Reviewerly/exHarmony huggingface dataset:
Cooperative Path Planning for Multi-vehicle Systems by an Integrated Intelligent Algorithm of IABC and DWA

This brief mainly studies the issue of cooperative path planning (CPP) for multi-vehicle systems consisting of car-like models based on the integration of the improved artificial bee colony (IABC) algorithm and the dynamic window approach (DWA). An integrated intelligent algorithm is proposed by considering the objective function constructed of three different criteria formulated by IABC, including the goal shortest path, the collision avoidance between the vehicles and the obstacles, and the desired path smoothness, and then an optimized multi-objective evaluation function realized by DWA, which yield that an optimal collision-free smoothness path is successfully generated. Subsequently, the effectiveness and advantages of the developed CPP algorithm are furthermore illustrated by the simulation experiments, as well as the efficiency comparison between the standard artificial bee colony (ABC) algorithm and the IABC algorithm.
Design and Implementation of Integrated Operations Control Center Based on Cloud Architecture

As an important part of aerospace engineering, integrated operations control center is usually responsible for the reception, processing, storage and application analysis of satellite in-orbit test data. The traditional integrated operations control center is faced with some problems, such as tight coupling of software and hardware, low efficiency of resource sharing and poor system expansion ability, which limit the development of the aerospace TT &C system. In response to the above issues, an integrated operations control center system based on cloud architecture is proposed and designed to realize resource sharing and interaction, service customization and system expansion, which has been verified by space missions.
Improved GWO-WOA and Fuzzy NN DWA Based Path Planning Algorithm for the UAV in Dynamic Environment

An improved path planning algorithm for the unmanned aerial vehicles (UAVs) is developed, which is based on the combination of grey wolf optimizer-whale optimization algorithm (GWO-WOA) and fuzzy neural network dynamic window approach (FNN-DWA). It aims to realize dynamic path planning in a spherical obstacle environment. Firstly, an improved GWO and WOA based global planning algorithm is developed, in which an adaptive position updating equation is designed to improve GWO’s convergence speed. Meanwhile, inspired by the spiral bubble nets strategy in WOA and levy flight strategy in cuckoo search algorithm (CS), a random and the best individual’s information based random wandering strategy is developed to improve GWO’s global exploration ability. Then, an improved FNN-DWA based local planning algorithm is proposed, and the path points of GWO-WOA are set as goals for local planning. Based the proposed fuzzy logic based DWA (FDWA), FNN is utilized to train the input membership functions’ parameters and the connection weights. It aims to improve the algorithm’s adaptability to complex dynamic environments. Finally, simulations results are obtained to verify the effective performance of the designed scheme to realize dynamic path planning for the UAV.
Analyzing and Recognizing Interlocutors' Gaze Functions from Multimodal Nonverbal Cues

A novel framework is presented for analyzing and recognizing the functions of gaze in group conversations. Considering the multiplicity and ambiguity of the gaze functions, we first define 43 nonexclusive gaze functions that play essential roles in conversations, such as monitoring, regulation, and expressiveness. Based on the defined functions, in this study, a functional gaze corpus is created, and a corpus analysis reveals several frequent functions, such as addressing and thinking while speaking and attending by listeners. Next, targeting the ten most frequent functions, we build convolutional neural networks (CNNs) to recognize the frame-based presence/absence of each gaze function from multimodal inputs, including head pose, utterance status, gaze/avert status, eyeball direction, and facial expression. Comparing different input sets, our experiments confirm that the proposed CNN using all modality inputs achieves the best performance and an F value of 0.839 for listening while looking.
Efficient PBFT Algorithm Research Based on Credit Value Model

With the widespread attention to blockchain technology, the consensus algorithms that affect its performance have also come into focus. In response to the problem of complicated communication and inappropriate selection of master nodes in the PBFT algorithm consensus, an improved PBFT consensus algorithm (RC-PBFT) based on the credit value model is proposed. Firstly, the reputation value model is constructed according to the historical behavior of the nodes, the nodes are classified, and appropriate master nodes are selected to ensure reasonable selection. Secondly, by combining the UNL concept in the Ripple algorithm, the consistency protocol is optimized, reliable nodes are selected to carry out, and the algorithm efficiency is improved. Finally, the node type is dynamically adjusted, malicious nodes are deleted, and the security and scalability of the algorithm are improved. Through experimental analysis, it is shown that the RC-PBFT master nodes are safer and the consensus efficiency is higher, and in terms of throughput, consensus delay, communication overhead, etc., it is better than the PBFT algorithm.
Knowledge Management and Its Role in the Innovative Performance of the Iraqi Ministry of Youth and Sports

The study aimed to determine the role of knowledge management and its dimensions (knowledge creation - knowledge sharing - knowledge application - knowledge storage) in the innovative performance of the Iraqi Ministry of Youth and Sports in terms of connection and influence, as well as defining Levels of awareness, practice, and application of the study variables and their dimensions. To achieve the objectives, two main hypotheses were built. (H1-H2). The study dealt with the youth and sports sector represented by the Iraqi Ministry of Youth and Sports. The target community represented the directors and middle and senior leadership in the ministry with a total of (82) directors and a sample of (77) directors from various formations and levels of the ministry. Data were collected using several methods, including questionnaires, personal interviews, field experience, personal observation, reports, and official statements. To analyze the data, statistical program were used (SPSS_V.26) A set of statistical tools were used to reach the results, the most important of which is (mean - coefficient of variation - Standard deviation - Content validity - Simple linear regression - Pearson correlation coefficient), and one of the most important findings of the study is that there is a positive impact of knowledge management on innovative performance, and the knowledge application dimension was the most closely linked and Influencing innovative performance.
Overview of NLPCC 2023 Shared Task 6: Chinese Few-Shot and Zero-Shot Entity Linking

Entity Linking (EL) is the task of grounding a textual mention in context to a corresponding entity in a knowledge base. However, current EL systems demonstrate a popularity bias, significantly underperforming on tail and emerging entities. To this end, we organize NLPCC 2023 Shared Task 6, i.e., Chinese Few-shot and Zero-shot Entity Linking, which aims at testing the generalization ability of Chinese EL systems to less popular and newly emerging entities. The dataset for this task is a human-calibrated and multi-domain Chinese EL benchmark with Wikidata as KB, consisting of few-shot and zero-shot test sets. There are 22 registered teams and 13 submissions in total, and the highest accuracy is 0.6915. The submitted approaches focus on different aspects of this problem and use diverse techniques to boost the performance. All relevant information can be found at https://github.com/HITsz-TMG/Hansel/tree/main/NLPCC .
The impact of e-learning system adoption on the practical skills of accounting students in Jordan

The study aimed to explore the effectiveness of the e-learning system in teaching scientific and humanities disciplines and its suitability to provide accounting students (specifically) in Jordanian universities with the practical skills necessary for the labor market. The qualitative approach was adopted for collecting, analyzing and testing data. The researchers designed an electronic questionnaire that was distributed to 3000 accounting students in 30 Jordanian universities for data collection. Only 762 questionnaires were retrieved and analyzed which formed the sample for the study. The descriptive analysis and linear regression test were used to analyze data and test hypotheses. The results showed that accounting students in Jordanian universities think that the e-learning system is ineffective in fulfilling the teaching requirements for humanities and scientific disciplines with the necessary skills to practice the profession after graduation. The adoption of e-learning systems in the teaching process of accounting majors leads to a decline in the practical skills of accounting students. The study recommended that Jordanian universities conduct a permanent and continuous evaluation of e-learning systems to ensure their effectiveness and efficiency in achieving the desired goals. Furthermore, Jordanian universities have to build effective review, evaluation and testing mechanisms for students’ skills through e -learning to ensure the required practical skills.
Fantastic Gradients and Where to Find Them: Improving Multi-attribute Text Style Transfer by Quadratic Program

Unsupervised text style transfer (TST) is an important task with extensive implications in natural language generation (NLG). A prevalent approach involves editing the latent representations of text, guided by gradients from an attribute classifier. However, in multi-attribute TST, the simultaneous satisfaction of all required attributes remains challenging. In this paper, we unveil that the gradient direction during editing might conflict with certain attribute representations through empirical analysis. To tackle this problem, we introduce a mathematical programming method to impose constraints on the editing direction of multiple attributes, effectively mitigating potential attribute conflicts during the inference stage. Our proposed method considers the potential conflict between different attributes for the first time. Experimental results from the YELP benchmark showcase that our method can effectively improve the multi-attribute-transfer accuracy and quality without compromising single attribute performance. Moreover, our method can be readily integrated with pre-trained auto-encoders, providing an effective and scalable solution for multi-attribute scenarios.
Improving the Generalization Ability in Essay Coherence Evaluation Through Monotonic Constraints

Coherence is a crucial aspect of evaluating text readability and can be assessed through two primary factors when evaluating an essay in a scoring scenario. The first factor is logical coherence, characterized by the appropriate use of discourse connectives and the establishment of logical relationships between sentences. The second factor is the appropriateness of punctuation, as inappropriate punctuation can lead to confused sentence structure. To address these concerns, we propose a coherence scoring model consisting of a regression model with two feature extractors: a local coherence discriminative model and a punctuation correction model. We employ gradient-boosting regression trees as the regression model and impose monotonicity constraints on the input features. The results show that our proposed model better generalizes unseen data. The model achieved third place in track 1 of NLPCC 2023 shared task 7. Additionally, we briefly introduce our solution for the remaining tracks, which achieves second place for track 2 and first place for both track 3 and track 4.
Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting

Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attacks. Large Language Models (LLMs) also inevitably experience decreased performance when confronted with adversarial examples, even for the GPT-3.5 model. In this paper, we propose a simple yet very effective Cycle Self-Augmenting (CSA) method and conduct a thorough robustness evaluation of cutting-edge GEC methods with three different types of adversarial attacks. By leveraging the augmenting data generated from the GEC models themselves in the post-training stage and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs at an extra cost. Experiments indicate that our proposed training method can significantly enhance the robustness and performance of the state-of-the-art GEC model.
A Multi-granularity Similarity Enhanced Model for Implicit Event Argument Extraction

Implicit Event Argument Extraction (Implicit EAE) aims to extract the document event arguments given the event type. Influenced by the document length, the arguments scattered in different sentences can potentially lead to two challenges during extraction: long-range dependency and distracting context. Existing works rely on the contextual capabilities of pre-trained models and semantic features but lack a straightforward solution for these two challenges and may introduce noise. In this paper, we propose a Multi-granularity Similarity Enhanced Model to solve these issues. Specifically, we first construct a heterogeneous graph to incorporate global information, then design a supplementary task to tackle the above challenges. For long-range dependency, span-level enhancement can directly close the semantic distance between trigger and arguments across sentences; for distracting context, sentence-level enhancement makes the model concentrate more on effective content. Experimental results on RAMS and WikiEvents demonstrate that our proposed model can obtain state-of-the-art performance in Implicit EAE.
A Hybrid Summarization Method for Legal Judgment Documents Based on Lawformer

Legal Judgment Summarization (LJS) is a crucial task in the field of Legal Artificial Intelligence (LegalAI) since it can improve the efficiency of case retrieval for judicial work. However, most existing LJS methods are confronted with the challenges of long text and complex structural characteristics of legal judgment documents. To address these issues, we propose a hybrid method of extractive and abstractive summarization with encoding by Lawformer to enhance the quality of LJS. In this method, by segmentation, long legal judgment documents can be shortened into three relatively short parts according to their specific structure. Furthermore, Lawformer, a new pre-trained language model for long legal documents, is applied as an encoder to deal with the long text problem. Additionally, different summarization models are applied to summarize the corresponding part in terms of its structural characteristics, and the obtained summaries of each part are integrated into a high-quality summary involving both semantic and structural information. Extensive experiments are conducted to verify the performance of our method, and the comparative results show that the summary obtained by our method outperforms all other baselines in matching with the reference summary. It is indicated that our method is effective for LJS and has prospects for LegalAI applications.
WikiIns: A High-Quality Dataset for Controlled Text Editing by Natural Language Instruction

Text editing, i.e., the process of modifying or manipulating text, is a crucial step in human writing process. In this paper, we study the problem of controlled text editing by natural language instruction. According to a given instruction that conveys the edit intention and necessary information, an original draft text is required to be revised into a target text. Existing automatically constructed datasets for this task are limited because they do not have informative natural language instruction. The informativeness requires the information contained in the instruction to be enough to produce the revised text. To address this limitation, we build and release WikiIns, a high-quality controlled text editing dataset with improved informativeness. We first preprocess the Wikipedia edit history database to extract the raw data (WikiIns-Raw). Then we crowdsource high-quality validation and test sets, as well as a small-scale training set (WikiIns-Gold). With the high-quality annotated dataset, we further propose automatic approaches to generate a large-scale “silver” training set (WikiIns-Silver). Finally, we provide some insightful analysis on our WikiIns dataset, including the evaluation results and the edit intention analysis. Our analysis and the experiment results on WikiIns may assist the ongoing research on text editing. The dataset, source code and annotation guideline are available at https://github.com/CasparSwift/WikiIns .
RAC-BERT: Character Radical Enhanced BERT for Ancient Chinese

In recent years, Chinese pre-training language models have achieved significant improvements in the fields, such as natural language understanding (NLU) and text generation. However, most of these existing pre-trained language models focus on modern Chinese but ignore the rich semantic information embedded for Chinese characters, especially the radical information. To this end, we present RAC-BERT, a language-specific BERT model for ancient Chinese. Specifically, we propose two new radical-based pre-training tasks, which are: (1) replacing the masked tokens with random words of the same radical, that can mitigate the gap between the pre-training and fine-tuning stages; (2) predicting the radical of the masked token, not the original word, that reduces the computational effort. Extensive experiments were conducted on two ancient Chinese NLP datasets. The results show that our model significantly outperforms the state-of-the-art models on most tasks. And we conducted ablation experiments to demonstrate the effectiveness of our approach. The pre-trained model are publicly available at https://github.com/CubeHan/RAC-BERT
ERNIE-AT-CEL: A Chinese Few-Shot Emerging Entity Linking Model Based on ERNIE and Adversarial Training

This article proposes a Chinese few-shot emerging entity linking model based on ERNIE and adversarial training. The model utilizes ERNIE as the base model and achieves accurate linking of Chinese few-shot emerging entities by adding adversarial perturbations during the training process. Experimental results on standard entity linking evaluation datasets demonstrate significant performance improvements of our proposed model compared to baseline models. Moreover, we compare multiple candidate entity retrieval methods through comparative experiments to evaluate and compare their effectiveness in the entity linking task. The experimental results show that our appoarch achieved an F1 score of 0.60 and ranked second in the NLPCC 2023 Shared Task 6 (Chinese Few-shot and Zero-shot Entity Linking). Experimental results demonstrate that the model has high predictive performance and robustness in the Chinese few-shot emerging entity linking task, providing reference and inspiration for research and practice in related fields.
Two-Stage Topic Sentence Extraction for Chinese Student Essays

In this paper, we present the method proposed by our team for Track 2 of NLPCC 2023 Shared Task 7, which focuses on the extraction of paragraph-level and whole essay topic sentences in middle school student essays. This paper proposes a two-stage topic sentence extraction framework for each paragraph and the whole essay. In the first stage, we extract topic sentences for each paragraph, considering local semantic and contextual aspects. In the second stage, we derive the text topic sentence for the whole essay from the extracted paragraph-level topic sentences. Compared with the one-stage method, the two-stage method which can focus on the local semantic information of paragraphs related to the task has advantages in paragraph and full-text topic sentence extraction. Comparative experiments show that the extraction performance of the fine-tuned two-stage topic sentence extraction framework surpasses the few-shot large language models (GPT-3.5 et al.). The final comprehensive index also achieved the first-place result in this track.
Semantic Extension for Cross-Modal Retrieval of Medical Image-Diagnosis Report

In recent years, cross-modal hash retrieval has provided a new perspective for computer-aided diagnostic systems. However, in the field of cross-modal retrieval, the current mainstream methods all adopt a simple similarity discrimination strategy based on shared labels or the number of shared labels, which will lead to the isolation of semantics between labels and the loss of similar retrieval results. In this study, we propose a cross-modal retrieval method called Semantic Extension for Cross-Modal Retrieval of medical images and diagnostic reports (SECMR) designed to exploit hierarchal semantic associations between labels to constrain a hash code with more semantics. By mining the hierarchical association between disease labels and transforming it into multi-level semantic matrices, different weights are assigned according to the granularity level to guide the learning of image and text hash codes. In addition, we also introduce the focal loss function to mitigate class imbalance, which is a common problem with classification models based on medical data. Experiments conducted on the MIMIC-CXR dataset demonstrate that our cross-modal retrieval method surpasses other classic cross-modal retrieval models and achieves impressive performance.
SeSQL: A High-Quality Large-Scale Session-Level Chinese Text-to-SQL Dataset

As the first session-level Chinese dataset, CHASE contains two separate parts, i.e., 2,003 sessions manually constructed from scratch (CHASE-C), and 3,456 sessions translated from English SParC (CHASE-T). We find the two parts are highly discrepant and incompatible. In this work, we present SeSQL, a high-quality large-scale session-level Chinese text-to-SQL dataset, consisting of 5,028 sessions all manually constructed from scratch. Compared with previous datasets, in order to guarantee data quality, we adopt an iterative annotation workflow to facilitate intense and in-time review of previous-round natural language (NL) questions and SQL queries. Moreover, by completing all context-dependent NL questions, we obtain 27,012 context-independent question/SQL pairs, allowing SeSQL to be used as the largest dataset for single-round text-to-SQL parsing. We conduct benchmark session-level text-to-SQL parsing experiments on SeSQL via employing three competitive session-level parsers, and present detailed analysis.