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Works385 from public data
- Sentiment analysis using deep learning approaches: an overview186
This paper reviews deep learning approaches that have been applied to various sentiment analysis tasks and their trends of development, and provides the performance analysis of different deep learning models on a particular dataset at the end of each sentiment analysis task.
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- High-Throughput Calculations for High-Entropy Alloys: A Brief Review135
Four different calculation methods that are usually applied to accelerate the development of novel HEA compositions are presented, that is, empirical models, first-principles calculations, calculation of phase diagrams (CALPHAD), and machine learning.
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- Efficient multi-keyword ranked query over encrypted data in cloud computing116
This paper proposes a flexible multi-keyword query scheme, called MKQE, which greatly reduces the maintenance overhead during the keyword dictionary expansion and takes keyword weights and user access history into consideration when generating the query result.
- The Security and Privacy of Mobile-Edge Computing: An Artificial Intelligence Perspective99
This article comprehensively provides a survey of security and privacy in MEC from the perspective of AI, using European Telecommunications Standards Institute MEC reference architecture as a framework while merging the software-defined network (SDN) and network function virtualization (NFV) to better illustrate a serviceable platform of MEC.
- Hierarchical Local-Global Transformer for Temporal Sentence Grounding98
A novel Hierarchical Local-Global Transformer (HLGT) is proposed to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities for learning more fine-grained multi-modal representations.
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- Secure sensitive data sharing on a big data platform98
A proxy re-encryption algorithm based on heterogeneous ciphertext transformation and a user process protection method based on a virtual machine monitor are presented, which provides support for the realization of system functions and protects the security of users' sensitive data effectively and shares these data safely.
- Trajectory planning for UAV navigation in dynamic environments with matrix alignment Dijkstra80
A new forward exploration and backward navigation algorithm, matrix alignment Dijkstra (MAD), to pilot UAVs, which can achieve UAV path planning from one source cell to multiple target cells within one single run and can be accelerated on a GPU.
- Incremental K-clique clustering in dynamic social networks77
Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.
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- Blockchain-based verification framework for data integrity in edge-cloud storage71
This paper proposes a blockchain-based framework without TPA for data integrity verification in a decentralized edge-cloud storage (ECS) scenario and proposes sampling verification and develops rational sampling strategies to make sampling verification more effective.
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- A MapReduce task scheduling algorithm for deadline constraints69
An extensional MapReduce Task Scheduling algorithm for Deadline constraints in Hadoop platform: MTSD is proposed which can improved data locality observably to compare with default scheduler and it also can improve other scheduler’s locality.
- Optimization scheme of wind energy prediction based on artificial intelligence60
By improving the mode number selection in the variational mode decomposition, the characteristics of wind speed data can be better extracted and will help to promote the development of wind energy prediction field, help wind farms formulate wind power regulation strategies, and further promote the construction of green energy structure.
- EADP: An extended adaptive density peaks clustering for overlapping community detection in social networks59
An extended adaptive density peaks clustering for overlapping community detection, called EADP, which takes weights into consideration and incorporates a novel distance function based on common nodes to measure the distance between nodes.
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- Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey53
A large-scale benchmark is established to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches and the important challenges and future research directions in NCCL are discussed.
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- Not All Inputs Are Valid: Towards Open-Set Video Moment Retrieval using Language49
This paper proposes a novel model OpenVMR, which first distinguishes ID and OOD queries based on the normalizing flow technology, and then conducts moment retrieval based on ID queries, and first distinguishes ID and OOD queries based on the normalizing flow technology, and then conducts moment retrieval based on ID queries.
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- Role mining based on weights43
A weighted role mining algorithm to generate roles based on weights to reflect their importance to the system and introduces the concept of similarity between both users and permissions, and uses a similarity matrix to reinforce the similarity between permissions.
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- FedNLR: Federated Learning with Neuron-wise Learning Rates40
This paper proposes a novel and simple algorithm called FedNLR, which utilizes Neuron-wise Learning Rates during the FL local training process to enhance the learning of neurons bound to local classes on local data knowledge while reducing the decay of non-local classes knowledge stored in neurons.
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- A novel hybrid method to analyze security vulnerabilities in Android applications37
This paper designs dynamic executable scripts that record and perform manual operations to customize the execution path of the target application and shows that they can replace most manual operations, simplify the analysis process, and further verify the corresponding security vulnerabilities.
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- Personalized Federated Recommendation for Cold-Start Users via Adaptive Knowledge Fusion35
This work takes a dive into the federated recommendation for cold-start users, a novel scenario where the new participating users can directly obtain a promising recommendation without comprehensive training with all participating users by leveraging both transferred knowledge from the converged warm clients and the knowledge learned from the local data.
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- OSP28
A new synchronization parallel mechanism named overlap synchronization parallel (OSP), in which the waiting time is removed by conducting computation and communication in an overlapped manner, and it is theoretically proved that this mechanism could achieve the same convergence rate compared to the sequential SGD for non-convex problems.
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- Heterogeneity-aware Gradient Coding for Straggler Tolerance26
A novel heterogeneity-aware gradient coding scheme which can not only tolerate a predetermined number of stragglers but also fully utilize the computation capabilities of heterogenous workers is proposed.
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- Stock Price Prediction Using Time Convolution Long Short-Term Memory Network24
A novel deep learning model named Time Convolution Long Short-Term Memory (TC-LSTM) networks is proposed to learn long-term dependencies of stock prices and outperforms other three baseline models in the mean square error.
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- Bulk and grain boundary tracer diffusion in multiphase AlCoCrFeNiTi0.2 compositionally complex alloy22
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- UNGER: Generative Recommendation with A Unified Code via Semantic and Collaborative Integration20
A novel method is proposed, named UNGER, which integrates semantic and collaborative knowledge into a UNified code for Generative Recommendation, and adaptively learn an integrated embedding through the joint optimization of cross-modality knowledge alignment and next-item prediction tasks.
- FedBAT: Communication-Efficient Federated Learning via Learnable Binarization20
This work proposes Federated Binarization-Aware Training (FedBAT), a novel framework that directly learns binary model updates during the local training process, thus inherently reducing the approximation errors.
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- Decoupled Rationalization with Asymmetric Learning Rates: A Flexible Lipschitz Restraint19
The main idea of DR is to decouple the generator and predictor to allocate them with asymmetric learning rates, which can naturally and flexibly restrain the Lipschitz constant of the predictor, to address the problem of degeneration.
- Enhancing the Rationale-Input Alignment for Self-explaining Rationalization18
A novel approach called DAR (Discriminatively Aligned Rationalization), which utilizes an auxiliary module pretrained on the full input to discriminatively align the selected rationale and the original input, thereby overcoming the rationale shift problem.
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- Masked Random Noise for Communication-Efficient Federated Learning13
This paper proposes Federated Masked Random Noise (FedMRN), a novel framework that enables clients to learn a 1-bit mask for each model parameter and apply masked random noise to represent model updates to enhance communication efficiency.
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- The Panaceas for Improving Low-Rank Decomposition in Communication-Efficient Federated Learning10
This paper focuses on three key issues related to decomposition in FL: what to decompose, how to decompose, and how to aggregate, and introduces three novel techniques: Model Update Decomposition (MUD), Block-wise Kronecker Decomposition (BKD), and Aggregation-Aware Decomposition (AAD).
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- BoRA: Towards More Expressive Low-Rank Adaptation with Block Diversity8
Block Diversified Low-Rank Adaptation is proposed, which improves the rank of LoRA weights with a small number of additional parameters and introduces a unique diagonal matrix for each block multiplication to enhance the diversity of different block products.
- Adversarial Cooperative Rationalization: The Risk of Spurious Correlations in Even Clean Datasets8
This study investigates the self-rationalization framework constructed with a cooperative game, where a generator initially extracts the most informative segment from raw input, and a subsequent predictor utilizes the selected subset for its input, and introduces an instruction to prevent the predictor from learning the correlations.
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- Is Model Editing Built on Sand? Revealing Its Illusory Success and Fragile Foundation7
Empirical evidence shows that editing is likely to be based on shortcuts rather than full semantics, calling for an urgent reconsideration of the very basis of model editing before further advancements can be meaningfully pursued.
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- Mixed-Precision Embeddings for Large-Scale Recommendation Models7
This paper proposes a novel embedding compression method, termed Mixed-Precision Embeddings (MPE), which first group features by frequency and then search precision for each feature group, to reduce the size of the search space.
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- Privacy-Friendly Cross-Domain Recommendation via Distilling User-irrelevant Information6
These prototypes facilitate bridging the domain shift between distinct source and target domains in a privacy-friendly manner and enable the identification of top-k users in the target domain to substitute for cold-start users prediction.
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- Exploring Practical Gaps in Using Cross Entropy to Implement Maximum Mutual Information Criterion for Rationalization6
A simple yet effective method that introduces random vicinal1 perturbations to the selected rationale candidates and broadens the predictor’s assessment to a vicinity around the selected rationale candidate.
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- Rehearsal-Free Continual Federated Learning with Synergistic Synaptic Intelligence6
A simple yet effective regularization algorithm for CFL named FedSSI is proposed, which tailors the synaptic intelligence for the CFL with heterogeneous data settings and can not only reduce computational overhead without rehearsal but also address the data heterogeneity issue.
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- Fine-Grained Text Sentiment Transfer via Dependency Parsing3
A novel model that learns the latent representation without disentanglement and leverages sentiment intensity as input to decoder and leverages sentiment intensity as input to decoder for fine-grained sentiment control is proposed.
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- SNEAK: Synonymous Sentences-Aware Adversarial Attack on Natural Language Video Localization2
A new adversarial attack paradigm called synonymous sentences-aware adversarial attack on NLVL (SNEAK), which captures the cross-modality interplay between the vision and language sides is proposed, which captures the cross-modality interplay between the vision and language sides.
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- FedRE: Robust and Effective Federated Learning with Privacy Preference2
The PSI within data is studied and the PSI within data is defined with regard to the privacy preferences of each client, and FedRE is developed, that can simultaneously achieve robustness and effectiveness benefits with LDP protection.
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- Hypernetwork-driven centralized contrastive learning for federated graph classification2
This study introduces a novel hypernetwork-based method, termed CCL (Centralized Contrastive Learning), which is a server-centric innovation that effectively addresses the challenges posed by traditional client-centric approaches in heterogeneous datasets.
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- Efficient Knowledge Transfer in Federated Recommendation for Joint Venture Ecosystem1
This paper proposes an efficient and privacy-enhanced Federated Recommendation for the Joint Venture Ecosystem (FR-JVE) that each client transfers more common knowledge from other clients with a distilled user’s rating preference from the local dataset.
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- FedGIG: Graph Inversion from Gradient in Federated Learning1
This paper first explores GIA's impact on Federated Graph Learning (FGL) and introduces Graph Inversion from Gradient in Federated Learning (FedGIG), a novel GIA method specifically designed for graph-structured data.
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- Imbalance Rectification in Deep Logistic Regression for Multi-Label Image Classification Using Random Noise Samples1
This work finds that feeding randomly generated noise samples into an LR classifier is an effective way to detect class imbalances, and defines an informative imbalance metric named inference tendency based on noise sample analysis.
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- SimPattern: A Novel Byte-Pattern Matching based Chunk Similarity Detection for Post-Deduplication–
SimPattern is a similarity detection method motivated by the characteristics of delta compression that enables more fine-grained similarity detection and substantially improves the effectiveness of delta compression, reducing the final storage size by up to 42.68% compared with state-of-the-art methods.
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- Take a Further Step Beyond Data Augmentation: Augmented Knowledge Purification for Federated Recommendation–
This paper proposes FR-AKP, which performs the augmented knowledge purification on the basis of data augmentation at both the intra-client and inter-client levels, and significantly outperforms state-of-the-art methods.
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- ChatbotID: Identifying Chatbots with Granger Causality Test–
This work proposes an innovative approach named ChatbotID, which applies the Granger Causality Test (GCT) to extract a novel set of interactional features that capture the evolving, predictive relationships between conversational attributes, and significantly enhances the model’s ability to capture asymmetric influence in H-C dialogues.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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