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Advanced Algorithms and Control engineering (ICAACE), International Conference on

作者： Peng Zhang MengChen Zou HongXia Du Software Engineering Chongqing University of Posts and Telecommunications Chongqing China dept. iFLYTEK Big Data Chongqing City Vocational College Chongqing China

ISBN: (数字)9798331535087

ISBN: (纸本)9798331535094

Automatic terminology extraction holds significant application value in natural language processing tasks, particularly in domain-specific knowledge graph construction, information retrieval, and machine translation. However, existing terminology extraction methods demonstrate limited performance in cross-domain scenarios due to substantial variations in linguistic conventions, term distributions, and contextual dependencies across different domain-specific texts. This paper proposes a novel terminology extraction model that integrates document-level contextual features with domain adaptation techniques. The model achieves effective alignment between source and target domain distributions through cross-sentence dependency analysis and reverse Kullback-Leibler divergence optimization, thereby enhancing generalization capabilities. Experimental results demonstrate that: (1) Document-level contextual features yield an average 3.74% improvement in F1-score; (2) The domain adaptation mechanism provides additional 2.32% F1-score enhancement, particularly improving model adaptability in low-resource target domains. Our methodology offers new perspectives for applying terminology extraction in multi-domain scenarios.

关键词： Adaptation models Terminology Knowledge graphs Linguistics Feature extraction Robustness data mining Machine translation Optimization Context modeling

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Design and Implementation of Student Comprehensive Assessment System 12

Design and Implementation of Student Comprehensive Assessmen...

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12th International Conference on Orange Technology, ICOT 2024

作者： Liu, Zhongjie Cai, Jiachen Xu, Meifang Tao, Dawei Tseng, Shih-Pang School of Software and Big Data Changzhou College of Information Technology Changzhou China School of Cyberspace Security Changzhou College of Information Technology Changzhou China School of Electronic Engineering Changzhou College of Information Technology Changzhou China School of Information Science and Technology Sanda University Shanghai China

ISBN: (纸本)9798331523602

With the continuous deepening of educational informatization, student comprehensive assessment has become increasingly important as a core link in evaluating academic achievements and promoting the improvement of teaching quality. However, some schools still lack sufficient educational informatization and rely on Excel spreadsheets to analyze students comprehensive assessment data, resulting in low work efficiency. This paper proposes a student comprehensive assessment system based on the Django framework, which deeply investigates the functional requirements of existing student comprehensive assessments and designs corresponding functional modules such as honor declaration, moral education evaluation, academic performance inquiry, dormitory performance accounting, and comprehensive performance inquiry. Based on the different weights of the entered data, the system can quickly calculate the comprehensive assessment scores for each student. The system also integrates a large language model from Qianfan, developing an intelligent question assistant to deeply learn the school's policy knowledge base to answer questions encountered by teachers and students in their learning and daily lives. The deployment and implementation of the system have verified the accuracy of the student comprehensive assessment results, improved the level of educational informatization management in student work, and promoted the development of the student comprehensive assessment system. © 2024 IEEE.

关键词： Students

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Research on Malicious software Detection Based on CNN-LSTM Hybrid Model 5

Research on Malicious Software Detection Based on CNN-LSTM H...

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5th International Conference on Machine Learning, big data and Business Intelligence, MLBDBI 2023

作者： Qi, Xiaoxia Johar, Md Gapar Md Khatibi, Ali Tham, Jacquline Zhang, Rui Mao, Sijie Anhui Xinhua University School of Big Data and Artificial Intelligence Hefei China Management and Science University Software Engineering and Digital Innovation Centre Shah Alam Malaysia Management and Science University School of Graduate Studies Shah Alam Malaysia

ISBN: (纸本)9798350359930

The proliferation of malicious applications has increased rapidly, requiring a more effective approach than traditional methods for auditing and matching code. The behavior of malicious software provides valuable information, with the API call sequence, which represents the order in which operations are generated by both malicious and benign software in a sandbox environment, has become a crucial data source. In this paper, we propose a hybrid model that combines convolutional neural network (CNN) and long short-term memory network (LSTM) to detect and classify malware. We train and classify the model using a large dataset of API sequences called by both malicious and non-malicious software. Compared to traditional machine learning methods, our model eliminates the need for manual extraction of specific instruction calls from assembly code. Furthermore, compared to other deep learning models, hybrid model with LSTM shows significantly improved detection performance. Specifically, the CNN-LSTM model achieves a 24% and 22% increase in accuracy and at least a 16% and 14% higher F1 score compared to other models. These results demonstrate the effectiveness of our proposed model in enhancing malware detection capabilities. © 2023 IEEE.

关键词： Long short-term memory

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Biqiao Xin Qiang Li Qianchen Mao Jinbao Wang Bingshu Wang School of Software Northwestern Polytechnical University Xi’an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches.

关键词： Wavelet transforms Frequency-domain analysis Semantics Signal processing Feature extraction Excavation Robustness Real-time systems Complexity theory Speech processing

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An Exact Potential Game-Based End-Edge-Cloud Collaborative Task Offloading Approach 19

An Exact Potential Game-Based End-Edge-Cloud Collaborative T...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Shen, Yue Liu, Bowen Dai, Fei Dou, Wanchun Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shenzhen China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China

ISBN: (纸本)9798350346558

With the development of Industrial Internet of Things(IIoT), massive industrial devices produce a large number of delay-sensitive and computing-intensive tasks. Offloading tasks under the end-edge-cloud collaboration mode can improve the efficiency of completing tasks. However, on the one hand, tasks need to be finished before corresponding deadlines and pay for consuming resources, so delay and cost constitute the main factors affecting task offloading decisions. On the other hand, limited real-time resources in the system may not meet the needs of all devices, so we should ensure that as many devices can complete tasks on time as possible while optimizing task delay and cost of each device. To tackle the challenges, we propose an end-edge-cloud collaborative task offloading approach based on exact potential game. Aiming at optimizing delay-cost of each device and task completion rate of the whole system, the end-edge-cloud collaborative task offloading problem is modeled as an exact potential game and solved. Extensive experiments show that the approach can improve the task completion rate of the system while optimizing the delay-cost of each device. © 2022 IEEE.

关键词： Edge computing

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Experimentally Study on the Effect of RIE Etching Power on Etching Rate of β-Ga2O3Thin Film 29

Experimentally Study on the Effect of RIE Etching Power on E...

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29th International Symposium on Semiconductor Manufacturing, ISSM 2022

作者： Xu, Wang Yang, Ran Jing Lai, Yang Shun, Yang Fa Kui, Ma College of Big Data and Information Engineering Guizhou University China Guizhou Provincial Key Laboratory of Micro and Nano Electronics and Software Technology Guizhou Province Guiyang City China

ISBN: (纸本)9781665471336

As a member of ultra-wide band gap semiconductor materials, β-Ga2O3 materials have attracted wide attention from researchers in the semiconductor field in recent years. Etching process is crucial to realize semiconductor devices and integrated circuits based on β-Ga2O3 materials. Based on the reaction ion etching process commonly used in silicon-based semiconductor technology, the etching experiment research of β-Ga2O3 thin film is carried out. The β-Ga2O3film is etched with SF6, based on the induction coupled reaction ion etching. The effect of RIE etching power, excitation power and bias power, on etching rate of β-Ga2O3 thin film has been studied. SEM characterization results show that the etching rate is the highest at 600W excitation power. The etching rate increases with the increase of bias power. The etching rate at 200W bias power is slightly higher than that at 150W bias power. However, the photoresist used as the etch mask will be damaged at 200W bias power. © 2022 IEEE.

关键词： Etching

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MLRP-KG: Mine Landslide Risk Prediction Based on Knowledge Graph

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第1期3卷 78-87页

作者： Ma, Lianbo Wang, Jingwei Cheng, Jian Wang, Xingwei Zhu, Wancheng Northeastern University Shenyang China College of Software Northeastern University Shenyang China Research Institute of Mine Big Data China Coal Research Institute Beijing China College of Computer Science and Engineering Northeastern University Shenyang China School of Resource and Civil Engineering Northeastern University Shenyang China

The mine landslide risk prediction is a fundamental task for the safety management of the digital mining system, which is dependent on the analysis of the open pit mine exploitation slope stability. Such stability analysis involves a lot of local natural and human factors. Training a learning model using these factors, with the aim of discovering their relationship with the slope stability, is an intuitive way for the mine landslide risk prediction. The key issue is how to explore the complex nonlinear relations among these factors by using a small amount of high-dimensional historical slope data. Traditional factor-led methods for the issue only focus on the impacts of landslide factors, but ignore the correlations between historical slope data, which in fact contain more useful information. In this article, we propose a new mine landslide risk prediction model using knowledge graph. The gradient boosting decision tree is applied to further exploit the crossed features within the historical data, and then a landslide semantic network is constructed using knowledge graph with the consideration of correlation information between historical slope data. In this way, the model is suitable to deal with the small set of high-dimensional data, and it makes a joint use of the features within the landslide factor values and correlations between historical slope data. We conduct a set of experiments on real historical slope data from the real-world open pit mining scene. The experiment results validate the effectiveness and efficiency of the proposed model in predicting landslide risks with a small set of high-dimensional historical data. © 2020 IEEE.

关键词： Landslides

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Attribute Enhanced Graph Attention Networks for Session-based Recommendation 11

Attribute Enhanced Graph Attention Networks for Session-base...

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11th International Conference on Advanced Cloud and big data, CBD 2023

作者： Xu, Yidan Wang, Ning Wu, Jiaqi Xu, Shuai Li, Bohan Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Natl. Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi'an China

ISBN: (纸本)9798350345346

Session-based recommendation (SBR) aims to predict the next item that the anonymous users will interact with based on their short behavior sequence. However, most of the current approaches towards SBR only focus on modeling users' interest preferences based on a series of items, ignoring extra item attributes, i.e., the brand attribute. This paper proposes a novel approach, called the Attribute Enhanced Graph Attention Networks for Session-based Recommendation (AGSR), using the brand attribute to better model user preferences. Specifically, (i) AGSR constructs the session graph of the current session to capture the user's session-level item embedding;(ii) AGSR constructs the brand global graph to capture the brand-level item embedding and brand embedding;(iii) Based on the learned node embeddings, AGSR apply attention layers to extract users' interest and brand preferences. Finally, AGSR makes the recommendation based on item features and user interest and brand preferences, so as to provide better recommendations. Extensive experiments on three real-world datasets demonstrate the effectiveness of our approach. © 2023 IEEE.

关键词： Embeddings

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Decomposing and Fusing Intra- and Inter-Sensor Spatio-Temporal Signal for Multi-Sensor Wearable Human Activity Recognition 39

Decomposing and Fusing Intra- and Inter-Sensor Spatio-Tempor...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xie, Haoyu Li, Haoxuan Zheng, Chunyuan Yuan, Haonan Liao, Guorui Liao, Jun Liu, Li School of Big Data and Software Engineering Chongqing University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China School of Computer Science and Engineering Beihang University China

ISBN: (纸本)157735897X

Wearable Human Activity Recognition (WHAR) is a prominent research area within ubiquitous computing. Multi-sensor synchronous measurement has proven to be more effective for WHAR than using a single sensor. However, existing WHAR methods use shared convolutional kernels for indiscriminate temporal feature extraction across each sensor variable, which fails to effectively capture spatio-temporal relationships of intra-sensor and inter-sensor variables. We propose the DecomposeWHAR model consisting of a decomposition phase and a fusion phase to better model the relationships between modality variables. The decomposition creates high-dimensional representations of each intra-sensor variable through the improved Depth Separable Convolution to capture local temporal features while preserving their unique characteristics. The fusion phase begins by capturing relationships between intra-sensor variables and fusing their features at both the channel and variable levels. Long-range temporal dependencies are modeled using the State Space Model (SSM), and later cross-sensor interactions are dynamically captured through a self-attention mechanism, highlighting inter-sensor spatial correlations. Our model demonstrates superior performance on three widely used WHAR datasets, significantly outperforming state-of-the-art models while maintaining acceptable computational efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Ambient intelligence

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Coordinated Spectral Efficiency Prediction for Real-World 5G CoMP Systems

arXiv

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arXiv 2024年

作者： Chen, Zhixing Fan, Zhaoyu Li, Yang Kang, Yibin Yan, Qi Shi, Qingjiang The School of Software Engineering Tongji University Shanghai China The Shenzhen Research Institute of Big Data Shenzhen China The Networking and User Experience Lab Huawei Technologies Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China

Coordinated multipoint (CoMP) systems incur substantial resource consumption due to the management of backhaul links and the coordination among various base stations (BSs). Accurate prediction of coordinated spectral efficiency (CSE) can guide the optimization of network parameters, resulting in enhanced resource utilization efficiency. However, characterizing the CSE is intractable due to the inherent complexity of the CoMP channel model and the diversity of the 5G dynamic network environment, which poses a great challenge for CSE prediction in real-world 5G CoMP systems. To address this challenge, in this letter, we propose a data-driven model-assisted approach. Initially, we leverage domain knowledge to preprocess the collected raw data, thereby creating a well-informed dataset. Within this dataset, we explicitly define the target variable and the input feature space relevant to channel statistics for CSE prediction. Subsequently, a residual-based network model is built to capture the high-dimensional non-linear mapping function from the channel statistics to the CSE. The effectiveness of the proposed approach is validated by experimental results on real-world data. Copyright © 2024, The Authors. All rights reserved.

关键词： Spectrum efficiency

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