American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algori...
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Cyber-attack detection is crucial for assuring computer network security. Contemporary research is based on the supervised learning paradigm for cyber-attack detection. Supervised learning techniques require labeled d...
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In the field of computer vision, general single-stage object detection methods employ two individual subnets within detection head, serving classification and localization purposes respectively. However, the lack of e...
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In the field of computer vision, general single-stage object detection methods employ two individual subnets within detection head, serving classification and localization purposes respectively. However, the lack of explicit modeling for distinctions and associations poses challenges for aligning the spatial feature perception of these two tasks, consequently leading to sub-optimal detection performance. Although some methods utilize classification to evaluate localization, it is a compromise rather than multi-task optimization. In this paper, we propose a Task-coordinated Single-stage Object Detector (TSOD) to enhance the coordination of multiple tasks. Firstly, we introduce a Task-decoupled Feature Alignment Mechanism (TFAM), which adaptively provides compatible features for different tasks by decoupling spatial information. For classification and localization, the network adaptively samples from category-sensitive regions and boundary-separable regions, respectively. Secondly, we propose a Task-interactive Enhancement Mechanism (TEM), which explicitly combines different task-sensitive features for joint classification score prediction and selects samples with high task consistency for training. Through this interaction mechanism, consistency between tasks is bolstered. We conduct extensive experiments on the COCO, Cityscapes, CrowdHuman and WiderFace datasets to evaluate the performance of TSOD. The results demonstrate that our model outperforms several state-of-the-art detectors, achieving a 2.0 AP improvement over the baseline on COCO minival and a remarkable 50.4 AP at single-model single-scale testing on COCO test-dev. Additionally, our model, equipped with ResNet-50, performs significantly better than other representative detectors on the Cityscapes, CrowdHuman, and WiderFace datasets, showcasing its robustness and generalizability. Our study contributes a new perspective to the design of single-stage object detectors by emphasizing the importance of decoupl
Success of any Over the Top (OTT) platform depends on how the platform is providing best user experience along with content to its customers. Being in media and entertainment space customers need to access content fro...
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Chest X-ray scans are one of the most often used diagnostic tools for identifying chest diseases. However, identifying diseases in X-ray images needs experienced technicians and is frequently noted as a time-consuming...
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In Aspect-based Sentiment Analysis (ABSA), accurately determining the sentiment polarity of specific aspects within text requires a nuanced understanding of linguistic elements, including syntax. Traditional ABSA appr...
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In Aspect-based Sentiment Analysis (ABSA), accurately determining the sentiment polarity of specific aspects within text requires a nuanced understanding of linguistic elements, including syntax. Traditional ABSA approaches, particularly those leveraging attention mechanisms, have shown effectiveness but often fall short in integrating crucial syntax information. Moreover, while some methods employ Graph Neural Networks (GNNs) to extract syntax information, they face significant limitations, such as information loss due to pooling operations. Addressing these challenges, our study proposes a novel ABSA framework that bypasses the constraints of GNNs by directly incorporating syntax-aware insights into the analysis process. Our approach, the Syntax-Informed Attention Mechanism Vector (SIAMV), integrates syntactic distances obtained from dependency trees and part-of-speech (POS) tags into the attention vectors, ensuring a deeper focus on linguistically relevant elements. This not only substantially enhances ABSA accuracy by enriching the attention mechanism but also maintains the integrity of sequential information, a task managed by adopting Long Short-Term Memory (LSTM) networks. The LSTM’s inputs, consisting of syntactic distance, POS tags, and the sentence itself, are processed to generate a syntax vector. This vector is then combined with the attention vector, offering a robust model that adeptly captures the nuances of language. Moreover, the sequential processing capability of LSTM ensures minimal information loss across the text by preserving the context and dependencies inherent in the sentence structure, unlike traditional pooling methods. Our experimental findings demonstrate that this innovative combination of SIAMV and LSTM significantly outperforms existing GNN-based ABSA models in accuracy, thereby setting a new standard for sentiment analysis research. By overcoming the traditional reliance on GNNs and their pooling-induced information loss, our method
This paper introduces a new approach to switch authentication within a network environment, addressing the challenges associated with multiple switch configurations. The proposed continuous authentication process is s...
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Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on...
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Partition testing is one of the most fundamental and popularly used software testing *** first di-vides the input domain of the program under test into a set of disjoint partitions,and then creates test cases based on these *** by the theory of software cybernetics,some strategies have been proposed to dynamically se-lect partitions based on the feedback information gained during *** basic intuition of these strategies is to assign higher probabilities to those partitions with higher fault-detection potentials,which are judged and updated mainly ac-cording to the previous test *** a feedback-driven mechanism can be considered as a learning process—it makes decisions based on the observations acquired in the test ***,advanced learning techniques could be leveraged to empower the smart partition selection,with the purpose of further improving the effectiveness and efficiency of partition *** this paper,we particularly leverage reinforcement learning to enhance the state-of-the-art adaptive partition testing *** algorithms,namely RLAPT_Q and RLAPT_S,have been developed to implement the proposed *** studies have been conducted to evaluate the performance of the proposed approach based on seven object programs with 26 *** experimental results show that our approach outperforms the existing partition testing techniques in terms of the fault-detection capability as well as the overall testing *** study demonstrates the applicability and effectiveness of reinforcement learning in advancing the performance of software testing.
In this study, we utilize a recently proposed non-parametric metaheuristic algorithm known as geometric mean optimization (GMO) to adjust the hidden layer input weights and bias of six ANN variants, namely PSNN, SPNN,...
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The current study is defined by two main aims. An effective strategy for improving local search is to combine the Set Algebra-Based Heuristic Algorithm (SAHA) algorithm with the Nelder-Mead simplex method. The approac...
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