In the very beginning,the computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the *** computerscience developed as a discipline in its own righ...
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In the very beginning,the computer Laboratory of the University of Cambridge was founded to provide computing service for different disciplines across the *** computerscience developed as a discipline in its own right,boundaries necessarily arose between it and other disciplines,in a way that is now often detrimental to ***,it is necessary to reinvigorate the relationship between computerscience and other academic disciplines and celebrate exploration and creativity in *** do this,the structures of the academic department have to act as supporting scaffolding rather than *** examples are given that show the efforts being made at the University of Cambridge to approach this problem.
Mainstream math libraries for floating point (FP) do not produce correctly rounded results for all inputs. In contrast, CR-LIBM and RLibm provide correctly rounded implementations for a specific FP representation with...
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The world is facing a wide range of serious problems that require urgent attention. Inherent to understanding and solving these problems are research and development activities. Research and development skills are pri...
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ISBN:
(纸本)9781665458429
The world is facing a wide range of serious problems that require urgent attention. Inherent to understanding and solving these problems are research and development activities. Research and development skills are primarily taught at institutions of higher education and have translated into numerous benefits for students, such as enhanced cognitive skills and a broader awareness of the discipline. This paper presented a model for running undergraduate research courses at the computersciencedepartment, University of Guyana (UG). Key components of the model include preparatory assessments, two-semester offering of research courses (formal proposal and thesis), human resources, mechanisms for guidance and support, assessments and grading rubrics. The model was first implemented in 2017/2018, and was monitored and evaluated over a two-year period using a sample of 23 undergraduate students who were pursuing computer related BSc degree programs. Students provided feedback via a survey that was primarily focused on the research process, supervision, course structure, weekly research sessions, and general strengths and weaknesses of the undergraduate research model. The participants reported positive feedback such as having a good grasp of the research process, high levels of confidence in conducting independent research and preparedness for postgraduate studies. As such, this undergraduate research model may have wider applicability for the UG community and other institutions of higher learning.
Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Most computing departments offer one or two undergraduate programs, but the CSIT department at a mid-south state university offers three, all three accredited by the Accreditation Board for Engineering and Technology ...
Most computing departments offer one or two undergraduate programs, but the CSIT department at a mid-south state university offers three, all three accredited by the Accreditation Board for Engineering and Technology (ABET): computer Information Systems, computer Information Technology, and computerscience. In 2018, an integrated 3-in-1 B.S. program structure was adopted, which earned ABET accreditation in 2021--2022. With about 650 majors and 100--150 graduates annually, the streamlined curriculum has optimized resources and contributed to the department's success.
Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food *** these diseases early can help mitigate their impact and ensure healthy crop *** ...
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Plant disease detection has played a significant role in combating plant diseases that pose a threat to global agri-culture and food *** these diseases early can help mitigate their impact and ensure healthy crop *** learning algorithms have emerged as powerful tools for accurately identifying and classifying a wide range of plant diseases from trained image datasets of affected *** algorithms,including deep learning algorithms,have shown remarkable success in recognizing disease patterns and early signs of plant *** early detection,there are other potential benefits of machine learning algorithms in overall plant disease management,such as soil and climatic condition predictions for plants,pest identification,proximity detection,and many *** the years,research has focused on using machine-learning algorithms for plant disease ***,little is known about the extent to which the research community has ex-plored machine learning algorithms to cover other significant areas of plant disease *** view of this,we present a cross-comparative review of machine learning algorithms and applications designed for plant dis-ease detection with a specific focus on four(4)economically important plants:apple,cassava,cotton,and *** conducted a systematic review of articles published between 2013 and 2023 to explore trends in the research community over the *** filtering a number of articles based on our inclusion criteria,including articles that present individual prediction accuracy for classes of disease associated with the selected plants,113 articles were considered *** these articles,we analyzed the state-of-the-art techniques,challenges,and future prospects of using machine learning for disease identification of the selected *** from our re-view show that deep learning and other algorithms performed significantly well in detecting plant *** addition,we fou
Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous vali...
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Deep reinforcement learning(DRL) has demonstrated significant potential in industrial manufacturing domains such as workshop scheduling and energy system ***, due to the model's inherent uncertainty, rigorous validation is requisite for its application in real-world tasks. Specific tests may reveal inadequacies in the performance of pre-trained DRL models, while the “black-box” nature of DRL poses a challenge for testing model behavior. We propose a novel performance improvement framework based on probabilistic automata,which aims to proactively identify and correct critical vulnerabilities of DRL systems, so that the performance of DRL models in real tasks can be improved with minimal model ***, a probabilistic automaton is constructed from the historical trajectory of the DRL system by abstracting the state to generate probabilistic decision-making units(PDMUs), and a reverse breadth-first search(BFS) method is used to identify the key PDMU-action pairs that have the greatest impact on adverse outcomes. This process relies only on the state-action sequence and final result of each trajectory. Then, under the key PDMU, we search for the new action that has the greatest impact on favorable results. Finally, the key PDMU, undesirable action and new action are encapsulated as monitors to guide the DRL system to obtain more favorable results through real-time monitoring and correction mechanisms. Evaluations in two standard reinforcement learning environments and three actual job scheduling scenarios confirmed the effectiveness of the method, providing certain guarantees for the deployment of DRL models in real-world applications.
Neuromorphic technology has diversified considerably from its origins in the seminal work by Carver Mead and his group at Caltech in the 1980s [1]. That early work focussed on the analogy between the equations describ...
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Neuromorphic technology has diversified considerably from its origins in the seminal work by Carver Mead and his group at Caltech in the 1980s [1]. That early work focussed on the analogy between the equations describing the flow of ions in biological neurons and the equations describing the flow of carriers in field-effect transistors operating in the subthreshold region.
This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss *** multi-scale en...
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This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer(ViT)architectures and a dynamic multi-loss *** multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features,while the dynamic loss function adapts during training to optimize classification accuracy and retrieval *** approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets,yielding notable ***,on the ISIC-2018 dataset,our method achieves an F1-Score improvement of+4.84% compared to the standard ViT,with a precision increase of+5.46% for melanoma(MEL).On the ChestX-ray14 dataset,the method delivers an F1-Score improvement of 5.3%over the conventional ViT,with precision gains of+5.0% for pneumonia(PNEU)and+5.4%for fibrosis(FIB).Experimental results demonstrate that our approach outperforms traditional CNN-based models and existing ViT variants,particularly in retrieving relevant medical cases and enhancing diagnostic *** findings highlight the potential of the proposedmethod for large-scalemedical image analysis,offering improved tools for clinical decision-making through superior classification and case comparison.
With the increasing usage of cloud computing in many fields, concerns about the secrecy of data storage in the cloud have been growing. Many types of data are stored in cloud computing, such as text, images, audio, vi...
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