Reviewerly/exHarmony huggingface dataset:
Multi-Round Extraction and Dynamic Role Selection Framework For Document-Level Event Extraction

Document-level Event Extraction (DEE) aims to extract structured event information from a document, which is an indispensable downstream task for many NLP applications. Argument-scatter and multi-event are its two main challenges. Recent work decomposes this challenging DEE task into multiple steps such as entity recognition, contextual information modeling, and event arguments extraction. Besides, they all extract event arguments in a predefined fixed role order. Though it is effective, its cumbersome steps and fixed extraction order will bring about the problem of error propagation. To address this issue, we propose a Multi-round Extraction and Dynamic Role Selection (MREDRS) Framework for the DEE task. We model the DEE task in an end-to-end manner to avoid these cumbersome steps. Multi-round extraction can deal with multi-event problem. In order to avoid the error propagation problem caused by the fixed role extraction order, we dynamically select the next role according to the current extraction state. We conducted experiments on the commonly used DEE dataset and extensive experimental results demonstrated the effectiveness of our method.
Richer Information Transformer for Object Detection

While Convolutional Neural Networks (CNNs) have been dominated computer vision tasks such as object detection and instance segmentation for a long time, recently Vision Transformers (ViTs) are showing promising performance in these tasks nowadays. Though CNNs can efficiently decrease local redundancy by convolution within a small neighborhood, the limited receptive field makes it hard to capture global dependency. Alternatively, ViTs can effectively capture long-range dependency via self-attention mechanism which also produce quadratic computation complexity to the image size input. In this paper, we propose a module composed of several groups of convs and activation functions to make up for the lack of comprehensive information in ViTs for extracting features, so that conv and transformer can achieve a complementary advantage. We also introduce channel attention module to capture the channel information, which arising from the frequent manipulations to channels during the calculation process of self-attention. In the absence of pretrained data, our model achieves 40.3 box AP and 37.1 mask AP on COCO object detection task, surpassing state-of-art Swin Transformers backbone respectively by +8.8, +6.7 respectively under the similar FLOPs settings.
A Joint-BERT Method for Knowledge Base Question Answering

This paper proposes a Joint-BERT method to solve the knowledge base question answering (KBQA) task involved a single fact. It divides it into two subtasks, topic entity recognition and relation detection. For the entity recognition subtask, instead of treating it as a sequence labeling task, a simpler approach of applying a pointer network on the BERT encoder is used to predict the start and end positions of the topic entity. The subtask of relation detection, sharing the BERT encoder, computes candidate predicates rankings from both local and global matching perspectives, and finally trains the two tasks jointly in a multi-task learning framework, so that the two tasks benefit from each other. Experiments show that: Joint-BERT model achieves competitive results on the SimpleQuestions benchmark.
SPCPFS: a pseudo-label filtering strategy with fusion of perplexity and confidence

In the pseudo-label filtering for semi-supervised Mongolian speech recognition, the correctness of word combinations and the correctness of speech and word correspondences in the self-training set cannot be guaranteed simultaneously. To solve this problem, we propose a pseudo-label filtering strategy with fusion of perplexity and confidence, which is called sentence perplexity confidence. The strategy simultaneously evaluates the semantic relations of pseudo-labels and the correspondence between pseudo-labels and acoustic features of unlabeled speech, which improves the accuracy of the self-training set and thus the performance of the target speech recognition model output by semi-supervised training. We conducted ablation experiments and comparison experiments of sentence perplexity confidence on Mongolian datasets IMUT-MC and IMUT-MC-SMI. The experimental results show that the sentence perplexity confidence is ahead of the sentence-level confidence and perplexity in terms of accuracy improvement ability of the self-training set, and the output target speech recognition models reach 14.7% and 16.1% for WER and SER respectively.
Crowd counting method based on a fusion of high and low altitude information

In this paper, we design a crowd counting method based on the fusion of high and low altitude information, and establish a fusion mechanism of high and low altitude view images. Typical crowd counting models are mainly used for single-view scenarios and cannot be fully applied to crowd counting problems in large-view scenarios. For the problems of incomplete coverage of low-altitude view and unclear texture features of high-altitude view in large-view scenes, this paper achieves the complementary regional information within the high-altitude view images through density similarity to infer the global number of people in large-view scenes. The experiments showed that the method was more accurate in counting.
Cross-Domain Sentiment Classification via Deep Reinforcement Learning

Cross-domain sentiment classification aims to transferring the labeled knowledge of the source domain to the target domain for sentiment classification. In the case of lacking labeled target domain data, it reduces manual marking cost of labeling data. Most existing works perform transfer learning by extracting pivot and non-pivot features between domains. These methods are susceptible to noise data and ignore important sentiment information. To solve the above problem, we propose the cross-domain sentiment classification via deep reinforcement learning. We propose a deep reinforcement learning framework, which formulates feature selection policy to solve the noise data problem and pay attention to important data features. The framework is integrated with a policy network and a classification network. The policy network is applied to the feature selection. The classification network makes predictions based on feature selection and calculate the reward of the policy network. Experiment results on the Amazon review datasets demonstrate that the proposed method considerably outperforms other state-of-the-art methods.
Distributed Cloud Detection Method for 5G Charging Devices Based on Invariance Constraint

In this paper, an invariance constraint-based federated learning method is proposed to implement the distributed cloud detection for 5G charging devices. In order to solve the problem that the charging devices have large numbers and scattered distribution, a distributed scenario modeling of the charging devices is proposed. Each charging device is regarded as a child node, and a central node is constructed on cloud end. In this modeling scenario, the 5G network is used to transmit only the model and gradient parameters between the central node and each child node, which avoids high cost of data transmission and high burden of centralized data storage. In addition, the invariance-based loss function is designed to solve the problem that the data distribution of each child node has significant statistical heterogeneity in the train stage. The proposed method makes the model generalize better, and improves the accuracy of the safety detection of charging devices.
Chinese medical named entity recognition based on zero-shot learning

To solve the problem that existing named entity recognition models lacked of ability to deal with unseen classes, the zero-shot learning was proposed to be used in the task of Chinese medical named entity recognition. Zero-shot learning utilizes the description information of the entity's class to establish the connection between the entity and the class, and transfers information from the observed classes to the unseen target classes. The model proposed by this paper was mainly based on BERT, which was used to model the relationship between the entity and the description. Moreover, the static word embedding, which is as a supplementary information, is concatenated with the features obtained from the BERT to solve the problem that BERT is not suitable for a specific field. At the same time, Correlation Searchers are added between the transformers of BERT to search for the word information most relevant to the character, so as to solve the problem that the model cannot obtain complete word information with characters as the input unit. Experiments show that the model's recognition performance has been significantly improved after adding the static word embedding and word information.
Multi-channel Graph Convolution for Aspect-level Sentiment Classification of Online Student Reviews

Abstract—It is important for schools and online learning platforms to mine student attitudes and opinions from student reviews so teachers know what needs to be improved. Previous studies have only focused on the semantic features of sentences, ignoring syntactic dependency relations in sentences. To address this problem, we build a graph convolutional network (GCN) on the syntactic dependency tree of sentences to exploit syntactic information for the first time in the field of online student reviews. Furthermore, in order to exploit positional relations and alleviate over-smoothing, we add relative aspect distance relations and stochastic edge elimination. On this basis, a novel multi-channel graph convolution model (MCGCN) is proposed, which constructs three GCN channels for syntactic dependency relations, relative aspect distance relations and stochastic edge elimination to extract relational features. Finally we use average pooling to fuse three relational features. Experiments on the CR23k dataset demonstrate that the overall performance of the model outperforms the state-of-the-art methods.
Gentle Teaching Assistant Model For Improved Accuracy In Question-Answering

With the rise of deep neural networks, question-answering based on machine reading comprehension has attracted more and more attention. Current question-answering systems, including transformer-based models, suffer from increasing model complexity and a large number of parameters, which makes them inefficient in extracting answers in constrained devices. In this paper, we propose an efficient knowledge distillation approach to tackle both model complexity and the huge amount of parameter problems. To be Specific, we proposed architecture employs an intermediate model to work as the teacher assistant, and then the "teacher assistant" can teach the student more efficiently without consuming massive resources. We conduct experiments on the SQuAD dataset and these experiments show that we can achieve better experimental results than the conventional knowledge distillation model.
Investigating Hybrid and Ensemble Deep Learning Architectures for Leukemia Detection

This research designed and implemented a leukemia diagnostic system targeted for a real clinical environment based on deep learning approaches. The dataset consists of 15,135 total images. This research develops four independent models (VGG19 1 epoch, VGG19 30 epochs, ResNet50 1 epoch, and ResNet50 30 epochs), two Hybrid models (trained at 1 epoch and 30 epochs), and four Ensemble models (two Ensemble models of VGG19 and ResNet50 and two Ensemble models with an additional Hybrid model). All models are pre-trained on ImageNet. By using transfer learning, the models were fine-tuned (further trained) on the leukemia domain at a much greater speed as the existing layers will have benefitted from the pre-training done on ImageNet. This research indicates that Hybrid models can help improve predictive capabilities by leveraging different feature patterns extracted from running images through two different architectures. Meanwhile, Ensemble models will take the prediction votes from multiple final model outputs to further incorporate different model capabilities and also help generalize. Among all independent models, the best model is ResNet50 30 epochs, which achieved an accuracy of 84%. Among the Hybrid models, the best model is Hybrid 30 epochs, which achieved an accuracy of 84%. Ensemble 4 (Hybrid 30 epochs, VGG19 1 epoch, and ResNet50 1 epoch) achieved an accuracy of 86%, which is 2% better than the second-best model, Ensemble 2. In principle, the diagnostic system developed in this research can be applied in other medical diagnostic applications.
An effective Time-Aware Encoder for Temporal Knowledge Graph Reasoning

There are many studies on static knowledge graph (KG) reasoning that predicts missing facts for its completeness. As the facts in real world usually are time-dependent, temporal knowledge graph (TKG) has received great attention lately. Entities and their relations in TKGs may change over time, and how to predict future facts from past facts has become a fundamental subject of TKGs. In this paper, a novel effective time-aware encoder (TAE) is proposed for TKG reasoning. It encodes the influence of time on entities and relations into accurate time-specific embedding representations. Then the embedding representations of entities and relations under different timestamps are employed for the prediction of future facts. The evaluations on four public TKG datasets have demonstrated that TAE outperforms all baseline models on TKG reasoning tasks.
A Method of Machine Learning for Social Bot Detection Combined with Sentiment Analysis

Social Bot exists widely in major social networks. Some maliciously use a social bot to guide public opinion, steal user privacy, and create rumors, which seriously affects the security of social networks. Past approaches mainly extracted large amounts of contents but ignored bots’ text sentiment features, and it is hard to detect social bot just based on contents. This paper proposes a malicious social bot detection method that combines sentiment features in response to this problem. It trains a Bidirectional Long Short-Term Memory model(Bi-LSTM) with an Attention Mechanism to perform sentiment calculation on the online text information of social accounts and analyze the sentiment fluctuations of accounts to get the new sentiment features; Then, it inputs the new features combined with metadata features into different machine learning models for analysis and comparison. Through this method, different machine learning detection models have improved the detection accuracy after combining sentiment features.
Ranking Published Images and Bidding for Corporate Purpose

All these years, information or the data on the web has grown incredibly, guiding so many researchers at the sector of Machine Language and NLP concepts. Research papers regarding Sentiment Analysis has its own compulsive topics. This paper is a literature survey on Sentiment Analysis in the corporate sector that tells you about the methods, advantages and their applications. Here a literature survey on sentiment analysis in the corporate sector is based on image ranking (which is done with the help of users comments in the images).
Semantic Representation Based on AMR Graph for Document Level Question Generation

Question Generation (QG) is the task of generating questions from reference sentences and answers specified in the sentences. Intuitively, as one type of semantic representations, Abtract Meaning Representation (AMR) can be helpful for question generation by enforcing meaning preservation and handling data sparsity due to being abstracted from syntactic representations. However, little work has been done to leverage rich semantics in AMR for question generation. Therefore, we design AMR Question Generation (AMRQG) model to construct document-level AMR graphs from sentence-level AMR graphs. The model solves the alignment issue between nodes of AMR graph and words in sequence text at graph-level and node-level. To our knowledge, we are the first to leverage AMR representation into question generation for document-level input. Automatic evaluation results demonstrate that incorporating AMR as additional knowledge can significantly improve a sequence-to-sequence natural question generation model and show our model’s superiority.
Interactive VBE and IPSL Aid for the Disabled using a GRU-based Model

In a world of billions communication becomes the radical thing that makes us human. Without communication, people are mere objects with a soul. Unfortunately, not all of us are blessed with being able to communicate freely. Interaction between the disabled and the ordinary poses a huge challenge. This paper proposes to solve this problem by developing a sign language identifier by combining machine learning and web development techniques to aid the needy. IPSL (Indo-Pakistani Sign Language) is one such language that is not represented as much as it should be. Despite being used actively by more than 15 Million people in the deaf community, IPSL is unknown to most of the world. IPSL will be made detectable with the proposed model that is combined with a VBE (Voice Based Email) system to aid not only the deaf but also the blind, using a GRU model for the same. GRU is already a well defined model, but this paper intends to show how this deep learning model can be used for IPSL, having never been done before, as it is usually done for the popular languages like ASL and BSL.
ABC-DE-WOA: A New Hybrid Algorithm for Optimization Problems

One of the most effective swarm intelligence-based algorithms for many global optimization issues is the Artificial Bee Colony (ABC) strategy. Despite the fact that there are many Artificial Bee Colony (ABC) variants, the algorithm normally has a low convergence rate. Therefore, it is still essential to moderate an algorithm's intensity and diversity. In this instance, the standard Artificial Bee Colony (ABC) algorithm has been combined with the Whale Optimization Algorithm (WOA) and Differential Evolution Algorithm (DE) to generate a novel Hybrid Artificial Bee Colony algorithm (ABC), Artificial Bee Colony-Differential Evolution-Whale Optimization Algorithm (ABC-DE-WOA). For simple benchmark problems with up to 100 dimensions, 50 dimensions, 30 dimensions, and 10 dimensions, the proposed hybrid technique is compared with Artificial Bee Colony (ABC) variants like Artificial Bee Colony-Whale Optimization Algorithm (ABC-WOA), Artificial Bee Colony-Differential Evolution Algorithm (ABC-DE), and original Artificial Bee Colony Algorithm (ABC). The results show that the proposed technique performs better than its competitors in terms of convergence speed.
A Streaming End-to-End Speech Recognition Approach Based on WeNet for Tibetan Amdo Dialect

Speech recognition is a technique to transcribe acoustic features into text sequences. However, traditional speech recognition model cannot get an effective performance, when dealed with Tibetan Amdo dialect dataset which requires a large amount of linguistic knowledge. In order to solve this issue, we propose an end-to-end speech streaming recognition model which can not only realize the transcription of the Tibetan Amdo dialect but also solve the Tibetan Amdo dialect streaming recognition problem. In the model, we choose Tibetan syllables as the modeling unit and MFCC as the acoustic features. Furthermore, extensive experimental results show good results on our self-built thousand-hour level dataset. Finally, the Character Error Rate(CER) of speech streaming recognition on ourself dataset is 10.73%, which is a relative improvement is 17.65% compared to the baseline model. The CER of speech transcription is 10.23%, which is a relative improvement is 16.01% compared to the baseline model.
Learning Comprehensive Multimodal Representation for Cancer Survival Prediction

Making accurate cancer survival prediction plays a key role in helping clinicians to plan appropriate treatments, which can enhance the life quality of cancer patients and reduce the rate of cancer death. Recent works demonstrate that combining information from different modalities, e.g., genomic data and histopathology images, is crucial for improving the performance of cancer survival prediction. Although these works have obtained promising results, the heterogeneity of data distributions poses a great challenge to efficiently learning comprehensive multimodal representations. To solve this problem, we propose a novel method, which learns both modality-invariant and -specific representations to provide a holistic view of multimodal data. Experimental results suggest that our method achieves remarkable performance and outperforms other approaches.
Automation of Server Security Assessment

While system hardening concepts are general, one of the leading causes of the breaches is human error in the misconfiguration. Depending on the type of hardening, different tools and techniques are used. The whole lifespan of technology, from initial installation through setup, maintenance, and support, to end-of-life decommissioning, necessitates system hardening. Additionally, mandated by regulations like PCI DSS (Payment Card Industry Data Security Standard.) and HIPAA (Health Insurance Portability and Accountability Act), systems hardening is something that cyber insurers are increasingly requesting. This paper explains how to automate server security assessments using an ansible agentless framework and utilize them to continue security audits and compliance evaluations throughout risk assessments. The technique and ideas discussed in this paper are more effective when the server environment is undergoing continual change.
A Blockchain-Based Framework for Building and Sharing Machine Learning Models

Artificial Intelligence (AI) and blockchain are two disruptive technologies proving to be a powerful combination, improving virtually every industry. Research in Artificial Intelligence has come a long way, with many corporations, organizations and individuals greatly benefiting from Machine Learning services. This research work focuses on creating a Blockchain-based framework for building and sharing Machine Learning models. We break the framework into three components to understand it: Publishing AI Services, Contribution to AI Services, and Fetching Private Predictions from AI Services. In the first component, the framework allows corporations, organizations and individuals to publish their AI Services. In the second component, the framework incorporates machine learning practitioners who can contribute to AI Service Publishers by building models and datasets in exchange for a reward. Thirdly, the framework allows corporations, organizations and individuals with problems that could be solved with AI to solicit solutions from AI Service Publishers without sharing their data. This research utilizes the benefits of existing multi-party computation (MPC) libraries to build Machine Learning models that provide predictions on encrypted data.
A Faster Method For Generating Chinese Text Summaries-Combining Extractive Summarization And Abstractive Summarization

Extractive summarization and generative summarization are the two main ways to generate summarization.However,previous work treats both of them as two independent subtasks.In this paper,we obtain new summarization by combining extractive summarization and generative summarization.This method extracts the key information of the article firstly,and then generates the summarization of the extracted information.The experimental result shows that this method can significantly improve the quality of the generative text compared with extractive summarization,and can significantly improve the generative speed compared with generative summarization.