Most papers are machine-translated to English; some are originally in English.
Showing 50 of 116 papers in Computer Science
With the development of financial technology, fintech courses in universities face challenges such as an overemphasis on theory, insufficient practical application, and limited teaching tools, making it difficult for students to master end-to-end investment research operational capabilities. This pa…
Cryptography is the cornerstone of trusted computing. Currently, classical cryptographic systems are facing severe challenges from quantum computing. While post-quantum cryptography (PQC) can resist known quantum attacks, with the continuous development and maturation of quantum computing, the emerg…
In large-scale Internet of Things (IoT) deployments, terminal nodes are often constrained by factors such as power consumption, cost, and installation location, preventing them from accessing the Internet directly and forcing them to rely on nearby devices to forward data. If the forwarding path lac…
The advent of large language models represents a new revolution in information technology, and their application will bring new vitality to various industries. This paper focuses on the application of large models in the maritime field, proposing the application prospects as well as the risks and ch…
This study aims to construct a unified, systematic, and referable knowledge framework for the annotation of art image datasets, addressing the issues of ambiguous definitions and inconsistent results caused by the lack of common standards during the annotation process. To achieve this goal, based on…
We point out that neural networks are not black boxes, and their generalization stems from the ability to dynamically map a dataset to the extrema of the model function. We further prove that the number of extrema in a neural network is positively correlated with the number of its parameters. We the…
Abstract Currently, global cultural competition is becoming increasingly intense. Domestic cultural products face difficulties in "breaking out of the circle," and their "overseas expansion" to gain followers is further challenged by issues such as a lack of local adaptation and cultural discount. A…
Systematically excavating strategies with long-term evolutionary advantages in multi-agent systems constitutes a pivotal challenge in evolutionary game theory, complex systems science, and artificial intelligence research. This paper proposes and implements a unified strategy search framework—AI4Gam…
[Objective] To address the "technical silo" issue in the full drug discovery and development pipeline, where target screening, molecular design, efficacy evaluation, and clinical translation rely on isolated models and lack a lightweight end-to-end integrated framework. [Methods] Based on the AutoGe…
Safety early warning and optimal dispatching of hydropower systems rely on efficient analysis of long-term high-precision time-series monitoring data (such as minute-level dam water levels, inflow rates, etc.). However, existing monitoring modes still primarily rely on manual retrieval, suffering fr…
Brain-computer interfaces (BCIs) establish direct communication channels between the human brain and external devices, enabling not only novel human-computer interaction paradigms but also providing an important technological foundation for innovative clinical treatments. BCI decoding methods can be…
[Abstract] This study proposes a method for constructing a prosodic lexicon for Lhasa Tibetan based on a continuous speech database, applicable to low-resource and complex languages. The prosodic lexicon constructed from a small set of high-quality data (3.77 hours, 2,526 sentences) significantly im…
Named Entity Recognition (NER, NamedEntityRecognition) is a critical component in natural language processing systems, widely applied in tasks such as question answering, information retrieval, and relation extraction. Although NER systems have undergone decades of research and development, named en…
This paper comprehensively surveys the evolutionary trajectory, technical principles, market applications, development trends, and challenges confronting reconfigurable chip technology. With the rapid advancement of information technology, emerging technologies have imposed increasingly stringent de…
Advances in technology and the development of the Internet have enabled the storage and distribution of large-scale unstructured data (such as audio, video, and natural language text). However, any such storage and distribution incurs certain costs, naturally prompting considerations for the efficie…
Fractal image decoding has been effectively accelerated by using the range-averaged image (RAI) as the initial image in decoding process, and only one iteration is needed to obtain the decoded image quality with acceptable quality. To further improve the decoded image quality while maintaining real-…
We point out that neural networks are not black boxes, and their generalization stems from the ability to dynamically map a dataset to the local extrema of the model function. We further prove that the number of local extrema in a neural network is positively correlated with the number of its parame…
This study proposes and validates a novel psycho-judicial attack paradigm termed "Cognitive Trial." Unlike traditional prompt injection, this paradigm is completed through a multi-stage game, exploiting core architectural vulnerabilities in Large Language Models (LLMs) that arise from their pursuit …
Micro-expressions refer to brief facial actions that individuals unconsciously display when attempting to suppress genuine emotions. Owing to their non-invasive nature, they possess significant application value in domains such as national security and public security. To address challenges includin…
Mathematical reasoning presents a significant challenge for Large Language Models (LLMs) as it requires ensuring the correctness of each reasoning step. Researchers have been strengthening the mathematical reasoning abilities of LLMs through supervised fine-tuning, but due to the inability to suppre…
Natural language is considered closely intertwined with human cognition, with linguistic structures posited to offer profound insights into the cognitive system. However, as a coding system, natural language encodes diverse objects into unified forms; its prominent formal features capture people’s a…
With the rapid penetration of artificial intelligence across multiple industries and scenarios, integrating the language understanding advantages of general large models with industry-specific knowledge bases in complex real-world applications has become a key challenge in constructing next-generati…
Objective: With the development of internet big data and machine learning methods, an increasing number of studies combine text analysis and machine learning to predict satisfaction. In research on building life satisfaction prediction models, to address the challenge of obtaining large amounts of e…
This paper analyzes the major challenges currently facing Large Language Models (LLMs) and proposes concrete solutions. It identifies that the representation and computation of probabilistic conceptual structure models is crucial, provides a brief overview of the related technology—Deep Semantic Mod…
OpenAI's Generative Pre-trained Transformer 4 (GPT-4) is a powerful large language model with a certain degree of intelligence in understanding and generating coherent text. We are exploring whether GPT-4 is capable of acting as a die, i.e. generating random numbers. We show that GPT-4 does not appe…
Until now, image quality assessment has not addressed color issues. The literature on image quality assessment primarily evaluates the degree of degradation (deterioration) of image quality during image processing procedures such as compression and transmission. A planar image is essentially a two-d…
To address issues such as low registration efficiency and large errors in the registration of two partially overlapping point clouds, we propose a point cloud registration algorithm based on mixed features sampled from the overlapping region. First, we predict the overlap score for each point throug…
Bone age assessment is a commonly used method for detecting endocrine and growth abnormalities in children; however, low-quality hand X-ray images in deep learning methods reduce the final assessment accuracy. To address this issue, we propose an alignment network that increases the region of intere…
Employing reinforcement learning to address robot manipulation problems offers numerous advantages; however, traditional reinforcement learning algorithms encounter challenges associated with sparse rewards, and the policies obtained are difficult to directly deploy in real-world environments. To en…
To address the problems of ambiguous boundaries between high-value and low-value parts and unreasonable disassembly depth of waste products, a multi-objective disassembly scheme decision-making method for products considering functional degradation of parts is proposed. The influence of specific par…
In recent years, the application of unmanned aerial vehicles (UAVs) has become increasingly widespread, and the coordination of multiple UAVs to accomplish tasks has substantially improved operational efficiency. Motivated by this phenomenon, numerous scholars have devoted themselves to research on …
To resist quantum algorithm attacks and address the vulnerability where malicious signers exploit the complete anonymity of ring signature technology to output multiple signatures for double-spending attacks, while simultaneously solving the problem of unnecessary system overhead waste, a novel latt…
To address the issues of high latency in existing low-orbit satellite network authentication schemes that utilize centralized authentication methods and significant computational overhead arising from complex bilinear mappings, we introduce a certificateless authentication model and propose an effic…
Traditional ciphertext retrieval schemes in cloud environments generate document vectors and query vectors based on statistical models, without considering the deep semantic information of documents and requests. This paper proposes a deep semantic ciphertext retrieval model based on a hybrid cloud …
To address the problems of low delivery timeliness and poor customer value in the logistics industry resulting from increasing vehicle ownership and escalating traffic congestion, this study proposes a time-varying routing optimization method for truck-drone collaborative delivery, comprehensively c…
Synthetic Minority Over-sampling Technique (SMOTE) is one of the effective methods for addressing class imbalance problems. However, SMOTE's linear interpolation mechanism restricts synthetic samples to the line connecting original samples, resulting in a lack of diversity in new samples, and this l…
To address the fresh food closed-loop supply chain network design problem, a robust optimization model for fresh food closed-loop supply chain networks is established to resolve uncertainties in the supply chain network. First, for a fresh food supply chain network structure encompassing 5 nodes, a …
To enhance the solution efficiency for the Multi-Depot Vehicle Routing Problem (MDVRP), we propose an end-to-end deep reinforcement learning framework. First, MDVRP is formulated as a Markov Decision Process (MDP), encompassing definitions of state, action, and reward. Additionally, we propose an im…
Text representation needs to address the ambiguity of textual terms and accurately define their semantic features within specific contextual environments. To tackle the problems of word polysemy and contextual characteristics, we propose an SCDVAB model for text semantic disambiguation. The main inn…
To address the issues that the Raft algorithm cannot resist attacks from Byzantine nodes and that logs are susceptible to tampering and forgery, we design an RB-Raft (Resist Byzantine-Raft) algorithm that resists Byzantine nodes. First, we employ a hash chain approach to perform iterative hashing on…
To address the issues of time-consuming training, large memory consumption, and difficulty in model updating associated with traditional offline hashing algorithms, as well as the phenomenon of substantial label loss in real-world image datasets, we propose an online hashing algorithm for balanced l…
To investigate the effects of hazard factors such as temperature, smoke, and CO concentration on evacuation in fire scenarios, a fire evacuation model based on dynamic coupling of FDS and cellular automata is established. The model establishes a one-to-one correspondence between FDS grids and cellul…
To achieve accurate classification of breast pathology WSI images, we propose a gated convolutional neural network classification method based on hybrid connections. A hybrid module incorporating local residual connections and global dense connections is constructed, with squeeze-and-excitation gate…
Complex event processing is a technique for analyzing event streams in dynamic environments. Typically implemented based on finite state automata, complex event processing techniques generate a large number of overlapping partial matches on the event stream during the matching process, requiring the…
A bi-level programming model is established to address the actual battery swapping characteristics of automated guided vehicles (AGVs) in automated container terminals, aiming to reduce the total task completion time and total battery swapping time of AGVs while rationally planning the number of bat…
To address the issues of slow convergence and susceptibility to local optima in existing ant colony optimization-based algorithms for solving distributed constraint optimization problems, this paper proposes a Random disturbance based multi-population ant colony algorithm to solve distributed constr…
To address the suboptimal segmentation performance of existing visible-infrared (RGB-T) image semantic segmentation models, this paper proposes a nested segmentation network based on deep difference feature complementary fusion. Specifically, the encoder and decoder components are interconnected via…
Mobile crowdsensing data contains image and spatio-temporal contextual information that can be utilized for detecting changes in street view images; however, such data is typically low-quality and non-standard. To accurately detect changes in street scenes, this paper primarily addresses the data qu…
To address the deficiencies of the School-Based Optimization (SBO) algorithm, including poor search performance and susceptibility to local optima, this paper proposes an SBO algorithm integrated with educational psychology (SBO based on Educational Psychology, SBO-EP). During the teaching phase, th…
To improve the accuracy of overlapping community detection, we propose CLPANNI (Cycle Label Propagation Algorithm with Neighbor Node Influence), an optimized algorithm based on cycle structures derived from LPANNI. The algorithm extracts minimal cycle information of nodes, measures node importance u…
