Time Pub Date : 2022-01-01 DOI: 10.4230/LIPIcs.TIME.2022.13

G. Pagliarini, Simone Scaboro, Giuseppe Serra, G. Sciavicco, Eduard Ionel Stan

引用次数: 1

Getting to the CORE of Complex Event Recognition (Invited Talk) 复杂事件识别的核心(特邀演讲)

Time Pub Date : 2022-01-01 DOI: 10.4230/LIPIcs.TIME.2022.3

Stijn Vansummeren

{"title":"Getting to the CORE of Complex Event Recognition (Invited Talk)","authors":"Stijn Vansummeren","doi":"10.4230/LIPIcs.TIME.2022.3","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2022.3","url":null,"abstract":"In this talk, I will give an overview of our recent work on complex event recognition. 2012 ACM Subject Classification Information systems → Query languages for non-relational engines; Information systems → Stream management; Theory of computation → Formal languages and automata theory Abstract Complex Event Recognition (CER for short) refers to the activity of processing high-velocity streams of primitive events by evaluating queries that detect complex events : collections of primitive events that satisfy some pattern. In particular, CER queries match incoming events on the basis of their content; where they occur in the input stream; and how this order relates to other events in the stream. CER has been successfully applied in diverse domains such as maritime monitoring, network intrusion detection, industrial control systems and real-time analytics, among others. In this talk, I will survey our recent work on developing a formal framework for specifying and evaluating CER queries. This framework consist of a formal, core query language called Complex Event Logic (CEL) for specifying CER queries [4]. In contrast to previous proposals, CEL has a compositional and denotational semantics, and encompasses all operators that are considered “common base operators” in the literature. Using CEL, we have been able to get a better understanding of the relative expressiveness of these operators as well as the impact of common evaluation heuristics such as selection policies The framework also consists of an formal computational for CEL,","PeriodicalId":75226,"journal":{"name":"Time","volume":"101 1","pages":"3:1-3:2"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77365568","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

A Neuro-Symbolic Approach for Real-World Event Recognition from Weak Supervision 弱监督下现实世界事件识别的神经符号方法

Time Pub Date : 2022-01-01 DOI: 10.4230/LIPIcs.TIME.2022.12

Gianluca Apriceno, Andrea Passerini, L. Serafini

{"title":"A Neuro-Symbolic Approach for Real-World Event Recognition from Weak Supervision","authors":"Gianluca Apriceno, Andrea Passerini, L. Serafini","doi":"10.4230/LIPIcs.TIME.2022.12","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2022.12","url":null,"abstract":"Events are structured entities involving different components (e.g, the participants, their roles etc.) and their relations. Structured events are typically defined in terms of (a subset of) simpler, atomic events and a set of temporal relation between them. Temporal Event Detection (TED) is the task of detecting structured and atomic events within data streams, most often text or video sequences, and has numerous applications, from video surveillance to sports analytics. Existing deep learning approaches solve TED task by implicitly learning the temporal correlations among events from data. As consequence, these approaches often fail in ensuring a consistent prediction in terms of the relationship between structured and atomic events. On the other hand, neuro-symbolic approaches have shown their capability to constrain the output of the neural networks to be consistent with respect to the background knowledge of the domain. In this paper, we propose a neuro-symbolic approach for TED in a real world scenario involving sports activities. We show how by incorporating simple knowledge involving the relative order of atomic events and constraints on their duration, the approach substantially outperforms a fully neural solution in terms of recognition accuracy, when little or even no supervision is available on the atomic events.","PeriodicalId":75226,"journal":{"name":"Time","volume":"1 1","pages":"12:1-12:19"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90075722","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

Reasoning on Dynamic Transformations of Symbolic Heaps 符号堆动态变换的推理

Time Pub Date : 2022-01-01 DOI: 10.4230/LIPIcs.TIME.2022.9

N. Peltier

引用次数: 0

Uprooted. 扎根。

Time Pub Date : 2021-10-26 DOI: 10.2307/j.ctv20rsk12.9

D. Cray

引用次数: 20

Sick and Tired: 生病和疲劳:

Time Pub Date : 2021-10-01 DOI: 10.2307/j.ctv1zjgbcm.11

引用次数: 0

Temporal Logic with Recursion 递归的时间逻辑

Time Pub Date : 2021-09-01 DOI: 10.4230/LIPIcs.TIME.2020.6

Florian Bruse, M. Lange

引用次数: 3

Living large. 生活宽裕。

Time Pub Date : 2021-02-23 DOI: 10.2307/j.ctt5vkgw0.23

A. Park

引用次数: 3

Olisipo: A Probabilistic Approach to the Adaptable Execution of Deterministic Temporal Plans 确定性时间计划适应性执行的概率方法

Time Pub Date : 2021-01-01 DOI: 10.4230/LIPIcs.TIME.2021.15

Tomás Ribeiro, Oscar Lima, Michael Cashmore, A. Micheli, R. Ventura

{"title":"Olisipo: A Probabilistic Approach to the Adaptable Execution of Deterministic Temporal Plans","authors":"Tomás Ribeiro, Oscar Lima, Michael Cashmore, A. Micheli, R. Ventura","doi":"10.4230/LIPIcs.TIME.2021.15","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2021.15","url":null,"abstract":"The robust execution of a temporal plan in a perturbed environment is a problem that remains to be solved. Perturbed environments, such as the real world, are non-deterministic and filled with uncertainty. Hence, the execution of a temporal plan presents several challenges and the employed solution often consists of replanning when the execution fails. In this paper, we propose a novel algorithm, named Olisipo, which aims to maximise the probability of a successful execution of a temporal plan in perturbed environments. To achieve this, a probabilistic model is used in the execution of the plan, instead of in the building of the plan. This approach enables Olisipo to dynamically adapt the plan to changes in the environment. In addition to this, the execution of the plan is also adapted to the probability of successfully executing each action. Olisipo was compared to a simple dispatcher and it was shown that it consistently had a higher probability of successfully reaching a goal state in uncertain environments, performed fewer replans and also executed fewer actions. Hence, Olisipo offers a substantial improvement in performance for disturbed environments. 2012 ACM Subject Classification Computing methodologies → Robotic planning","PeriodicalId":75226,"journal":{"name":"Time","volume":"4 1","pages":"15:1-15:15"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74550069","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

Interval Temporal Random Forests with an Application to COVID-19 Diagnosis 区间时间随机森林在COVID-19诊断中的应用

Time Pub Date : 2021-01-01 DOI: 10.4230/LIPIcs.TIME.2021.7

F. Manzella, G. Pagliarini, G. Sciavicco, Eduard Ionel Stan

{"title":"Interval Temporal Random Forests with an Application to COVID-19 Diagnosis","authors":"F. Manzella, G. Pagliarini, G. Sciavicco, Eduard Ionel Stan","doi":"10.4230/LIPIcs.TIME.2021.7","DOIUrl":"https://doi.org/10.4230/LIPIcs.TIME.2021.7","url":null,"abstract":"Symbolic learning is the logic-based approach to machine learning. The mission of symbolic learning is to provide algorithms and methodologies to extract logical information from data and express it in an interpretable way. In the context of temporal data, interval temporal logic has been recently proposed as a suitable tool for symbolic learning, specifically via the design of an interval temporal logic decision tree extraction algorithm. Building on it, we study here its natural generalization to interval temporal random forests, mimicking the corresponding schema at the propositional level. Interval temporal random forests turn out to be a very performing multivariate time series classification method, which, despite the introduction of a functional component, are still logically interpretable to some extent. We apply this method to the problem of diagnosing COVID-19 based on the time series that emerge from cough and breath recording of positive versus negative subjects. Our experiment show that our models achieve very high accuracies and sensitivities, often superior to those achieved by classical methods on the same data. Although other recent approaches to the same problem (based on different and more numerous data) show even better statistical results, our solution is the first logic-based, interpretable, and explainable one. © Federico Manzella, Giovanni Pagliarini, Guido Sciavicco, and Ionel Eduard Stan;licensed under Creative Commons License CC-BY 4.0 28th International Symposium on Temporal Representation and Reasoning (TIME 2021).","PeriodicalId":75226,"journal":{"name":"Time","volume":"1 1","pages":"7:1-7:18"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79935896","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

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