Reviewerly/exHarmony huggingface dataset:
Analysis of Forced Aligner Performance on L2 English Speech

There is growing interest in how speech technologies perform on L2 speech. Largely omitted from this discussion are tools used in the early data processing steps, such as forced aligners, that can introduce errors and biases. This study adds to the conversation and tests how well a model pre-trained for the alignment of L1 American English speech performs on L2 English speech. We test and discuss the impact of language variety, demographic factors, and segment type on the performance of the forced aligner. We also examine systematic errors encountered. Forty-five speakers representing nine L2 varieties were selected from the Speech Accent Archive and force aligned using the Montreal Forced Aligner. The phoneme-level boundary placements were manually corrected in order to assess differences between the automatic and manual alignments. Results show marked variation in the performance across language groups and segment types for the two metrics used to assess accuracy: Onset Boundary Displacement, a distance metric between the automatic and manual boundary placements, and Overlap Rate, which indicates to what extent the automatically aligned segment overlaps with the manually aligned segment. The highest accuracy on both measures was obtained for German and French, and lowest accuracy for Russian. The aligner's performance on all varieties was comparable to that on conversational American English and non-standard varieties of English. Furthermore, the percentage of boundary placements within 10 and 20ms of the corrected boundary was similar to that observed between transcribers. Apart from errors due to variety mismatch, most issues encountered in the alignment were due to issues not exclusive to L2 speech such as inaccurate orthographic transcriptions, hesitations, specific voice qualities, and background noise. The results of this study can inform the use of automatic aligners on L2 English speech and provide a baseline of potential errors and information to help the development of more robust alignment tools for further development of automatic systems using L2 English.
The role of lifestyle factors, biological sex, and racial identity for (Visually Induced) Motion Sickness Susceptibility: Insights from an online survey

Motion sickness (MS) and visually induced motion sickness (VIMS) are common side-effects when travelling or when using visual devices, respectively. A variety of individual factors may determine one’s susceptibility to MS/VIMS. The role of lifestyle factors including video-game usage, physical activity, diet, and substance use on self-reported susceptibility to MS/VIMS was investigated. Additionally, the role of biological sex and racial identity on the overall prevalence of MS/VIMS were analyzed. A total of 711 responses to an online survey were collected from adults between the ages of 17-49 years. Method of analyses included correlations, comparison tests, and multiple regressions. The results showed that overall, VIMS is a prevalent issue for most participants, with approximately 24% of users reporting nausea at least sometimes when using visual displays. Although some lifestyle factors (video game usage, assorted dietary elements) were identified as potentially relevant for MS/VIMS susceptibility, these factors only explained a minimal amount of the variance in MS/VIMS susceptibility. Significant sex differences were found, with female participants reporting higher susceptibility for MS/VIMS than male participants. Additionally, East Asians participants reported higher MS susceptibility. Our findings do not suggest that lifestyle factors are prominent predictors for the susceptibility of VIMS and MS.
Shape-constrained deformable brain segmentation: Methods and quantitative validation

MRI-guided neuro interventions require rapid, accurate, and reproducible segmentation of anatomical brain structures for identification of targets during surgical procedures and post-surgical evaluation of intervention efficiency. Segmentation algorithms must be validated and cleared for clinical use. This work introduces a methodology for shape-constrained deformable brain segmentation, describes the quantitative validation used for its clinical clearance, and presents a comparison with manual expert segmentation and FreeSurfer, an open source software for neuroimaging data analysis. ClearPoint Maestro is software for fully-automatic brain segmentation from T1-weighted MRI that combines a shape-constrained deformable brain model with voxel-wise tissue segmentation within the cerebral hemispheres and the cerebellum. The performance of the segmentation was validated in terms of accuracy and reproducibility. Segmentation accuracy was evaluated with respect to training data and independently traced ground truth. Segmentation reproducibility was quantified and compared with manual expert segmentation and FreeSurfer. Quantitative reproducibility analysis indicates superior performance compared to both manual expert segmentation and FreeSurfer. The shape-constrained methodology results in accurate and highly reproducible segmentation. Inherent point based-correspondence provides consistent target identification ideal for MRI-guided neuro interventions.
A framework for efficient cross-chain token transfers in blockchain networks

The proliferation of blockchain technology has resulted in diverse token standards, posing challenges for compatibility, security, and performance in existing cross-chain bridges. This paper introduces a novel framework capable of concurrently facilitating fungible token exchange, as well as the processing of both individual and batch non-fungible tokens (NFTs). We deploy token bridges that meet different token standards to support cross-chain staking and unlocking of ERC20, ERC721, and ERC1155. To minimize both waiting times and handling fees, we relocate processes necessitating frequent transactions and verifications to the sidechain. Additionally, we adopt a batch-processing approach for tokens necessitating cross-chain transfers, leveraging payment channels to facilitate efficiency. The system’s reliability is upheld through the validator group. Validators acquire an initial reputation value by making deposits and enhance both their rewards and reputation by successfully completing NFT auction tasks on the sidechain. We use OpenZeppelin’s security library functions to standardize token operations, and carefully design the validator’s reward, punishment, and reputation mechanisms. Our comprehensive contract security audit and system analysis validate our solution’s effectiveness in mitigating common vulnerabilities and internal threats. Implementation and testing with Ethereum and its test network demonstrate substantial reductions in transmission time for key cross-chain token steps by nearly half. Moreover, our framework showcases efficiency and cost-effectiveness with an average gas cost of 693,379.
Cross-validated tree-based models for multi-target learning

Multi-target learning (MTL) is a popular machine learning technique which considers simultaneous prediction of multiple targets. MTL schemes utilize a variety of methods, from traditional linear models to more contemporary deep neural networks. In this work we introduce a novel, highly interpretable, tree-based MTL scheme which exploits the correlation between the targets to obtain improved prediction accuracy. Our suggested scheme applies cross-validated splitting criterion to identify correlated targets at every node of the tree. This allows us to benefit from the correlation among the targets while avoiding overfitting. We demonstrate the performance of our proposed scheme in a variety of synthetic and real-world experiments, showing a significant improvement over alternative methods. An implementation of the proposed method is publicly available at the first author's webpage.
Cenozoic morphotectonic evolution of the eastern edge of Paraná Sedimentary Basin, southern Brazil

The Paraná Sedimentary Basin (PSB) is one of the most important geotectonic structures on the South American continent and its tectono-sedimentary evolution has been widely investigated, especially during the Paleozoic and Mesozoic. However, the post-rift Cenozoic dynamics need further studies to fully understand its evolution. Hence, this research aims to add data and interpretations regarding the morphostructural features inherent in the Cenozoic tectonic framework, especially in the south/southeast region of Brazil. Here, we explore the Cenozoic processes of tectonic reactivation of preexisting structural features, as well as the neoformation of structural features, considering that Mesozoic sedimentary and volcanic strata of the Serra Geral Group in the Paraná Basin were deformed by Cenozoic joints and faults. For this purpose, structural and geomorphological (morphometric) analyses were used in the Itararé and Cinzas rivers catchments, on the eastern edge of the PSB. The study area is part of the Ponta Grossa Arch domain, in the northeast of Paraná state and southwest of São Paulo state. The structural data collected, when associated with the morphometric analyses (qualitative and quantitative), were compatible, showing the influence of tectonic structuring on the geomorphological framework of the investigated watersheds, resulting in the morphotectonic compartmentalization of the surveyed area. Alterations to the topographic surface, asymmetry patterns in the watersheds, disturbances in the longitudinal river profiles and anomalies in the drainage network patterns are the result of reactivation of ancient NE-SW and NW-SE faults in the crystalline basement and present in the units that fill the sedimentary basin, as well as new families of faults, mainly in N–S and E-W direction. The structural analysis supported the determination of the Cenozoic paleostress fields, defined from the analysis of the fault population, spatial arrangement and through statistical and mechanical treatment of the kinematic structural data. The results of the paleostress tensors indicate a landscape evolution associated with at least four tectonic pulses, which compressive SHmax values are oriented in NE-SW, N–S, NW-SE and E-W directions, respectively. These are in agreement with the main regional scientific studies in southern and southeastern Brazil, which involve tectonic events from the Upper Cretaceous and Paleogene, advancing into the Neogene and up to at least the Pleistocene.
Graph Neural Network contextual embedding for Deep Learning on tabular data

All industries are trying to leverage Artificial Intelligence (AI) based on their existing big data which is available in so called tabular form, where each record is composed of a number of heterogeneous continuous and categorical columns also known as features. Deep Learning (DL) has constituted a major breakthrough for AI in fields related to human skills like natural language processing, but its applicability to tabular data has been more challenging. More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modelling interactions among tabular features. Its results outperform those of a recently published survey with DL benchmark based on seven public datasets, also achieving competitive results when compared to boosted-tree solutions.
Data-driven model selections of second-order particle dynamics via integrating Gaussian processes with low-dimensional interacting structures

In this paper, we focus on the data-driven discovery of a general second-order particle-based model that contains many state-of-the-art models for modeling the aggregation and collective behavior of interacting agents of similar size and body type. This model takes the form of a high-dimensional system of ordinary differential equations parameterized by two interaction kernels that appraise the alignment of positions and velocities. We propose a Gaussian Process-based approach to this problem, where the unknown model parameters are marginalized by using two independent Gaussian Process (GP) priors on latent interaction kernels constrained to dynamics and observational data. This results in a nonparametric model for interacting dynamical systems that accounts for uncertainty quantification. We also show that our estimators can be efficiently derived through the use of preconditioned techniques, ensuring scalability. Moreover, we perform a theoretical analysis to interpret the methodology and investigate the conditions under which the kernels can be recovered. We demonstrate the effectiveness of the proposed approach on various prototype systems, including the selection of the order of the systems and the types of interactions. In particular, we present applications to modeling two real-world fish motion datasets that display flocking and milling patterns up to 248 dimensions. Despite the use of small data sets, the GP-based approach learns an effective representation of the nonlinear dynamics in these spaces and outperforms competitor methods.
Pedagogy and technology: the transformative impact of digital tools in education

This bibliographic review addressed the transformative impact of digital tools on education, focusing on the interplay between pedagogy and technology. The central problem was to understand how technology reshapes education, both in pedagogical practices and in the development of 21st-century skills. The main objective was to explore the nature and extent of this impact, considering opportunities and challenges for students and teachers. The methodology adopted was a literature review, allowing a critical and comprehensive analysis of relevant publications. The results showed that digital technologies offer significant opportunities to innovate in education, but also present challenges such as unequal access, the need for teacher training, and the adaptation of methodologies. Successful experiences highlighted how effective integration of technology can promote critical skills and educational inclusion. It was concluded that technology is a transformative element in education, capable of improving the quality and effectiveness of teaching. However, overcoming existing barriers is essential to maximize its potential. The research underlined the continuous need for innovation and investigation in educational technology.
Few-Shot Semantic Segmentation via Mask Aggregation

Abstract Few-shot semantic segmentation aims to recognize novel classes with only very few labelled data. This challenging task requires mining of the correlation between the query image and the support images. Previous works have typically regarded it as a pixel-wise classification problem. Therefore, various models have been designed to explore the correlation of pixels between the query image and the support images. However, they focus only on pixel-wise correspondence and ignore the overall correlation of objects. In this paper, we introduce a mask-based classification method for addressing this problem. The mask aggregation network, which is a simple mask classification model, is proposed to simultaneously generate a fixed number of masks and their probabilities of being targets. Then, the final segmentation result is obtained by aggregating all the masks according to their locations. Experiments on both the PASCAL- $$5^i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> and COCO- $$20^i$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>20</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> datasets show that our method performs comparably to the state-of-the-art pixel-based methods. This competitive performance demonstrates the potential of mask classification as an alternative baseline method for few-shot semantic segmentation.
Service Level Agreement in cloud computing: Taxonomy, prospects, and challenges

Cloud computing represents a distributed computing paradigm, offering hosted services via the internet on a pay-as-you-go basis. The Service Level Agreement (SLA) serves as a pivotal element of communication between Cloud Service Providers (CSPs) and Cloud Service Users (CSUs), detailing Service Level Objectives (SLOs) pertinent to service functionalities and Quality of Service (QoS). Within an SLA, service terms are formally documented in a contract between the CSP and CSU, ensuring that outsourcing customers establish an SLA with their vendors, thereby holding vendors accountable for financial consequences or payments should the set objectives not be achieved. In recent years, SLA methodologies in cloud computing have attracted considerable attention from the research community, with numerous strategies being developed to tackle challenges that may impede the efficient provisioning and management of QoS. This survey paper offers a comprehensive review of SLA techniques, presenting a detailed taxonomy based on their distinctive attributes. It discusses the evaluation parameters and platforms utilized in analyzing SLA approaches. Furthermore, the paper outlines design objectives and highlights open research issues that should be addressed when proposing new SLA techniques.
Bayesian Nonparametric Multivariate Mixture of Autoregressive Processes with Application to Brain Signals

One of neuroscience's goals is to study the interactions between different brain regions during rest and while performing specific cognitive tasks. Multivariate Bayesian autoregressive decomposition (MBMARD) is proposed as an intuitive and novel Bayesian non-parametric model to represent high-dimensional signals as a low-dimensional mixture of univariate uncorrelated latent oscillations. Each latent oscillation captures a specific underlying oscillatory activity and, hence, is modeled as a unique second-order autoregressive process due to a compelling property, namely, that its spectral density's shape is characterized by a unique frequency peak and bandwidth, parameterized by a location and a scale parameter. The posterior distributions of the latent oscillation parameters are computed using a Metropolis-within-Gibbs algorithm. One of the advantages of the MBMARD model is its higher robustness against misspecification, compared with standard models. The main scientific questions addressed by the MBMARD model relate to the effects of long-term alcohol abuse on memory. These effects were examined by analyzing the electroencephalogram records of alcoholic and non-alcoholic subjects performing a visual recognition experiment. The MBMARD model yielded novel and interesting findings, including the identification of subject-specific clusters of low- and high-frequency oscillations in different brain regions.
Average consensus of whole-process privacy protection: A scale parameter method

Privacy-preserving average consensus is a distributed control/estimation framework where a group of agents cooperatively compute the average of initial state. Meanwhile, the initial state and real-time state (such as initial opinion, initial location, real-time location, real-time active power ratio) in the communication and computation process may contain some sensitive information, which do not want other nodes of the network to obtain. However, existing privacy protection results mainly focused on protecting the initial state, and rarely took into account the privacy effect from the beginning to the end. To deal with this problem, a novel and interesting scale parameter method is proposed. First, a group of privacy coefficients is designed and assigned in advance for each node, which ensures that only their own parameters can be known and has no knowledge of others. Correspondingly, the initial state and real-time state can be hidden in the false value after multiplying these scale parameters. Then, the correctness analysis and privacy analysis of this method are provided respectively. Later, the generalization on directed networks is discussed. Finally, numerical simulations are conducted to show the effectiveness of obtained theoretical results.
Network traffic classification based on federated semi-supervised learning

Traffic Classification (TC) has been applied to a wide range of applications, from security monitoring to quality of service (QoS) provisioning in network Internet Service Providers (ISPs). In recent years, many researchers have applied Machine Learning (ML) or Deep Learning (DL) to TC, namely AI-TC. However, AI-TC methods face significant challenges, including high data dependency, exhaustively costly traffic labeling, and network subscribers’ privacy. This paper proposes a TC framework for smart home networks using Federated Learning (FL) that protects traffic data privacy by performing local training and inference of TC models. Firstly, we design a DPI-based traffic labeling method on edge home gateways as FL nodes, which enables these nodes to have data labeling capability while protecting data privacy. Then, a semi-supervised TC model based on an autoencoder (AE) is proposed to reduce the dependence of the model on labeled traffic samples. Finally, an XAI-based method is utilized to interpret the model to ensure its explainability. We validate the proposed method on public and real datasets using benchmarking methods. The experimental results show that the method can achieve high performance using a small number of samples while protecting data privacy and improving the model’s credibility. Experimental code can be found in the following url: https://github.com/PrinceXuan12138/HGW-TC-Experimental-code.
Institute for Artificial Intelligence and Fundamental Interactions (IAIFI): Infusing physics intelligence into artificial intelligence

Abstract The NSF AI Institute for Artificial Intelligence and Fundamental Interactions (IAIFI, pronounced /aI‐faI/) is one of the inaugural NSF AI research institutes ( https://iaifi.org ). The IAIFI is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles from fundamental physics. By combining state‐of‐the‐art research with early career talent and a growing AI + physics community in the Boston area and beyond, the IAIFI is enabling researchers to develop AI technologies to tackle some of the most challenging problems in physics, and transfer these technologies to the broader AI community. Since trustworthy AI is as important for physics discovery as it is for other applications of AI in society, IAIFI researchers are applying physics principles to develop more robust AI tools and to illuminate existing AI technologies. To cultivate human intelligence, the IAIFI promotes training, education, and public engagement at the intersection of physics and AI. In these ways, the IAIFI is fusing deep learning with deep thinking to gain a deeper understanding of our universe and AI.