Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences最新文献_第3页

Power Solitons in Inverter-based Electric Energy Systems: Observation, Analysis and Implications 基于逆变器的电力系统中的功率孤子:观察、分析和启示

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-10-16 DOI: 10.2172/1892470

John N. Jiang

引用次数: 0

Machine Learning in Transaction Monitoring: The Prospect of xAI 事务监控中的机器学习:xAI的前景

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-10-14 DOI: 10.48550/arXiv.2210.07648

Julie Gerlings, Ioanna D. Constantiou

{"title":"Machine Learning in Transaction Monitoring: The Prospect of xAI","authors":"Julie Gerlings, Ioanna D. Constantiou","doi":"10.48550/arXiv.2210.07648","DOIUrl":"https://doi.org/10.48550/arXiv.2210.07648","url":null,"abstract":"Banks hold a societal responsibility and regulatory requirements to mitigate the risk of financial crimes. Risk mitigation primarily happens through monitoring customer activity through Transaction Monitoring (TM). Recently, Machine Learning (ML) has been proposed to identify suspicious customer behavior, which raises complex socio-technical implications around trust and explainability of ML models and their outputs. However, little research is available due to its sensitivity. We aim to fill this gap by presenting empirical research exploring how ML supported automation and augmentation affects the TM process and stakeholders' requirements for building eXplainable Artificial Intelligence (xAI). Our study finds that xAI requirements depend on the liable party in the TM process which changes depending on augmentation or automation of TM. Context-relatable explanations can provide much-needed support for auditing and may diminish bias in the investigator's judgement. These results suggest a use case-specific approach for xAI to adequately foster the adoption of ML in TM.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"70 1","pages":"3474-3483"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79128392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Codeless App Development: Evaluating A Cloud-Native Domain-Specific Functions Approach 无代码应用程序开发:评估云原生领域特定功能方法

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-10-04 DOI: 10.48550/arXiv.2210.01647

Chuhao Wu, José Miguel Pérez-Álvarez, Adrian Mos, John Millar Carroll

{"title":"Codeless App Development: Evaluating A Cloud-Native Domain-Specific Functions Approach","authors":"Chuhao Wu, José Miguel Pérez-Álvarez, Adrian Mos, John Millar Carroll","doi":"10.48550/arXiv.2210.01647","DOIUrl":"https://doi.org/10.48550/arXiv.2210.01647","url":null,"abstract":"Mobile applications play an important role in the economy today and there is an increasing trend for app enablement on multiple platforms. However, creating, distributing, and maintaining an application remain expert tasks. Even for software developers, the process can be error-prone and resource-consuming, especially when targeting different platforms simultaneously. Researchers have proposed several frameworks to facilitate cross-platform app development, but little attention has been paid to non-technical users. In this paper, we described the Flow framework, which takes the advantage of domain-specific languages to enable no-code specification for app modeling. The cloud-native coordination mechanism further supports non-technical users to execute, monitor, and maintain apps for any target platforms. User evaluations were conducted to assess the usability and user experience with the system. The results indicated that users can develop apps in Flow with ease, but the prototype could be optimized to reduce learning time and workload.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"31 1","pages":"6904-6913"},"PeriodicalIF":0.0,"publicationDate":"2022-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76909415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images 航空图像中炸弹弹坑的深度域自适应检测

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.11299

Marco Geiger, Dominik Martin, Niklas Kühl

引用次数: 0

Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study 使用环境同步量数据的动态响应恢复:一个综合的德克萨斯互联案例研究

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-09-22 DOI: 10.48550/arXiv.2209.11105

Shaohui Liu, Hao Zhu, V. Kekatos

{"title":"Dynamic Response Recovery Using Ambient Synchrophasor Data: A Synthetic Texas Interconnection Case Study","authors":"Shaohui Liu, Hao Zhu, V. Kekatos","doi":"10.48550/arXiv.2209.11105","DOIUrl":"https://doi.org/10.48550/arXiv.2209.11105","url":null,"abstract":"Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. This paper puts forth a comprehensive framework for inferring the dynamic responses in the small-signal regime using ubiquitous fast-rate ambient data collected during normal grid operations. We have shown that the impulse response between any pair of locations can be recovered in a model-free fashion by cross-correlating angle and power flow data streams collected only at these two locations, going beyond previous work based on frequency data only. The result has been established via model-based analysis of linearized second-order swing dynamics under certain conditions. Numerical validations demonstrate its applicability to realistic power system models including nonlinear, higher-order dynamics. In particular, the case study using synthetic PMU data on a synthetic Texas Interconnection (TI) system strongly corroborates the benefit of using angle PMU data over frequency data for real-world power system dynamic modeling.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"80 1","pages":"2651-2660"},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84161672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Process Mining Meets Visual Analytics: The Case of Conformance Checking 过程挖掘与可视化分析:一致性检查的案例

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-09-20 DOI: 10.48550/arXiv.2209.09712

Jana-Rebecca Rehse, Luise Pufahl, Michael Grohs, Lisa-Marie Klein

引用次数: 1

Kernel-Segregated Transpose Convolution Operation 核分离转置卷积运算

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-09-08 DOI: 10.48550/arXiv.2209.03704

Vijay Srinivas Tida, Sai Venkatesh Chilukoti, X. Hei, Sonya Hsu

{"title":"Kernel-Segregated Transpose Convolution Operation","authors":"Vijay Srinivas Tida, Sai Venkatesh Chilukoti, X. Hei, Sonya Hsu","doi":"10.48550/arXiv.2209.03704","DOIUrl":"https://doi.org/10.48550/arXiv.2209.03704","url":null,"abstract":"Transpose convolution has shown prominence in many deep learning applications. However, transpose convolution layers are computationally intensive due to the increased feature map size due to adding zeros after each element in each row and column. Thus, convolution operation on the expanded input feature map leads to poor utilization of hardware resources. The main reason for unnecessary multiplication operations is zeros at predefined positions in the input feature map. We propose an algorithmic-level optimization technique for the effective transpose convolution implementation to solve these problems. Based on kernel activations, we segregated the original kernel into four sub-kernels. This scheme could reduce memory requirements and unnecessary multiplications. Our proposed method was $3.09 (3.02) times$ faster computation using the Titan X GPU (Intel Dual Core CPU) with a flower dataset from the Kaggle website. Furthermore, the proposed optimization method can be generalized to existing devices without additional hardware requirements. A simple deep learning model containing one transpose convolution layer was used to evaluate the optimization method. It showed $2.2 times$ faster training using the MNIST dataset with an Intel Dual-core CPU than the conventional implementation.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"60 1","pages":"6934-6943"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88944167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fox in the Henhouse: The Delegation of Regulatory and Privacy Enforcement to Big Tech 鸡窝里的狐狸:监管和隐私执法向大型科技公司的授权

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-09-08 DOI: 10.24251/hicss.2022.320

W. Bendix, J. Mackay

引用次数: 0

Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection 用于Covid-19疾病检测的隐私保护深度学习模型

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-09-07 DOI: 10.48550/arXiv.2209.04445

Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya Hsu, X. Hei

{"title":"Privacy-Preserving Deep Learning Model for Covid-19 Disease Detection","authors":"Vijay Srinivas Tida, Sai Venkatesh Chilukoti, Sonya Hsu, X. Hei","doi":"10.48550/arXiv.2209.04445","DOIUrl":"https://doi.org/10.48550/arXiv.2209.04445","url":null,"abstract":"Recent studies demonstrated that X-ray radiography showed higher accuracy than Polymerase Chain Reaction (PCR) testing for COVID-19 detection. Therefore, applying deep learning models to X-rays and radiography images increases the speed and accuracy of determining COVID-19 cases. However, due to Health Insurance Portability and Accountability (HIPAA) compliance, the hospitals were unwilling to share patient data due to privacy concerns. To maintain privacy, we propose differential private deep learning models to secure the patients' private information. The dataset from the Kaggle website is used to evaluate the designed model for COVID-19 detection. The EfficientNet model version was selected according to its highest test accuracy. The injection of differential privacy constraints into the best-obtained model was made to evaluate performance. The accuracy is noted by varying the trainable layers, privacy loss, and limiting information from each sample. We obtained 84% accuracy with a privacy loss of 10 during the fine-tuning process.","PeriodicalId":74512,"journal":{"name":"Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences","volume":"169 1","pages":"3287-3296"},"PeriodicalIF":0.0,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77859651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Combinations of Affinity Functions for Different Community Detection Algorithms in Social Networks 社交网络中不同社区检测算法的亲和力函数组合

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences Pub Date : 2022-08-26 DOI: 10.24251/HICSS.2022.265

Javier Fumanal Idocin, O. Cordón, M. Minárová, Amparo Alonso Betanzos, H. Bustince

引用次数: 0