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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kai He Bing Mao Xiangyu Zhou Yufei Li Tieliang Gong Chen Li Jialun Wu School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Coreference resolution aims at linking all mentions that refer to the same entity, which are widely adopted in many biomedical and bioinformatics tasks, such as biomedical knowledge graph construction and metabolic pathway integration. Many recent studies focus on improving neural model structures. However, we argue that a practical method that integrates commonsense knowledge can further improve coreference resolution performance, because commonsense delivers extra prior knowledge for reasoning and can enhance related representations, rather than naive mention-context occurrence modeling. In this work, we propose an effective method to integrate external commonsense knowledge into a neural coreference resolution model. Specially, a gated attention mechanism is employed in our method to leverage commonsense according to different contexts. By using ConceptNet as the knowledge base in three span-ranking backbone models, the models can yield significant performance gains on used datasets. We also achieve improvements in tasks of long-term mention detection and cross-sentence coreferences after incorporating knowledge.

关键词： Biological system modeling knowledge based systems Logic gates Performance gain Task analysis Bioinformatics Commonsense reasoning

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Feature Aggregating Network with Inter-Frame Interaction for Efficient Video Super-Resolution

Feature Aggregating Network with Inter-Frame Interaction for...

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IEEE International Conference on data Mining (ICDM)

作者： Yawei Li Zhao Zhang Suiyi Zhao Jicong Fan Haijun Zhang Mingliang Xu School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Kunming China Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China Department of Computer Science Harbin Institute of Technology Shenzhen China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China

Video super-resolution (VSR) on mobile devices aims to restore high-resolution frames from their low-resolution counterparts, satisfying the requirements of performance, FLOPs and latency. On one hand, partial feature processing, as a classic and acknowledged strategy, is developed in current studies to reach an appropriate trade-off between FLOPs and accuracy. However, the splitting of partial feature processing strategy are usually performed in a blind manner, thereby reducing the computational efficiency and performance gains. On the other hand, current methods for mobile platforms primarily treat VSR as an extension of single-image super-resolution to reduce model calculation and inference latency. However, lacking inter-frame information interaction in current methods results in a suboptimal latency and accuracy trade-off. To this end, we propose a novel architecture, termed Feature Aggregating Network with Inter-frame Interaction (FANI), a lightweight yet considering frame-wise correlation VSR network, which could achieve real-time inference while maintaining superior performance. Our FANI accepts adjacent multi-frame low-resolution images as input and generally consists of several fully-connection-embedded modules, i.e., Multi-stage Partial Feature Distillation (MPFD) for capturing multi-level feature representations. Moreover, considering the importance of inter-frame alignment, we further employ a tiny Attention-based Frame Alignment (AFA) module to promote inter-frame information flow and aggregation efficiently. Extensive experiments on the well-known dataset and real-world mobile device demonstrate the superiority of our proposed FANI, which means that our FANI could be well adapted to mobile devices and produce visually pleasing results.

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Smart Contract Vulnerability Detection Based on Semantic Graph and Residual Graph Convolutional Networks with Edge Attention

SSRN

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SSRN 2022年

作者： Chen, Da Feng, Lin Fan, Yuqi Shang, Siyuan Wei, Zhenchun The Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230009 China

It is imperative to assure the security of smart contracts via intelligent vulnerability detection tools before deploying smart contracts on blockchains. The existing deep learning-based approaches fail to effectively capture the rich syntax and semantic information embedded in smart contracts. In this paper, we detect smart contract vulnerabilities at the function level by constructing a novel semantic graph (SG) for each function and learning the SGs using a new graph convolutional network EA-RGCN. Our proposed method consists of three stages. In the first stage, we create the SG which characterizes rich data-data, instruction-instruction and instruction-data relationships in the function code. In the second stage, we propose EA-RGCN which contains three parts: node and edge representation via word2vec, content feature extraction with a residual GCN (RGCN) module, and semantic feature extraction using an edge attention (EA) module. Finally, we concatenate the code content features and the semantic features to obtain the global code feature and use a Multilayer Perceptron (MLP) to identify whether the function is vulnerable. We conduct experiments on the dataset constructed from real-world smart contracts. Experimental results demonstrate that the proposed semantic graph and the EA-RGCN model can achieve superior performance. © 2022, The Authors. All rights reserved.

关键词： Smart contract

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Movie Recommendation Algorithm Based on Sentiment Analysis and LDA

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Procedia Computer Science 2022年 199卷 871-878页

作者： Yilin Zhang Lingling Zhang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved.

关键词： Sentiment Analysis Topic Extraction Content-based Recommendation Movie Reviews

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LogADCH: Hyperspheric-Based Log Message Anomaly Detection And Classification

LogADCH: Hyperspheric-Based Log Message Anomaly Detection An...

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Frontiers of Robotics and Software engineering (FRSE), International Conference on

作者： Shiming He Jinpan Xiao Bowen Chen Ying Lei School of Computer and Communication Engineering the Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science & Technology Changsha China

Web services have been integrated with all walks of life in society. Abnormalities in the network and services seriously affect user experience and company revenue. The system log records various information of the system, and the information-rich log messages usually show different anomalies. It is particularly important to accurately and timely identify the anomalies of the log messages and classify the anomalies. In addition, most of the current existing work only detects anomalies and does not classify them, and a few existing works require anomaly labels to classify anomalies. Based on these potential challenges, we propose an unsupervised hypersphere-based log message anomaly detection and classification method, LogADCH. We use the hypersphere loss function to let the BERT model learn compact representations of log messages and perform anomaly detection. Finally, K-Means clustering is performed on the semantic feature vectors detected as anomalies to form clusters of different types of anomalies. Compared with other works, LogADCH further classifies the abnormal types after detecting the abnormal logs, helping the operation and maintenance personnel to quickly locate the fault. We compare our method with other methods on two public datasets, and the experimental results show that LogADCH has superior performance in anomaly detection as well as anomaly classification.

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Kubernetes-based Scripted Remote Sensing Process Service Platform

Kubernetes-based Scripted Remote Sensing Process Service Pla...

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Liming Zhou Mengyu Liu Wencheng Yang Zhehao Liu Hang Zhao Hongyu Han Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer and Information Engineering Henan University Kaifeng China

With the formation of the high-resolution satellite system, the information carried in remote sensing data is becoming more and more abundant, and the types of data are also increasing. In order to realize the efficient processing of remote sensing data, the construction of remote sensing process is very important. At the same time, higher requirements are put forward for the remote sensing system in terms of dynamic resource scheduling, easy maintenance, elastic expansion, controllable complexity, and flexible deployment. In response to the above problems, this paper first designs a scripted process construction technology, which realizes the efficient construction of various complex processes. And introduce Docker container technology and Kubernetes container cluster management technology, split the remote sensing system into different micro-service modules, run independently in mutually isolated containers, so that the system has the characteristics of flexible load scheduling and elastic scaling.

关键词： Satellites Codes Microservice architectures Containers Dynamic scheduling Hardware Real-time systems

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K-ON: Stacking knowledge On the Head Layer of Large Language Model 39

K-ON: Stacking Knowledge On the Head Layer of Large Language...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Guo, Lingbing Zhang, Yichi Bo, Zhongpu Chen, Zhuo Sun, Mengshu Zhang, Zhiqiang Zhang, Wen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Ant Group Joint Lab of Knowledge Graph China Ant Group China School of Software Technology Zhejiang University China Zhejiang Key Laboratory of Big Data Intelligent Computing China

ISBN: (纸本)157735897X

Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an entity requires at least several tokens. This leads to a granularity mismatch between KGs and natural languages. To address this issue, we propose K-ON, which integrates KG knowledge into the LLM by employing multiple head layers for next k-step prediction. K-ON can not only generate entity-level results in one step, but also enables contrastive loss against entities, which is the most powerful tool in KG representation learning. Experimental results show that K-ON outperforms state-of-the-art methods that incorporate text and even the other modalities. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Fighting Fake News: Two Stream Network for Deepfake Detection via Learnable SRM

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity Science 2021年第3期3卷 320-331页

作者： Han, Bing Han, Xiaoguang Zhang, Hua Li, Jingzhi Cao, Xiaochun State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong at Shenzhen Shenzhen518172 China

Benefitting from the development of deep generative networks, modern fake news generation methods called Deepfake rapidly go viral over the Internet, calling for efficient detection methods. Existing Deepfake detection methods basically use binary classification networks trained on frame-level inputs and lack leveraging temporal information in videos. Besides, the accuracy of these methods will rapidly decrease when processing low-quality data. In this work, we propose a two-stream network to detect Deepfake in video level with the capability of handling low-quality data. The proposed architecture firstly divides the input video into segments and then feeds selected frames of each segment into two streams: The first stream takes RGB information as input and tries to learn the semantic inconsistency. The second stream parallelly leverages noise features extracted by spatial rich model (SRM) filters. Additionally, our experiments found that traditional SRM filters with fixed weights contribute insignificant improvement, we thus design novel learnable SRM filters, which can better fit the noise inconsistency in tampered regions. Segmental fusion and stream fusion are conducted at last to combine the information from segments and streams. We evaluate our algorithm on the existing largest Deepfake dataset FaceForensics++ and the experimental results show that we obtain state-of-the-art performance. © 2019 IEEE.

关键词： Semantics

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The Applications of Artificial Intelligence in Digestive System Neoplasms:A Review

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Health data Science 2023年第1期3卷 1-16页

作者： Shuaitong Zhang Wei Mu Di Dong Jingwei Wei Mengjie Fang Lizhi Shao Yu Zhou Bingxi He Song Zhang Zhenyu Liu Jianhua Liu Jie Tian School of Engineering Medicine Beihang UniversityBeijingChina. Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina. CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina. Department of Oncology Guangdong Provincial People's Hospital/Second Clinical Medical College of Southern Medical University/Guangdong Academy of Medical SciencesGuangzhouGuangdongChina.

Importance:Digestive system neoplasms(DSNs)are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%.Subjective evaluation of medical images including endoscopic images,whole slide images,computed tomography images,and magnetic resonance images plays a vital role in the clinical practice of DSNs,but with limited performance and increased workload of radiologists or *** application of artificial intelligence(AI)in medical image analysis holds promise to augment the visual interpretation of medical images,which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor ***:We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis,assessment of treatment response,and prognosis prediction on 4 typical DSNs including esophageal cancer,gastric cancer,colorectal cancer,and hepatocellular ***:AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of *** technical issues should be overcome before its application into clinical practice of DSNs.

关键词： diagnosis neoplasms holds

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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