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We propose an automatic method for generating flight summaries of prescribed duration, given any planed 3D trajectory of a flying object. The challenge is to select relevant time‐ellipses, while keeping and adequately framing the most interesting parts of the trajectory, and enforcing cinematographic rules between the selected shots. Our solution optimizes the visual quality of the output video both in terms of camera view and film editing choices, thanks to a new optimization technique, designed to jointly optimize the selection of the interesting parts of a flight, and the camera animation parameters over time. To our best knowledge, this solution is the first one to address camera control, film editing, and trajectory summarizing at once. Ablation studies demonstrate the visual quality of the flights summaries we generate compared to alternative methods. [ABSTRACT FROM AUTHOR]

We study the complexity of infinite-domain constraint satisfaction problems (CSPs): our basic setting is that a complexity classification for the CSPs of first-order expansions of a structure 21 can be transferred to a classification of the CSPs of first-order expansions of another structure 53. We exploit a product of structures (the algebraic product) that corresponds to the product of the respective polymorphism clones and present a complete complexity classification of the CSPs for first-order expansions of the n-fold algebraic power of (ℚ;<). This is proved by various algebraic and logical methods in combination with knowledge of the polymorphisms of the tractable first-order expansions of (ℚ;<) and explicit descriptions of the expressible relations in terms of syntactically restricted first-order formulas. By combining our classification result with general classification transfer techniques, we obtain surprisingly strong new classification results for highly relevant formalisms such as Allen's Interval Algebra, the n-dimensional Block Algebra, and the Cardinal Direction Calculus, even if higher-arity relations are allowed. Our results confirm the infinite-domain tractability conjecture for classes of structures that have been difficult to analyze with older methods. For the special case of structures with binary signatures, the results can be substantially strengthened and tightly connected to Ord-Horn formulas; this solves several longstanding open problems from the artificial intelligence (AI) literature. [ABSTRACT FROM AUTHOR]

As lawmakers produce norms, the underlying normative system is affected showing the intrinsic dynamism of law. Through undertaken actions of legal change, the normative system is continuously modified. In a usual legislative practice, the time for an enacted legal provision to be in force may differ from that of its inclusion to the legal system, or from that in which it produces legal effects. Even more, some provisions can produce effects retroactively in time. In this article we study a simulation of such process through the formalisation of a temporalised logical framework upon which a novel belief revision model tackles the dynamic nature of law. Represented through intervals, the temporalisation of sentences allows differentiating the temporal parameters of norms. In addition, a proposed revision operator allows assessing change to the legal system by including a new temporalised literal while preserving the time-based consistency. This can be achieved either by pushing out conflictive pieces of pre-existing norms or through the modification of intervals in which such norms can be either in force, or produce effects. Finally, the construction of the temporalised revision operator is axiomatically characterised and its rational behavior proved through a corresponding representation theorem. [ABSTRACT FROM AUTHOR]

The management of time is essential in most AI-related applications. In addition, we know that temporal information is often not precise. In fact, in most cases, it is necessary to deal with imprecision and/or uncertainty. On the other hand, there is the need to handle the implicit common-sense information present in many temporal statements. In this paper, we present FTCProlog, a logic programming language capable of handling fuzzy temporal constraints soundly and efficiently. The main difference of FTCProlog with respect to its predecessor, PROLogic, is its ability to associate a certainty index with deductions obtained through SLD-resolution. This resolution is based on a proposal within the theoretical logical framework FTCLogic. This model integrates a first-order logic based on possibilistic logic with the Fuzzy Temporal Constraint Networks (FTCNs) that allow efficient time management. The calculation of the certainty index can be useful in applications where one wants to verify the extent to which the times elapsed between certain events follow a given temporal pattern. In this paper, we demonstrate that the calculation of this index respects the properties of the theoretical model regarding its semantics. FTCProlog is implemented in Haskell. [ABSTRACT FROM AUTHOR]

Question Answering Over Temporal Knowledge Graphs (TKGQA) is an important topic in question answering. TKGQA focuses on accurately understanding questions involving temporal constraints and retrieving accurate answers from knowledge graphs. In previous research, the hierarchical structure of question contexts and the constraints imposed by temporal information on different sentence components have been overlooked. In this paper, we propose a framework called "Semantic-Enhanced Reasoning Question Answering" (SERQA) to tackle this problem. First, we adopt a pretrained language model (LM) to obtain the question relation representation vector. Then, we leverage syntactic information from the constituent tree and dependency tree, in combination with Masked Self-Attention (MSA), to enhance temporal constraint features. Finally, we integrate the temporal constraint features into the question relation representation using an information fusion function for answer prediction. Experimental results demonstrate that SERQA achieves better performance on the CRONQUESTIONS and ImConstrainedQuestions datasets. In comparison with existing temporal KGQA methods, our model exhibits outstanding performance in comprehending temporal constraint questions. The ablation experiments verified the effectiveness of combining the constituent tree and the dependency tree with MSA in question answering. [ABSTRACT FROM AUTHOR]

Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer's patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent "TimeOntoImperfection", a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of "TimeOntoImperfection", then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step. [ABSTRACT FROM AUTHOR]

Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems. [ABSTRACT FROM AUTHOR]

Visual representations of data are widely used for communication and understanding, particularly in science, technology, engineering, and mathematics (STEM). However, despite their importance, many people have difficulty understanding data-based visualizations. This work presents a series of three studies that examine how understanding time-based Earth-science data visualizations are influenced by scale and the different directions time can be represented (e.g., the Geologic Time Scale represents time moving from bottom-to-top, whereas many calendars represent time moving left-to-right). In Study 1, 316 visualizations from two top scholarly geoscience journals were analyzed for how time was represented. These expert-made graphs represented time in a range of ways, with smaller timescales more likely to be represented as moving left-to-right and larger scales more likely to be represented in other directions. In Study 2, 47 STEM novices were recruited from an undergraduate psychology experiment pool and asked to construct four separate graphs representing change over two scales of time (Earth's history or a single day) and two phenomena (temperature or sea level). Novices overwhelmingly represented time moving from left-to-right, regardless of scale. In Study 3, 40 STEM novices were shown expert-made graphs where the direction of time varied. Novices had difficulty interpreting the expert-made graphs when time was represented moving in directions other than left-to-right. The study highlights the importance of considering representations of time and scale in STEM education and offers insights into how experts and novices approach visualizations. The findings inform the development of educational resources and strategies to improve students' understanding of scientific concepts where time and space are intrinsically related. [ABSTRACT FROM AUTHOR]

We introduce negation under the stable model semantics in DatalogMTL - a temporal extension of Datalog with metric temporal operators. As a result, we obtain a rule language which combines the power of answer set programming with the temporal dimension provided by metric operators. We show that, in this setting, reasoning becomes undecidable over the rational timeline, and decidable in ExpSpace in data complexity over the integer timeline. We also show that, if we restrict our attention to forward-propagating programs, reasoning over the integer timeline becomes PSpace-complete in data complexity, and hence, no harder than over positive programs; however, reasoning over the rational timeline in this fragment remains undecidable. [ABSTRACT FROM AUTHOR]

Descriptions of durational relations can be ambiguous, for example, the description "one meeting happened during another" couldmean that onemeeting started before the other ended, or it couldmean that themeetings started and ended simultaneously. A recent theory posits that people mentally simulate descriptions of durational events by representing their starts and ends along a spatial axis, that is, an iconic representation of time. To draw conclusions from this iconic mental model, reasoners consciously scan it in the direction of earlier to later timepoints. The account predicts an iconicity bias: People should prefer descriptions that are congruent with an iconic scanning procedure--descriptions that mention the starts of events before the ends of events--over logically equivalent but incongruent descriptions. Six experiments corroborated the prediction; they show that iconicity biases in temporal reasoning manifest in cases when reasoners consciously evaluate the durations of events. [ABSTRACT FROM AUTHOR]

Temporal reasoning is crucial for action recognition tasks. The previous works use 3D CNNs to jointly capture spatiotemporal information, but it causes a lot of computational costs as well. To improve the above problems, we propose a general channel split spatiotemporal network (CSST-Net) to achieve effective spatiotemporal feature representation learning. The CSST module consists of the grouped spatiotemporal modeling (GSTM) module and the parameter-free feature fusion (PFFF) module. The GSTM module decomposes features into spatial and temporal parts along the channel dimension in parallel, which focuses on spatial and temporal clues, respectively. Meanwhile, we utilize the combination of group-wise convolution and point-wise convolution to reduce the number of parameters in the temporal branch, thus alleviating the overfitting of 3D CNNs. Furthermore, for the problem of spatiotemporal feature fusion, the PFFF module performs the recalibration and fusion of spatial and temporal features by a soft attention mechanism, without introducing extra parameters, thus ensuring the correct network information flow effectively. Finally, extensive experiments on three benchmark databases (Sth-Sth V1, Sth-Sth V2, and Jester) indicate that the proposed CSST-Net can achieve competitive performance compared to existing methods, and significantly reduces the number of parameters and FLOPs of 3D CNNs baseline. [ABSTRACT FROM AUTHOR]

Action recognition is a challenging task that requires understanding the temporal relationships between frames. However, capturing and processing spatio-temporal and motion features is computationally expensive, making it difficult to apply to practical situations. We propose a novel approach called the Spatio-Temporal-Wise (STW) network to address this problem. The STW network inserts STW blocks, consisting of a Spatio-Temporal Fusion Module and a Temporal-Wise Module, into an existing 2D CNN. This approach requires very little additional computational overhead but brings huge performance improvements in recognizing human actions. The proposed method is evaluated on several public datasets, including Something-Something v1 & v2, Kinetics-400, UCF101, and HMDB51. STW achieved comparable or better performance on these datasets compared to state-of-the-art methods. Notably, the STW network improves recognition accuracy by 26.6% and 34.6% on the Something-Something v1 & v2 datasets, respectively, with less than 2% additional computational overhead. The results demonstrate that the STW network can significantly improve performance in action recognition tasks while requiring only a small additional computational overhead, which represents a promising direction for developing more efficient and effective approaches to handling temporal reasoning in action recognition, which may have important applications in the future.

Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level sentiment analysis categorizes a target's sentiment polarity in a specific environment. Using topic modelling in aspect-level sentiment analysis enables the identification of more accurate aspect-based tweet sentiments. The existing sentiment classification techniques used for the development of recommendation systems do not focus on the aspect-based approach modelled using deep learning classifier with temporal analysis on the social media data. Hence, this paper proposes an efficient sentiment classification model that highlights the impact of topic modelling-based word feature embedding for improvising the classification of Twitter sentiments and product reviews based on temporal reasoning and analysis for performing predictive analysis. For tweets context analysis, Latent Dirichlet Allocation based topic modelling is used in this work which generates the topics. For each topic, the sentiment is calculated separately and the topic guided feature expansion is done using Senti-wordnet. Moreover, an extended deep learning classification algorithm called Long Short-Term Memory (LSTM) with word embedding and temporal reasoning(LSTMWTR) is proposed in this paper for improving the classification accuracy. Finally, the labelled data are classified using the existing machine learning algorithms namely Naïve Bayes, Support Vector Machines and also using the deep learning models such as Convolution Neural Network(CNN),LSTM, Recurrent Neural Networks (RNN) and the transformer model namelyBi-directional Encoder Representation from Transformers (BERT),Convolution Bi-directional Recurrent Neural Network (CBRNN) and the proposed deep learning algorithm namelyLSTMWTR. These sentiment classification algorithms have been evaluated with word embedding for tweet sentiment classification and product review classification. The results obtained from this work show that the proposed LSTMWTR algorithm emerges as the highly accurate model for tweet sentiment and product review classification. [ABSTRACT FROM AUTHOR]

Descriptions of durational relations can be ambiguous, e.g., thedescription ‘two different meetings happened at the same time’could mean that one meeting started before the other ended, orit could mean that the meetings both started and endedsimultaneously. A recent theory posits that people mentallysimulate events with durations by representing the starts andends of events along a chronological axis (Khemlani et al.,2015). To draw conclusions from this durational mental model,reasoners consciously scan it in the direction of earlier timepoints to later time points. The account predicts that peopleshould prefer descriptions that are congruent with achronological scanning procedure, e.g., descriptions thatmention the starts of events before the ends of events. Twoexperiments corroborate the prediction, and show thatchronological biases in temporal reasoning manifest in caseswhen reasoners consciously evaluate the durations of events.

Multimorbidity is defined as the co-existence of two or more chronic diseases in a patient, and it is increasing in prevalence. This condition poses new problems for clinical reasoning. Few studies inquire regarding the construct of reasoning in multimorbidity and the teaching/learning methods. The objectives of this scoping review were searching for a definition of the construct of clinical reasoning in multimorbidity and the related learning methods, and special ways in which information technology can help. We searched PubMed, Scopus, ERIC and CORE databases. After an iterative process of selection and thematic analysis, we selected 30 articles, that were thematized in three classes: the multimorbid patient as a teacher (8 articles), defining a framework of competence (11 articles), representing multimorbidity and related clinical reasoning (11 articles). In this last theme were also grouped studies using technology to enhance learning. The construct of clinical reasoning in multimorbidity expands over three domains: clinical (including managing uncertainty, anticipating, and detecting evolutions and conflicting guidelines, and setting priorities); relational (concerning communicating uncertainty and developing a feasible, shared plan of care with the patient; organizational) (managing the wide system of resources needed to take care of a multimorbid patient). The preferred teaching methods are based on the encounter with real or expert patients, technology enhanced case-based learning and graphical representations of clinical cases. Perspectives of research should be addressed to permit the learner to experience a patient’s life-long experience by moving forward and back over time while exploring interactions among diseases and social determinants with respect to possibly conflicting treatments. Perspectives on rich, technology-enhanced simulations should be researched.

Question Answering Over Temporal Knowledge Graphs is an important topic in question answering, which aims to find an entity or timestamp to answer temporal reasoning questions from temporal knowledge graphs. Answering complex questions remains a major challenge for question answering over temporal knowledge graphs because it is associated with complex temporal reasoning. The performance of the existing state-of-the-art model falls short when the question contains constraints (e.g., 'before/after', 'first/last' and 'during') that require complex temporal reasoning based on multiple relevant facts. In this paper, we propose an improving reasoning method called the Complex Temporal Reasoning Network, which improves the complex temporal reasoning for temporal reasoning questions. For each question, we capture implicit temporal features and relation representation and then integrate them to generate implicit temporal relation representation. The experimental results on the CRONQUESTIONS dataset demonstrate that our method significantly outperforms all baselines. In particular, we demonstrate the effectiveness of our method on complex questions. The source code of CTRN will be available at https://github.com/2399240664/CTRN. [ABSTRACT FROM AUTHOR]

After a power system failure, a large number of alarms are generated and uploaded to the dispatching center. Based on this information, a fast and accurate fault diagnosis method can provide key decision support for further failure emergency treatment. Existing fault diagnosis methods considering timing information are complicated and temporal reasoning is time-consuming. A new approach to power system fault diagnosis based on fuzzy temporal order Petri net is proposed. The temporal order of alarms information is directly combined with the hierarchical Petri net model. Temporal constraints of operations of protective relays and circuit breakers are also exploited. An automatic model construction algorithm is presented to improve the applicability of the proposed method. The reasoning efficiency is enhanced by introducing the layered matrix reasoning algorithm. After the fault diagnosis result is obtained, a backward reasoning process can be conducted to evaluate alarms information. Finally, the proposed method is tested on multiple cases and simulation results show that the all faults are correctly identified even when information distortion exists.

Fuzzy and temporal reasoning can effectively improve the accuracy of fault diagnosis methods. However, there are challenges in practical applications, such as missing alarm messages, temporal reasoning with complex calculations, and complex modeling processes. Therefore, this study proposes an improved temporal fuzzy reasoning spiking neural P (ITFRSNP) system for power system fault diagnosis. First, the ITFRSNP system and its reasoning method are proposed to perform association reasoning between confidence degrees and temporal constraints. Second, a general fault diagnosis model and process are developed based on the ITFRSNP system to diagnose various faulty components and simplify the modeling process. In addition, a search method is provided for identifying suspected faulty components, considering the missing alarm message of the circuit breaker. Simulation results of fault cases demonstrate that the proposed method exhibits high accuracy and fault tolerance. It can precisely identify faulty components despite incorrect operations or inaccurate alarm messages of protective relays and circuit breakers. Moreover, the search method effectively narrows down the diagnostic scope without missing suspected faulty components in scenarios where alarms from boundary circuit breakers are missing, thereby enhancing the fault diagnosis efficiency. The fault diagnosis model features a straightforward structure and reasoning process with minimal computational complexity, making it suitable for real-time diagnosis of complex faults within power systems.

Few experiments have examined how people reason aboutdurative relations, e.g., "during". Such relations posechallenges to present theories of reasoning, but manyresearchers argue that people simulate a mental timeline whenthey think about sequences of events. A recent theory positsthat to mentally simulate durative relations, reasoners do notrepresent all of the time points across which an event mightendure. Instead, they construct discrete tokens that stand inplace of the beginnings and endings of those events. The theorypredicts that when reasoners need to build multiple simulationsto solve a reasoning problem, they should be more prone toerror. To test the theory, an experiment provided participantswith sets of premises describing durative relations; theyassessed whether the sets were consistent or inconsistent. Theresults of the experiment validated the theory's prediction. Weconclude by situating the study in recent work on temporalthinking.

Summary Illustration showing archaeological stratigraphic laws and principles Illustration of archaeological stratigraphic laws and principles. Taken from What is The Matrix? How do archaeologists use stratigraphy? YouTube Stratigraphic data and relationships form the backbone of all the related archaeological records from each excavated site and, along with the phasing and interpretive information derived through stratigraphic analysis, are essential for chronological modelling, broader synthesis of inter-site phases and periods and, we argue in this paper and elsewhere, stratigraphic data should be a required component in digital archives of the growing body of archaeological information and reports generated through the commercial archaeological sector in the UK and internationally. Not every site has complex stratigraphy, but understanding the nature of the stratigraphy, be that deep or shallow, complex or otherwise, enables researchers to piece together the underlying details of how the excavator(s) arrived at the interpretations they have made about the site. The stratigraphic record, including associated relationships and data, which in the case of complex stratigraphy are usually visualized in the form of a stratigraphic matrix diagram, acts as a primary, if not the primary evidence for how, and in what order, the site was excavated. As such the stratigraphic data can be the key mechanism that enables anyone less familiar with the site, to re-visit and re-use the excavation records; understand what data is most relevant for addressing certain research questions; or grasp the nature of the chronological sequence encountered; and piece together the underlying details of how the excavator(s) arrived at their interpretations. However such records are often only held on paper or as scanned image copies (as PDFs) of matrix diagrams that cannot easily be re-used with all the associated data. This article presents outcomes from The Matrix project (AHRC AH/T002093/1) that address the current problems caused by the lack of standardized approaches to digital archiving of archaeological data using the case study of stratigraphic and phasing data.

The use of ontologies to model human behaviours that affect health is challenging since this process involves data from multiple inter-related domains that unfold and evolve over time. However, while current ontology development methodologies are generic enough to model any domain of interest, they do not provide design guidelines for modelling time-related aspects. This paper proposes a methodology for ontology development that entails the requirements for behaviours modelling based on passive temporal data. Its main focus is on temporal representations of classes and their holistic relations since no other methodology approaches ontology design from its temporal perspective. We exemplify these ideas by modelling the sleep behaviour domain, its relations to other behavioural aspects, and its effects on health. [ABSTRACT FROM AUTHOR]

A semantic understanding of road traffic can help people understand road traffic flow situations and emergencies more accurately and provide a more accurate basis for anomaly detection and traffic prediction. At present, the overview of computer vision in traffic mainly focuses on the static detection of vehicles and pedestrians. There are few in-depth studies on the semantic understanding of road traffic using visual methods. This paper aims to review recent approaches to the semantic understanding of road traffic using vision sensors to bridge this gap. First, this paper classifies all kinds of traffic monitoring analysis methods from the two perspectives of macro traffic flow and micro road behavior. Next, the techniques for each class of methods are reviewed and discussed in detail. Finally, we analyze the existing traffic monitoring challenges and corresponding solutions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Temporal expression recognition and normalization (TERN) is the foundation for all higher-level temporal reasoning tasks in natural language processing, such as timeline extraction, so it must be performed well to limit error propagation. Achieving new heights in state-of-the-art performance for TERN in clinical texts requires knowledge of where current systems struggle. In this work, we summarize the results of a detailed error analysis for three top performing state-of-the-art TERN systems that participated in the 2012 i2b2 Clinical Temporal Relation Challenge, and compare our own home-grown system Chrono to identify specific areas in need of improvement. Performance metrics and an error analysis reveal that all systems have reduced performance in normalization of relative temporal expressions, specifically in disambiguating temporal types and in the identification of the correct anchor time. To address the issue of temporal disambiguation we developed and integrated a module into Chrono that utilizes temporally fine-tuned contextual word embeddings to disambiguate relative temporal expressions. Chrono now achieves state-of-the-art performance for temporal disambiguation of relative temporal expressions in clinical text, and is the only TERN system to output dual annotations into both TimeML and SCATE schemes.

Unmanned aerial vehicles (UAVs) are widely applied for purposes of inspection, search, and rescue operations by the virtue of low-cost, large-coverage, real-time, and high-resolution data acquisition capacities. Massive volumes of aerial videos are produced in these processes, in which normal events often account for an overwhelming proportion. It is extremely difficult to localize and extract abnormal events containing potentially valuable information from long video streams manually. Therefore, we are dedicated to developing anomaly detection methods to solve this issue. In this article, we create a new dataset, named Drone-Anomaly, for anomaly detection in aerial videos. This dataset provides 37 training video sequences and 22 testing video sequences from seven different realistic scenes with various anomalous events. There are 87488 color video frames (51635 for training and 35853 for testing) with the size of 640 $\times640$ at 30 frames/s. Based on this dataset, we evaluate existing methods and offer a benchmark for this task. Furthermore, we present a new baseline model, anomaly detection with Transformers (ANDTs), which treats consecutive video frames as a sequence of tubelets, utilizes a Transformer encoder to learn feature representations from the sequence, and leverages a decoder to predict the next frame. Our network models normality in the training phase and identifies an event with unpredictable temporal dynamics as an anomaly in the test phase. Moreover, to comprehensively evaluate the performance of our proposed method, we use not only our Drone-Anomaly dataset but also another dataset. We will make our dataset and code publicly available. A demo video is available at https://youtu.be/ancczYryOBY. We make our dataset and code publicly available (https://gitlab.lrz.de/ai4eo/reasoning/drone-anomaly https://github.com/Jin-Pu/Drone-Anomaly). [ABSTRACT FROM AUTHOR]

Copyright of Praxis Filosófica is the property of Universidad del Valle and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Time words like "yesterday" and "tomorrow" are abstract, and are interpreted relative to the context in which they are produced: the word "tomorrow" refers to a different point in time now than in 24 h. We tested 112 three- to five-year-old English- and Hindi-speaking children on their knowledge of "yesterday" and "tomorrow," which are represented by the same word in Hindi-Urdu: "kal." We found that Hindi learners performed better than English learners when tested on actual past and future events, but that performance for hypothetical events was poor for both groups. Compatible with a "syntactic cues" account, we conclude that syntactic tense information-which is necessary for differentiating "yesterday" from "tomorrow" in Hindi-may play a stronger role in learning the deictic status of these words than mapping of specific words to particular past and future events ("event mapping")., (© 2024 John Wiley & Sons Ltd.)

With the modernization of industry and introduction of IoT, maintenance practices have been moving from reactive to proactive and predictive approaches. The identification of faults often relies on the analysis of real-time data provided by streams and unstructured sources. Ontologies have been applied to the maintenance field in order to add a semantic layer to the data and facilitate interoperability, and combined with other approaches for explainability and fault diagnosis, among others. In such a time-sensitive domain, it is important that ontologies go beyond static representations of the domain and allow not only for the incorporation of time related knowledge, but must also be able to adapt to new knowledge and evolve. This systematic review presents four research questions to provide a general understanding of the state of the art of the representation of time and ontology evolution in the predictive maintenance field. The results have shown that there are several ways of representing the evolution of knowledge that are fairly established and several specific evolutionary actions are discriminated and analyzed. Similarly, there is a diverse group of metrics that can be exploited to measure change and to establish evolutionary trends and even predict future stages of the ontology. Studies on the representation of time show us that it can be done either quantitative or qualitatively, with some approaches combining the two. Applications of these to the problem of ontology evolution are still in the open. Finally, results show that while applications of ontologies to the field of predictive maintenance are plenty, there are not many studies focusing on their evolution or in the effective application of their ability to reason with time constraints. The results obtained in this systematic review are particularly relevant for devising solutions that make use of the ontology's potential for time representation and evolution in the predictive maintenance field. [ABSTRACT FROM AUTHOR]