In recent years, we see more degradation in the quality of cyber security graduates, especially in terms of not meeting the employer's perspective. They lack a certain amount of skills to be competent enough for t...
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ISBN:
(数字)9781728173108
ISBN:
(纸本)9781728173115
In recent years, we see more degradation in the quality of cyber security graduates, especially in terms of not meeting the employer's perspective. They lack a certain amount of skills to be competent enough for the industry. This study aims to inculcate interest in cyber security at a younger age, specifically from secondary-level education. After exploring various methods of approaching this problem, Capture the Flag (CTF) has been chosen to introduce secondary school students to the cyber security competitive scene in the hope that they find fun factors to be more interested in the field of area. A one-day event was conducted with the implementation of the CTF competition at the end. A pre-test and post-test questionnaire were constructed and given to the participants to answer prior and after the event to measure any changes made to their view and interest of cyber security.
Image emotion recognition has become an increasingly popular research domain in the area of image processing and affective computing. Despite fast-improving classification performance in this task, the understanding a...
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ISBN:
(数字)9781728163956
ISBN:
(纸本)9781728163963
Image emotion recognition has become an increasingly popular research domain in the area of image processing and affective computing. Despite fast-improving classification performance in this task, the understanding and interpretability of its performance are still lacking as there are limited studies on which part of an image would invoke a particular emotion. In this work, we propose a Multi-GAP deep neural network for image emotion classification, which is extensible to accommodate multiple streams of information. We also incorporate feature dependency into our network blocks by adding a bidirectional GRU network to learn transitional features. We report extensive results on the variants of our proposed network and provide valuable perspectives into the class-activated regions via Grad-CAM, and network depth contributions by truncation strategy.
Query rewriting (QR) is an increasingly important technique to reduce customer friction caused by errors in a spoken language understanding pipeline, where the errors originate from various sources such as speech reco...
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Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that closely resemble the source sentences. ...
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Security vulnerabilities of traditional single factor authentication has become a major concern for security practitioners and researchers. To mitigate single point failures, new and technologically advanced Multi-Fac...
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This paper introduces an adaptive parallel pipeline pattern which follows the GRASP (grid-adaptive structured parallelism) methodology. GRASP is a generic methodology to incorporate structural information at compile t...
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This paper introduces an adaptive parallel pipeline pattern which follows the GRASP (grid-adaptive structured parallelism) methodology. GRASP is a generic methodology to incorporate structural information at compile time into a parallel program that enables it to adapt automatically to dynamic variations in resource performance. GRASP instruments the pipeline with a series of pragmatic rules, which depend on particular performance thresholds based on the computation/communication patterns of the program and the availability of resources in the grid. Our parallel pipeline pattern is implemented as a parameterisable C/MPI API using a variable-size input data vector and a stage function array. We have evaluated its efficiency using a numerical benchmark stage function in a non-dedicated computational grid environment.
One of the most important and commonly used operations in many linear algebra functions is matrix-matrix multiplication (GEMM), which is also a key component in obtaining high performance of many scientific codes. It ...
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The automated generation of memory aids known as mnemonics is an application of AI that has great potential to improve education. We used an optimisation method based on techniques from natural language processing (NL...
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ISBN:
(数字)9781665415507
ISBN:
(纸本)9781665415514
The automated generation of memory aids known as mnemonics is an application of AI that has great potential to improve education. We used an optimisation method based on techniques from natural language processing (NLP), corpus linguistics, and genetic algorithms to generate mnemonics called elaborated acronyms (EAs) for four technical lists. The efficacy of these computer-generated mnemonics was then tested in a higher education environment. 54 Malaysian university students were asked to learn the four technical lists containing a total of 30 words, each in strict sequence. Half of the students used the mnemonics generated by computer and the other half used rote learning. We assessed learning immediately after the learning phase and also after a one-week delay. Recall performance of mnemonics users was found to be significantly better than rote learners, both immediately and one week later, suggesting that the generation of mnemonics by computer is a learning technology worth developing.
This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient communication and storage. The proposed approach exploits Sparse Gaussian Process to encode pointcloud into a com...
This paper presents a framework to represent high-fidelity pointcloud sensor observations for efficient communication and storage. The proposed approach exploits Sparse Gaussian Process to encode pointcloud into a compact form. Our approach represents both the free space and the occupied space using only one model (one 2D Sparse Gaussian Process) instead of the existing two-model framework (two 3D Gaussian Mixture Models). We achieve this by proposing a variance-based sampling technique that effectively discriminates between the free and occupied space. The new representation requires less memory footprint and can be transmitted across limited-bandwidth communication channels. The framework is extensively evaluated in simulation and it is also demonstrated using a real mobile robot equipped with a 3D LiDAR. Our method results in a 70~100 times reduction in the communication rate compared to sending the raw pointcloud. We have provided a demonstration video 1 1 Video: https://***/BQZzXiCFGrM and open-sourced our code 2 2 Code: https://***/mahmoud-a-ali/vsgp_pcl.
It is important to understand which view is better recognizing and reconstructing a scene for many robotic applications, especially in a cluttered environment, where objects interact and may occlude one another in all...
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ISBN:
(纸本)9781479969241
It is important to understand which view is better recognizing and reconstructing a scene for many robotic applications, especially in a cluttered environment, where objects interact and may occlude one another in all views. In this paper, we introduce a novel, learning-based approach to evaluate scene recognizability from a view based on the quality and quantity of recognized objects, the recognition uncertainty, and the background recognizability, rather than the visibility. Our study shows that increasing visibility does not guarantee better recognizability of objects. The introduced view evaluator can better characterize which view is more useful for the purpose of autonomous object recognition and scene reconstruction. The approach is validated through experiments, and the effects of many factors to scene recognizability are discussed based on the experimental results.
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