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Abstract—This manuscript introduces the Chaos Fusion Mutation INFO algorithm (CFMINFO), which integrates multiple strategies and updates vector positions through three core processes. These processes incorporate Good Point Set initialization, Sine-Tent-Cosine (STC) chaotic parameterization, and Normal Cloud Mutation strategies. The algorithm is characterized by its simplicity, rapid convergence, and ability to avoid local optima. To validate its performance, CFMINFO is applied to the optimization of linear arrays.

Binary classification is the most suitable task considering the common use cases in MCUs. Numerous datasets for image classification have been proposed. The Visual Wake Words (VWW) dataset, which is derived from the COCO dataset, distinguishes between ‘w/ person’ and ‘w/o person’ and is designed for object detection on MCUs. Therefore, datasets for binary classification and object detection exist. However, the dataset for binary classification has not been proposed for the semantic segmentation task.

Dataset with syntentic TLSA DNS records for DNSSEC evaluation. Created with several RSA2048, RSA3072, RSA4096, ECDSAP256, ECDSAP384, or ECDSAP512 keys. Note: ECDSAP256 conforms to prime256v1, ECDSAP348 conforms to secp384r1 and  ECDSAP512 conforms to secp521r1

Keys and certificates are repeated using a factor of 500. Keys and certificates are available in https://gitlab.gast.it.uc3m.es/dnsseccollisions/tlsadataset

The experiments were conducted at RAICo Lab in West Cumbria. The data were real-time data collected during experiments to validate the CAP system. The underwater robot moves autonomously along three different paths, while the surface robot follows the underwater robot's movement simultaneously. The uploaded file consists of nine data sets. Three experiments were conducted for each trajectory, resulting in a total of nine datasets. The nine subsets are: Lawnmower1-3, Random1-3, and Square1-3.

We introduce a dataset comprised of energy consumption data from smart meters in French households, capturing detailed, disaggregated time series for various home appliances. This dataset covers a six-month period with a one-minute sampling rate across five different households. The objective of this dataset is to support the development of models that learn disentangled representations of time series energy data, which can significantly enhance model generalization across both in-distribution and out-of-distribution scenarios.

e used a mixed dataset\cite{ye2023geneface}in our experiments, where part of the data was referenced from the publicly available dataset provided by GaussianTalking\cite{li2025talkinggaussian}, and additional data was collected by ourselves. Specifically, we selected four high-definition talking video clips from the publicly available dataset, including two male portraits, "Macron" and "Obama" and one female portrait, "May". These video clips are centered on the subject, with an average length of 6500 frames and a frame rate of 25 FPS.

About the data

The publicly available LIDC/IDRI database. This data uses the Creative Commons Attribution 3.0 Unported License. We excluded scans with a slice thickness greater than 2.5 mm. In total, 888 CT scans are included. The LIDC/IDRI database also contains annotations which were collected during a two-phase annotation process using 4 experienced radiologists. Each radiologist marked lesions they identified as non-nodule, nodule < 3 mm, and nodules >= 3 mm. See this publication for the details of the annotation process.

This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between bad'' connections, called intrusions or attacks, andgood'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment.

This dataset extends the standard Myers-Briggs Type Indicator (MBTI) dataset, widely available on Kaggle, by incorporating advanced data augmentation techniques leveraging GPT-based Transformers. The augmentation addresses inherent class imbalance and data sparsity issues in the original dataset, significantly enriching the volume and diversity of textual samples while maintaining linguistic and contextual fidelity to the MBTI personality types.

This dataset comprises raw CAN bus messages collected from five different EV car manufacturers. The primary focus of the dataset is on battery-related messages, although it also includes other general car communication messages. These raw CAN bus messages represent the fundamental data exchanged between various components of the electric vehicle, such as the battery management system (BMS), motor controller, and other electronic control units (ECUs).