Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effec...
Reasoning over Temporal Knowledge Graphs (TKGs) aims to forecast future events based on historical ones. Existing approaches typically enforce strict temporal order constraints among past events; however, such rigidity limits the effective exploitati...
Temporal knowledge graphs (TKGs) incorporate temporal information into traditional triplets, enhancing the dynamic representation of real-world events. Temporal knowledge graph reasoning aims to infer unknown quadruples at future timestamps through d...
Temporal knowledge graphs aim to enhance the dynamic and evolutionary representation of knowledge while enabling time-based reasoning. However, the reasoning based on temporal knowledge graphs in real geographic environments suffers from low accuracy...
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...
Video Question Answering (VideoQA) requires a deep understanding of dynamic video content, integrating spatial reasoning, temporal dependencies, and language comprehension. Existing methods often struggle with long or semantically complex videos due...
Predicting future events is crucial for temporal reasoning, providing valuable insights for decision-making across diverse domains. However, the intricate global interactions and temporal–causal relationships at the document level event present...
The modeling, analysis and visualization of dynamic geospatial phenomenahas been identified as a key developmental challenge for next-generation GeographicInformation Systems (GIS). In this context, the envisaged paradigmatic extensions tocontemporar...
The accurate recognition of cattle behaviours is crucial for improving animal welfare and production efficiency in precision livestock farming. However, existing methods pay limited attention to recognising behaviours under occlusion or those involvi...
Path reasoning in knowledge graphs is a pivotal task for uncovering complex relational patterns and facilitating advanced inference processes. It also holds significant potential in domains such as power electronics, where real-time reasoning over dy...
Current generative AI systems, despite extraordinary progress, face fundamental limitations in temporal reasoning, contextual understanding, and ethical decision-making. These systems process information statistically without authentic comprehension...
Nowadays, a noteworthy temporal alteration of traditional hydrological patterns is being observed, producing a higher variability and more unpredictable extreme events worldwide. This is largely due to global warming, which is generating a growing un...
Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships...
Zero-shot learning (ZSL) enables models to recognize categories not encountered during training, which is crucial for categories with limited data. Existing methods overlook efficient temporal modeling in multimodal data. This paper proposes a Tempor...
Temporal knowledge graphs (TKGs) are crucial for modeling evolving real-world facts and are widely applied in event forecasting and risk analysis. However, current TKG reasoning models struggle to separate causal signals from noisy observations, alig...
We present Stratified Metric Temporal Logic (SMTL), a novel formalism for specifying and verifying the properties of complex cyber–physical systems that exhibit behaviors across multiple temporal and abstraction scales. SMTL extends existing te...
A temporal knowledge graph represents temporal information between entities in a multi-relational graph. Its reasoning aims to infer and predict potential links among entities. Predicting time-aware entities is a challenging task due to significant d...
Hosts generating unsolicited network traffic increasingly operate in a coordinated manner rather than in isolation. Scanning and exploitation activities are often distributed across multiple hosts that share common infrastructure, toolchains, and beh...
Temporal reasoning is an important part of the field of time geography and spatio-temporal data science. Recent advances in qualitative temporal reasoning have developed a set of 74 relations that apply between discretized time intervals of at least...
Temporal knowledge graphs (TKGs) effectively represent dynamic facts by incorporating a temporal dimension, yet they frequently encounter data incompleteness issues that constrain downstream applications. Concurrently, TKG prediction tasks, which ena...
Contemporary artificial intelligence excels at pattern recognition but lacks genuine understanding, temporal awareness, and ethical reasoning. Critics argue that AI systems manipulate statistical correlations without grasping concepts, time, or moral...
Deep learning approaches for egocentric video understanding often lack a principled theoretical treatment of stability, particularly when dealing with the sparse, noisy, and temporally ambiguous observations characteristic of first-person imaging. In...
Temporal knowledge graphs (KGs) have recently attracted increasing attention. The temporal KG forecasting task, which plays a crucial role in such applications as event prediction, predicts future links based on historical facts. However, current stu...
Temporal knowledge graphs play an increasingly prominent role in scenarios such as social networks, finance, and smart cities. As such, research on temporal knowledge graphs continues to deepen. In particular, research on temporal knowledge graph rea...
In 1983, B. Moszkowski introduced a first interval-interpreted temporal logic system, the so-called Interval Temporal Logic (ITL), as a system suitable to express mutual relations inside intervals for reasonings about digital circuits. In 1991, Halpe...
There is voluminous literature concerning the scope of topological relations that span various embedding spaces from
The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-...
In the realm of artificial intelligence, knowledge graphs (KGs) serve as an essential structured framework, capturing intricate relationships between diverse entities and supporting a broad spectrum of AI applications. Despite their utility, the stat...
Video scene graph generation (ViDSGG), the creation of video scene graphs that helps in deeper and better visual scene understanding, is a challenging task. Segment-based and sliding-window based methods have been proposed to perform this task. Howev...
Environmentally Friendly Farming Practices (EFFPs) are tools aimed at providing ecosystem services or mitigating the environmental impacts of intensive agriculture. A large literature has explored the factors affecting the adoption of EFFPs by farmer...
Knowledge bases in complex domains must take into account many attributes describing numerous objects that are themselves components of complex objects. Temporal case-based reasoning (TCBR) requires comparing the structural evolution of component obj...
Contemporary artificial intelligence is dominated by generative systems that excel at extracting patterns but fail to grasp meaning, sense, context, and experiential temporality. This limitation highlights the need for new computational wisdom that c...
Strategic evaluation is essential for decision-making under uncertainty. Yet existing qualitative and quantitative methods—including chat-oriented large language model (LLM) evaluations—are difficult to deploy in complex, dynamic environm...
The accurate detection of electrical equipment states and faults is crucial for the reliable operation of such equipment and for maintaining the health of the overall power system. The state of power equipment can be effectively monitored through dee...
Industrial pump systems require real-time fault diagnosis for predictive maintenance, but conventional Chain-of-Thought (COT) reasoning faces computational bottlenecks when processing high-frequency vibration data. This paper proposes Vibration-Aware...
The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance p...
Video question answering (QA) aims to understand the video scene and underlying plot by answering video questions. An algorithm that can competently cope with this task needs to be able to: (1) collect multi-modal information scattered in the video f...
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...
Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have u...
With the continuous improvement of power system intelligence, multimodal data generated during distribution network maintenance have grown exponentially. However, existing power multimodal datasets commonly suffer from issues such as low sample quali...
Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowi...
The concept of sustainability is assumed for this research from a temporal perspective. Rivers represent natural systems with an inherent internal memory on their runoff and, by extension, to their hydrological behavior, that should be identified, ch...
Visual tracking from the unmanned aerial vehicle (UAV) perspective has been at the core of many low-altitude remote sensing applications. Most of the aerial trackers follow “tracking-by-detection” paradigms or their temporal-context-embed...
Smart manufacturing applications increasingly rely on time-series data from Industrial IoT sensors, yet these data streams often contain data quality issues (DQIs) that affect analysis and disrupt production. While traditional Machine Learning method...
Objective: Sepsis is a life-threatening response to infection and a major cause of hospital mortality. Machine learning (ML) models have demonstrated better sepsis prediction performance than integer risk scores but are less widely used in clinical s...
The current international society has entered an era of large-scale power transfer. Government interests have gradually transferred from national strength to national influence. As such, how to quantitatively present the fuzzy geopolitical influence...
In the Internet of Things (IoT), multiple sensors and devices are generating heterogeneous streams of data. To perform meaningful analysis over multiple of these streams, stream processing needs to support expressive reasoning capabilities to infer i...
The task of joint dialogue act recognition (DAR) and sentiment classification (DSC) aims to predict both the act and sentiment labels of each utterance in a dialogue. Existing methods mainly focus on local or global semantic features of the dialogue...
Point-of-interest (POI) recommendation is a crucial task in location-based social networks, especially for enhancing personalized travel experiences in smart tourism. Recently, large language models (LLMs) have demonstrated significant potential in t...
As the proper noun for geographical entities, place names provide an intuitive way to identify and access specific geographic locations, playing a key role in semantic expression and spatial retrieval. However, existing research has insufficiently ex...
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