In the era of the popularization of the Internet of Things (IOT), analyzing people’s daily life behavior through the data collected by devices is an important method to mine potential daily requirements. The network method is an important means to an...
The extant event detection models, which rely on dependency parsing, have exhibited commendable efficacy. However, for some long sentences with more words, the results of dependency parsing are more complex, because each word corresponds to a directe...
Biomedical texts are relatively obscure in describing relations between specialized entities, and the automatic extraction of drug–drug or drug–disease relations from massive biomedical texts presents a challenge faced by many researchers...
The task of aspect-based sentiment analysis (ABSA) is to detect the sentiment polarity toward given aspects. Contemporary methods predominantly utilize graph neural networks and incorporate attention mechanisms to dynamically connect aspect terms wit...
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them mere...
Complexity of open source operating systems constantly increase on account of their widespread application. It is increasingly difficult to understand the collaboration between components in the system. Extant research of open source operating system...
Background/Objectives: Motor imagery electroencephalography (MI-EEG) contains weak, non-stationary patterns that evolve across channels and time. Effective decoding requires local sensorimotor structure, trial-wide context, and ordered temporal evolu...
Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene a...
Knowledge base question answering (KBQA) can be divided into two types according to the type of complexity: questions with constraints and questions with multiple hops of relationships. Previous work on knowledge base question answering have mostly f...
Geopolitical risks—including export controls, entity listings, and end-use restrictions—have become a major source of disruptions in semiconductor supply chains. The impact of such disruptions depends not only on the policy trigger itself...
Shadow information technology (SIT) revolves around systems that are hidden but are still managed by the same business entities. It consists of the use of devices, software, systems and applications without the information technology (IT) department&...
Sixth-generation communication networks must be highly dependable due to the foreseen connectivity of critical infrastructures through them. Dependability is a compound metric of four well-known concepts—reliability, availability, safety, and s...
Recurrent neural networks (RNNs) play a pivotal role in natural language processing and computer vision. Long short-term memory (LSTM), as one of the most representative RNNs, is built upon relatively complex architecture with an excessive number of...
Carbon regulation may not reduce total supply chain emissions if buyers shift carbon-intensive activities upstream. Drawing on Resource Dependence Theory (RDT), this study examines how supplier dependence on buyers relates to carbon intensity across...
In a world where the interconnection and interaction between human and artificial agents are continuously increasing, the dynamics of social bonds and dependence networks play a fundamental role. The core of our investigation revolves around the intr...
Real-world systems interact with one another via dependency connectivities. Dependency connectivities make systems less robust because failures may spread iteratively among systems via dependency links. Most previous studies have assumed that two nod...
Extreme opinion propagation in social information networks often appears as a low-frequency but high-impact process, in which abnormal activity becomes synchronized across structurally related users or communities during crisis periods. Conventional...
Wireless sensor networks have been considered as an effective solution to a wide range of applications due to their prominent characteristics concerning information retrieving and distributed processing. When visual information can be also retrieved...
Current two-stage object detectors extract the local visual features of Regions of Interest (RoIs) for object recognition and bounding-box regression. However, only using local visual features will lose global contextual dependencies, which are helpf...
Low-carbon technology innovation serves as the core driver for multiple countries striving to achieve their dual-carbon goals. Therefore, building efficient low-carbon technology innovation networks and accelerating low-carbon technological innovatio...
This study investigates how structural properties of dependence networks and trust-based partner selection jointly shape cooperation in multi-agent societies. We develop a simulation framework grounded in a Block World environment in which agents pur...
Researchers frequently use visualizations such as scatter plots when trying to understand how random variables are related to each other, because a single image represents numerous pieces of information. Dependency measures have been widely used to a...
Variations in microbial community structure and metabolic efficiency are governed in part by oxygen availability, which is a function of water content, diffusion distance, and oxygen demand; for this reason, the volume, connectivity, and geometry of...
The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite,...
Wireless sensor networks are being increasingly used in several application areas, particularly to collect data and monitor physical processes. Non-functional requirements, like reliability, security or availability, are often important and must be a...
Inter-city networks of cultural producers offer one measurable window on urban cultural resilience. Using Wikidata records, with Getty ULAN identifiers for authority control, this study builds birthplace-to-work-location networks for 15,816 painters,...
Aspect-based sentiment analysis is a fine-grained task where the key goal is to predict sentiment polarities of one or more aspects in a given sentence. Currently, graph neural network models built upon dependency trees are widely employed for aspect...
We study the problem of quickly computing point-to-point shortest paths in massive road networks with traffic predictions. Incorporating traffic predictions into routing allows, for example, to avoid commuter traffic congestions. Existing techniques...
Higher-order information brings significant accuracy gains in semantic dependency parsing. However, modeling higher-order information is non-trivial. Graph neural networks (GNNs) have been demonstrated to be an effective tool for encoding higher-orde...
Expressway networks represent evolving complex systems whose topological properties significantly impact regional development. This paper presents a decision support framework for addressing the expressway infrastructure sequencing problem using comp...
With the rapid development of information and automation technology, the manufacturing system is evolving towards more complexity and integration. The system components will inevitably suffer from degeneration, and the impact of component-level failu...
In this paper, we propose a new differentiable neural network with an alignment mechanism for text-dependent speaker verification. Unlike previous works, we do not extract the embedding of an utterance from the global average pooling of the temporal...
We consider a queuing network with single-server nodes and heterogeneous customers. The number of customers, which can obtain service simultaneously, is restricted. Customers that cannot be admitted to the network upon arrival make repeated attempts...
This paper studies the multi-depot joint distribution vehicle routing problem considering energy consumption with time-dependent networks (MDJDVRP-TDN). Aiming at the multi-depot joint distribution vehicle routing problem where the vehicle travel tim...
Optimizing sensor placement for the accurate localization of an uncertain source is crucial for a variety of distributed sensor network applications. To handle the uncertainty of the source locations, objective functions are typically written as an a...
Long-term dependence is an essential feature for the predictability of time series. Estimating the parameter that describes long memory is essential to describing the behavior of time series models. However, most long memory estimation methods assume...
Wireless Sensor Networks (WSNs) are being increasingly adopted in critical applications, where verifying the correct operation of sensor nodes is a major concern. Undesired events may undermine the mission of the WSNs. Hence, their effects need to be...
Aiming at the practical constraints of high resource occupancy and complex calculations in the existing Spike Neural Network (SNN) image classification model, in order to seek a more lightweight and efficient machine vision solution, this paper propo...
This paper proposes a physics-informed neural network (PINN) for predicting the early-age time-dependent behaviors of prestressed concrete beams. The PINN utilizes deep neural networks to learn the time-dependent coupling among the effective prestres...
This research investigates the controllability of Hilfer fractional dynamical systems with state-dependent delays in
The analytic hierarchy process (AHP) has been a widely used method for handling multi-criteria decision-making (MCDM) problems since the 1980s. However, it postulates that criteria are independent and static, which may not always hold true in realist...
Recurrent neural networks (RNNs) can model the time-dependency of time-series data. It has also been widely used in text-dependent speaker verification to extract speaker-and-phrase-discriminant embeddings. As with other neural networks, RNNs are tra...
This paper investigates the synchronization problem for a class of master–slave neural networks with state-dependent impulses. Different from fixed-time impulsive systems, the impulsive instants considered here depend on the current states of t...
Traditional scheduling theory optimizes initial task assignments under static assumptions, yet operational systems face repeated disruptions requiring both immediate rescheduling and long-term structural adaptation. Existing approaches treat each dis...
People have become accustomed to studying or working with the guidance of artificial intelligence (AI) in recent years. Studies have begun investigating the risk factors of AI dependence, though most have used hypothesis-testing methods. The present...
This paper investigates the event-triggered robust dissipative filtering problem for discrete-time networked nonlinear systems subject to dynamic quantization and stochastic cyber attacks. The networked nonlinear system under consideration is represe...
The factors that affect learning efficiency in different environments have been considered in many studies, but multimode mood-consistent learning has not been considered specifically. In this paper, a neural network circuit based on memristors to de...
In the fabrication of ultrathin multilayer ceramic capacitors (MLCCs), the long-term stability of ceramic slurries is a critical yet often overlooked factor that can significantly influence coating uniformity, interfacial adhesion, and process reprod...
To accurately assess the time-dependent reliability of bridge piers in marine environments, this paper proposes a time-dependent reliability evaluation method for bridge piers based on a Dynamic Bayesian Network (DBN). By establishing a resistance de...
Complex network theory has been successfully introduced into the field of software engineering. Many works in the literature have built complex networks in software, usually called software networks, to represent software structure. Such software net...
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