Semantic segmentation and stereo matching, respectively analogous to the ventral and dorsal streams in our human brain, are two key components of autonomous driving perception systems. Addressing these two tasks with ...
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Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manua...
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A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. This paper aims to fill this...
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ISBN:
(数字)9798331507589
ISBN:
(纸本)9798331507596
A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. This paper aims to fill this research gap by undertaking a systematic literature review on the use of quantization in distributed learning for privacy enhancement. We explore peer-reviewed literature that utilizes quantization for privacy-preserving purposes. Our analysis identifies the limitations and challenges of current approaches. It also highlights the need to integrate quantization techniques for dual objectives (privacy and communication efficiency) in distributed learning frameworks.
We have developed a system that separates and measures the optical properties of skin, i.e., the surface reflection, diffuse reflection, and sub-surface scattering components of the skin. This system includes two pola...
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Due to the increasing importance of complex systems, the problems of modelling and simulation (M&S) of these systems, their approaches and solutions have been studied extensively these last years. In this paper, w...
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Effectively identifying and locating plants on the forest floor is essential for successful reforestation efforts and forest health assessment. This task is challenging due to the diverse range of plants, the high var...
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ISBN:
(数字)9789532901382
ISBN:
(纸本)9798350354614
Effectively identifying and locating plants on the forest floor is essential for successful reforestation efforts and forest health assessment. This task is challenging due to the diverse range of plants, the high variance in appearance, the limited availability of accurately labeled data, and the complexity of data collection in post-harvest forest areas. These challenges are addressed by integrating field-surveyed plant data with images captured by Unmanned Aerial Vehicles (UAVs) to produce labeled training data. A custom Faster-RCNN model is trained using the aforementioned data to detect individual plants and clustered plant groups. Two different annotation approaches (unified class and distinct classes) are explored to address plant groupings and compare their performance using Mean Average Precision at 50% overlap (mAP50) and F1 score. The model trained using the unified class approach shows promise in detecting plants and plant groups larger than 0.15 square meters (F1 score: 0.44) but struggles with those smaller than 0.15 square meters. The inclusion of field-labeled data makes detection more challenging but ensures reliability. Meanwhile, the distinct class approach shows limited success. This work underscores the value of high-resolution imagery and comprehensive temporal analysis in enhancing detection accuracy and reliability.
The development and application of artificial intelligence-based computer vision systems in medicine, environment, and industry are playing an increasingly prominent role. Hence, the need for optimal and efficient hyp...
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Studying mental health through social media data has become an emerging area of research, notably for the detection of depression and anxiety. In this regard, many researches have been conducted, yielding very satisfa...
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
Studying mental health through social media data has become an emerging area of research, notably for the detection of depression and anxiety. In this regard, many researches have been conducted, yielding very satisfactory results (e.g., [1]–[3]). However, most of these studies have addressed each of these two mental disorders separately. This is due to the overlap of symptoms associated with depression and anxiety that makes distinguishing between them significantly challenging. Based on this context, this work leverages pretrained large language models (LLMs) to develop efficient multi-class models for predicting both depression and anxiety. For this purpose, a small, multi-labeled Twitter dataset is first constructed. Then, a pre-trained BERT-based uncased model is fine-tuned for six epochs on the dataset, resulting in a model referred to as DAC-BERT. Finally, the DAC-BERT model is evaluated and compared against both hybrid deep learning models and other LLM-based models. The obtained results show that DAC-BERT model outperforms existing approaches, achieving accuracies of up to 97.20% on a multi-labeled dataset containing normal, depressive, and anxious tweets, and up to 96.50% on a dataset with only normal and depressive tweets. These findings highlight the promising potential of fine-tuning LLMs for predicting mental health disorders, particularly depression and anxiety.
In this paper, we consider the Hermitian {P,k+1}-(anti-)reflexive solutions to the quaternion matrix equation AXB+CXD=E and AX=E, respectively. We use the complex representation method to obtain the necessary and suff...
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Euler Lagrange Skeletal Animation (ELSA) is the novel and fast model for skeletal animation, based on the Euler Lagrange equations of motion and configuration and phase space notion. Single joint’s animation is an in...
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