Reviewerly/exHarmony huggingface dataset:
Exploring the Issue of Resignation among External Teachers in Z Region's Higher Vocational Colleges: Based on the Theory of Psychological Contract

As the enrollment size of higher vocational institutions in China swiftly amplifies, and with a simultaneous rise in the number of new specializations, existing full-time faculty members grapple with meeting the mounting prerequisites of vocational education. Challenges encompass a low teacher-student ratio, inconsistent quality, and varying teaching abilities. Given such conditions, vocational institutions have turned to hiring professional technicians from enterprises, a strategy that mitigates faculty shortage, economizes educational expenses, bolsters students' practical skills, and augments the scholarly ambiance. The Annual Report on Quality of China's Higher Vocational Education indicates a teacher-student ratio in Chinese vocational institutions of merely 1:15.6, with some well-sourced institutions even failing to satisfy the qualified standard of 1:18. Post expansion, a lag in administration has resulted in a persistently high turnover rate among faculty hires, thus engendering new challenges. There has been a noticeable yearly upsurge in turnover rates since 2017 among faculty hires. Additionally, diverse issues in faculty management within vocational institutions profoundly influence teaching quality. This article investigates 285 faculty hires from various vocational colleges in region Z. Utilizing the SPSS statistical analysis tool, it is revealed that the transactional, relational, and developmental dimensions of psychological contracts of External faculty all significantly correlate negatively (P&lt;0.05) with turnover intentions. A multiple linear regression analysis further disclosed the developmental psychological contract as exerting the most substantial impact on faculty hire turnover rates. The paper concludes by offering suggestions to refine human resource management in vocational colleges in region Z.
Research on the Application of New Technologies in Digital International Chinese Education Textbooks

With the advent of the information age, the traditional teaching concepts and the form of international Chinese education textbooks have been unable to meet the development needs of international Chinese education. Giving full play to the advantages of information technology in international Chinese education and using new technological means to develop digital International Chinese education textbooks will contribute to the spread of international Chinese education in the new era. In view of the current situation of the development of Digital International Chinese education textbooks, this paper describes and analyzes the morphological characteristics of several different levels of Digital International Chinese education textbooks, such as paper-based textbooks, multimedia digital textbooks, interactive digital textbooks and centralized digital textbooks, and analyzes the two-dimensional code or web page link and mobile terminal technology, VR technology and AR technology in the development of Digital International Chinese education textbooks The application of new technical means such as human-computer interaction and artificial intelligence technology is discussed to provide reference for the research of Digital International Chinese education textbooks and better promote the innovation and development of international Chinese education.
A Method to Annotate Who Speaks a Text Line in Manga and Speaker-Line Dataset for Manga109

Speaker estimation in a manga is one component that needs to be recognized in conducting research using manga. To identify the speaker of a text line in a manga, a dataset of who speaks the lines is needed. In order to construct such a dataset easily, we proposed a method to annotate who speaks a text line based on characteristics of information design and the human factor. Then, we developed a prototype system, constructed a dataset that mapped between text lines and speakers in the Manga109 dataset, and distributed the dataset on the Web. Then, we analyzed the dataset and showed that the perfect match rate was about 80% when there were five annotators. In addition, we found that variation in annotation occurred even with human judgment and that this was partly due to lines requiring reference to other frames. We also found that it was difficult for annotators to map speakers in scenes involving science fiction and battles by calculating the Evaluation Consistency Indicators.
Vision-Based Human Activity Recognition Methods Using Pose Estimation

Human Activity Recognition (HAR) has been a theme of great interest in research, especially thanks to the possible practical applications in the fields of video surveillance, human-machine interaction, gaming, autonomous driving, and health care. Despite this, the problem still remains a very complex challenge and whose definitive solution is still far away. Since the quality of the feature extracted impacts dramatically on the results of the overall system, the Human Pose Estimation (HPE) phase is crucial for the HAR. However, the problem remains of establishing how to use the extracted pose according to the network created for the HAR. Our work aims to analyze the ways in which the pose estimation, based on 2D poses extracted from monocular images, can affect a HAR system consisting of a recurrent network. For this purpose, various possible recurring structures are examined, at whose input the pose is used in different formats. The analysis carried out shows how the numerical simplification of the inputs facilitates learning compared to a “human” approach (which, on the contrary, could consider it easier to start from the graphic visualization of the skeleton).
An efficient and scalable vaccine passport verification system based on ciphertext policy attribute-based encryption and blockchain

Abstract Implementing a trust and secure immunity or vaccine passport verification system is now crucial for many countries. The system typically aims to enable the secure access control and verification of vaccination records which will be used by trusted parties. However, the issues related to the system scalability in supporting a large number of data access requests, the enforcement of the user consent for data sharing, and the flexibility in delegating the access capability to trusted parties have not been resolved by existing works. In this paper, we propose a Universal Vaccine Passport Verification System (UniVAC) to support a decentralized, scalable, secure, and fine-grained, access control for Covid-19 vaccine passport data sharing and verification. At a core of our scheme, we employ the ciphertext policy attribute-based encryption (CP-ABE) to support secure and fine-grained access control and use the blockchain to record access transactions and provide data indexing. Furthermore, we propose a ciphertext retrieval method based on regional blockchain segmentation and introduce the outsourced CP-ABE decryption as a part of the proxy re-encryption (PRE) process to enable scalable and secure ciphertext delivery of the encrypted vaccine passport under the requestor’s public key. Finally, we conducted the extensive experiments in real cloud environment and the results showed that our proposed scheme is more efficient and scalable than related works.
Puzzles Like You: Lightbulb puzzle

I found this great puzzle hidden in an app aimed at children. There are 300 lightbulbs in a row, labelled 1 to 300, each with an on/off switch. We count from 1 to 300 and flick the switches as follows: on “1” all lightbulbs are switched on. On “2” lightbulbs 2, 4, 6, 8 etc. and switched (turning them off). On “3” lightbulbs 3, 6, 9 etc. are switched. If a lightbulb is on when switched it goes off (and vice versa). How many lightbulbs are on by the time we...
Fixing a hole: reinstalling pi-hole

I run two pi-holes on my home network (details here). The older of the two was in need of a refresh. Here are some notes on how I did a reinstall. For those that don’t know, a pi-hole is a simple ad-blocking solution. You can donate to the project here. Problem The pi-hole itself can be easily updated over ssh using pihole -up however the OS also needs occasional updating for security. OS updates can be done using sudo apt update and sudo apt full-upgrade My install was...
Effects of Metacognition-based Outdoor Play Math Activities on Young Children’s Play Flow and Self Initiative

This study examined the effects of metacognition-based outdoor play math activities on young children's play flow and self initiative. The main results are presented as follows. First, the effect of metacognition-based outdoor play math activities on the total play flow and sub-factors of play flow of young children was examined. As a result of dividing the comparison group and the experimental group into before and after, the post-score was higher than the pre-score in both the entire play group and sub-factors. In addition, as a result of examining the difference between the comparison group and the experimental group by conducting covariate analysis, it was found that there was a significant difference between the two groups in both total flow in play and sub-factors(play challenge, play concentration, play control, goal and process, motivation and emotion, and sense of time). Second, the effect of metacognitive-based outdoor play mathematical activities on infants' self-directed and self-directed sub-factors was examined. First of all, as a result of classifying the control group and the experimental group into pre- and post-mortem, it was found that the average of post-score was higher than the pre-score in both the overall self-directed and sub-factors. In addition, covariate analysis was conducted to confirm the difference between the control group and the experimental group, and it was confirmed that there was a significant difference between the two groups in both overall self-initiative and sub-factors (emotional ability, communication, and achievement motivation). Therefore, it has been confirmed that metacognition-based outdoor play mathematics activities were effective, and in order to enhance infants' play immersion and self-directedness, teachers themselves should recognize the importance of metacognition, have knowledge of the subject, and train infants with metacognitive skills.
Uncertainty-Aware Data Augmentation for Offline Reinforcement Learning

One of the key challenges in Offline Reinforcement Learning is that it cannot conduct further environment exploration and performs poorly in terms of out-of-distribution generalizations. Data augmentation is commonly used to solve the issue of limited coverage of the full state-action space in static offline dataset. However, the existing data augmentation methods for proprioceptive observation suffer from the dilemma where the data coverage is often limited by tight constraints, while aggressive methods may exacerbate the performance. At the heart of this phenomenon are the diverged action distribution and the high uncertainty of the value function. In this paper, we propose to extend the static offline datasets during training by adding gradient-based perturbation to the state and utilizing the estimated uncertainty of the value function to constrain the range of the gradient. The estimated uncertainty of the value function works as a guidance to adjust the range of augmentation automatically, ensuring the adaptability and reliability of the state perturbation. The proposed algorithm Uncertainty-Aware Data Augmentation(UADA), is plugged into various standard offline RL algorithms and evaluated on several offline rein-forcement learning tasks. The empirical results confirm that UADA substantially improves the performance and achieves better model stability compared with the original algorithms.
Knowledge Distillation with Deep Supervision

Knowledge distillation aims to enhance the performance of a lightweight student model by exploiting the knowledge from a pre-trained cumbersome teacher model. However, in the traditional knowledge distillation, teacher predictions are only used to provide the supervisory signal for the last layer of the student model, which may result in those shallow student layers lacking accurate training guidance in the layer-by-layer back propagation and thus hinders effective knowledge transfer. To address this issue, we propose Deeply-Supervised Knowledge Distillation (DSKD), which fully utilizes class predictions and feature maps of the teacher model to supervise the training of shallow student layers. A loss-based weight allocation strategy is developed in DSKD to adaptively balance the learning process of each shallow layer, so as to further improve the student performance. Extensive experiments on CIFAR-100 and TinyImageNet with various teacher-student models show significantly performance, confirming the effectiveness of our proposed method. Code is available at: https://github.com/luoshiya/DSKD
C2SFormer: Rethinking the Local-Global Design for Efficient Visual Recognition Model

Vision Transformers are born with the property of data-dependent and long-range dependencies, accomplishing a number of astonishing results against their contemporary competitor CNNs. To alleviate the excessive computational burden, previous methods apply the local operation (e.g., convolution, local attention) in the high-resolution stages. Although these designs are efficient for local relations learning, especially for the high redundancy stages, they inevitably lead to the losses of non-locality and are constrained by the limited receptive field. In this paper, we present an effective hybrid-style vision backbone that is explicitly built with dynamic convolution and self-attention to respectively undertake both local and global interaction, dubbed C2SFormer. We adopt two homogeneous modules whose structure follows the typical Transformers. For local relations learning, we take the parallel multi-scale design and additive aggregation as simple but effective ideas, named MS-SCDC. For the global context modeling, we leverage the efficient factorized self-attention mechanism proposed in CoaT and apply it with the MS-SCDC in a cross-stacking manner over the high-resolution stages. Additionally, we further introduce a general approach for multi-scale learning of transformer-based modules, named MS-MHSA. The experiments conducted on a variety of general-purpose vision tasks demonstrate the superiority of the proposed model.
PromptMix: Text-to-image diffusion models enhance the performance of lightweight networks

Many deep learning tasks require annotations that are too time consuming for human operators, resulting in small dataset sizes. This is especially true for dense regression problems such as crowd counting which requires the location of every person in the image to be annotated. Techniques such as data augmentation and synthetic data generation based on simulations can help in such cases. In this paper, we introduce PromptMix, a method for artificially boosting the size of existing datasets, that can be used to improve the performance of lightweight networks. First, synthetic images are generated in an end-to-end data-driven manner, where text prompts are extracted from existing datasets via an image captioning deep network, and subsequently introduced to text-to-image diffusion models. The generated images are then annotated using one or more high-performing deep networks, and mixed with the real dataset for training the lightweight network. By extensive experiments on five datasets and two tasks, we show that PromptMix can significantly increase the performance of lightweight networks by up to 26%.
Emp-USIR: A Unidirectional Synchronous Interactive Reasoning Model for Empathetic Dialogue

The goal of an empathetic dialogue generative sys-tem is to generate coherently and relate emotional responses after finite turns of dialogue. Most of the previous work guided the dialogue system to generate empathetic responses from the dialogue history or emotional reasons. However, in people's daily communication, the empathetic response is often generated by deep reasoning through dialogue history and existing commonsense knowledge. To fill this gap, we propose an empathetic dialogue model of unidirectional synchronous interactive reasoning, Emp- Usir.Specifically, according to the existing commonsense knowledge base, we extract commonsense knowledge with emotional representation from it, and use it as the connection feature of dialogue history and remaining common-sense knowledge to guide the interactive reasoning between multi-turns dialogue history and commonsense knowledge, to imitate human reasoning based on commonsense to generate responses. Then, we propose a cross-token level attention mechanism to learn emotional dependence from reasoning features, to produce more smooth and diverse empathy responses. The experimental results and manual evaluation prove the effectiveness of the Emp-USIR model proposed in this paper on the widely-used benchmark dataset.
DUP-Net: Double U-PoolFormer Networks for Renal Artery Segmentation in CT Urography

Renal artery segmentation plays a fundamental role in nephrectomy, which can help surgeons get a better under-standing of vascular structures. However, the similar intensity between the renal arteries and cortex, the complex variations and tiny structures of arteries, bring challenges to accurate segmentation. To address these issues, we construct double U-PoolFormer networks (DUP-Net) to establish a coarse-to-fine framework for renal artery segmentation. Specifically, we use 2- D U - N et for the kidney extraction and then create 3-D DUP-Net for artery segmentation. DUP-Net is a serial network architecture that uses two U-PoolFormer modules to extract long-range spatial dependencies to create tree-like constraints while removing mis-segmentation of renal cortex through the serial structure. While DUP-Net improving the segmentation accuracy, it reduces memory cost during segmengtation. We evaluated our method on 70 cases of computed tomography urography data, with the experimental results showing that our proposed method certainly outperforms current 2-D and 3-D network models. Particularly, the average dice similarity coefficient of our method was improved from 81.51 % to 88.35%.
Real-time Night Surveillance Video Retrieval through Calibrated Denoising and Super-resolution

Real-time video surveillance cameras have been widely deployed over the last few years. In case of incidents such as natural disasters, it provides vital guidance in real time to aid the rescue operations. However, the quality of the captured video is far from satisfactory due to the limited camera hardware and low network bandwidth. Noise is often observed especially at night and the resolution is low. To this end, we are motivated to retrieve the nighttime surveillance video through calibrated denoising and super-resolution. We only use the preceding and current frames, but not the future frames. Thus, we avoid the additional delay of waiting for future frames, which is not suitable for real-time video applications. We design the pipeline semantically beneficial for both denoising and super-resolution, and achieve high rendering quality, especially for real-world noise. Moreover, we propose a novel calibration method for collecting paired noisy and clean observations in the real world, which provides more effective training data. We conduct experiments using the real-world dataset collected under low-light conditions, and benchmark with state-of-the-art video denoising and super-resolution methods. Results show that our method achieves significant performance gain while introducing small delay compared with the benchmarks, suitable for real-time videos.
Graph Neural Network with Virtual Edge Message Passing for Heterophilous Graphs

Graph Neural Networks (GNNs) have achieved great success in graph representation learning. Modern GNNs are built upon the homophily assumption and iteratively aggregate messages from immediate neighbors through the message-passing mechanism, which limits the ability of GNNs to represent graphs with heterophily. Existing GNNs considering heterophily adapt to the heterophilous graphs through reconstructing neighborhood and message fusion. However, noise information is constantly superimposed during message passing, due to the mixed propagation of different-order messages. In this paper, we propose a special graph convolutional network with virtual edge message passing (VEGCN), which consists of three important components: virtual edge message passing, message attention, and residual connection. Different from the message-passing mechanism of existing GNNs, virtual edge message passing can directly transfer messages from the second-order neighbors to the target nodes. By bypassing the first-order neighbors, VEGCN can avoid interference from first-order neighbors. In addition, we design an attention mechanism to adaptively obtain messages from first-order and second-order neighbors. This attention mechanism can distinguish the different importance of the messages from first-order neighbors and second-order neighbors. Finally, we introduce a residual connection to enhance the features of the nodes themselves and alleviate over-smoothing. We validate the effectiveness of VEGCN on several benchmark datasets including graphs with homophily and heterophily. Experimental results show that VEGCN outperforms representative baselines. Furthermore, we also designed ablation experiments to verify the role of the core components.
Graph Learning by Dynamic Sampling

Graph neural networks based on message-passing rely on the principle of neighborhood aggregation which has shown to work well for many graph tasks. In other cases these approaches appear insufficient, for example, when graphs are heterophilic. In such cases, it can help to modulate the aggregation method depending on the characteristic of the current neighborhood. Furthermore, when considering higher-order relations, heterophilic settings become even more important. In this work, we investigate a sparse version of message-passing that allows selective neighbor integration and aims for learning to identify most salient nodes that are then integrated over. In our approach, information on individual nodes is encoded by generating distinct walks. Because these walks follow distinct trajectories, the higher-order neighborhood grows only linearly which mitigates information bottlenecks. Overall, we aim to find the most salient substructures by deploying a learnable sampling strategy. We validate our method on commonly used graph benchmarks and show the effectiveness especially in heterophilic graphs. We finally discuss possible extensions to the framework.
3D Super Resolution for Non-Isotropic Medical Image Through Multi-Input 3D ResUnet

Fluid-attenuated inversion recovery imaging (FLAIR) is a magnetic resonance (MR) method that is frequently utilized to diagnose brain lesions. However, it usually provides transverse section imaging in high resolution but with very low resolution in the other axis. This non-isotropy huddles its wide utilization in research including machine learning. In this study, we applied a deep-learning based super resolution (SR) technique to convert non-isotropic FLAIR images into isotropic one. We proposed a multi-input 3D ResUnet that uses both FLAIR and T1-weighted MR images as its inputs. As a result, our proposed model successfully reconstructed isotropic FLAIR images (SSIM: 0.9947 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 0.0009)$</tex> , and out-performed the FLAIR only single-input 3D ResUnet.