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AsmDocGen: Generating Functional natural language Descriptions for Assembly Code

AsmDocGen: Generating Functional Natural Language Descriptio...

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19th international conference on Software Technologies, ICSOFT 2024

作者： Yuki, Jesia Quader Amouei, Mohammadhossein Fung, Benjamin C. M. Charland, Philippe Walenstein, Andrew School of Information Studies McGill University MontrealQC Canada Mission Critical Cyber Security Section Defence R&D Canada QuebecQC Canada BlackBerry Limited WaterlooON Canada

ISBN: (纸本)9789897587061

This study explores the field of software reverse engineering through the lens of code summarization, which involves generating informative and concise summaries of code functionality. A significant aspect of this research is the application of assembly code summarization in malware analysis, highlighting its critical role in understanding and mitigating potential security threats. Although there have been recent efforts to develop code summarization techniques for high-level programming languages, to the best of our knowledge, this study is the first attempt to generate comments for assembly code. For this purpose, we first built a carefully curated dataset of assembly function-comment pairs. We then focused on automatic assembly code summarization using transfer learning with pre-trained natural language processing (NLP) models, including BERT, DistilBERT, RoBERTa, and CodeBERT. The results of our experiments show a notable advantage of CodeBERT: despite its initial training on high-level programming languages alone, it excels in learning assembly language, outperforming other pre-trained NLP models. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Reverse engineering

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natural language processing to Extract Contextual Structure from Requirements 16

Natural Language Processing to Extract Contextual Structure ...

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16th Annual IEEE international Systems conference (SysCon)

作者： Vierlboeck, Maximilian Dunbar, Daniel Nilchiani, Roshanak Stevens Inst Technol Sch Syst & Enterprises Hoboken NJ 07030 USA

ISBN: (纸本)9781665439923

The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Such benefits have been identified amongst others for the area of Requirements engineering. By assessing the natural language processing for Requirement engineering status quo and literature, a necessity for an automatic and universal approach to elicit structure from requirement and specification documents was identified. This paper outlines the first steps and results towards a modularized approach that splits the core algorithm from the text corpus as an input and underlying rule/knowledge base. This separation of functions allows for individual modification of the included parts and eases or potentially removes restrictions as well as limitations, such as input rules or the necessity for human supervision. Furthermore, contextual information and links via ontology inference can be considered that are not explicit on a textual level. The initial results of the approach show the successful extraction of structural information from requirement text, which was validated by comparing the results to human interpretations for small and public sample sets. In addition, the contextual consideration and inference via ontologies is described conceptually. At the current stage, limitations still exist regarding scalability and handling of text ambiguities, but solutions for these caveats have been developed and are being tested. Overall, the approach and results presented will be integrated and are part of a novel requirement complexity assessment framework.

关键词： requirements engineering natural language processing complexity structure ontology contextual information

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Enhanced and Efficient Gated Recurrent Unit and Long Short-Term Memory Architectures for Detecting False Information

Enhanced and Efficient Gated Recurrent Unit and Long Short-T...

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2024 international conference on knowledge engineering and Communication Systems, ICKECS 2024

作者： Raman, Ramakrishnan Kumar, Vikram Sanjay, C.P. Rabadiya, Dhaval Patre, Smruti Meenakshi, R. India SRV Media Pvt. Ltd. India It & Business Analytics India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India India Chennai Institute of Technology Computer Science and Engineering India

ISBN: (纸本)9798350359688

In the digital era, the rapid dissemination of false information has emerged as a formidable challenge, undermining the credibility of online platforms and posing a threat to informed public discourse. Addressing this issue, our research introduces innovative enhancements to Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures, aimed at bolstering the efficiency and accuracy of false information detection mechanisms. Leveraging the inherent strengths of GRU and LSTM models in capturing temporal dependencies within data, our proposed modifications incorporate adaptive learning rates and a novel hybrid attention mechanism. These enhancements facilitate a more nuanced understanding of the contextual and stylistic nuances characteristic of false information, thereby significantly improving the models' predictive performance. Through extensive experimentation on a diverse dataset comprising various forms of digital content, our findings reveal that the enhanced GRU and LSTM architectures outperform existing models in terms of detection accuracy, while simultaneously reducing computational overhead. This advancement in deep learning techniques represents a pivotal step towards more reliable and efficient false information detection, with potential applications spanning across social media platforms, news outlets, and beyond. Our research not only contributes to the academic discourse on natural language processing and misinformation studies but also offers practical solutions for safeguarding the integrity of information in the digital landscape. © 2024 IEEE.

关键词： Long short-term memory

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Summarization of Elicitation Conversations to Locate Requirements-Relevant Information 1

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29th international Working conference on Requirements engineering - Foundation for Software Quality (REFSQ)

作者： Spijkman, Tjerk de Bondt, Xavier Dalpiaz, Fabiano Brinkkemper, Sjaak Univ Utrecht Dept Informat & Comp Sci Utrecht Netherlands Fizor Utrecht Netherlands

ISBN: (数字)9783031297861

ISBN: (纸本)9783031297854;9783031297861

[Context and motivation] Conversations around requirements, such as interviews and workshops, are a key activity of requirements elicitation, and play a significant role in the creation of requirements specifications. [Question/problem] While these conversations contain a wealth of knowledge, requirements engineers use them mainly through note-taking during the conversation and by recalling the information from their memory. There is potential for supporting practitioners by retrieving important information from the recordings of these conversations. [Principal ideas/results] Although transcriptions can be automatically generated with good accuracy, they often contain excessive text to be efficiently used for processing requirements elicitation sessions. Thus, we observed a need to transform these datasets into a useful format for requirements engineers to analyze. [Contribution] We present REConSum, a prototype that utilizes natural language processing (NLP) to summarize requirements conversations. REConSum takes as input a transcribed conversation, and it filters the speaker turns by keeping only those that include a question and that are expected to contain, or to be answered with, requirements-relevant information. In addition to presenting REConSum, we experiment with different algorithms to assess the most effective combination.

关键词： Requirements Elicitation natural language processing Conversational RE Requirements-Relevant Information

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Disease Diagnosis and Diet Plan Recommendation using KNN model

Disease Diagnosis and Diet Plan Recommendation using KNN mod...

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2023 international conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Durga, Chinni Roshini Vemuri, Sandeep Lahari, Veeranki Kavya Velagapudi Ramakrishna Siddhartha Engineering College Department of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350344387

Healthcare is essential for every individual in order to maintain a healthy and peaceful life. However, it is very difficult to have a doctor's consultation for every small health issue. So rather than consulting the doctor every time, it is better to use a medical chatbot for healthcare purposes for predicting the disease and for having the awareness of very basic details of the disease before the doctor's consultation. It not only reduces the cost of the consultation but also improves the medical knowledge of the user by means of a chatbot. The chatbot is a computer program which uses natural language to interact with the users and uses NLP techniques to identify the symptoms provided by the users. The proposed system uses the KNN model to predict the most probable diseases. It diagnoses the disease using the symptoms provided by the user which is taken as input. It also gives the list of most probable diseases and their probabilities based on the user-selected number of probable diseases he wants to know. Later on, it also provides a diet plan to the users based on their answers in order to control the disease up to some extent. © 2023 IEEE.

关键词： Chatbot Diet Plan Disease Diagnosis Healthcare KNN NLP (natural language processing)

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Learnable Position-based Joint Entity-Relation Extraction Filtering Model 2

Learnable Position-based Joint Entity-Relation Extraction Fi...

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2nd international conference on Computing, Communication, Perception and Quantum Technology, CCPQT 2023

作者： Liu, Jingjing Zhang, Juan Wu, Ming Shanghai University of Engineering Science School of Electronic and Electrical Engineering Shanghai China

ISBN: (纸本)9798350342697

The goal of entity relation joint extraction is to simultaneously extract entities and their relationships from text, representing this information in a structured manner, typically presented in the form of triples (entity1, relationship, entity2). This task holds significant importance in natural language processing applications such as knowledge graph construction and expansion, automatic question answering, information retrieval, and text mining. However, existing relation extraction methods still face several challenges. The core idea behind Seq2Seq models is to transform input sequences into output sequences, typically consisting of an encoder and a decoder. While Seq2Seq models excel in multiple tasks, they still encounter certain issues. For instance, they may be limited in dealing with challenges like redundancy, long-distance dependencies, and polysemy. To address these challenges, this study introduces an innovative approach. Firstly, we incorporate a "filter layer"inspired by signal processing to alleviate the decline in extraction performance caused by data redundancy. Secondly, we adopt variable-length positional encoding techniques to effectively capture word positions in sequences, enhancing the model's understanding of text. Additionally, we introduce dense connections, successfully mitigating the vanishing gradient problem in deep learning networks, thereby improving information flow and performance. Through these innovative methods, our model demonstrates significant performance improvements on two benchmark datasets, affirming its effectiveness and practicality in entity relation joint extraction tasks. These contributions are expected to drive advancements in the field of relation extraction, enhance the effectiveness of natural language processing tasks and hold significant implications for knowledge graph construction and expansion, among other domains. © 2023 IEEE.

关键词： Extraction

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End-to-End Rationale Reconstruction 7

End-to-End Rationale Reconstruction

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37th IEEE/ACM international conference on Automated Software engineering (ASE)

作者： Dhaouadi, Mouna Oakes, Bentley James Famelis, Michalis Univ Montreal DIRO Montreal PQ Canada

ISBN: (纸本)9781450394758

The logic behind design decisions, called design rationale, is very valuable. In the past, researchers have tried to automatically extract and exploit this information, but prior techniques are only applicable to specific contexts and there is insufficient progress on an end-to-end rationale information extraction pipeline. Here we outline a path towards such a pipeline that leverages several Machine Learning (ML) and natural language processing (NLP) techniques. Our proposed context-independent approach, called Kantara, produces a knowledge graph representation of decisions and of their rationales, which considers their historical evolution and traceability. We also propose validation mechanisms to ensure the correctness of the extracted information and the coherence of the development process. We conducted a preliminary evaluation of our proposed approach on a small example sourced from the Linux Kernel, which shows promising results.

关键词： Software rationale natural language processing

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CDF-Net: A zero-shot 3D classification network with CLIP Decoder for Feature Fusion 24

CDF-Net: A zero-shot 3D classification network with CLIP Dec...

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2024 international conference on Intelligent Perception and Pattern Recognition, IPPR 2024

作者： Yan, Hao Bai, Jing Liu, Lu School of Computer Science and Engineering North Minzu University China The Key Laboratory of images Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798400707315

Zero-shot 3D model classification requires neural networks to correctly classify models with unseen categories, which has important practical implications for fields such as autonomous driving and robotics. PointCLIP demonstrates that transferring knowledge from the 2D image domain to the 3D model domain is beneficial for zero-shot 3D model classification, even without incorporating new 3D-specific knowledge. However, its effectiveness is limited by the lack of 3D knowledge. As a result, subsequent improvements have focused on refining 3D-to-2D projection algorithms rather than exploiting 3D information. Our approach aims to exploit both image domain knowledge and the full potential of 3D models. To this end, we propose CDF-Net, a learnable model architecture. CDF-Net consists of two components: an initial feature encoding module using the Contrastive language-Image Pre-training network to extract 2D knowledge from multi-view representations of 3D models, and a learnable module to extract 3D knowledge and integrate it with 2D knowledge. Evaluations on two benchmarks consisting of four datasets show that CDF-Net achieves competitive classification accuracies, demonstrating the effectiveness of our method. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Scrutinization and Abstraction of Unstructured Electronic Health Records using natural language processing and Deep Learning 1

Scrutinization and Abstraction of Unstructured Electronic He...

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1st IEEE international conference on Contemporary Computing and Communications, InC4 2023

作者： Nancy Deborah, R. Alwyn Rajiv, S. Vinora, A. Priyadharshini, J. Priyanga, P. Vidhya Lakshmi, K. Velammal College of Engineering and Technology Department of Information Technology Tamilnadu Madurai India Kamaraj College of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu Viruthunagar India

ISBN: (纸本)9798350335774

The healthcare industry produces patients' medical records in great numbers on daily basis. In hospitals, these records are generated in both organized and unstructured formats. EHR commonly called Electronic Health Records are the digital copy of the medical summary of the patient, medical treatment, lab reports of the patients, and their medical history. Electronic health records hold an enormous amount of data which is found useful for research in the medical field but the limitation is that it takes time to process those data. To overcome this disadvantage, we are introducing the deep learning concept of Recurrent Neural Network (RNN) for rapid analysis in EHRs. And the other limitation is that due to the presence of medical entities in EHR, these are hard to process. To overcome this limitation NLP is used for the automatic processing of records. NLP has shown improvement in the performance of processing a large amount of data. The proposed work is about combining the knowledge of deep learning approaches with natural language processing to handle the EHR datasets to enhance the already existing methods in an EHR system. In this paper, we study the patient's unstructured EHRs and propose a novel algorithm named Clinical-NERT. We predict the patient's medical conditions based on the symptoms they experience. We train our model with the degree of symptoms for the disease along with its severity. Here, deep learning techniques are used to provide a way to boost the performance of the system by enhancing the precision of the decision-making process regarding patients' disease prediction by processing the vibrantly rich information from the medical records. © 2023 IEEE.

关键词： Recurrent neural networks

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Research on Named Entity Recognition Based on ERNIE-BiLSTM-CRF 4

Research on Named Entity Recognition Based on ERNIE-BiLSTM-C...

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4th international conference on Computers, Information processing and Advanced Education, CIPAE 2023

作者： Bao, Zhongyuan Zhang, Lejun Lu, Zhengyu Shanghai Normal University College of Information Mechanical and Electrical Engineering Shanghai China Donghua University College of Mechanical Engineering Shanghai China

ISBN: (纸本)9798350342710

Named entity recognition, as well as the knowledge extraction of text data, is one of the important tasks of information mining. However, due to the unique text structure and word distribution of some specific sentences, the traditional models cannot well inject the related entities into the exact representation. To better represent entities by word vectors to improve the annotation performance of named entities of the model, this paper proposes an ERNIE-BiLSTM-CRF model. Firstly, the dynamic word vectors of text input are obtained by ERNIE pre-trained model, and then the corresponding feature vectors are acquired through BiLSTM, and finally, the features are decoded and the label sequence is calculated by CRF. The experiment result shows that the Precision, Recall, and F1-score of the proposed model are 69.74%, 66.55%, and 68.10% respectively, which proves the effectiveness of the method. © 2023 IEEE.

关键词： natural language processing systems

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