OpenRaiser/Intern-Atlas huggingface dataset:
Optimised Design and Performance Comparison of Soft Robotic Manipulators
Venue: arXiv cs.RO 2022
Year: 2022

Soft robotic manipulators are attractive for a range of applications such as medical interventions or industrial inspections in confined environments. A myriad of soft robotic manipulators have been proposed in the literature, but their designs tend to be relatively similar, and generally offer a relatively low force. This limits the payload they can carry and therefore their usability. A comparison of force of the different designs is not available under a common framework, and designs present different diameters and features that make them hard to compare. In this paper, we present the design of a soft robotic manipulator that is optimised to maximise its force while respecting typical application constraints such as size, workspace, payload capability, and maximum pressure. The design presented here has the advantage that it morphs to an optimal design as it is pressurised to move in different directions, and this leads to higher lateral force. The robot is designed using a set of principles and thus can be adapted to other applications. We also present a non-dimensional analysis for soft robotic manipulators, and we apply it to compare the performance of the design proposed here with other designs in the literature. We show that our design has a higher force than other designs in the same category. Experimental results confirm the higher force of our proposed design.
Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control
Venue: arXiv cs.DC 2022
Year: 2022

The optimal offloading of tasks in heterogeneous edge-computing scenarios is of great practical interest, both in the selfish and fully cooperative setting. In practice, such systems are typically very large, rendering exact solutions in terms of cooperative optima or Nash equilibria intractable. For this purpose, we adopt a general mean-field formulation in order to solve the competitive and cooperative offloading problems in the limit of infinitely large systems. We give theoretical guarantees for the approximation properties of the limiting solution and solve the resulting mean-field problems numerically. Furthermore, we verify our solutions numerically and find that our approximations are accurate for systems with dozens of edge devices. As a result, we obtain a tractable approach to the design of offloading strategies in large edge-computing scenarios with many users.
Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction
Venue: arXiv cs.LG 2022
Year: 2022

In recent years, graph neural networks (GNNs) combined with variants of recurrent neural networks (RNNs) have reached state-of-the-art performance in spatiotemporal forecasting tasks. This is particularly the case for traffic forecasting, where GNN models use the graph structure of road networks to account for spatial correlation between links and nodes. Recent solutions are either based on complex graph operations or avoiding predefined graphs. This paper proposes a new sequence-to-sequence architecture to extract the spatiotemporal correlation at multiple levels of abstraction using GNN-RNN cells with sparse architecture to decrease training time compared to more complex designs. Encoding the same input sequence through multiple encoders, with an incremental increase in encoder layers, enables the network to learn general and detailed information through multilevel abstraction. We further present a new benchmark dataset of street-level segment traffic data from Montreal, Canada. Unlike highways, urban road segments are cyclic and characterized by complicated spatial dependencies. Experimental results on the METR-LA benchmark highway and our MSLTD street-level segment datasets demonstrate that our model improves performance by more than 7% for one-hour prediction compared to the baseline methods while reducing computing resource requirements by more than half compared to other competing methods.
Valuing Players Over Time
Venue: arXiv cs.LG 2022
Year: 2022

In soccer (or association football), players quickly go from heroes to zeroes, or vice-versa. Performance is not a static measure but a somewhat volatile one. Analyzing performance as a time series rather than a stationary point in time is crucial to making better decisions. This paper introduces and explores I-VAEP and O-VAEP models to evaluate actions and rate players' intention and execution. Then, we analyze these ratings over time and propose use cases to fundament our option of treating player ratings as a continuous problem. As a result, we present who were the best players and how their performance evolved, define volatility metrics to measure a player's consistency, and build a player development curve to assist decision-making.
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
Venue: arXiv cs.CL 2022
Year: 2022

In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus. We address the Causal Relation Identification (CRI) task by exploiting a set of simple yet complementary techniques for fine-tuning language models (LMs) on a small number of annotated examples (i.e., a few-shot configuration). We follow a prompt-based prediction approach for fine-tuning LMs in which the CRI task is treated as a masked language modeling problem (MLM). This approach allows LMs natively pre-trained on MLM problems to directly generate textual responses to CRI-specific prompts. We compare the performance of this method against ensemble techniques trained on the entire dataset. Our best-performing submission was fine-tuned with only 256 instances per class, 15.7% of the all available data, and yet obtained the second-best precision (0.82), third-best accuracy (0.82), and an F1-score (0.85) very close to what was reported by the winner team (0.86).
The Science Gateway Community Institute's Consulting Services Program: Lessons for Research Software Engineering Organizations
Venue: arXiv cs.SE 2022
Year: 2022

The Science Gateways Community Institute (SGCI) is an NSF Software Infrastructure for Sustained Innovation (S2I2) funded project that leads and supports the science gateway community. Major activities for SGCI include a) sustainability training, including the Focus Week week-long course designed to help science gateway operators develop sustainability plans, and the Jumpstart virtual short-course; b) usability and user experience consulting; c) a community catalog of science gateways and science gateway software; d) workforce development activities, including a coding institute for students, internship opportunities, and hackathons; e) an annual conference; and f) in-depth technical support for client gateway projects. The goals of SGCI's Embedded Technical Support component are to help the institute's clients to create new science gateways or to significantly enhance existing science gateways. Examples of the latter include helping to implement major new capabilities and to implement significant usability improvements suggested by SGCI's usability consultants. The Embedded Technical Support component was managed by Indiana University and involved research software engineers at San Diego Supercomputer Center, Texas Advanced Computing Center, Indiana University, and Purdue University (through 2019). Since 2016, the component has involved 20 research software engineers as consultants and has conducted 59 client consultations. This short paper provides a summary of lessons learned from the Embedded Technical Support program that may be useful for the research software engineering community.
FedDAR: Federated Domain-Aware Representation Learning
Venue: arXiv cs.LG 2022
Year: 2022

Cross-silo Federated learning (FL) has become a promising tool in machine learning applications for healthcare. It allows hospitals/institutions to train models with sufficient data while the data is kept private. To make sure the FL model is robust when facing heterogeneous data among FL clients, most efforts focus on personalizing models for clients. However, the latent relationships between clients' data are ignored. In this work, we focus on a special non-iid FL problem, called Domain-mixed FL, where each client's data distribution is assumed to be a mixture of several predefined domains. Recognizing the diversity of domains and the similarity within domains, we propose a novel method, FedDAR, which learns a domain shared representation and domain-wise personalized prediction heads in a decoupled manner. For simplified linear regression settings, we have theoretically proved that FedDAR enjoys a linear convergence rate. For general settings, we have performed intensive empirical studies on both synthetic and real-world medical datasets which demonstrate its superiority over prior FL methods.
Impact of automation during innovative remanufacturing processes in circular economy: a state of the art
Venue: arXiv cs.RO 2022
Year: 2022

With the increasing demand of raw materials nowadays, and the decrease in supplies, the industrial sector is suffering. The environment and the society are also indirectly affected. The goal to reach a sustainable development imposes several studies on the economic, environmental and community level. The aim of this paper is to provide an overview of the existing body of literature on automated remanufacturing, and its potential impacts on the three pillars of sustainability. A particular interest is given to the growing use of cobots promoted by the principle of industry 4.0. The investigation that covers each part of the remanufacturing process will help in formalizing an approach about the automation of such processes. It highlights the challenges found and aims to improve the remanufacturing sector towards a more sustainable industry.
Generating Contextual Load Profiles Using a Conditional Variational Autoencoder
Venue: arXiv cs.LG 2022
Year: 2022

Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network architecture, which is challenging due to the highly variable nature of such profiles. Generated contextual load profiles were conditioned on the month of the year and typical power exchange with the grid. Moreover, the quality of generations was both visually and statistically evaluated. The experimental results demonstrate our proposed CVAE model can capture temporal features of historical load profiles and generate `realistic' data with satisfying univariate distributions and multivariate dependencies.
Analysis of the Power Imbalance in Power-Domain NOMA on Correlated Rayleigh Fading Channels
Venue: arXiv cs.IT 2022
Year: 2022

This paper analyzes the power imbalance issue in power-domain NOMA (PD-NOMA) in the presence of channel correlations, typically encountered on the downlink of cellular systems when the base station antennas have an insufficient separation. In a recent paper, the authors analyzed this issue for a typical uplink scenario with uncorrelated channels, and the study revealed an astounding result that the optimum in terms of average error probability is achieved when the user signals are perfectly balanced in terms of power as in multi-user MIMO with power control. This result led to some questioning of the concept of PD-NOMA for uncorrelated Rayleigh fading channels. In the present paper, we make a similar analysis for the downlink, and the study gives a very clear insight into the influence of the power imbalance at different levels of channel correlation. First, with full correlation (user signals transmitted from the same antenna), the PD-NOMA concept reduces to simple signal constellation design. The optimum is achieved when the power imbalance between the user signals is such that the resulting constellation has uniform spacing. Any deviation from this optimum will lead to a hierarchical constellation with performance loss. Also, this optimum power imbalance is shown to hold for a range of strong channel correlations, but for moderate and low correlation values perfectly power balanced NOMA takes over as in the presence of uncorrelated channels.
Domain-specific Learning of Multi-scale Facial Dynamics for Apparent Personality Traits Prediction
Venue: arXiv cs.CV 2022
Year: 2022

Human personality decides various aspects of their daily life and working behaviors. Since personality traits are relatively stable over time and unique for each subject, previous approaches frequently infer personality from a single frame or short-term behaviors. Moreover, most of them failed to specifically extract person-specific and unique cues for personality recognition. In this paper, we propose a novel video-based automatic personality traits recognition approach which consists of: (1) a \textbf{domain-specific facial behavior modelling} module that extracts personality-related multi-scale short-term human facial behavior features; (2) a \textbf{long-term behavior modelling} module that summarizes all short-term features of a video as a long-term/video-level personality representation and (3) a \textbf{multi-task personality traits prediction module} that models underlying relationship among all traits and jointly predict them based on the video-level personality representation. We conducted the experiments on ChaLearn First Impression dataset, and our approach achieved comparable results to the state-of-the-art. Importantly, we show that all three proposed modules brought important benefits for personality recognition.
Post-Quantum Oblivious Transfer from Smooth Projective Hash Functions with Grey Zone
Venue: arXiv cs.CR 2022
Year: 2022

Oblivious Transfer (OT) is a major primitive for secure multiparty computation. Indeed, combined with symmetric primitives along with garbled circuits, it allows any secure function evaluation between two parties. In this paper, we propose a new approach to build OT protocols. Interestingly, our new paradigm features a security analysis in the Universal Composability (UC) framework and may be instantiated from post-quantum primitives. In order to do so, we define a new primitive named Smooth Projective Hash Function with Grey Zone (SPHFwGZ) which can be seen as a relaxation of the classical Smooth Projective Hash Functions, with a subset of the words for which one cannot claim correctness nor smoothness: the grey zone. As a concrete application, we provide two instantiations of SPHFwGZ respectively based on the Diffie-Hellman and the Learning With Errors (LWE) problems. Hence, we propose a quantum-resistant OT protocol with UC-security in the random oracle model.
Multi-grained Label Refinement Network with Dependency Structures for Joint Intent Detection and Slot Filling
Venue: arXiv cs.CL 2022
Year: 2022

Slot filling and intent detection are two fundamental tasks in the field of natural language understanding. Due to the strong correlation between these two tasks, previous studies make efforts on modeling them with multi-task learning or designing feature interaction modules to improve the performance of each task. However, none of the existing approaches consider the relevance between the structural information of sentences and the label semantics of two tasks. The intent and semantic components of a utterance are dependent on the syntactic elements of a sentence. In this paper, we investigate a multi-grained label refinement network, which utilizes dependency structures and label semantic embeddings. Considering to enhance syntactic representations, we introduce the dependency structures of sentences into our model by graph attention layer. To capture the semantic dependency between the syntactic information and task labels, we combine the task specific features with corresponding label embeddings by attention mechanism. The experimental results demonstrate that our model achieves the competitive performance on two public datasets.
Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions
Venue: arXiv cs.AI 2022
Year: 2022

The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment with reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies for adopting the Metaverse for healthcare, the potential applications and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.
Estimating Multi-label Accuracy using Labelset Distributions
Venue: arXiv cs.LG 2022
Year: 2022

A multi-label classifier estimates the binary label state (relevant vs irrelevant) for each of a set of concept labels, for any given instance. Probabilistic multi-label classifiers provide a predictive posterior distribution over all possible labelset combinations of such label states (the powerset of labels) from which we can provide the best estimate, simply by selecting the labelset corresponding to the largest expected accuracy, over that distribution. For example, in maximizing exact match accuracy, we provide the mode of the distribution. But how does this relate to the confidence we may have in such an estimate? Confidence is an important element of real-world applications of multi-label classifiers (as in machine learning in general) and is an important ingredient in explainability and interpretability. However, it is not obvious how to provide confidence in the multi-label context and relating to a particular accuracy metric, and nor is it clear how to provide a confidence which correlates well with the expected accuracy, which would be most valuable in real-world decision making. In this article we estimate the expected accuracy as a surrogate for confidence, for a given accuracy metric. We hypothesise that the expected accuracy can be estimated from the multi-label predictive distribution. We examine seven candidate functions for their ability to estimate expected accuracy from the predictive distribution. We found three of these to correlate to expected accuracy and are robust. Further, we determined that each candidate function can be used separately to estimate Hamming similarity, but a combination of the candidates was best for expected Jaccard index and exact match.
Anomaly Detection through Unsupervised Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning (FL) is proving to be one of the most promising paradigms for leveraging distributed resources, enabling a set of clients to collaboratively train a machine learning model while keeping the data decentralized. The explosive growth of interest in the topic has led to rapid advancements in several core aspects like communication efficiency, handling non-IID data, privacy, and security capabilities. However, the majority of FL works only deal with supervised tasks, assuming that clients' training sets are labeled. To leverage the enormous unlabeled data on distributed edge devices, in this paper, we aim to extend the FL paradigm to unsupervised tasks by addressing the problem of anomaly detection in decentralized settings. In particular, we propose a novel method in which, through a preprocessing phase, clients are grouped into communities, each having similar majority (i.e., inlier) patterns. Subsequently, each community of clients trains the same anomaly detection model (i.e., autoencoders) in a federated fashion. The resulting model is then shared and used to detect anomalies within the clients of the same community that joined the corresponding federated process. Experiments show that our method is robust, and it can detect communities consistent with the ideal partitioning in which groups of clients having the same inlier patterns are known. Furthermore, the performance is significantly better than those in which clients train models exclusively on local data and comparable with federated models of ideal communities' partition.
Conversion of Acoustic Signal (Speech) Into Text By Digital Filter using Natural Language Processing
Venue: arXiv cs.AI 2022
Year: 2022

One of the most crucial aspects of communication in daily life is speech recognition. Speech recognition that is based on natural language processing is one of the essential elements in the conversion of one system to another. In this paper, we created an interface that transforms speech and other auditory inputs into text using a digital filter. Contrary to the many methods for this conversion, it is also possible for linguistic faults to appear occasionally, gender recognition, speech recognition that is unsuccessful (cannot recognize voice), and gender recognition to fail. Since technical problems are involved, we developed a program that acts as a mediator to prevent initiating software issues in order to eliminate even this little deviation. Its planned MFCC and HMM are in sync with its AI system. As a result, technical errors have been avoided.
Non-isothermal direct bundle simulation of SMC compression molding with a non-Newtonian compressible matrix
Venue: arXiv cs.CE 2022
Year: 2022

Compression molding of Sheet Molding Compounds (SMC) is a manufacturing process in which a stack of discontinuous fiber-reinforced thermoset sheets is formed in a hot mold. The reorientation of fibers during this molding process can be either described by macroscale models based on Jeffery's equation or by direct mesoscale simulations of individual fiber bundles. In complex geometries and for long fibers, direct bundle simulations outperform the accuracy of state-of-the-art macroscale approaches in terms of fiber orientation and fiber volume fraction. However, it remains to be shown that they are able to predict the necessary compression forces considering non-isothermal, non-Newtonian and compaction behavior. In this contribution, both approaches are applied to the elongational flow in a press rheometer and compared to experiments with 23% glass fiber volume fraction. The results show that both models predict contributions to the total compression force and orientation reasonably well for short flow paths. For long flow paths and thick stacks, complex deformation mechanisms arise and potential origins for deviation between simulations models and experimental observations are discussed. Furthermore, Jeffery's basic model is able to predict orientations similar to the high-fidelity mesoscale model. For planar SMC flow, this basic model appears to be even better suited than the more advanced orientation models with diffusion terms developed for injection molding.