Analyzing nationwide electricity consumption is challenged by data volume and regional heterogeneity. This study proposes a deterministic non-Euclidean representation framework using symmetric relative deviation. By transforming raw consumption into...
Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based mode...
Using the information geometry approach, we determine the volume of the set of two-qubit states with maximally disordered subsystems. Particular attention is devoted to the behavior of the volume of sub-manifolds of separable and entangled states wit...
We present a new method of self-supervised learning and knowledge distillation based on multi-views and multi-representations (MV–MR). MV–MR is based on the maximization of dependence between learnable embeddings from augmented and non-au...
In this paper we introduce a systemic approach for predictive and prescriptive maintenance framed within the larger system of systems, as exemplified by a use case in mining. Developments are presented as systematic, while there is a focus on improve...
There is an increasing demand for robotic manipulators to perform more complex and versatile tasks. In order to fulfill this need, expeditious calibration and estimation techniques are required as a first step for the correct usage of the manipulator...
Fundus is the only structure that can be observed without trauma to the human body. By analyzing color fundus images, the diagnosis basis for various diseases can be obtained. Recently, fundus image segmentation has witnessed vast progress with the d...
One might perceive the Middle Ages as an era of certain rights and privileges. Social stratification or the conformation of a group’s identity were all established around privileges in the Kingdom of Hungary. In the medieval period, as opposed...
Obtaining meaning-rich representations of social media inputs, such as Tweets (unstructured and noisy text), from general-purpose pre-trained language models has become challenging, as these inputs typically deviate from mainstream English usage. The...
This paper proposes a continuous-time physics-based framework for smartphone battery state-of-charge (SOC) and time-to-empty (TTE) prediction using multi-source sensing and device operating information. A first-order RC equivalent circuit representat...
Text vectorization is the basic work of natural language processing tasks. High-quality vector representation with rich feature information can guarantee the quality of entity recognition and other downstream tasks in the field of traditional Chinese...
Contemporary research on electric vehicle (EV) consumption is predominantly focused on the vehicle’s powertrain and battery technology. However, the analyses indicate that the actual state of the various electrical subsystems in the vehicle can...
In this paper we present a novel composite methodology for obtaining spatial projections of the impacts and opportunities arising from the integration of plug-in electric vehicles with future smart electricity grids. We link models of future plug-in...
Software quantum simulators are essential tools for designing and testing quantum algorithms on classical computing architectures, especially given the current limitations of physical quantum hardware. This work focuses on studying and evaluating mem...
Deep neural networks, despite their remarkable success in computer vision tasks, often face deployment challenges due to high computational demands and memory usage. Addressing this, we introduce a probabilistic framework for automated model compress...
Developing a high-quality, real-time, dense visual SLAM system poses a significant challenge in the field of computer vision. NeRF introduces neural implicit representation, marking a notable advancement in visual SLAM research. However, existing neu...
Personalized location recommendations aim to recommend places that users want to visit, which can save their decision-making time in daily life. However, the recommending task faces a serious data sparsity problem because users have only visited a sm...
In recent years, graph-based learning methods have gained significant traction in point-of-interest (POI) recommendation systems due to their strong generalization capabilities. These approaches commonly transform user check-in records into graph-str...
Predicting the futures from previous spatiotemporal data remains a challenging topic. There have been many previous works on predictive learning. However, mainstream models suffer from huge memory usage or the gradient vanishing problem. Enlightened...
Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation. Graph Convolutional Neural Networks (GCNs), as a representative m...
This paper thoroughly reviews the current state of digital simulation technology and its strategic implementation across applications, primarily focusing on advanced compression garments. Despite the significant impact of three-dimensional digital gar...
Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single...
Graph query languages such as Cypher are widely adopted to match and retrieve data in a graph representation, due to their ability to retrieve and transform information. Even though the most natural way to match and transform information is through r...
Quantum image representation (QIR) is the basic idea behind quantum image processing. It explains how a normal image is converted into quantum states so that it can be processed using quantum computers. The commonly used models for QIR are Flexible R...
Simultaneous localization and mapping (SLAM) is one of the cornerstones of autonomous navigation systems in robotics and the automotive industry. Visual SLAM (V-SLAM), which relies on image features, such as keypoints and descriptors to estimate the...
Considering the case of a continual bundle of controlled dynamic processes, the authors have studied the functional-geometric conditions of existence of non-stationary coefficients-operators from the differential realization of this bundle in the cla...
Modern massively-parallel Graphics Processing Units (GPUs) and Machine Learning (ML) frameworks enable neural network implementations of unprecedented performance and sophistication. However, state-of-the-art GPU hardware platforms are extremely powe...
For safe UAV navigation and to avoid collision, it is essential to have accurate and real-time perception of the environment surrounding the UAV, such as free area detection and recognition of dynamic and static obstacles. The perception system of th...
This experimental study defines the usage of a computer-aided surgical simulation process that is effective, safe, user-friendly, and low-cost, that achieves a detailed and realistic representation of the anatomical region of interest. The chosen too...
Point cloud is a versatile geometric representation that could be applied in computer vision tasks. On account of the disorder of point cloud, it is challenging to design a deep neural network used in point cloud analysis. Furthermore, most existing...
Multimodal Emotion Recognition in Conversation (ERC) has attracted attention as a key technology in human–computer interaction, mental healthcare, and intelligent services. However, deploying ERC in real-world settings remains challenging due t...
Non-Intrusive Load Monitoring (NILM) aims to disaggregate a household’s total aggregated power consumption into appliance-level usage, enabling intelligent energy management without the need for intrusive metering. While deep learning has impro...
Government agencies, energy consumers, and other societal groups have all shown concern and attention for the energy management of buildings. Relevant statistical data, however, indicate that most public buildings continue to consume large amounts of...
The meeting between Natural Language Processing (NLP) and Quantum Computing has been very successful in recent years, leading to the development of several approaches of the so-called Quantum Natural Language Processing (QNLP). This is a hybrid field...
Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural as...
BDS (Beidou Navigation Satellite System) integrates the legacy PNT (Positioning, Navigation, Timing) service and the authorized SBAS (Satellite-Based Augmentation Services) service. To support the requirement of decimeter-level positioning, four type...
A large and ever increasing quantity of high throughput sequencing (HTS) data is stored in FASTQ files. Various methods for data compression are used to mitigate the storage and transmission costs, from the still prevalent general purpose Gzip to sta...
This paper presents the visualization of national budget, a tool based on Semantic Web technologies that shows by graphic representations the Chilean budget law published annually, and their execution by each state agency. We describe the processes f...
Techniques to provide in vitro tissue culture have undergone significant changes during the last decades, and current applications involve interactions of cells and organoids, three-dimensional cell co-cultures, and organ/body-on-chip tools. Efficien...
The surface mesh reconstruction from point clouds has been a fundamental research topic in Computer Vision and Computer Graphics. Recently, the Neural Implicit Representation (NIR)-based reconstruction has revolutionized this research topic. This wor...
The COVID-19 pandemic necessitated significant changes in foreign language education, forcing teachers to reconstruct their identities and redefine their roles as language educators. To better understand these adaptations and perspectives, it is cruc...
Resource-constrained edge devices require efficient and adaptive health monitoring to ensure reliable operation under dynamic workloads. This paper proposes an LLM-driven, context-aware framework that integrates real-time system metrics, historical d...
Wheeled bipedal robots are promising for industrial mobility because they combine tight turning, agile balancing, and efficient rolling. Their inherently unstable and underactuated dynamics make reliable reference tracking challenging, particularly i...
Accurate forecasting of container resource usage is crucial for efficient resource scheduling and ensuring Quality of Service (QoS) in cloud data centers. The inherent complexity of container workloads, characterized by strong temporal dependencies,...
This paper proposes a novel quantum image transmission framework to address the limitations of existing single-qubit time domain systems, which struggle with noise resilience and scalability. The framework integrates frequency domain processing with...
The elastodynamic stress field near a crack tip propagating at a constant speed in isotropic quasi-brittle material was investigated, taking into account the strain gradient and inertia gradient effects. An asymptotic solution for a steady-state Mode...
Background: Sex and gender are vitally important in the study of epigenetic mechanisms for various types of cancer. However, little has been done to assess the state of sex and gender-based analyses (SGBA) in this field. The aim was to undertake a cr...
Concept drift can degrade encrypted-traffic classifiers deployed at the network edge as applications, protocols, and usage patterns evolve. This paper formulates federated continual learning under asynchronous real- and virtual drift and proposes Dri...
Extracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cyb...
The concept of a mission is important to system design and development, especially in system of systems (SoS) engineering. However, the diverse usage of the term ’mission’ across disciplines often results in ambiguity regarding its role i...
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