Article
Automation & Control Systems
Jiabao Han, Hongzhi Wang
Summary: Text understanding and reasoning are crucial areas of artificial intelligence, and while ongoing advancements in connectionist models based on neural networks have shown significant progress, they still struggle with logical reasoning. The proposed GMR approach, utilizing graph matching for question-answering, has demonstrated superior anti-noise interference capabilities, high stability, and the ability to handle diverse tasks without sacrificing performance. Through comprehensive analysis and comparison, this symbolic approach offers optimal parameter configurations and stable model performance across various tasks.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Sinan Tan, Mengmeng Ge, Di Guo, Huaping Liu, Fuchun Sun
Summary: This paper proposes a novel Knowledge-based Embodied Question Answering (K-EQA) task where an agent explores the environment to answer questions using knowledge. The agent can use external knowledge to understand complex questions, rather than explicitly specifying the target object. To address this problem, a framework based on neural program synthesis reasoning is proposed, which performs joint reasoning of external knowledge and 3D scene graph for navigation and question answering. The use of the 3D scene graph improves the efficiency of multi-turn question answering. Experimental results show that the proposed framework is capable of answering complex and realistic questions in the embodied environment, and is applicable to multi-agent scenarios.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Aarthi Paramasivam, S. Jaya Nirmala
Summary: Question answering is a classic application in Natural Language Processing that consists of multiple subtasks. Textual Entailment is one approach used to capture semantic inference.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Peng Yang, Wenjun Li, Guangzhen Zhao, Xianyu Zha
Summary: Multi-hop Question Answering over heterogeneous data is a challenging task in NLP, and we propose a new approach based on RHGN to accomplish multi-hop QA over both textual and tabular data. RHGN consists of two phases: row selection phase and row reading comprehension phase. In the row selection phase, a retriever and a pre-training language model are used to find the appropriate answer row. In the row reading comprehension phase, a row-based hierarchical graph network and a gated mechanism are utilized for graph reasoning. Experimental results show the effectiveness of RHGN on the HybridQA dataset.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Computer Science, Artificial Intelligence
Huayi Zhan, Peixi Xiong, Xin Wang, Lan Yang
Summary: The method proposed in this paper utilizes key features such as entity-attribute graphs, query graphs, reinforcement learning models, and inference schemes to efficiently process visual tasks and accurately answer questions.
Review
Chemistry, Multidisciplinary
Emmanuel Mutabazi, Jianjun Ni, Guangyi Tang, Weidong Cao
Summary: The study reviewed medical textual question-answering systems based on deep learning approaches and thoroughly explored recent architectures of MQA systems. An in-depth analysis of deep learning approaches used in different MQA system tasks was provided, with recommendations to effectively address the different critical challenges posed by MQA systems.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Artificial Intelligence
Xunlin Zhan, Yinya Huang, Xiao Dong, Qingxing Cao, Xiaodan Liang
Summary: In this paper, the PathReasoner approach is proposed to explain and facilitate commonsense question answering by explicitly incorporating structured information and external reasoning paths. Experimental results demonstrate the effectiveness of this method, and a case study reveals that reasoning paths provide explainable information for question answering through the PathReasoner.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Jerome Abdelnour, Jean Rouat, Giampiero Salvi
Summary: The goal of the Acoustic Question Answering (AQA) task is to answer free-form text questions about acoustic scenes. This paper proposes a new benchmark for AQA, called CLEAR2, which addresses the challenges of handling variable duration scenes and different elementary sounds. The neural architecture NAAQA, which utilizes 1D convolutions to process acoustic content, achieves promising results with reduced complexity compared to previous models.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Information Systems
Zhaoyang Xu, Jinguang Gu, Maofu Liu, Guangyou Zhou, Haidong Fu, Chen Qiu
Summary: This paper investigates the potential of reasoning graph network on multi-hop reasoning questions. By constructing a cross-modal interaction module and a multi-hop reasoning graph network, the model dynamically updates the inter-associated instruction between two modalities to infer an answer. The experiments show that graph-based multi-hop reasoning improves visual question answering tasks significantly.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Luping Liu, Meiling Wang, Xiaohai He, Linbo Qing, Honggang Chen
Summary: This study proposes an effective framework for fact-based visual question answering (FVQA) by coordinating a perception module and an explicit reasoning module. Experimental results show that the model outperforms other baselines on two public datasets, and it also provides interpretations of the reasoning process.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Chunxiao Fan, Wentong Chen, Yuexin Wu
Summary: Knowledge base question answering (KBQA) refers to combining the information in the knowledge base to obtain an answer for an objective question. Most existing methods involve adding hand-crafted features or constraints to improve model performance, resulting in complex KBQA systems with limited improvement. This work presents a novel method that avoids using hand-crafted features or constraints, transforming the problem into a text matching task. The Path Matching Model (PMM) is employed to match the question and a series of edges in the knowledge base (KB). On the WebQuestions benchmark, the method achieves a 3% improvement compared to the state-of-the-art approach.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Information Systems
Arijit Das, Diganta Saha
Summary: A study on a Bengali question answering system using word embedding clustering and deep feature representation to improve efficiency in retrieving Bengali textual content relevant to user queries.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Meiling Wang, Xiaohai He, Luping Liu, Linbo Qing, Honggang Chen, Yan Liu, Chao Ren
Summary: Medical visual question answering (Med-VQA) is a task to accurately answer clinical questions about medical images. This paper proposes a novel Med-VQA framework that addresses the challenges of diverse clinical questions and the relationship between candidate responses. The framework includes a question-type reasoning module, attention mechanism, and semantic constraint space to extract valuable question features and consider the correlation between answers. Experimental results demonstrate improved performance compared to state-of-the-art methods.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Xunlin Zhan, Yuan Li, Xiao Dong, Xiaodan Liang, Zhiting Hu, Lawrence Carin
Summary: This paper introduces the elBERto framework, which improves model's ability to leverage rich commonsense in context through five self-supervised tasks. Experimental results show that elBERto outperforms other methods in challenging questions and achieves substantial improvements.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Hao Li, Jinfa Huang, Peng Jin, Guoli Song, Qi Wu, Jie Chen
Summary: TextVQA aims to produce correct answers for questions about images with multiple scene texts. This paper introduces 3D geometric information into the spatial reasoning process to capture contextual knowledge. Experimental results show that the proposed method achieves state-of-the-art performance on TextVQA and ST-VQA datasets.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2023)
Article
Computer Science, Artificial Intelligence
Maqbool Ali, Jamil Hussain, Sungyoung Lee, Byeong Ho Kang, Kashif Sattar
Article
Computer Science, Hardware & Architecture
Taqdir Ali, Muhammad Afzal, Hyeong Won Yu, Ubaid Ur Rehman, Ho-Seong Han, June Young Choi, Arif Jamshed, Jamil Hussain, Muhammad Bilal Amin, Musarrat Hussain, Usman Akhtar, Wajahat Ali Khan, Sungyoung Lee, Byeong Ho Kang, Maqbool Hussain
Article
Computer Science, Information Systems
Sudheer Kumar Battula, Saurabh Garg, James Montgomery, Byeong Kang
IEEE TRANSACTIONS ON SERVICES COMPUTING
(2020)
Article
Computer Science, Theory & Methods
Ankur Lohachab, Saurabh Garg, Byeong Ho Kang, Muhammad Bilal Amin
Summary: The researchers designed a novel P2P energy trading framework to improve resource utilization and address the electricity crisis challenge, evaluating the results based on different system parameters for validation. Performance bottlenecks and optimal configurations were determined through independent investigations, with the use of benchmark tools aiding application designers and developers in selecting suitable implementation models.
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2021)
Article
Computer Science, Artificial Intelligence
Wenli Yang, Saurabh Garg, Zhiqiang Huang, Byeong Kang
Summary: Conversation systems often face challenges in knowledge management from multiple human experts. Current knowledge-based conversation systems are typically centralized on servers, leading to potential issues in transparency and security. Blockchain solutions are being proposed to enhance security and efficiency in various domains, but the selection of blockchain platforms for knowledge-based conversation systems is still under development. The proposed decision model in this paper utilizes multiple methods such as AHP, FAHP, and FTOPSIS to analyze and generate consistent results, aiding in the selection of blockchain platforms and improving decision-making efficiency.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Automation & Control Systems
Zeyi Liu, Fuyuan Xiao, Chin-Teng Lin, Byeong Ho Kang, Zehong Cao
Summary: This study proposes a representative value method for handling interval criteria, using neural networks to adjust parameters to enhance decision accuracy and effectiveness.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2021)
Article
Operations Research & Management Science
Ali Raza, Lachlan Hardy, Erin Roehrer, Soonja Yeom, Byeong Ho Kang
Summary: Advancements in technology have led to better integration of personal computing devices into people's lives and homes, raising concerns about controlling access to sensitive information. The rise of blockchain technology allows for immutable audit trails of locational data, which can be controlled through inexpensive equipment at home. This paper presents a blockchain-based family security system for outdoor tracking and in-house monitoring, integrating sensors and AI to detect anomalies in users' activities while ensuring data security within the family unit.
JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING
(2021)
Article
Computer Science, Hardware & Architecture
Khizar Hameed, Saurabh Garg, Muhammad Bilal Amin, Byeong Kang, Abid Khan
Summary: This paper presents an efficient scheme for detecting clone node attacks on mobile IoT networks by using semantic information to securely locate IoT devices. The proposed location proof mechanism combines location proofs and batch verification to accelerate the verification process at trusted nodes. Additionally, a model for selecting trustworthy IoT devices based on their capabilities is introduced for the location proof-verification procedure, resulting in high detection accuracy and reduced resource overheads.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Riseul Ryu, Soonja Yeom, David Herbert, Julian Dermoudy
Summary: This study aims to determine how implicit authentication can observe students' behavior without disrupting their learning activities. A structured systematic review of the existing literature reveals future research directions, including considering diverse authenticators, addressing template ageing issues, and exploring evaluation approaches for context-aware implicit authentication systems.
Proceedings Paper
Computer Science, Artificial Intelligence
Riseul Ryu, Soonja Yeom, David Herbert, Julian Dermoudy
Summary: Online learning environments require robust authentication systems to ensure the authenticity of students' identities and work, and to prevent academic malpractices. This paper proposes a design for an adaptive biometric authentication system that dynamically selects biometric combinations based on a user's authenticating device.
ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS AND DOCTORAL CONSORTIUM, PT II
(2022)
Article
Computer Science, Interdisciplinary Applications
Ananda Maiti, Ali Raza, Byeong Ho Kang
Summary: The Internet-of-Things (IoT) is a set of technologies that integrate physical devices into human activities, gaining popularity among developers and consumers. It is crucial for IoT to be included in higher education curriculum using a project-based learning approach.
IEEE TRANSACTIONS ON LEARNING TECHNOLOGIES
(2021)
Review
Computer Science, Information Systems
Riseul Ryu, Soonja Yeom, Soo-Hyung Kim, David Herbert
Summary: Building safeguards against illegitimate access and authentication is crucial for system security. Continuous multimodal biometric authentication systems have been proposed as a reliable solution to address the challenges in existing user authentication schemes. However, there is a lack of critical analysis on current progress in the field, highlighting the need for further research and development in this area.
Review
Mathematical & Computational Biology
Yuchen Wei, Son Tran, Shuxiang Xu, Byeong Kang, Matthew Springer
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2020)
Proceedings Paper
Computer Science, Theory & Methods
Md Anwarul Kaium Patwary, Saurabh Garg, Byeong Kang
PROCEEDINGS OF THE AUSTRALASIAN COMPUTER SCIENCE WEEK MULTICONFERENCE (ACSW 2019)
(2019)
Article
Computer Science, Information Systems
Wenli Yang, Erfan Aghasian, Saurabh Garg, David Herbert, Leandro Disiuta, Byeong Kang
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)