Reviewerly/exHarmony huggingface dataset:
Multivariate Time Series Anomaly Detection Method Based on mTranAD

Multivariate time series anomaly detection is a crucial area of research in several domains, including finance, logistics, and manufacturing. Successfully identifying abnormal behaviors or events can help prevent disruptions, but the high false positive rate in this field is a significant challenge that affects detection accuracy. In this paper, we propose a novel method, mTranAD, which improves upon the TranAD algorithm by leveraging the benefits of Transformer and variational autoencoder (VAE) in multivariate unsupervised anomaly detection. Specifically, mTranAD replaces TranAD’s autoencoder structure with a VAE and trains it using the VAE’s loss function. The incorporation of latent variables in the VAE model enables accurate reconstruction of data and mapping of data to a lower dimensional latent space, allowing for a more efficient description of input data complexity with fewer parameters. By utilizing these latent variables, the model can effectively handle high-dimensional, complex data and exhibit greater flexibility when generating new data. We conduct experiments on four public datasets (NAB, MBA, SMAP and WADI) and compare mTranAD’s performance with 11 other state-of-the-art methods, including TranAD, MERLIN, LSTM-NDT, OmniAnomaly, USAD, and DAGMM. The experimental results demonstrate that mTranAD outperforms these methods in terms of performance, accuracy, and reliability. The primary purpose of this paper is to improve the TranAD algorithm and enhance the accuracy of multivariate time series anomaly detection by reducing the false positive rate.
Speaker-Aware Dialogue Discourse Parsing with Meta-Path Based Heterogeneous Graph Neural Network

Dialogue Discourse Parsing aims to identify the discourse links and relations between utterances, which has attracted more and more research interest in recent years. However, the speaker, the most essential characteristic of dialogue, has not been fully considered in most previous works. Accordingly, we propose a speaker-aware meta-path based heterogeneous graph neural network, including node-level aggregation and meta-path level aggregation. Concretely, firstly we construct a novel heterogeneous graph and define two meta-paths: intra-speaker meta-path and inter-speaker meta-path. Secondly, node-level aggregation aims to aggregate corresponding neighbors along each meta-path and then meta-path level aggregation uses a fusion gate to get the optimal combination of neighbors from different meta-paths. Experiments on Molweni and STAC demonstrate the effectiveness of our model, compared with previous state of the art systems.
Deep Reinforced Active Learning for Time Series Anomaly Detection

Massive time series data are recorded in various domains. Identifying exceptional data objects within time series can help avoid potential faults, dangers, and accidents, which is significant in maintaining the target system’s health and stability. Recent years have witnessed a long list of unsupervised time-series anomaly detection models that can only work without any control after deployment. This motivates us to consider an intriguing question: can we devise a model that adaptively and iteratively evolves according to the interaction with human analysts. We intend to install a handle on the model, thus realizing a “human-in-the-loop” learning paradigm. However, it is still a non-trivial task due to the difficulty of (i) accurately exploring valuable data from the unlabeled set as query samples and (ii) fully exploiting returned annotated data. To tackle these challenges, this paper proposes a novel reinforced active time series anomaly detection algorithm. We first propose to use the ensemble of unsupervised anomaly scoring, and by leveraging the derived anomaly scores, we devise two reward strategies. The learning process is guided by these reward strategies, during which the agent is encouraged to explore possible anomalies hidden in the unlabeled set. These potential anomalies are submitted as queries for human labeling and further exploited in our reward functions to supervise the agent taking expected actions. Extensive experiments on real-world datasets demonstrate that our method substantially outperforms five state-of-the-art competitors and obtains 12.1%–60.3% $${F}_{1}$$ score improvement, and 11.5%–59.1% AUC-PR improvement.
A Novel Algorithm to Multi-view TSK Classification Based on the Dirichlet Distribution

With the help of multi-view classification technology, the classification performance can be effectively improved. However, the traditional multi-view TSK classification method has the problem of dimension explosion when superimposing the features of multiple views. This paper proposes an innovative multi-view TSK classification method, which makes individual decisions in each view, and then selects the most credible decision results to obtain comprehensive classification results. Compared with other algorithms, our algorithm shows strong competitiveness in accuracy rate. This study also conducts experimental validation on different datasets, proving that this method exhibits good performance in multiple domains. Different views are dynamically integrated by the multi-view decision TSK algorithm by at the evidence level, providing a feasible idea for multi-view TSK classification. A new method of multi-view TSK classification has been developed in this study, which addresses the issue of high dimensionality present in traditional multi-view TSK classification methods. The experimental results provide strong evidence in support of the feasibility and potential for this proposed method of implementation and application.
Improved YOLOv5s Method for Nut Detection on Ultra High Voltage Power Towers

As an important power transmission facility, the operational stability and safety of ultra high voltage (UHV) power transmission towers are crucial to energy supply and social stability. With the continuous development of science and technology and artificial intelligence technology, the research and development of UHV tower maintenance robots has become an inevitable trend. However, the existing vision-based maintenance robot research is not yet mature, and there are problems such as poor real-time performance, low positioning accuracy, large distance measurement error, and poor performance of embedded devices. To solve these problems, this paper proposes a lightweight nut object detection algorithm based on YOLOv5s and MobileNetV3. In addition, experiments were conducted on the generated nut dataset. Compared with YOLOv5s, the proposed method can reduce the model size by 77.78% and increase the detection speed by 4.17%, and improved accuracy by 0.73%. The experimental results show that the improved algorithm greatly reduces the model size and improves the detection speed while maintaining the original accuracy, and effectively solves the problems of poor real-time detection and poor performance of embedded devices in existing methods.
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error

Training agents via off-policy deep reinforcement learning algorithm requires a replay memory storing past experiences that are sampled uniformly or non-uniformly to create the batches for training. When calculating the loss function, off-policy algorithms commonly assume that all samples are of equal importance. We introduce a novel algorithm that assigns unequal importance, in the form of a weighting factor, to each experience, based on their distribution of temporal difference (TD) error, for the training objective. Results obtained with uniform sampling from the experiments in eight environments of the OpenAI Gym suite show that the proposed algorithm achieves in one environment 10% increase in convergence speed along with a similar success rate and in the other seven environments 3%–46% increases in success rate or 3%–14% increases in cumulative reward, along with similar convergence speed. The algorithm can be combined with existing prioritization method employing non-uniform sampling. The combined technique achieves 20% increase in convergence speed as compared to the prioritization method alone.
RA-KD: Random Attention Map Projection for Knowledge Distillation

Pretrained language models such as BERT, ELMO, and GPT have proven to be effective for natural language processing tasks. However, deploying them on computationally constrained devices poses a challenge. Moreover, the practical application of these models is affected by the training and deployment of large-scale pretrained language models. While knowledge distillation (KD) in the intermediate layer can improve standard KD techniques, especially for large-scale pretrained language models, distillation in the intermediate layers brings excessive computational burdens and engineering difficulties in mapping the middle layer of a student model with a variable number of layers. The attention map is one of the essential blocks in the intermediate layer. To address these problems, we propose an approach called random attention map projection, in which the intermediate layers are randomly selected from the teacher model. Then, the attention map knowledge is extracted to the student model's attention block. This approach enables the student model to capture deeper semantic information while reducing the computational cost of the intermediate layer distillation method. We conducted experiments on the GLUE dataset to verify the effectiveness of our approach. Our proposed RA-KD approach performs considerably better than other KD approaches in both performance and training time.
An Unsupervised Video Summarization Method Based on Multimodal Representation

A good video summary should convey the whole story and feature the most important content. However, the importance of video content is often subjective, and users should have the option to personalize the summary by using natural language to specify what is important to them. Moreover, existing methods usually apply only visual cues to solve generic video summarization tasks, while this work introduces a single unsupervised multi-modal framework for addressing both generic and query-focused video summarization. We use a multi-head attention model to represent the multi-modal feature. We apply a Transformer-based model to learn the frame scores based on their representative, diversity and reconstruction losses. Especially, we develop a novel representative loss to train the model based on both visual and semantic information. We outperform previous state-of-the-art work with superior results on both generic and query-focused video summarization datasets.
FRVidSwin:A Novel Video Captioning Model with Automatical Removal of Redundant Frames

Video captioning aims to generate natural language sentences that describe the visual content of given videos, which requires long-range temporal modeling and consumes significant computational resources. Existing methods typically operate on frames uniformly sampled from videos, leading to time scale inconsistency and redundancy in contiguous frames. In this paper, we propose a transformer-based architecture called Frame-Reduce Swin transformer (FRVidSwin) for video captioning. Our method takes a frame sequence along with the frame indices sampled from a video as input and outputs a natural language sentence describing its content. The FRVidSwin Encoder automatically evaluates the importance of each frame in the video using self-attention and discards redundant ones, reducing computational cost. This allows the model to focus on informative frames to generate high-quality features, improving text synthesis. We propose the Time Index Position Encoding based on Roformer, where the frame indices in the original video are kept and directly encoded. This preserves the time flow consistent with the original video, facilitating the model’s perception of slow and fast movements. Experimental results show that our model can generate high-quality captions and outperforms mainstream models, such as HMN and ORG-TRL, on MSVD and MSR-VTT benchmarks.
A Sentence Quality Evaluation Framework for Machine Reading Comprehension Incorporating Pre-trained Language Model

Multi-choice Machine Reading Comprehension (MRC) task involves selecting the correct answer from a limited set of options given a passage and a question. MRC tasks have experienced two main peaks: the explosion of deep neural networks and the evolution of contextual language models. However, the traditional paradigm suffers from inconsistency between the pre-trained and fine-tuning phases of the task. Pre-trained Language Models (PrLMs) are limited to a fixed input length, which can introduce noisy information irrelevant to the question. To address these issues, we propose a MacBERT-based Chinese MRC model that discards the traditional mask and adopts a similar word approach. This approach provides better contextual information and solves the problem of multiple meanings of words, better fitting the nature of the MRC task. Additionally, an unsupervised algorithm is used to filter passage sentences, controlling the model input and solving the problem of too much irrelevant content in long reading materials. We demonstrate that the proposed methods achieve better results compared to the baseline model on the Chinese MRC dataset C3.
Employing Beautiful Sentence Evaluation to Automatic Chinese Essay Scoring

Language learning relies on language sense. To write a good essay, the author not only needs skills but also original accumulation. In this paper, we approach from two perspectives of essay scoring and essay writing, utilizing beautiful sentence evaluation and generation to assist in essay scoring and writing. We first established a beautiful sentence dataset and trained a beautiful sentence evaluation model using the dataset, and then applied the evaluation model to automatic Chinese essay scoring. The experimental results demonstrated the effectiveness of beautiful sentence features in the essay scoring tasks. Moreover, we trained a beautiful sentence generator using the pre-trained model GPT-2 to improve writing ability of essay writers. The experimental results demonstrated the effectiveness of the beautiful sentence generator in essay writing assistance.
MISC: A Multimodal Approach for Sentiment Classification of Classical Chinese Poetry

Classical Chinese poetry is a literary genre that is rich in emotion and lyricism. However, sentiment classification on such poetry poses a challenge as they are typically short texts in classical Chinese, which are characterized by sparse features and non-standard information. To address these challenges, we propose a novel approach called MISC (the Multimodal Imagery based Sentiment Classification), which takes the multimodal information of imagery into account. Imagery is widely used in classical Chinese poetry and has a strong positive correlation with the poet’s emotional expression. Considering that imagery words are closely related to visual modality, we have introduced visual modality information from imagery words in addition to the text modality. Our approach utilizes imagery words in poems and applies multi-head attention to the poem text, imagery words, and imagery pictures, respectively. Finally, we integrate multimodal features of the poem, and use it on sentiment classification task which output ternary classification results of negative, neutral and positive. Our results demonstrate an improvement in sentiment analysis of classical Chinese poetry through the incorporation of visual modalities.
Chinese Named Entity Recognition Based on Multi-feature Fusion

Pre-trained language models usher in a new era of named entity recognition, but more additional relevant knowledge is needed to improve its performance on specific problems. In particular, in Chinese government named entity recognition, most entities are lengthy and have vague boundaries, and this entity length and boundary uncertainty makes the entity recognition task difficult or incorrectly identified. To address this problem, this paper proposes a Chinese named entity recognition model based on multi-feature fusion, in which lexical features, word boundary features and pinyin features are fused together through a multi-headed attention mechanism to enhance the model’s semantic representation of government texts. Meanwhile, this paper also studied the contribution of different features to entity recognition, and finds that pinyin features have unique advantages in recognising government entities. This study provides new ideas and methods for the research and application of Chinese governmental entity recognition, and also provides some insights into the problem of named entity recognition in other language domains. The experimental results show that the model proposed in this paper has better performance compared to the baseline model.
Explainable Artificial Intelligence 101: Techniques, Applications and Challenges

Artificial Intelligence (AI) systems have grown commonplace in modern life, with various applications from customized suggestions to self-driving vehicles. As these systems get more complicated, the necessity for transparency in their decision-making processes becomes more critical. Explainability refers to an AI system’s ability to explain how and why it made a certain judgement or prediction. Recently, there has been a surge of interest in constructing explainable AI (XAI) systems that give insights into the decision-making processes of machine learning models. This paper discloses and elaborates upon a selection of XAI techniques, identifies current challenges and possible future directions in XAI research.
Ponzi Scheme Identification of Smart Contract Based on Multi Feature Fusion

Due to the anonymity and imperfect supervision of the blockchain, criminal acts committed by criminals on the blockchain are difficult to be investigated. In recent years, scams based on district smart contracts have emerged one after another, among which the losses caused by Ponzi schemes have reached millions of dollars. However, there is little research on smart contract fraud identification at present, and for fraud detection, information utilization is not comprehensive, only based on a single feature or simply fused multiple features directly, without considering the duplication and connection between features. In order to solve these problems, in this paper, we propose a multi feature fusion scheme identification model (MFFSI) for smart contracts. Our contributions mainly include the following two points: 1) In terms of information use, the operands containing the information about the jump relationship of the opcode execution are retained; 2) In feature fusion, the operation code (opcode) and application binary interface (ABI) sequence features are extracted, and attention modules are used to guide the fusion of features to alleviate the interference of irrelevant features on classification. The results show that the model proposed in this paper has a good detection effect. The F1 score is higher than 86%, which is better than the previous.
Zero-Shot Learning Based on Weighted Reconstruction of Hybrid Attribute Groups

For the zero-shot learning, the rational description and utilization of attributes are valid approaches to build the bridge between training classes and testing classes. In order to improve the descriptive ability of attributes and further construct the appropriate mapping between attributes and features, a novel zero-shot learning method based on weighted reconstruction of hybrid attribute groups (WRHAG) is proposed. First, original semantic attributes are divided into groups by using the hierarchical clustering, and grouped semantic attributes are further enhanced by the broad learning. The semantic attribute groups and enhanced attribute groups together constitute hybrid attribute groups, which effectively improve the attribute description ability. Then, the mutual mapping between attributes and features is obtained by constructing a weighted autoencoder, in which the structured sparse L21 norm and attribute group coefficients are adopted to choose the discriminative attributes and consider the differences between attribute groups. Finally, the zero-shot classification is achieved by calculating the similarity between features of testing sample and predicted class features in the feature space. Comparative experiments on typical CUB dataset demonstrate that the proposed WRHAG model yields better performance in zero-shot image classification.
K-means Based Transfer Learning Algorithm

Focused on the issue that most transfer learning methods ignore the intra-domain distribution structures of the target domain, an algorithm based on K-means (K-means Transfer Learning, KTL) is proposed to enhance the transfer learning performance of the classification algorithms. In view of the poor clustering behavior of K-means on non-convex data sets, the multi-core version of KTL is proposed by clustering data into more small clusters to better fit the non-convex distribution of data (MKTL, Multi-core K-means Transfer Learning). The experimental results show that MKTL achieves the best average accuracy in 3 datasets. Compared with the original methods (kNN, TCA, GFK, JDA), the performance of MKTL is improved by 2.5 ~ 12.8 percentage in high computational efficiency.
Chinese Event Extraction with Small-Scale Language Model

Event extraction, as an important part of information extraction, is the main way to transform unstructured text into valuable structured text. To address the problems of long training time and large model volume commonly found in current event extraction models, the paper proposes a Chinese event extraction model based on ABBSAC model. Reducing model size with ALBERT pre-trained models, using Bi-SRU + + to capture the internal association information of the text, and incorporating the attention mechanism to improve the model accuracy, and finally using the output of CRF as the extraction result. Comparative experiments were conducted on an independently constructed corpus based on Sina News. The model achieved higher precision, recall and F1-score with an increase in training speed of about 10% and a cut in the number of model parameters of about 82%, demonstrating the advancedness of the proposed model. Also on the ACE05 and DUEE benchmark datasets, the F1-score for trigger extraction were improved by 1.7% and 0.3%, respectively, and the F1-score for argument role extraction were improved by 5.4% and 0.1%, when compared with the frontier method, effectively improving the effectiveness of the Chinese event extraction task.
KDCE: Effective Lifelong Learning for Code Pre-train Language Model

In today's world, various software applications and systems are constantly increasing, from financial transactions to social media, resulting in a continually expanding codebase. However, current Code Pre-trained Language Models (CPTLM) usually only use static code data for training. As the codebase grows, it becomes increasingly difficult to pack more knowledge into a fixed-size code pre-training language model. Without proper knowledge stimulation, these models often fail to integrate all code information and suffer from catastrophic forgetting. This paper proposes a Knowledge Distillation-based Code Language Expansion Model (KDCE). Specifically, we increase the model's capacity to learn more new knowledge by flexibly expanding the width and depth of the existing model in lifelong learning. We also add a code language recognition task to enhance the model's ability to recognize code language and use distillation learning to mitigate catastrophic forgetting. We conducted experiments on data from four code libraries using KDCE. The results show that KDCE outperforms various baselines in downstream tasks.