Domain-independent System Research Articles

Next-generation predictive maintenance: leveraging blockchain and dynamic deep learning in a domain-independent system.

The fourth industrial revolution, often referred to as Industry 4.0, has revolutionized the manufacturing sector by integrating emerging technologies such as artificial intelligence (AI), machine and deep learning, Industrial Internet of Things (IIoT), cloud computing, cyber physical systems (CPSs) and cognitive computing, throughout the production life cycle. Predictive maintenance (PdM) emerges as a critical component, utilizing data analytic to track machine health and proactively detect machinery failures. Deep learning (DL), is pivotal in this context, offering superior accuracy in prediction through neural networks' data processing capabilities. However, DL adoption in PdM faces challenges, including continuous model updates and domain dependence. Meanwhile, centralized DL models, prevalent in PdM, pose security risks such as central points of failure and unauthorized access. To address these issues, this study presents an innovative decentralized PdM system integrating DL, blockchain, and decentralized storage based on the InterPlanetary File System (IPFS) for accurately predicting Remaining Useful Lifetime (RUL). DL handles predictive tasks, while blockchain secures data orchestration. Decentralized storage safeguards model metadata and training data for dynamic models. The system features synchronized two DL pipelines for time series data, encompassing prediction and training mechanisms. The detailed material and methods of this research shed light on the system's development and validation processes. Rigorous validation confirms the system's accuracy, performance, and security through an experimental testbed. The results demonstrate the system's dynamic updating and domain independence. Prediction model surpass state-of-the-art models in terms of the root mean squared error (RMSE) score. Blockchain-based scalability performance was tested based on smart contract gas usage, and the analysis shows efficient performance across varying input and output data scales. A comprehensive CIA analysis highlights the system's robust security features, addressing confidentiality, integrity, and availability aspects. The proposed decentralized predictive maintenance (PdM) system, which incorporates deep learning (DL), blockchain technology, and decentralized storage, has the potential to improve predictive accuracy and overcome significant security and scalability obstacles. Consequently, this system holds promising implications for the advancement of predictive maintenance in the context of Industry 4.0.

An Architecture of Domain Independent and Extensible Intelligent Tutoring System based on Concept Dependencies and Subject Paths

An Architecture of Domain Independent and Extensible Intelligent Tutoring System based on Concept Dependencies and Subject Paths

Automating the Repair of Faulty Logical Theories

AbstractThis thesis aims to develop a domain-independent system for repairing faulty Datalog-like theories by combining three existing techniques: abduction, belief revision, and conceptual change. Accordingly, the proposed system is named the ABC repair system (ABC). Given an observed assertion and a current theory, abduction adds axioms, which explain that observation by making the corresponding assertion derivable from the expanded theory. Belief revision incorporates a new piece of information which conflicts with the input theory by deleting old axioms. Conceptual change uses the reformation algorithm for blocking unwanted proofs or unblocking wanted proofs. The former two techniques change an axiom as a whole, while reformation changes the language in which the theory is written. These three techniques are complementary. But they have not previously been combined into one system. We are working on aligning these three techniques in ABC, which is capable of repairing logical theories with better result than each individual technique alone. In addition, ABC extends abduction and belief revision to operate on preconditions: the former deletes preconditions from rules, and the latter adds preconditions to rules. Datalog is used as the underlying logic of theories in this thesis, but the proposed system has the potential to be adapted to theories in other logics.Abstract prepared by Xue Li by taking directly from the thesis.E-mail: xuerr.lee@gmail.com

Development Cycle Modeling: Resource Estimation

This paper presents results produced by a domain-independent system development model that enables objective and quantitative calculation of certain development cycle characteristics. The presentation recounts the model’s motivation and includes an outline of the model’s structure. The outline shows that the model is constructive. As such, it provides an explanatory mechanism for the results that it produces, not just a representation of qualitative observations or measured data. The model is a Statistical Agent-based Model of Development and Evaluation (SAbMDE); and it appears to be novel with respect to previous design theory and methodology work. This paper focuses on one development cycle characteristic: resource utilization. The model’s resource estimation capability is compared to Boehm’s long-used software development estimation techniques. His Cone of Uncertainty (COU) captures project estimation accuracy empirically at project start but intuitively over a project’s duration. SAbMDE calculates estimation accuracy at start up and over project duration; and SAbMDE duplicates the COU’s empirical values. Additionally, SAbMDE produces results very similar to the Constructive Cost Model (COCOMO) effort estimation for a wide range of input values.

Domain Independent Automatic Labeling system for Large-scale Social Data using Lexicon and Web-based Augmentation

Recently, with the large-scale adoption of social media, people have begun to express their opinion on these sites in the form of reviews. Potential consumers often forced to wade through huge amount of reviews to make informed decision. Sentiment analysis has become rapid and effective way to automatically gauge consumers’ opinion. However, such analysis often requires tedious process of manual tagging of large training examples or manually building a lexicon for the purpose of classifying reviews as positive or negative. In this paper, we present a method to automate the tedious process of labeling large textual data in an unsupervised, domain independent and scalable manner. The proposed method combines the lexicon-based and Web-based Point Wise Mutual Information (PMI) statistics to find the Semantic Orientation (SO) of opinion expressed in a review. Based on proposed methods a system called Domain Independent Automatic Labeling System (DIALS) has been implemented, which takes collection of text from any domain as input and generates fully labeled dataset in an unsupervised and scalable manner. The result generated can be used to track and summarize online discussion and/or use to train any classifier in the next stage of development. The effectiveness of system is tested by comparing it with baseline machine learning and lexicon-based methods. Experiments on multi-domains dataset has shown that proposed method consistently shown improved recall and accuracy as compared to baseline machine learning and lexicon-based methods.

CHOpinionMiner: An unsupervised system for Chinese opinion target extraction

SummaryOpinion target extraction (OTE) is an important task in fine‐grained sentiment analysis field, which focuses on the identification of the targets of users' opinions or sentiments from online reviews. Existing approaches to OTE are mainly based on unsupervised rule‐based methods or supervised machine learning methods. However, the latter needs a large number of labeled samples to train their models and lack of labeled corpus limits the research progress on OTE of Chinese. In this paper, we proposed a novel unsupervised and domain independent system called CHOpinionMiner. First, noun phrases are extracted as candidate opinion targets based on phrase structure grammar, then the syntactic paths of the candidate opinion targets and the opinion words in one opinionated sentence are extracted. After that, the legal paths, or rather, the opinion targets‐opinion lexicon pairs that are syntactically associated are selected according to the syntactic rules. Finally, not only the formal targets but also the orientations are obtained. Experiments on data sets consisting of microblog topics and product reviews demonstrate that our approach outperforms the existing state‐of‐the‐art methods.

LANGUAGE CHALLENGES IN ASPECT-BASED SENTIMENT ANALYSIS: A REVIEW OF ALBANIAN LANGUAGE

Beside the advantages of the typical sentiment analysis, which focuses on predicting the positive or negative polarity of the given sentence(s), there are two main drawbacks of performing sentiment analysis on higher level, namely on sentence and document level. Firstly, gaining the overall sentiment of a sentence or a paragraph may not lead to accurate and precise information. The polarity will be valid for a broader context and not for particular targets. Secondly, many sentences or paragraphs may have opposing polarities towards different targets. This makes it difficult or impossible to give an accurate overall polarity. The necessity for detecting aspect terms and their corresponding polarity gave rise to aspect-based sentiment analysis (ABSA). To meet the objectives of aspect-based sentiment analysis systems, the process can be summarized in three main tasks: Aspect Term Extraction, Aspect-term and Opinion-word Separation and Sentiment Polarity Classification. Most commonly, supervised learning approaches are used for ABSA. However, having to build the tagged training and testing corpora for each language and each domain is highly time consuming and can often be achieved only manually. This is why we have used a semi-supervised model for designing a language- and domain-independent system that is based on novel machine learning approaches through which we are focused on analyzing Albanian texts and make use of Albanian data in the digital world. In this approach where we try to extract the aspects and the polarity of their corresponding opinions through almost unsupervised learning, the biggest challenge is to reach high accuracy in natural language processing. In order to achieve this, in language-independent systems there must be taken into consideration all the differences and similarities of the languages. In this paper our aim is to define the biggest challenges that appear in Albanian language in comparison with English; and after analyzing certain amount of data, we have identified the following issues: inflections, negation, homonyms, dialects, irony, sarcasm and stop-words’ presence in aspect terms. This is not an exhaustive list of the language issues, since we have selected and discussed only the ones that have greater impact in the process of extracting the aspect-terms and opinions, and can highly affect the accuracy of the final polarity classification of the texts.

Hindi to Punjabi machine translation system based on statistical approach

Machine Translation problem is an open area of research. It is a challenge to build a domain independent, effective and efficient MT System. The major approaches to machine translation encountered in literature are Word-for-Word Translation, Syntactic Transfer, Semantic Transfer, Interlingua Approach, Example Based Translation, and Statistical Approach to Translation. The existing Hindi to Punjabi MT systems have been developed using rule bases approach and word-for-word translation approach. The present paper is an attempt to study the Architecture of Hindi to Punjabi MT System based on Statistical Approach.

Sentilo: Frame-Based Sentiment Analysis

Sentilo is an unsupervised, domain-independent system that performs sentiment analysis by hybridizing natural language processing techniques and semantic Web technologies. Given a sentence expressing an opinion, Sentilo recognizes its holder, detects the topics and subtopics that it targets, links them to relevant situations and events referred to by it and evaluates the sentiment expressed on each topic/subtopic. Sentilo relies on a novel lexical resource, which enables a proper propagation of sentiment scores from topics to subtopics, and on a formal model expressing the semantics of opinion sentences. Sentilo provides its output as a RDF graph, and whenever possible it resolves holders’ and topics’ identity on Linked Data.

The Generalized Intelligent Framework for Tutoring (GIFT) as a Tool for Human Factors Professionals

The Generalized Intelligent Framework for Tutoring (GIFT) is an open-source domain-independent intelligent tutoring system (ITS) framework. It provides the tools and capabilities to design a complete ITS, manage student instruction, and serve as a testbed for experimentation and analysis. GIFT was developed to provide a cost-effective and efficient means for developing ITSs that have interchangeable parts and can be reused. Additionally, GIFT provides the ability for human factors professionals to develop and run complete experiments in a computer-based environment. This demonstration will provide an introduction to GIFT, along with the motivation behind its design. Further, a demonstration of the tools that are useful to human factors practitioners and psychologists will be provided, and examples of courses that have been developed using GIFT will be shown.

과학 기술 문헌 분석을 위한 기계학습 기반 범용 전문용어 인식 시스템

문헌에서의 전문용어 인식 연구는 정보검색, 정보추출, 시맨틱 웹, 질의응답 분야 등의 연구를 위한 선행 연구로서, 지금까지 대부분 특정 분야, 특히 생의학 분야에서 집중되어 연구되어 왔다. 그러나 기존 연구들이 특정 도메인 또는 문헌 내부 통계 정보를 활용함으로써 범용적인 전문용어 인식에 한계점을 보여 왔기 때문에, 본 연구에서는 웹 검색 결과와 사전, 후보용어의 문형 특징 등을 활용하는 기계 학습 기반 범용 전문용어 인식 방법을 제안하였다. 제안한 방법을 문헌의 지역 통계 정보를 사용하는 방법(C-value)과 비교 실험하여 80.8%의 F-값으로 6.5%의 성능향상을 보였다. 다양한 응집도 자질들을 접목한 두 번째 실험에서는 Normalized Google Distance 방법과 접목한 방식이 F-값 81.8%의 성능으로 최고의 성능을 나타냈다. 기계 학습 방법으로는 로지스틱 회귀분석, C4.5, SVMs 등을 적용하였는데, 일반적으로 이진 분류에 좋은 성능을 보이는 SVMs과 로지스틱 회귀분석 방법보다 결정 트리 방식의 C4.5가 전반적으로 좋은 성능을 보였다. Terminology recognition system which is a preceding research for text mining, information extraction, information retrieval, semantic web, and question-answering has been intensively studied in limited range of domains, especially in bio-medical domain. We propose a domain independent terminology recognition system based on machine learning method using dictionary, syntactic features, and Web search results, since the previous works revealed limitation on applying their approaches to general domain because their resources were domain specific. We achieved F-score 80.8 and 6.5% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies. In the second experiment with various combinations of unithood features, the method combined with NGD(Normalized Google Distance) showed the best performance of 81.8 on F-score. We applied three machine learning methods such as Logistic regression, C4.5, and SVMs, and got the best score from the decision tree method, C4.5.

Modeling Beliefs and Solution Strategies in a Distributed Learning System

We present algorithms guiding the identification of student solution strategies in the Domain-Independent Adaptive Tutoring System (DIATS). The DIATS is a distributed computer-assisted instruction system with a prototype in the domain of psychiatric mental disorders. Problems are solved using differential diagnosis decision trees from the DMS-IV-TR. Student solution strategy identification is performed by the Response Analysis Unit of the modeler. The Domain-Independent Adaptive Tutoring System (DIATS) has a distributed architecture that combines shared-data and client-server styles.

LADDER, a sketching language for user interface developers

Sketch recognition systems are currently being developed for many domains, but can be time consuming to build if they are to handle the intricacies of each domain. In order to aid sketch-based user interface developers, we have developed tools to simplify the development of a new sketch recognition interface. We created LADDER, a language to describe how sketched diagrams in a domain are drawn, displayed, and edited. We then automatically transform LADDER structural descriptions into domain specific shape recognizers, editing recognizers, and shape exhibitors for use in conjunction with a domain independent sketch recognition system, creating a sketch recognition system for that domain. We have tested our framework by writing several domain descriptions and automatically generating a domain specific sketch recognition system from each description.

Reasoning with Numeric and Symbolic Time Information

Representing and reasoning about time is fundamental in many applications of Artificial Intelligence as well as of other disciplines in computer science, such as scheduling, planning, computational linguistics, database design and molecular biology. The development of a domain-independent temporal reasoning system is then practically important. An important issue when designing such systems is the efficient handling of qualitative and metric time information. We have developed a temporal model, TemPro, based on the Allen interval algebra, to express and manage such information in terms of qualitative and quantitative temporal constraints. TemPro translates an application involving temporal information into a Constraint Satisfaction Problem (CSP). Constraint satisfaction techniques are then used to manage the different time information by solving the CSP. In order for the system to deal with real time applications or those applications where it is impossible or impractical to solve these problems completely, we have studied different methods capable of trading search time for solution quality when solving the temporal CSP. These methods are exact and approximation algorithms based respectively on constraint satisfaction techniques and local search. Experimental tests were performed on randomly generated temporal constraint problems as well as on scheduling problems in order to compare and evaluate the performance of the different methods we propose. The results demonstrate the efficiency of the MCRW approximation method to deal with under constrained and middle constrained problems while Tabu Search and SDRW are the methods of choice for over constrained problems.

A speech-act-based negotiation protocol

Existing negotiation protocols used in Distributed Artificial Intelligence (DAI) systems rarely take into account the results from negotiation research. We propose a negotiation protocol, SANP (Speech-Act-based Negotiation Protocol), which is based on Ballmer and Brennenstuhl's speech act classification and on negotiation analysis literature. The protocol is implemented as a domain-independent system using Strudel, which is an electronic mail toolkit. A small study tested the potential use of the protocol. Although a number of limitations were found in the study, the protocol appears to have potential in domains without these limitations, and it can serve as a building block to design more general negotiation protocols.

DIDS: rapidly prototyping configuration design systems

The domain independent design system (DIDS) provides a set of tools for rapidly constructing new configuration design systems from a library of reusable software elements called mechanisms. A DIDS user begins by creating a model of the problem domain and the task to be automated. This includes describing a library of parts from which new artifacts could be configured, optimization and preference criteria, and functionality constraints. DIDS analyzes this input and automatically builds an operational prototype system by selecting and combining mechanisms. DIDS' ability to automate this process is derived from its model of configuration design, which enables reusable mechanisms to be identified and automatically selected based on a problem's characteristics. The use of DIDS is illustrated by showing how DIDS solved an elevator-configuration problem.

A distributed artificial intelligence view on general purpose vision systems (abstract)

A General Purpose Vision System (GPVS) is an open and domain-independent system that is able to build, maintain, and use an internal representation of the external world from data provided by physical sensors such as cameras. The experience in constructing the Vision As Process (VAP) and SATURNE systems using a Distributed Artificial Intelligence (DAI) approach has enabled us to compare, from a DAI point of view, these two systems with other major existing GPVS's. As far as we know this work represents the first large-scale comparison between GPVS's at the module and module-integration levels using a DAI formalism. This has lead us to the identification of several basic common features within the systems studied. Furthermore we think these common features are essential for the construction of a GPVS, namely: 1/the distribution of the knowledge representation provides level-agents that denote the same knowledge at a given level of representation 2/the distribution of the knowledge processing provides focus-agents which realize the system's "focus of attention" 3/intersection of level-agents and focus-agents determines the active basic-agents that explicitly or implicitly constitute the system at a given time. The description of GPVSs in terms of basic-agents is a novel feature of our approach and may also be applicable to other domains such as robotics.

Inducing and refining rule-based knowledge from inexact examples

The knowledge acquisition bottle-neck ( Feigenbaum, 1981) greatly obstructs the development of expert systems. This paper describes AKARS-1, a domain independent Automatic Knowledge Acquisition and Refinement System which can automatically induce and refine knowledge in rule form (exact and approximate) from exact and inexact examples. Its major components, AKA-2 (Automatic Knowledge Acquisition system) and HERES (HEuristic REfinement System), are detailed. AKA-2 employs a new discriminatory coefficient to evaluate discriminatory ability of each attribute/value pair and a novel method for calculating certainty factors of rules. HERES collects performance statistics of every rule and calculates the overall adequacy of the initial knowledge base, and then employs heuristics and the information collected to determine how to refine the knowledge base. The functionality and effectiveness of AKARS-1 are verified through various case studies. It has been verified that AKARS-1 can successfully induce and refine knowledge bases. The learning and refinement methods can handle imprecise and uncertain examples and generate approximate rules. In this aspect, they are better than other famous learning algorithms like ID3 ( Quinlan, 1983), AQ11 and INDUCE ( Michalski, 1973, 1980, 1983). AKARS-1's methods are currently unique in processing inexact examples and creating approximate rules.

ML-SMART: A Problem Solver for Learning from Examples

This paper describes ML-SMART, a knowledge based system for inducing conceptual description from examples. It is organized as a problem solver and makes an integrated use of multiple search strategies, including characterization, constructive learning and deduction. The acquired knowledge consists of production rules organized into a network, which can be seen as a generalization of a decision tree. The condition part of the rules is expressed in a first order logic language also containing numerical quantification. The use of variables and functions allows highly structured concepts to be easily described. ML-SMART (Similarity-based Multiple-concept Acquisition and Reasoning Tool) is a domain independent system, provided with a friendly interface to ease the task of configuring a new application. It is well suited to work in noisy environments and has been tested in different domains for the sake of comparison with other existing systems.

An intelligent user interface for browsing satellite data catalogs

A large scale domain-independent spatial data management expert system that serves as a front-end to databases containing spatial data is described. This system is unique for two reasons. First, it uses spatial search techniques to generate a list of all the primary keys that fall within a user's spatial constraints prior to invoking the database management system, thus substantially decreasing the amount of time required to answer a user's query. Second, a domain-independent query expert system uses a domain-specific rule base to preprocess the user's English query, effectively mapping a broad class of queries into a smaller subset that can be handled by a commercial natural language processing system. The methods used by the spatial search module and the query expert system are explained, and the system architecture for the spatial data management expert system is described. The system is applied to data from the International Ultraviolet Explorer (IUE) satellite, and results are given.