OpenRaiser/Intern-Atlas huggingface dataset:
She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models
Venue: arXiv cs.SE 2023
Year: 2023

Implicit gender bias in software development is a well-documented issue, such as the association of technical roles with men. To address this bias, it is important to understand it in more detail. This study uses data mining techniques to investigate the extent to which 56 tasks related to software development, such as assigning GitHub issues and testing, are affected by implicit gender bias embedded in large language models. We systematically translated each task from English into a genderless language and back, and investigated the pronouns associated with each task. Based on translating each task 100 times in different permutations, we identify a significant disparity in the gendered pronoun associations with different tasks. Specifically, requirements elicitation was associated with the pronoun "he" in only 6% of cases, while testing was associated with "he" in 100% of cases. Additionally, tasks related to helping others had a 91% association with "he" while the same association for tasks related to asking coworkers was only 52%. These findings reveal a clear pattern of gender bias related to software development tasks and have important implications for addressing this issue both in the training of large language models and in broader society.
Hybrid Classic-Quantum Computing for Staging of Invasive Ductal Carcinoma of Breast
Venue: arXiv cs.AI 2023
Year: 2023

Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do not provide with optimal solutions. For this, one promising option could be the use of the concepts and ideas of Quantum Mechanics, for the construction of quantum-based Artificial Intelligence systems. From a hybrid classical-quantum perspective, this article deals with the application of quantum computing techniques for the staging of Invasive Ductal Carcinoma of the breast. It includes: (1) a general explanation of a classical, and well-established, approach for medical reasoning, (2) a description of the clinical problem, (3) a conceptual model for staging invasive ductal carcinoma, (4) some basic notions about Quantum Rule-Based Systems, (5) a step-by-step explanation of the proposed approach for quantum staging of the invasive ductal carcinoma, and (6) the results obtained after running the quantum system on a significant number of use cases. A detailed discussion is also provided at the end of this paper.
Data-centric Artificial Intelligence: A Survey
Venue: arXiv cs.LG 2023
Year: 2023

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI
A machine learning and feature engineering approach for the prediction of the uncontrolled re-entry of space objects
Venue: arXiv cs.LG 2023
Year: 2023

The continuously growing number of objects orbiting around the Earth is expected to be accompanied by an increasing frequency of objects re-entering the Earth's atmosphere. Many of these re-entries will be uncontrolled, making their prediction challenging and subject to several uncertainties. Traditionally, re-entry predictions are based on the propagation of the object's dynamics using state-of-the-art modelling techniques for the forces acting on the object. However, modelling errors, particularly related to the prediction of atmospheric drag may result in poor prediction accuracies. In this context, we explore the possibility to perform a paradigm shift, from a physics-based approach to a data-driven approach. To this aim, we present the development of a deep learning model for the re-entry prediction of uncontrolled objects in Low Earth Orbit (LEO). The model is based on a modified version of the Sequence-to-Sequence architecture and is trained on the average altitude profile as derived from a set of Two-Line Element (TLE) data of over 400 bodies. The novelty of the work consists in introducing in the deep learning model, alongside the average altitude, three new input features: a drag-like coefficient (B*), the average solar index, and the area-to-mass ratio of the object. The developed model is tested on a set of objects studied in the Inter-Agency Space Debris Coordination Committee (IADC) campaigns. The results show that the best performances are obtained on bodies characterised by the same drag-like coefficient and eccentricity distribution as the training set.
ESP32: QEMU Emulation within a Docker Container
Venue: arXiv cs.OS 2023
Year: 2023

The ESP32 is a popular microcontroller from Espressif that can be used in many embedded applications. Robotic joints, smart car chargers, beer vat agitators and automated bread mixers are a few examples where this system-on-a-chip excels. It is cheap to buy and has a number of vendors providing low-cost development board kits that come with the microcontroller and many external connection points with peripherals. There is a large software ecosystem for the ESP32. Espressif maintains an SDK containing many C-language sample projects providing a starting point for a huge variety of software services and I/O needs. Third party projects provide additional sample code as well as support for other programming languages. For example, MicroPython is a mature project with sample code and officially supported by Espressif. The SDK provides tools to not just build an application but also merge a flash image, flash to the microcontroller and monitor the output. Is it possible to build the ESP32 load and emulate on another host OS? This paper explores the QEMU emulator and its ability to emulate the ethernet interface for the guest OS. Additionally, we look into the concept of containerizing the entire emulator and ESP32 load package such that a microcontroller flash image can successfully run with a one-step deployment of a Docker container.
Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
Venue: arXiv cs.CV 2023
Year: 2023

This paper presents a new methodology to alleviate the fundamental trade-off between accuracy and latency in spiking neural networks (SNNs). The approach involves decoding confidence information over time from the SNN outputs and using it to develop a decision-making agent that can dynamically determine when to terminate each inference. The proposed method, Dynamic Confidence, provides several significant benefits to SNNs. 1. It can effectively optimize latency dynamically at runtime, setting it apart from many existing low-latency SNN algorithms. Our experiments on CIFAR-10 and ImageNet datasets have demonstrated an average 40% speedup across eight different settings after applying Dynamic Confidence. 2. The decision-making agent in Dynamic Confidence is straightforward to construct and highly robust in parameter space, making it extremely easy to implement. 3. The proposed method enables visualizing the potential of any given SNN, which sets a target for current SNNs to approach. For instance, if an SNN can terminate at the most appropriate time point for each input sample, a ResNet-50 SNN can achieve an accuracy as high as 82.47% on ImageNet within just 4.71 time steps on average. Unlocking the potential of SNNs needs a highly-reliable decision-making agent to be constructed and fed with a high-quality estimation of ground truth. In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Energy-Efficient Control of Cable Robots Exploiting Natural Dynamics and Task Knowledge
Venue: arXiv cs.RO 2023
Year: 2023

This paper focusses on the energy-efficient control of a cable-driven robot for tasks that only require precise positioning at few points in their motion, and where that accuracy can be obtained through contacts. This includes the majority of pick-and-place operations. Knowledge about the task is directly taken into account when specifying the control execution. The natural dynamics of the system can be exploited when there is a tolerance on the position of the trajectory. Brakes are actively used to replace standstill torques, and as passive actuation. This is executed with a hybrid discrete-continuous controller. A discrete controller is used to specify and coordinate between subtasks, and based on the requirements of these specific subtasks, specific, robust, continuous controllers are constructed. This approach allows for less stiff and thus saver, and cheaper hardware to be used. For a planar pick-and-place operation, it was found that this results in energy savings of more than 30%. However, when the payload moves with the natural dynamics, there is less control of the followed trajectory and its timing compared to a traditional trajectory-based execution. Also, the presented approach implies a fundamentally different way to specify and execute tasks.
Hybrid Systems Neural Control with Region-of-Attraction Planner
Venue: arXiv cs.RO 2023
Year: 2023

Hybrid systems are prevalent in robotics. However, ensuring the stability of hybrid systems is challenging due to sophisticated continuous and discrete dynamics. A system with all its system modes stable can still be unstable. Hence special treatments are required at mode switchings to stabilize the system. In this work, we propose a hierarchical, neural network (NN)-based method to control general hybrid systems. For each system mode, we first learn an NN Lyapunov function and an NN controller to ensure the states within the region of attraction (RoA) can be stabilized. Then an RoA NN estimator is learned across different modes. Upon mode switching, we propose a differentiable planner to ensure the states after switching can land in next mode's RoA, hence stabilizing the hybrid system. We provide novel theoretical stability guarantees and conduct experiments in car tracking control, pogobot navigation, and bipedal walker locomotion. Our method only requires 0.25X of the training time as needed by other learning-based methods. With low running time (10-50X faster than model predictive control (MPC)), our controller achieves a higher stability/success rate over other baselines such as MPC, reinforcement learning (RL), common Lyapunov methods (CLF), linear quadratic regulator (LQR), quadratic programming (QP) and Hamilton-Jacobian-based methods (HJB). The project page is on https://mit-realm.github.io/hybrid-clf.
HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique. The existing MIM methods adopt the strategy to mask random patches of the image and reconstruct the missing pixels, which only considers semantic information at a lower level, and causes a long pre-training time.This paper presents HybridMIM, a novel hybrid self-supervised learning method based on masked image modeling for 3D medical image segmentation.Specifically, we design a two-level masking hierarchy to specify which and how patches in sub-volumes are masked, effectively providing the constraints of higher level semantic information. Then we learn the semantic information of medical images at three levels, including:1) partial region prediction to reconstruct key contents of the 3D image, which largely reduces the pre-training time burden (pixel-level); 2) patch-masking perception to learn the spatial relationship between the patches in each sub-volume (region-level).and 3) drop-out-based contrastive learning between samples within a mini-batch, which further improves the generalization ability of the framework (sample-level). The proposed framework is versatile to support both CNN and transformer as encoder backbones, and also enables to pre-train decoders for image segmentation. We conduct comprehensive experiments on four widely-used public medical image segmentation datasets, including BraTS2020, BTCV, MSD Liver, and MSD Spleen. The experimental results show the clear superiority of HybridMIM against competing supervised methods, masked pre-training approaches, and other self-supervised methods, in terms of quantitative metrics, timing performance and qualitative observations. The codes of HybridMIM are available at https://github.com/ge-xing/HybridMIM
Local-to-Global Panorama Inpainting for Locale-Aware Indoor Lighting Prediction
Venue: arXiv cs.CV 2023
Year: 2023

Predicting panoramic indoor lighting from a single perspective image is a fundamental but highly ill-posed problem in computer vision and graphics. To achieve locale-aware and robust prediction, this problem can be decomposed into three sub-tasks: depth-based image warping, panorama inpainting and high-dynamic-range (HDR) reconstruction, among which the success of panorama inpainting plays a key role. Recent methods mostly rely on convolutional neural networks (CNNs) to fill the missing contents in the warped panorama. However, they usually achieve suboptimal performance since the missing contents occupy a very large portion in the panoramic space while CNNs are plagued by limited receptive fields. The spatially-varying distortion in the spherical signals further increases the difficulty for conventional CNNs. To address these issues, we propose a local-to-global strategy for large-scale panorama inpainting. In our method, a depth-guided local inpainting is first applied on the warped panorama to fill small but dense holes. Then, a transformer-based network, dubbed PanoTransformer, is designed to hallucinate reasonable global structures in the large holes. To avoid distortion, we further employ cubemap projection in our design of PanoTransformer. The high-quality panorama recovered at any locale helps us to capture spatially-varying indoor illumination with physically-plausible global structures and fine details.
Deterministic Rendezvous Algorithms
Venue: arXiv cs.DC 2023
Year: 2023

The task of rendezvous (also called {\em gathering}) calls for a meeting of two or more mobile entities, starting from different positions in some environment. Those entities are called mobile agents or robots, and the environment can be a network modeled as a graph or a terrain in the plane, possibly with obstacles. The rendezvous problem has been studied in many different scenarios. Two among many adopted assumptions particularly influence the methodology to be used to accomplish rendezvous. One of the assumptions specifies whether the agents in their navigation can see something apart from parts of the environment itself, for example other agents or marks left by them. The other assumption concerns the way in which the entities move: it can be either deterministic or randomized. In this paper we survey results on deterministic rendezvous of agents that cannot see the other agents prior to meeting them, and cannot leave any marks.
Multi-Semantic Interactive Learning for Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Single-branch object detection methods use shared features for localization and classification, yet the shared features are not fit for the two different tasks simultaneously. Multi-branch object detection methods usually use different features for localization and classification separately, ignoring the relevance between different tasks. Therefore, we propose multi-semantic interactive learning (MSIL) to mine the semantic relevance between different branches and extract multi-semantic enhanced features of objects. MSIL first performs semantic alignment of regression and classification branches, then merges the features of different branches by semantic fusion, finally extracts relevant information by semantic separation and passes it back to the regression and classification branches respectively. More importantly, MSIL can be integrated into existing object detection nets as a plug-and-play component. Experiments on the MS COCO, and Pascal VOC datasets show that the integration of MSIL with existing algorithms can utilize the relevant information between semantics of different tasks and achieve better performance.
A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models
Venue: arXiv cs.CL 2023
Year: 2023

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on, have gained considerable attention due to their exceptional natural language processing capabilities. However, despite the abundance of research on the difference in capabilities between GPT series models and fine-tuned models, there has been limited attention given to the evolution of GPT series models' capabilities over time. To conduct a comprehensive analysis of the capabilities of GPT series models, we select six representative models, comprising two GPT-3 series models (i.e., davinci and text-davinci-001) and four GPT-3.5 series models (i.e., code-davinci-002, text-davinci-002, text-davinci-003, and gpt-3.5-turbo). We evaluate their performance on nine natural language understanding (NLU) tasks using 21 datasets. In particular, we compare the performance and robustness of different models for each task under zero-shot and few-shot scenarios. Our extensive experiments reveal that the overall ability of GPT series models on NLU tasks does not increase gradually as the models evolve, especially with the introduction of the RLHF training strategy. While this strategy enhances the models' ability to generate human-like responses, it also compromises their ability to solve some tasks. Furthermore, our findings indicate that there is still room for improvement in areas such as model robustness.
Byzantine-Resilient Federated Learning at Edge
Venue: arXiv cs.DC 2023
Year: 2023

Both Byzantine resilience and communication efficiency have attracted tremendous attention recently for their significance in edge federated learning. However, most existing algorithms may fail when dealing with real-world irregular data that behaves in a heavy-tailed manner. To address this issue, we study the stochastic convex and non-convex optimization problem for federated learning at edge and show how to handle heavy-tailed data while retaining the Byzantine resilience, communication efficiency and the optimal statistical error rates simultaneously. Specifically, we first present a Byzantine-resilient distributed gradient descent algorithm that can handle the heavy-tailed data and meanwhile converge under the standard assumptions. To reduce the communication overhead, we further propose another algorithm that incorporates gradient compression techniques to save communication costs during the learning process. Theoretical analysis shows that our algorithms achieve order-optimal statistical error rate in presence of Byzantine devices. Finally, we conduct extensive experiments on both synthetic and real-world datasets to verify the efficacy of our algorithms.
Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation
Venue: arXiv cs.CV 2023
Year: 2023

Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains continuously without source data or target supervision. It has wide applications, such as robotic vision and autonomous driving. The main underlying challenge of CVDA is to learn helpful information only from the unsupervised target data while avoiding forgetting previously learned knowledge catastrophically, which is out of the capability of previous Video-based Unsupervised Domain Adaptation methods. Therefore, we propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA. Firstly, to learn from unsupervised domains, we propose to learn from pseudo labels. However, in continuous adaptation, prediction errors can accumulate rapidly in pseudo labels, and CART effectively tackles this problem with two key modules. Specifically, The first module generates refined pseudo labels using model predictions and deploys a novel attentive learning strategy. The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors. Extensive experiments suggest that the CVDA performance of CART outperforms existing methods by a considerable margin.
Examining the Potential for Conversational Exploratory Search using a Smart Speaker Digital Assistant
Venue: arXiv cs.HC 2023
Year: 2023

Online Digital Assistants, such as Amazon Alexa, Google Assistant, Apple Siri are very popular and provide a range or services to their users, a key function is their ability to satisfy user information needs from the sources available to them. Users may often regard these applications as providing search services similar to Google type search engines. However, while it is clear that they are in general able to answer factoid questions effectively, it is much less obvious how well they support less specific or exploratory type search tasks. We describe an investigation examining the behaviour of the standard Amazon Alexa for exploratory search tasks. The results of our study show that it not effective in addressing these types of information needs. We propose extensions to Alexa designed to overcome these shortcomings. Our Custom Alexa application extends Alexa's conversational functionality for exploratory search. A user study shows that our extended Alexa application both enables users to more successfully complete exploratory search tasks and is well accepted by our test users.