Reviewerly/exHarmony huggingface dataset:
Bidirectional Model-Based Policy Optimization Based on Adaptive Gaussian Noise and Improved Confidence Weights

Model-Based Reinforcement Learning (MBRL) has been gradually applied in the field of Robot Learning due to its excellent sample efficiency and asymptotic performance. However, for high-dimensional learning tasks in complex scenes, the exploration and stable training capabilities of the robot still need enhancement. In light of policy planning and policy optimization, we propose a bidirectional model-based policy optimization algorithm based on adaptive gaussian noise and improved confidence weights (BMPO-NW). The algorithm parameterizes bidirectional policy networks into noise networks by adding different adaptive Gaussian noises to the connection weights and biases. This can improve the randomness of policy search and induce efficient exploration for the robot. Simultaneously, the confidence weight of improved activation function is introduced into the Q-function update formula of SAC, which can reduce the error propagation problem of target Q-network, and enhance the robot’s training stability. Finally, we implement the improved algorithm based on the framework of bidirectional model-based policy optimization algorithm (BMPO) to ensure asymptotic performance and sample efficiency. Experimental results in MuJoCo benchmark environments demonstrate that the learning speed of BMPO-NW is about 20% higher than baseline methods, the average reward is about 15% higher than other MBRL methods, and 50%-70% higher than MFRL methods, while the training process is more stable. Ablation experiments and different variant design experiments further verify the feasibility and robustness. The research results provide theoretical support for the conclusion of this paper and hold significant practical value for MBRL to help the robot realize applications in complex scenarios.
DeFeeNet: Consecutive 3D Human Motion Prediction with Deviation Feedback

Let us rethink the real-world scenarios that require human motion prediction techniques, such as human-robot collaboration. Current works simplify the task of predicting human motions into a one-off process of forecasting a short future sequence (usually no longer than 1 second) based on a historical observed one. However, such simplification may fail to meet practical needs due to the neglect of the fact that motion prediction in real applications is not an isolated “observe then predict” unit, but a consecutive process composed of many rounds of such unit, semi-overlapped along the entire sequence. As time goes on, the predicted part of previous round has its corresponding ground truth observable in the new round, but their deviation in-between is neither exploited nor able to be captured by existing isolated learning fashion. In this paper, we propose DeFeeNet, a simple yet effective network that can be added on existing one-off prediction models to realize deviation perception and feedback when applied to consecutive motion prediction task. At each prediction round, the deviation generated by previous unit is first encoded by our DeFeeNet, and then incorporated into the existing predictor to enable a deviation-aware prediction manner, which, for the first time, allows for information transmit across adjacent prediction units. We design two versions of DeFeeNet as MLP-based and GRU-based, respectively. On Human3.6M and more complicated BABEL, experimental results indicate that our proposed network improves consecutive human motion prediction performance regardless of the basic model.
Multi-Granularity Archaeological Dating of Chinese Bronze Dings Based on a Knowledge-Guided Relation Graph

The archaeological dating of bronze dings has played a critical role in the study of ancient Chinese history. Current archaeology depends on trained experts to carry out bronze dating, which is time-consuming and labor-intensive. For such dating, in this study, we propose a learning-based approach to integrate advanced deep learning techniques and archaeological knowledge. To achieve this, we first collect a large-scale image dataset of bronze dings, which contains richer attribute information than other existing fine-grained datasets. Second, we introduce a multihead classifier and a knowledge-guided relation graph to mine the relationship between attributes and the ding era. Third, we conduct comparison experiments with various existing methods, the results of which show that our dating method achieves a state-of-the-art performance. We hope that our data and applied networks will enrich fine-grained classification re-search relevant to other interdisciplinary areas of expertise. The dataset and source code used are included in our supplementary materials, and will be open after submission owing to the anonymity policy. Source codes and data are available at: https://github.com/zhourixin/bronze-Ding
DiffusionRig: Learning Personalized Priors for Facial Appearance Editing

We address the problem of learning person-specific facial priors from a small number (e.g., 20) of portrait photos of the same person. This enables us to edit this specific person's facial appearance, such as expression and lighting, while preserving their identity and high-frequency facial details. Key to our approach, which we dub DiffusionRig, is a diffusion model conditioned on, or “rigged by,“ crude 3D face models estimated from single in-the-wild images by an off-the-shelf estimator. On a high level, DiffusionRig learns to map simplistic renderings of 3D face models to realistic photos of a given person. Specifically, DiffusionRig is trained in two stages: It first learns generic facial priors from a large-scale face dataset and then person-specific priors from a small portrait photo collection of the person of interest. By learning the CGI-to-photo mapping with such personalized priors,DiffusionRig can “rig“ the lighting, facial expression, head pose, etc. of a portrait photo, conditioned only on coarse 3D models while preserving this person's identity and other high-frequency characteristics. Qualitative and quantitative experiments show that DiffusionRig outperforms existing approaches in both identity preservation and photorealism. Please see the project website: https://diffusionrig.github.io for the supplemental material, video, code, and data.
Multi-Ship Tracking by Robust Similarity metric

Multi-ship tracking (MST) as a core technology has been proven to be applied to situational awareness at sea and the development of a navigational system for autonomous ships. Despite impressive tracking outcomes achieved by multi-object tracking (MOT) algorithms for pedestrian and vehicle datasets, these models and techniques exhibit poor performance when applied to ship datasets. Intersection of Union (IoU) is the most popular metric for computing similarity used in object tracking. The low frame rates and severe image shake caused by wave turbulence in ship datasets often result in minimal, or even zero, Intersection of Union (IoU) between the predicted and detected bounding boxes. This issue contributes to frequent identity switches of tracked objects, undermining the tracking performance. In this paper, we address the weaknesses of IoU by incorporating the smallest convex shapes that enclose both the predicted and detected bounding boxes. The calculation of the tracking version of IoU(TIoU) metric considers not only the size of the overlapping area between the detection bounding box and the prediction box, but also the similarity of their shapes. Through the integration of the TIoU into state-of-the-art object tracking frameworks, such as DeepSort and ByteTrack, we consistently achieve improvements in the tracking performance of these frameworks.
Unsupervised Fall Detection on Edge Devices

Automatic fall detection is a crucial task in healthcare as falls pose a significant risk to the health of elderly individuals. This paper presents a lightweight acceleration-based fall detection method that can be implemented on edge devices. The proposed method uses Autoencoders, a type of unsupervised learning, within the framework of anomaly detection, allowing for network training without requiring extensive labeled fall data. One of the challenges in fall detection is the difficulty in collecting fall data. However, our proposed method can overcome this limitation by training the neural network without fall data, using the anomaly detection framework of Autoencoders. Additionally, this method employs an extremely lightweight Autoencoder that can run independently on an edge device, eliminating the need to transmit data to a server and minimizing privacy concerns. We conducted experiments comparing the performance of our proposed method with that of a baseline method using a unique fall detection dataset. Our results confirm that our method outperforms the baseline method in detecting falls with higher accuracy.
Research on Data-driven College English Teaching Model Based on Reinforcement Learning and Virtual Reality through Online Gaming

The contemporary education community has been trying to use video games or game elements in teaching in recent years.The pioneers in this field have tried to improve the quality of education by means of educational games or gamified teaching, but it has been difficult to be widely promoted for various reasons.In recent years, attempts have found that although there are certain advantages over educational video games and gamified teaching, like the former, there are still considerable difficulties in popularizing them in practice.Therefore, this paper proposes a learning structure design based on the big data video game task mode, hoping to make the learning process really arouse the learners' interest in learning, achieve good learning results in a relaxed and focused learning atmosphere, and achieve unsupervised high efficiency.Self-learning.Finally, the F1 value of simple teaching resources is increased by 8.6% on average compared with the other four algorithms, and it can reach 0.888 when the test set is 10%; when recommending complex teaching resources, the F1 value is increased by an average of 5.6% compared with the other four algorithms.It can reach 0.958 at 10% of the test set.The above data show that the CUPMF method can effectively improve the recommendation accuracy and improve the recommendation effect.
Design and Implementation of Online Gaming for Learning Motivation and Achievement Improvement in Computer Information Technology Curriculum

The contemporary education community has been trying to use video games or game elements in teaching in recent years.The pioneers in this field have tried to improve the quality of education by means of educational games or gamified teaching, but it has been difficult to be widely promoted for various reasons.In recent years, attempts have found that although there are certain advantages over educational video games and gamified teaching, like the former, there are still considerable difficulties in popularizing them in practice.Therefore, this paper proposes a learning structure design based on the big data video game task mode, hoping to make the learning process really arouse the learners' interest in learning, achieve good learning results in a relaxed and focused learning atmosphere, and achieve unsupervised high efficiency.Self-learning.Finally, the F1 value of simple teaching resources is increased by 8.6% on average compared with the other four algorithms, and it can reach 0.888 when the test set is 10%; when recommending complex teaching resources, the F1 value is increased by an average of 5.6% compared with the other four algorithms.It can reach 0.958 at 10% of the test set.The above data show that the CUPMF method can effectively improve the recommendation accuracy and improve the recommendation effect.
Perceptions of Civil Aviation Training Center Students About the Use of The Test of English for International Communication Results Before Studying at Catc and Co-operative Education Enrollment

This research was carried out in order to investigate the perceptions of Civil Aviation Training Center (CATC) students about the use of the Test of English for International Communication (TOEIC) results before studying at CATC and before co-operative education enrollment. The objective of this research was to study the perception of using TOEIC results before studying and before cooperative education enrollment and the factors that affected the perception of learning English for the Test of English for International Communication (TOEIC) and study the development of their English language skills for TOEIC preparation in order to propose guidelines for the development of English learning to the TOEIC test by studying and collecting data from 378 participants of 1st and 3rd year undergraduate students of CATC, Bachelor of Technology in Aviation Program in Aviation Management and Bachelor of Engineering in the academic year 2019 by using questionnaires and a random interview of 68 students. The results showed that most respondents get 405-600 English TOEIC score or Elementary Proficiency Plus level. Students were aware that CATC has clearly publicized that TOEIC result would be used for consideration before enrolling and before joining cooperative education. They realized that TOEIC was the one of a reliable and useful English language proficiency assessment. Regarding self-test preparation, it was found that most of the students were good self-learners by being aware that it must be well prepared before the test. They set their own goals in the test and also prepared their own materials used in test preparation. In the analysis of one's own ability, students knew their weaknesses and were able to improve themselves. Students realized that they would receive a higher score after their good preparation. And we also found that the helps and advices from lecturers would make students felt more confident during the TOEIC preparation process.
A Case Study of Phosphorite Investigation in Jhabua District, Madhya Pradesh, India: By Barefoot Geologist

The Jhabua phosphorite is the second largest deposit in India after the famous Jhamerkotra deposit of Rajasthan. Though geographically they are separated by state boundaries but geologically they belong to the same domain. The Jhabua deposit is hosted with in the Proterozoic meta-sediments of Aravalli Supergroup. The host litho-units for the stromatolite bearing phosphate mineralization are limestone and chert. The limestone is siliceous in nature and has large variation in terms of impurities and grades to dolomite. The phosphate content is higher in cherts compared to limestone. The stromatolites are the important field guides to locate the phosphate mineralization. But need of geochemical investigation method arises in locating the non stromatolite bearing rock phosphate mineralization. The paper demonstrates very vividly the methodology of exploration for rock phosphate mineralization. This can be seen here as how the basic and conventional techniques of exploration are effectively utilized to first locate the geochemical anomalies on the ground followed by drilling the anomalies and establishing the resources. This is significant here to observe that no state of art technology has been used here during the entire course of exploration, in establishing the resources. This is to underline the importance of conventional exploration techniques which is getting a beating by the frequent use of AI in geology. This case study demonstrates as how the time tested conventional methods are still very much valid and can yield fruitful results if utilized meticulously.
Breast Cancer Histopathological Images Segmentation Using Deep Learning

Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still not exploitable because to make its valorization possible, the images require an annotation which remains a costly and difficult task. Thus, the use of an unsupervised segmentation method could facilitate the process. In this article, we propose two approaches for the semantic segmentation of breast cancer histopathology images. On the one hand, an autoencoder architecture for unsupervised segmentation is proposed, and on the other hand, an improvement U-Net architecture for supervised segmentation is proposed. We evaluate these models on a public dataset of histological images of breast cancer. In addition, the performance of our segmentation methods is measured using several evaluation metrics such as accuracy, recall, precision and F1 score. The results are competitive with those of other modern methods.
A GAN-Based Framework Combining Memory and Self-Attention Mechanisms for Video Anomaly Detection in Online Gaming Environments

The contemporary education community has been trying to use video games or game elements in teaching in recent years.The pioneers in this field have tried to improve the quality of education by means of educational games or gamified teaching, but it has been difficult to be widely promoted for various reasons.In recent years, attempts have found that although there are certain advantages over educational video games and gamified teaching, like the former, there are still considerable difficulties in popularizing them in practice.Therefore, this paper proposes a learning structure design based on the big data video game task mode, hoping to make the learning process really arouse the learners' interest in learning, achieve good learning results in a relaxed and focused learning atmosphere, and achieve unsupervised high efficiency.Self-learning.Finally, the F1 value of simple teaching resources is increased by 8.6% on average compared with the other four algorithms, and it can reach 0.888 when the test set is 10%; when recommending complex teaching resources, the F1 value is increased by an average of 5.6% compared with the other four algorithms.It can reach 0.958 at 10% of the test set.The above data show that the CUPMF method can effectively improve the recommendation accuracy and improve the recommendation effect.
What Does A Typical CNN “See” In An Emotional Facial Image?

The objective of this research is to understand the current capabilities of artificial neural network algorithms and contrast them to the human visual system, in order to identify the most effective features to support affective automation.This can, in turn, aid in optimisation of resources used for storage and transmission by understanding which level of information can be used to augment and potentially accelerate accurate identification of emotional facial expressions.For the first part of our experiment, which we present in this paper, we focused on evaluating feature selection of facial expression images using machine learning.70 (10 examples of each basic emotion) randomly selected from the NIMSTIM dataset images were used, which were split into train (56) and test ( 14) sets.The testing images were then processed using Singular Vector Decomposition to vary the levels of information shown in the image.Next, the training dataset was used to train a Convolutional Neural Network algorithm with 18 layers (with convolutional, max pooling, dropout, flattening and activation layers) and 66,884,615 trainable parameters.The validation accuracy was 45% and the confusion matrix showed that the emotion disgust was predicted at almost 100% accuracy, surprise at 55%, and sorrow/happiness/neutral at 46-47%.As expected, the granularity level of the test images had an effect on the successful predictions.A feature map visualisation was performed to demonstrate what the CNN "sees" (i.e., the feature selection) in the image in order to accurately predict the emotional expression type.For the next phase of our experiment, we plan on contrasting the features and performance to that of the human visual system using an experimental design with eye tracking.
NLP-based Traffic Scene Retrieval via Representation Learning

Many automated systems require the interpretation of visual information, i.e., images, videos, and natural language input, i.e., speech or text, to comprehend their surroundings and communicate with interacting humans.One such hybrid application of computer vision using images and videos and natural language processing (NLP) recognizes traffic scenes, a crucial and challenging problem in automated transportation systems.Scene classification is just one of many areas where recent convolutional neural network (CNN) frameworks have proven to be highly effective.Still to be fully explored for application to problem-solving in the real world is CNN's impressive, truly representative learning capability.However, newer CNN implementations, such as YOLO and DeepSort, show promise for object detection.The BERT model is the benchmark for text embeddings and the most efficient method currently available.Hence, we aim to retrieve the vehicles from the traffic videos using natural language-based description, i.e., text.The paper proposes a novel approach that combines YOLOv7, the recent version of YOLO, DeepSort algorithms for object detection, i.e., detecting the vehicles from the traffic scene from the frames of the videos and the transfer learning model, i.e., BERT model for text embeddings.Additionally, a Kalman filter is utilized to track the cars by providing the id and will retain them in the other frames of the videos.The machine learning model performs the similarity checking, i.e., siamese neural networks.The experiments are performed on the standard dataset of AI city challenge 2022.Moreover, the results depict that the proposed approach achieves 28.49 % of Recall@5, 42.08 % of Recall@10, and 20.73 % of MRR, indicating the proposed method's effective approach.
The role of local government in tackling a global pandemic: A lesson from Nepal

This paper critically analyses the scope of leadership in Nepal at the local government level when tackling the wider impacts of COVID-19, with a particular focus on health service delivery in line with World Health Organisation (WHO) frameworks. In-depth interviews with 66 representatives from local governments (LG) in the Madhesh and Sudurpaschim provinces were conducted, along with analysis of routine data from health management information systems. We found that Maternal Child Health (MCH) services at local health institutions were largely unimpacted, stocks of essential medicine were available, and the LGs exhibited determination and capability in getting to grips with the crisis, despite inadequate knowledge and resources. Nevertheless, there was sizeable shrinkage in the utilisation of services, which triggered public health concerns of a different nature, the LGs were inadequately prepared in regards to the capacity and availability of human resources, and there were explicit gaps in terms of coordination among all three tiers of government and a lack of role clarity that delayed the response to the pandemic at the local level. Our recommendations include continued investment in local MCH services, capacity building for local leaders with a focus on human resource management in emergency contexts, and the simplification of public procurement processes, particularly during crises, enabling LGs and other local actors to expediate procurement and improve response times.