Anticancer peptides (ACPs) are short bioactive sequences that selectively target tumor cells with minimal toxicity, positioning them as promising candidates for next-generation cancer therapies. However, existing computational models face limitations in sequence representation and class imbalance. To address these challenges, we propose UACD-ACPs, a unified fusion-driven framework that integrates a diffusion-inspired noise-conditioned classifier for ACP prediction and a diffusion-based peptide generation module with cancer-type-aware organization for targeted downstream screening. The classification module integrates ProtBERT-based semantic embeddings with physicochemical descriptors via the Multiscale Embedding Compression Strategy (MECS) and a diffusion-inspired noise-conditioned encoder, substantially enhancing predictive robustness and accuracy, particularly under challenging imbalanced multi-class settings. In the generative pipeline, we introduce a denoising diffusion-based generative framework augmented by two novel fusion modules: the Bitemporal Fusion Module (BFM) and the Temporal Feature Attention Module (TFAM). These modules perform multi-scale temporal and semantic fusion to promote the generation of structurally coherent and functionally relevant peptide candidates. Experimental results demonstrate that UACD-ACPs outperforms state-of-the-art methods in terms of accuracy, F1-score, and AUC-ROC. The generated peptides exhibit favorable physicochemical properties, diverse secondary structures, and strong structural stability, as validated by molecular dynamics simulations and membrane-binding analyses. Overall, this study highlights the potential of fusion-driven diffusion-based frameworks for alleviating class imbalance and data heterogeneity in anticancer peptide modeling, paving the way for scalable and biologically grounded ACP discovery.

In deep space environment, some objects may split into several small fragments during movement, and these deep space objects often appear as points in satellite images. In this article, we conduct research on multi-object tracking (MOT) for these objects. First, we propose a simulation dataset, ScatterDataset, which simulates the movement and separation of objects in deep space background. By assigning two IDs to a trajectory, we describe the trajectory’s relationship before and after separation. Second, we present an end-to-end motion association model, ScatterNet, which encodes the position information of trajectories and detections into motion features. These features are processed through temporal aggregation by a Transformer encoder and spatial aggregation by a graph network; then, we get the association results by calculating the similarity between these features. Finally, we introduce a tracker, ScatterTracker, which is suitable for tracking in scenarios with object separation. Experiments with state-of-the-art tracking methods on ScatterDataset demonstrate that our approach has achieved significant performance improvements in deep space scenarios. The code is available at:https://github.com/wht-bupt/ScatterTrack.

Greybox fuzzing is a powerful testing technique. Given a set of initial seeds, greybox fuzzing continuously generates new test inputs to execute the program under test and drives executions with code coverage as feedback. Seed prioritization is an important step of greybox fuzzing that helps greybox fuzzing choose promising seeds for input generation in priority. However, mainstream greybox fuzzers like AFL++ and Zest tend to neglect the importance of seed prioritization. They may pick seeds plainly according to the sequential order of the seeds being queued or an order produced with a random-based approach, which may consequently degrade their performance in exploring code and exposing bugs. In the meantime, existing state-of-the-art techniques like Alphuzz and K-Scheduler adopt complex strategies to schedule seeds. Although powerful, such strategies also inevitably incur great overhead and will reduce the scalability of the proposed technique. In this article, we propose a novel distance-based seed prioritization approach named DiPri to facilitate greybox fuzzing. Specifically, DiPri evaluates the queued seeds according to seed distances and chooses the outlier ones, which are the farthest from the others, in priority to improve the probabilities of discovering previously unexplored code regions. To make a profound evaluation of DiPri , we prototype DiPri on AFL++ and conduct large-scale experiments with four baselines and 24 C/C++ fuzz targets, where eight are from widely adopted real-world projects, eight are from the coverage-based benchmark FuzzBench, and eight are from the bug-based benchmark Magma. The results obtained through a fuzzing exceeding 50,000 CPU hours suggest that DiPri can (1) insignificantly influence the host fuzzer’s capability of code coverage by slightly improving the branch coverage on the eight targets from real-world projects and slightly reducing the branch coverage on the eight targets from FuzzBench, and (2) improve the host fuzzer’s capability of finding bugs by triggering five more Magma bugs. Besides the evaluation with the three C/C++ benchmarks, we integrate DiPri into the Java fuzzer Zest and conduct experiments on a Java benchmark composed of five real-world programs for more than 8,000 CPU hours to empirically study the scalability of DiPri . The results with the Java benchmark demonstrate that DiPri is pretty scalable and can help the host fuzzer find bugs more consistently.

This replicated computational results (RCR) report describes how to (1) set up DiPri and (2) replicate the experimental results. The primary artifact is the C/C++ prototype of DiPri , which is essentially an extension of the state-of-the-art greybox fuzzer AFL++ (version 4.06). Other artifacts include the Java implementation of DiPri on Zest, the materials for integrating DiPri into FuzzBench and Magma, and the scripts for running docker and processing data. All artifacts can be found at our GitHub repository 1 and Zenodo archive. 2