ACM Transactions on Knowledge Discovery from Data (TKDD)最新文献

Machine Learning-based Short-term Rainfall Prediction from Sky Data 基于机器学习的天空数据短期降雨预测

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-07-29 DOI: 10.1145/3502731

Fu Jie Tey, Tin-Yu Wu, Jiann-Liang Chen

引用次数: 1

Incremental Feature Spaces Learning with Label Scarcity 标签稀缺性下的增量特征空间学习

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-06-27 DOI: 10.1145/3516368

Shilin Gu, Yuhua Qian, Chenping Hou

{"title":"Incremental Feature Spaces Learning with Label Scarcity","authors":"Shilin Gu, Yuhua Qian, Chenping Hou","doi":"10.1145/3516368","DOIUrl":"https://doi.org/10.1145/3516368","url":null,"abstract":"Recently, learning and mining from data streams with incremental feature spaces have attracted extensive attention, where data may dynamically expand over time in both volume and feature dimensions. Existing approaches usually assume that the incoming instances can always receive true labels. However, in many real-world applications, e.g., environment monitoring, acquiring the true labels is costly due to the need of human effort in annotating the data. To tackle this problem, we propose a novel incremental Feature spaces Learning with Label Scarcity (FLLS) algorithm, together with its two variants. When data streams arrive with augmented features, we first leverage the margin-based online active learning to select valuable instances to be labeled and thus build superior predictive models with minimal supervision. After receiving the labels, we combine the online passive-aggressive update rule and margin-maximum principle to jointly update the dynamic classifier in the shared and augmented feature space. Finally, we use the projected truncation technique to build a sparse but efficient model. We theoretically analyze the error bounds of FLLS and its two variants. Also, we conduct experiments on synthetic data and real-world applications to further validate the effectiveness of our proposed algorithms.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124814616","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

Multi-objective Learning to Overcome Catastrophic Forgetting in Time-series Applications 多目标学习克服时间序列应用中的灾难性遗忘

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-06-17 DOI: 10.1145/3502728

Reem A. Mahmoud, Hazem M. Hajj

{"title":"Multi-objective Learning to Overcome Catastrophic Forgetting in Time-series Applications","authors":"Reem A. Mahmoud, Hazem M. Hajj","doi":"10.1145/3502728","DOIUrl":"https://doi.org/10.1145/3502728","url":null,"abstract":"One key objective of artificial intelligence involves the continuous adaptation of machine learning models to new tasks. This branch of continual learning is also referred to as lifelong learning (LL), where a major challenge is to minimize catastrophic forgetting, or forgetting previously learned tasks. While previous work on catastrophic forgetting has been focused on vision problems; this work targets time-series data. In addition to choosing an architecture appropriate for time-series sequences, our work addresses limitations in previous work, including the handling of distribution shifts in class labels. We present multi-objective learning with three loss functions to minimize catastrophic forgetting, prediction error, and errors in generalizing across label shifts, simultaneously. We build a multi-task autoencoder network with a hierarchical convolutional recurrent architecture. The proposed method is capable of learning multiple time-series tasks simultaneously. For cases where the model needs to learn multiple new tasks, we propose sequential learning, starting with tasks that have the best individual performances. This solution was evaluated on four benchmark human activity recognition datasets collected from mobile sensing devices. A wide set of baseline comparisons is performed, and an ablation analysis is run to evaluate the impact of the different losses in the proposed multi-objective method. The results demonstrate an up to 4% performance improvement in catastrophic forgetting compared to the use of loss functions in state-of-the-art solutions while demonstrating minimal losses compared to upper bound methods of traditional fine-tuning (FT) and multi-task learning (MTL).","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124918689","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

Combining Filtering and Cross-Correlation Efficiently for Streaming Time Series 流时间序列滤波和互相关的有效结合

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-05-24 DOI: 10.1145/3502738

Sheng Zhong, Vinicius M. A. Souza, A. Mueen

{"title":"Combining Filtering and Cross-Correlation Efficiently for Streaming Time Series","authors":"Sheng Zhong, Vinicius M. A. Souza, A. Mueen","doi":"10.1145/3502738","DOIUrl":"https://doi.org/10.1145/3502738","url":null,"abstract":"Monitoring systems have hundreds or thousands of distributed sensors gathering and transmitting real-time streaming data. The early detection of events in these systems, such as an earthquake in a seismic monitoring system, is the base for essential tasks as warning generations. To detect such events is usual to compute pairwise correlation across the disparate signals generated by the sensors. Since the data sources (e.g., sensors) are spatially separated, it is essential to consider the lagged correlation between the signals. Besides, many applications require to process a specific band of frequencies depending on the event’s type, demanding a pre-processing step of filtering before computing correlations. Due to the high speed of data generation and a large number of sensors in these systems, the operations of filtering and lagged cross-correlation need to be efficient to provide real-time responses without data losses. This article proposes a technique named FilCorr that efficiently computes both operations in one single step. We achieve an order of magnitude speedup by maintaining frequency transforms over sliding windows. Our method is exact, devoid of sensitive parameters, and easily parallelizable. Besides our algorithm, we also provide a publicly available real-time system named Seisviz that employs FilCorr in its core mechanism for monitoring a seismometer network. We demonstrate that our technique is suitable for several monitoring applications as seismic signal monitoring, motion monitoring, and neural activity monitoring.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130132557","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

Segment-Wise Time-Varying Dynamic Bayesian Network with Graph Regularization 基于图正则化的分段时变动态贝叶斯网络

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-05-04 DOI: 10.1145/3522589

Xingxuan Yang, Chen Zhang, Baihua Zheng

引用次数: 2

HW-Forest: Deep Forest with Hashing Screening and Window Screening HW-Forest:带散列筛选和窗口筛选的深森林

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-05-04 DOI: 10.1145/3532193

Pengfei Ma, Youxi Wu, Y. Li, Lei Guo, He Jiang, Xingquan Zhu, X. Wu

引用次数: 8

A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting 基于新闻的发现和跟踪城市地区概况框架:Covid-19背景下的评估

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-04-25 DOI: 10.1145/3532186

A. Bechini, Alessandro Bondielli, José Luis Corcuera Bárcena, P. Ducange, F. Marcelloni, Alessandro Renda

{"title":"A News-Based Framework for Uncovering and Tracking City Area Profiles: Assessment in Covid-19 Setting","authors":"A. Bechini, Alessandro Bondielli, José Luis Corcuera Bárcena, P. Ducange, F. Marcelloni, Alessandro Renda","doi":"10.1145/3532186","DOIUrl":"https://doi.org/10.1145/3532186","url":null,"abstract":"In the last years, there has been an ever-increasing interest in profiling various aspects of city life, especially in the context of smart cities. This interest has become even more relevant recently when we have realized how dramatic events, such as the Covid-19 pandemic, can deeply affect the city life, producing drastic changes. Identifying and analyzing such changes, both at the city level and within single neighborhoods, may be a fundamental tool to better manage the current situation and provide sound strategies for future planning. Furthermore, such fine-grained and up-to-date characterization can represent a valuable asset for other tools and services, e.g., web mapping applications or real estate agency platforms. In this article, we propose a framework featuring a novel methodology to model and track changes in areas of the city by extracting information from online newspaper articles. The problem of uncovering clusters of news at specific times is tackled by means of the joint use of state-of-the-art language models to represent the articles, and of a density-based streaming clustering algorithm, properly shaped to deal with high-dimensional text embeddings. Furthermore, we propose a method to automatically label the obtained clusters in a semantically meaningful way, and we introduce a set of metrics aimed at tracking the temporal evolution of clusters. A case study focusing on the city of Rome during the Covid-19 pandemic is illustrated and discussed to evaluate the effectiveness of the proposed approach.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132346378","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

MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining Poset族的蒙特卡洛Rademacher平均和近似模式挖掘

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-04-25 DOI: 10.1145/3532187

Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato

{"title":"MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining","authors":"Leonardo Pellegrina, Cyrus Cousins, Fabio Vandin, Matteo Riondato","doi":"10.1145/3532187","DOIUrl":"https://doi.org/10.1145/3532187","url":null,"abstract":"“I’m an MC still as honest” – Eminem, Rap God We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both (1) statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and (2) approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This flexibility offered by MCRapper is a big advantage over previously proposed solutions, which could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining, by appropriately computing approximations of the negative and positive borders of the collection of patterns of interest, which allow an effective pruning of the pattern space and the computation of strong bounds to the supremum deviation. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116047692","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

Synthesis of Longitudinal Human Location Sequences: Balancing Utility and Privacy 纵向人类定位序列的综合:平衡效用与隐私

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-04-24 DOI: 10.1145/3529260

Maya Benarous, Eran Toch, I. Ben-Gal

{"title":"Synthesis of Longitudinal Human Location Sequences: Balancing Utility and Privacy","authors":"Maya Benarous, Eran Toch, I. Ben-Gal","doi":"10.1145/3529260","DOIUrl":"https://doi.org/10.1145/3529260","url":null,"abstract":"People’s location data are continuously tracked from various devices and sensors, enabling an ongoing analysis of sensitive information that can violate people’s privacy and reveal confidential information. Synthetic data have been used to generate representative location sequences yet to maintain the users’ privacy. Nonetheless, the privacy-accuracy tradeoff between these two measures has not been addressed systematically. In this article, we analyze the use of different synthetic data generation models for long location sequences, including extended short-term memory networks (LSTMs), Markov Chains (MC), and variable-order Markov models (VMMs). We employ different performance measures, such as data similarity and privacy, and discuss the inherent tradeoff. Furthermore, we introduce other measurements to quantify each of these measures. Based on the anonymous data of 300 thousand cellular-phone users, our work offers a road map for developing policies for synthetic data generation processes. We propose a framework for building data generation models and evaluating their effectiveness regarding those accuracy and privacy measures.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130036552","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

Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection 基于剖面分解的混合迁移学习冷启动数据异常检测

ACM Transactions on Knowledge Discovery from Data (TKDD) Pub Date : 2022-04-24 DOI: 10.1145/3530990

Ziyue Li, Haodong Yan, F. Tsung, Ke Zhang

{"title":"Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection","authors":"Ziyue Li, Haodong Yan, F. Tsung, Ke Zhang","doi":"10.1145/3530990","DOIUrl":"https://doi.org/10.1145/3530990","url":null,"abstract":"Anomaly detection is an essential task for quality management in smart manufacturing. An accurate data-driven detection method usually needs enough data and labels. However, in practice, there commonly exist newly set-up processes in manufacturing, and they only have quite limited data available for analysis. Borrowing the name from the recommender system, we call this process a cold-start process. The sparsity of anomaly, the deviation of the profile, and noise aggravate the detection difficulty. Transfer learning could help to detect anomalies for cold-start processes by transferring the knowledge from more experienced processes to the new processes. However, the existing transfer learning and multi-task learning frameworks are established on task- or domain-level relatedness. We observe instead, within a domain, some components (background and anomaly) share more commonality, others (profile deviation and noise) not. To this end, we propose a more delicate component-level transfer learning scheme, i.e., decomposition-based hybrid transfer learning (DHTL): It first decomposes a domain (e.g., a data source containing profiles) into different components (smooth background, profile deviation, anomaly, and noise); then, each component’s transferability is analyzed by expert knowledge; Lastly, different transfer learning techniques could be tailored accordingly. We adopted the Bayesian probabilistic hierarchical model to formulate parameter transfer for the background, and “L2,1+L1”-norm to formulate low dimension feature-representation transfer for the anomaly. An efficient algorithm based on Block Coordinate Descend is proposed to learn the parameters. A case study based on glass coating pressure profiles demonstrates the improved accuracy and completeness of detected anomaly, and a simulation demonstrates the fidelity of the decomposition results.","PeriodicalId":435653,"journal":{"name":"ACM Transactions on Knowledge Discovery from Data (TKDD)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126670152","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: 10