediction into an efficient compact auxiliary task. SESDI gracefully incorporates irregularities in data with its novel model architecture. We believe SESDI is the first successful demonstration of end-to-end learning on real seismic data. SESDI achieves SSIM of over 0.8 on velocity inversion task on real proprietary data from the Gulf of Mexico and outperforms the state-of-the-art U-Net model on synthetic datasets.
Active Selection of Classification Features
Venue: arXiv cs.LG 2021
Year: 2021

Some data analysis applications comprise datasets, where explanatory variables are expensive or tedious to acquire, but auxiliary data are readily available and might help to construct an insightful training set. An example is neuroimaging research on mental disorders, specifically learning a diagnosis/prognosis model based on variables derived from expensive Magnetic Resonance Imaging (MRI) scans, which often requires large sample sizes. Auxiliary data, such as demographics, might help in selecting a smaller sample that comprises the individuals with the most informative MRI scans. In active learning literature, this problem has not yet been studied, despite promising results in related problem settings that concern the selection of instances or instance-feature pairs. Therefore, we formulate this complementary problem of Active Selection of Classification Features (ASCF): Given a primary task, which requires to learn a model f: x-> y to explain/predict the relationship between an expensive-to-acquire set of variables x and a class label y. Then, the ASCF-task is to use a set of readily available selection variables z to select these instances, that will improve the primary task's performance most when acquiring their expensive features z and including them to the primary training set. We propose two utility-based approaches for this problem, and evaluate their performance on three public real-world benchmark datasets. In addition, we illustrate the use of these approaches to efficiently acquire MRI scans in the context of neuroimaging research on mental disorders, based on a simulated study design with real MRI data.
Casual and Hardcore Player Traits and Gratifications of Pokémon GO, Harry Potter: Wizards Unite, Ingress
Venue: arXiv cs.HC 2021
Year: 2021

Location-based games (LBG) impose virtual spaces on top of physical locations. Studies have explored LBG from various perspectives. However, a comprehensive study of who these players are, their traits, their gratifications, and the links between them is conspicuously absent from the literature. In this paper, we aim to address this lacuna through a series of surveys with 2390 active LBG players utilizing Tondello's Player Traits Model and Scale of Game playing Preferences, and Hamari's scale of LBG gratifications. Our findings (1) illustrate an association between player satisfaction and social aspects of the studied games, (2) explicate how the core-loops of the studied games impact the expressed gratifications and the affine traits of players, and (3) indicate a strong distinction between hardcore and casual players based on both traits and gratifications. Overall our findings shed light into the players of LBG, their traits, and gratifications they derive from playing LBGs.
An Optimal Deterministic Algorithm for Geodesic Farthest-Point Voronoi Diagrams in Simple Polygons
Venue: arXiv cs.CG 2021
Year: 2021

Given a set $S$ of $m$ point sites in a simple polygon $P$ of $n$ vertices, we consider the problem of computing the geodesic farthest-point Voronoi diagram for $S$ in $P$. It is known that the problem has an $Ω(n+m\log m)$ time lower bound. Previously, a randomized algorithm was proposed [Barba, SoCG 2019] that can solve the problem in $O(n+m\log m)$ expected time. The previous best deterministic algorithms solve the problem in $O(n\log \log n+ m\log m)$ time [Oh, Barba, and Ahn, SoCG 2016] or in $O(n+m\log m+m\log^2 n)$ time [Oh and Ahn, SoCG 2017]. In this paper, we present a deterministic algorithm of $O(n+m\log m)$ time, which is optimal. This answers an open question posed by Mitchell in the Handbook of Computational Geometry two decades ago.
A Brief Survey of Current Software Engineering Practices in Continuous Integration and Automated Accessibility Testing
Venue: arXiv cs.SE 2021
Year: 2021

It's long been accepted that continuous integration (CI) in software engineering increases the code quality of enterprise projects when adhered to by it's practitioners. But is any of that effort to increase code quality and velocity directed towards improving software accessibility accommodations? What are the potential benefits quoted in literature? Does it fit with the modern agile way that teams operate in most enterprises? This paper attempts to map the current scene of the software engineering effort spent on improving accessibility via continuous integration and it's hurdles to adoption as quoted by researchers. We also try to explore steps that agile teams may take to train members on how to implement accessibility testing and introduce key diagrams to visualize processes to implement CI based accessibility testing procedures in the software development lifecycle.
Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning
Venue: arXiv cs.LG 2021
Year: 2021

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets.
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation
Venue: arXiv cs.CV 2021
Year: 2021

Active Shape Model (ASM) is a statistical model of object shapes that represents a target structure. ASM can guide machine learning algorithms to fit a set of points representing an object (e.g., face) onto an image. This paper presents a lightweight Convolutional Neural Network (CNN) architecture with a loss function being assisted by ASM for face alignment and estimating head pose in the wild. We use ASM to first guide the network towards learning a smoother distribution of the facial landmark points. Inspired by transfer learning, during the training process, we gradually harden the regression problem and guide the network towards learning the original landmark points distribution. We define multi-tasks in our loss function that are responsible for detecting facial landmark points as well as estimating the face pose. Learning multiple correlated tasks simultaneously builds synergy and improves the performance of individual tasks. We compare the performance of our proposed model called ASMNet with MobileNetV2 (which is about 2 times bigger than ASMNet) in both the face alignment and pose estimation tasks. Experimental results on challenging datasets show that by using the proposed ASM assisted loss function, the ASMNet performance is comparable with MobileNetV2 in the face alignment task. In addition, for face pose estimation, ASMNet performs much better than MobileNetV2. ASMNet achieves an acceptable performance for facial landmark points detection and pose estimation while having a significantly smaller number of parameters and floating-point operations compared to many CNN-based models.
Meta-Learning with Graph Neural Networks: Methods and Applications
Venue: arXiv cs.LG 2021
Year: 2021

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different meta-learning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.
A Differential Testing Approach for Evaluating Abstract Syntax Tree Mapping Algorithms
Venue: arXiv cs.SE 2021
Year: 2021

Abstract syntax tree (AST) mapping algorithms are widely used to analyze changes in source code. Despite the foundational role of AST mapping algorithms, little effort has been made to evaluate the accuracy of AST mapping algorithms, i.e., the extent to which an algorihtm captures the evolution of code. We observe that a program element often has only one best-mapped program element. Based on this observation, we propose a hierarchical approach to automatically compare the similarity of mapped statements and tokens by different algorithms. By performing the comparison, we determine if each of the compared algorithms generates inaccurate mappings for a statement or its tokens. We invite 12 external experts to determine if three commonly used AST mapping algorithms generate accurate mappings for a statement and its tokens for 200 statements. Based on the experts' feedback,we observe that our approach achieves a precision of 0.98--1.00 and a recall of 0.65--0.75. Furthermore, we conduct a large-scale study with a dataset of ten Java projects, containing a total of 263,165 file revisions. Our approach determines that GumTree, MTDiff and IJM generate inaccurate mappings for 20%--29%, 25%--36% and 21%--30% of the file revisions, respectively. Our experimental results show that state-of-art AST mapping agorithms still need improvements.
Unsupervised Representations Predict Popularity of Peer-Shared Artifacts in an Online Learning Environment
Venue: arXiv cs.CY 2021
Year: 2021

In online collaborative learning environments, students create content and construct their own knowledge through complex interactions over time. To facilitate effective social learning and inclusive participation in this context, insights are needed into the correspondence between student-contributed artifacts and their subsequent popularity among peers. In this study, we represent student artifacts by their (a) contextual action logs (b) textual content, and (c) set of instructor-specified features, and use these representations to predict artifact popularity measures. Through a mixture of predictive analysis and visual exploration, we find that the neural embedding representation, learned from contextual action logs, has the strongest predictions of popularity, ahead of instructor's knowledge, which includes academic value and creativity ratings. Because this representation can be learnt without extensive human labeling effort, it opens up possibilities for shaping more inclusive student interactions on the fly in collaboration with instructors and students alike.
Lifelong Learning based Disease Diagnosis on Clinical Notes
Venue: arXiv cs.AI 2021
Year: 2021

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i.e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks. What is worse, the trained diagnosis system would be fixed once deployed but collecting training data that covers enough diseases is infeasible, which inspires us to develop a lifelong learning diagnosis system. In this work, we propose to adopt attention to combine medical entities and context, embedding episodic memory and consolidation to retain knowledge, such that the learned model is capable of adapting to sequential disease-diagnosis tasks. Moreover, we establish a new benchmark, named Jarvis-40, which contains clinical notes collected from various hospitals. Our experiments show that the proposed method can achieve state-of-the-art performance on the proposed benchmark.
Incorporating Domain Knowledge into Deep Neural Networks
Venue: arXiv cs.NE 2021
Year: 2021

We present a survey of ways in which domain-knowledge has been included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines two broad approaches to encode such knowledge--as logical and numerical constraints--and describes techniques and results obtained in several sub-categories under each of these approaches.
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers
Venue: arXiv cs.LG 2021
Year: 2021

Nowadays, Neural Networks represent a major expectation for the realization of powerful Deep Learning algorithms, which can determine several physical systems' behaviors and operations. Computational resources required for model, training, and running are large, especially when related to the amount of data that Neural Networks typically need to generalize. The latest TinyML technologies allow integrating pre-trained models on embedded systems, allowing making computing at the edge faster, cheaper, and safer. Although these technologies originated in the consumer and industrial worlds, many sectors can greatly benefit from them, such as the automotive industry. In this paper, we present a framework for implementing Neural Network-based models on a family of automotive Microcontrollers, showing their efficiency in two case studies applied to vehicles: intrusion detection on the Controller Area Network bus and residual capacity estimation in Lithium-Ion batteries, widely used in Electric Vehicles.
Improved Two Sample Revenue Guarantees via Mixed-Integer Linear Programming
Venue: arXiv cs.GT 2021
Year: 2021

We study the performance of the Empirical Revenue Maximizing (ERM) mechanism in a single-item, single-seller, single-buyer setting. We assume the buyer's valuation is drawn from a regular distribution $F$ and that the seller has access to {\em two} independently drawn samples from $F$. By solving a family of mixed-integer linear programs (MILPs), the ERM mechanism is proven to guarantee at least $.5914$ times the optimal revenue in expectation. Using solutions to these MILPs, we also show that the worst-case efficiency of the ERM mechanism is at most $.61035$ times the optimal revenue. These guarantees improve upon the best known lower and upper bounds of $.558$ and $.642$, respectively, of [Daskalakis & Zampetakis, '20].
Singularity-aware motion planning for multi-axis additive manufacturing
Venue: arXiv cs.RO 2021
Year: 2021

Multi-axis additive manufacturing enables high flexibility of material deposition along dynamically varied directions. The Cartesian motion platforms of these machines include three parallel axes and two rotational axes. Singularity on rotational axes is a critical issue to be tackled in motion planning for ensuring high quality of manufacturing results. The highly nonlinear mapping in the singular region can convert a smooth toolpath with uniformly sampled waypoints defined in the model coordinate system into a highly discontinuous motion in the machine coordinate system, which leads to over-extrusion / under-extrusion of materials in filament-based additive manufacturing. The problem is challenging as both the maximal and the minimal speeds at the tip of a printer head must be controlled in motion. Moreover, collision may occur when sampling-based collision avoidance is employed. In this paper, we present a motion planning method to support the manufacturing realization of designed toolpaths for multi-axis additive manufacturing. Problems of singularity and collision are considered in an integrated manner to improve the motion therefore the quality of fabrication.
Automated Generation of Interorganizational Disaster Response Networks through Information Extraction
Venue: arXiv cs.SI 2021
Year: 2021

When a disaster occurs, maintaining and restoring community lifelines subsequently require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating Stakeholder Collaboration Networks (SCNs), this paper proposes a systematic approach to reliable information extraction for stakeholder collaboration and automated network generation. Specifically, stakeholders and their interactions are extracted from texts through Named Entity Recognition (NER), one of the techniques in natural language processing. Once extracted, the collaboration information is transformed into structured datasets to generate the SCNs automatically. A case study of stakeholder collaboration during Hurricane Harvey was investigated and it had demonstrated the feasibility and applicability of the proposed method. Hence, the proposed approach was proved to significantly reduce practitioners' interpretation and data collection workloads. In the end, discussions and future work are provided.
On Optimizing the Trade-off between Privacy and Utility in Data Provenance
Venue: arXiv cs.DB 2021
Year: 2021

Organizations that collect and analyze data may wish or be mandated by regulation to justify and explain their analysis results. At the same time, the logic that they have followed to analyze the data, i.e., their queries, may be proprietary and confidential. Data provenance, a record of the transformations that data underwent, was extensively studied as means of explanations. In contrast, only a few works have studied the tension between disclosing provenance and hiding the underlying query. This tension is the focus of the present paper, where we formalize and explore for the first time the tradeoff between the utility of presenting provenance information and the breach of privacy it poses with respect to the underlying query. Intuitively, our formalization is based on the notion of provenance abstraction, where the representation of some tuples in the provenance expressions is abstracted in a way that makes multiple tuples indistinguishable. The privacy of a chosen abstraction is then measured based on how many queries match the obfuscated provenance, in the same vein as k-anonymity. The utility is measured based on the entropy of the abstraction, intuitively how much information is lost with respect to the actual tuples participating in the provenance. Our formalization yields a novel optimization problem of choosing the best abstraction in terms of this tradeoff. We show that the problem is intractable in general, but design greedy heuristics that exploit the provenance structure towards a practically efficient exploration of the search space. We experimentally prove the effectiveness of our solution using the TPC-H benchmark and the IMDB dataset.