OpenRaiser/Intern-Atlas huggingface dataset:
The Right Tool for the Job: Open-Source Auditing Tools in Machine Learning
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, discussions about fairness in machine learning, AI ethics and algorithm audits have increased. Many entities have developed framework guidance to establish a baseline rubric for fairness and accountability. However, in spite of increased discussions and multiple frameworks, algorithm and data auditing still remain difficult to execute in practice. Many open-source auditing tools are available, but users aren't always aware of the tools, what they are useful for, or how to access them. Model auditing and evaluation are not frequently emphasized skills in machine learning. There are also legal reasons for the proactive adoption of these tools that extend beyond the desire for greater fairness in machine learning. There are positive social issues of public perception and goodwill that matter in our highly connected global society. Greater awareness of these tools and the reasons for actively utilizing them may be helpful to the entire continuum of programmers, data scientists, engineers, researchers, users and consumers of AI and machine learning products. It is important for everyone to better understand the input and output differentials, how they are occurring, and what can be done to promote FATE (fairness, accountability, transparency, and ethics) in machine- and deep learning. The ability to freely access open-source auditing tools removes barriers to fairness assessment at the most basic levels of machine learning. This paper aims to reinforce the urgent need to actually use these tools and provides motivations for doing so. The exemplary tools highlighted herein are open-source with software or code-base repositories available that can be used immediately by anyone worldwide.
Learning Neuro-Symbolic Skills for Bilevel Planning
Venue: arXiv cs.RO 2022
Year: 2022

Decision-making is challenging in robotics environments with continuous object-centric states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches, such as task and motion planning (TAMP), address these challenges by decomposing decision-making into two or more levels of abstraction. In a setting where demonstrations and symbolic predicates are given, prior work has shown how to learn symbolic operators and neural samplers for TAMP with manually designed parameterized policies. Our main contribution is a method for learning parameterized polices in combination with operators and samplers. These components are packaged into modular neuro-symbolic skills and sequenced together with search-then-sample TAMP to solve new tasks. In experiments in four robotics domains, we show that our approach -- bilevel planning with neuro-symbolic skills -- can solve a wide range of tasks with varying initial states, goals, and objects, outperforming six baselines and ablations. Video: https://youtu.be/PbFZP8rPuGg Code: https://tinyurl.com/skill-learning
Making the case for audience design in conversational AI: Rapport expectations and language ideologies in a task-oriented chatbot
Venue: arXiv cs.CL 2022
Year: 2022

Chatbots are more and more prevalent in commercial and science contexts. They help customers complain about a product or service or support them to find the best travel deals. Other bots provide mental health support or help book medical appointments. This paper argues that insights into users' language ideologies and their rapport expectations can be used to inform the audience design of the bot's language and interaction patterns and ensure equitable access to the services provided by bots. The argument is underpinned by three kinds of data: simulated user interactions with a chatbot facilitating health appointment bookings, users' introspective comments on their interactions and users' qualitative survey comments post engagement with the booking bot. In closing, I will define audience design for conversational AI and discuss how user-centred analyses of chatbot interactions and sociolinguistically informed theoretical approaches, such as rapport management, can be used to support audience design.
Performance Prediction Under Dataset Shift
Venue: arXiv cs.LG 2022
Year: 2022

ML models deployed in production often have to face unknown domain changes, fundamentally different from their training settings. Performance prediction models carry out the crucial task of measuring the impact of these changes on model performance. We study the generalization capabilities of various performance prediction models to new domains by learning on generated synthetic perturbations. Empirical validation on a benchmark of ten tabular datasets shows that models based upon state-of-the-art shift detection metrics are not expressive enough to generalize to unseen domains, while Error Predictors bring a consistent improvement in performance prediction under shift. We additionally propose a natural and effortless uncertainty estimation of the predicted accuracy that ensures reliable use of performance predictors. Our implementation is available at https: //github.com/dataiku-research/performance_prediction_under_shift.
Multi-Omic Data Integration and Feature Selection for Survival-based Patient Stratification via Supervised Concrete Autoencoders
Venue: arXiv cs.LG 2022
Year: 2022

Cancer is a complex disease with significant social and economic impact. Advancements in high-throughput molecular assays and the reduced cost for performing high-quality multi-omics measurements have fuelled insights through machine learning . Previous studies have shown promise on using multiple omic layers to predict survival and stratify cancer patients. In this paper, we developed a Supervised Autoencoder (SAE) model for survival-based multi-omic integration which improves upon previous work, and report a Concrete Supervised Autoencoder model (CSAE), which uses feature selection to jointly reconstruct the input features as well as predict survival. Our experiments show that our models outperform or are on par with some of the most commonly used baselines, while either providing a better survival separation (SAE) or being more interpretable (CSAE). We also perform a feature selection stability analysis on our models and notice that there is a power-law relationship with features which are commonly associated with survival. The code for this project is available at: https://github.com/phcavelar/coxae
Low-latency Imaging and Inference from LoRa-enabled CubeSats
Venue: arXiv cs.NI 2022
Year: 2022

Recent years have seen the rapid deployment of low-cost CubeSats in low-Earth orbit, primarily for research, education, and Earth observation. The vast majority of these CubeSats experience significant latency (several hours) from the time an image is captured to the time it is available on the ground. This is primarily due to the limited availability of dedicated satellite ground stations that tend to be bulky to deploy and expensive to rent. This paper explores using LoRa radios in the ISM band for low-latency downlink communication from CubeSats, primarily due to the availability of extensive ground LoRa infrastructure and minimal interference to terrestrial communication. However, the limited bandwidth of LoRa precludes rich satellite Earth images to be sent - instead, the CubeSats can at best send short messages (a few hundred bytes). This paper details our experience in communicating with a LoRa-enabled CubeSat launched by our team. We present Vista, a communication system that makes software modifications to LoRa encoding onboard a CubeSat and decoding on commercial LoRa ground stations to allow for satellite imagery to be communicated, as well as wide-ranging machine learning inference on these images. This is achieved through a LoRa-channel-aware image encoding that is informed by the structure of satellite images, the tasks performed on it, as well as the Doppler variation of satellite signals. A detailed evaluation of Vista through trace-driven emulation with traces from the LoRa-CubeSat launch (in 2021) shows 4.56 dB improvement in LoRa image PSNR and 1.38x improvement in land-use classification over those images.
Online Modeling and Control of Soft Multi-fingered Grippers via Koopman Operator Theory
Venue: arXiv cs.RO 2022
Year: 2022

Soft grippers are gaining momentum across applications due to their flexibility and dexterity. However, the infinite-dimensionality and non-linearity associated with soft robots challenge modeling and closed-loop control of soft grippers to perform grasping tasks. To solve this problem, data-driven methods have been proposed. Most data-driven methods rely on intensive model learning in simulation or offline, and as such it may be hard to generalize across different settings not explicitly trained upon and in physical robot testing where online control is required. In this paper, we propose an online modeling and control algorithm that utilizes Koopman operator theory to update an estimated model of the underlying dynamics at each time step in real-time. The learned and continuously updated models are then embedded into an online Model Predictive Control (MPC) structure and deployed onto soft multi-fingered robotic grippers. To evaluate the performance, the prediction accuracy of our approach is first compared against other model-extraction methods among different datasets. Next, the online modeling and control algorithm is tested experimentally with a soft 3-fingered gripper grasping objects of various shapes and weights unknown to the controller initially. Results indicate a high success ratio in grasping different objects using the proposed method. Sample trials can be viewed at https://youtu.be/i2hCMX7zSKQ.
A method for ethical AI in Defence: A case study on developing trustworthy autonomous systems
Venue: arXiv cs.CY 2022
Year: 2022

What does it mean to be responsible and responsive when developing and deploying trusted autonomous systems in Defence? In this short reflective article, we describe a case study of building a trusted autonomous system - Athena AI - within an industry-led, government-funded project with diverse collaborators and stakeholders. Using this case study, we draw out lessons on the value and impact of embedding responsible research and innovation-aligned, ethics-by-design approaches and principles throughout the development of technology at high translation readiness levels.
An Empirical Study On Correlation between Readme Content and Project Popularity
Venue: arXiv cs.SE 2022
Year: 2022

Readme in GitHub repositories serves as a preliminary source of information, and thus helps developers in understanding about the projects, for reuse or extension. Different types of contextual and structural content, which we refer to as categories of the content and features in the content respectively, are present in readme files, and could determine the extent of comprehension about project. Consequently, the structural and contextual aspects of the content could impact the project popularity. Studying the correlation between the content and project popularity could help in focusing on the aspects that could improve popularity, while designing the readme files. However, existing studies explore the categories of content and types of features in readme files, and do not explore their usefulness towards project popularity. Hence, we present an empirical study to understand correlation between readme file content and project popularity. We perform the study on 1950 readme files of public GitHub projects, spanning across ten programming languages, and observe that readme files in majority of the popular projects are well organised using lists and images, and comprise links to external sources. Also, repositories with readme files containing contribution guidelines and references were observed to be associated with higher popularity.
iTiger: An Automatic Issue Title Generation Tool
Venue: arXiv cs.SE 2022
Year: 2022

In both commercial and open-source software, bug reports or issues are used to track bugs or feature requests. However, the quality of issues can differ a lot. Prior research has found that bug reports with good quality tend to gain more attention than the ones with poor quality. As an essential component of an issue, title quality is an important aspect of issue quality. Moreover, issues are usually presented in a list view, where only the issue title and some metadata are present. In this case, a concise and accurate title is crucial for readers to grasp the general concept of the issue and facilitate the issue triaging. Previous work formulated the issue title generation task as a one-sentence summarization task. A sequence-to-sequence model was employed to solve this task. However, it requires a large amount of domain-specific training data to attain good performance in issue title generation. Recently, pre-trained models, which learned knowledge from large-scale general corpora, have shown much success in software engineering tasks. In this work, we make the first attempt to fine-tune BART, which has been pre-trained using English corpora, to generate issue titles. We implemented the fine-tuned BART as a web tool named iTiger, which can suggest an issue title based on the issue description. iTiger is fine-tuned on 267,094 GitHub issues. We compared iTiger with the state-of-the-art method, i.e., iTAPE, on 33,438 issues. The automatic evaluation shows that iTiger outperforms iTAPE by 29.7%, 50.8%, and 34.1%, in terms of ROUGE-1, ROUGE-2, ROUGE-L F1-scores. The manual evaluation also demonstrates the titles generated by BART are preferred by evaluators over the titles generated by iTAPE in 72.7% of cases. Besides, the evaluators deem our tool as useful and easy-to-use. They are also interested to use our tool in the future.
MultiEarth 2022 Deforestation Challenge -- ForestGump
Venue: arXiv cs.CV 2022
Year: 2022

The estimation of deforestation in the Amazon Forest is challenge task because of the vast size of the area and the difficulty of direct human access. However, it is a crucial problem in that deforestation results in serious environmental problems such as global climate change, reduced biodiversity, etc. In order to effectively solve the problems, satellite imagery would be a good alternative to estimate the deforestation of the Amazon. With a combination of optical images and Synthetic aperture radar (SAR) images, observation of such a massive area regardless of weather conditions become possible. In this paper, we present an accurate deforestation estimation method with conventional UNet and comprehensive data processing. The diverse channels of Sentinel-1, Sentinel-2 and Landsat 8 are carefully selected and utilized to train deep neural networks. With the proposed method, deforestation status for novel queries are successfully estimated with high accuracy.
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
Venue: arXiv cs.LG 2022
Year: 2022

Federated Learning (FL) is a machine learning paradigm to distributively learn machine learning models from decentralized data that remains on-device. Despite the success of standard Federated optimization methods, such as Federated Averaging (FedAvg) in FL, the energy demands and hardware induced constraints for on-device learning have not been considered sufficiently in the literature. Specifically, an essential demand for on-device learning is to enable trained models to be quantized to various bit-widths based on the energy needs and heterogeneous hardware designs across the federation. In this work, we introduce multiple variants of federated averaging algorithm that train neural networks robust to quantization. Such networks can be quantized to various bit-widths with only limited reduction in full precision model accuracy. We perform extensive experiments on standard FL benchmarks to evaluate our proposed FedAvg variants for quantization robustness and provide a convergence analysis for our Quantization-Aware variants in FL. Our results demonstrate that integrating quantization robustness results in FL models that are significantly more robust to different bit-widths during quantized on-device inference.
Parallel Pre-trained Transformers (PPT) for Synthetic Data-based Instance Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Recently, Synthetic data-based Instance Segmentation has become an exceedingly favorable optimization paradigm since it leverages simulation rendering and physics to generate high-quality image-annotation pairs. In this paper, we propose a Parallel Pre-trained Transformers (PPT) framework to accomplish the synthetic data-based Instance Segmentation task. Specifically, we leverage the off-the-shelf pre-trained vision Transformers to alleviate the gap between natural and synthetic data, which helps to provide good generalization in the downstream synthetic data scene with few samples. Swin-B-based CBNet V2, SwinL-based CBNet V2 and Swin-L-based Uniformer are employed for parallel feature learning, and the results of these three models are fused by pixel-level Non-maximum Suppression (NMS) algorithm to obtain more robust results. The experimental results reveal that PPT ranks first in the CVPR2022 AVA Accessibility Vision and Autonomy Challenge, with a 65.155% mAP.
I^2R-Net: Intra- and Inter-Human Relation Network for Multi-Person Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

In this paper, we present the Intra- and Inter-Human Relation Networks (I^2R-Net) for Multi-Person Pose Estimation. It involves two basic modules. First, the Intra-Human Relation Module operates on a single person and aims to capture Intra-Human dependencies. Second, the Inter-Human Relation Module considers the relation between multiple instances and focuses on capturing Inter-Human interactions. The Inter-Human Relation Module can be designed very lightweight by reducing the resolution of feature map, yet learn useful relation information to significantly boost the performance of the Intra-Human Relation Module. Even without bells and whistles, our method can compete or outperform current competition winners. We conduct extensive experiments on COCO, CrowdPose, and OCHuman datasets. The results demonstrate that the proposed model surpasses all the state-of-the-art methods. Concretely, the proposed method achieves 77.4% AP on CrowPose dataset and 67.8% AP on OCHuman dataset respectively, outperforming existing methods by a large margin. Additionally, the ablation study and visualization analysis also prove the effectiveness of our model.
Decoupling the ascending and descending phases in Abstract Interpretation
Venue: arXiv cs.PL 2022
Year: 2022

Abstract Interpretation approximates the semantics of a program by mimicking its concrete fixpoint computation on an abstract domain $\mathbb{A}$. The abstract (post-) fixpoint computation is classically divided into two phases: the ascending phase, using widenings as extrapolation operators to enforce termination, is followed by a descending phase, using narrowings as interpolation operators, so as to mitigate the effect of the precision losses introduced by widenings. In this paper we propose a simple variation of this classical approach where, to more effectively recover precision, we decouple the two phases: in particular, before starting the descending phase, we replace the domain $\mathbb{A}$ with a more precise abstract domain $\mathbb{D}$. The correctness of the approach is justified by casting it as an instance of the A$^2$I framework. After demonstrating the new technique on a simple example, we summarize the results of a preliminary experimental evaluation, showing that it is able to obtain significant precision improvements for several choices of the domains $\mathbb{A}$ and $\mathbb{D}$.
A Study on Modularity Density Maximization: Column Generation Acceleration and Computational Complexity Analysis
Venue: arXiv cs.SI 2022
Year: 2022

Community detection is a fundamental network-analysis primitive with a variety of applications in diverse domains. Although the modularity introduced by Newman and Girvan (2004) has widely been used as a quality function for community detection, it has some drawbacks. The modularity density introduced by Li et al. (2008) is known to be an effective alternative to the modularity, which mitigates one of the drawbacks called the resolution limit. A large body of work has been devoted to designing exact and heuristic methods for modularity density maximization, without any computational complexity analysis. In this study, we investigate modularity density maximization from both algorithmic and computational complexity aspects. Specifically, we first accelerate column generation for the modularity density maximization problem. To this end, we point out that the auxiliary problem appearing in column generation can be viewed as a dense subgraph discovery problem. Then we employ a well-known strategy for dense subgraph discovery, called the greedy peeling, for approximately solving the auxiliary problem. Moreover, we reformulate the auxiliary problem to a sequence of $0$--$1$ linear programming problems, enabling us to compute its optimal value more efficiently and to get more diverse columns. Computational experiments using a variety of real-world networks demonstrate the effectiveness of our proposed algorithm. Finally, we show the NP-hardness of a slight variant of the modularity density maximization problem, where the output partition has to have two or more clusters, as well as showing the NP-hardness of the auxiliary problem.
Template-based Approach to Zero-shot Intent Recognition
Venue: arXiv cs.CL 2022
Year: 2022

The recent advances in transfer learning techniques and pre-training of large contextualized encoders foster innovation in real-life applications, including dialog assistants. Practical needs of intent recognition require effective data usage and the ability to constantly update supported intents, adopting new ones, and abandoning outdated ones. In particular, the generalized zero-shot paradigm, in which the model is trained on the seen intents and tested on both seen and unseen intents, is taking on new importance. In this paper, we explore the generalized zero-shot setup for intent recognition. Following best practices for zero-shot text classification, we treat the task with a sentence pair modeling approach. We outperform previous state-of-the-art f1-measure by up to 16\% for unseen intents, using intent labels and user utterances and without accessing external sources (such as knowledge bases). Further enhancement includes lexicalization of intent labels, which improves performance by up to 7\%. By using task transferring from other sentence pair tasks, such as Natural Language Inference, we gain additional improvements.
Comparing Formulaic Language in Human and Machine Translation: Insight from a Parliamentary Corpus
Venue: arXiv cs.CL 2022
Year: 2022

A recent study has shown that, compared to human translations, neural machine translations contain more strongly-associated formulaic sequences made of relatively high-frequency words, but far less strongly-associated formulaic sequences made of relatively rare words. These results were obtained on the basis of translations of quality newspaper articles in which human translations can be thought to be not very literal. The present study attempts to replicate this research using a parliamentary corpus. The text were translated from French to English by three well-known neural machine translation systems: DeepL, Google Translate and Microsoft Translator. The results confirm the observations on the news corpus, but the differences are less strong. They suggest that the use of text genres that usually result in more literal translations, such as parliamentary corpora, might be preferable when comparing human and machine translations. Regarding the differences between the three neural machine systems, it appears that Google translations contain fewer highly collocational bigrams, identified by the CollGram technique, than Deepl and Microsoft translations.