Artificial Intelligence

Data were generated from ChatGPT’s responses to 80 counseling questions that college students asked during a school counseling setting. All the responses generated during these simulated counseling sessions were then analyzed using three primary metrics—warmth, empathy, and acceptance—following APA guidelines. The analysis adopted several natural language processing methodologies for emotion detection and empathy measurement to quantify ChatGPT’s high efficacy in presenting the appropriate emotions and reactions for counseling. 

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The 5G cellular technology has introduced advanced radio communication protocols and new frequency bands and enabled faster data exchange. These improvements increase network capacity and establish a foundation for high-bandwidth, low-latency services, helping the development of applications like the Internet of Things (IoT). However, information security poses significant challenges, particularly concerning attacks such as Fake Base Stations (FBS) and Stream Control Transmission Protocol (SCTP) Session Hijacking.

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General video captioning datasets are not suitable for challenges of fast moving camera, highly overlapping vocabulary and caption for camera-carrier in driving scenes. To address this issue, we develop the Traffic Scenarios Event Caption (TSEC) Dataset to describe the key events of ego vehicle, road environment and other traffic participants. To acquire diverse types of traffic scenarios, we select the videos that we take with our on-board camera and other public dataset videos. We also download from BiliBili and Youtube to get the traffic accident videos.

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the dataset is related to an app-based framework for multivariate next-day price prediction using
GRU attention networks with rolling averages.

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Decentralized Collaborative Simultaneous Localization and Mapping (C-SLAM) is essential to enable multi-robot missions in unknown environments without relying on pre-existing localization and communication infrastructure. This technology is anticipated to play a key role in the exploration of the Moon, Mars, and other planets. In this work, we introduce a novel dataset collected during C-SLAM experiments involving three robots operating on a Mars analogue terrain.

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The benchmarking dataset, GenAI on the Edge, contains performance metrics from evaluating Large Language Models (LLMs) on edge devices, utilizing a distributed testbed of Raspberry Pi devices orchestrated by Kubernetes (K3s). It includes performance data collected from multiple runs of prompt-based evaluations with various LLMs, leveraging Prometheus and the Llama.cpp framework. The dataset captures key metrics such as resource utilization, token generation rates/throughput, and detailed inference timing for stages such as Sample, Prefill, and Decode.

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This study introduces a multimodal dataset collected to investigate the psychophysiological relationship between perceived fear and muscle activity in climbers. The dataset includes physiological, motion, and subjective data from 19 climbers during \textit{lead} and \textit{top rope climbing} ascent styles, which differ in perceived risk due to varying fall distances. Physiological data were recorded using EMG, ECG, and IMU sensors. Additionally, subjective fear ratings were collected at distinct phases of the climb.

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This is a wheat breeding phenotyping and yield dataset, including canopy height (CH, m), canopy volume (CV, m3), and leaf area index (LAI) collected in the field; vegetation index (VI) generated by multispectral data acquired by UAV remote sensing; trial site weather (Weather); and yield (Yield, kg). The data comes from field trials.

Data acquisition and processing are described in the relevant part of the manuscript.

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These are the codes and models used in our experiments regarding our submitted article “Cheby-KANs:
Advanced Kolmogorov-Arnold Networks for Applying Geometric Deep Learning in Quantum Chemistry
Applications”. The code is developed using python programming language. In our paper we hae
developed the B-spline based KANs with a more powerful and much faster polynomials “shifted-
Chebyshev polynomials” of the first kind. Also, we integrated our new architecture with geometric deep

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We proposes a new task in the field of Answering Subjective Induction Question on Products (SUBJPQA). The answer to this kind of question is non-unique, but can be interpreted from many perspectives. For example, the answer to ‘whether the phone is heavy’ has a variety of different viewpoints. A satisfied answer should be able to summarize these subjective opinions from multiple sources and provide objective knowledge, such as the weight of a phone.

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