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- Untangling Blockchain: A Data Processing View of Blockchain Systems1,186
This paper conducts a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric, and discusses several research directions for bringing blockchain performance closer to the realm of databases.
- BLOCKBENCH: A Framework for Analyzing Private Blockchains933
BLOCKBENCH is described, the first evaluation framework for analyzing private blockchains and it serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices, and is released for public use.
- Social contextual recommendation317
This paper investigates social recommendation on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence, and proposes a novel probabilistic matrix factorization method to fuse them in latent spaces.
- FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting180
This work proposes FuseFormer, a Transformer model designed for video inpainting via fine-grained feature fusion based on novel Soft Split and Soft Composition operations, which surpasses state-of-the-art methods in both quantitative and qualitative evaluations.
- DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network95
This paper proposes a novel DivCo framework to properly constrain both "positive" and "negative" relations between the generated images specified in the latent space and introduces a novel latent-augmented contrastive loss, which encourages images generated from adjacent latent codes to be similar and those generated from distinct latent code to be dissimilar.
- HyperNews: Simultaneous News Recommendation and Active-Time Prediction via a Double-Task Deep Neural Network68
This paper proposes a novel approach dubbed HyperNews which explicitly models the effect of timeliness on news recommendation and introduces an auxiliary task of predicting the so-called "active-time" that users spend on each news article.
- Discrete Content-aware Matrix Factorization66
A Discrete Content-aware Matrix Factorization (DCMF) model is proposed to derive compact yet informative binary codes at the presence of user/item content information and an efficient discrete optimization algorithm for parameter learning is developed.
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- Using Twitter to Predict When Vulnerabilities will be Exploited53
This paper proposes a novel framework to predict when a vulnerability will be exploited via Twitter discussion, without using CVSS score information, and introduces the unique concept of a family of CVE-Author-Tweet (CAT) graphs and builds a novel set of features based on such graphs.
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- StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching50
The proposed end-to-end training framework introduces two novel losses, i.e., bidirectional multi-scale feature re-projection loss and correlation consistency loss, to help translate all synthetic stereo images into realistic ones as well as maintain epipolar constraints.
- Conditional Adversarial Generative Flow for Controllable Image Synthesis49
This paper proposes a novel flow-based generative model named conditional adversarial generative flow (CAGlow), which can synthesize images with conditional information like categories, attributes, and even some unknown properties and ensures the independence of different conditions and outperforms regular Glow to a significant extent.
- Discrete Factorization Machines for Fast Feature-based Recommendation47
A convergent updating rule is proposed that resolves the challenging discrete optimization of DFM and shows that DFM consistently outperforms state-of-the-art binarized recommendation models, and DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss.
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- Real-Time Anomaly Detection in Edge Streams37
Algorithms proposed have the following properties: they detects microcluster anomalies while providing theoretical guarantees about the false positive probability; they are online, thus processing each edge in constant time and constant memory, and also processes the data orders- of-magnitude faster than state-of-the-art approaches.
- Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU37
Evaluation results indicate that nearly all GPU-accelerated applications are vulnerable to such attacks, and adversaries can launch attacks without requiring any special privileges both on traditional multi-user operating systems, and emerging cloud computing scenarios.
- A hierarchical similarity based job recommendation service framework for university students35
This research proposes a student profiling based re-ranking framework that recommends a list of potential jobs based on those who have graduated and obtained job offers over the past few years, and shows this method’s potential.
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- GHPO: Adaptive Guidance for Stable and Efficient LLM Reinforcement Learning34
The Guided Hybrid Policy Optimization (GHPO), a novel difficulty-aware reinforcement learning framework, is introduced, which significantly enhances both training stability and final reasoning performance, thus offering a scalable and efficient solution for developing powerful and robust reasoning models.
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- Privacy-Preserving Cloud Auditing for Multiple Users Scheme With Authorization and Traceability21
This work uses certificateless signature technology to construct a privacy-preserving cloud auditing scheme for multiple users with authorization and traceability and introduces an identity authentication process between the third-party auditor (TPA) and the CSP to prevent the denial of service attack.
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- Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native17
An AI-native computing paradigm that harnesses the power of both cloud-native technologies and advanced machine learning runtime is described, which aims to optimize costs-of-goods-sold (COGS) and improve resource accessibility.
- DAX17
DAX relies on multi-master replication and Dynamo-style flexible consistency, which enables it to run in multiple data centers and hence be disaster tolerant, and provides high availability and disaster tolerance for the DBMS storage tier.
- BayesGaze: A Bayesian Approach to Eye-Gaze Based Target Selection16
BayesGaze, a Bayesian approach of determining the selected target given an eye-gaze trajectory that improves target selection accuracy and speed over a dwell-based selection method, and the Center of Gravity Mapping (CM) method.
- Predicting Bugs’ Components via Mining Bug Reports15
This work digs out the components of 28,829 bugs in Eclipse bug project that have been specified wrongly and modified at least once and builds classifiers based on support vector machines and Na\"ive Bayes to predict the component of a new bug.
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- PCAM: A Data-driven Probabilistic Cyber-alert Management Framework11
Using statistics derived from 44 days of real-world alert data, PCAM is able to minimize the expected number of true alerts that are not manually examined by a team consisting of junior, senior, and principal analysts and is robust to various kinds of errors in the statistics used.
- ASFNet: Adaptive multiscale segmentation fusion network for real‐time semantic segmentation10
This article designs an adaptive multiscale segmentation fusion module based on an attention mechanism that achieves 70.9% mIoU of accuracy in the Cityspace test set, processing images at 61 FPS when the input is 1024 × 2048.
- Image Stitching Method of Bone Stick Fragment Based on Similarity Freeman Code Matching9
The experiment results show that the algorithm proposed in this paper can effectively assist the stitching work of bone stick cultural relics.
- Employer Oriented Recruitment Recommender Service for University Students8
In this research, a student file based employer oriented job recommendation framework is proposed, which is firstly modelled by the personal features and also academic features and which has shown its potential in real applications.
- A Space-bit-plane Scrambling Algorithm for Image Based on Chaos8
The simulation results and the performance analysis show that the proposed scrambling algorithm has large secret-key space, high security, fast scrambling speed and strong robustness, and is suitable for practical use to protect the security of digital image information over the Internet.
- Path planning for autonomous vehicles based on the improved ant colony algorithm7
Experimental results showed that autonomous vehicles could move from the starting point to the end point along the simulated paths using the improved ant colony algorithm, and the findings are of considerable significance for autonomous vehicles.
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- A Hybrid Virtualization Approach to Emulate Network Nodes of Heterogeneous Architectures7
Functional verification and performance evaluation experiments have led to the conclusion that the elaborated hybrid virtualization approach can effectively dispose of the emulation incompatibility problem with affordable performance degradations.
- Genetic investigation of haemoglobinopathies in a large cohort of asymptomatic individuals reveals a higher carrier rate for β-thalassaemia in Sichuan Province (Southwestern China)7
The main abnormal haemoglobin genotype (HbE) and thalassaemia genotype were consistent with those in other regions of China, but the carrier rate of β-thalassaemic in Sichuan was higher than that of α-thalASSaemia.
- cmFSM: a scalable CPU-MIC coordinated drug-finding tool by frequent subgraph mining7
The implemented parallel acceleration tool for classical frequent subgraph mining algorithm called cmFSM is implemented to employ parallel techniques to parallelize extension tasks, so as to reduce computation time and presents a promising scalability of massive mining tasks in multi-node scenario.
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- PerCoNet: News Recommendation with Explicit Persona and Contrastive Learning6
This paper presents PerCoNet, a novel deep learning approach to personalized news recommendation which features two new findings: representing users through explicit persona analysis based on the prominent entities in their recent news reading history could be more effective than latent persona analysis employed by most existing work.
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- Real-Time Streaming Anomaly Detection in Dynamic Graphs6
This work proposes MIDAS, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data, and introduces a conditional merge step, which updates the algorithm's data structures after each time tick, to reduce the 'poisoning' effect of newly arriving edges.
- The design of distributed real-time video analytic system6
A distributed scalable infrastructure VAP (Video Analytic Platform) for supporting real-time video stream analysis, which leverages UIMA (Unstructured Information Management Architecture) framework as the data flow control engine and multiple commodity databases as the storage and computation resources.
- Multi-scale 3D-Modeling6
3D-modeling activities at the German DLR Institute of Robotics and Mechatronics are reviewed, which describes systems which acquire information containing geometry and texture at different scales of scenery, ranging from indoor scenes to planetary explorations.
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- A Multimodal Bone Stick Matching Approach Based on Large-Scale Pre-Trained Models and Dynamic Cross-Modal Feature Fusion4
A novel multimodal bone stick matching approach that integrates image, inscription, and archeological information to enhance the accuracy and efficiency of matching fragmented bone stick artifacts, providing robust technical support for the research and restoration of bone stick cultural heritage.
- HiABP: Hierarchical Initialized ABP for Unsupervised Representation Learning4
Hierarchical Initialized Alternating Backpropagation (HiABP) is proposed for efficient Bayesian inference of hierarchical latent representation with controllable image synthesis and can outperform other popular deep generative models in modeling natural images and learning from incomplete data.
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- Medical Image Registration Algorithm Based on Bounded Generalized Gaussian Mixture Model3
The bounded generalized Gaussian mixture model is used to approach the joint intensity of source medical images, formulated based on a maximum likelihood framework, and is solved by an expectation-maximization algorithm.
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- Global and Local Information Based Spherical Marginal Fisher Analysis for Face Recognition3
Experimental results on the ORL and Yale face databases show the proposed new face recognition algorithm, termed Spherical Marginal Fisher Analysis, outperforms other linear methods.
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- 3D Human Motion Retrieval Based on ISOMAP Dimension Reduction3
This work adopts the scarcity of neighboring-graph to improve ISOMAP algorithm for making it apply the massive human motion database and builds a motion string index for database, deploy Smith-Waterman algorithm to compare the retrieval samples' motion string with motion strings of candidate datasets, and obtains the similar motion sequence.
- A Novel Proactive Defense Strategy Design for Cyber-Physical Systems in the Presence of FDI Attacks2
A novel proactive defense strategy tailored to address false-data-injection attacks in cyber-physical systems (CPSs) using watermarking and moving target defense mechanisms, with quantitative comparisons demonstrating the efficacy of the proposed methods against FDI attacks.
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- EQRC: A secure QR code-based E-coupon framework supporting online and offline transactions2
This paper introduces a novel fragment coding-based approach for QR codes using the idea of visual cryptography, and proposes a QR code scheme with high security by combining the fragment coding with the commitment technique, and presents an enhanced QR code-based secure e-coupon transaction framework.
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- A New Chaotic Image Scrambling Scheme Based on Spatial Bit-Level Map2
A new image scrambling algorithm based on chaotic sequence and spatial bit-level map that can scramble image based on the pixel’s position and the value and their corresponding probability is proposed.
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- Spatio-temporal feature-based encrypted traffic classification model ASTNet1
ASTNet, an encrypted traffic classification model based on spatio-temporal features, is proposed and experiments show that the model achieves better results in encrypted traffic classification.
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- Research on a watermarking recognition method for PDF documents based on natural language processing1
By collecting a large number of PDF documents, a KenLM language model based on language model probability and conditional probability calculation rules is established to identify PDF document watermarks, which effectively improves the accuracy of PDF documentWatermark recognition.
- An Efficient and Reliable Blockchain-based Trust Management Model for Electricity Trading Terminal1
The experimental results show that, compared with similar methods, the ERBTM model can effectively deal with the endogenous security threats of terminals in the electricity trading environment, and has significant advantages in terms of efficiency and reliability.
- Intrusion Detection Based on Feature Reduction and Model Pruning in Electricity Trading Network1
A comparative analysis of different pruning thresholds and determined that reducing the dimensionality to 49 dimensions yields superior model performance, making it particularly suitable for resource-constrained electricity trading network.
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- Unsupervised Cross-Modal Hashing with Soft Constraint1
This paper proposes a state-of-the-art model named Soft Constraint Hashing (SCH), using a special soft constraint term defined as an “information tunnel” to achieve the goal that conveys information from one approach to another.
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- The design and implementation of invisible eavesdropping protection application1
A scheme was proposed in this paper using the access authority of mobile phone's microphone, network access, user trust and etc to implement the function of alarm and threat position in the open source Android mobile platform.
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- Preoperative assessment of axillary lymph node tumor burden in cT1-2N0 breast cancer patients with a modality-adaptive network based on sentinel lymph node ultrasound images–
A deep learning model based on ultrasound (US) images of sentinel lymph nodes (SLNs) detected by contrast-enhanced lymphatic ultrasound (CEUS) that provided a direct and efficient method for accurate preoperative assessment of ALN tumor burden in cT1-2N0 breast cancer patients.
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- Based on improved unsuperpoint image stitching method–
The backbone network of the unsupervised model, the points of interest and the descriptor loss function are optimized to improve the efficiency of the network and the accuracy of feature point extraction.
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- Research on Task Scheduling Algorithm for Edge Computing in Low-voltage transformer the area network–
- 3D Human Motion Retrieval Based on Graph Model–
A new method for human motion retrieval based on graph model based on Maximum Range of the Distance (MRD) is proposed, which is better than the previous methods of motion retrieval in many ways.
- A Latent Factor Model Based on Elastic Network for Recommender Systems–
This work integrates the elastic network optimization scheme into an LF model, where utilized on HiDS matrices, and adopts an efficient learning algorithms, i.e., stochastic proximal gradient descent, to train desired latent factors in an SENLf-based model, resulting in a novel SENLF-based models relying on the learning scheme.
- Quantum Gate Circuit Neural Network Optimization Algorithm Based on Performance Function–
Research prove WQG CA and RQGCA have better pattern classification ability relative to QGCA and RZGCA has a higher classification accuracy than WQgCA.
- Elastic Resource Provisioning for Cloud Based on Docker–
The results show that the proposed elastic resource provisioning approach can meet the resource supply requirements of elastic cloud with excellent scalability, great agility, strong robustness and high resource utilization.
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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 2026-10-15. 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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