An Internet meme is a catchphrase, concept, or piece of media that is spread by means of the Internet, regularly through online media platforms and particularly for comical purposes. Detection of Online Hateful speech...
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The long-term usability of digital building documents is essential for the maintenance and optimization of infrastructure portfolios. It supports the preservation of building-specific knowledge and the cultural herita...
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Internet of Softwarized Things (IoST) is a promising and dynamic programmable technology that has the capability to interconnect sensor devices with an objective to share the accumulated data in the network without th...
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Poor software quality can lead to crashes, failures or downtime, the consequences of which may be lost profits, financial costs, leakage or loss of data, accidents, human casualties, material losses or environmental d...
Poor software quality can lead to crashes, failures or downtime, the consequences of which may be lost profits, financial costs, leakage or loss of data, accidents, human casualties, material losses or environmental disasters. The total cost of poor software quality for IT companies in the USA is estimated at ${\$}$2.41 trillion per year and tends to increase. Among the identified costs, the cost of unsuccessful projects is estimated at ${\$}$260 billion, and the total cost of operational failures caused by poor quality software is estimated at ${\$}$1.81 trillion. Late finding and fixing of software defects directly affects the success of the project as a whole, since it significantly increases the cost and development time, and affects its quality. This paper discussed the relationship between factors affecting the software development process and various groups of defects. It has been found that the leading groups of defects affecting the software quality are the defects in software requirements and defects in the design of software interfaces for human-computer interaction with the total percentage of influence of 33.3%.
Species interaction networks are a powerful tool for describing ecological communities;they typically contain nodes representing species, and edges representing interactions between those species. For the purposes of ...
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The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-dri...
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ISBN:
(数字)9798350373332
ISBN:
(纸本)9798350373349
The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from ₹3000 to just ₹300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners
Money laundering is an unlawful activity that has caused underdevelopment to Africa, it's a ravaging vices that cripples economic growth, increases criminality and leads to economic sabotage. Many works previously...
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ISBN:
(数字)9798350376838
ISBN:
(纸本)9798350376845
Money laundering is an unlawful activity that has caused underdevelopment to Africa, it's a ravaging vices that cripples economic growth, increases criminality and leads to economic sabotage. Many works previously done on money laundering detection, it was observed that it takes times before fraudulent transactions can be detected, it is also less effective, meaning that is can easily be circumvents and the data sampling methods used in previous researches makes the model to suffer from overfitting and loss of information, however this research adopts SMOTE (Synthetic Minority Oversampling Techniques) algorithm method to balance the dataset so as to have a fair representation of the two classes in order for machine learning model to accurately evaluate the models. The Objective of this research is to adapt and implement decision tree ensemble learning algorithms to detect money laundering activities, also design and implement an Anti Money Laundering (AML) query system to mitigate money laundering activities based on detection results and finally to carry out the performance evaluation of decision tree ensemble algorithms using standard performance metrics.
The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is ...
The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0°&90° to 60°&150°, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections.
During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of informatio...
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This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations bet...
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This paper describes our approach to automatically identify paired Discourse Connectives (DCs) in Chinese texts. Discourse Connectives (DCs) are terms that connect two text spans and signal the discourse relations between them. Most DCs consist of a consecutive words (eg. as a result); however paired DCs are composed of non-consecutive words that together signal the discourse relation (eg. on one hand … on the other hand). Although paired DCs are not common in English, they are very frequent in Chinese. The contribution of this paper in two-fold: First, we propose a methodology for the automatic identification of Chinese paired DCs. Second, we present a new corpus based on the Chinese Discourse Treebank (CDTB) [1] annotated with paired DCs. To identify paired DCs, we experimented with two main approaches: hypothesis testing and supervised machine learning. Although the hypothesis testing approaches led to lower than expected results, the simple machine learning models achieved F-scores between 72.5%–75.6% with no fine-tuning.
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