检索结果-内蒙古大学图书馆

Process Mining Approach to Analyze Engagement in Campaign Activities

学校读者我要写书评

暂无评论

Process Mining Approach to Analyze Engagement in Campaign Ac...

Information Technology International Seminar (ITIS)

作者： Aryo Nugroho Awalludiyah Ambarwati Arsi Raniah Faza Ardi Ilham Falah Oddy Virgantara Putra Ika Widiastuti Department of Information System Universitas Narotama Surabaya Indonesia Department of Accounting UPN Veteran Jawa Timur Surabaya Indonesia Department of Soft Computing AONO Research Center Surabaya Indonesia Department of Informatics Universitas Darussalam Gontor Ponorogo Indonesia Department of Computer Science & Engineering. Ewha Womans University Seoul Republic of Korea

Campaign strategies are evolving because of developments in communication technologies. Campaign activities have become more attractive to the younger generation with the existence of social media. There is much research related to campaigns, but it focuses on data collection and content. This research tries to look at it from the other side, namely from the perspective of the campaign team. How can teams improve performance and ensure that strategies are executed correctly? The analysis was carried out using process mining. The methodology used is the Project Mining Methodology, supported by data, identification, conformity checking, and quality improvement. The data used was taken from a Facebook group that was specifically created and operated during the 100 days of the official campaign. Interactions occurred with 2215 members with 975 active members, of which there were 357 posts, 8755 reactions, 1275 comments, and 280 shares. The results obtained from this process show weekly posting patterns and responses. Furthermore, we can see the involvement of key figures in the activities of the campaign group.

关键词：

Multi-Class Classification of Alzheimer’s Disease Using Frontal Cortex Non-invasive fNIRS 8th

学校读者我要写书评

暂无评论

Multi-Class Classification of Alzheimer’s Disease Using Fro...

8th International Conference on the Development of Biomedical engineering, BME8 2020

作者： Ho, Thi Kieu Khanh Kim, Minhee Garg, Harish Kim, Byeong C. Lee, Jang Jae Choi, Kyu Yeong Kim, Jae Gwan Lee, Kun Ho Song, Jong-In Gwak, Jeonghwan School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Gwangju61005 Korea Republic of Department of Biomedical Science and Engineering Institute of Science and Technology Gwangju61005 Korea Republic of School of Mathematics Thapar Institute of Engineering and Technology Deemed University Patiala Punjab147004 India Department of Neurology Chonnam National University Medical School Gwangju61469 Korea Republic of Cohort Research Center Chosun University Gwangju61452 Korea Republic of Department of Biomedical Science Chosun University Gwangju61452 Korea Republic of Aging Neuroscience Research Group Korea Brain Research Institute Daegu41062 Korea Republic of Department of Software Korea National University of Transportation Chungju27469 Korea Republic of

ISBN: (纸本)9783030755058

Early and accurate diagnosis of Alzheimer’s disease (AD) is especially important for neurodegenerative disorders allowing patients, who exhibit different patterns of severity and progression risks, to take prevention and treatments before brain damages are shaped. Functional Near-Infrared Spectroscopy (fNIRS), as a clinical test, can be employed in an ecological setting to support AD diagnosis at early stages. In this study, we aimed to comprehensively study the hemodynamic response patterns among four subject groups from 140 participants by visualizing the 4-class distributions based on t-SNE visualization techniques and showing the fNIRS topographic mapping to have a pictorial representation on the surface of the prefrontal cortex region. We demonstrated that the changes in hemodynamic concentrations of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (Hb) from healthy controls to three stages of AD did exist during four stages of our experimental protocols. Results also revealed that various machine learning techniques were capable of both analyzing AD patterns and its related features and classifying multiple pathophysiologic mechanisms with the highest accuracies of 74.39 ± 4.7% and 84.91 ± 4.01% corresponding to raw and SMOTE-based features, respectively. These findings demonstrate the feasibility of fNIRS-based approaches to investigate the stages of AD progression. © 2022, Springer Nature Switzerland AG.

关键词： Machine learning

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics

学校读者我要写书评

暂无评论

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

International Conference on Data engineering

作者： Yu Chen Sheng Zhang Ziying Zhou Xiaokun Wang Yu Liang Ning Chen Yuting Yan Mingjun Xiao Jie Wu Zhuzhong Qian Harry Xu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Systems Science Beijing Normal University China School of Computer and Electronic Information / School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China P.R. China Center for Networked Computing Temple University USA Computer Science Department Samueli School of Engineering University of California Los Angeles USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

关键词： Crowdsourcing Accuracy Visual analytics Games Data engineering Outsourcing Task analysis

Modeling Inter-Intra Heterogeneity for Graph Federated Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

Differential evolution variants for Searching D- and A-optimal designs

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Tong, Lyuyang National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China Department of Computer Science City University of Hong Kong Hong Kong

Optimal experimental design is an essential subfield of statistics that maximizes the chances of experimental success. The D- and A-optimal design is a very challenging problem in the field of optimal design, namely minimizing the determinant and trace of the inverse Fisher information matrix. Due to the flexibility and ease of implementation, traditional evolutionary algorithms (EAs) are applied to deal with a small part of experimental optimization design problems without mathematical derivation and assumption. However, the current EAs remain the issues of determining the support point number, handling the infeasible weight solution, and the insufficient experiment. To address the above issues, this paper investigates differential evolution (DE) variants for finding D- and A-optimal designs on several different statistical models. The repair operation is proposed to automatically determine the support point by combining similar support points with their corresponding weights based on Euclidean distance and deleting the support point with less weight. Furthermore, the repair operation fixes the infeasible weight solution into the feasible weight solution. To enrich our optimal design experiments, we utilize the proposed DE variants to test the D- and A-optimal design problems on 12 statistical models. Compared with other competitor algorithms, simulation experiments show that LSHADE can achieve better performance on the D- and A-optimal design problems. Copyright © 2022, The Authors. All rights reserved.

关键词： Evolutionary algorithms

Understanding Dynamic Associations: Gait Recognition via Cross-view Spatiotemporal Aggregation Network

学校读者我要写书评

暂无评论

IEEE Transactions on Circuits and Systems for Video Technology 2022年 1-1页

作者： Chen, Jianyu Wang, Zhongyuan Zheng, Caixia Zeng, Kangli Zou, Qin Xiong, Zixiang School of Computer Science National Engineering Research Center for Multimedia Software Wuhan University Wuhan China Key Laboratory of Applied Statistics of MOE Northeast Normal University Changchun China School of Computer Science Wuhan University Wuhan China Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Gait recognition is a technique to recognize subjects under long-distance conditions. Existing gait recognition methods generally rely on extracting different motion representations of body parts to capture the gait information. However, the differences in appearance silhouettes between subjects are not discriminative enough to identify the distinct gaits. Therefore, exploring the representations of unique motion differences of subjects from the temporal domain is an effective way to boost gait recognition performance. In this paper, we propose a cross-view spatiotemporal aggregation network (CSTAN) to learn the most discriminative motion representations of subjects from multi-scale features. Particularly, CSTAN uses temporal contextual feature aggregation (TCFA) block to interact multi-scale features, increasing the diversity of temporal representations in feature sequences and producing suitable motion representations. Besides, a potential spatial feature assessment (PSFA) block is proposed to address the problem of missing silhouettes due to occlusion. Considering the multi-view context, we propose a cross-view prediction learning (CVPL) block to mitigate the impact of the view itself. By aggregating features in the spatiotemporal and view domains, CSTAN can learn a more appropriate representation of human motion differences. Extensive experiments on two public gait recognition datasets demonstrate the state-of-the-art performance of our proposed CSTAN. It is worth mentioning that the performance of CSTAN achieves a breakthrough in some complex environments, e.g., the subjects wearing different coats or carrying a bag. IEEE

关键词： Data mining

Robust Generalization against Photon-Limited Corruptions via Worst-Case Sharpness Minimization

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Huang, Zhuo Zhu, Miaoxi Xia, Xiaobo Shen, Li Yu, Jun Gong, Chen Han, Bo Du, Bo Liu, Tongliang Syndey Ai Centre The University of Sydney Australia National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China Jd Explore Academy China Department of Automation University of Science and Technology of China China Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong

Robust generalization aims to tackle the most challenging data distributions which are rare in the training set and contain severe noises, i.e., photon-limited corruptions. Common solutions such as distributionally robust optimization (DRO) focus on the worst-case empirical risk to ensure low training error on the uncommon noisy distributions. However, due to the over-parameterized model being optimized on scarce worst-case data, DRO fails to produce a smooth loss landscape, thus struggling on generalizing well to the test set. Therefore, instead of focusing on the worst-case risk minimization, we propose SharpDRO by penalizing the sharpness of the worst-case distribution, which measures the loss changes around the neighbor of learning parameters. Through worst-case sharpness minimization, the proposed method successfully produces a flat loss curve on the corrupted distributions, thus achieving robust generalization. Moreover, by considering whether the distribution annotation is available, we apply SharpDRO to two problem settings and design a worst-case selection process for robust generalization. Theoretically, we show that SharpDRO has a great convergence guarantee. Experimentally, we simulate photon-limited corruptions using CIFAR10/100 and ImageNet30 datasets and show that SharpDRO exhibits a strong generalization ability against severe corruptions and exceeds well-known baseline methods with large performance gains. Copyright © 2023, The Authors. All rights reserved.

关键词： Optimization

How Deep is Love in LLMs’ Hearts? Exploring Semantic Size in Human-like Cognition

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yao, Yao Yang, Yifei Ma, Xinbei Yang, Dongjie Zhang, Zhuosheng Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 Shanghai China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan University Wuhan430072 China

How human cognitive abilities are formed has long captivated researchers. However, a significant challenge lies in developing meaningful methods to measure these complex processes. With the advent of large language models (LLMs), which now rival human capabilities in various domains, we are presented with a unique testbed to investigate human cognition through a new lens. Among the many facets of cognition, one particularly crucial aspect is the concept of semantic size—the perceived magnitude of both abstract and concrete words or concepts. This study seeks to investigate whether LLMs exhibit similar tendencies in understanding semantic size, thereby providing insights into the underlying mechanisms of human cognition. We begin by exploring metaphorical reasoning, comparing how LLMs and humans associate abstract words with concrete objects of varying sizes. Next, we examine LLMs’ internal representations to evaluate their alignment with human cognitive processes. Our findings reveal that multi-modal training is crucial for LLMs to achieve more human-like understanding, suggesting that real-world, multi-modal experiences are similarly vital for human cognitive development. Lastly, we examine whether LLMs are influenced by attention-grabbing headlines with larger semantic sizes in a real-world web shopping scenario. The results show that multi-modal LLMs are more emotionally engaged in decision-making, but this also introduces potential biases, such as the risk of manipulation through clickbait headlines. Ultimately, this study offers a novel perspective on how LLMs interpret and internalize language, from the smallest concrete objects to the most profound abstract concepts like ‘love’. The insights gained not only improve our understanding of LLMs but also provide new avenues for exploring the cognitive abilities that define human intelligence. Our code and dataset is publicly available at https://***/Zoeyyao27/LLM-Semantic-Size-Understanding. © 2025, CC BY.

关键词： Semantics

Resource Optimization for LEO Constellation Networks: A Multi-satellite Cooperative Coverage Design

学校读者我要写书评

暂无评论

IEEE Transactions on Communications 2025年

作者： Ma, Xu Zhang, Haijun Song, Wei Lu, Yang Wu, Yuan Leung, Victor C.M University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China China Electric Power Research Institute Co. Ltd Beijing102209 China University of Macau State Key Laboratory of Internet of Things for Smart City Macau China University of Macau Department of Computer and Information Science Macau China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services. © 1972-2012 IEEE.

关键词： Multiobjective optimization