Reviewerly/exHarmony huggingface dataset:
The psychological and behavioral impact of secondary school students during the examination period

This study aims to know the views and impressions of high school students during the exam period, what are their behaviors and psychology towards recalling their lessons during the exam period, and do they maintain or divide the periods of sleep, study and eat meals, and do they maintain their activity and the extent of their study hours for the test subjects, and do they get a lot of stimuli during their review of their lessons because of their fear and the extent of their anxiety about failing and fearing the consequences from the parents.An electronic question was handed through WhatsApp friendly networking implementation, where it was doled out to about 660 people in the city of Mecca, And 550 replied were gained.
UGV Path Programming Based on the DQN With Noise in the Output Layer

The path programming of the unmanned ground vehicle (UGV) was studied under the framework of the deep <i>Q</i>-network (DQN) algorithm. To improve the exploration efficiency, the DQN algorithm was applied through discretization of the continuous state into the discrete state. To balance between exploration and exploitation, the Gaussian noise was added only in the output layer of the network, and a progressive reward function was designed. Finally, experiments were carried out in the Gazebo simulation environment. The simulation results show that, first, this strategy can quickly program a collision-free route from the initial point to the target point, and the convergence speed is significantly higher than those of the <i>Q</i>-learning algorithm, the DQN algorithm and the noisynet_DQN algorithm; second, this strategy has the generalization ability about the initial point, the target point and the obstacles, as well as verified effectiveness and robustness.
A Face Detection and Standardized Mask-Wearing Recognition Algorithm

In the era of coronavirus disease (COVID-19), wearing a mask could effectively protect people from the risk of infection and largely reduce transmission in public places. To prevent the spread of the virus, instruments are needed in public places to monitor whether people are wearing masks, which has higher requirements for the accuracy and speed of detection algorithms. To meet the demand for high accuracy and real-time monitoring, we propose a single-stage approach based on YOLOv4 to identify the face and whether to regulate the wearing of masks. In this approach, we propose a new feature pyramidal network based on the attention mechanism to reduce the loss of object information that can be caused by sampling and pooling in convolutional neural networks. The network is able to deeply mine the feature map for spatial and communication factors, and the multi-scale feature fusion makes the feature map equipped with location and semantic information. Based on the complete intersection over union (CIoU), a penalty function based on the norm is proposed to improve positioning accuracy, which is more accurate at the detection of small objects; the new bounding box regression function is called Norm CIoU (NCIoU). This function is applicable to various object-detection bounding box regression tasks. A combination of the two functions to calculate the confidence loss is used to mitigate the problem of the algorithm bias towards determinating no objects in the image. Moreover, we provide a dataset for recognizing faces and masks (RFM) that includes 12,133 realistic images. The dataset contains three categories: face, standardized mask and non-standardized mask. Experiments conducted on the dataset demonstrate that the proposed approach achieves mAP@.5:.95 69.70% and AP75 73.80%, outperforming the compared methods.
Level Set Interactive Segmentation on Perforated Road Image using Region Based Active Countours Method with im’4

An image will have a lot of interpretation meaning when compared to a text. However, there is nothing wrong with the original image being manipulated to achieve a certain goal. In digital image processing (PCD) there are image manipulation techniques without touching other objects, this is due to the difficulty of separating one object from another. One of the digital image segmentation techniques that is widely used for PCD and computer vision is level set. In this research, the strength of active contour will be tested which will segment area-based objects with potholes using Matlab R2016a. inner region taking into account im = 4 with imresize(mask, 8) iterations of 500.
A Transformer-Based Unsupervised Domain Adaptation Method for Skeleton Behavior Recognition

In recent years, skeleton-based action recognition has received extensive attention, and a large number of researches have achieved excellent performance. This paper investigates on unsupervised domain adaptation (UDA) method (STT-DA) used in skeleton-based action recognition tasks, which is challenging in real scenes. In domain adaptation tasks, the labels are only available on source domain but unavailable on target domain. Different from other traditional approaches for UDA like the adversarial learning-based methods, this paper adapts a Transformer mechanism based on cross-attention to align different domains. It learns from both source and target domain to reduce the domain shift between different skeleton datasets, thus reducing the effect of pseudo-labels errors which is generated in domain adaptation process. Taking the particularity of skeleton data into account, this paper proposes bidirectional normalized alignment algorithm to align skeleton sequences from source and target domain and explore the feature representation in both spatial and temporal dimensions. Especially, it focuses on the adjacency dependency of skeleton joints, that is, each joint is a weight summary of adjacent joints. It enables the network to pay attention to the global characteristics of skeleton data and consider the local characteristics of joint connections. Meanwhile, skeleton sequences are divided into several parts, called <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$subs$ </tex-math></inline-formula> , to reduce the time cost of the model. We conduct experiments on five datasets for skeleton-based action recognition, including two large-scale datasets (NTU RGB+D, NW-UCLA). Extensive results demonstrate that the accuracy of the proposed method (STT-DA) reaches 82.5% on NTU <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> UCLA domain adaptation task and it also performs better on other datasets. With the application of skeleton sequences alignment algorithm and local attention weights, the accuracy improves largely.
EXPONENTIAL STABILITY OF BAM-TYPE NEURAL NETWORKS WITH CONFORMABLE DERIVATIVE

This paper investigates the fractional exponential stability of bidirectional associative memory (BAM) neural networks with conformable derivative.Applying the contraction mapping theorem existence and uniqueness of the equilibrium is studied.Furthermore, an appropriate Lyapunov function is stated to get exponential stability.Numerical examples are given to illustrate the obtained results.
E-learning and the factors that influence the fear of failing an academic year in the era of Covid-19

The aim of this research has enabled us to establish the influential relationship between factors associated with e-learning, such as individual impact and depression, and the mediating role in psychological distress and computer anxiety related to the fear of failing an academic year in times of COVID-19 in university students at two universities in the Arequipa region of Peru. The methodology used was developed in two stages; in the first stage, an Exploratory Factor Analysis (EFA) was performed, which enabled us to compare the initial results from the theoretical matrix proposed with the observed matrix; in the following stage, a Confirmatory Factor Analysis (CFA) was performed, as an analytic condition for the factors and to determine the robustness of the proposed model. For this purpose, the Structural Equation Modeling (PLS-SEM) was used, based on partial minimum squares. The sample for the study was made up by 252 students. It is concluded that there are significant influential relationships among the variables observed, such as the individual impact of e-learning, depression, mediated by psychological distress and anxiety for fear of failing the academic year.
GPU Acceleration of Clustering Meta-feature Extraction using RAPIDS

Although machine learning algorithms have been successful when applied to several tasks, the selection of the most suitable for a given dataset is not straightforward. The recommendation of machine learning algorithms can be automated through the use of meta-learning, but this requires efficient methods for the characterizations of datasets, i.e. meta-features extraction. In this work we propose to accelerate the extraction of clustering-based meta-features on GPUs, taking advantage of the optimized libraries and API from the RAPIDS framework. We parallelized a well-known meta-feature extraction tool (MFE) via RAPIDS to accelerate the clustering meta-features extraction process. Our experiment shows that significantly less time is required to complete the extraction, up to 10x faster than the MFE implementation. These results are promising and suggest greater feasibility for large-scale experiments involving meta-learning.
Learning Logical Reasoning Using an Intelligent Tutoring System: A Hybrid Approach to Student Modeling

In our previous works, we presented Logic-Muse as an Intelligent Tutoring System that helps learners improve logical reasoning skills in multiple contexts. Logic-Muse components were validated and argued by experts throughout the designing process (ITS researchers, logicians, and reasoning psychologists). A catalog of reasoning errors (syntactic and semantic) has been established, in addition to an explicit representation of semantic knowledge and the structures and meta-structures underlying conditional reasoning. A Bayesian network with expert validation has been developed and used in a Bayesian Knowledge Tracing (BKT) process that allows the inference of the learner skills. This paper presents an evaluation of the learner-model components in Logic-Muse (a bayesian learner model). We conducted a study and collected data from nearly 300 students who processed 48 reasoning activities. These data were used to develop a psychometric model for initializing the learner's model and validating the structure of the initial Bayesian network. We have also developed a neural architecture on which a model was trained to support a deep knowledge tracing (DKT) process. The proposed neural architecture improves the initial version of DKT by allowing the integration of expert knowledge (through the Bayesian Expert Validation Network) and allowing better generalization of knowledge with few samples. The results show a significant improvement in the predictive power of the learner model. The analysis of the results of the psychometric model also illustrates an excellent potential for improving the Bayesian network's structure and the learner model's initialization process.
Theory of Mind: A Familiar Aspect of Humanity to Give Machines

My research focuses on machine models of theory of mind, a set of skills that helps humans cooperate with each other. Because these skills present themselves in behavior, inference-based measurements must be carefully designed to rule out alternate hypotheses. Producing models that display these skills requires an extensive understanding of experiences and mechanisms sufficient for learning, and the models must have robust generalization to be effective in varied domains. To address these problems, I intend to evaluate computational models of ToM using a variety of tests.
DPAUC: Differentially Private AUC Computation in Federated Learning

Federated learning (FL) has gained significant attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple participants. The prior work on FL has mostly studied how to protect label privacy during model training. However, model evaluation in FL might also lead to the potential leakage of private label information. In this work, we propose an evaluation algorithm that can accurately compute the widely used AUC (area under the curve) metric when using the label differential privacy (DP) in FL. Through extensive experiments, we show our algorithms can compute accurate AUCs compared to the ground truth. The code is available at https://github.com/bytedance/fedlearner/tree/master/example/privacy/DPAUC
Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning

We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal representations. Specifically, dense point cloud segments are first input into an encoder to extract embeddings. All but the last ones are then aggregated by a context-aware autoregressor to make predictions for the last target segment. Towards the goal of modeling multi-granularity structures, local and global contrastive learning are performed between predictions and targets. To further improve the generalization of representations, the predictions are also utilized to reconstruct raw point cloud sequences by a decoder, where point cloud colorization is employed to discriminate against different frames. By combining classic contrast and reconstruction paradigms, it makes the learned representations with both global discrimination and local perception. We conduct experiments on four point cloud sequence benchmarks, and report the results on action recognition and gesture recognition under multiple experimental settings. The performances are comparable with supervised methods and show powerful transferability.
Hardness of Learning AES Key (Student Abstract)

We show hardness of learning AES key from pairs of ciphertexts under the assumption of computational closeness of AES to pairwise independence. The latter is motivated by a recent result on statistical closeness of AES to pairwise independence.
The Analysis of Deep Neural Networks by Information Theory: From Explainability to Generalization

Despite their great success in many artificial intelligence tasks, deep neural networks (DNNs) still suffer from a few limitations, such as poor generalization behavior for out-of-distribution (OOD) data and the "black-box" nature. Information theory offers fresh insights to solve these challenges. In this short paper, we briefly review the recent developments in this area, and highlight our contributions.
Deep Learning for Medical Prediction in Electronic Health Records

The widespread adoption of electronic health records (EHRs) has opened up new opportunities for using deep neural networks to enhance healthcare. However, modeling EHR data can be challenging due to its complex properties, such as missing values, data scarcity in multi-hospital systems, and multimodal irregularity. How to tackle various issues in EHRs for improving medical prediction is challenging and under exploration. I separately illustrate my works to address these issues in EHRs and discuss potential future directions.
On Analyzing the Role of Image for Visual-Enhanced Relation Extraction (Student Abstract)

Multimodal relation extraction is an essential task for knowledge graph construction. In this paper, we take an in-depth empirical analysis that indicates the inaccurate information in the visual scene graph leads to poor modal alignment weights, further degrading performance. Moreover, the visual shuffle experiments illustrate that the current approaches may not take full advantage of visual information. Based on the above observation, we further propose a strong baseline with an implicit fine-grained multimodal alignment based on Transformer for multimodal relation extraction. Experimental results demonstrate the better performance of our method. Codes are available at https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal.
Backforward Propagation (Student Abstract)

In this paper we introduce Backforward Propagation, a method of completely eliminating Internal Covariate Shift (ICS). Unlike previous methods, which only indirectly reduce the impact of ICS while introducing other biases, we are able to have a surgical view at the effects ICS has on training neural networks. Our experiments show that ICS has a weight regularizing effect on models, and completely removing it enables for faster convergence of the neural network.
Human-Aware AI – A Foundational Framework for Human-AI Interaction

We are living through a revolutionary moment in AI history. We are seeing the development of impressive new AI systems at a rate that was unimaginable just a few years ago. However, AI's true potential to transform society remains unrealized, in no small part due to the inability of current systems to work effectively with people. A major hurdle to achieving such coordination is the inherent asymmetry between the AI system and its users. In this talk, I will discuss how the framework of Human-Aware AI (HAAI) provides us with the tools required to bridge this gap and support fluent and intuitive coordination between the AI system and its users.
Multi-Horizon Learning in Procedurally-Generated Environments for Off-Policy Reinforcement Learning (Student Abstract)

Value estimates at multiple timescales can help create advanced discounting functions and allow agents to form more effective predictive models of their environment. In this work, we investigate learning over multiple horizons concurrently for off-policy reinforcement learning by using an advantage-based action selection method and introducing architectural improvements. Our proposed agent learns over multiple horizons simultaneously, while using either exponential or hyperbolic discounting functions. We implement our approach on Rainbow, a value-based off-policy algorithm, and test on Procgen, a collection of procedurally-generated environments, to demonstrate the effectiveness of this approach, specifically to evaluate the agent's performance in previously unseen scenarios.
Debiasing Intrinsic Bias and Application Bias Jointly via Invariant Risk Minimization (Student Abstract)

Demographic biases and social stereotypes are common in pretrained language models (PLMs), while the fine-tuning in downstream applications can also produce new biases or amplify the impact of the original biases. Existing works separate the debiasing from the fine-tuning procedure, which results in a gap between intrinsic bias and application bias. In this work, we propose a debiasing framework CauDebias to eliminate both biases, which directly combines debiasing with fine-tuning and can be applied for any PLMs in downstream tasks. We distinguish the bias-relevant (non-causal factors) and label-relevant (causal factors) parts in sentences from a causal invariant perspective. Specifically, we perform intervention on non-causal factors in different demographic groups, and then devise an invariant risk minimization loss to trade-off performance between bias mitigation and task accuracy. Experimental results on three downstream tasks show that our CauDebias can remarkably reduce biases in PLMs while minimizing the impact on downstream tasks.
Cooperative Multi-Agent Learning in a Complex World: Challenges and Solutions

Over the past few years, artificial intelligence (AI) has achieved great success in a variety of applications, such as image classification and recommendation systems. This success has often been achieved by training machine learning models on static datasets, where inputs and desired outputs are provided. However, we are now seeing a shift in this paradigm. Instead of learning from static datasets, machine learning models are increasingly being trained through feedback from their interactions with the world. This is particularly important when machine learning models are deployed in the real world, as their decisions can often have an impact on other agents, turning the decision-making process into a multi-agent problem. As a result, multi-agent learning in complex environments is a critical area of research for the next generation of AI, particularly in the context of cooperative tasks. Cooperative multi-agent learning is an essential problem for practitioners to consider as it has the potential to enable a wide range of multi-agent tasks. In this presentation, we will review the background and challenges of cooperative multi-agent learning, and survey our research that aims to address these challenges.
READ: Aggregating Reconstruction Error into Out-of-Distribution Detection

Detecting out-of-distribution (OOD) samples is crucial to the safe deployment of a classifier in the real world. However, deep neural networks are known to be overconfident for abnormal data. Existing works directly design score function by mining the inconsistency from classifier for in-distribution (ID) and OOD. In this paper, we further complement this inconsistency with reconstruction error, based on the assumption that an autoencoder trained on ID data cannot reconstruct OOD as well as ID. We propose a novel method, READ (Reconstruction Error Aggregated Detector), to unify inconsistencies from classifier and autoencoder. Specifically, the reconstruction error of raw pixels is transformed to latent space of classifier. We show that the transformed reconstruction error bridges the semantic gap and inherits detection performance from the original. Moreover, we propose an adjustment strategy to alleviate the overconfidence problem of autoencoder according to a fine-grained characterization of OOD data. Under two scenarios of pre-training and retraining, we respectively present two variants of our method, namely READ-MD (Mahalanobis Distance) only based on pre-trained classifier and READ-ED (Euclidean Distance) which retrains the classifier. Our methods do not require access to test time OOD data for fine-tuning hyperparameters. Finally, we demonstrate the effectiveness of the proposed methods through extensive comparisons with state-of-the-art OOD detection algorithms. On a CIFAR-10 pre-trained WideResNet, our method reduces the average FPR@95TPR by up to 9.8% compared with previous state-of-the-art.