This paper presents a novel frequency-based algorithm which solves the maximal square problem with improved practical speed performance while maintaining optimal asymptotic complexity. My approach tracks the columnar ...
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Microorganisms, comprising bacteria, viruses, fungi, and protozoa, are pivotal across various domains including human health, agriculture, biotechnology, and environmental science. Understanding these microorganisms i...
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In-storage computing with computational SSDs is emerging as one effective solution for I/O bottlenecks in big data applications such as AI learning model training. Specifically, with in-SSD computing, computation can ...
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ISBN:
(纸本)9798350323481
In-storage computing with computational SSDs is emerging as one effective solution for I/O bottlenecks in big data applications such as AI learning model training. Specifically, with in-SSD computing, computation can be pushed down to SSDs and the volume of the output data that will be transferred back to the host can be greatly reduced. However, there are several fundamental issues for applications to fully exploit in-SSD computing with simple and efficient function offloading. In this paper, we present three challenges for in-SSD computing, namely, data model, programming framework, and storage/computing integration, and discuss possible research directions.
The sign language retrieval system, while similar to conventional video retrieval systems, faces distinct challenges that require a thorough understanding of the meanings conveyed through human actions depicted in the...
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Instruction tuning is crucial for adapting large language models (LLMs) to align with user intentions. Numerous studies emphasize the significance of the quality of instruction tuning (IT) data, revealing a strong cor...
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In this work, we present milliGait , a user identification using gait patterns captured by millimeter wave (mmWave) radar technology. milliGait takes into account the unique movement signatures of individuals to enabl...
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ISBN:
(数字)9798331531195
ISBN:
(纸本)9798331531201
In this work, we present milliGait , a user identification using gait patterns captured by millimeter wave (mmWave) radar technology. milliGait takes into account the unique movement signatures of individuals to enable privacy secured accurate user identification. Using a Commercial-Off-the-self (COTS) mmWave radar we have collected mmWave features, particularly the range information and range-doppler profile features, extracted from the raw I/Q samples. To categorize people according to their leg movement patterns, we developed a Convolutional Neural Network (CNN) model using the mmWave features. The two features namely the range information and the range-doppler profile is fed into two concurrent CNN models (1D and 2D CNN models respectively). Finally a fully-connecyed classifier is employed to identify the subjects. We have evaluated milliGait with 3 different subjects with an authentication accuracy of over 80%.
Depression, a psychiatric condition marked by profound sadness and apathy, influences cognitive processes, emotions, and behaviors, causing psychological and physiological challenges. Everyday tasks become arduous, li...
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ISBN:
(数字)9798331515911
ISBN:
(纸本)9798331515928
Depression, a psychiatric condition marked by profound sadness and apathy, influences cognitive processes, emotions, and behaviors, causing psychological and physiological challenges. Everyday tasks become arduous, life loses vibrancy, and in extreme cases, may lead to suicidal tendencies. University students face unique stressors due to academic, social, and personal pressures, underscoring the need for mental health support. This thesis investigates depression diagnosis among Metropolitan University teachers and students using machine learning techniques. Survey data, comprising 516 anonymized responses covering mental well-being, mood, and behavior, form the basis. The researchers preprocess data, visualize it, and train models to detect depression indicators, evaluating techniques like Logistic Regression, Decision Trees, and Gradient Boosting for effectiveness. The research highlights that the reasons for depression differ between females and males. The aim is to enhance early detection and support mechanisms, fostering proactive intervention within the university community.
There has been a deluge of data-driven deep learning approaches to detect COVID-19 from computed tomography (CT) images over the pandemic, most of which use ad-hoc deep learning black boxes of little to no relevance t...
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This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. B...
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ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
This paper introduces a secure authentication framework by applying Mutual Authentication Coupled with Optimised AES (Advanced Encryption Standard) Encryption to improve the security of the multi-server environment. By addressing inefficiencies in traditional mutual authentication protocols without encryption, this framework significantly improves both performance and resilience. Encryption time was reduced by 25% and authentication time by 16.67% while CPU (Central Processing Unit) usage was reduced by 23.08%, with a corresponding 21.43% improvement in memory efficiency. The proposed system demonstrates a 70% improvement in attack resistance. Dynamic revocation mechanisms mitigate unauthorized access and malicious activity in real time. The protocol is designed for resource-constrained environments to ensure secure, scalable, and efficient communication in multi-server architectures, further increasing system reliability and user trust in cloud-based services.
Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos f...
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ISBN:
(数字)9798331510831
ISBN:
(纸本)9798331510848
Action detection aims to detect (recognize and localize) human actions spatially and temporally in videos. Existing approaches focus on the closed-set setting where an action detector is trained and tested on videos from a fixed set of action categories. However, this constrained setting is not viable in an open world where test videos inevitably come beyond the trained action categories. In this paper, we address the practical yet challenging Open-Vocabulary Action Detection (OVAD) problem. It aims to detect any action in test videos while training a model on a fixed set of action categories. To achieve such an open-vocabulary capability, we propose a novel method OpenMixer that exploits the inherent semantics and localizability of large vision-language models (VLM) within the family of query-based detection transformers (DETR). Specifically, the OpenMixer is developed by spatial and temporal OpertMixer blocks (S-OMB and T-OMB), and a dynamically fused alignment (DFA) module. The three components collectively enjoy the merits of strong generalization from pretrained VLMs and end-to-end learning from DETR design. Moreover, we established OVAD benchmarks under various settings, and the experimental results show that the OpenMixer performs the best over baselines for detecting seen and unseen actions. We release the codes, models, and dataset splits at https://***/Cogito2012/0penMixer.
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