Reviewerly/exHarmony huggingface dataset:
Perceptions of online education among 16-18-year-olds: Differences and similarities in their interests and preferred formats according to where they live

This research employs a quantitative and cross-sectional approach, utilizing an online survey (N=600), to examine the preferences of Spanish students aged 16 to 18 regarding different modes of education (face-to-face or online). It also explores potential variances based on their place of residence. Additionally, the study identifies the subjects of interest to these students for integration into formal learning and determines their preferred online learning formats. The findings reveal significant differences between students residing in cities with a population of 50,000 or more and those in smaller habitats, particularly in relation to their experiences and evaluations of online education, precisely specific training in received digital technologies, their topics of interest, their self-study learning methods, and their preferred online learning formats. These study outcomes can contribute to the enhancement of online education and better preparedness of the educational system for future challenges that may necessitate the adoption of this instructional mode. Based on our study, several recommendations for online education can be made. Firstly, there is a need for teachers to acquire enhanced digital skills. Secondly, it is important not to assume that young individuals possess inherent digital skills due to their age; instead, it is crucial to emphasize the acquisition of critical digital skills during their education. Thirdly, motivating young students by aligning teaching with their areas of interest should be encouraged, considering their significant habitat preferences. Lastly, interactive methodologies that stimulate students, such as immediate evaluation questionnaires, animations, and interactive exercises, should be prioritized, particularly for students residing in densely populated areas.
The synergy between AI and radiologist in advancing digital mammography: comparative study between stand-alone radiologist and concurrent use of artificial intelligence in BIRADS 4 and 5 female patients

Abstract Background Recent significant advancements in speed and machine learning have profoundly changed artificial intelligence (AI). In order to evaluate the value of AI in the detection and diagnosis of BIRADS 4 and 5 breast lesions visible on digital mammography pictures, we compared it to a radiologist. The gold standard was tissue core biopsy and pathology. A total of 130 individuals with 134 BIRADS 4 or 5 mammography lesions were included in the study, and all relevant digital mammography pictures were exported to an AI software system. Objectives The goal of this investigation was to determine how well artificial intelligence performs in digital mammography when compared to a radiologist in identifying and diagnosing BIRADS 4 and 5 breast lesions. Methods A total of 134 BIRADS 4 and 5 breast lesions in 130 female patients were discovered using digital mammography on both the craniocaudal and mediolateral oblique planes. All mammograms were transferred to an AI software system for analysis, and the results were compared in accordance with the histopathological results, which served as standard of reference in all lesions. Results Artificial intelligence was found to be more accurate (90.30%) than radiologist (82.84%) and shows higher positive predictive value (94.5%) than radiologist (82.8%) regarding suspecting malignancy in digital mammography with BIRADS 4 and 5 lesions, while the radiologist achieved higher sensitivity (100%) than AI (93.7%) in detecting malignancy in BIRADS 4 and 5 lesions. Conclusions Radiologist was found to be more sensitive than AI in detecting malignancy in BIRADS 4 and 5 lesions but AI had a higher positive predictive value. However, specificity as well as negative predictive value could not be assessed for the radiologist, hence could not be compared with AI values because the inclusion criteria of the study did not include BIRADS 1, 2 and 3 so benign-looking lesions by digital mammography were not involved to measure specificity and negative predictive values. All in all, based on the available data, AI was found to be more accurate than radiologist regarding suspecting malignancy in digital mammography. AI can run hand in hand with human experience to give best health-care service in screening and/or diagnosing patients with breast cancer.
An End-to-End Dense Connected Heterogeneous Graph Convolutional Neural Network

Graph convolutional networks (GCNs) are powerful models for graph-structured data learning task. However, most existing GCNs may confront with two major challenges when dealing with heterogeneous graph: (1) Predefined meta-paths are required to capture the semantic relations between nodes from different types, which may not exploit all the useful information in the graph; (2) Performance degradation and semantic confusion may happen with the growth of the network depth, which limits their ability to capture long-range dependencies. To meet these challenges, we propose Dense-HGCN, an end-to-end dense connected heterogeneous convolutional neural network to learn node representation. Dense-HGCN computes the attention weights between different nodes and incorporates the information of previous layers into each layer's aggregation process via a specific fuse function. Moreover, Dense-HGCN leverages multi-scale information for node classification or other downstream tasks. Experimental results on real-world datasets demonstrate the superior performance of Dense-HGCN in enhancing the representational power compared with several state-of-the-art methods.
An Adaptive Detector for Few Shot Object Detection

Few-shot object detection has made progress in recent years. However, most research assumes that base and new classes come from the same domain. In real-world applications, they often come from different domains, resulting in poor adaptability of existing methods. To address this problem, we designed an adaptive few-shot object detection framework. Based on the Meta R-CNN framework, we added an image domain classifier after the backbone's last layer to reduce domain discrepancy. To avoid class feature confusion caused by image feature distribution alignment, we also added a feature filter module (CAFFM) to filter out features irrelevant to specific classes. We tested our method on three base/new splits and found significant performance improvements compared to the base model Meta R-CNN. In base/new split2, mAP50 increased by $$\pm 8 \% $$ , and in the remaining two splits, mAP50 improved by $$\pm 3 \% $$ . Our method outperforms state-of-the-art methods in most cases for the three different base/new splits, validating the efficacy and generality of our approach.
SORA: Improving Multi-agent Cooperation with a Soft Role Assignment Mechanism

Role-based multi-agent reinforcement learning (MARL) holds the promise of achieving scalable multi-agent cooperation by decomposing complex tasks through the concept of roles and has enjoyed great success in various tasks. However, conventional role-based MARL methods typically assign a single role to each agent, limiting the agent's behavior in certain scenarios. In real life, an individual usually performs multiple responsibilities in a given task. To meet such situations, we propose a novel soft role assignment (SORA) process that enables an agent to play multiple roles simultaneously. Concretely, SORA first generates a role distribution via the attention mechanism to interpret the agent's identity as a combination of different roles. To ensure consistent behavior with an agent's assigned role, we also introduce role-specific Q networks for decision-making. By virtue of these advances, our proposed method makes a prominent improvement over the prior state-of-the-art approaches on StarCraft multi-agent challenges and Google Research Football.
Theoretical Analysis of Gradient-Zhang Neural Network for Time-Varying Equations and Improved Method for Linear Equations

Solving time-varying equations is fundamental in science and engineering. This paper aims to find a fast-converging and high-precision method for solving time-varying equations. We combine two classes of feedback neural networks, i.e., gradient neural network (GNN) and Zhang neural network (ZNN), to construct a continuous gradient-Zhang neural network (GZNN) model. Our research shows that GZNN has the advantages of high convergence precision of ZNN and fast convergence speed of GNN in certain cases, i.e., all the eigenvalues of Jacobian matrix of the time-varying equations multiplied by its transpose are larger than 1. Furthermore, we conduct the different detailed mathematical proof and theoretical analysis to establish the stability and convergence of the GZNN model. Additionally, we discretize the GZNN model by utilizing time discretization formulas (i.e., Euler and Taylor-Zhang discretization formulas), to construct corresponding discrete GZNN algorithms for solving discrete time-varying problems. Different discretization formulas can construct discrete algorithms with varying precision. As the number of time sampling instants increases, the precision of discrete algorithms can be further improved. Furthermore, we improve the matrix inverse operation in the GZNN model and develop inverse-free GZNN algorithms to solve linear problems, effectively reducing their time complexity. Finally, numerical experiments are conducted to validate the feasibility of GZNN model and the corresponding discrete algorithms in solving time-varying equations, as well as the efficiency of the inverse-free method in solving linear equations.
Wasserstein Diversity-Enriched Regularizer for Hierarchical Reinforcement Learning

Hierarchical reinforcement learning composites subpolicies in different hierarchies to accomplish complex tasks. Automated subpolicies discovery, which does not depend on domain knowledge, is a promising approach to generating subpolicies. However, the degradation problem is a challenge that existing methods can hardly deal with due to the lack of consideration of diversity or the employment of weak regularizers. In this paper, we propose a novel task-agnostic regularizer called the Wasserstein Diversity-Enriched Regularizer (WDER), which enlarges the diversity of subpolicies by maximizing the Wasserstein distances among action distributions. The proposed WDER can be easily incorporated into the loss function of existing methods to boost their performance further. Experimental results demonstrate that our WDER improves performance and sample efficiency in comparison with prior work without modifying hyperparameters, which indicates the applicability and robustness of the WDER.
Communication-Efficient Distributed Minimax Optimization via Markov Compression

Recently, the minimax problem has attracted a lot of attention due to its wide applications in modern machine learning fields such as GANs. With the exponential growth of data volumes and increasing problem sizes, the design of distributed algorithms to train high-performance models has become imperative. However, distributed algorithms often suffer from communication bottlenecks. To address this challenge, in this paper, we propose a communication-efficient distributed compressed stochastic gradient descent ascent algorithm, abbreviated as DCSGDA, in a parameter-server setting. To reduce the communication cost, each client in DCSGDA transmits the compressed gradients of the primal and dual variables to the server at each iteration. In particular, we leverage a Markov compression mechanism that allows both unbiased and biased compressors to mitigate the negative effect of compression errors on convergence. Namely, we show theoretically that the DCSGDA algorithm can still achieve linear convergence in the presence of compression errors, provided that the local objective function is strongly-convex-strongly-concave. Finally, numerical experiments demonstrate the desirable communication efficiency and efficacy of the proposed DCSGDA.
Actor-Critic with Variable Time Discretization via Sustained Actions

Reinforcement learning (RL) methods work in discrete time. In order to apply RL to inherently continuous problems like robotic control, a specific time discretization needs to be defined. This is a choice between sparse time control, which may be easier to train, and finer time control, which may allow for better ultimate performance. In this work, we propose SusACER, an off-policy RL algorithm that combines the advantages of different time discretization settings. Initially, it operates with sparse time discretization and gradually switches to a fine one. We analyze the effects of the changing time discretization in robotic control environments: Ant, HalfCheetah, Hopper, and Walker2D. In all cases our proposed algorithm outperforms state of the art.
MS3DAAM: Multi-scale 3-D Analytic Attention Module for Convolutional Neural Networks

In this paper, we propose a compact and effective module, called multi-scale 3-D analytic attention module (MS3DAAM) to address this challenge. We significantly reduce model complexity by developing a decoupling-and-coupling strategy. Firstly, we factorize the regular attention along channel, height and width directions and then efficiently encode the information via 1-D convolutions, which greatly saves the computational power. Secondly, we multiply the weighted embedding results of the three direction vectors to regain a better 3-D attention map, which allocates an independent weight to each neuron, thus developing a unified measurement method for attention. Furthermore, multi-scale method is introduced to further strengthen our module capability in locating by capturing both the inter-channel relationships and long-range spatial interactions from different receptive fields. Finally, we develop a structural re-parameterization technique for multi-scale 1-D convolutions to boost the inference speed. Extensive experiments in classification and object detection verify the superiority of our proposed method over other state-of-the-art counterparts. This factorizing-and-combining mechanism with the beauty of brevity can be further extended to simplify similar network structures.
Generalization of graph network inferences in higher-order graphical models

Abstract Probabilistic graphical models provide a powerful tool to describe complex statistical structure, with many real-world applications in science and engineering from controlling robotic arms to understanding neuronal computations. A major challenge for these graphical models is that inferences such as marginalization are intractable for general graphs. These inferences are often approximated by a distributed message-passing algorithm such as Belief Propagation, which does not always perform well on graphs with cycles, nor can it always be easily specified for complex continuous probability distributions. Such difficulties arise frequently in expressive graphical models that include intractable higher-order interactions. In this paper we define the Recurrent Factor Graph Neural Network (RF-GNN) to achieve fast approximate inference on graphical models that involve many-variable interactions. Experimental results on several families of graphical models demonstrate the out-of-distribution generalization capability of our method to different sized graphs, and indicate the domain in which our method outperforms Belief Propagation (BP). Moreover, we test the RF-GNN on a real-world Low-Density Parity-Check dataset as a benchmark along with other baseline models including BP variants and other GNN methods. Overall we find that RF-GNNs outperform other methods under high noise levels.
ADEQ: Adaptive Diversity Enhancement for Zero-Shot Quantization

Zero-shot quantization (ZSQ) is an effective way to compress neural networks, especially when real training sets are inaccessible because of privacy and security issues. Most existing synthetic-data-driven zero-shot quantization methods introduce diversity enhancement to simulate the distribution of real samples. However, the adaptivity between the enhancement degree and network is neglected, i.e., whether the enhancement degree benefits different network layers and different classes, and whether it reaches the best match between the inter-class distance and intra-class diversity. Due to the absence of the metric for class-wise and layer-wise diversity, maladaptive enhancement degree run the vulnerability of mode collapse of the inter-class inseparability. To address this issue, we propose a novel zero-shot quantization method, ADEQ. For layer-wise and class-wise adaptivity, the enhancement degree of different layers is adaptively initialized with a diversity coefficient. For inter-class adaptivity, an incremental diversity enhancement strategy is proposed to achieve the trade-off between inter-class distance and intra-class diversity. Extensive experiments on the CIFAR-100 and ImageNet show that our ADEQ is observed to have advanced performance at low bit-width quantization. For example, when ResNet-18 is quantized to 3 bits, we improve top-1 accuracy by 17.78% on ImageNet compared to the advanced ARC. Code at https://github.com/dangsingrue/ADEQ .
Toward anthropologies of the metaverse

Abstract Anthropology is good for understanding the metaverse, the emerging domain of digital culture that includes virtual worlds, online games, and social media. In the wake of COVID, there has been heightened interest in the metaverse's potential, particularly after Facebook renamed itself Meta in October 2021. Yet current understandings of the metaverse are deeply muddled, warped by rhetorics of promotion ranging from entrepreneurial zeal to rampant hucksterism. In response, anthropology can defamiliarize ways of thinking that might otherwise take on the status of common sense. I show that the metaverse is mischaracterized when its optional aspects are repackaged as obligatory. Four such mischaracterizations are particularly damaging: that the metaverse must use virtual reality (VR, which I rename “sensory immersion,” or SI), that it must be interoperable, that it must be massive, and that it must employ crypto. By clearing this conceptual underbrush, we can move beyond both hype and anti‐hype to delineate what anthropology offers to the analysis, decolonization, and future transformation of the metaverse.
Attribution Guided Layerwise Knowledge Amalgamation from Graph Neural Networks

Knowledge Amalgamation (KA), aiming to transfer knowledge from multiple well-trained teacher networks to a multi-talented and compact student, is gaining attention due to its crucial role in resource-constrained scenarios. Previous literature on KA, although exhibiting promising results, is primarily geared toward Convolutional Neural Networks (CNNs). However, when transferred to Graph Neural Networks (GNNs) with non-grid data, KA techniques face new challenges that can be difficult to overcome. Moreover, the layerwise aggregation of GNNs produces significant noise as they progress from a shallow to a deep level, which can impede KA students' deep-level semantic comprehension. This work aims to overcome this limitation and propose a novel strategy termed LAyerwIse Knowledge Amalgamation (LaiKA). It involves Hierarchical Feature Alignment between the teachers and the student, which enables the student to directly master the feature aggregation rules from teacher GNNs. Meanwhile, we propose a Selective Attribution Transfer (SAT) module that identifies task-relevant topological substructures to assist the capacity-limited student in mitigating noise and enhancing performance. Extensive experiments conducted on six datasets demonstrate that our proposed method equips a single student GNN to handle tasks from multiple teachers effectively and achieve comparable or superior results to those of the teachers without human annotations.
AM-RRT*: An Automatic Robot Motion Planning Algorithm Based on RRT

Motion planning is a very important part of robot technology, where the quality of planning directly affects the energy consumption and safety of robots. Focusing on the shortcomings of traditional RRT methods such as long, unsmooth paths, and uncoupling with robot control system, an automatic robot motion planning method was proposed based on Rapid Exploring Random Tree called AM-RRT* (automatic motion planning based on RRT*). First, the RRT algorithm was improved by increasing the attractive potential fields of the target points of the environment, making it more directional during the sampling process. Then, a path optimization method based on a dynamic model and cubic B-spline curve was designed to make the planned path coupling with the robot controller. Finally, an RRT speed planning algorithm was added to the planned path to avoid dynamic obstacles in real time. To verify the feasibility of AM-RRT*, a detailed comparison was made between AM-RRT* and the traditional RRT series algorithms. The results showed that AM-RRT* improved the shortcomings of RRT and made it more suitable for robot motion planning in a dynamic environment. The proposal of AM-RRT* can provide a new idea for robots to replace human labor in complex environments such as underwater, nuclear power, and mines.
Application of Deep Learning Methods in the Diagnosis of Coronary Heart Disease Based on Electronic Health Record

With the development of Internet technology, the number of electronic health record data has surged. Also, artificial intelligence simulates the ability of human beings to solve problems and make decisions by conducting complex and fast calculation on a large amount of data. Based on the electronic health records of hypertension patients in Peking University Shenzhen Hospital, we proposed the use of deep learning methods to achieve intelligent coronary heart disease diagnosis for hypertensive patients. We firstly conducted statistical data analysis and effective feature selection experiments. Then, we established an intelligent diagnosis model for coronary heart disease based on the Transformer and contrastive learning. The model integrates multiple types of health record data such as patient's personal information, symptoms, concurrent diseases and test data, and the results proved that our model achieved the best classification performance and accuracy compared with CNN, RNN and LSTM, with AUC value reached 0.9349. In the future, this model can be extended to the diagnosis of general chronic diseases.
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs. As a result, they only perform well in limited scenarios and are sensitive to data drift. In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem. EdgeMA extracts the gray level co-occurrence matrix based statistical texture feature and uses the Random Forest classifier to detect the domain shift. Moreover, we have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our results illustrate that EdgeMA significantly improves inference accuracy.