Miracle-dz/UniFAR-FTUs huggingface dataset:
Unveiling Emotions from EEG: A GRU-Based Approach

One of the most important study areas in affective computing is emotion identification using EEG data. In this study, the Gated Recurrent Unit (GRU) algorithm, which is a type of Recurrent Neural Networks (RNNs), is tested to see if it can use EEG signals to predict emotional states. Our publicly accessible dataset consists of resting neutral data as well as EEG recordings from people who were exposed to stimuli evoking happy, neutral, and negative emotions. For the best feature extraction, we pre-process the EEG data using artifact removal, bandpass filters, and normalization methods. With 100% accuracy on the validation set, our model produced outstanding results by utilizing the GRU's capacity to capture temporal dependencies. When compared to other machine learning techniques, our GRU model's Extreme Gradient Boosting Classifier had the highest accuracy. Our investigation of the confusion matrix revealed insightful information about the performance of the model, enabling precise emotion classification. This study emphasizes the potential of deep learning models like GRUs for emotion recognition and advances in affective computing. Our findings open up new possibilities for interacting with computers and comprehending how emotions are expressed through brainwave activity.
Prompt Guided Copy Mechanism for Conversational Question Answering

Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions. In this paper, we propose a pluggable approach for extractive methods that introduces a novel prompt-guided copy mechanism to improve the fluency and appropriateness of the extracted answers. Our approach uses prompts to link questions to answers and employs attention to guide the copy mechanism to verify the naturalness of extracted answers, making necessary edits to ensure that the answers are fluent and appropriate. The three prompts, including a question-rationale relationship prompt, a question description prompt, and a conversation history prompt, enhance the copy mechanism's performance. Our experiments demonstrate that this approach effectively promotes the generation of natural answers and achieves good results in the CoQA challenge.
Discrete weak duality of hybrid high-order methods for convex minimization problems

This paper derives a discrete dual problem for a prototypical hybrid high-order method for convex minimization problems. The discrete primal and dual problem satisfy a weak convex duality that leads to a priori error estimates with convergence rates under additional smoothness assumptions. This duality holds for general polytopal meshes and arbitrary polynomial degree of the discretization. A nouvelle postprocessing is proposed and allows for a~posteriori error estimates on simplicial meshes using primal-dual techniques. This motivates an adaptive mesh-refining algorithm, which performs superiorly compared to uniform mesh refinements.
DiCuPIT: Distributed Cuckoo Filter-based Pending Interest Table

Named data networking is one of the recommended {\color{red}architectures} for the future of the Internet. In this communication architecture, the content name is used instead of the IP address. To achieve this purpose, a new data structure is added to the nodes of named data networking which is called Pending Interest Table (PIT). Scalability, memory consumption, and integration are the significant challenges in PIT design {\color{red} as} it needs to be updated for each packet, and it saves the name of the packet. This paper introduces a new data structure for PIT called DiCuPIT. DiCuPIT is a distributed data structure for the PIT table, {\color{red} that works} based on the Cuckoo filter and can cover the three features as above-mentioned. {\color{red} By} implementing this PIT, {\color{red} the lookup} time shows {\color{red} a 36\% reduction} compared to the methods based on the Bloom filter and 40\% based on hash tables. Moreover, the memory consumption is reduced by 68\% compared to the hash tables-based mechanisms and 31\% compared to the methods based on the Bloom filter.
Canadian healthcare workers’ mental health and health behaviours during the COVID-19 pandemic: results from nine representative samples between April 2020 and February 2022

In the context of COVID-19, Canadian healthcare workers (HCWs) worked long hours, both to respond to the pandemic and to compensate for colleagues who were not able to work due to infection and burnout. This may have had detrimental effects on HCWs’ mental health, as well as engagement in health-promoting behaviours. This study aimed to identify changes in mental health outcomes and health behaviours experienced by Canadian HCWs throughout the COVID-19 pandemic. Nine representative samples (Ntotal = 1615 HCWs) completed the iCARE survey using an online polling firm between April 2020 (Time 1) and February 2022 (Time 9). Participants were asked about the psychological effects of COVID-19 (e.g., feeling anxious) and about changes in their health behaviours (e.g., alcohol use, physical activity). A majority of the HCWs identified as female (65%), were younger than 44 years old (66%), and had a university degree (55%). Female HCWs were more likely than male HCWs to report feeling anxious (OR = 2.68 [1.75, 4.12]), depressed (OR = 1.63 [1.02, 2.59]), and irritable (OR = 1.61 [1.08, 2.40]) throughout the first two years of the pandemic. Female HCWs were more likely than their male counterparts to report eating more unhealthy diets (OR = 1.54 [1.02, 2.31]). Significant differences were also revealed by age, education level, income, parental status, health status, and over time. Results demonstrate that the impacts of COVID-19 on HCWs’ mental health and health behaviours were significant, and varied by sociodemographic characteristics (e.g., sex, age, income).
Health Disparity Curricula for Ophthalmology Residents: Current Landscape, Barriers, and Needs

Abstract Background Social determinants of health play a critical role in visual health outcomes. Yet, there exists no structured curriculum for ophthalmology residents to identify and address health disparities relevant to eye care or no a standard assessment of health disparities education within ophthalmology residency programs. This study aims to characterize current health disparity curricula in ophthalmology residency programs in the United States, determine resident confidence in addressing health disparities in the clinical setting, and identify perceived barriers and needs of program directors (PDs) and residents in this area. Design This was a cross-sectional survey study. Methods A closed-ended questionnaire with comments was distributed to the Accreditation Council for Graduate Medical Education-accredited ophthalmology residency PDs and residents in April 2021 and May 2022. The questionnaire solicited characteristics of any existing health disparity curricula, PD and resident perceptions of these curricula, and residents' experience with and confidence in addressing health disparities in the delivery of patient care. Results In total, 29 PDs and 96 residents responded. Sixty-six percent of PDs stated their program had a formal curriculum compared to fifty-three percent of residents. Forty-one percent of PDs and forty-one percent of residents stated their program places residents in underserved care settings for more than 50% of their training. Most residents (72%) were confident in recognizing health disparities. Sixty-six percent were confident in managing care in the face of disparities and fifty-nine percent felt they know how to utilize available resources. Residents were most concerned with the lack of access to resources to help patients. Forty-five percent of PDs felt the amount of time dedicated to health disparities education was adequate. Forty-nine percent of residents reported they felt the amount of training they received on health disparities to be adequate. The top barrier to curriculum development identified by PDs was the availability of trained faculty to teach. Time in the curriculum was a major barrier identified by residents. Conclusions Roughly half of ophthalmology residency programs who responded had a health disparity curriculum; however, both PDs and residents felt inadequate time is dedicated to such education. National guidance on structured health disparity curricula for ophthalmology residents may be warranted as a next step.
Exploring the Physical World Adversarial Robustness of Vehicle Detection

Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-intensive experiments. Virtual simulations offer an alternative, but the absence of standardized benchmarks hampers progress. Addressing this, we propose an innovative instant-level data generation pipeline using the CARLA simulator. Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks. Our findings highlight diverse model performances under adversarial conditions. YOLO v6 demonstrates remarkable resilience, exhibiting just a marginal 6.59% average drop in average precision (AP). In contrast, the ASA attack yields a substantial 14.51% average AP reduction, twice the effect of other algorithms. We also note that static scenes yield higher recognition AP values, and outcomes remain relatively consistent across varying weather conditions. Intriguingly, our study suggests that advancements in adversarial attack algorithms may be approaching its “limitation”. In summary, our work underscores the significance of adversarial attacks in real-world contexts and introduces the DCI dataset as a versatile benchmark. Our findings provide valuable insights for enhancing the robustness of detection models and offer guidance for future research endeavors in the realm of adversarial attacks.
A Short Note on the Potential of Utilization of Spectral AERONET-Derived Depolarization Ratios for Aerosol Classification

We herein present the spectral linear particle depolarization ratios (δp) from an Aerosol Robotics NETwork (AERONET) sun/sky radiometer with respect to the aerosol type. AERONET observation sites, which are representative of each aerosol type, were selected for our study. The observation data were filtered using the Ångström exponent (Å), fine-mode fraction (FMF) and single scattering albedo (ω) to ensure that the obtained values of δp were representative of each aerosol condition. We report the spectral δp values provided in the recently released AERONET version 3 inversion product for observation of the following aerosol types: dust, polluted dust, smoke, non-absorbing, moderately-absorbing and high-absorbing pollution. The AERONET-derived δp values were generally within the range of the δp values measured from lidar observations for each aerosol type. In addition, it was found that the spectral variation of δp differed according to the aerosol type. From the obtained results, we concluded that our findings provide potential insight into the identification and classification of aerosol types using remote sensing techniques.
Network Security in the Industrial Control System: A Survey

Along with the development of intelligent manufacturing, especially with the high connectivity of the industrial control system (ICS), the network security of ICS becomes more important. And in recent years, there has been much research on the security of the ICS network. However, in practical usage, there are many types of protocols, which means a high vulnerability in protocols. Therefore, in this paper, we give a complete review of the protocols that are usually used in ICS. Then, we give a comprehensive review on network security in terms of Defence in Depth (DiD), including data encryption, access control policy, intrusion detection system, software-defined network, etc. Through these works, we try to provide a new perspective on the exciting new developments in this field.
BioBERT Based SNP-traits Associations Extraction from Biomedical Literature

Scientific literature contains a considerable amount of information that provides an excellent opportunity for developing text mining methods to extract biomedical relationships. An important type of information is the relationship between singular nucleotide polymorphisms (SNP) and traits. In this paper, we present a BioBERT-GRU method to identify SNP- traits associations. Based on the evaluation of our method on the SNPPhenA dataset, it is concluded that this new method performs better than previous machine learning and deep learning based methods. BioBERT-GRU achieved the result a precision of 0.883, recall of 0.882 and F1-score of 0.881.
XAI in Automated Fact-Checking? The Benefits Are Modest And There's No One-Explanation-Fits-All

Fact-checking is a popular countermeasure against misinformation but the massive volume of information online has spurred active research in the automation of the task. Like expert fact-checking, it is not enough for an automated fact-checker to just be accurate, but also be able to inform and convince the user of the validity of its prediction. This becomes viable with explainable artificial intelligence (XAI). In this work, we conduct a study of XAI fact-checkers involving 180 participants to determine how users' actions towards news and their attitudes towards explanations are affected by the XAI. Our results suggest that XAI has limited effects on users' agreement with the veracity prediction of the automated fact-checker and on their intents to share news. However, XAI does nudge them towards forming uniform judgments of news veracity, thereby signaling a reliance on the explanations. We also found polarizing preferences towards XAI, raising several design considerations on these.
Knowledge-Aware Collaborative Filtering With Pre-Trained Language Model for Personalized Review-Based Rating Prediction

Personalized review-based rating prediction aims at leveraging existing reviews to model user interests and item characteristics for rating prediction. Most of the existing studies mainly encounter two issues. First, the rich knowledge contained in the fine-grained aspects of each review and the knowledge graph is rarely considered to complement the pure text for better modeling user-item interactions. Second, the power of pre-trained language models is not carefully studied for personalized review-based rating prediction. To address these issues, we propose an approach named Knowledge-aware Collaborative Filtering with Pre-trained Language Model (KCF-PLM). For the first issue, to utilize rich knowledge, KCF-PLM develops a transformer network to model the interactions of the extracted aspects w.r. t. a user-item pair. For the second issue, to better represent users and items, KCF-PLM takes all the historical reviews of a user or an item as input to pre-trained language models. Moreover, KCF-PLM integrates the transformer network and the pre-trained language models through representation propagation on the knowledge graph and user-item guided attention of the aspect representations. Thus KCF-PLM combines review text, aspect, knowledge graph, and pre-trained language models together for review-based rating prediction. We conduct comprehensive experiments on several public datasets, demonstrating the effectiveness of KCF-PLM.
Towards Autocomplete Strategies for Visualization Construction

Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality.A free copy of this paper and all supplemental materials are available on our online repository: https://osf.io/nu4z3/? view_only= 594baee54d114a99ab381886fb32a126.
Graphene-based RRAM devices for neural computing

Resistive random access memory is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to build highly accurate crossbar arrays. Traditional RRAM designs make use of various filament-based oxide materials for creating a channel that is sandwiched between two electrodes to form a two-terminal structure. They are often subjected to mechanical and electrical stress over repeated read-and-write cycles. The behavior of these devices often varies in practice across wafer arrays over these stresses when fabricated. The use of emerging 2D materials is explored to improve electrical endurance, long retention time, high switching speed, and fewer power losses. This study provides an in-depth exploration of neuro-memristive computing and its potential applications, focusing specifically on the utilization of graphene and 2D materials in RRAM for neural computing. The study presents a comprehensive analysis of the structural and design aspects of graphene-based RRAM, along with a thorough examination of commercially available RRAM models and their fabrication techniques. Furthermore, the study investigates the diverse range of applications that can benefit from graphene-based RRAM devices.
Learning-based Parameter Optimization for a Class of Orbital Tracking Control Laws

This paper presents a learning algorithm for tuning the parameters of a family of stabilizing nonlinear controllers for orbital tracking, in order to minimize a cost function which combines convergence time and fuel consumption. The main feature of the proposed approach is that it achieves performance optimization while guaranteeing closed-loop stability of the resulting controller. This property is exploited also to restrict the class of admissible controllers and hence to expedite the training process. The learning algorithm is tested on three case studies: two different orbital transfers and a rendezvous mission. Numerical simulations show that the learned control parameters lead to a significant improvement of the considered performance measure.
Spatially Varying Nanophotonic Neural Networks

The explosive growth of computation and energy cost of artificial intelligence has spurred strong interests in new computing modalities as potential alternatives to conventional electronic processors. Photonic processors that execute operations using photons instead of electrons, have promised to enable optical neural networks with ultra-low latency and power consumption. However, existing optical neural networks, limited by the underlying network designs, have achieved image recognition accuracy far below that of state-of-the-art electronic neural networks. In this work, we close this gap by embedding massively parallelized optical computation into flat camera optics that perform neural network computation during the capture, before recording an image on the sensor. Specifically, we harness large kernels and propose a large-kernel spatially-varying convolutional neural network learned via low-dimensional reparameterization techniques. We experimentally instantiate the network with a flat meta-optical system that encompasses an array of nanophotonic structures designed to induce angle-dependent responses. Combined with an extremely lightweight electronic backend with approximately 2K parameters we demonstrate a reconfigurable nanophotonic neural network reaches 72.76\% blind test classification accuracy on CIFAR-10 dataset, and, as such, the first time, an optical neural network outperforms the first modern digital neural network -- AlexNet (72.64\%) with 57M parameters, bringing optical neural network into modern deep learning era.
Towards Improving Harmonic Sensitivity and Prediction Stability for Singing Melody Extraction

In deep learning research, many melody extraction models rely on redesigning neural network architectures to improve performance. In this paper, we propose an input feature modification and a training objective modification based on two assumptions. First, harmonics in the spectrograms of audio data decay rapidly along the frequency axis. To enhance the model's sensitivity on the trailing harmonics, we modify the Combined Frequency and Periodicity (CFP) representation using discrete z-transform. Second, the vocal and non-vocal segments with extremely short duration are uncommon. To ensure a more stable melody contour, we design a differentiable loss function that prevents the model from predicting such segments. We apply these modifications to several models, including MSNet, FTANet, and a newly introduced model, PianoNet, modified from a piano transcription network. Our experimental results demonstrate that the proposed modifications are empirically effective for singing melody extraction.
The effect of Quantum Statistics on the sensitivity in an SU(1,1) interferometer

We theoretically study the effect of quantum statistics of the light field on the quantum enhancement of parameter estimation based on cat state input the SU(1,1) interferometer. The phase sensitivity is dependent on the relative phase $\theta$ between two coherent states of Schr\"{o}dinger cat states. The optimal sensitivity is achieved when the relative phase is $\pi$% , i.e., odd coherent states input. For a coherent state input into one port, the phase sensitivity of the odd coherent state into the second input port is inferior to that of the squeezed vacuum state input. However, in the presence of losses the Schr\"{o}dinger cat states are more resistant to loss than squeezed vacuum states. As the amplitude of Schr\"{o}dinger cat states increases, the quantum enhancement of phase sensitivity decreases, which shows that the quantum statistics of Schr\"{o}dinger cat states tends towards Poisson statistics from sub-Poisson statistics or super-Poisson statistics.
SeACo-Paraformer: A Non-Autoregressive ASR System with Flexible and Effective Hotword Customization Ability

Hotword customization is one of the concerned issues remained in ASR field - it is of value to enable users of ASR systems to customize names of entities, persons and other phrases to obtain better experience. The past few years have seen effective modeling strategies for ASR contextualization developed, but they still exhibit space for improvement about training stability and the invisible activation process. In this paper we propose Semantic-Augmented Contextual-Paraformer (SeACo-Paraformer) a novel NAR based ASR system with flexible and effective hotword customization ability. It possesses the advantages of AED-based model's accuracy, NAR model's efficiency, and explicit customization capacity of superior performance. Through extensive experiments with 50,000 hours of industrial big data, our proposed model outperforms strong baselines in customization. Besides, we explore an efficient way to filter large-scale incoming hotwords for further improvement. The industrial models compared, source codes and two hotword test sets are all open source.