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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Diseases

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Potential effects of a role-playing digital gaming learning system on the learning performance and motivation in a humanities course

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Interactive Learning Environments 2023年第8期31卷 5078-5091页

作者： Chin, Kai-Yi Chen, Yen-Lin Department of Data Science Soochow University Taipei City Taiwan Department of Computer Science Information Engineering National Taipei University of Technology Taipei City Taiwan

Using digital learning content to realize learning in games is a rapidly-developing direction of interest for teachers and researchers. This study has developed a digital role-playing gaming system to review Social Studies course content to a fifth Grade class at an elementary school. It allows students to experience the historical storyline of Tamsui, located in the north of Taiwan, by playing the role of Dr. Mackay. An experiment was conducted to evaluate the potential effects of the proposed system when applied to the reviewing activity of Humanities courses. The results of the post-test scores showed that the proposed system is not superior to the traditional teaching method which uses PowerPoint-based slides with the teacher’s oral instruction, but it still enhances their overall learning performance after the reviewing activity. Moreover, the results of the questionnaire survey showed that the students who reviewed by using the proposed system showed a higher learning motivation than those reviewing with the traditional teaching method. It is therefore concluded that our role-playing digital gaming system can be used to assist the teachers’ teaching activities, because it allows the students to complete their reviewing activity by themselves, and it effectively enhances their learning performances and motivation. © 2021 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Digital game-based learning elementary education humanities course role-playing game technology-enhanced learning

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt Engineering

An Entity Relation Extraction Framework Based on Large Langu...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Geng, Haibin Shi, Chenglong Jiang, Xuesong Kong, Zan Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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A Transformer-Based Video Colorization Method Fusing Local Self-attention and Bidirectional Optical Flow 10th

A Transformer-Based Video Colorization Method Fusing Local S...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Yuan, Lin Duan, Xun Xiang, Lingjie Kong, Guangqian Wu, Yun State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819787456

Video colorization encounters two principal challenges: colorization quality and temporal flicker. Balancing colorization quality and temporal consistency is a significant challenge. To address the aforementioned issues, we employ the transformer model in the field of video colorization and introduce a pioneering video colorization method named VCTR. Initially, to guarantee the quality of video coloring, we employ a pretrained image coloring network to add color to the grayscale video frame. Next, the feature extraction module for VCTR is utilized to perform feature extraction and propagation on the color frames during the colorization process. Finally, the transformer module is designed to fully leverage local information via the local feature self-attention layer. Additionally, motion information from bidirectional optical flow is utilized to identify correlations across video frames for feature fusion, guaranteeing both the coloring effect and temporal consistency. The experimental results demonstrate that VCTR outperforms existing methods on two publicly available datasets, namely, DAVIS and Videvo. VCTR attains the top ranking in the long-series dataset, Videvo, based on the CTBI (Colorization and Temporal-Consistency Balance Index). This achievement underscores VCTR’s ability to strike a commendable balance between colorization quality and temporal consistency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical flows

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VAEPose: 6D Pose Estimation with Visual Appearance Enhancement for Low-Light Conditions 25

VAEPose: 6D Pose Estimation with Visual Appearance Enhanceme...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Gao, Yan Chen, Lu Liu, Jingyang Insti. Of Big Data Science & Industry School Of Computer & Information Tech. Shanxi University Taiyuan China

ISBN: (纸本)9798350340266

6D pose estimation is a key technology in the field of computer vision, and has great application potential in the fields of virtual reality, augmented reality, robot operation, and intelligent driving. When using deep neural network technology to accomplish this advanced vision task, the quality of the data is demanding. Currently, the task achieves excellent results in normal lighting conditions, but the task presents serious challenges due to the significant degradation of data quality in low-light conditions. The results are not satisfactory when the object pose is directly estimated using a one-stage processing mode in low-light conditions. Therefore, in view of this observation, we choose a two-stage processing mode to solve the challenges. In this work, we propose a Low-light Pose Estimation Network based on the Visual Appearance Enhancement module (VAEPose). Among them, the Low-light Appearance Enhancement Module (LAEM) is designed to improve data quality and show clearer details, helping the network model to be more adaptable to light and less dependent on data. In the LAEM module, an attention mechanism is introduced to achieve the fusion of channel and spatial attention of the extracted RGB features, resulting in finer color and texture information. The Virtual Low-light dataset (VLD) used in the experiment is created by us in virtual environment. Experiments verify the effectiveness of the proposed method. © 2024 IEEE.

关键词： Image enhancement

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Optimization Models for Teaching Load Formation of University Teachers 1

Optimization Models for Teaching Load Formation of Universit...

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IEEE AITU: 1st International Student Conference: "Digital Generation", AITU 2024

作者： Chigambayeva, Diana Goryakin, Makar Batova, Polina Department of Computational and Data Science Astana IT University Astana Kazakhstan Depatment of Computer Engineering Astana IT University Astana Kazakhstan

ISBN: (纸本)9798350364378

Functional and mathematical models for the distribution of academic workload at the stage of preparing the educational process at a university are considered, which make it possible to largely determine the uniformity of teachers’ workload with various types of academic work when fulfilling the criteria and restrictions adopted for the distribution. ©2024 IEEE.

关键词： functional model mathematical model study load educational process educational work types of educational work

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Adaptive Successive POI Recommendation via Trajectory Sequences Processing and Long Short-Term Preference Learning

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computers, Materials & Continua 2024年第10期81卷 685-706页

作者： Yali Si Feng Li Shan Zhong Chenghang Huo Jing Chen Jinglian Liu School of Computer Science and Engineering Changshu Institute of TechnologyChangshu215500China Suzhou Industrial Intelligence and Big Data Laboratory Suzhou215000China School of Information Science and Engineering Yanshan UniversityQinhuangdao066004China College of Electronic and Information Engineering Guangdong Ocean UniversityZhanjiang524088China

Point-of-interest(POI)recommendations in location-based social networks(LBSNs)have developed rapidly by incorporating feature information and deep learning ***,most studies have failed to accurately reflect different users’preferences,in particular,the short-term preferences of inactive *** better learn user preferences,in this study,we propose a long-short-term-preference-based adaptive successive POI recommendation(LSTP-ASR)method by combining trajectory sequence processing,long short-term preference learning,and spatiotemporal ***,the check-in trajectory sequences are adaptively divided into recent and historical sequences according to a dynamic time ***,an adaptive filling strategy is used to expand the recent check-in sequences of users with inactive check-in behavior using those of similar active *** further propose an adaptive learning model to accurately extract long short-term preferences of users to establish an efficient successive POI recommendation system.A spatiotemporal-context-based recurrent neural network and temporal-context-based long short-term memory network are used to model the users’recent and historical checkin trajectory sequences,*** experiments on the Foursquare and Gowalla datasets reveal that the proposed method outperforms several other baseline methods in terms of three evaluation *** specifically,LSTP-ASR outperforms the previously best baseline method(RTPM)with a 17.15%and 20.62%average improvement on the Foursquare and Gowalla datasets in terms of the Fβmetric,respectively.

关键词： Location-based social networks adaptive successive point-of-interest recommendation long short-term preference trajectory sequences

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Oja's Algorithm for Streaming Sparse PCA 38

Oja's Algorithm for Streaming Sparse PCA

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kumar, Syamantak Sarkar, Purnamrita Department of Computer Science UT Austin United States Department of Statistics and Data Sciences UT Austin United States

Oja's algorithm for Streaming Principal Component Analysis (PCA) for n datapoints in a d dimensional space achieves the same sin-squared error O(reff/n) as the offline algorithm in O(d) space and O(nd) time and a single pass through the datapoints. Here reff is the effective rank (ratio of the trace and the principal eigenvalue of the population covariance matrix Σ). Under this computational budget, we consider the problem of sparse PCA, where the principal eigenvector of Σ is s-sparse, and reff can be large. In this setting, to our knowledge, there are no known single-pass algorithms that achieve the minimax error bound in O(d) space and O(nd) time without either requiring strong initialization conditions or assuming further structure (e.g., spiked) of the covariance matrix. We show that a simple single-pass procedure that thresholds the output of Oja's algorithm (the Oja vector) can achieve the minimax error bound under some regularity conditions in O(d) space and O(nd) time. We present a nontrivial and novel analysis of the entries of the unnormalized Oja vector, which involves the projection of a product of independent random matrices on a random initial vector. This is completely different from previous analyses of Oja's algorithm and matrix products, which have been done when the reff is bounded. © 2024 Neural information processing systems foundation. All rights reserved.

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Coins Game - A Novel Decision Problem and Its Solving Using Reinforcement Learning 4

Coins Game - A Novel Decision Problem and Its Solving Using ...

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4th Asia Conference on Information Engineering, ACIE 2024

作者： Chen, Bo Lin, Ziqing Chen, Guohong Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University School of Economics and Management Fuzhou China

ISBN: (纸本)9798350371925

Recent years have witnessed continuous optimization and innovation of reinforcement learning algorithms. Games, as a key application paradigm, have been widely employed to develop superior reinforcement learning models. Different game environments present diverse challenges to reinforcement learning agents;however, mainstream gaming paradigms have not yet specifically addressed issues such as variable action spaces and varying tasks. In this regard, this paper introduces a two-player adversarial game with a configurable player action space. The game allows for the diversification of task challenges by configuring opponent strategies. Additionally, we propose a reinforcement learning method to facilitate the decision-making of the AI player (the agent) in the game. The inclusion of an action masking algorithm enables effective handling of variable action space issues. Experimental results indicate that the decision-making behavior of the agent adjusts with changes in opponent behavior and continuously improves with policy updates. The trained agent exhibits impressive performance in this game, it shows that the proposed method could serve as a baseline for the decision-making in the novel game, and a robust foundation for further research and applications is provided. © 2024 IEEE.

关键词： Decision making

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A structural variation genotyping algorithm enhanced by CNV quantitative transfer

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Frontiers of computer science 2022年第6期16卷 159-165页

作者： Tian ZHENG Xinyang QIAN Jiayin WANG Department of Computer Science and Technology School of Electronic and Information EngineeringXi’an Jiaotong UniversityXi’an710049China Institute of Data Science and Information Quality Shaanxi Engineering Research Center of Medical and Health Big DataXi’an Jiaotong UniversityXi’an710049China

Genotyping of structural variations considering copy number variations(CNVs)is an infancy and challenging ***,a prevalent form of critical genetic variations that cause abnormal copy numbers of large genomic regions in cells,often affect transcription and contribute to a variety of *** characteristics of CNVs often lead to the ambiguity and confusion of existing genotyping features and algorithms,which may cause heterozygous variations to be erroneously genotyped as homozygous variations and seriously affect the accuracy of downstream *** the allelic copy number increases,the error rate of genotyping increases *** instances with different copy numbers play an auxiliary role in the genotyping classification problem,but some will seriously interfere with the accuracy of the *** by these,we propose a transfer learning-based method to genotype structural variations accurately considering *** method first divides the instances with different allelic copy numbers and trains the basic machine learning framework with different genotype *** maximizes the weights of the instances that contribute to classification and minimizes the weights of the instances that hinder correct *** adjusting the weights of the instances with different allelic copy numbers,the contribution of all the instances to genotyping can be maximized,and the genotyping errors of heterozygote variations caused by CNVs can be *** applied the proposed method to both the simulated and real datasets,and compared it to some popular algorithms including GATK,Facets and *** experimental results demonstrate that the proposed method outperforms the others in terms of accuracy,stability and *** source codes have been uploaded at github/TrinaZ/CNVtransfer for academic use only.

关键词： genotyping copy number variations transfer earning

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