Nations, governments, corporations, and individuals are under threat following the implications of crimes in the Internet. Windows Vista launched by Microsoft contains special features that appear to make commercial p...
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Nations, governments, corporations, and individuals are under threat following the implications of crimes in the Internet. Windows Vista launched by Microsoft contains special features that appear to make commercial products redundant for which, developers of such products have inevitably sought to stress the perceived flaws of Microsoft's release. Patchguard, Vista's 64-bit version makes it difficult for people to interfere with the windows kernel, which is the very center of its operating system. Future versions of Patchguard is expected to be secured by the use of more advanced processor chips, although Patchguard makes it difficult to interfere with Windows. Windows Vista also includes the Windows Defender system, which was earlier known as Microsoft AntiSpyware and Windows Vista also incorporated an improved firewall.
In this paper we examine the possibility of using artificial intelligence (AI) to improve academic advisement of students within the school of computing and Information technology (SCIT) at the University of Technolog...
In this paper we examine the possibility of using artificial intelligence (AI) to improve academic advisement of students within the school of computing and Information technology (SCIT) at the University of technology, Jamaica (Utech). Described as one of the important challenges facing academics [1], academic advisement plays a vital role in student completion. All students at Utech are assigned academic advisors and encouraged to access advisors for advisement. Each faculty manages the process internally. Students are not mandated to seek advisement but are strongly encouraged to do so to allow them to make informed choices related to module selection, academic probation, grade forgiveness, etc. Within SCIT the rate of take up is less than desired resulting in some students going on academic probation, having to switch programs in some cases or failing out of their program. We will explore the automation of the academic advisement process by using AI to push relevant information to students related to their performance. The system will be coded to recognize common situations and contact the students providing information relevant to the situation and schedule an advisement session with the academic advisor (AA).
Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both enti...
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Temporal knowledge graph(TKG) reasoning, has seen widespread use for modeling real-world events, particularly in extrapolation settings. Nevertheless, most previous studies are embedded models, which require both entity and relation embedding to make predictions, ignoring the semantic correlations among different entities and relations within the same timestamp. This can lead to random and nonsensical predictions when unseen entities or relations occur. Furthermore, many existing models exhibit limitations in handling highly correlated historical facts with extensive temporal depth. They often either overlook such facts or overly accentuate the relationships between recurring past occurrences and their current counterparts. Due to the dynamic nature of TKG, effectively capturing the evolving semantics between different timestamps can be *** address these shortcomings, we propose the recurrent semantic evidenceaware graph neural network(RE-SEGNN), a novel graph neural network that can learn the semantics of entities and relations simultaneously. For the former challenge, our model can predict a possible answer to missing quadruples based on semantics when facing unseen entities or relations. For the latter problem, based on an obvious established force, both the recency and frequency of semantic history tend to confer a higher reference value for the current. We use the Hawkes process to compute the semantic trend, which allows the semantics of recent facts to gain more attention than those of distant facts. Experimental results show that RE-SEGNN outperforms all SOTA models in entity prediction on 6 widely used datasets, and 5 datasets in relation prediction. Furthermore, the case study shows how our model can deal with unseen entities and relations.
This study presents a revolutionary deep-learning architecture that focuses on feature extraction, feature selection, and sales forecasting. The technique begins with a pre-processing step using median imputation and ...
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Residential burglary is a severe crime that affects millions of residents each year. It is critical to analyze patterns of human behavior in surveillance video data and discover suspicious actions to avoid and deter t...
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The emergence of multimodal disease risk prediction signifies a pivotal shift towards healthcare by integrating information from various sources and enhancing the reliability of predicting susceptibility to specific d...
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The disease that contains the highest mortality and morbidity across the world is cardiac disease. Annually millions of people are affected and deaths take place due to cardiac diseases worldwide. There are various di...
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Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, ...
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Software security poses substantial risks to our society because software has become part of our life. Numerous techniques have been proposed to resolve or mitigate the impact of software security issues. Among them, software testing and analysis are two of the critical methods, which significantly benefit from the advancements in deep learning technologies. Due to the successful use of deep learning in software security, recently,researchers have explored the potential of using large language models(LLMs) in this area. In this paper, we systematically review the results focusing on LLMs in software security. We analyze the topics of fuzzing, unit test, program repair, bug reproduction, data-driven bug detection, and bug triage. We deconstruct these techniques into several stages and analyze how LLMs can be used in the stages. We also discuss the future directions of using LLMs in software security, including the future directions for the existing use of LLMs and extensions from conventional deep learning research.
For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven mode...
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For the diagnostics and health management of lithium-ion batteries, numerous models have been developed to understand their degradation characteristics. These models typically fall into two categories:data-driven models and physical models, each offering unique advantages but also facing ***-informed neural networks(PINNs) provide a robust framework to integrate data-driven models with physical principles, ensuring consistency with underlying physics while enabling generalization across diverse operational conditions. This study introduces a PINN-based approach to reconstruct open circuit voltage(OCV) curves and estimate key ageing parameters at both the cell and electrode *** parameters include available capacity, electrode capacities, and lithium inventory capacity. The proposed method integrates OCV reconstruction models as functional components into convolutional neural networks(CNNs) and is validated using a public dataset. The results reveal that the estimated ageing parameters closely align with those obtained through offline OCV tests, with errors in reconstructed OCV curves remaining within 15 mV. This demonstrates the ability of the method to deliver fast and accurate degradation diagnostics at the electrode level, advancing the potential for precise and efficient battery health management.
Reinforcement learning (RL) systems can be complex and non-interpretable, making it challenging for non-AI experts to understand or intervene in their decisions. This is due in part to the sequential nature of RL in w...
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