Most papers are machine-translated to English; some are originally in English.
Showing 50 of 116 papers in Computer Science
Social media account credibility assessment is an important component in ensuring the healthy development of online social ecosystems. To address issues such as multi-dimensional credibility assessment indicators for social media accounts and diverse data information uncertainties, a credibility ass…
In fifth-generation mobile communications, network slicing is employed to provide an optimal network for diverse services. In the context of RAN slicing scenarios across multiple base stations, conventional resource allocation methods fail to meet slice requirements when the number of slices varies …
Link prediction is an important problem in the field of data mining. Similarity methods based on random walks generally assume that the probability of a walking particle transferring to neighboring nodes is equal, ignoring the influence of node degree values on the transfer probability. To address t…
Named Data Networking (NDN), as a novel Internet architecture, aims to address the ever-increasing data traffic. Based on its consumer-driven content retrieval model, NDN naturally supports consumer mobility. However, producer mobility remains a challenging problem, requiring additional mechanisms t…
Bullens et al. left an open problem in CSI-Fish, namely designing an identification protocol that allows the system challenge space to be #1;, rather than a small set #1;. This paper proposes a zero-knowledge proof scheme based on supersingular isogenies. The scheme treats the challenge C as an isog…
Existing mobile crowdsensing task recommendation methods share common drawbacks: on one hand, they fail to fully consider the impact of spatio-temporal information on worker preferences, resulting in low recommendation accuracy; on the other hand, they ignore the influence of task popularity on reco…
In the IoT service discovery process, users typically express their requirements through their own intentions, whereas service descriptions constitute explanations of service functionalities; consequently, mismatches between these two elements impact the accuracy of service discovery. Simultaneously…
Due to their limited battery life and computing power, autonomous vehicles struggle to meet the processing demands of latency-sensitive or compute-intensive tasks while ensuring driving range. To address this issue, this paper proposes an autonomous vehicle task offloading strategy based on deep Q-n…
Analyzing student evaluation data for teachers in teaching evaluation systems enables teachers to comprehend students' authentic attitudes toward instructors, summarize teaching experience, improve subsequent teaching methods, and enhance teaching quality. However, during teaching evaluations, issue…
In deploying blockchain-based IoT applications, technical obstacles have emerged, including lack of fine-grained privacy protection, low transaction processing efficiency, high latency, and insufficient flexibility and dynamism. To further advance the popularization and practical deployment of block…
Action Recognition (AR) represents a research hotspot in the field of computer vision, with extensive application prospects in security surveillance, autonomous driving, production safety, and other domains. First, the connotation and extension of action recognition are analyzed, and the technical c…
To further improve the accuracy and speed of optical flow matching in visual SLAM, this paper proposes an improved optical flow matching algorithm that integrates Inertial Measurement Unit (IMU) information to remove motion blur. The algorithm first employs a point spread function computed from IMU …
SM9-IBS is an identity-based signature algorithm industry standard published by China in 2016. Although identity-based signature algorithms reduce the complexity of system management of user public keys, they suffer from the difficult problem of key revocation. Furthermore, the special structure of …
To address the issue that gate reallocation algorithm results struggle to satisfy the operating habits of different operators, a gate reallocation recommendation algorithm that aligns with the operational habits of actual operational staff is proposed. First, a decision environment space model is co…
In response to the current challenges of load uncertainty, renewable energy integration, and "dual carbon" objectives in power systems, this paper establishes a real-time pricing model that incorporates load uncertainty and carbon trading within the smart grid context, while fully considering the we…
To address the issues of the TextRank algorithm ignoring syntactic information and thematic information when extracting document keywords, we propose a document keyword extraction model based on syntactic analysis and thematic distribution. The model extracts document keywords through a two-stage pr…
Integrating knowledge graphs into recommendation systems can leverage the semantic relationships between entities in knowledge graphs to learn user and item representations. Embedding propagation-based methods utilize the graph structure of knowledge graphs to learn relevant features, but as the pro…
To improve text classification accuracy and address the issue of insufficient utilization of node features in text graph convolutional neural networks, a novel text classification model is proposed that intrinsically integrates the advantages of text graph convolution and Stacking ensemble learning …
Currently, research on the Stochastic Duration Distributed Resource-Constrained Multi-Project Scheduling Problem (SDRCMPSP) is scarce, with most studies focusing on static scheduling schemes that cannot adjust and optimize strategies in real-time in response to environmental changes or promptly addr…
With the goal of enhancing the cognitive reasoning capability of recommendation algorithm models and overcoming the current limitation where traditional recommendation algorithms heavily rely on data quality, resulting in constrained performance, we propose an implicit deep collaborative recommendat…
To reduce data storage requirements and transmission overhead in wireless Internet of Things (IoT), a local directed acyclic graph blockchain scheme (LDB) based on directed acyclic graph (DAG) is proposed. This scheme addresses node storage constraints and transmission overhead by enabling wireless …
In industrial applications, fully supervised semantic segmentation for surface imprinted character images incurs high dataset annotation costs for enterprises. To address this issue, we propose a dual-branch feature fusion domain adaptation segmentation method (Dual-branch Feature Fusion Domain Adap…
We propose a multi-description coding image enhancement method based on joint side and central decoding feature learning, which simultaneously considers both side decoding image enhancement and central decoding image enhancement, thereby enabling better network training through joint optimization of…
Aspect-level sentiment analysis aims to determine the sentiment polarity toward specific aspects in reviews; however, few studies have investigated the impact of complex sentences on sentiment classification. Based on this, we propose an aspect-level sentiment analysis model based on BERT and a self…
Based on comment data from Eastmoney.com's Shenzhen Component Index stock forum between January 1, 2018 and December 31, 2019, this study extracts investor sentiment embedded therein using a deep learning BERT model, and employs a TVP-VAR model to investigate the time-varying dynamic relationships a…
This paper presents a comprehensive survey of subword tokenization methods in neural network-based natural language processing. It first elaborates on the out-of-vocabulary (OOV) problem stemming from closed vocabulary limitations in such approaches, and introduces three prevalent methods for addres…
In self-service baggage check-in and sorting for civil aviation, automatically detecting whether pallets are added to self-dropped baggage is an essential function; however, the pallets are largely obscured by the embedded baggage, which poses a challenging problem. To address this issue, a fast det…
The novel view synthesis task refers to generating novel-view images of a scene from multiple reference images. However, in multi-object scenes, inter-object occlusion leads to incomplete acquisition of object information, resulting in artifacts and misalignment issues in the generated novel-view sc…
To address the limitations of conventional salient object detection methods in detecting multiple salient objects at different scales, we propose a salient object detection algorithm based on deep reuse of multi-scale features. The network model consists of vertically stacked bidirectional dense fea…
Deep Reinforcement Learning (DRL) can be widely applied in the field of urban traffic signal control; however, in existing research, the vast majority of DRL agents utilize only the current traffic state for decision-making, resulting in limited control effectiveness under conditions of significant …
Hand-drawn sketch image translation is a challenging research topic in computer vision, with significant application value in artistic design and e-commerce. Currently, GAN-based hand-drawn sketch image translation remains in its infancy. This article analyzes the challenging issues confronting sket…
In mobile crowdsensing systems, tasks exhibit spatiotemporal coverage overlap, which may lead to redundant data collection and consequently cause data redundancy issues. To address this, we propose a task allocation method that can simultaneously control both intra-task and inter-task data redundanc…
To address the challenges of next-generation networks in coverage, deployment cost, and capacity, Mobile Edge Computing (MEC) often necessitates the assistance of relay nodes to execute computation-intensive and delay-sensitive tasks. This paper first introduces the fundamental architecture of relay…
Emerging intelligent transportation systems hold significant promise for improving traffic flow, optimizing fuel efficiency, reducing delays, and enhancing overall driving experience. Currently, traffic congestion constitutes an extremely serious challenge for humanity, particularly severe at urban …
For the Dual-Resource Constrained Flexible Job Shop Scheduling Problem (DRCFJSP), a Cuckoo Algorithm with an improved decoding scheme is proposed to optimize the makespan. Since DRCFJSP requires consideration of both machine allocation and worker processing status, the traditional decoding method is…
In open source communities, the promptness and quality of responses to developers' issues critically determine community vitality. Therefore, identifying and recommending appropriate problem solvers for newly submitted issues facilitates community development. This paper proposes a problem solver re…
Existing short-term demand forecasting models for public bicycles have neglected the nature differences in how various environmental factors influence user demand, as well as the temporal dependencies of variable environmental factors. This paper distinguishes environmental factors into invariant fa…
Max-SAT is the optimization version of the SAT problem, with the objective of finding a variable assignment that maximizes the number of satisfied clauses in a given clause set. This problem is typically NP-hard. With the in-depth development of big data and artificial intelligence, existing algorit…
To address issues such as base station coverage holes and local traffic overload in urban vehicular networks, this paper proposes a dynamic pre-deployment scheme based on vehicle trajectory prediction information. First, to train a unified Seq2Seq-GRU trajectory prediction model, multiple UAVs equip…
To improve the real-time estimation accuracy of human vigilance, a real-time vigilance estimation method based on differential entropy (DE), improved moving average, and bidirectional two-dimensional principal component analysis (TD-2DPCA) is proposed. First, the total frequency band is decomposed i…
Given that edge clouds lack more powerful computational processing capabilities compared to central clouds, they are susceptible to unnecessary scaling jitter or insufficient resource processing capability when handling dynamic workloads. This paper therefore conducts experimental evaluations on mic…
Few-shot relation extraction is a prominent research problem in natural language processing, which aims to train relation extraction models with low-cost annotated data. The currently widely-used prototype networks suffer from issues of inaccurate and incomplete class prototype representation. To ad…
When swarm intelligence heuristic algorithms solve the Discounted {0-1} Knapsack Problem (D{0-1}KP), some repair and optimization strategy is required to transform abnormal encoding individuals into encoding individuals that satisfy the solution constraints, in order to improve solution efficiency a…
Traffic flow prediction constitutes a crucial component of intelligent transportation systems. Due to the complexity of traffic data, long-term and accurate traffic flow prediction has consistently remained one of the most challenging tasks in time series forecasting. In recent years, researchers ha…
Recommendation systems have found extensive applications across diverse domains, profoundly influencing daily life. Training an effective recommendation system typically requires vast amounts of "user-item" interaction data; however, data obtained in practice is often extremely sparse, frequently le…
Slater voting rule is a tournament-based voting rule that primarily constructs an acyclic tournament, finds one with minimal difference from the original tournament, and selects the winner from it. For the NP-hard Slater voting algorithm, a Picat method for optimized solving of the Slater problem ba…
To address the issue of attackers exploiting background knowledge and other information to launch attacks during user location privacy protection, this paper proposes a location privacy protection method for mobile terminals. The scheme leverages k-anonymity and local differential privacy techniques…
Graph Convolutional Networks (GCNs) can extract effective information from graph data through graph convolutions, but they are vulnerable to adversarial attacks that degrade model performance. While adversarial training can enhance neural network robustness, the discrete nature of graph structures a…
To address the issues of insufficient feature extraction for facial expressions and inadequate generalization capability of lightweight networks in complex environments, as well as ambiguous expressions caused by single-label datasets' inability to effectively describe complex emotional tendencies, …
To address the issue that inpainting networks in generative adversarial networks fail to simultaneously preserve global and local consistency of images while incurring substantial computational load, we incorporate the concept of progressive inpainting into an asymmetric U-Net architecture. First, w…
