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Hybrid Movie Recommendation System using Neural Collaborative Filtering with Gradient Decent and Association Rule Mining 1

Hybrid Movie Recommendation System using Neural Collaborativ...

引用

1st International conference on software, Systems and Information Technology, SSITCON 2024

作者： Cindhamani, J. Pogaku, Rajkumar Sahana, M.P. Adnan, Myasar M. Suganya, M. Vel Tech Multi Tech Dr. Rangrajan Dr. Sakunthala Engineering College Avadi Department of Artificial Intelligence and Data Science Chennai India Maisammaguda School of Computer Science and Engineering Hyderabad India Dayananda Sagar College of Engineering Department of Computer Science and Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350352931

In recent days, most of the people are entertained by watching movies and shows, so developing a recommendation system help people across the world in saving the time to search for high rated movies. Usually, movies are recommended based on the user interests in the different movie genres such as horror, drama, romantic and comedy. Previous researchers suggested various methods for movie recommendation using Content based Filtering (CBF), Collaborative Filtering (CF) which failed to predict the non-linear relationship recommendation due to the availability of many genres. To overcome this challenge, we proposed hybrid model namely Neural Collaborative Filtering (NCF) with gradient decent and Association Rule Mining (ARM). Initially, MovieLens dataset is employed and then data cleaning data organization is incorporated in pre-processing. Further, the features are extracted through the Term Frequency-Inverse Document Frequency (TF-IDF) and Latent Semantic Analysis (LSA) to convert the text data into numerical data and increase the performance respectively. Finally, the developed model was measured using Root mean square error (RMSE), precision, recall, and F 1 score. Hence, the proposed hybrid recommendation model provides higher accuracy value when compared to knowledge-aware Attentional Neural Network (KANN). © 2024 IEEE.

关键词： Collaborative filtering

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AutoLog: A Log Sequence Synthesis Framework for Anomaly Detection 38

AutoLog: A Log Sequence Synthesis Framework for Anomaly Dete...

引用

38th IEEE/ACM International conference on Automated software engineering (ASE)

作者： Huo, Yintong Li, Yichen Su, Yuxin He, Pinjia Xie, Zifan Lyu, Michael R. Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China Sun Yat Sen Univ Sch Software Engn Zhuhai Peoples R China Chinese Univ Hong Kong Sch Data Sci Shenzhen CUHK Shenzhen Shenzhen Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China

ISBN: (纸本)9798350329964

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets. While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events;(2) scalability over diverse systems;and (3) flexibility of log utility. To address these limitations, we propose AUTOLOG, the first automated log generation methodology for anomaly detection. AUTOLOG uses program analysis to generate runtime log sequences without actually running the system. AU-TOLOG starts with probing comprehensive logging statements associated with the call graphs of an application. Then, it constructs execution graphs for each method after pruning the call graphs to find log-related execution paths in a scalable manner. Finally, AUTOLOG propagates the anomaly label to each acquired execution path based on human knowledge. It generates flexible log sequences by walking along the log execution paths with controllable parameters. Experiments on 50 popular Java projects show that AUTOLOG acquires significantly more (9x-58x) log events than existing log datasets from the same system, and generates log messages much faster (15x) with a single machine than existing passive data collection approaches. AUTOLOG also provides hyper-parameters to adjust the data size, anomaly rate, and component indicator for simulating different real-world scenarios. We further demonstrate AUTOLOG's practicality by showing that AUTOLOG enables log-based anomaly detectors to achieve better performance (1.93%) compared to existing log datasets. We hope AUTOLOG can facilitate the benchmarking and adoption of automated log analysis techniques.

关键词： Anomaly detection

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ComPAT: A Compiler Principles Course Assistant 1

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17th International conference on knowledge Science, engineering and Management (KSEM)

作者： Cai, Shubin Chen, Honglong Huang, Youyi Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Lab Artificial Intelligence & Digital Econ Shenzh Shenzhen Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (数字)9789819754892

ISBN: (纸本)9789819754885;9789819754892

Students in compiler principle course often encounter difficulties in understanding abstract programming concepts and need first-aid assistants in the context of independent learning. An innovative Large Language Model-based assistant ComPAT (Compiler Principles course AssisTant) is proposed in this paper. ComPAT uses vector database for text segmentation and similarity queries, meta prompting for answer refinement and challenging questions for heuristic learning. A compiler principles domain-specific dataset with 1520 questions is constructed. Compared to ChatGPT3.5, ERNIE3.5, and ChatGLM4, ComPAT has shown superior performance in handling fill-in-the-blank and question-and-answer. ComPAT demonstrates an innovative and promising use of LLM-based teaching/learning assistant system in computer education.

关键词： Computer Education Large Language Model Teaching/Learning Assistant

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An Ontology-based knowledge Base System for Military software Testing 9

An Ontology-based Knowledge Base System for Military Softwar...

引用

9th International conference on Dependable Systems and Their Applications, DSA 2022

作者： Gao, Chiyang Luo, Wenbing Xie, Fei Beijing CCID Software Test Engineering Technology Center Co. Ltd Beijing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

software testing is a knowledge-intensive activity of software quality assurance. Due to professionalism and time constraints, the military software testing task brings many challenges to the validity and reliability of the testing results for the testing industry. The large amount of historical test data accumulated by the testing institution contains rich test experience and domain knowledge, which have not been used effectively to meet these challenges. Given great importance to knowledge for software testing, using semantic web technologies, an ontology-based knowledge base system and its technical implementation scheme are introduced to military domain, which is an effective means to support knowledge representation, processing, storage and retrieval. At last, we analysis how software testing ontologies and knowledge base system can be used to improve acquiring and sharing of organizational knowledge, as well as supporting the engineering practices of military software testing. © 2022 IEEE.

关键词： software testing

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DDCL: A Dual Decision-making Continuous Reinforcement Learning Method based on Sim2Real 35

DDCL: A Dual Decision-making Continuous Reinforcement Learni...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： Xiao, Wenwen Wang, Xinzhi Luo, Xiangfeng Xie, Shaorong School of Computer Engineering and Science Shanghai University Shanghai200444 China

Continuous reinforcement learning carries potential security risks when applied in real-world scenarios, which could have significant societal implications. While its field of application is expanding, the majority of applications still remain confined to virtual environments. if only a single continuous learning method is applied to an unmanned system, it will still forget previously learned experiences, and retraining will be required when it encounters unknown environments. This reduces the learning efficiency of the unmanned system. To address these issues, some scholars have suggested prioritizing the experience playback pool and using transfer learning to apply previously learned strategies to new environments. However, these methods only alleviate the speed at which the unmanned system forgets its experiences and do not fundamentally solve the problem. Additionally, they cannot prevent dangerous actions and falling into local optima. Therefore, we propose a dual decision-making continuous learning method based on Simulation to Reality (Sim2Real). This method employs a knowledge body to eliminate the local optimal dilemma, and corrects bad strategies in a timely manner to ensure that the unmanned system makes the best decision every time. Our experimental results demonstrate that our method has a 30% higher success rate than other state-of-the-art methods, and the model transfer to real scenes is still highly effective. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Learning systems

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LLMs as Collaborator: Demands-Guided Collaborative Retrieval-Augmented Generation for Commonsense knowledge-Grounded Open-Domain Dialogue Systems

LLMs as Collaborator: Demands-Guided Collaborative Retrieval...

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2024 conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Yu, Jiong Wu, Sixing Chen, Jiahao Zhou, Wei National Pilot School of Software Yunnan University Kunming China Engineering Research Center of Cyberspace Yunnan University Kunming China

ISBN: (纸本)9798891761681

Capturing the unique knowledge demands for each dialogue context plays a crucial role in commonsense knowledge-grounded response generation. However, current CoT-based and RAG-based methods are still unsatisfactory in the era of LLMs because 1) CoT often overestimates the capabilities of LLMs and treats them as isolated knowledge Producers;thus, CoT only uses the inherent knowledge of LLM itself and then suffers from the hallucination and outdated knowledge, and 2) RAG underestimates LLMs because LLMs are the passive Receivers that can only use the knowledge retrieved by external retrievers. In contrast, this work regards LLMs as interactive Collaborators and proposes a novel DCRAG1 (Demands-Guided Collaborative RAG) to leverage the knowledge from both LLMs and the external knowledge graph. Specifically, DCRAG designs three Thought-then-Generate stages to collaboratively investigate knowledge demands, followed by a Demands-Guided knowledge Retrieval to retrieve external knowledge by interacting with LLMs. Extensive experiments and in-depth analyses on English DailyDialog and Chinese Diamante datasets proved DCRAG can effectively capture knowledge demands and bring higher-quality responses. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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An Implementation of Integrated Approach in Product Life-Cycle Management Tool to Ensure Requirements-In-Loop During Complex Product Development: A Cubesat Case Study 20th

An Implementation of Integrated Approach in Product Life-Cyc...

引用

20th International conference on Product Lifecycle Management: Leveraging Digital Twins, Circular Economy, and knowledge Management for Sustainable Innovation (PLM)

作者： Rehman, Mubeen Ur Fortin, Clement Skolkovo Inst Sci & Technol Moscow 121205 Russia

ISBN: (纸本)9783031625800;9783031625787;9783031625770

The complexity of innovative products is increasing due to implementation of emerging technologies and rise in system complexity because of greater system and software functionality and inter connectivity, to address the challenge, multiple engineering disciplines must be well-coordinated in product lifecycle management tools. Complexity of novel products have also demanded the establishment of life cycle spanning development from the conceptualization till product realization. The application of MBSE inside PLM tools to keep requirements-in-loop becomes paramount for timely decision support. Unfortunately, keeping a requirements traceability within product development is still a challenge. In aerospace domain, due to innate complexity results in huge number of mission critical requirements which need to be verified and validated to make well informed design decisions throughout the product development process. In this paper, we have proposed a method to implement MBSE using RFLP approach in PLM tool using a Cubesat case study, to keep requirements-in-loop during initial phase of product development.

关键词： Model based Systems engineering RFLP Requirements-in-Loop Product Life-cycle Management Decision Support

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Make LLM a Testing Expert: Bringing Human-Like Interaction to Mobile GUI Testing via Functionality-Aware Decisions 24

Make LLM a Testing Expert: Bringing Human-Like Interaction t...

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44th ACM/IEEE International conference on software engineering, ICSE 2024

作者： Liu, Zhe Chen, Chunyang Wang, Junjie Chen, Mengzhuo Wu, Boyu Che, Xing Wang, Dandan Wang, Qing State Key Laboratory of Intelligent Game Beijing China Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Technical University of Munich Munich Germany DiDi Global Inc Science & Technology on Integrated Information System Laboratory China

ISBN: (纸本)9798400702174

Automated Graphical User Interface (GUI) testing plays a crucial role in ensuring app quality, especially as mobile applications have become an integral part of our daily lives. Despite the growing popularity of learning-based techniques in automated GUI testing due to their ability to generate human-like interactions, they still suffer from several limitations, such as low testing coverage, inadequate generalization capabilities, and heavy reliance on training data. Inspired by the success of Large Language Models (LLMs) like ChatGPT in natural language understanding and question answering, we formulate the mobile GUI testing problem as a Q&A task. We propose GPTDroid, asking LLM to chat with the mobile apps by passing the GUI page information to LLM to elicit testing scripts, and executing them to keep passing the app feedback to LLM, iterating the whole process. Within this framework, we have also introduced a functionality-aware memory prompting mechanism that equips the LLM with the ability to retain testing knowledge of the whole process and conduct long-term, functionality-based reasoning to guide exploration. We evaluate it on 93 apps from Google Play and demonstrate that it outperforms the best baseline by 32% in activity coverage, and detects 31% more bugs at a faster rate. Moreover, GPTDroid identifies 53 new bugs on Google Play, of which 35 have been confirmed and fixed. © 2024 ACM.

关键词： Graphical user interfaces

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Multimodal knowledge Triple Extraction based on Representation Learning 5

Multimodal Knowledge Triple Extraction Based on Representati...

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5th International conference on Electronic engineering and Informatics, EEI 2023

作者： Xian, Guangming Zhang, Wencong Lan, Fucai Lin, Yifan Lin, Yanhang School of Software South China Normal University Guangdong Foshan China

ISBN: (纸本)9798350327076

knowledge based visual question-answering is an emerging technique that combines computer vision and natural language processing to address image-based questions. This approach requires the model to possess internal reasoning ability and incorporate external knowledge to enhance its generalization performance. knowledge graphs are commonly employed to integrate various types of knowledge, such as image knowledge, text knowledge, and basic common sense, into visual language question answering models, significantly enhancing their interpretability. However, introducing knowledge into these models often involves retrieval and pre-training methods, which can introduce noise due to the local correlation among knowledge sources and the existence of modality gaps, thereby affecting the model's performance. To mitigate this issue, this paper proposes a multimodal knowledge extraction approach based on distributed representation learning. The approach models inexpressible multimodal facts using explicit triples, considering the semantic gap between visual and textual modalities. Cross-modal representations are obtained through an attention mechanism, resulting in knowledge triplets comprising visual features, cross-modal representations, and text features. By incorporating these knowledge triplets into the visual language question answering model, the task is transformed into pattern matching using knowledge triples. The proposed approach comprehensively considers multiple factors, is not restricted to specific forms of knowledge, and can effectively incorporate a substantial amount of knowledge. Experimental results demonstrate its superiority, with a performance improvement of 0.5% on the OKVQA dataset compared to the baseline model, highlighting its strong generalization ability. © 2023 IEEE.

关键词： Visual languages

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Object-Level Contrast Learning for 3D Sparse Object Detection in Ocean Scene 35

Object-Level Contrast Learning for 3D Sparse Object Detectio...

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35th International conference on software engineering and knowledge engineering, SEKE 2023

作者： He, Yuheng Yang, Wenbin Luo, Xiangfeng Ma, Liyan Xie, Shaorong College of Computer Engineering and Science Shanghai University Shanghai China

LiDAR-based 3D object detection provides the necessary high-precision environmental sensing information for the safe navigation of smart ships. However, relying on viewpoint projections, voxelized point clouds, or using inefficient point sampling methods, current LiDAR 3D object detection methods treat all objects uniformly and quantitatively while ignoring the specificity of sparse objects in the scene, which leaves less useful information about sparse objects. In this paper, we propose an end-to-end two-stage architecture, Object-Level Contrast Learning 3D Object Detection network (OCL), for better construction of sparse object features and improving the ability of model to detect sparse objects. In the first stage, the Contrast Learning based Sparse Object Feature Enhancement training strategy is proposed to decrease the feature discrepancy between sparse and regular objects in object-level. In the second stage, we use the Point-level Feature Multiple Aggregation Strategy to aggregate finer point-level features of sparse objects. Extensive experiments show that OCL achieves excellent performance on both Ship dataset and KITTI dataset. Furthermore, our work proposes a promising new idea for applying contrast learning to 3D object detection. © 2023 knowledge Systems Institute Graduate School. All rights reserved.

关键词： Object detection

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