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PrivComp-KG: Leveraging KG and LLM for Compliance Verification

PrivComp-KG: Leveraging KG and LLM for Compliance Verificati...

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IEEE international conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Leon Garza Lavanya Elluri Aritran Piplai Anantaa Kotal Deepti Gupta Anupam Joshi Computer Science The University of Texas at El Paso El Paso TX USA Computer Information Systems Texas A&M University Central Texas Killeen TX USA CSEE University of Maryland Baltimore County Baltimore MD USA

ISBN: (数字)9798350386745

ISBN: (纸本)9798350386752

Regulatory documents are complex and lengthy, making full compliance a challenging task for businesses. Similarly, privacy policies provided by vendors frequently fall short of the necessary legal standards due to insufficient detail. To address these issues, we propose a solution that leverages a Large language Model (LLM) in combination with Semantic web technology. this approach aims to clarify regulatory requirements and ensure that organizations’ privacy policies align with the relevant legal frameworks, ultimately simplifying the compliance process, reducing privacy risks, and improving efficiency. In this paper, we introduce a novel tool, the Privacy Policy Compliance Verification Knowledge Graph, referred to as PrivComp-KG. PrivComp-KG is designed to efficiently store and retrieve comprehensive information related to privacy policies, regulatory frameworks, and domain-specific legal knowledge. By utilizing LLM and Retrieval Augmented Generation (RAG), we can accurately identify relevant sections in privacy policies and map them to the corresponding regulatory rules. Our LLM-based retrieval system has demonstrated a high level of accuracy, achieving a correctness score of 0.9, outperforming other models in privacy policy analysis. the extracted information from individual privacy policies is then integrated into the PrivComp-KG. By combining this data with contextual domain knowledge and regulatory rules, PrivComp-KG can be queried to assess each vendor’s compliance with applicable regulations. We demonstrate the practical utility of PrivComp-KG by verifying the compliance of privacy policies across various organizations. this approach not only helps policy writers better understand legal requirements but also enables them to identify gaps in existing policies and update them in response to evolving regulations.

关键词： Semantic web Privacy Law Large language models Retrieval augmented generation Standards organizations Organizations Knowledge graphs Regulation Security

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Keywords-oriented Data Augmentation for Chinese

Keywords-oriented Data Augmentation for Chinese

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international conference on Computer and Communications (ICCC)

作者： Fang Yuan Xianbin Hong Cheng Yuan Xiang Fei Sheng-Uei Guan Dawei Liu Wei Wang AI Group ENC Digital Technology Co. Ltd Shanghai China Research Institute of Big Data Analytics Xi'an Jiaotong-Liverpool University Suzhou China AI Group Beijing Livedata Technology Co. LTD Beijing China School of Computing Electronics and Maths Coventry University Coventry UK Cyber Technology Institute De Montfort University Leicester UK

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

In natural language processing tasks, data is very important, but data collection is not cheap. Large volume data can well serve a series of tasks, especially for deep learning tasks. Data augmentation methods are solutions to data problems, which can work well on rising data quality and quantity, such as generating text without meaning changing and expanding the diversity of data distribution. A user-friendly method of the data augmentation is to sample words in a text then augmenting them. the sampling method is often implemented by a random probability. Although the performance of this solution has been proved over the past few years, random sampling is not the best choice for the data augmentation as it has a chance of randomly introducing some noise into initial data, like stop words. the generated data could interfere with the subsequent tasks and drop the accuracy of the tasks' solutions. Hence, this paper aims to introduce a novel data augmentation method that could avoid involving such noisy data. the strategy is keywords-oriented data augmentation for Chinese (KDA). the KDA proposed in this paper indicates a method of extracting keywords based on category labels, and an augmenting method based on the keywords. In contrast to randomness, the proposed technique firstly selects the key information data, then expands the selected data. the experimental section is compared with another two typical data augmentation techniques on three Chinese data sets for text classification tasks. the result shows that the KDA technique has a better performance in the data augmentation task than the compared two.

关键词： Deep learning Data integrity Text categorization Sampling methods Natural language processing Noise measurement Task analysis

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An Object Detection System for Automatic Driving: MEC-YOLO Based on "Cloud-Edge-End"

An Object Detection System for Automatic Driving: MEC-YOLO B...

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Natural language processing (ICNLP), international conference on

作者： Siyuan Liang Xu Zhang School of Communications and Information Engineering Xi’an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

the massive machine type of communication (mMTC) of 5G is the basis for IoT (Internet of things) development, which can promote automobile industry upgrades: intelligent and green. Automatic driving (AD) is an important trend in intelligent vehicles. With the development of computer vision (CV) technology, pure vision sensor-based solutions are dominating the AD of intelligent vehicles. Due to CV hardware platform energy and cost consumption constraints, meeting the detection speed and accuracy of vehicle AD requirements have become an obvious challenge, especially for new energy vehicles. Combining the advantages of edge computing and IoT based on mMTC, this paper proposes a system called mobile edge computing-you only look once (MEC-YOLO) to solve this problem, which is an energy-efficient and low-cost instance-level vision perception system for AD. In this system, the local hardware platform uses the improved object detection (OD) network to extract features from the locally collected visual data, processes them into a feature extraction queue, uploads them to the edge network nodes to complete the OD task, and transmits the prediction results and trains weights back to the local devices. In addition, the cloud computing center resource can be invoked to supplement edge nodes when the edge computing capacity is insufficient. Furthermore, we develop an AD platform equipped with Jetson for system validation, which improved the detection speed by 93.2% and reduced the GPU resource occupation rate by 90.8% compared to YOLOv4 on the KITTI dataset. the test results verified that the MEC-YOLO proposed in this paper has the advantages of fast detection speed, high accuracy, and low resource occupation rate compared with the traditional OD scheme.

关键词： Cloud computing Accuracy Intelligent vehicles Image edge detection Graphics processing units Object detection Feature extraction Hardware Internet of things Edge computing

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A Multi-modal Dialog System for Business Transactions

A Multi-modal Dialog System for Business Transactions

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6～(th) international conference on Spoken language processing

作者： JoyceChai SylvieLevesque MargorzataBudzikowska VeronikaHorvath NandaKambhatla NicolasNicolov WlodekZadrozny IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA IBM T. J. Watson Research Center 30 Saw Mill River Road Hawthorne NY 10532 USA

this paper presents a general framework for conversational agents for business applications that supports multi-channel, multi-modal interactions through the use of a channel and modality independent Dialog Move Markup language. In particular, we describe a prototype system as an instantiation of a general dialog architecture that supports web-based interaction through a combination of modalities such as natural language dialog and user interface components. User studies have revealed that the prototype syste(?) has enhanced user experience in an online shopping environment by significantly reducing the length of interactions in terms of time and the number of clicks. Furthermore, the success in exter ding the general architecture to a prototype system demons(?)rates the applicability and potentiality of such framework in business applications.

关键词： Dialog A Multi-modal Dialog System for Business Transactions

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Hydropower Generation Forecasting via Deep Neural Network

Hydropower Generation Forecasting via Deep Neural Network

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international conference on information Science and Control Engineering (ICISCE)

作者： Liang Li Fuming Yao Ying Huang Fan Zhou School of Information and Software Engineering University of Electronic Science and Technology of China China Energy Investment Corporation Sichuan Dahui Big Data Service CO.Ltd.

ISBN: (数字)9781728157122

ISBN: (纸本)9781728157139

With the advances of deep learning, its applications in our daily life have attracted considerable attention from both academics and industry. However, most of the existing works focus on computer vision and natural language processing, while few studies in the real industrial manufactures. the main reasons are that the data resources are difficult to obtain and the relationships between industrial data are too complex to be modeled. In this paper, we propose a deep neural network based approach for hydroelectric power generation prediction, which, to our knowledge, is the first attempt modeling power generation data with the combination of residual neural networks and recurrent neural networks. Furthermore, we consider different grains of the hydropower generation by dividing the data into four-levels, i.e. recent, daily, weekly, and time-series sequences, which can greatly improve the prediction performance. To this end, we employ a multi-information fusion method to fuse the four components (i.e. closeness, period, trend, long-period) predicted results, among which different component is learned with different weights to determine their influence on final hydropower prediction. Experiments conducting on the real hydropower generation prove the effectiveness of the proposed model, which significantly outperform the baselines. We hope this research will open a new perspective of improving data usage in the industry, especially in power generation areas.

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R-TES: Regularized Template Style for Generative Joint Relational Triple Extraction 23

R-TES: Regularized Template Style for Generative Joint Relat...

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Proceedings of the 2023 6th international conference on Machine Learning and Natural language processing

作者： Liqi Zhang Lianhe Yang Yinhao Bai Hongfei Zhu Xingyu Liu School of Computer Science and Technology Tiangong University China College of Software Nankai University China School of Software Tiangong University China

ISBN: (纸本)9798400709241

Joint Relational Triple Extraction (RTE) is an important task in the field of information extraction. With the development of the pre-trained language models, the sequence-to-sequence (seq2seq) approaches have become one of the promising methods for this task, utilizing a predefined template to convert the relational triples into a structure target sequence, which can be easily decoded as relational triples. However, most existing seq2seq studies focus on the improvement of methods but ignore that template styles also have impacts on performance. Inspired by this idea, we first explore the effects of different template styles on performance and find that some template styles can help generate models to achieve better performance. Based on the above findings, we argue that different template styles lead to various understandings of the relation triple. therefore, we propose Regularized template style (R-TES) to improve the performance of a main template by reducing the gap between it and other selected templates. Specifically, R-TES uses the pre-trained language model to select the templates with kullback-leibler (KL) divergence. then, we further reduce the gap between the main template and these selected templates by minimizing KL divergence. Experimental results show that our method outperforms state-of-the-art methods on the publicly available dataset.

关键词： information extraction language model neural networks

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Resource Recommendation Based on Topic Model for Educational System

Resource Recommendation Based on Topic Model for Educational...

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2011 6th IEEE Joint international information technology and Artificial Intelligence conference(ITAIC 2011)

作者： Wei Kuang Computer and information Management Center Tsinghua University Beijing 100084,P.R.China Nianlong Luo Computer and information Management Center Tsinghua University Beijing 100084,P.R.China Zilei Sun Computer and information Management Center Tsinghua University Beijing 100084,P.R.China

In this paper,we propose a method for resource recommendation based on topic model in an e-learning *** web provides an extremely large and dynamic source of *** it is now increasingly popular to provide personalized service in document *** service can reduce information overload and,hence,increase user *** model is a generative model for text mining,which has significant effects in both efficiency and *** Dirichlet Allocation(LDA),an approach to building topic models based on a formal generative model of documents,is and feasible and effective algorithm in text *** propose an LDA-based interest model within the language modeling framework,and evaluate it on an e-learning *** an e-learning system,topic model can provide a good vector model for the course *** with the help of the topic model,we can build an exact model for users' interests,because in an e-learning system,we can get the users' access action and users' learning condition from the *** the system can adopts interest mining technology and topic model to automatically identify the learner's interests and recommend interest-related resources to specific *** this paper,we only focus on interests modeling and resource *** interest modeling system using proposed approach based on topic model is more ***,the recommendation system based on user interests also gets better result.

关键词： Topic Model Interests Mining Interests Model Resource Recommendation LDA

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Learning from Auxiliary Sources in Argumentative Revision Classification 24th

Learning from Auxiliary Sources in Argumentative Revision Cl...

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24th international conference on Artificial Intelligence in Education (AIED)

作者： Afrin, Tazin Litman, Diane Univ Pittsburgh Pittsburgh PA 15260 USA

ISBN: (纸本)9783031363351;9783031363368

We develop models to classify desirable reasoning revisions in argumentative writing. We explore two approaches - multi-task learning and transfer learning - to take advantage of auxiliary sources of revision data for similar tasks. Results of intrinsic and extrinsic evaluations show that both approaches can indeed improve classifier performance over baselines. While multi-task learning shows that training on different sources of data at the same time may improve performance, transfer-learning better represents the relationship between the data.

关键词： Writing Revision Natural language processing

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Heterogeneous Graph Neural Network for Chinese Financial Event Extraction 22

Heterogeneous Graph Neural Network for Chinese Financial Eve...

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Proceedings of the 2022 6th international conference on Electronic information technology and Computer Engineering

作者： Shunyu Yao Jie Hu Chuxiong Sun Zhiqiao Gao Ning Liu Big Data and Artificial Intelligence Institute China Telecom Research Institute China

ISBN: (纸本)9781450397148

Financial event extraction aims to detect events from financial announcements and extract corresponding event arguments. this task is challenging because financial announcements are often long text, the arguments of an event are always scattered among different sentences in the document, and multiple events can coexist in the same document. It requires a comprehensive understanding of the document and the ability to aggregate arguments across multiple sentences. Most existing sentence-level event extraction methods only extract event arguments within the sentence range. these methods are not very effective for this task, and it is difficult to handle a large number of financial announcements. To address these issues, we propose a novel heterogeneous graph-based model HGCFEE with six types of edges designed to capture the interactions between sentences and entities using heterogeneous graphs. In-depth experiments and comprehensive analysis demonstrate the superiority of HGCFEE over baseline methods.

关键词： Natural language processing Artificial Intelligence Neural Network Event Extraction

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Named Entity Recognition on COVID-19 Scientific Papers 22

Named Entity Recognition on COVID-19 Scientific Papers

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Proceedings of the 2022 6th international conference on Natural language processing and information Retrieval

作者： An Tuan Dao Akiko Aizawa Yuji Matsumoto Aizawa Lab Computer Science Department Graduate School of Information Science and Technology The University of Tokyo Japan and Knowledge Acquisition Team RIKEN AIP Japan Digital Content and Media Sciences Research Division National Institute of Informatics Japan and Knowledge Acquisition Team RIKEN AIP Japan Knowledge Acquisition Team RIKEN AIP Japan

ISBN: (纸本)9781450397629

Text mining techniques, especially named entity recognition (NER), play a vital role in supporting researchers for keeping track of hundred thousand of papers on COVID-19 related literature. Although a few research has been performed NER on COVID-19 scientific papers, very little is currently known concerning the behaviors of current entity recognition models in this new domain. therefore, this ongoing study attempts to analyze current NER models’ performance and limitations on the CORD-19 dataset. By examining three NER models, this study showed that NER performance is improved with the similarity between the testing and pretraining data. When there are little manually annotated resources for COVID-19 NER exist, our analysis suggested that for training purposes, enhancing the dictionary for seed annotation is effective (not necessarily requiring costly human annotation).

关键词： COVID-19 coronavirus named entity recognition

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