Reviewerly/exHarmony huggingface dataset:
Research on a Text Quality Assessment System based on BERT Deep Learning

Traditional composition correction is often manually graded by teachers according to certain standards, which will be subjective. Western languages, represented by English, have widely used mature automatic composition evaluation systems in IELTS, TOEFL and other language tests. At present, the Chinese composition automatic evaluation technology is in a rapid development stage, and there are few Chinese composition automatic evaluation systems that can be applied on a large scale. Therefore, with the development of NLP, it is necessary for Chinese educators and learners to use Deep Learning technology to improve the intelligence of Chinese language assessment. Due to the lack of the assessment grade corpus of primary school students’ composition, we have established a corpus of nearly 210,000 compositions manually graded by experts. Through comparative analysis of five kinds of models closely related to neural networks, it is finally found that BERT achieved the best result, up to 45.52%. Therefore, we believe that the BERT Deep Learning model, which is easy to deploy and model, can be used to assist language educators, language test organizers and language learners in the intelligent and auxiliary grade evaluation of primary school students’ compositions.
Women in Scrum Master Role: Challenges and Opportunities*

The demand for workforce in the IT industry is increasing exponentially. Opportunities to attract human resources to this domain need to be identified. Due to the exponential development of the domain, several roles associated with software development emerged, including the role of Scrum Master. We had a series of interviews with persons in leading positions and a focus group with Scrum Masters to establish what are the desired skills for this role, both technical and soft, and the main challenges associated with this role. According to the focus group discussion, the Scrum Master role can be performed as a “single role” or can overlap with managerial or technical roles, depending on the project. We investigated the gender diversity associated with the Scrum Master role and concluded that women have good soft skills (communication, conflict management, empathy) that make them a good fit for this role.
Vision-Based Multiobject Tracking Through UAV Swarm

Conventional multi-sensor multi-object tracking algorithms usually fuse local positions to construct the global situation. However, low-cost cameras that have been widely-used in small-scale UAVs only provide bearing angle measurement but not target positions, which prohibits the application of conventional tracking paradigms. We propose a solution of vision based multi-object tracking through UAV swarm. Given the videos captured by UAVs and the states of the UAVs, the proposed solution fuses visual and geometry information to tackle three tasks: (1) associating the targets reported by different UAVs. (2) computing the targets’ positions in inertial coordinate system. (3) associating the targets reported at different instants. The effectiveness of the proposed solution is evaluated by offline ablation experiments, field scene experiments, and online closed-loop simulation. The source code is available at https://github.com/bitshenwenxiao/MOTL and the simulation video is available at https://youtu.be/erCiENAOEaM.
A Contrastive-Based Adversarial Training Algorithm for HRRP Target Recognition

In recent years, deep learning methods have significantly improved the recognition performance of high-resolution range profiles (HRRP). However, the vulnerability of the deep network to attacks poses a serious threat to the security of radar target recognition systems. In this paper, an adversarial training algorithm based on contrastive learning is proposed that introduces the N-pair loss function and balances the feature space to smooth the decision boundary and improve the robustness. The experimental analysis demonstrates that the proposed method achieves better defense performance than the traditional adversarial training algorithms. The work presented in this paper provides an important step towards improving the security and reliability of deep learning-based radar recognition systems.
Cost-Effective Gaussian Processes Based Extended Target Tracking

With the increasing development of high-resolution sensor techniques, extended target tracking (ETT) attracts more and more attention in civilian and military fields. In order to track an irregular-shape extended target with a large number of measurements per scan in heavy clutter, a cost-effective Gaussian processes (GP)-based probabilistic data association (CGP-PDA) algorithm is proposed in this paper. It consists of the following enhancements: Firstly, a generalized measurement-to-contour association algorithm is proposed to preselect the validated measurements that are associated with the target contour. This algorithm mitigates the influence of clutter within the validation gate on the accuracy of target states estimation. Secondly, based on the above association algorithm and the Fisher information matrix (FIM)-based sub-modular function, a measurement selection method is proposed to choose the informative measurements. These measurements are defined to provide the primary information for target states estimation. This selection method greatly limits the scale of data association events and hence increases the real-time performance of the filter. Finally, the computational complexity of the CGP-PDA together with that of GP-PDA is investigated in detail. The floating-point operations (FLOPs) are invoked to analyze the computational complexity of the above algorithms, and the lower/upper bound on the relative difference of FLOPs between CGP-PDA and GP-PDA is analyzed. The effectiveness of the proposed algorithm is evaluated in simulatiodn.
Subspace Clustering Technique Using Multi-objective Functions for Multi-class Categorical Data

Clustering categorical data has been a challenging task. Most known clustering techniques for such data often achieve excellent performance over binary data, however, their performance on multi-class data has not been as good. We believe this could be improved by taking into account the overlapping and imbalance issues in the clustering process. This would allow detecting small and informative clusters as well, and hence improved cluster quality. In this paper, we introduce a novel subspace technique for multi-class data. For this, we use the notion of entropy from information theory in conjunction with Coa's density function to identify and combine potential sub-clusters from the same classes, revealing smaller clusters. We compare our results to those of well-known subspace clustering approaches, density-based algorithms, and ensemble clustering. The results of our numerous experiments indicate that our proposed technique outperforms the majority of existing solutions.
Multi-task Graph Neural Network for Optimizing the Structure Fairness

Graph neural networks, the mainstream paradigm of graph data mining, optimize the traditional feature-based node classification models with supplementing spatial topology. However, those isolated nodes not well connected to the whole graph are difficult to capture effective information through structural aggregation and sometimes even bring the negative local over-smoothing phenomenon, which is called structure fairness problem. To the best of our knowledge, current methods mainly focus on amending the network structure to improve the expressiveness with absence of the influence of the isolated parts. To facilitate this line of research, we innovatively propose a Multi-task Graph Neural Network for Optimizing the Structure Fairness (GNN-OSF). In GNN-OSF, nodes set is divided into diverse positions with a comprehensive investigation of the correlation between node position and accuracy in global topology. Besides, the link matrix is constructed to express the consistency of node labels, which expects isolated nodes to learn the same embedding and label when nodes share similar features. Afterward, the GNN-OSF network structure is explored by introducing the auxiliary link prediction task, where the task-shared and task-specific layer of diverse tasks are integrated with the auto-encoder architecture. Our comprehensive experiments demonstrate that GNN-OSF achieves superior node classification performance on both public benchmark and real-world industrial datasets, which effectively alleviates the structure unfairness of the isolated parts and leverages off-the -shelf models with the interaction of auxiliary tasks.
Interpreting Deep Text Quantification Models

Quantification learning is a relatively new deep learning task. Differing from a classic classification problem where the class of a single instance is predicted, a quantification model predicts the distribution of classes within a given set of instances. Quantification learning has applications in various domains. For example, in designing political campaign ads, it is important to know the proportion of different aspects voters care about. QuaNet is a recent deep learning quantification model that was shown to achieve good quantification performance. Like many deep learning models, there is no explanation about the contributions of different inputs QuaNet uses to predict a class distribution. In this study, we propose a method to provide such an explanation, which is important to increase users' trust in the model. Our method is the first work on interpreting deep learning quantification models.
Construction of Two Kinds of Optimal Wide-Gap Frequency-Hopping Sequence Sets

The frequency hopping sequence plays a crucial role in determining the system's anti-jamming performance, in frequency hopping communication systems. If the adjacent frequency points of FHS can ensure wide-gap, it will better improve the anti-interference capability of the FH communication system. Moreover, if the period of the sequence is expanded, and each frequency point does not repeat in the same sequence, the system's ability to resist electromagnetic interference will be enhanced. And a one-coincidence frequency-hopping sequence set consists of FHSs with maximum Hamming autocorrelation 0 and cross-correlation 1. In this paper, we present two constructions of wide-gap frequency-hopping sequence sets. One construction is a new class of wide-gap one-coincidence FHS set, and the other is a WGFHS set with long period. These two WGFHS sets are optimal with respect to WG-Peng-Fan bound. And each sequence of these WGFHS sets is optimal with respect to WG-Lempel-Greenberger bound.
Ontology Population from French Classified Ads

Understanding texts written in natural language is a challenging task. Semantic Web technologies, in particular ontologies, can be used to represent knowledge from a specific domain and reason like a human. Ontology population from texts aims to transform textual contents into ontological assertions. This paper deals with an approach of automatic ontology population from French textual descriptions. This approach has been designed to be domain-independent, as long as a domain ontology is provided. It relies on text-based and knowledge-based analyses, which are fully explained. Experiments performed on French classified advertisements are discussed and provide encouraging results.
Target and Precursor Named Entities Recognition from Scientific Texts of High-Temperature Steel Using Deep Neural Network

Named Entity Recognition (NER) is an essential task in natural language processing, especially in the domain of scientific texts. This paper presents a study of NER for scientific texts in high-temperature steel, a type of alloy used in various applications where high temperatures prevail. We propose a NER system using Bi-LSTM with a domain-specific embedding approach and evaluate its performance on a test dataset. The study results show that the proposed NER system achieves an F1 score of 0.99, indicating that it can accurately identify and classify named entities in scientific texts about high-temperature steel with high precision and recall. The proposed approach was more effective than the classical machine learning-based approach. Our results suggest that the domain-specific embedded Bi-LSTM technique can be an effective approach for NER in scientific texts, especially in specialized domains such as high-temperature steel.
Financial Argument Quality Assessment in Earnings Conference Calls

Arguments are ubiquitous. Yet, the definition of what is a good argument depends on the goal, and the settings. Although various business communication studies confirm the crucial role of argumentation, no work has shaped financial argument quality in a way that is concise enough for a practical application. In this paper, we aim to close this research gap by modeling the quality of managers' arguments, during the Q &A sessions of earnings conference calls. To this end, we propose various quality dimensions at both levels of argument and argument units. Our quality model establishes a well-considered link between, on the one hand, insights as they are expressed in financial text analysis literature, and, on the other hand, insights derived from empirical quality descriptions as provided by argumentation discourse linguistics and computational models. We further conducted the related annotation study and produced FinArgQuality, the first financial dataset annotated with argument quality. This corpus composes of 14,146 sentences in a total of 80 earnings calls transcripts. Our proposed quality assessment dimensions, and final annotated corpus are publicly available, and can serve as strong baselines for future work in both FinNLP and computational argumentation disciplines. We further discuss some potential optimization goals and financial applications of this data, and highlight future directions.
CF-SAFF: Collaborative Filtering Based on Self-attention Mechanism and Feature Fusion

User features and item features are important information for recommendation systems, and their interaction significantly improves the accuracy of recommendations. Existing work classifies types of interactions as internal and cross interactions. However, it does not take into consideration the different importance of information in cross interactions, nor captures more complex dependencies between features with higher-order interactions. To solve this problem we propose Collaborative Filtering based on Self-Attention Mechanism and Feature Fusion (CF-SAFF). It explicitly uses internal interactions for user and item feature learning, and assigns different weights to cross interactions based on the Self-Attention mechanism to represent the different importance of interaction nodes, thereby performing preference matching on recommendations. At the same time, the fusion operation of internal interaction information and cross interaction information is used to discover high-order feature combination information, which improves the prediction accuracy and generalization ability of the model. The model has conducted extensive experiments on public standard datasets, and the results show that the model has achieved better results than previous mainstream models in all performance indicators.
Deep Multi-interaction Hidden Interest Evolution Network for Click-Through Rate Prediction

Click-Through Rate (CTR) prediction plays a crucial role in the field of recommendation systems. Some previous works treat the user's historical behavior as a sequence to uncover the hidden interests behind it. However, these works often ignore the dependencies and dynamic interests between different user behaviors evolving over time, as well as hidden information by user representation. To solve the above problems, we propose Deep Multi-Interaction Hidden Interest Evolution Network (MIHIEN). Specifically, we first design Hidden Interest Extraction Layer (HIE) to initially mine the hidden interests of users evolving over time from it, which can better reflect the user representation. The deeper interests of users are then explored in two types of interactions in the Item-to-Item Sub-network (IISN) and the User-to-Item Sub-network (UISN), respectively. The experimental results show that our proposed MIHIEN model outperforms other previous mainstream models.
Efficient Video Captioning with Frame Similarity-Based Filtering

Video captioning combines computer vision and Natural Language Processing (NLP) to perform the challenging task of scene understanding. The rapid advancements in artificial intelligence have led to a growing interest in video captioning, which involves generating natural language descriptions based on the visual content of videos. In this paper, we present a novel approach to video caption generation. The proposed method first extracts frames from the video and reduces the number of frames based on their similarity. The remaining frames are then processed by a Convolution Neural Network (CNN) to extract a feature vector, which is then fed into a Long Short-Term Memory (LSTM) network to generate the captions. The results are compared with the state-of-the-art models which demonstrate that the proposed approach outperforms the existing methods on MSVD, M-VAD, and MPII-MD datasets.
Hierarchy-Aware Bilateral-Branch Network for Imbalanced Hierarchical Text Classification

Hierarchical text classification is an essential task in natural language processing. Existing studies focus only on label hierarchy structure, such as building classifiers for each level of labels or employing the label taxonomic hierarchy to improve the hierarchy classification performance. However, these methods ignore issues with imbalanced datasets, which present tremendous challenges to text classification performance, especially for the tail categories. To this end, we propose Hierarchy-aware Bilateral-Branch Network (HiBBN) to address this problem, where we introduce the bilateral-branch network and apply a hierarchy-aware encoder to model text representation with label dependencies. In addition, HiBBN has two network branches that cooperate with the uniform sampler and reversed sampler, which can deal with the data imbalance problem sufficiently. Therefore, our model handles both hierarchical structural information and modeling of tail data simultaneously, and extensive experiments on benchmark datasets indicate that our model achieves better performance, especially for fine-grained categories.
CAGAIN: Column Attention Generative Adversarial Imputation Networks

Imputation for missing values is a key operation in building data analysis models. In this paper, we target numerical and categorical values in tabular data. While previous studies have demonstrated the effectiveness of state-of-the-art methods, a major limitation is that these methods lack robustness and their performance significantly varies across datasets and the missing rate of values, hence posing considerable overhead of selecting and tuning models in a real-world scenario. To tackle this problem, we propose a Column Attention Generative Adversarial Imputation Network (CAGAIN), an imputation model which employs a generative adversarial network (GAN) and the attention mechanism. The generator of CAGAIN mimics the distribution of original data and generates imputed samples similar to real ones. The discriminator of CAGAIN distinguishes real and generated samples, so as to improve the quality of the imputed data. At the same time, the attention mechanism captures the correlation between attributes and focuses on the most significant attributes that determine the values of the missing positions. By inheriting the advantages of GAN and the attention mechanism, our model is endowed with robustness to shifting datasets and missing rates, which is demonstrated by experiments using 9 real datasets.
Except-Condition Generative Adversarial Network for Generating Trajectory Data

Location data shared on social media is collected and processed as trajectory data, which exposes individuals to leakage of sensitive information, such as sensitive geographic areas. A typical countermeasure is a generative adversarial network (GAN) model that ensures data anonymity. However, generating data selectively by identifying only sensitive areas is difficult. In this study, we propose an except-condition GAN (exGAN) model that generates synthetic data while maintaining the original's utility. This model ensures the anonymity of sensitive areas and maintains the distribution of data in relatively less sensitive areas. It uses a method that assigns the remaining labels except for specific selected labels as a condition. The selected labels represent points of high sensitivity, and the trajectory data generated by the model contains only points corresponding to the labels, except for the selected labels. Furthermore, in our comparative evaluation of the exGAN model, it outperformed the original GAN model.
Few-Shot Multi-label Aspect Category Detection Utilizing Prototypical Network with Sentence-Level Weighting and Label Augmentation

Multi-label aspect category detection is intended to detect multiple aspect categories occurring in a given sentence. Since aspect category detection often suffers from limited datasets and data sparsity, the prototypical network with attention mechanisms has been applied for few-shot aspect category detection. Nevertheless, most of the prototypical networks used so far calculate the prototypes by taking the mean value of all the instances in the support set. This seems to ignore the variations between instances in multi-label aspect category detection. Also, several related works utilize label text information to enhance the attention mechanism. However, the label text information is often short and limited, and not specific enough to discern categories. In this paper, we first introduce support set attention along with the augmented label information to mitigate the noise at word-level for each support set instance. Moreover, we use a sentence-level attention mechanism that gives different weights to each instance in the support set in order to compute prototypes by weighted averaging. Finally, the calculated prototypes are further used in conjunction with query instances to compute query attention and thereby eliminate noises from the query set. Experimental results on the Yelp dataset show that our proposed method is useful and outperforms all baselines in four different scenarios.
Postmodern Human-Machine Dialogues: Pedagogical Inquiry Experiments

In the classical Human-Machine Dialogue (HMD) setting, existing research has mainly focused on the objective quality of the machine answer. However, it has been recently shown that humans do not perceive in the same manner a human made answer and respectively a machine made answer. In this paper, we put ourselves in the context of conversational Artificial Intelligence software and introduce the setting of postmodern human machine dialogues by focusing on the factual relativism of the human perception of the interaction. We demonstrate the above-mentioned setting in a practical setting via a pedagogical experiment using ChatGPT3.