ACM Transactions on Intelligent Systems and Technology (TIST)最新文献_第9页

Significant DBSCAN+: Statistically Robust Density-based Clustering 显著DBSCAN+:统计上健壮的基于密度的聚类

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-10-31 DOI: 10.1145/3474842

Yiqun Xie, X. Jia, S. Shekhar, Han Bao, Xun Zhou

{"title":"Significant DBSCAN+: Statistically Robust Density-based Clustering","authors":"Yiqun Xie, X. Jia, S. Shekhar, Han Bao, Xun Zhou","doi":"10.1145/3474842","DOIUrl":"https://doi.org/10.1145/3474842","url":null,"abstract":"Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115595325","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: 5

KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines 用非参数回归样条连接大型非线性时间序列中的因果结

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-10-31 DOI: 10.1145/3480971

Georgios Koutroulis, L. Botler, Belgin Mutlu, K. Diwold, Kay Römer, Roman Kern

{"title":"KOMPOS: Connecting Causal Knots in Large Nonlinear Time Series with Non-Parametric Regression Splines","authors":"Georgios Koutroulis, L. Botler, Belgin Mutlu, K. Diwold, Kay Römer, Roman Kern","doi":"10.1145/3480971","DOIUrl":"https://doi.org/10.1145/3480971","url":null,"abstract":"Recovering causality from copious time series data beyond mere correlations has been an important contributing factor in numerous scientific fields. Most existing works assume linearity in the data that may not comply with many real-world scenarios. Moreover, it is usually not sufficient to solely infer the causal relationships. Identifying the correct time delay of cause-effect is extremely vital for further insight and effective policies in inter-disciplinary domains. To bridge this gap, we propose KOMPOS, a novel algorithmic framework that combines a powerful concept from causal discovery of additive noise models with graphical ones. We primarily build our structural causal model from multivariate adaptive regression splines with inherent additive local nonlinearities, which render the underlying causal structure more easily identifiable. In contrast to other methods, our approach is not restricted to Gaussian or non-Gaussian noise due to the non-parametric attribute of the regression method. We conduct extensive experiments on both synthetic and real-world datasets, demonstrating the superiority of the proposed algorithm over existing causal discovery methods, especially for the challenging cases of autocorrelated and non-stationary time series.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131610902","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: 3

Shifting Capsule Networks from the Cloud to the Deep Edge 将胶囊网络从云端转移到深边缘

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-10-06 DOI: 10.1145/3544562

Miguel Costa, Diogo Costa, T. Gomes, S. Pinto

{"title":"Shifting Capsule Networks from the Cloud to the Deep Edge","authors":"Miguel Costa, Diogo Costa, T. Gomes, S. Pinto","doi":"10.1145/3544562","DOIUrl":"https://doi.org/10.1145/3544562","url":null,"abstract":"Capsule networks (CapsNets) are an emerging trend in image processing. In contrast to a convolutional neural network, CapsNets are not vulnerable to object deformation, as the relative spatial information of the objects is preserved across the network. However, their complexity is mainly related to the capsule structure and the dynamic routing mechanism, which makes it almost unreasonable to deploy a CapsNet, in its original form, in a resource-constrained device powered by a small microcontroller (MCU). In an era where intelligence is rapidly shifting from the cloud to the edge, this high complexity imposes serious challenges to the adoption of CapsNets at the very edge. To tackle this issue, we present an API for the execution of quantized CapsNets in Arm Cortex-M and RISC-V MCUs. Our software kernels extend the Arm CMSIS-NN and RISC-V PULP-NN to support capsule operations with 8-bit integers as operands. Along with it, we propose a framework to perform post-training quantization of a CapsNet. Results show a reduction in memory footprint of almost 75%, with accuracy loss ranging from 0.07% to 0.18%. In terms of throughput, our Arm Cortex-M API enables the execution of primary capsule and capsule layers with medium-sized kernels in just 119.94 and 90.60 ms, respectively (STM32H755ZIT6U, Cortex-M7 @ 480 MHz). For the GAP-8 SoC (RISC-V RV32IMCXpulp @ 170 MHz), the latency drops to 7.02 and 38.03 ms, respectively.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131132827","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

GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning GTG-Shapley:联邦学习中高效准确的参与者贡献评估

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-09-05 DOI: 10.1145/3501811

Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, Li-zhen Cui

{"title":"GTG-Shapley: Efficient and Accurate Participant Contribution Evaluation in Federated Learning","authors":"Zelei Liu, Yuanyuan Chen, Han Yu, Yang Liu, Li-zhen Cui","doi":"10.1145/3501811","DOIUrl":"https://doi.org/10.1145/3501811","url":null,"abstract":"Federated Learning (FL) bridges the gap between collaborative machine learning and preserving data privacy. To sustain the long-term operation of an FL ecosystem, it is important to attract high-quality data owners with appropriate incentive schemes. As an important building block of such incentive schemes, it is essential to fairly evaluate participants’ contribution to the performance of the final FL model without exposing their private data. Shapley Value (SV)–based techniques have been widely adopted to provide a fair evaluation of FL participant contributions. However, existing approaches incur significant computation costs, making them difficult to apply in practice. In this article, we propose the Guided Truncation Gradient Shapley (GTG-Shapley) approach to address this challenge. It reconstructs FL models from gradient updates for SV calculation instead of repeatedly training with different combinations of FL participants. In addition, we design a guided Monte Carlo sampling approach combined with within-round and between-round truncation to further reduce the number of model reconstructions and evaluations required. This is accomplished through extensive experiments under diverse realistic data distribution settings. The results demonstrate that GTG-Shapley can closely approximate actual Shapley values while significantly increasing computational efficiency compared with the state-of-the-art, especially under non-i.i.d. settings.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130900918","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: 45

Impact of Driving Behavior on Commuter’s Comfort During Cab Rides: Towards a New Perspective of Driver Rating 驾驶行为对乘客乘坐出租车舒适度的影响:基于驾驶员评价新视角的研究

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-08-24 DOI: 10.1145/3523063

Rohit Verma, Sugandh Pargal, Debasree Das, Tanusree Parbat, Sai Shankar Kambalapalli, Bivas Mitra, Sandip Chakraborty

{"title":"Impact of Driving Behavior on Commuter’s Comfort During Cab Rides: Towards a New Perspective of Driver Rating","authors":"Rohit Verma, Sugandh Pargal, Debasree Das, Tanusree Parbat, Sai Shankar Kambalapalli, Bivas Mitra, Sandip Chakraborty","doi":"10.1145/3523063","DOIUrl":"https://doi.org/10.1145/3523063","url":null,"abstract":"Commuter comfort in cab rides affects driver rating as well as the reputation of ride-hailing firms like Uber/Lyft. Existing research has revealed that commuter comfort not only varies at a personalized level but also is perceived differently on different trips for the same commuter. Furthermore, there are several factors, including driving behavior and driving environment, affecting the perception of comfort. Automatically extracting the perceived comfort level of a commuter due to the impact of the driving behavior is crucial for a timely feedback to the drivers, which can help them to meet the commuter’s satisfaction. In light of this, we surveyed around 200 commuters who usually take such cab rides and obtained a set of features that impact comfort during cab rides. Following this, we develop a system Ridergo which collects smartphone sensor data from a commuter, extracts the spatial time series feature from the data, and then computes the level of commuter comfort on a five-point scale with respect to the driving. Ridergo uses a Hierarchical Temporal Memory model-based approach to observe anomalies in the feature distribution and then trains a multi-task learning-based neural network model to obtain the comfort level of the commuter at a personalized level. The model also intelligently queries the commuter to add new data points to the available dataset and, in turn, improve itself over periodic training. Evaluation of Ridergo on 30 participants shows that the system could provide efficient comfort score with high accuracy when the driving impacts the perceived comfort.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131487420","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

Cross-Silo Federated Learning for Multi-Tier Networks with Vertical and Horizontal Data Partitioning 具有垂直和水平数据划分的多层网络的跨筒仓联邦学习

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-08-19 DOI: 10.1145/3543433

Anirban Das, Timothy Castiglia, Shiqiang Wang, S. Patterson

引用次数: 5

DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction DeepExpress:基于异构和耦合序列的快递预测模型

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-08-18 DOI: 10.1145/3526087

Siyuan Ren, Bin Guo, Longbing Cao, Ke Li, Jiaqi Liu, Zhiwen Yu

{"title":"DeepExpress: Heterogeneous and Coupled Sequence Modeling for Express Delivery Prediction","authors":"Siyuan Ren, Bin Guo, Longbing Cao, Ke Li, Jiaqi Liu, Zhiwen Yu","doi":"10.1145/3526087","DOIUrl":"https://doi.org/10.1145/3526087","url":null,"abstract":"The prediction of express delivery sequence, i.e., modeling and estimating the volumes of daily incoming and outgoing parcels for delivery, is critical for online business, logistics, and positive customer experience, and specifically for resource allocation optimization and promotional activity arrangement. A precise estimate of consumer delivery requests has to involve sequential factors such as shopping behaviors, weather conditions, events, business campaigns, and their couplings. Despite that various methods have integrated external features to enhance the effects, extant works fail to address complex feature-sequence couplings in the following aspects: weaken the inter-dependencies when processing heterogeneous data and ignore the cumulative and evolving situation of coupling relationships. To address these issues, we propose DeepExpress—a deep-learning-based express delivery sequence prediction model, which extends the classic seq2seq framework to learn feature-sequence couplings. DeepExpress leverages an express delivery seq2seq learning, a carefully designed heterogeneous feature representation, and a novel joint training attention mechanism to adaptively handle heterogeneity issues and capture feature-sequence couplings for accurate prediction. Experimental results on real-world data demonstrate that the proposed method outperforms both shallow and deep baseline models.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129791323","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: 4

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion 利用大规模多模式数据融合识别Instagram上的非法毒贩

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-08-18 DOI: 10.1145/3472713

Chuanbo Hu, Minglei Yin, Bing Liu, Xin Li, Yanfang Ye

{"title":"Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion","authors":"Chuanbo Hu, Minglei Yin, Bing Liu, Xin Li, Yanfang Ye","doi":"10.1145/3472713","DOIUrl":"https://doi.org/10.1145/3472713","url":null,"abstract":"Illicit drug trafficking via social media sites such as Instagram have become a severe problem, thus drawing a great deal of attention from law enforcement and public health agencies. How to identify illicit drug dealers from social media data has remained a technical challenge for the following reasons. On the one hand, the available data are limited because of privacy concerns with crawling social media sites; on the other hand, the diversity of drug dealing patterns makes it difficult to reliably distinguish drug dealers from common drug users. Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG). Nearly 4,000 user accounts, of which more than 1,400 are drug dealers, have been collected from Instagram with multiple data sources including post comments, post images, homepage bio, and homepage images. We then design a quadruple-based multimodal fusion method to combine the multiple data sources associated with each user account for drug dealer identification. Experimental results on the constructed IDDIG dataset demonstrate the effectiveness of the proposed method in identifying drug dealers (almost 95% accuracy). Moreover, we have developed a hashtag-based community detection technique for discovering evolving patterns, especially those related to geography and drug types.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127970878","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: 4

Modeling Customer Experience in a Contact Center through Process Log Mining 通过流程日志挖掘建模呼叫中心的客户体验

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-08-12 DOI: 10.1145/3468269

Teng Fu, Guido Zampieri, David Hodgson, C. Angione, Yifeng Zeng

{"title":"Modeling Customer Experience in a Contact Center through Process Log Mining","authors":"Teng Fu, Guido Zampieri, David Hodgson, C. Angione, Yifeng Zeng","doi":"10.1145/3468269","DOIUrl":"https://doi.org/10.1145/3468269","url":null,"abstract":"The use of data mining and modeling methods in service industry is a promising avenue for optimizing current processes in a targeted manner, ultimately reducing costs and improving customer experience. However, the introduction of such tools in already established pipelines often must adapt to the way data is sampled and to its content. In this study, we tackle the challenge of characterizing and predicting customer experience having available only process log data with time-stamp information, without any ground truth feedback from the customers. As a case study, we consider the context of a contact center managed by TeleWare and analyze phone call logs relative to a two months span. We develop an approach to interpret the phone call process events registered in the logs and infer concrete points of improvement in the service management. Our approach is based on latent tree modeling and multi-class Naïve Bayes classification, which jointly allow us to infer a spectrum of customer experiences and test their predictability based on the current data sampling strategy. Moreover, such approach can overcome limitations in customer feedback collection and sharing across organizations, thus having wide applicability and being complementary to tools relying on more heavily constrained data.","PeriodicalId":123526,"journal":{"name":"ACM Transactions on Intelligent Systems and Technology (TIST)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124441794","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

Parallel Connected LSTM for Matrix Sequence Prediction with Elusive Correlations 基于模糊关联矩阵序列预测的并行连通LSTM

ACM Transactions on Intelligent Systems and Technology (TIST) Pub Date : 2021-08-12 DOI: 10.1145/3469437

Qi Zhao, Chuqiao Chen, Guangcan Liu, Qingshan Liu, Shengyong Chen

引用次数: 0