OpenRaiser/Intern-Atlas huggingface dataset:
HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association
Venue: arXiv cs.NI 2023
Year: 2023

Federated learning (FL) is a promising paradigm that enables collaboratively learning a shared model across massive clients while keeping the training data locally. However, for many existing FL systems, clients need to frequently exchange model parameters of large data size with the remote cloud server directly via wide-area networks (WAN), leading to significant communication overhead and long transmission time. To mitigate the communication bottleneck, we resort to the hierarchical federated learning paradigm of HiFL, which reaps the benefits of mobile edge computing and combines synchronous client-edge model aggregation and asynchronous edge-cloud model aggregation together to greatly reduce the traffic volumes of WAN transmissions. Specifically, we first analyze the convergence bound of HiFL theoretically and identify the key controllable factors for model performance improvement. We then advocate an enhanced design of HiFlash by innovatively integrating deep reinforcement learning based adaptive staleness control and heterogeneity-aware client-edge association strategy to boost the system efficiency and mitigate the staleness effect without compromising model accuracy. Extensive experiments corroborate the superior performance of HiFlash in model accuracy, communication reduction, and system efficiency.
Towards an Automatic Consolidation of French Law
Venue: arXiv cs.CL 2023
Year: 2023

We present preliminary results about Legistix, a tool we are developing to automatically consolidate the French and European law. Legistix is based both on regular expressions used in several compound grammars, similar to the successive passes of a compiler, and on a new specialized language of functional type, allowing to describe the changes applied to the texts. Instead of creating manually a full consolidated version of a text at each modification date, Legistix generates automatically programs from legal documents written in natural language to automatically create the consolidated versions.
Towards social embodied cobots: The integration of an industrial cobot with a social virtual agent
Venue: arXiv cs.RO 2023
Year: 2023

The integration of the physical capabilities of an industrial collaborative robot with a social virtual character may represent a viable solution to enhance the workers' perception of the system as an embodied social entity and increase social engagement and well-being at the workplace. An online study was setup using prerecorded video interactions in order to pilot potential advantages of different embodied configurations of the cobot-avatar system in terms of perceptions of Social Presence, cobot-avatar Unity and Social Role of the system, and explore the relation of these. In particular, two different configurations were explored and compared: the virtual character was displayed either on a tablet strapped onto the base of the cobot or on a large TV screen positioned at the back of the workcell. The results imply that participants showed no clear preference based on the constructs, and both configurations fulfill these basic criteria. In terms of the relations between the constructs, there were strong correlations between perception of Social Presence, Unity and Social Role (Collegiality). This gives a valuable insight into the role of these constructs in the perception of cobots as embodied social entities, and towards building cobots that support well-being at the workplace.
Automate migration to microservices architecture using Machine Learning techniques
Venue: arXiv cs.SE 2023
Year: 2023

The microservice architectural style has many advantages such as scalability, reusability, and easy maintainability. Microservices have therefore become a popular architectural choice when developing new applications. Reaping these benefits requires redesigning the monolithic application and moving them to a microservices-based architecture. This process is inherently complex and costly, so automating this task is critical. This project proposes a method by which potential microservices can be identified from a given monolithic application while treating this problem as a clustering thematic. Our method takes as input the source code of the source application in order to apply different approaches to devise the one box application into its different microservices. In this report we detail each of these techniques while finishing with a discussion and comparison of the results.
Robot Duck Debugging: Can Attentive Listening Improve Problem Solving?
Venue: arXiv cs.RO 2023
Year: 2023

While thinking aloud has been reported to positively affect problem-solving, the effects of the presence of an embodied entity (e.g., a social robot) to whom words can be directed remain mostly unexplored. In this work, we investigated the role of a robot in a "rubber duck debugging" setting, by analyzing how a robot's listening behaviors could support a thinking-aloud problem-solving session. Participants completed two different tasks while speaking their thoughts aloud to either a robot or an inanimate object (a giant rubber duck). We implemented and tested two types of listener behavior in the robot: a rule-based heuristic and a deep-learning-based model. In a between-subject user study with 101 participants, we evaluated how the presence of a robot affected users' engagement in thinking aloud, behavior during the task, and self-reported user experience. In addition, we explored the impact of the two robot listening behaviors on those measures. In contrast to prior work, our results indicate that neither the rule-based heuristic nor the deep learning robot conditions improved performance or perception of the task, compared to an inanimate object. We discuss potential explanations and shed light on the feasibility of designing social robots as assistive tools in thinking-aloud problem-solving tasks.
Pose Metrics: a New Paradigm for Character Motion Edition
Venue: arXiv cs.GR 2023
Year: 2023

In animation, style can be considered as a distinctive layer over the content of a motion, allowing a character to achieve the same gesture in various ways. Editing existing animation to modify the style while keeping the same content is an interesting task, which can facilitate the re-use of animation data and cut down on production time. Existing animation edition methods either work directly on the motion data, providing precise but tedious tools, or manipulate semantic style categories, taking control away from the user. As a middle ground, we propose a new character motion edition paradigm allowing higher-level manipulations without sacrificing controllability. We describe the concept of pose metrics, objective value functions which can be used to edit animation, leaving the style interpretation up to the user. We then propose an edition pipeline to edit animation data using pose metrics.
Optimal Mobility Aware Wireless Edge Cloud Support for the Metaverse
Venue: arXiv cs.NI 2023
Year: 2023

Mobile augmented reality (MAR) applications extended in the metaverse could provide mixed and immersive experiences by amalgamating the virtual and physical world. However, the joint consideration between MAR and metaverse seeks the reliable and high quality support for foreground interactions and background contents from these applications, which intensifies their consumption in energy, caching and computing resources. To tackle these challenges, a more flexible request assignment and resource allocation with more efficient processing are proposed in this paper through anchoring decomposed metaverse AR services at different edge nodes and proactively caching background metaverse region models embedded with target Augmented Reality Objects (AROs).Advanced terminals are also considered to further reduce service delay at an acceptable cost of energy consumption.We then propose and solve a joint optimization problem that explicitly considers the balance between service delay and energy consumption under the constraint of user perception quality in a mobility event.By also explicitly taking into account capabilities of user terminals, the proposed optimized scheme is compared to its terminal oblivious version in this paper. According to a wide set of numerical investigations, the proposed scheme owns advantages in service latency and energy efficiency over other nominal baseline schemes which neglect capacities of terminals, user physical mobility, service decomposition and the inherent multi modality of the metaverse MAR service.
Stuck in the Permissions With You: Developer & End-User Perspectives on App Permissions & Their Privacy Ramifications
Venue: arXiv cs.HC 2023
Year: 2023

While the literature on permissions from the end-user perspective is rich, there is a lack of empirical research on why developers request permissions, their conceptualization of permissions, and how their perspectives compare with end-users' perspectives. Our study aims to address these gaps using a mixed-methods approach. Through interviews with 19 app developers and a survey of 309 Android and iOS end-users, we found that both groups shared similar concerns about unnecessary permissions breaking trust, damaging the app's reputation, and potentially allowing access to sensitive data. We also found that developer participants sometimes requested multiple permissions due to confusion about the scope of certain permissions or third-party library requirements. Additionally, most end-user participants believed they were responsible for granting a permission request, and it was their choice to do so, a belief shared by many developer participants. Our findings have implications for improving the permission ecosystem for both developers and end-users.
Data-Driven Encoding: A New Numerical Method for Computation of the Koopman Operator
Venue: arXiv cs.LG 2023
Year: 2023

This paper presents a data-driven method for constructing a Koopman linear model based on the Direct Encoding (DE) formula. The prevailing methods, Dynamic Mode Decomposition (DMD) and its extensions are based on least squares estimates that can be shown to be biased towards data that are densely populated. The DE formula consisting of inner products of a nonlinear state transition function with observable functions does not incur this biased estimation problem and thus serves as a desirable alternative to DMD. However, the original DE formula requires knowledge of the nonlinear state equation, which is not available in many practical applications. In this paper, the DE formula is extended to a data-driven method, Data-Driven Encoding (DDE) of Koopman operator, in which the inner products are calculated from data taken from a nonlinear dynamic system. An effective algorithm is presented for the computation of the inner products, and their convergence to true values is proven. Numerical experiments verify the effectiveness of DDE compared to Extended DMD. The experiments demonstrate robustness to data distribution and the convergent properties of DDE, guaranteeing accuracy improvements with additional sample points. Furthermore, DDE is applied to deep learning of the Koopman operator to further improve prediction accuracy.
A Deep Learning & Fast Wavelet Transform-based Hybrid Approach for Denoising of PPG Signals
Venue: arXiv cs.IT 2023
Year: 2023

This letter presents a novel hybrid method that leverages deep learning to exploit the multi-resolution analysis capability of the wavelets, in order to denoise a photoplethysmography (PPG) signal. Under the proposed method, a noisy PPG sequence of length N is first decomposed into L detailed coefficients using the fast wavelet transform (FWT). Then, the clean PPG sequence is reconstructed as follows. A custom feedforward neural network (FFNN) provides the binary weights for each of the wavelet sub-signals outputted by the inverse-FWT block. This way, all those sub-signals which correspond to noise or artefacts are discarded during reconstruction. The FFNN is trained on the Beth Israel Deaconess Medical Center (BIDMC) dataset under the supervised learning framework, whereby we compute the mean squared-error (MSE) between the denoised sequence and the reference clean PPG signal, and compute the gradient of the MSE for the back-propagation. Numerical results show that the proposed method effectively denoises the corrupted PPG and video-PPG signal.
Error-related Potential Variability: Exploring the Effects on Classification and Transferability
Venue: arXiv cs.HC 2023
Year: 2023

Brain-Computer Interfaces (BCI) have allowed for direct communication from the brain to external applications for the automatic detection of cognitive processes such as error recognition. Error-related potentials (ErrPs) are a particular brain signal elicited when one commits or observes an erroneous event. However, due to the noisy properties of the brain and recording devices, ErrPs vary from instance to instance as they are combined with an assortment of other brain signals, biological noise, and external noise, making the classification of ErrPs a non-trivial problem. Recent works have revealed particular cognitive processes such as awareness, embodiment, and predictability that contribute to ErrP variations. In this paper, we explore the performance of classifier transferability when trained on different ErrP variation datasets generated by varying the levels of awareness and embodiment for a given task. In particular, we look at transference between observational and interactive ErrP categories when elicited by similar and differing tasks. Our empirical results provide an exploratory analysis into the ErrP transferability problem from a data perspective.
PromptShots at the FinNLP-2022 ERAI Tasks: Pairwise Comparison and Unsupervised Ranking
Venue: arXiv cs.CL 2023
Year: 2023

This report describes our PromptShots submissions to a shared task on Evaluating the Rationales of Amateur Investors (ERAI). We participated in both pairwise comparison and unsupervised ranking tasks. For pairwise comparison, we employed instruction-based models based on T5-small and OpenAI InstructGPT language models. Surprisingly, we observed OpenAI InstructGPT language model few-shot trained on Chinese data works best in our submissions, ranking 3rd on the maximal loss (ML) pairwise accuracy. This model works better than training on the Google translated English data by a large margin, where the English few-shot trained InstructGPT model even performs worse than an instruction-based T5-small model finetuned on the English data. However, all instruction-based submissions do not perform well on the maximal potential profit (MPP) pairwise accuracy where there are more data and learning signals. The Chinese few-shot trained InstructGPT model still performs best in our setting. For unsupervised ranking, we utilized many language models, including many financial-specific ones, and Bayesian lexicons unsupervised-learned on both Chinese and English words using a method-of-moments estimator. All our submissions rank best in the MPP ranking, from 1st to 3rd. However, they all do not perform well for ML scoring. Therefore, both MPP and ML scores need different treatments since we treated MPP and ML using the same formula. Our only difference is the treatment of market sentiment lexicons.
IOPathTune: Adaptive Online Parameter Tuning for Parallel File System I/O Path
Venue: arXiv cs.DC 2023
Year: 2023

Parallel file systems contain complicated I/O paths from clients to storage servers. An efficient I/O path requires proper settings of multiple parameters, as the default settings often fail to deliver optimal performance, especially for diverse workloads in the HPC environment. Existing tuning strategies have shortcomings in being adaptive, timely, and flexible. We propose IOPathTune, which adaptively tunes PFS I/O Path online from the client side without characterizing the workloads, doing expensive profiling, and communicating with other machines. We implemented IOPathTune on Lustre and leveraged CloudLab to conduct the evaluations on 20 different Filebench workloads in three different scenarios. We observed either on-par or better performance than the default configuration, as high as 231% on standalone executions. IOPathTune also delivers 89.57% better overall performance than CAPES in multiple client executions.
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Deep learning methods typically depend on the availability of labeled data, which is expensive and time-consuming to obtain. Active learning addresses such effort by prioritizing which samples are best to annotate in order to maximize the performance of the task model. While frameworks for active learning have been widely explored in the context of classification of natural images, they have been only sparsely used in medical image segmentation. The challenge resides in obtaining an uncertainty measure that reveals the best candidate data for annotation. This paper proposes Test-time Augmentation for Active Learning (TAAL), a novel semi-supervised active learning approach for segmentation that exploits the uncertainty information offered by data transformations. Our method applies cross-augmentation consistency during training and inference to both improve model learning in a semi-supervised fashion and identify the most relevant unlabeled samples to annotate next. In addition, our consistency loss uses a modified version of the JSD to further improve model performance. By relying on data transformations rather than on external modules or simple heuristics typically used in uncertainty-based strategies, TAAL emerges as a simple, yet powerful task-agnostic semi-supervised active learning approach applicable to the medical domain. Our results on a publicly-available dataset of cardiac images show that TAAL outperforms existing baseline methods in both fully-supervised and semi-supervised settings. Our implementation is publicly available on https://github.com/melinphd/TAAL.
Data-aware customization of activation functions reduces neural network error
Venue: arXiv cs.LG 2023
Year: 2023

Activation functions play critical roles in neural networks, yet current off-the-shelf neural networks pay little attention to the specific choice of activation functions used. Here we show that data-aware customization of activation functions can result in striking reductions in neural network error. We first give a simple linear algebraic explanation of the role of activation functions in neural networks; then, through connection with the Diaconis-Shahshahani Approximation Theorem, we propose a set of criteria for good activation functions. As a case study, we consider regression tasks with a partially exchangeable target function, \emph{i.e.} $f(u,v,w)=f(v,u,w)$ for $u,v\in \mathbb{R}^d$ and $w\in \mathbb{R}^k$, and prove that for such a target function, using an even activation function in at least one of the layers guarantees that the prediction preserves partial exchangeability for best performance. Since even activation functions are seldom used in practice, we designed the ``seagull'' even activation function $\log(1+x^2)$ according to our criteria. Empirical testing on over two dozen 9-25 dimensional examples with different local smoothness, curvature, and degree of exchangeability revealed that a simple substitution with the ``seagull'' activation function in an already-refined neural network can lead to an order-of-magnitude reduction in error. This improvement was most pronounced when the activation function substitution was applied to the layer in which the exchangeable variables are connected for the first time. While the improvement is greatest for low-dimensional data, experiments on the CIFAR10 image classification dataset showed that use of ``seagull'' can reduce error even for high-dimensional cases. These results collectively highlight the potential of customizing activation functions as a general approach to improve neural network performance.
Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment
Venue: arXiv cs.CV 2023
Year: 2023

As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays. It opens up new opportunities in the technology-mediated dancing space. These platforms primarily rely on passive and continuous human pose estimation as an input capture mechanism. Existing solutions are mainly based on RGB or RGB-Depth cameras for dance games. The former suffers in low-lighting conditions due to the motion blur and low sensitivity, while the latter is too power-hungry, has a low frame rate, and has limited working distance. With ultra-low latency, energy efficiency, and wide dynamic range characteristics, the event camera is a promising solution to overcome these shortcomings. We propose YeLan, an event camera-based 3-dimensional high-frequency human pose estimation(HPE) system that survives low-lighting conditions and dynamic backgrounds. We collected the world's first event camera dance dataset and developed a fully customizable motion-to-event physics-aware simulator. YeLan outperforms the baseline models in these challenging conditions and demonstrated robustness against different types of clothing, background motion, viewing angle, occlusion, and lighting fluctuations.
Quantifying and Managing Impacts of Concept Drifts on IoT Traffic Inference in Residential ISP Networks
Venue: arXiv cs.LG 2023
Year: 2023

Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically inferring active IoT devices per household and notifying users of vulnerable ones. Developing a scalable inference method that can perform robustly across thousands of home networks is a non-trivial task. This paper focuses on the challenges of developing and applying data-driven inference models when labeled data of device behaviors is limited and the distribution of data changes (concept drift) across time and space domains. Our contributions are three-fold: (1) We collect and analyze network traffic of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behavior of IoT devices; (2) We analyze the performance of two inference strategies, namely "global inference" (a model trained on a combined set of all labeled data from training homes) and "contextualized inference" (several models each trained on the labeled data from a training home) in the presence of concept drifts; and (3) To manage concept drifts, we develop a method that dynamically applies the ``closest'' model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in 20% of scenarios.