Sorting and merging are two problems that commonly arise in Computer Science especially in data processing tasks. To solve these problems, several algorithms have been developed. Similarly, existing merge and sorting algorithms have been improved to provide more efficient and accurate results. In this paper, selection and merging algorithms were developed on an octa-core processing machine using System.nanoTime methods in Java in order to compare their running times. The results obtained show that Merge Sort performs far better than selection sort with careful implementations by taking advantage of multiple processing cores in the test machine and some concurrency utility in Java. It was concluded that implementing algorithms using a machine with multiple numbers of cores in their Central Processing Unit (CPU) will result in a significant improvement in the performance of both algorithms.
Internet technology has given Banks the opportunity to provide customers robust, convenient and flexible banking services including, but not limited to fund transfers, account checking and payment of bills. Despite these huge benefits, e-banking has given rise to so many security concerns arising from countless threats. The rise in security threats against e-banking has caused a decline in the use of online banking and has negatively affected customer confidence in the ability of banks to protect their money and information and are looking up to the banks to fix the problems. This research, Improved Online Security Framework for e-banking services is geared towards developing an improved security framework that solves the issues of authentication, confidentiality, integrity and non-repudiation as it pertains to online banking attacks. Data was collected from primary and secondary sources ranging from interviewing relevant stakeholders that use internet banking to consultations of related journals articles and technical reports. Design and modeling tools such as UML usecases, Entity relationship (E-R) diagrams, process flow modeling and MySQL for a robust database design were used to capture basic system functionalities and artifacts required. The entire design was implemented on Visual studio platform. Upon running and testing on a XAMPP server, the system was found to meet all design objectives and operationally effective.
Hate speech on online social networks is a general problem across social media platforms that has the potential of causing physical harm to the society. The growing number of hateful comments on the Internet and the rate at which tweets and posts are published per second on social media make it a challenging task to manually identify and remove the hateful commentsfrom such posts. Although numerous publications have proposed machine learning approaches to detect hate speech and other antisocial online behaviours without concentrating on blocking the hate speech from being published on social media. Similarly, prior publications on deep learning and multi-platform approaches did not work on the topic of detecting hate speech in Englishlanguage comments on Twitter and Facebook. This paper proposed a deep learning approach based on a hybrid of convolutional neural network (CNN) and long short-term memory (LSTM) with pre-trained GloVe words embedding to automatically detect and block hate speech on multiple social media platforms including Twitter and Facebook. Thus, datasets were collected from Twitter and Facebook which were annotated as hateful and non-hateful. A set of features were extracted from the datasets based on word embedding mechanism, and the word embeddings were fed into our deep learning framework. The experiment was carried out as a three independent tasks approach. The results show that our hybrid CNN-LSTM approach in Task 1 achieved an f1-score of 0.91, Task 2 obtained an f1-score of 0.92, and Task 3 achieved an f1-score of 0.87. Thus, there is outstanding performance in classifying text as Hate speech or non-hate speech in all the considered metrics. Based on the findings, we conclude that hatespeech can be detected and blocked on social media before it can reach the public.
Background: The processor affinity library in Java can switch ON all the processing cores available in a multi-processing environment. Using this feature will enable concurrent programmers to fully utilize the benefits of processing power available in multi-core processors. Therefore, this study aims to use processor affinity to develop four different frameworks that could optimize the efficiency of quick sorting algorithms on multi-core platforms. Methods: Benchmarking is the method used to carry out all the experiments and test the developed algorithm's efficiencies using all four frameworks. An Octa-core machine with eight (8) processing cores was used to develop and run the algorithms to measure their running times and compare their performances. JDK 12.0 is the version of Java used for development. An array data structure containing One Million Elements (1,000,000) was used as the preferred data structure. Results: The results obtained show that processor affinity can improve the performance of quick sorting algorithms by ensuring no processing core is idled during the computation of results. The results also show that processor affinity and Work-Stealing-Action perform similar functions by ensuring that available cores in a machine are fully utilized to handle tasks and improve performance. It was further found that the algorithm developed using the Executor Services framework outperformed all the three other frameworks implemented using Naïve, Fork-Join, and Sequential implementations. Conclusion: It was concluded that processor affinity improves the performances of all the four different implementations of quick sorts. It was also concluded that in the fork-join framework, Work-Stealing-Action performed by the worker-threads has an effect similar to that of affinity by ensuring that all the processing cores are fully utilized to improve performance. Further investigations can be carried out using machines with more processing cores in their CPU using a different data structure such as a Link List Array of the same or different data size to see whether the same or different conclusions can be drawn.
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