OpenRaiser/Intern-Atlas huggingface dataset:
Greedier is Better: Selecting Multiple Neighbors per Iteration for Sparse Subspace Clustering
Venue: arXiv cs.LG 2022
Year: 2022

Sparse subspace clustering (SSC) using greedy-based neighbor selection, such as orthogonal matching pursuit (OMP), has been known as a popular computationally-efficient alternative to the popular L1-minimization based methods. This paper proposes a new SSC scheme using generalized OMP (GOMP), a soup-up of OMP whereby multiple neighbors are identified per iteration, along with a new stopping rule requiring nothing more than a knowledge of the ambient signal dimension. Compared to conventional OMP, which identifies one neighbor per iteration, the proposed GOMP method involves fewer iterations, thereby enjoying lower algorithmic complexity; advantageously, the proposed stopping rule is free from off-line estimation of subspace dimension and noise power. Under the semi-random model, analytic performance guarantees, in terms of neighbor recovery rates, are established to justify the advantage of the proposed GOMP. The results show that, with a high probability, GOMP (i) is halted by the proposed stopping rule, and (ii) can retrieve more true neighbors than OMP, consequently yielding higher final data clustering accuracy. Computer simulations using both synthetic data and real human face data are provided to validate our analytic study and evidence the effectiveness of the proposed approach.
Data-Driven Approach for Log Instruction Quality Assessment
Venue: arXiv cs.SE 2022
Year: 2022

In the current IT world, developers write code while system operators run the code mostly as a black box. The connection between both worlds is typically established with log messages: the developer provides hints to the (unknown) operator, where the cause of an occurred issue is, and vice versa, the operator can report bugs during operation. To fulfil this purpose, developers write log instructions that are structured text commonly composed of a log level (e.g., "info", "error"), static text ("IP {} cannot be reached"), and dynamic variables (e.g. IP {}). However, as opposed to well-adopted coding practices, there are no widely adopted guidelines on how to write log instructions with good quality properties. For example, a developer may assign a high log level (e.g., "error") for a trivial event that can confuse the operator and increase maintenance costs. Or the static text can be insufficient to hint at a specific issue. In this paper, we address the problem of log quality assessment and provide the first step towards its automation. We start with an in-depth analysis of quality log instruction properties in nine software systems and identify two quality properties: 1) correct log level assignment assessing the correctness of the log level, and 2) sufficient linguistic structure assessing the minimal richness of the static text necessary for verbose event description. Based on these findings, we developed a data-driven approach that adapts deep learning methods for each of the two properties. An extensive evaluation on large-scale open-source systems shows that our approach correctly assesses log level assignments with an accuracy of 0.88, and the sufficient linguistic structure with an F1 score of 0.99, outperforming the baselines. Our study shows the potential of the data-driven methods in assessing instructions quality and aid developers in comprehending and writing better code.
Failure Identification from Unstable Log Data using Deep Learning
Venue: arXiv cs.SE 2022
Year: 2022

The reliability of cloud platforms is of significant relevance because society increasingly relies on complex software systems running on the cloud. To improve it, cloud providers are automating various maintenance tasks, with failure identification frequently being considered. The precondition for automation is the availability of observability tools, with system logs commonly being used. The focus of this paper is log-based failure identification. This problem is challenging because of the instability of the log data and the incompleteness of the explicit logging failure coverage within the code. To address the two challenges, we present CLog as a method for failure identification. The key idea presented herein based is on our observation that by representing the log data as sequences of subprocesses instead of sequences of log events, the effect of the unstable log data is reduced. CLog introduces a novel subprocess extraction method that uses context-aware neural network and clustering methods to extract meaningful subprocesses. The direct modeling of log event contexts allows the identification of failures with respect to the abrupt context changes, addressing the challenge of insufficient logging failure coverage. Our experimental results demonstrate that the learned subprocesses representations reduce the instability in the input, allowing CLog to outperform the baselines on the failure identification subproblems - 1) failure detection by 9-24% on F1 score and 2) failure type identification by 7% on the macro averaged F1 score. Further analysis shows the existent negative correlation between the instability in the input event sequences and the detection performance in a model-agnostic manner.
Co-constructing Shared Values and Ethical Practice for the Next Generation: Lessons Learned from a Curriculum on Information Ethics
Venue: arXiv cs.HC 2022
Year: 2022

We present the motivation, design, outline, and lessons learned from an online course in scientific integrity, research ethics, and information ethics provided to over 2000 doctoral and engineering students in STEM fields, first at the University Paris-Saclay, and now expanded to an online MOOC available to students across the world, in English. Unlike a course in scientific domains, meant to provide students with methods, tools, and concepts they can apply in their future career, the goal of such a training is not so much to equip them, but to make them aware of the impact of their work on society, care about the responsibilities that befall on them, and make them realize not all share the same opinions on how should technology imprint society. While we provide conceptual tools, this is more to sustain interest and engage students. We want them to debate on concrete ethical issues and realize the difficulty of reconciling positions on contemporary dilemma such as dematerialized intellectual property, freedom of expression online and its counterparts, the protection of our digital selves, the management of algorithmic decision, the control of autonomous systems, and the resolution of the digital divide. As a bold shortcut, our course is about introducing and motivating Hegelian dialectics in STEM curricula, usually more bent on an Aristotelian perspective.
GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets
Venue: arXiv cs.LG 2022
Year: 2022

Recent years have seen the advent of molecular simulation datasets that are orders of magnitude larger and more diverse. These new datasets differ substantially in four aspects of complexity: 1. Chemical diversity (number of different elements), 2. system size (number of atoms per sample), 3. dataset size (number of data samples), and 4. domain shift (similarity of the training and test set). Despite these large differences, benchmarks on small and narrow datasets remain the predominant method of demonstrating progress in graph neural networks (GNNs) for molecular simulation, likely due to cheaper training compute requirements. This raises the question -- does GNN progress on small and narrow datasets translate to these more complex datasets? This work investigates this question by first developing the GemNet-OC model based on the large Open Catalyst 2020 (OC20) dataset. GemNet-OC outperforms the previous state-of-the-art on OC20 by 16% while reducing training time by a factor of 10. We then compare the impact of 18 model components and hyperparameter choices on performance in multiple datasets. We find that the resulting model would be drastically different depending on the dataset used for making model choices. To isolate the source of this discrepancy we study six subsets of the OC20 dataset that individually test each of the above-mentioned four dataset aspects. We find that results on the OC-2M subset correlate well with the full OC20 dataset while being substantially cheaper to train on. Our findings challenge the common practice of developing GNNs solely on small datasets, but highlight ways of achieving fast development cycles and generalizable results via moderately-sized, representative datasets such as OC-2M and efficient models such as GemNet-OC. Our code and pretrained model weights are open-sourced.
An Algebraic Approach to Learning and Grounding
Venue: arXiv cs.CL 2022
Year: 2022

We consider the problem of learning the semantics of composite algebraic expressions from examples. The outcome is a versatile framework for studying learning tasks that can be put into the following abstract form: The input is a partial algebra $\alg$ and a finite set of examples $(φ_1, O_1), (φ_2, O_2), \ldots$, each consisting of an algebraic term $φ_i$ and a set of objects~$O_i$. The objective is to simultaneously fill in the missing algebraic operations in $\alg$ and ground the variables of every $φ_i$ in $O_i$, so that the combined value of the terms is optimised. We demonstrate the applicability of this framework through case studies in grammatical inference, picture-language learning, and the grounding of logic scene descriptions.
WeChat uptake of Chinese scholarly journals: an analysis of CSSCI-indexed journals
Venue: arXiv cs.DL 2022
Year: 2022

The study of how science is discussed and how scholarly actors interact on social media has increasingly become popular in the field of scientometrics in recent years. While most prior studies focused on research outputs discussed on global platforms, such as Twitter or Facebook, the presence of scholarly journals on local platforms was seldom studied, especially in the Chinese social media context. To fill this gap, this study investigates the uptake of WeChat (a Chinese social network app) by the Chinese scholarly journals indexed by the Chinese Social Sciences Citation Index (CSSCI). The results show that 65.3% of CSSCI-indexed journals have created WeChat public accounts and posted over 193 thousand WeChat posts in total. At the journal level, bibliometric indicators (e.g., citations, downloads, and journal impact factors) and WeChat indicators (e.g., clicks, likes, replies, and recommendations) are weakly correlated with each other, reinforcing the idea of fundamentally differentiated dimensions of indicators between bibliometrics and social media metrics. Results also show that journals with WeChat public accounts slightly outperform those without WeChat public accounts in terms of citation impact, suggesting that the WeChat presence of scientific journals is mostly positively associated with their citation impact.
EMMT: A simultaneous eye-tracking, 4-electrode EEG and audio corpus for multi-modal reading and translation scenarios
Venue: arXiv cs.CL 2022
Year: 2022

We present the Eyetracked Multi-Modal Translation (EMMT) corpus, a dataset containing monocular eye movement recordings, audio and 4-electrode electroencephalogram (EEG) data of 43 participants. The objective was to collect cognitive signals as responses of participants engaged in a number of language intensive tasks involving different text-image stimuli settings when translating from English to Czech. Each participant was exposed to 32 text-image stimuli pairs and asked to (1) read the English sentence, (2) translate it into Czech, (3) consult the image, (4) translate again, either updating or repeating the previous translation. The text stimuli consisted of 200 unique sentences with 616 unique words coupled with 200 unique images as the visual stimuli. The recordings were collected over a two week period and all the participants included in the study were Czech natives with strong English skills. Due to the nature of the tasks involved in the study and the relatively large number of participants involved, the corpus is well suited for research in Translation Process Studies, Cognitive Sciences among other disciplines.
Simple and Effective Synthesis of Indoor 3D Scenes
Venue: arXiv cs.CV 2022
Year: 2022

We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining 3D consistency. Existing approaches are highly complex, with many separately trained stages and components. We propose a simple alternative: an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images. On the Matterport3D and RealEstate10K datasets, our approach significantly outperforms prior work when evaluated by humans, as well as on FID scores. Further, we show that our model is useful for generative data augmentation. A vision-and-language navigation (VLN) agent trained with trajectories spatially-perturbed by our model improves success rate by up to 1.5% over a state of the art baseline on the R2R benchmark. Our code will be made available to facilitate generative data augmentation and applications to downstream robotics and embodied AI tasks.
Evolutionary Programmer: Autonomously Creating Path Planning Programs based on Evolutionary Algorithms
Venue: arXiv cs.NE 2022
Year: 2022

Evolutionary algorithms are wildly used in unmanned aerial vehicle path planning for their flexibility and effectiveness. Nevertheless, they are so sensitive to the change of environment that can't adapt to all scenarios. Due to this drawback, the previously successful planner frequently fail in a new scene. In this paper, a first-of-its-kind machine learning method named Evolutionary Programmer is proposed to solve this problem. Concretely, the most commonly used Evolutionary Algorithms are decomposed into a series of operators, which constitute the operator library of the system. The new method recompose the operators to a integrated planner, thus, the most suitable operators can be selected for adapting to the changing circumstances. Different from normal machine programmers, this method focuses on a specific task with high-level integrated instructions and thus alleviate the problem of huge search space caused by the briefness of instructions. On this basis, a 64-bit sequence is presented to represent path planner and then evolved with the modified Genetic Algorithm. Finally, the most suitable planner is created by utilizing the information of the previous planner and various randomly generated ones.
Multi-task nonparallel support vector machine for classification
Venue: arXiv cs.LG 2022
Year: 2022

Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the dual problems, so it costs much running time. Moreover, kernel trick cannot be directly utilized in the nonlinear case. To effectively avoid above problems, a novel multi-task nonparallel support vector machine (MTNPSVM) including linear and nonlinear cases is proposed in this paper. By introducing epsilon-insensitive loss instead of square loss in DMTSVM, MTNPSVM effectively avoids matrix inversion operation and takes full advantage of the kernel trick. Theoretical implication of the model is further discussed. To further improve the computational efficiency, the alternating direction method of multipliers (ADMM) is employed when solving the dual problem. The computational complexity and convergence of the algorithm are provided. In addition, the property and sensitivity of the parameter in model are further explored. The experimental results on fifteen benchmark datasets and twelve image datasets demonstrate the validity of MTNPSVM in comparison with the state-of-the-art algorithms. Finally, it is applied to real Chinese Wine dataset, and also verifies its effectiveness.
Using Interactive Feedback to Improve the Accuracy and Explainability of Question Answering Systems Post-Deployment
Venue: arXiv cs.CL 2022
Year: 2022

Most research on question answering focuses on the pre-deployment stage; i.e., building an accurate model for deployment. In this paper, we ask the question: Can we improve QA systems further \emph{post-}deployment based on user interactions? We focus on two kinds of improvements: 1) improving the QA system's performance itself, and 2) providing the model with the ability to explain the correctness or incorrectness of an answer. We collect a retrieval-based QA dataset, FeedbackQA, which contains interactive feedback from users. We collect this dataset by deploying a base QA system to crowdworkers who then engage with the system and provide feedback on the quality of its answers. The feedback contains both structured ratings and unstructured natural language explanations. We train a neural model with this feedback data that can generate explanations and re-score answer candidates. We show that feedback data not only improves the accuracy of the deployed QA system but also other stronger non-deployed systems. The generated explanations also help users make informed decisions about the correctness of answers. Project page: https://mcgill-nlp.github.io/feedbackqa/
VALUE: Understanding Dialect Disparity in NLU
Venue: arXiv cs.CL 2022
Year: 2022

English Natural Language Understanding (NLU) systems have achieved great performances and even outperformed humans on benchmarks like GLUE and SuperGLUE. However, these benchmarks contain only textbook Standard American English (SAE). Other dialects have been largely overlooked in the NLP community. This leads to biased and inequitable NLU systems that serve only a sub-population of speakers. To understand disparities in current models and to facilitate more dialect-competent NLU systems, we introduce the VernAcular Language Understanding Evaluation (VALUE) benchmark, a challenging variant of GLUE that we created with a set of lexical and morphosyntactic transformation rules. In this initial release (V.1), we construct rules for 11 features of African American Vernacular English (AAVE), and we recruit fluent AAVE speakers to validate each feature transformation via linguistic acceptability judgments in a participatory design manner. Experiments show that these new dialectal features can lead to a drop in model performance. To run the transformation code and download both synthetic and gold-standard dialectal GLUE benchmarks, see https://github.com/SALT-NLP/value
Urdu Morphology, Orthography and Lexicon Extraction
Venue: arXiv cs.CL 2022
Year: 2022

Urdu is a challenging language because of, first, its Perso-Arabic script and second, its morphological system having inherent grammatical forms and vocabulary of Arabic, Persian and the native languages of South Asia. This paper describes an implementation of the Urdu language as a software API, and we deal with orthography, morphology and the extraction of the lexicon. The morphology is implemented in a toolkit called Functional Morphology (Forsberg & Ranta, 2004), which is based on the idea of dealing grammars as software libraries. Therefore this implementation could be reused in applications such as intelligent search of keywords, language training and infrastructure for syntax. We also present an implementation of a small part of Urdu syntax to demonstrate this reusability.
An Instrumented Wheel-On-Limb System of Planetary Rovers for Wheel-Terrain Interactions: System Conception and Preliminary Design
Venue: arXiv cs.RO 2022
Year: 2022

Understanding the wheel-terrain interaction is of great importance to improve the maneuverability and traversability of the rovers. A well-developed sensing device carried by the rover would greatly facilitate the complex risk-reducing operations on sandy terrains. In this paper, an instrumented wheel-on-limb (WOL) system of planetary rovers for wheel-terrain interaction characterization is presented. Assuming the function of a passive suspension of the wheel, the WOL system allows itself to follow the terrain contour, and keep the wheel remain lowered onto the ground during rover motion including climbing and descending, as well as deploy and place the wheel on the ground before a drive commanding. The system concept, functional requirements, and pre-design work, as well as the system integration are presented.
P4BID: Information Flow Control in P4
Venue: arXiv cs.PL 2022
Year: 2022

Modern programmable network switches can implement custom applications using efficient packet processing hardware, and the programming language P4 provides high-level constructs to program such switches. The increase in speed and programmability has inspired research in dataplane programming, where many complex functionalities, e.g., key-value stores and load balancers, can be implemented entirely in network switches. However, dataplane programs may suffer from novel security errors that are not traditionally found in network switches. To address this issue, we present a new information-flow control type system for P4. We formalize our type system in a recently-proposed core version of P4, and we prove a soundness theorem: well-typed programs satisfy non-interference. We also implement our type system in a tool, P4bid, which extends the type checker in the p4c compiler, the reference compiler for the latest version of P4. We present several case studies showing that natural security, integrity, and isolation properties in networks can be captured by non-interference, and our type system can detect violations of these properties while certifying correct programs.
Do They Accept or Resist Cybersecurity Measures? Development and Validation of the 13-Item Security Attitude Inventory (SA-13)
Venue: arXiv cs.CR 2022
Year: 2022

We present SA-13, the 13-item Security Attitude inventory. We develop and validate this assessment of cybersecurity attitudes by conducting an exploratory factor analysis, confirmatory factor analysis, and other tests with data from a U.S. Census-weighted Qualtrics panel (N=209). Beyond a core six indicators of Engagement with Security Measures (SA-Engagement, three items) and Attentiveness to Security Measures (SA-Attentiveness, three items), our SA-13 inventory adds indicators of Resistance to Security Measures (SA-Resistance, four items) and Concernedness with Improving Compliance (SA-Concernedness, three items). SA-13 and the subscales exhibit desirable psychometric qualities; and higher scores on SA-13 and on the SA-Engagement and SA-Attentiveness subscales are associated with higher scores for security behavior intention and for self-reported recent security behaviors. SA-13 and the subscales are useful for researchers and security awareness teams who need a lightweight survey measure of user security attitudes. The composite score of the 13 indicators provides a compact measurement of cybersecurity decisional balance.
Sigma Workbook: A Spreadsheet for Cloud Data Warehouses
Venue: arXiv cs.DB 2022
Year: 2022

Cloud data warehouses (CDWs) bring large-scale data and compute power closer to users in enterprises. However, existing tools for analyzing data in CDWs are either limited in ad-hoc transformations or difficult to use for business users. Here we introduce Sigma Workbook, a new interactive system that enables business users to easily perform a visual analysis of data in CDWs at scale. For this, Sigma Workbook provides an accessible spreadsheet-like interface for analysis through direct manipulation. Sigma Workbook dynamically constructs matching SQL queries from user interactions, building on the versatility and expressivity of SQL. Constructed queries are directly executed on CDWs, leveraging the superior characteristics of the new generation CDWs, including scalability. We demonstrate Sigma Workbook through 3 real-life use cases -- cohort analysis, sessionization, and data augmentation -- and underline Workbook's ease of use, scalability, and expressivity.