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2020 International Conference on INnovations in Intelligent systems and Applications, INISTA 2020

作者： Amin, Kareem Kapetanakis, Stelios Polatidis, Nikolaos Althoff, Klaus-Dieter Dengel, Andreas Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (纸本)9781728167992

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture. © 2020 IEEE.

关键词： Case based reasoning

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RPR-Net: A Point Cloud-based Rotation-aware Large Scale Place Recognition Network

arXiv

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arXiv 2021年

作者： Fan, Zhaoxin Song, Zhenbo Zhang, Wenping Liu, Hongyan He, Jun Du, Xiaoyong Key Laboratory of Data Engineering and Knowledge Engineering of MOE School of Information Renmin University of China Beijing100872 China Department of Management Science and Engineering Tsinghua University Beijing100084 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Point cloud-based large scale place recognition is an important but challenging task for many applications such as Simultaneous Localization and Mapping (SLAM). Taking the task as a point cloud retrieval problem, previous methods have made delightful achievements. However, how to deal with catastrophic collapse caused by rotation problems is still under-explored. In this paper, to tackle the issue, we propose a novel Point Cloud-based Rotation-aware Large Scale Place Recognition Network (RPR-Net). In particular, to solve the problem, we propose to learn rotation-invariant features in three steps. First, we design three kinds of novel Rotation-Invariant Features (RIFs), which are low-level features that can hold the rotation-invariant property. Second, using these RIFs, we design an attentive module to learn rotation-invariant kernels. Third, we apply these kernels to previous point cloud features to generate new features, which is the well-known SO(3) mapping process. By doing so, high-level scene-specific rotation-invariant features can be learned. We call the above process an Attentive Rotation-Invariant Convolution (ARIConv). To achieve the place recognition goal, we build RPR-Net, which takes ARIConv as a basic unit to construct a dense network architecture. Then, powerful global descriptors used for retrieval-based place recognition can be sufficiently extracted from RPR-Net. Experimental results on prevalent datasets show that our method achieves comparable results to existing state-of-the-art place recognition models and significantly outperforms other rotation-invariant baseline models when solving rotation problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Network architecture

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NTIRE 2023 Challenge on Efficient Super-Resolution: Methods and Results

NTIRE 2023 Challenge on Efficient Super-Resolution: Methods ...

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Li, Yawei Zhang, Yulun Timofte, Radu Van Gool, Luc Yu, Lei Li, Youwei Li, Xinpeng Jiang, Ting Wu, Qi Han, Mingyan Lin, Wenjie Jiang, Chengzhi Luo, Jinting Fan, Haoqiang Liu, Shuaicheng Wang, Yucong Cai, Minjie Li, Mingxi Zhang, Yuhang Fan, Xian-Jun Sheng, Yankai Mao, Yanyu Zhang, Nihao Wang, Qian Zheng, Mingjun Sun, Long Pan, Jinshan Dong, Jiangxin Tang, Jinhui Yang, Zhongbao Wang, Yan Pan, Erlin Cai, Qixuan Dai, Xinan Zhussip, Magauiya Kalyazin, Nikolay Vyal, Dmitry Zou, Xueyi Yan, Youliang Chung, Heaseo Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Liao, Bohao Du, Zhibo Wu, Yu-Liang Shi, Gege Peng, Long Wang, Yang Cao, Yang Zha, Zhengjun Huang, Zhi-Kai Chen, Yi-Chung Chiang, Yuan-Chun Yang, Hao-Hsiang Chen, Wei-Ting Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Liu, Xin Pan, Jiahao Yu, Hongyuan Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Yin, Xuanwu Zuo, Kunlong Deng, Weijian Yuan, Hongjie Lu, Zengtong Ouyang, Mingyu Ma, Wenzhuo Liu, Nian Zheng, Hanyou Zhang, Yuantong Zhang, Junxi Chen, Zhenzhong Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhu, Yurui Wang, Xi Fu, Xueyang Zha, Zheng-Jun Yin, Daheng Liu, Mengyang Chen, Baijun Li, Ao Luo, Lei Jin, Kangjun Zhu, Ce Zhang, Xiaoming Xie, Chengxing Li, Linze Meng, Haiteng Zhang, Tianlin Li, Tianrui Zhao, Xiaole Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Ren, Jiahuan Hu, Kang Shi, Jingpeng Wu, Zhijian Huang, Dingjiang Zhu, Jinchen Li, Hui Xv, Qianru Liu, Tianle Weng, Shizhuang Wu, Gang Jiang, Junpeng Liu, Xianming Jiang, Junjun Zhang, Mingjian Hu, Jing Wu, Chengxu Fan, Qinrui Feng, Chengming Luo, Ziwei Hu, Shu Lyu, Siwei Wu, Xi Wang, Xin Computer Vision Lab ETH Zurich Switzerland Computer Vision Lab University of Würzburg Germany Megvii Technology China MicroBT China College of Computer Science and Electronic Engineering Hunan University China Attrsense Xian University of Posts and Telecommunications Xi'an China National Engineering Laboratory for Cyber Event Warning and Control Technologies China Nanjing University of Science and Technology China Nankai University China University of Electronic Science and Technology of China China Tianjin University China Noah's Ark Lab Huawei Technologies McMaster University Canada Espresomedia Korea Republic of AntGroup China University of Science and Technology of China China Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan ServiceNow United States China Mobile Research Institute China Chongqing University of Technology China Multimedia Department Xiaomi Inc. China Institute of Automation Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China School of Information and Communication Engineering Communication University of China China Smart Classroom Division Ruijie Networks Co. Ltd. China School of Remote Sensing and Information Engineering Wuhan University China Micro-Nano Electronics Department Shanghai Jiao Tong University Shanghai200240 China Electrical Engineering Department Faculty of Engineering Assiut University Assiut71516 Egypt Southeast University China University of Electronic Science and Technology of China Chengdu China Chongqing University of Posts and Telecommunications Chongqing China Information Technology Co. Ltd Hangzhou China Southwest Jiaotong University China National Space Science Center Chinese Academy of Science China Hefei University of Technology Hefei China Anhui University China Fried Ric

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on efficient single-image super-resolution with a focus on the proposed solutions and results. The aim of this challenge is to devise a network that reduces one or several aspects such as runtime, parameters, FLOPs, activations, memory footprint, and depth of RFDN while at least maintaining the PSNR of 29.00dB on DIV2K validation datasets. The challenge had 272 registered participants, and 35 teams made valid submissions. They gauge the state-of-the-art for efficient single-image super-resolution. © 2023 IEEE.

关键词： Optical resolving power

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Size-fixed group discovery via multi-constrained graph pattern matching

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information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Exploring the Potential of Large Language Models for Estimating the Reading Comprehension Question Difficulty

arXiv

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arXiv 2025年

作者： Jain, Yoshee Hollander, John He, Amber Tang, Sunny Zhang, Liang Sabatini, John Siebel School of Computing and Data Science University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Psychology and Counseling Arkansas State University JonesboroAR72401 United States School of Computer Science Carnegie Mellon University PittsburghPA15213 United States Heinz College of Information Systems and Public Policy Carnegie Mellon University PittsburghPA15213 United States Department of Electrical and Computer Engineering University of Memphis MemphisTN38152 United States Institute for Intelligent Systems University of Memphis MemphisTN38152 United States Department of Psychology University of Memphis MemphisTN38152 United States

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. There is potential for Large Language Models (LLMs) to automate question difficulty estimation;however, this area remains underexplored. Our study investigates the effectiveness of LLMs, specifically OpenAI’s GPT-4o and o1, in estimating the difficulty of reading comprehension questions using the Study Aid and Reading Assessment (SARA) dataset. We evaluated both the accuracy of the models in answering comprehension questions and their ability to classify difficulty levels as defined by IRT. The results indicate that, while the models yield difficulty estimates that align meaningfully with derived IRT parameters, there are notable differences in their sensitivity to extreme item characteristics. These findings suggest that LLMs can serve as the scalable method for automated difficulty assessment, particularly in dynamic interactions between learners and Adaptive Instructional systems (AIS), bridging the gap between traditional psychometric techniques and modern AIS for reading comprehension and paving the way for more adaptive and personalized educational assessments. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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Analyze Wireless Sensor Network Structures for Intellectual Monitoring System

Analyze Wireless Sensor Network Structures for Intellectual ...

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International Conference on information Science and Communications Technologies (ICISCT)

作者： Ilkhomjon Siddikov Halimjon Khujamatov Doston Khasanov Ernazar Reypnazarov Abdurasul Iminov Department of “Use of electrical technologies and electrical equipment” Tashkent institute of irrigation and agricultural mechanization engineers National research university Tashkent Uzbekistan “Komputer engineering” Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of “Data Communication Networks and Systems” Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Tashkent Uzbekistan Department of “Information Technologies” Academy of the Ministry of Internal Affairs of the Republic of Uzbekistan Tashkent Uzbekistan

As one of the modern trends in the development of data transmission networks, wireless sensor networks are of great importance for solving problems related to the creation of tools for remote intelligent monitoring of devices. Wireless sensor networks are used in environmental monitoring, smart home, smart energy, smart transportation systems, smart agriculture, smart medicine and many other fields. Therefore, it is appropriate to model and research the hybrid energy supply sources of telecommunication systems as information transmission technologies in the IoT-based intelligent monitoring system, not based on a general approach to researching the characteristics of wireless sensor networks, but based on the parameters and specific characteristics of the system in which it is used.

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Modeling Prototypical Preference Behavior and Diversity using Rank Score Characteristic Functions

Modeling Prototypical Preference Behavior and Diversity usin...

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IEEE International Conference on Cognitive Informatics

作者： Christina Schweikert Shinsuke Shimojo Hannah Glasser Rebecca Hendsey Rami Alsaber D. Frank Hsu St. John's University Queens NY USA Division of Biology & Biological Engineering / Computation & Neural Systems California Institute of Technology Pasadena CA USA Department of Computer and Information Science Laboratory of Informatics and Data Mining Fordham University New York NY USA

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling.

关键词： Computational modeling Decision making Behavioral sciences Cognitive systems Faces

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A hybrid model for post-treatment mortality rate classification of patients with breast cancer

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Healthcare Analytics 2023年 4卷 100254-100254页

作者： Folorunso, Sakinat Oluwabukonla Awotunde, Joseph Bamidele Adigun, Adepeju Abeke Prasad, L.V. Narasimha Lalitha, Vuyyuru Lakshmi Department of Mathematical Sciences Olabisi Onabanjo University Ogun State Ago-Iwoye Nigeria Artificial Intelligent Systems Research Group (ArISRG) Department of Mathematical Sciences Olabisi Onabanjo University Ago-iwoye Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin 240003 Nigeria Department of Information and Communication Technology Osun State University Osogbo Nigeria Department of CSE Institute of Aeronautical Engineering Hyderabad India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation India

Terminal cancer is not curable and eventually results in death. Breast cancer (BC) is a prevalent malignancy affecting women. Although there are prognostic indicators, BC prognosis is still challenging because of the intricate connections between various survival factors and influencing factors. This study proposes an ensemble classifier for predicting BC survivability using a new BC post-treatment dataset for the number of survivals. However, the classes survival cases dataset for BC is skewed, which caused a sub-optimal classification performance. Hence, a hybrid sampling scheme of Synthetic Minority Over-Sampling TEchnique (SMOTE) and Wilson's Edited Nearest Neighbor (ENN) is employed to treat the class imbalance in the dataset. Random Forest (RF) ensemble classifier is for classifying the dataset. The proposed framework performs well in terms of accuracy, recall of the two classes, Receiver Operating Characteristics (ROC) and Kappa Statistic (KS) metric on the dataset. The results demonstrated that the RF, with 97.0% accuracy on the holdout sample, is the best predictor. This prediction accuracy is superior to any noted in the literature, compared with Logistic Regression (LR) and Bagging classifiers. © 2023 The Authors

关键词： Breast cancer Class imbalance Ensemble learners Machine learning Random forest Sampling

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Distributed training and scalability for the particle clustering method UCluster

arXiv

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arXiv 2021年

作者： Gudnadottir, Olga Sunneborn Gedon, Daniel Desmarais, Colin Bernander, Karl Bengtsson Sainudiin, Raazesh Suarez, Rebeca Gonzalez Department of Physics and Astronomy Division of High Energy Physics Uppsala University Department of Information Technology Division of Systems and Control Uppsala University Department of Information Technology Division of Visual Information & Interaction Uppsala University Department of Mathematics Uppsala University Combient Competence Centre for Data Engineering Sciences Uppsala University

In recent years, machine-learning methods have become increasingly important for the experiments at the Large Hadron Collider (LHC). They are utilised in everything from trigger systems to reconstruction and data analysis. The recent UCluster method is a general model providing unsupervised clustering of particle physics data, that can be easily modified to provide solutions for a variety of different decision problems. In the current paper, we improve on the UCluster method by adding the option of training the model in a scalable and distributed fashion, and thereby extending its utility to learn from arbitrarily large data sets. UCluster combines a graph-based neural network called ABCnet with a clustering step, using a combined loss function in the training phase. The original code is publicly available in TensorFlow v1.14 and has previously been trained on a single GPU. It shows a clustering accuracy of 81% when applied to the problem of multi-class classification of simulated jet events. Our implementation adds the distributed training functionality by utilising the Horovod distributed training framework, which necessitated a migration of the code to TensorFlow v2. Together with using parquet files for splitting data up between different compute nodes, the distributed training makes the model scalable to any amount of input data, something that will be essential for use with real LHC data sets. We find that the model is well suited for distributed training, with the training time decreasing in direct relation to the number of GPU’s used. However, further improvements by a more exhaustive and possibly distributed hyper-parameter search is required in order to achieve the reported accuracy of the original UCluster method. Copyright © 2021, The Authors. All rights reserved.

关键词： Learning systems

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Optimal kernel choice for score function-based causal discovery 24

Optimal kernel choice for score function-based causal discov...

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Proceedings of the 41st International Conference on Machine Learning

作者： Wenjie Wang Biwei Huang Feng Liu Xinge You Tongliang Liu Kun Zhang Mingming Gong School of Mathematics and Statistics The University of Melbourne Australia and Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Halicioğlu Data Science Institute (HDSI) University of California San Diego School of Computing and Information Systems The University of Melbourne Australia Huazhong University of Science and Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates and Department of Philosophy Carnegie Mellon University

Score-based methods have demonstrated their effectiveness in discovering causal relationships by scoring different causal structures based on their goodness of fit to the data. Recently, Huang et al. (2018) proposed a generalized score function that can handle general data distributions and causal relationships by modeling the relations in reproducing kernel Hilbert space (RKHS). The selection of an appropriate kernel within this score function is crucial for accurately characterizing causal relationships and ensuring precise causal discovery. However, the current method involves manual heuristic selection of kernel parameters, making the process tedious and less likely to ensure optimality. In this paper, we propose a kernel selection method within the generalized score function that automatically selects the optimal kernel that best fits the data. Specifically, we model the generative process of the variables involved in each step of the causal graph search procedure as a mixture of independent noise variables. Based on this model, we derive an automatic kernel selection method by maximizing the marginal likelihood of the variables involved in each search step. We conduct experiments on both synthetic data and real-world benchmarks, and the results demonstrate that our proposed method outperforms heuristic kernel selection methods.

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