Artificial Intelligence

The dataset1 includes fake&real news propagation networks on Twitter built according to fact-check information and the news retweet graphs were originally extracted by [FakeNewsNet](https://github.com/KaiDMML/FakeNewsNet).The statistics of the dataset is shown below:| Data | #Graphs | #Total Nodes | #Total Edges | #Avg.

This study investigates the integration of artificial intelligence (AI) to enhance endpoint management solutions. The research explores AI's impact on security, efficiency, and compliance within enterprise environments (R1). Through case studies and empirical analysis, the paper highlights the benefits and challenges of such integrations, offering insights into future developments.

This dataset has been meticulously curated to evaluate the efficiency of Retrieval-Augmented Generation (RAG) pipelines in both retrieval and generative accuracy, with a particular focus on scenarios involving overlapping contexts. The dataset comprises two primary components: Motor data and Employee data. The Motor dataset includes master data of various motor models along with their corresponding manuals, linked by the motor's model name. Similarly, the Employee dataset encompasses employee master data and associated policy documents, linked by department.

We construct the triple-modality dataset, VGG-sound+, comprising image-text-audio data. Based on VGG-sound, VGG-sound+ consists of 200,000 audio-visual data entries categorized as video data, including metadata label- ing the category of each video clip. We define the image-text-audio triplet modalities of VGG-sound+ as the dataset Di = (Ii , Ti , Ai), where Ii represents an image snap- shot of the video, Ti denotes a textual description of the video, and Ai signifies the audio clip.