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Towards ontology-driven navigation of the lipid bibliosphere

BMC BIOINFORMATICS 2008年第s1期9卷 S5-S5页

作者： Baker, Christopher J. O. Kanagasabai, Rajaraman Ang, Wee Tiong Veeramani, Anitha Low, Hong-Sang Wenk, Markus R. Inst Infocomm Res Dept Data Min Singapore 119613 Singapore Natl Univ Singapore Ctr Life Sci Dept Biochem Singapore 119260 Singapore Natl Univ Singapore Ctr Life Sci Dept Biol Sci Singapore 119260 Singapore

Background: the indexing of scientific literature and content is a relevant and contemporary requirement within life science information systems. Navigating information available in legacy formats continues to be a challenge both in enterprise and academic domains. the emergence of semantic web technologies and their fusion with artificial intelligence techniques has provided a new toolkit with which to address these data integration challenges. In the emerging field of lipidomics such navigation challenges are barriers to the translation of scientific results into actionable knowledge, critical to the treatment of diseases such as Alzheimer's syndrome, Mycobacterium infections and cancer. Results: We present a literature-driven workflow involving document delivery and natural language processing steps generating tagged sentences containing lipid, protein and disease names, which are instantiated to custom designed lipid ontology. We describe the design challenges in capturing lipid nomenclature, the mandate of the ontology and its role as query model in the navigation of the lipid bibliosphere. We illustrate the extent of the description logic-based A-box query capability provided by the instantiated ontology using a graphical query composer to query sentences describing lipid-protein and lipid-disease correlations. Conclusion: As scientists accept the need to readjust the manner in which we search for information and derive knowledge we illustrate a system that can constrain the literature explosion and knowledge navigation problems. Specifically we have focussed on solving this challenge for lipidomics researchers who have to deal with the lack of standardized vocabulary, differing classification schemes, and a wide array of synonyms before being able to derive scientific insights. the use of the OWL-DL variant of the web Ontology language (OWL) and description logic reasoning is pivotal in this regard, providing the lipid scientist with advanced query access to

关键词： Natural language processing Text Mining Entity Recognition Visual Query Datatype Property

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Sequence Recognition of Scene Text Based on CRNN and CTPN Models 22

Sequence Recognition of Scene Text Based on CRNN and CTPN Mo...

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

作者： Yiyi Liu Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College China

ISBN: (纸本)9781450397148

Image-based sequence recognition has lately emerged as a prominent study subject in the science of computer vision, while text detection and identification in natural situations has emerged as an active research field. Based on scene text data, this paper addresses the theory of deep learning-based CRNN and CTPN models and the process of processing text. Using CRNN, text recognition can be turned into a time-dependent sequence learning issue, which is commonly employed for indeterminate-length text sequences. Contextual relationships between text images are learned using BLSTM and CTC, thus effectively improving text recognition accuracy and making the model more robust. It also excels in text recognition tests for wordless and lexical-based scenes, as it is not constrained by any predefined language. It produces a more efficient, but smaller, model that is more suited to real-world settings. CRNN recognition accuracy is lower for short texts with large morphological changes, such as artistic words, or texts with large changes in natural scenes. Because of the Anchor setting, CTPN can only detect horizontally distributed text, but a small improvement can detect vertical text by adding horizontal Anchor. As a result of the limitations of the framework, the irregularly inclined text can be detected very broadly.

关键词： LSTM OCR Sequence recognition CTPN

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A Deep Learning-Based Bengali Visual Question Answering System Using Contrastive Loss

A Deep Learning-Based Bengali Visual Question Answering Syst...

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international conference on Electrical Engineering and information & Communication technology (ICEEICT)

作者： Md. Tareq Zaman Md. Yousuf Zaman Faisal Muhammad Shah Eusha Ahmed Mahi Department of Computer Science and Engineering Ahsanullah University of Science And Technology Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Visual Question Answering (VQA) is an interdis-ciplinary research area that uses image recognition, natural language processing, and cognitive understanding to interpret visual data and answer appropriate queries. this study aims to advance Bengali visual question answering (VQA) by proposing a contrastive loss approach to improve bengali VQA models contextual understanding between images and questions. We introduce the new VQA Bengali 1.0 dataset containing 1,864 social media images with 3,728 corresponding yes/no questions, which are splitted into train/test/val sets. Our approach involves extracting image features using ResNet50 and textual features with BangIa BERT variants. these features are fused through an attention mechanism. We incorporate contrastive loss during training to increase discrimination between positive and negative image-text pairs. Comparing combinations of ResNet50 with Sagor Sarker BangIa BERT and Sahaj BangIa BERT models, experiments demonstrate contrastive loss boosts accuracy over cross-entropy loss. the best model with ResNet50, Sahaj BangIa BERT and contrastive loss achieves 93.21 % accuracy. this work advances Bengali VQA through proposing an effective contrastive loss methodology and releasing a balanced dataset. By enhancing model contextual understanding, it has significant potential for further research innovations.

关键词： Training Visualization Technological innovation Image recognition Social networking (online) Feature extraction Question answering (information retrieval)

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Investigation of the Effectiveness of Applying ChatGPT in Dialogic Teaching of Electronic information Using Electroencephalography

Investigation of the Effectiveness of Applying ChatGPT in Di...

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Computer Science and Technologies in Education (CSTE), international conference on

作者： Jiayue Zhang Yiheng Liu Wenqi Cai Senqing Qi Taotao Long Bao Ge School of Physics and Information Technology Shaanxi Normal University Xi'an China School of Psychology Shaanxi Normal University Xi'an China Key Laboratory of Modern Teaching Technology Ministry of Education Shaanxi Normal University Xi'an China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China

ISBN: (数字)9798350351804

ISBN: (纸本)9798350351811

In recent years, the rapid development of artificial intelligence technology, especially the emergence of large language models (LLMs) such as ChatGPT, has presented significant prospects for application in the field of education. LLMs possess the capability to interpret knowledge, answer questions, and consider context, thus providing support for dialogic teaching to students. therefore, an examination of the capacity of LLMs to effectively fulfill instructional roles, thereby facilitating student learning akin to human educators within dialogic teaching scenarios, is an exceptionally valuable research topic. this research recruited 34 undergraduate students as participants, who were randomly divided into two groups. the experimental group engaged in dialogic teaching using ChatGPT, while the control group interacted with human teachers. Both groups learned the histogram equalization unit in the information-related course “Digital Image processing”. the research findings show comparable scores between the two groups on the retention test. However, students who engaged in dialogue with ChatGPT exhibited lower performance on the transfer test. Electroencephalography data revealed that students who interacted with ChatGPT exhibited higher levels of cognitive activity, suggesting that ChatGPT could help students establish a knowledge foundation and stimulate cognitive activity. However, its strengths on promoting students' knowledge application and creativity were insignificant. Based upon the research findings, it is evident that ChatGPT cannot fully excel in fulfilling teaching tasks in the dialogue teaching in information related courses. Combining ChatGPT with traditional human teachers might be a more ideal approach. the synergistic use of both can provide students with more comprehensive learning support, thus contributing to enhancing the quality of teaching.

关键词： Computer science Histograms Large language models Digital images Education Chatbots Electroencephalography

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A Multi-source Data-driven Approach to IGBT Remaining Useful Life Prediction

A Multi-source Data-driven Approach to IGBT Remaining Useful...

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Natural language processing (ICNLP), international conference on

作者： Xiaoyu Hao Qiang Wang Yahong Yang Hongbo Ma Xianzhi Wang Gaige Chen School of Communications and Information Engineering & School of Artificial Intelligence Shaanxi Union Research Center of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi’an University of Posts and Telecommunications Xi’an China Shanghai Institute of Space Power-Sources Shanghai China School of Mechano-Electronic Engineering Xidian University Xi'an China School of Automation Xi'an University of Posts and Telecommunications Xi’an China

ISBN: (数字)9798350349115

ISBN: (纸本)9798350349122

IGBT (insulated gate bipolar transistor) undergo aging failures due to environmental effects during operation. It is of theoretical importance and engineering value to study how to predict the remaining useful life (RUL) of IGBT using multi-source data. In this paper, we propose a multi-source data-driven IGBT RUL prediction method, which extracts the time-domain features of the degradation process by utilizing the operating characteristics of IGBT and employs a bi-directional gated recurrent unit neural network to improve the prediction performance. First, the corresponding current-voltage signals are extracted from the aging data according to the IGBT operating characteristics, and the time-domain features are calculated; second, the random forest algorithm is used to rank the importance of the features, and the important features are screened out to reduce the redundancy of the features; finally, the RUL prediction model based on the BiGRU network is established, and the validity of the method is verified by the actual IGBT degradation feature data. the results show that the proposed method combines a variety of aging data, fully considers multiple factors in the working condition, and has higher RUL prediction accuracy and stability.

关键词： Insulated gate bipolar transistors Degradation Accuracy Predictive models Aging Feature extraction Prediction algorithms Stability analysis Data mining Time-domain analysis

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EMERGING thREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT 22

EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT

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Proceedings of the 6th international conference on Future Networks & Distributed Systems

作者： Murooj Amer Taha Wijdan Mahood Khudhair Ahmed Mahmood Khudhur Omar Abdulkareem Mahmood Yousif I. Hammadi Riyam Shihab Ahmed Al-husseinawi Ahmed Aziz Department of Computer Engineering Techniques Bilad Alrafidain University College Iraq Department of Communications Engineering University of Diyala Iraq Department of Medical Instruments Engineering Techniques Bilad Alrafidain University College Iraq Department of International Business Management Tashkent State University of Economics Uzbekistan

ISBN: (纸本)9781450399050

Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. these procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. this study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting.

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NEW DISTANCE MEASURES FOR TEXT-INDEPENDENT SPEAKER IDENTIFICATION

NEW DISTANCE MEASURES FOR TEXT-INDEPENDENT SPEAKER IDENTIFIC...

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6～(th) international conference on Spoken language processing

作者： DavidLubensky IBM T. J. Watson research Center Yorktown Heights NY 10598 USA

Distance measures[1][2][3] based on the covariance matrix of feature vectors were applied to text-independent speaker verification and identification. However, some of them do not satisfy the symmetric property which is fundamental to a distance measure. In this paper, we propose several sym-metric distance measures based on the covariance matrix of feature vectors, and then construct some advanced measures using the data fusion method [4]. these new distance measures have good mathematic properties and impose little overhead in calculation. We apply these distance measures to text-independent speaker identification and handset detection. A new robust technique is developed for crosshandset speaker identification, and find that compensating the second order statistics is important when dealing with the mismatch caused by different handsets. the experiment uses the cb1 and cb2 data in the LLHDB corpus [5] for same-handset and cross-handset speaker identification test. We find that the use of delta cepstra decreases the speaker identification error rate by as much as 38％. Data fusion technique could further decrease the error rate by 11％. Applying these distance measures to 2-handset detection problem, the error rate is 12％. Using our new robust technique, the cross-handset speaker identification error rate is could be decreased by around 17％.

关键词： NEW DISTANCE MEASURES FOR TEXT-INDEPENDENT SPEAKER IDENTIFICATION

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A Semi-supervised Knowledge Assessment Paradigm Based on T-CNN Algorithm for the Industrial Massage System

A Semi-supervised Knowledge Assessment Paradigm Based on T-C...

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international conference on Systems and Informatics (ICSAI)

作者： Sheng Wang Jinkuan Wang Yinghua Han Qiang Zhao Northeastern University Shenyang China Northeastern University at Qinhuangdao Qinhuangdao China

ISBN: (数字)9781728152561

ISBN: (纸本)9781728152578

With the popularity of the Industrial Internet and information technology, most data resources in the industrial production process could be exploited and utilized completely. But there are some fragmented knowledges, like product data, project documentation and multimedia materials, that need to be collected and organized. For instance, Industrial Massage System (IMS) is a platform for operating field professionals to exchange and share their experiences and knowledge containing significant potential value. In order to effectively utilize the fragmented information, a semi-supervised knowledge assessment paradigm is proposed for this system using Tri-training method based on Convolutional Neural Network (T-CNN). the method initially trains three CNN classifiers employing a small number of labeled examples, and a large pool of unlabeled examples are then assigned pseudo-labels under certain conditions. In further training, these classifiers are fine-tuned through original labeled examples and pseudo-examples. Specifically, in the iteration of the tri-training, a pseudo-example for a classifier is obtained from the reliable hypothesis if the other two classifiers have the same predictions on the labeling. Compared to previous training method, it does not require enormous original labeled data to initialize the model, nor does it need to depend immoderately on expert's domain to label examples. And there are also no demands of the instance space to be described with sufficient and redundant views, like previous co-training style algorithms. Experiments tested on two benchmarks indicate that the algorithm can effectively exploit unlabeled data to enhance the learning performance and achieves better classification capability.

关键词： Training Labeling Classification algorithms Feature extraction Natural language processing Semantics Reliability

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ChulaDAISY: an automated DAISY audio book generation 12

ChulaDAISY: an automated DAISY audio book generation

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Proceedings of the 6th international conference on Rehabilitation Engineering & Assistive technology

作者： Proadpran Punyabukkana Surapol Vorapatratorn Nat Lertwongkhanakool Pawanrat Hirankan Natthawut Kertkeidkachorn Atiwong Suchato Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789810726638

DAISY is the current standard for Digital Talking Books (DTBs) authored in many countries, including thailand. Still, the number of book titles available in the form of DTB is limited partly due to their time-consuming production process. Although DTB authoring tools capable of producing Audio/Full Text DTBs are widely available, they do not fully support thai, a language in which text segmentation cannot be performed straight-forwardly. In this work, we propose a DTB authoring tool called ChulaDAISY to be used for producing DTBs whose contents contain thai texts, either manually or automatically. the program is also capable of uploading the finished DTBs through its GUI onto its online DTB archiving system where DTBs can be downloaded via web browsers. this consideration aims at tackling possible difficulties in commuting to libraries by the blinds. Experiments are conducted in order to evaluate the usability of the program as well as to measure the DTB production rate using ChulaDAISY in comparison to the one following a traditional procedure. We have found that ChulaDAISY yields an approximately 35% improvement in productivity over the traditional procedure.

关键词： daisy DAISY production tool digital accessible information system audio book people with disabilities human computer interface digital talking book

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An Examination of the Possible Application of Artificial Intelligence Audit Process in Organizations

An Examination of the Possible Application of Artificial Int...

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international conference on Cybernetics and Intelligent System (ICORIS)

作者： Wahyu Sardjono Dewi Sagita Pranata Muhammad Isamesal Information Systems Management Department BINUS Graduate Program – Master of Information Systems Management Bina Nusantara University Jakarta Indonesia Cyber Security Studi Program Department of Informatics Faculty of Natural Mathematical & Engineering Sciences King's College London Strands West Minster England Master of Management Department Faculty of Economics and Busines Universitas of Airlangga Surabaya Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

this study investigates how artificial intelligence (AI) can transform audit processes, including how AI can be used in businesses to execute non-technical audit tasks and what effect this will have on those tasks. A qualitative case study methodology is used in the research design to give detailed insights into how AI is being adopted by businesses. this study aims to analyze the aspects that impact AI implementation from the viewpoints of managers, auditors, and other stakeholders. the findings show that artificial intelligence (AI) has a great deal of promise for non-technical audit procedures in businesses. AI can automate tedious jobs, evaluate large data sets to identify hazards and enhance teamwork and communication. these developments may lead to increased productivity, maybe better audit quality, and more effective processes. But there are still difficulties. Data quality is essential to the success of AI integration, and auditors may exhibit bias in favor of human professionals. Careful thought must also be given to ethical issues about transparency and possible job displacement. All things considered, this study adds significant knowledge to both the academic literature and real-world auditing applications. It improves our comprehension of the function of AI in the industry and provides useful information for people involved in information technology development, audit, and legislation.

关键词： Productivity Ethics Data analysis Reviews Data integrity Transforms Natural language processing Teamwork Stakeholders Artificial intelligence

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