OpenRaiser/Intern-Atlas huggingface dataset:
PRIM project: what contributions for disabilities?
Venue: arXiv cs.HC 2023
Year: 2023

In the PRIM project, we aim at giving people the power to create scenagrams (interaction scenarios between a human and digital devices) without the need to learn programming or to ask for computer scientists. In this project, software design follows an unconventional approach, far from classical codes, to embody human thinking (based on interactions) instead of computer logic (based on algorithms). The main idea rests on a new time representation using a PRIM-specific timeline instead of a standardized timeline. We evaluated acceptability and cognitive compatibility of this new timeline with 50 participants. Results are very promising. In this paper, we detail qualitative evaluation results about the interest of such software in the field of disability.
ARcode: HPC Application Recognition Through Image-encoded Monitoring Data
Venue: arXiv cs.DC 2023
Year: 2023

Knowing HPC applications of jobs and analyzing their performance behavior play important roles in system management and optimizations. The existing approaches detect and identify HPC applications through machine learning models. However, these approaches rely heavily on the manually extracted features from resource utilization data to achieve high prediction accuracy. In this study, we propose an innovative application recognition method, ARcode, which encodes job monitoring data into images and leverages the automatic feature learning capability of convolutional neural networks to detect and identify applications. Our extensive evaluations based on the dataset collected from a large-scale production HPC system show that ARcode outperforms the state-of-the-art methodology by up to 18.87% in terms of accuracy at high confidence thresholds. For some specific applications (BerkeleyGW and e3sm), ARcode outperforms by over 20% at a confidence threshold of 0.8.
ATP: Adaptive Tensor Parallelism for Foundation Models
Venue: arXiv cs.DC 2023
Year: 2023

Foundation models have impressive performance and generalization capabilities across a wide range of applications. The increasing size of the models introduces great challenges for the training. Tensor parallelism is a critical technique that is currently used in almost all foundation model training and has a significant impact on overall training performance. However, current tensor parallelism in machine learning frameworks misses optimization opportunities in fitting various interconnection topologies. In this work, we present ATP, an adaptive tensor parallelism framework for foundation models, which can automatically select the optimal parallel strategy on different interconnections. We propose column- and row-first tensor parallelism based on 2D device meshes and construct a search space. Combined with the hierarchical communication matrix, ATP can identify the optimal strategy in the search space. We also propose chunk-based overlapping to reduce communication overhead. Our evaluations show ATP consistently outperforms the state-of-the-art approaches for various model sizes and interconnects, achieving end-to-end training performance improvements of up to 37-64% on specific interconnects. Based on our theoretical model, the communication overhead of ATP decreases with scaling, indicating a qualitative leap forward.
Massively Parallel Genetic Optimization through Asynchronous Propagation of Populations
Venue: arXiv cs.NE 2023
Year: 2023

We present Propulate, an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, Propulate omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare Propulate to the established optimization tool Optuna. We find that Propulate is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulate
Verse: A Python library for reasoning about multi-agent hybrid system scenarios
Venue: arXiv cs.SE 2023
Year: 2023

We present the Verse library with the aim of making hybrid system verification more usable for multi-agent scenarios. In Verse, decision making agents move in a map and interact with each other through sensors. The decision logic for each agent is written in a subset of Python and the continuous dynamics is given by a black-box simulator. Multiple agents can be instantiated and they can be ported to different maps for creating scenarios. Verse provides functions for simulating and verifying such scenarios using existing reachability analysis algorithms. We illustrate several capabilities and use cases of the library with heterogeneous agents, incremental verification, different sensor models, and the flexibility of plugging in different subroutines for post computations.
The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts
Venue: arXiv cs.AI 2023
Year: 2023

Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy and diseased status and examine briefly the role of augmentation methods. Finally, we discuss the trade-offs of four approaches for sampling digital cohorts and the associated potential for study bias with selecting specific patient distributions.
Domain-agnostic and Multi-level Evaluation of Generative Models
Venue: arXiv cs.LG 2023
Year: 2023

While the capabilities of generative models heavily improved in different domains (images, text, graphs, molecules, etc.), their evaluation metrics largely remain based on simplified quantities or manual inspection with limited practicality. To this end, we propose a framework for Multi-level Performance Evaluation of Generative mOdels (MPEGO), which could be employed across different domains. MPEGO aims to quantify generation performance hierarchically, starting from a sub-feature-based low-level evaluation to a global features-based high-level evaluation. MPEGO offers great customizability as the employed features are entirely user-driven and can thus be highly domain/problem-specific while being arbitrarily complex (e.g., outcomes of experimental procedures). We validate MPEGO using multiple generative models across several datasets from the material discovery domain. An ablation study is conducted to study the plausibility of intermediate steps in MPEGO. Results demonstrate that MPEGO provides a flexible, user-driven, and multi-level evaluation framework, with practical insights on the generation quality. The framework, source code, and experiments will be available at https://github.com/GT4SD/mpego.
Impact of PCA-based preprocessing and different CNN structures on deformable registration of sonograms
Venue: arXiv cs.CV 2023
Year: 2023

Central venous catheters (CVC) are commonly inserted into the large veins of the neck, e.g. the internal jugular vein (IJV). CVC insertion may cause serious complications like misplacement into an artery or perforation of cervical vessels. Placing a CVC under sonographic guidance is an appropriate method to reduce such adverse events, if anatomical landmarks like venous and arterial vessels can be detected reliably. This task shall be solved by registration of patient individual images vs. an anatomically labelled reference image. In this work, a linear, affine transformation is performed on cervical sonograms, followed by a non-linear transformation to achieve a more precise registration. Voxelmorph (VM), a learning-based library for deformable image registration using a convolutional neural network (CNN) with U-Net structure was used for non-linear transformation. The impact of principal component analysis (PCA)-based pre-denoising of patient individual images, as well as the impact of modified net structures with differing complexities on registration results were examined visually and quantitatively, the latter using metrics for deformation and image similarity. Using the PCA-approximated cervical sonograms resulted in decreased mean deformation lengths between 18% and 66% compared to their original image counterparts, depending on net structure. In addition, reducing the number of convolutional layers led to improved image similarity with PCA images, while worsening in original images. Despite a large reduction of network parameters, no overall decrease in registration quality was observed, leading to the conclusion that the original net structure is oversized for the task at hand.
Differential Privacy in Cooperative Multiagent Planning
Venue: arXiv cs.MA 2023
Year: 2023

Privacy-aware multiagent systems must protect agents' sensitive data while simultaneously ensuring that agents accomplish their shared objectives. Towards this goal, we propose a framework to privatize inter-agent communications in cooperative multiagent decision-making problems. We study sequential decision-making problems formulated as cooperative Markov games with reach-avoid objectives. We apply a differential privacy mechanism to privatize agents' communicated symbolic state trajectories, and then we analyze tradeoffs between the strength of privacy and the team's performance. For a given level of privacy, this tradeoff is shown to depend critically upon the total correlation among agents' state-action processes. We synthesize policies that are robust to privacy by reducing the value of the total correlation. Numerical experiments demonstrate that the team's performance under these policies decreases by only 3 percent when comparing private versus non-private implementations of communication. By contrast, the team's performance decreases by roughly 86 percent when using baseline policies that ignore total correlation and only optimize team performance.
A Review of the Trends and Challenges in Adopting Natural Language Processing Methods for Education Feedback Analysis
Venue: arXiv cs.CL 2023
Year: 2023

Artificial Intelligence (AI) is a fast-growing area of study that stretching its presence to many business and research domains. Machine learning, deep learning, and natural language processing (NLP) are subsets of AI to tackle different areas of data processing and modelling. This review article presents an overview of AI impact on education outlining with current opportunities. In the education domain, student feedback data is crucial to uncover the merits and demerits of existing services provided to students. AI can assist in identifying the areas of improvement in educational infrastructure, learning management systems, teaching practices and study environment. NLP techniques play a vital role in analyzing student feedback in textual format. This research focuses on existing NLP methodologies and applications that could be adapted to educational domain applications like sentiment annotations, entity annotations, text summarization, and topic modelling. Trends and challenges in adopting NLP in education were reviewed and explored. Contextbased challenges in NLP like sarcasm, domain-specific language, ambiguity, and aspect-based sentiment analysis are explained with existing methodologies to overcome them. Research community approaches to extract the semantic meaning of emoticons and special characters in feedback which conveys user opinion and challenges in adopting NLP in education are explored.
Consistency vs. Availability in Distributed Real-Time Systems
Venue: arXiv cs.DC 2023
Year: 2023

In distributed applications, Brewer's CAP theorem tells us that when networks become partitioned (P), one must give up either consistency (C) or availability (A). Consistency is agreement on the values of shared variables; availability is the ability to respond to reads and writes accessing those shared variables. Availability is a real-time property whereas consistency is a logical property. We have extended the CAP theorem to relate quantitative measures of these two properties to quantitative measures of communication and computation latency (L), obtaining a relation called the CAL theorem that is linear in a max-plus algebra. This paper shows how to use the CAL theorem in various ways to help design real-time systems. We develop a methodology for systematically trading off availability and consistency in application-specific ways and to guide the system designer when putting functionality in end devices, in edge computers, or in the cloud. We build on the Lingua Franca coordination language to provide system designers with concrete analysis and design tools to make the required tradeoffs in deployable software.
The Risk-Taking Software Engineer: A Framed Portrait
Venue: arXiv cs.SE 2023
Year: 2023

Background: Risk-taking is prevalent in a host of activities performed by software engineers on a daily basis, yet there is scant research on it. Aims and Method: We study if software engineers' risk-taking is affected by framing effects and by software engineers' personality. To this end, we perform a survey experiment with 124 software engineers. Results: We find that framing substantially affects their risk-taking. None of the "Big Five" personality traits are related to risk-taking in software engineers after correcting for multiple testing. Conclusions: Software engineers and their managers must be aware of framing effects and account for them properly.
A Hybrid Data-Driven Web-Based UI-UX Assessment Model
Venue: arXiv cs.HC 2023
Year: 2023

Today, a large proportion of end user information systems have their Graphical User Interfaces (GUI) built with web-based technology (JavaScript, CSS, and HTML). Some of these web-based systems include: Internet of Things (IOT), Infotainment (in vehicles), Interactive Display Screens (for digital menu boards, information kiosks, digital signage displays at bus stops or airports, bank ATMs, etc.), and web applications/services (on smart devices). As such, web-based UI must be evaluated in order to improve upon its ability to perform the technical task for which it was designed. This study develops a framework and a processes for evaluating and improving the quality of web-based user interface (UI) as well as at a stratified level. The study develops a comprehensive framework which is a conglomeration of algorithms such as the multi-criteria decision making method of analytical hierarchy process (AHP) in coefficient generation, sentiment analysis, K-means clustering algorithms and explainable AI (XAI).
Poor Man's Quality Estimation: Predicting Reference-Based MT Metrics Without the Reference
Venue: arXiv cs.CL 2023
Year: 2023

Machine translation quality estimation (QE) predicts human judgements of a translation hypothesis without seeing the reference. State-of-the-art QE systems based on pretrained language models have been achieving remarkable correlations with human judgements yet they are computationally heavy and require human annotations, which are slow and expensive to create. To address these limitations, we define the problem of metric estimation (ME) where one predicts the automated metric scores also without the reference. We show that even without access to the reference, our model can estimate automated metrics ($ρ$=60% for BLEU, $ρ$=51% for other metrics) at the sentence-level. Because automated metrics correlate with human judgements, we can leverage the ME task for pre-training a QE model. For the QE task, we find that pre-training on TER is better ($ρ$=23%) than training for scratch ($ρ$=20%).
Exploiting Out-of-band Motion Sensor Data to De-anonymize Virtual Reality Users
Venue: arXiv cs.CR 2023
Year: 2023

Virtual Reality (VR) is an exciting new consumer technology which offers an immersive audio-visual experience to users through which they can navigate and interact with a digitally represented 3D space (i.e., a virtual world) using a headset device. By (visually) transporting users from the real or physical world to exciting and realistic virtual spaces, VR systems can enable true-to-life and more interactive versions of traditional applications such as gaming, remote conferencing, social networking and virtual tourism. However, as with any new consumer technology, VR applications also present significant user-privacy challenges. This paper studies a new type of privacy attack targeting VR users by connecting their activities visible in the virtual world (enabled by some VR application/service) to their physical state sensed in the real world. Specifically, this paper analyzes the feasibility of carrying out a de-anonymization or identification attack on VR users by correlating visually observed movements of users' avatars in the virtual world with some auxiliary data (e.g., motion sensor data from mobile/wearable devices held by users) representing their context/state in the physical world. To enable this attack, this paper proposes a novel framework which first employs a learning-based activity classification approach to translate the disparate visual movement data and motion sensor data into an activity-vector to ease comparison, followed by a filtering and identity ranking phase outputting an ordered list of potential identities corresponding to the target visual movement data. Extensive empirical evaluation of the proposed framework, under a comprehensive set of experimental settings, demonstrates the feasibility of such a de-anonymization attack.
Resource-constrained FPGA Design for Satellite Component Feature Extraction
Venue: arXiv cs.CV 2023
Year: 2023

The effective use of computer vision and machine learning for on-orbit applications has been hampered by limited computing capabilities, and therefore limited performance. While embedded systems utilizing ARM processors have been shown to meet acceptable but low performance standards, the recent availability of larger space-grade field programmable gate arrays (FPGAs) show potential to exceed the performance of microcomputer systems. This work proposes use of neural network-based object detection algorithm that can be deployed on a comparably resource-constrained FPGA to automatically detect components of non-cooperative, satellites on orbit. Hardware-in-the-loop experiments were performed on the ORION Maneuver Kinematics Simulator at Florida Tech to compare the performance of the new model deployed on a small, resource-constrained FPGA to an equivalent algorithm on a microcomputer system. Results show the FPGA implementation increases the throughput and decreases latency while maintaining comparable accuracy. These findings suggest future missions should consider deploying computer vision algorithms on space-grade FPGAs.
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Venue: arXiv cs.SE 2023
Year: 2023

Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code representation, they have been further applied in multiple downstream tasks such as clone detection, code search and code translation. However, it is also observed that these state-of-the-art pre-trained models are susceptible to adversarial attacks. The performance of these pre-trained models drops significantly with simple perturbations such as renaming variable names. This weakness may be inherited by their downstream models and thereby amplified at an unprecedented scale. To this end, we propose an approach namely ContraBERT that aims to improve the robustness of pre-trained models via contrastive learning. Specifically, we design nine kinds of simple and complex data augmentation operators on the programming language (PL) and natural language (NL) data to construct different variants. Furthermore, we continue to train the existing pre-trained models by masked language modeling (MLM) and contrastive pre-training task on the original samples with their augmented variants to enhance the robustness of the model. The extensive experiments demonstrate that ContraBERT can effectively improve the robustness of the existing pre-trained models. Further study also confirms that these robustness-enhanced models provide improvements as compared to original models over four popular downstream tasks.
Initial Access Optimization for RIS-assisted Millimeter Wave Wireless Networks
Venue: arXiv cs.NI 2023
Year: 2023

Reconfigurable Intelligent Surfaces (RIS) are considered a key enabler to achieve the vision of Smart Radio Environments, where the propagation environment can be programmed and controlled to enhance the efficiency of wireless systems. These surfaces correspond to planar sheets comprising a large number of small and low-cost reflecting elements whose parameters are adaptively selected with a programmable controller. Hence, by optimizing these coefficients, the information signals can be directed in a customized fashion. On the other hand, the initial access procedure used in 5G is beam sweeping, where the base station sequentially changes the active beam direction in order to scan all users in the cell. This conventional protocol results in an initial access latency. The aim of this paper is to minimize this delay by optimizing the activated beams in each timeslot, while leveraging the presence of the RIS in the network. The problem is formulated as a hard optimization problem. We propose an efficient solution based on jointly alternating optimization and Semi Definite Relaxation (SDR) techniques. Numerical results are provided to assess the superiority of our scheme as compared to conventional beam sweeping.
Unifying Synergies between Self-supervised Learning and Dynamic Computation
Venue: arXiv cs.LG 2023
Year: 2023

Computationally expensive training strategies make self-supervised learning (SSL) impractical for resource constrained industrial settings. Techniques like knowledge distillation (KD), dynamic computation (DC), and pruning are often used to obtain a lightweightmodel, which usually involves multiple epochs of fine-tuning (or distilling steps) of a large pre-trained model, making it more computationally challenging. In this work we present a novel perspective on the interplay between SSL and DC paradigms. In particular, we show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting without any additional fine-tuning or pruning steps. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off and therefore yields a generic and multi-purpose architecture for application specific industrial settings. Extensive experiments on several image classification benchmarks including CIFAR-10/100, STL-10 and ImageNet-100, demonstrate that the proposed training strategy provides a dense and corresponding gated sub-network that achieves on-par performance compared with the vanilla self-supervised setting, but at a significant reduction in computation in terms of FLOPs, under a range of target budgets (td ).