In the past decade, online Peer-to-Peer (P2P) lending platforms have transformed industry, which has been historically dominated by commercial banks. Information technology breakthroughs such as big data-based financial technologies (Fintech) identified important disruptive driving forces for this paradigm shift. paper, we take an information economics perspective to investigate how data affects transformation of industry. By identifying signaling and search costs are reduced analytics...

Recently, Semantic Change Detection (SCD) has gained growing attention from the Remote Sensing (RS) research community due to its critical role in Earth observation applications. Typical approaches tackle task using a multi-task network, comprising one (CD) sub-task and two Segmentation (SS) sub-tasks. Although these have achieved good performance, crucial question persists: What is effective way handle feature interactions across SCD sub-tasks? To address this issue, paper first offers an...

In this paper, we focus on the problem of context-aware recommendation using tensor factorization. Traditional tensor-based models in scenario only consider user-item-context interactions. argue that rating can't be totally explained by interactions and also influenced combined impact overall mean, user bias, item bias context bias. Based hypothesis, propose a novel model named Bias Tensor Factorization, which take all factors into account. Additionally, traditional recommenders with...

Recent advancements in semantic change detection (SCD) often divide the task into two subtasks: segmentation (SS) and binary (BCD), typically employing three decoding heads optimized jointly based on their respective losses. However, this approach faces challenges aligning feature spaces across temporal domains, complicating construction of features. In paper, we propose novel PRO-HRSCD framework, which utilizes heads: one dedicated to BCD another for SS subtask. The subtasks bi-temporal...

The classification of transmission tower bolt images faces challenges such as class imbalance, sample scarcity, and the low pixel proportion pins. Traditional methods exhibit poor performance in identifying key categories with small proportions, fail to leverage correlation between line fittings bolts, suffer from severe false positive issues. This study proposes a novel approach that dynamically integrates two sampling strategies address imbalance problem while incorporating contrastive...

In the wake of 2008 financial tsunami, existing methods and tools for managing risk have been criticized weaknesses in monitoring alleviating risks at systemic level. A 2009 article Nature suggested new approaches to modeling economic meltdowns are needed prevent future crises. However, studies not focused on analysis individual bank level a banking network, which is essential mitigating contagious failures. To this end, we develop network approach management (NARM) analyzing systems. NARM...

Corrosion and crack defects often exist at the same time in pipelines. The interaction impact between these could potentially affect growth of fatigue crack. In this paper, a propagation method is proposed for pipelines with interacting corrosion defects. finite element models are built to obtain Stress Intensity Factors (SIFs) SIF ratio introduced describe effect on Two approaches based extreme gradient boosting (XGBoost) paper predict deepest point defect Crack size, size axial distance...

With the development and acceleration of urbanization, urban metro traffic is gradually growing up to a large network, structure topology between stations becomes more complex, which makes it increasingly difficult capture spatial dependency. The vertical horizontal interlacing multiple lines distributed topologically, traditional graph convolution implemented on adjacency matrix generated based priori knowledge, cannot reflect actual dependence stations. To address these problems, this...

Purpose The purpose of this paper is to investigate organizational information technology (IT) deployment from a dual decision-making perspective. This study builds on rational choice theory characterize how the costs and values incumbent IT those corresponding new cloud computing influence company's decision discontinuance acceptance. Design/methodology/approach chooses as research context, since it one most well-accepted ITs in current practice. By using survey methodology, data were...

Task scheduling plays a critical role in the performance of edge-cloud collaborative. Whether task is executed cloud and how it scheduled an important issue. On basis satisfying delay, this paper will schedule tasks on edge devices or present algorithm for that need to be transferred based catastrophic genetic (CGA) achieve global optimum. The quantifies total completion time penalty factor as fitness function. By improving roulette selection strategy, optimizing mutation crossover operator,...

Cognitive radio technology enables unauthorized users to use the same spectrum in absence of interference. And sensing for non-authorized perceive availability channel is a very important tool. But perceived need consume large energy, and this part energy can be reduced by holes. The reliable prediction methods, will only idle, which not reduce consumption, but also efficiency increased. In paper, we design neural network model spectrum, simulation, occupancy state predicted.

Person re-identification is an important task in the field of video surveillance that concentrates on identifying same person across different cameras. Some methods cannot learn effective image representations, due to low resolution pedestrian data sets. In this article, we propose a novel Siamese network architecture with layers specially designed address problem re-identification. The proposed work applied edge cloud infrastructure, which can accelerate speed retrieval. Our outputs...