This paper describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can somet...
Two sentences can be related in many different ways. Distinct tasks in natural language processing aim to identify different semantic relations between sentences. We developed several models for natural language inference and semantic textual similar...
Applied to the problem of automatic program generation, Genetic Programming often produces code bloat, or unexpected solutions that are, according to common belief, difficult to comprehend. To study the comprehensibility of the code produced by Genet...
Automatic intention recognition in financial service scenarios faces challenges such as limited corpus size, high colloquialism, and ambiguous intentions. This paper proposes a hybrid intention recognition framework for financial customer service, wh...
An important problem in machine learning is that, when using more than two labels, it is very difficult to construct and optimize a group of learning functions that are still useful when the prior distribution of instances is changed. To resolve this...
We study whether the semantic category of a hidden display terminal can be inferred from a wall-mediated indirect observation when the display remains outside the camera field of view under a controlled and calibrated scene configuration. This settin...
The semantic segmentation and understanding of remote sensing images applying computer technology has become an important component of monitoring mangrove forests’ ecological changes due to the rapid advancement of remote sensing technology. To...
WiFi Fine Timing Measurement (FTM) directly provides round-trip delay ranging information between a mobile terminal and an access point (AP), serving as a critical technical foundation for the development of low-cost indoor positioning systems. Howev...
Detecting previously unseen malware remains a critical challenge for modern cybersecurity systems due to the rapid evolution of malicious software and the limitations of traditional supervised detection models. This paper proposes a Dual-Channel Mamb...
Current indoor mapping approaches can detect accurate geometric information but are incapable of detecting the room type or dismiss this issue. This work investigates the feasibility of inferring the room type by using grammars based on geometric map...
Our previous study showed that automatically generated attribute grammars were harder to comprehend than manually written attribute grammars, mostly due to unexpected solutions. This study is an internally differentiated replication of the previous e...
Smart cities provide integrated management and operation of urban data emerging within a city, supplying the infrastructure for smart city services and resolving various urban challenges. Nevertheless, cities continue to grapple with substantial issu...
Reliable individual-tree mapping from unmanned aerial vehicle (UAV) orthomosaics requires segmentation and reconstruction across overlapping tiles. This study compares five semantic and instance segmentation pipelines for mapping Araucaria angustifol...
Most existing dialogue understanding methods rely on flat classification paradigms, failing to capture hierarchical semantic structures and cross-level dependencies. To address this limitation, we reformulate dialogue understanding as a hierarchical...
Cloud-based large language model (LLM) services are increasingly used to process natural-language queries that may contain private or sensitive information. Conventional privacy-preserving approaches, such as cryptographic protection and text sanitiz...
Unstructured POI name texts are widely used in fine-grained urban analysis, yet missing labels and semantic ambiguity often limit their value for spatial inference. This study proposes a large language model-based semantic–spatial inference fra...
Facial image generation or generative editing using diffusion models presents risks of identity leakage, potentially enabling unauthorized re-identification in forensic and content moderation applications. Diffusion editing is vulnerable to privacy-p...
In the dynamic landscape of healthcare, decision support systems (DSS) confront continuous challenges, especially in the era of big data. Background: This study extends a Q&A-based medical DSS framework that utilizes semantic technologies for dis...
Recent vision–language models (VLMs) achieve strong performance across multimodal benchmarks but suffer from high inference costs due to the large number of visual tokens. Prior studies have shown that many image tokens receive consistently low...
Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial...
Massive taxi trajectory data can be easily obtained in the era of big data, which is helpful to reveal the spatiotemporal information of human travel behavior but neglects activity semantics. The activity semantics reflect people’s daily activi...
In the domain of intelligent transportation systems, interpreting informative traffic signs requires more than recognizing isolated objects. Our study is dedicated to Traffic Sign Semantic Understanding (TSSU), with the goal of examining diverse traf...
This paper proposes a semantically oriented knowledge-representation method for transforming primary cyber-event data into structured semantic knowledge to support explainable reasoning in cybersecurity decision-making. The core contribution is a tra...
To be able to react adequately a smart environment must be aware of the context and its changes. Modeling the context allows applications to better understand it and to adapt to its changes. In order to do this an appropriate formal representation me...
This study conducts a case study of liǎnhóng (脸红) to examine how inference works in the diachronic change in conversational implicature. It proposes three types of inference, namely ad hoc inference, entrenched inference, an...
This study investigates the temporal evolution of e-commerce research topics from 1996 to 2025 using a reproducible semantic–temporal framework that combines a fixed-seed K = 20 BERTopic specification with adjacent-year Pearson correlation coef...
Friston proposed the Minimum Free Energy Principle (FEP) based on the Variational Bayesian (VB) method. This principle emphasizes that the brain and behavior coordinate with the environment, promoting self-organization. However, it has a theoretical...
Existing indoor semantic recognition schemes are mostly capable of discovering patterns through smartphone sensing, but it is hard to recognize rich enough high-level indoor semantics for map enhancement. In this work we present DeepMap+, an automati...
In complex ranch environments, ReID of cattle faces remains highly challenging due to postmortem appearance degradation, severe occlusion, environmental interference, and the extremely limited number of samples available for each individual. These fa...
The core challenge in video-text retrieval lies in measuring the cross-modal gap between visual and linguistic representations. While mainstream methods leverage deep learning to project video and text features into a shared space for similarity matc...
This paper investigates the nature of adjunct-inclusive interpretations in Japanese, which has long been debated in the literature. Previous studies have disagreed on whether these interpretations arise from V-stranding VP-ellipsis or adjunct ellipsi...
To enhance the security of vehicular software systems, inversely identifying the underlying function types of binary files plays a key role in threat discovery. However, existing function-type inference (FTI) methods can only provide a suboptimal per...
Semantic answer caching reduces inference cost by reusing previous responses but may repeatedly serve incorrect answers. Prior research primarily addresses cache matching, verification, and freshness, whereas this study investigates an error-attribut...
Tracert-retrieval-augmented generation (RAG) is a novel retrieval-augmented framework designed for efficient, document-level multi-hop reasoning. Unlike conventional RAG systems that retrieve top-k text segments based solely on dense similarity, Trac...
Temporal Knowledge Graph Inference (TKGI) is a cornerstone for intelligent decision-making in dynamic scenarios, but existing models face critical bottlenecks, including inadequate complex-context modeling, a lack of entity importance quantification,...
Wildfires are events in which fire spreads uncontrollably, destroying natural ecosystems and causing severe biodiversity loss. Driven by climate change, the frequency and intensity of these events have escalated, making effective risk mitigation a cr...
Efficient retrieval of mathematical and structural similarities in System Dynamics models remains a significant challenge for traditional lexical systems, which often fail to capture the contextual dependencies of simulation processes. This paper pre...
Discretely monitoring traffic systems and tracking payloads on vehicle targets can be challenging when traversal occurs off main roads where overhead traffic cameras are not present. This work proposes a portable roadside vehicle detection system as...
Persistent open-vocabulary navigation enables robots to search for sequential language-specified objects using reusable visual semantic evidence. On edge GPUs, the pipeline is constrained by foundation-model inference, semantic projection, persistent...
Volunteered Geographic Information (VGI) is often collected by non-expert users. This raises concerns about the quality and veracity of such data. There has been much effort to understand and quantify the quality of VGI. Extrinsic measures which comp...
Detecting small insects in high-resolution agricultural light-trap images is difficult because resizing obscures fine morphology and densely distributed insects often appear similar. We developed YOLO26-USW using PVTv2-B1-based UNet-PVT feature enhan...
Aiming at the recognition of intelligent retail dynamic visual container goods, two problems that lead to low recognition accuracy must be addressed; one is the lack of goods features caused by the occlusion of the hand, and the other is the high sim...
Visual perception is a critical task for autonomous driving. Understanding the driving environment in real time can assist a vehicle in driving safely. In this study, we proposed a multi-task learning framework for simultaneous traffic object detecti...
Cameras and IMUs on heavy mining trucks supply the visual signal that Advanced Driver Assistance Systems (ADASs) use in open-pit operations. Haul roads in a surface mine are unstructured and unmarked, so a perception model must be both accurate and f...
Deep-learning compiler transformations can change security-sensitive inference decisions without changing trained model parameters. Critical compilation strictness measures the smallest set of enforceable source/reference-semantic sites whose protect...
Semantic segmentation is increasingly being applied on mobile devices due to advancements in mobile chipsets, particularly in low-power consumption scenarios. However, the lightweight design of mobile devices poses limitations on the receptive field,...
When we compare the influences of two causes on an outcome, if the conclusion from every group is against that from the conflation, we think there is Simpson’s Paradox. The Existing Causal Inference Theory (ECIT) can make the overall conclusion...
Traditional pedestrian routing algorithms typically minimize physical distance or travel time, often overlooking contextual factors that influence route choice in digitally connected environments. As public WiFi infrastructure becomes increasingly pr...
Artificial intelligence presents an optimized alternative by performing problem-solving knowledge and problem-solving processes under specific conditions. This makes it possible to creatively examine various design alternatives under conditions that...
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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