An expert system for safety instrumented system in petroleum industry

The expert system technology has been developed since 1960s and now it has proven to be a useful and effective tool in many areas. It helps shorten the time required to accomplish a certain job and relieve the workload for human staves by implement the task automatically. This master thesis gives general introduction about the expert system and the technologies involved with it. We also discussed the framework of the expert system and how it will interact with the existing cause and effect matrix. The thesis describes a way of implementing automatic textual verification and the possibility of automatic information extraction in the designing process of safety instrumented systems. We use the Protégé application [*] to make models for the Cause and Effect Matrix and use XMLUnit to implement the comparison between two files of interest

Towards Building the Federated GPT: Federated Instruction Tuning

While "instruction-tuned" generative large language models (LLMs) have demonstrated an impressive ability to generalize to new tasks, the training phases heavily rely on large amounts of diverse and high-quality instruction data (such as ChatGPT and GPT-4). Unfortunately, acquiring high-quality data, especially when it comes to human-written data, can pose significant challenges both in terms of cost and accessibility. Moreover, concerns related to privacy can further limit access to such data, making the process of obtaining it a complex and nuanced undertaking. Consequently, this hinders the generality of the tuned models and may restrict their effectiveness in certain contexts. To tackle this issue, our study introduces a new approach called Federated Instruction Tuning (FedIT), which leverages federated learning (FL) as the learning framework for the instruction tuning of LLMs. This marks the first exploration of FL-based instruction tuning for LLMs. This is especially important since text data is predominantly generated by end users. Therefore, it is imperative to design and adapt FL approaches to effectively leverage these users' diverse instructions stored on local devices, while preserving privacy and ensuring data security. In the current paper, by conducting widely used GPT-4 auto-evaluation, we demonstrate that by exploiting the heterogeneous and diverse sets of instructions on the client's end with the proposed framework FedIT, we improved the performance of LLMs compared to centralized training with only limited local instructions. Further, in this paper, we developed a Github repository named Shepherd. This repository offers a foundational framework for exploring federated fine-tuning of LLMs using heterogeneous instructions across diverse categories.Comment: Project page: https://github.com/JayZhang42/FederatedGPT-Shepher

Towards Generalist Foundation Model for Radiology

In this study, we aim to initiate the development of Radiology Foundation Model, termed as RadFM.We consider the construction of foundational models from the perspectives of data, model design, and evaluation thoroughly. Our contribution can be concluded as follows: (i), we construct a large-scale Medical Multi-modal Dataset, MedMD, consisting of 16M 2D and 3D medical scans. To the best of our knowledge, this is the first multi-modal dataset containing 3D medical scans. (ii), We propose an architecture that enables visually conditioned generative pre-training, allowing for the integration of text input interleaved with 2D or 3D medical scans to generate response for diverse radiologic tasks. The model was initially pre-trained on MedMD and subsequently domain-specific fine-tuned on RadMD, a radiologic cleaned version of MedMD, containing 3M radiologic visual-language pairs. (iii), we propose a new evaluation benchmark that comprises five tasks, aiming to comprehensively assess the capability of foundation models in handling practical clinical problems. Our experimental results confirm that RadFM significantly outperforms existing multi-modal foundation models. The codes, data, and model checkpoint will all be made publicly available to promote further research and development in the field

Semantic Interoperability in Digital Library Systems

This report is a state-of-the-art overview of activities and research being undertaken in areas relating to semantic interoperability in digital library systems. It has been undertaken as part of the cluster activity of WP5: Knowledge Extraction and Semantic Interoperability (KESI). The authors and contributors draw on the research expertise and experience of a number of organisations (UKOLN, ICS-FORTH, NETLAB, TUC-MUSIC, University of Glamorgan) as well as several work-packages (WP5: Knowledge Extraction and Semantic Interoperability; WP3: Audio-Visual and Non-traditional Objects) within the DELOS2 NoE. In addition, a workshop was held [KESI Workshop Sept. 2004] (co-located with ECDL 2004) in order to provide a forum for the discussion of issues relevant to the topic of this report. We are grateful to those who participated in the forum and for their valuable comments, which have helped to shape this report. Definitions of interoperability, syntactic interoperability and semantic interoperability are presented noting that semantic interoperability is very much about matching concepts as a basis. The NSF Post Digital Libraries Futures Workshop: Wave of the Future [NSF Workshop] has identified semantic interoperability as being of primary importance in digital library research

Enabling the Development and Implementation of Digital Twins : Proceedings of the 20th International Conference on Construction Applications of Virtual Reality

Welcome to the 20th International Conference on Construction Applications of Virtual Reality (CONVR 2020). This year we are meeting on-line due to the current Coronavirus pandemic. The overarching theme for CONVR2020 is "Enabling the development and implementation of Digital Twins". CONVR is one of the world-leading conferences in the areas of virtual reality, augmented reality and building information modelling. Each year, more than 100 participants from all around the globe meet to discuss and exchange the latest developments and applications of virtual technologies in the architectural, engineering, construction and operation industry (AECO). The conference is also known for having a unique blend of participants from both academia and industry. This year, with all the difficulties of replicating a real face to face meetings, we are carefully planning the conference to ensure that all participants have a perfect experience. We have a group of leading keynote speakers from industry and academia who are covering up to date hot topics and are enthusiastic and keen to share their knowledge with you. CONVR participants are very loyal to the conference and have attended most of the editions over the last eighteen editions. This year we are welcoming numerous first timers and we aim to help them make the most of the conference by introducing them to other participants

지식 기반 대화에서의 대화 특성을 활용한 지식 선택 및 랭킹 방법

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·컴퓨터공학부, 2022. 8. 이상구.Knowledge grounded conversation (KGC) model aims to generate informative responses relevant to both conversation history and external knowledge. One of the most important parts of KGC models is to find the knowledge which provides the basis on which the responses are grounded. If the model selects inappropriate knowledge, it may produce responses that are irrelevant or lack knowledge. In this dissertation, we study the methods of leveraging conversational characteristics to select or rank the knowledge for knowledge grounded conversation. In particular, this dissertation provides novel two methods, where one of which focuses on the sequential structure of multi-turn conversation, and the other focuses on utilizing local context and topic of a long conversation. We first propose two knowledge selection strategies of which one preserves the sequential matching features and the other encodes the sequential nature of the conversation. Second, we propose a novel knowledge ranking model that composes an appropriate range of relevant documents by exploiting both the topic keywords and local context of a conversation. In addition, we apply the knowledge ranking model in quote recommendation with our new quote recommendation framework that provides hard negative samples to the model. Our experimental results show that the KGC models based on our proposed knowledge selection and ranking methods outperform the competitive models in terms of groundness and relevance.지식 기반 대화 모델은 대화 기록과 외부 지식 이 두 가지 모두에 관련된 응답을 생성하는 것을 목표로 한다. 지식 기반 대화 모델의 가장 중요한 부분 중 하나는 응답의 기반을 제공하는 지식을 찾는 것이다. 지식 기반 모델이 주어진 문맥에 부적합한 지식을 찾는 경우 관련성이 떨어지거나 지식이 부족한 응답이 생성될 수 있다. 이 문제를 해결하기 위해 이 논문에서는 지식 기반 대화를 위해 대화 여러 특성을 활용하여 지식을 선정하는 지식 선택 모델과 지식 순위 모델을 제시한다. 구체적으로 본 논문에서는 다중 턴 대화에서의 순차적 구조 또는 응답 이전 문맥과 대화의 주제를 활용하는 새로운 두 가지 방법을 제시한다. 첫 번째 방법으로써 본 논문은 두 가지 지식 선택 전략을 제안한다. 제안하는 전략 중 하나는 지식과 대화 턴 간의 순차적 매칭 특징을 보존하는 방법이고 다른 전략은 대화의 순차적 특성을 인코딩하여 지식을 선택하는 방법이다. 두 번째로 본 논문은 대화의 주제 키워드와 응답 바로 이전의 문맥을 모두 활용하여 적절한 범위의 관련 문서들로 검색 결과를 구성하는 새로운 지식 순위 모델을 제안한다. 마지막으로 지식 순위 모델의 적응성 검증을 위해 정답 인용구와 의미적으로 유사하지만 정답은 아닌 인용구의 집합을 인용구 순위 모델에 제공하는 인용구 추천 프레임워크를 제안한다. 제안된 지식 선택 및 순위 모델을 기반으로 하는 지식 기반 대화 모델이 경쟁 모델보다 외부 지식 및 대화 문맥과의 관련성 측면에서 우수하다는 것을 사람 간의 대화 데이터를 이용한 다수의 실험을 통해 검증하였다.Abstract 1 1. Introduction 17 2. Background and Related Works 25 2.1 Terminology 25 2.2 Overview of Technologies for Conversational Systems 27 2.2.1 Open-domain Dialogue System 27 2.2.2 Task-oriented Dialogue System 29 2.2.3 Question Answering System 29 2.3 Components of Knowledge Grounded Conversation Model 31 2.4 Related Works 36 2.4.1 KGC datasets 36 2.4.2 Soft Selection-based KGC Model 36 2.4.3 Hard Selection-based KGC Model 37 2.4.4 Retrieval-based KGC Models 39 2.4.5 Response Generation with Knowledge Integration 39 2.4.6 Quote Recommendation 42 2.5 Evaluation Methods 44 2.6 Problem Statements 47 3. Knowledge Selection with Sequential Structure of Conversation 48 3.1 Motivation 48 3.2 Reduce-Match Strategy & Match-Reduce Strategy 49 3.2.1 Backbone architecture 51 3.2.2 Reduce-Match Strategy-based Models 52 3.2.3 Match-Reduce Strategy-based Models 56 3.3 Experiments 62 3.3.1 Experimental Setup 62 3.3.2 Experimental Results 70 3.4 Analysis 72 3.4.1 Case Study 72 3.4.2 Impact of Matching Difficulty 75 3.4.3 Impact of Length of Context 77 3.4.4 Impact of Dialogue Act of Message 78 4. Knowledge Ranking with Local Context and Topic Keywords 81 4.1 Motivation 81 4.2 Retrieval-Augmented Knowledge Grounded Conversation Model 85 4.2.1 Base Model 86 4.2.2 Topic-aware Dual Matching for Knowledge Re-ranking 86 4.2.3 Data Weighting Scheme for Retrieval Augmented Generation Models 89 4.3 Experiments 90 4.3.1 Experimental Setup 90 4.3.2 Experimental Results 94 4.4 Analysis 98 4.4.1 Case Study 98 4.4.2 Ablation Study 99 4.4.3 Model Variations 104 4.4.4 Error Analysis 105 5. Application: Quote Recommendation with Knowledge Ranking 110 5.1 Motivation 110 5.2 CAGAR: A Framework for Quote Recommendation 112 5.2.1 Conversation Encoder 114 5.2.2 Quote Encoder 114 5.2.3 Candidate Generator 115 5.2.4 Re-ranker 116 5.2.5 Training and Inference 116 5.3 Experiments 117 5.3.1 Experimental Setup 117 5.3.2 Experimental Results 119 5.4 Analysis 120 5.4.1 Ablation Study 120 5.4.2 Case Study 121 5.4.3 Impact of Length of Context 121 5.4.4 Impact of Training Set Size per Quote 123 6. Conclusion 125 6.1 Contributions and Limitations 126 6.2 Future Works 128 Appendix A. Preliminary Experiments for Quote Recommendations 131 A.1 Methods 131 A.1.1 Matching Granularity Adjustment 131 A.1.2 Random Forest 133 A.1.3 Convolutional Neural Network 133 A.1.4 Recurrent Neural Network 134 A.2 Experiments 135 A.2.1 Baselines and Implementation Details 135 A.2.2 Datasets 136 A.2.3 Results and Discussions 137 초록 162박