Reviewerly/exHarmony huggingface dataset:
Learning Object Spatial Relationship from Demonstration

Object spatial relationship refers to the use of predicates to describe the relative positional relationship between two objects. For robots, understanding the spatial relationships between objects in scenes can contribute more robustness to the interaction process between robots and the environment in a closed-loop way. Traditional methods typically use models to learn 2D features of images to determine spatial relationships between objects, which has certain limitations in understanding 3D spatial relationships. In this paper, first, we propose a method to learn object spatial relationships from human demonstration, which utilizes spatial probability models to characterize spatial relationships between objects. Second, in order to facilitate the addition of understanding of new spatial relationships, we use a monocular 3D object detection method to construct new probability distribution models of spatial relationships from image and language demonstrations. Finally, we use SVM model to distinguish different spatial relationships and recognize spatial relationships between objects in images. We test the proposed method using the NUSCENES dataset and tabletop scenes to confirm its efficacy. The experimental results show that our method acquire high spatial relationship recognition accuracy and has the potential to enable robot manipulation more robust.
Compression Models via Meta-Learning and Structured Distillation for Named Entity Recognition

This paper addresses the issue of high resource consumption in named entity recognition (NER) under large models by utilizing meta-learning and structured distillation to generate lightweight models. Knowledge distillation from commonly used models in NER tasks poses challenges because of the exponentially large output space. Previous work treated it as a structured prediction task for distillation, but did not consider utilizing the feedback from the student model to optimize the student itself. Therefore, this paper proposes Meta-Structured Distillation (MSD). Specifically, this paper incorporates meta-learning into structured distillation, updating the teacher parameters based on the student's performance feedback on the dataset to obtain a better student model. Experimental results demonstrate the effectiveness of this approach, showing improvement over previous work in structured distillation.
Multi-View and Multi-Kernel Graph Neural Networks with Node Relationship Embedding for Graph Representation Learning

Graph neural networks (GNNs) have become increasingly popular for representation learning on graph data. However, when dealing with complex graph data, the representation capacity of GNNs is still limited. We find that previous methods tend to ignore the original representations in the eigenspace and the correlation between different kernels is not strong enough when using a multi-kernel method. In this paper, we propose a novel GNN architecture, named Multi-View and Multi-Kernel Graph Neural Network with Node Relationship Embedding (MVKGR), that leverages information from multiple kernels and constructs decision views to enhance representation learning. We introduce a relational view construction method to enhance the contribution of original representations in the decision view. Experimental results on semi-supervised node classification and LiDAR point cloud semantic segmentation tasks demonstrate that the graph representation capability of the model method proposed in this paper outperforms existing multi-view graph learning methods.
Grounding Language for Robotic Manipulation via Skill Library

Given the language instructions and a raw image, how can we enable robots to reason about semantic concepts and manipulate objects accordingly? Recent research on language-conditioned manipulation tasks has introduced end-to-end frameworks that combine the semantic understanding with the precise spatial reasoning. But these works require lots of training data and fail to generalize to more complex task scenes. We propose a novel Language-Goal-Skill architecture that decouples language-visual grounding and skill learning, which is more effective and generalizable. It leverages pre-trained models to infer manipulation skills, the scene objects and spatial relations, builds a skill library for diverse task scenes. Experiments in simulated settings suggest that our approach achieve a higher success rate for multi skills compared with baseline methods.
ConRS: Context-response Contrastive Learning for Multi-turn Response Selection

Multi-turn response selection in retrieval-based dialogue systems aims to select the most appropriate response from several candidates based on a context history, which is still worth the challenge. Nowadays, Pre-trained Language Models (PLMs) are superior in handling various tasks in natural language processing (NLP) field. In this paper, we apply BERT to the task, which is formulated as a context–response classification task. However, our preliminary experiments show that BERT pays much attention to the semantic relevance between dialogue context and response, but it is lacking of good ability to learn the temporal dependency between utterances within dialogue context. Moreover, we observe that the representations of dialogue context-response pairs sharing the same context are remarkably similar among each other, which means that they are clustered closely in the vector space, making it difficult for the model to distinguish and classify them. To solve these problems, we propose a general Context-response Contrastive learning framework for multi-turn Response Selection, called ConRS. Specifically, our ConRS learns more utterance-level interactions within the given context and helps to better distinguish positive and negative context-response pairs with high similarity. Furthermore, our ConRS is a self-supervised method tailored to context-response without additional labels, and thus is applicable to other existing models. Experiments on two benchmark datasets including corpus of Chinese and English indicate that our proposed ConRS brings significant improvement for the dialogue models.
Sentiment Analysis of Weibo Platform Based on LDA-SnowNLP Model

In order to perform sentiment analysis on Weibo platform posts, a sentiment analysis model combining the LDA model and the machine learning model Snownlp is used to analyze posts data. Python is used to crawl posts with specific keywords within a specified time range. TF-IDF is used for high-frequency word statistics, the LDA model is employed for topic extraction, and the Snownlp model is used for sentiment analysis. Experimental results demonstrate that the model has a relatively clear restorative effect on the topic evolution in Weibo posts. Furthermore, the use of sentiment scores in a line graph intuitively displays the trend of sentiment attitudes in a specific time period.
A Novel Federated Reinforcement Learning Algorithm with Historical Model Update Momentum

Reinforcement learning is a crucial approach for addressing intricate problems in online learning, real-time prediction, and control decision-making. However, the limited efficacy of conventional reinforcement learning algorithms remains a significant impediment to their practical application success. Reconciling the disparity between large sample demands and limited sampling efficiency, a federated reinforcement learning framework is introduced to enable information and model sharing among agents while ensuring data privacy and security. Traditional federated reinforcement learning algorithms still face issues such as unstable model training, limited exploration, huge gradient variance of model update, and susceptibility to local optimality. The present study proposes a Momentum-based Federated Reinforcement Learning (MFRL) algorithm. By incorporating the historical model update momentum into model aggregation, MFRL effectively mitigates the issue of excessive gradient variance during model updates, thereby accelerating algorithmic training speed, enhancing training stability in complex environments, and reducing the risk of local optima. The experimental results demonstrate that the MFRL algorithm outperforms the Federated Average (FedAvg) algorithm and baseline Soft Actor-Critic (SAC) algorithm in terms of average score and convergence speed in several classical continuous control tasks.
Cross-modal sentiment analysis based on Transformer and image-text collaborative interaction

In order to make full use of the interaction between images and texts, improve the extraction effect of each modal feature, and improve the accuracy of multi-modal classification, this paper proposed a sentiment classification method based on Transformer and image-text collaborative interaction. In this model, the image and text features are extracted based on Transformer, and the bidirectional long short-term memory network and attention mechanism are introduced while the text features are extracted, and the Vision Transformer is used to obtain the visual features of the image. Then, a fusion method based on information adaptive weight adjustment is designed, which adjusts the weight parameters of model fusion in real time according to the feature information of image and text to perform weighted fusion. Experiments show that the model improves the accuracy and F1 value.
Elevating Handwritten Mathematical Expression Recognition: Unveiling 2D Structural Insights Through Weak Supervision

Handwritten Mathematical Expression Recognition (HMER) poses a formidable challenge due to its intricate two-dimensional structure, variations in handwriting styles, and other complexities. The intricate nature of the problem offers numerous potential applications. The purpose of this paper is to suggest an architecture that utilizes weak supervision for extracting structural information from images, thereby enhancing the capabilities of HMER. We begin by defining a two-dimensional structural relationship by analyzing LaTeX syntax. Subsequently, we employ a decoder that utilizes Gated Recurrent Units (GRUs) to perceive the structural layout of handwritten expressions. Under weak supervision, Convolutional Neural Networks (CNNs) and a fully connected network work together to process the structure of mathematical expressions written by hand in two dimensions. Experimental results vividly demonstrate the superiority of this approach over alternative models, both in terms of structural and overall recognition accuracy. The model's recognition accuracy for the CROHME 2014 and 2016 datasets for online handwritten mathematical expressions is 55.13 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 52.44 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , respectively. Additionally, the model achieves a structural recognition accuracy of 76.9% and 73.4%.
Enhancing Offline Coverage Path Planning with Deep Reinforcement Learning

Coverage path planning is a critical challenge in various domains, including drones, robots, and automation devices. Traditional methods heavily rely on manually crafted heuristic rules, which struggle to adapt to complex environments and task requirements. This paper proposes a deep reinforcement learning-based algorithm for coverage path planning to achieve automated and intelligent solutions. Three significant contributions are presented. Firstly, curriculum learning is integrated to adjust the training difficulty, facilitating rapid convergence in deep reinforcement learning. Secondly, a coverage heat map is introduced, providing a visual representation of target areas and guiding the convolutional neural network to focus on relevant features, enhancing coverage performance. Lastly, a more reasonable reward function is designed, promoting desirable behavior and facilitating effective exploration and exploitation during learning. Experimental evaluations demonstrate the versatility of the training framework, successfully extending its application to larger and more complex environments. The proposed algorithm is thoroughly tested and exhibits stable and efficient coverage in maps with varying initial positions and random obstacles.
Design Discipline Teaching Reform and Innovation under Blockchain Technology

With the rapid development of technologies such as the Internet of Things, blockchain, artificial intelligence, big data, machine learning and automation, web 3.0 is bound to be a decentralized era. The traditional way of teaching art is unable to unify theory and practice, and can no longer respond to the needs of talents in the digital age. The new liberal arts building, which was started by the Ministry of Education, aims to improve the university teaching reform model and advance the comprehensive integration of contemporary technological education with traditional Chinese liberal arts majors. In this context, the article uses blockchain technology and integrates it into the teaching of design subjects in a targeted manner. The “incentive mechanism” and “distributed ledger” features of blockchain technology are utilized to support student learning, design motivation, and design work intellectual property protection. Implement theory and technology fundamentally to prepare pupils to become comprehensive Talent.
Portmanteau Word Detection Based on LD Algorithm

The advancement of Internet technology has promoted the development of network language, and the vocabulary in the traditional English dictionary can no longer be sufficient for on-trend expressions. People need new words to describe more precisely. The portmanteau word, or the lexical blend, is a common way to create new words from existing ones. This research develops an algorithm to identify whether an English word is a lexical blend and successfully applies it to real-world datasets.
Image Compression and Stable Reconstruction Based on Multi-Level Hybrid Feature Guidance

Image compression is a technique that reduces storage space and transmission bandwidth requirements by reducing the amount of image data, aiming to improve storage and transmission efficiency while maintaining acceptable image quality. In recent years, learning-based image compression methods have demonstrated superior rate-distortion performance compared to traditional image compression standards. Nevertheless, existing approaches often struggle to simultaneously achieve high compression rates and high-quality reconstruction. To tackle this issue, we present an image compression and stable reconstruction method based on multi-level hybrid feature guidance. Firstly, a hybrid attention module that combines spatial, channel, and shuffle attention is designed to improve the model's ability to capture local features of the image. Secondly, we utilize boundary prediction and frequency domain feature prediction auxiliary branches to guide the network for image compression and reconstruction. Moreover, we propose an image compression and reconstruction algorithm framework based on multi-level feature guidance. Based on experimental results, our method surpasses existing approaches in achieving superior rate-distortion performance. These research results are of great significance to the further development of image compression and reconstruction technology.
Robot path planning based on improved A* algorithm

The A* algorithm has been widely employed in the automatic navigation of mobile robots. However, the traditional A* algorithm faces challenges in handling complex environments and large-scale maps due to high computational complexity, large memory requirements, and suboptimal performance under real-time constraints. To address these issues and enhance search efficiency, reduce memory usage, and improve path quality, an improved A* algorithm based on a 24-neighborhood search is proposed. Firstly, a safety strategy is introduced to improve path safety by setting appropriate buffer zones and disabling unsafe search. Secondly, the 24-neighborhood is reduced to eight search intervals according to the orientation of the destination, which reduces the number of turning points and improves search speed. Then, the evaluation function is improved by adding an exponentially weighted turning cost, which speeds up the search process and reduces the total turning angle. Finally, the modified A* algorithm is simulated multiple times using Python, and the results show improvements in planning time, number of turning points, path safety, and total turning angle.
Improving Chinese-Centric Low-Resource Translation Using English-Centric Pivoted Parallel Data

The good performance of Neural Machine Trans-lation (NMT) normally relies on a large amount of parallel data, while the bilingual data between languages are usually insufficient. mBART improves the performance of low-resource translation by pre-training on multilingual monolingual data and then fine-tuning on bilingual data, but does not leverage parallel data which contains crucial alignment information between languages. In this paper, we propose to use English-centric parallel data in a Multilingual NMT (MNMT) manner with English as the pivot, to provide translation and alignment information for the translation between Chinese and other languages. We conduct experiments on the CCMT 2023 low-resource machine translation task between Chinese and the languages among “the Belt and Road”. Our method improves the zh <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rightarrow \text{vi}$</tex> , vi <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rightarrow \text{zh}$</tex> , zh <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rightarrow \text{mn}, \text{mn} \rightarrow \text{zh}$</tex> , zh <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rightarrow \text{cs}$</tex> and cs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\rightarrow \text{zh}$</tex> tasks by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$+1.65, +0.24,+0.91, +3.47, +2.88, +6.35$</tex> BLEU respectively over the strong mBART baseline, showing the effectiveness of our approach and the importance of English-centric parallel data.
A Statistical Study on the Number of Senses and Its Effects on English-Chinese One-to-Many Translation-Equivalent Word Pair Processing

To investigate the influence of the number of senses of the Chinese translation-equivalent words that an English word possesses on the processing of the English-Chinese one-to-many translation-equivalent word pairs by Chinese English as a Foreign Language (EFL) learners, we make a corpus-based calculation and categorization with reference to dictionary definitions to ascertain the numbers of senses of the Chinese translation-equivalent words corresponding to some high-frequency English words. Based upon this, a lexical decision task within the masked priming paradigm is carried out, whose results show a main effect of the number of senses, indicating that participants process the English-Chinese one-to-many translation equivalent word pairs with many senses faster than those with few senses, albeit with higher error rates. This study provides empirical evidence for viewpoints associated with the processing of translation-equivalent word pairs across languages with dissimilar writing scripts (e.g. Chinese and English).
Traditional martial arts boxing recognition based on YOLOv5s

Recognizing traditional Chinese martial arts punches in the video provides a new technical tool for martial arts education and research. There is no direct method to recognize traditional martial arts kung fu. We propose a fast and effective solution to address the low resolution of the video in the process of recognition and the fact that having similar stances can lead to misjudged kung fu. Firstly, adding SPD-Conv to the backbone network of yolov5s can improve the recognition accuracy in low-resolution videos. Secondly, an attention mechanism module that reinforces useful features is added to the C3 module to extract target features and improve recognition accuracy fully. The experimental findings demonstrate that the suggested approach can identify eight conventional fist styles of martial arts, namely Bagua Palm, Bajiquan, Taijiquan, Maze Boxing, Mantis Boxing, Tongbei Boxing, Form Yi Boxing, and Wing Chun Boxing. Compared with target detection algorithms such as YOLOv4 and YOLOv5s, it performs better and can effectively handle the behavioral recognition of actors.
Long-Term Memory for Large Language Models Through Topic-Based Vector Database

Large language models (LLMs) have garnered sub-stantial attention and significantly transformed the landscape of artificial intelligence, due to their human-like understanding and generation capabilities. However, despite their excellent capabilities, LLMs lack the latest information and are constrained by limited context memory, which limits their effectiveness in many real-time applications that require up-to-date information, such as personal AI assistants. Inspired by the recent study on enhancing LLMs with infinite external memory using vector database, this paper proposes a topic-based vector database to enable LLMs to achieve long-term personalized memory. By leveraging prompt engineering to fully utilize the semantic understanding capabilities of LLMs, an efficient topic-based per-sonalized memory management system is designed to store and update user's preferences and characteristics. This system can be applied in various AI assistant domains, such as companion robots, to efficiently store personal memories of users through conversations, ultimately fulfilling their needs in a personalized manner.
A Study of Backdoor Attacks Against the Object Detection Model YOLOv5

With the rapid advancement of hardware sources such as GPU computing capacity, implementing deep learning techniques in computer vision has become a trend. Object detection is one of the core challenges in computer vision research and serves as a crucial foundation for comprehending visual data. This task has been explored extensively in both academic and real-world applications, including surveillance security, autonomous driving, traffic monitoring, drone scene analysis, and robot vision[1]. Current object detection techniques rely primarily on neural networks, particularly large-scale ones, with parameter quantity far exceeding the training data. The resulting excess capacity parameter may pose security risks[3], such as backdoor attacks[13]. However, current research on the security of the model focuses primarily on the classification problem, and lacks relevant research on object detection models for broader applications and more complex problems. In this research, we investigated the viability of backdoor attacks against the traditional object detection model YOLOV5. We were motivated by the loss function of YOLOV5, and when poisoning the data, we took into account the size of the poisoning region and the placement of the poisoning such that the horse was attacked as if it were a human. In the PASACAL 2007 dataset, the improved poisoning approach increased the attack success rate on the detection model by 61.1% and 22.2%, respectively, in comparison to the traditional poisoning methods BadNets[1] and Blended Injection Attack[7].
Korean-Centered Cross-Lingual Parallel Sentence Corpus Construction Experiment

This paper proposes an efficient method for parallel sentence extraction based on LASER model. For our work, we mined reports in 4 languages from DONG-A ILBO website dating from 1999 to 2022. We designed custom sentence segmentation schema based on language features, and regulate the quality of selected sentence with suitable threshold. Experiment on a relatively small data of 2,000 articles (500 articles for each language) demonstrated the outstanding performance of our method, which has laid a solid foundation for our construction of large diachronic Korean-centered four-language parallel sentence corpus.