Software engineers today should be knowledgeable in computersystems, which encompasses electronics hardware, software, and networking. Because computersystemsengineering is a very broad and rapidly changing discipl...
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Software engineers today should be knowledgeable in computersystems, which encompasses electronics hardware, software, and networking. Because computersystemsengineering is a very broad and rapidly changing discipline, there is a need to have a unified methodology for both students and practitioners. Patterns, and pattern languages for software development have received considerable attention over the last few years, and this approach, based on work originally done in architecture and urban planning, supports methodology development, dissemination, and evolution. This paper discusses patterns and pattern languages of computersystems as unifying themes and underpinnings for undergraduate and postgraduate curricula. Most of a sample curriculum has been implemented within undergraduate and postgraduate degree programs in computersystemsengineering at the Royal Melbourne Institute of Technology in Australia. The sample curriculum is described here, along with preliminary feedback from students and lecturers. The net result is steps toward a methodology that allows students to learn to systematically develop computer software, electronics hardware, and networked systems.
Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors invo...
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Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle wi...
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Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation *** methods for extracting features from mesh edges or faces struggle with complex 3D models because edge-based approaches miss global contexts and face-based methods overlook variations in adjacent areas,which affects the overall *** address these issues,we propose the Feature Discrimination and Context Propagation Network(FDCPNet),which is a novel approach that synergistically integrates local and global features in mesh *** FDCPNet is composed of two modules:(1)the Feature Discrimination Module,which employs an attention mechanism to enhance the identification of key local features,and(2)the Context Propagation Module,which enriches key local features by integrating global contextual information,thereby facilitating a more detailed and comprehensive representation of crucial areas within the mesh *** Experiments on popular datasets validated the effectiveness of FDCPNet,showing an improvement in the classification accuracy over the baseline ***,even with reduced mesh face numbers and limited training data,FDCPNet achieved promising results,demonstrating its robustness in scenarios of variable complexity.
We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computa...
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Background: Foreign object debris (FOD) is an unwanted substance that damages vehicular systems, most commonly the wheels of vehicles. In airport runways, these foreign objects can damage the wheels or internal system...
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The IEEE 802.1Qbv (80.21Qbv) standard is designed for traffic requiring deterministic and bounded latencies through strict periodic time synchronization, as specified by IEEE 802.1AS standard. However, internal clock ...
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The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need a...
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With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Ear...
With the development of deep learning in recent years, code representation learning techniques have become the foundation of many software engineering tasks such as program classification [1] and defect detection. Earlier approaches treat the code as token sequences and use CNN, RNN, and the Transformer models to learn code representations.
Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part ...
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Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.
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