Aribiah Attoe took issue with the materialist and the non-materialist African conceptions of death by arguing that the reality of death puts pressure on the human conception of life’s meaning. He admits the reality of an afterlife experience through a causal principle that sees events in the world as the product of interactions between predetermined past events. It is an afterlife where a decomposing body continues interacting with other things in the world, not an afterlife involving consciousness. While conscious meaning-making (...) ends in the death of the body in Attoe’s materialist view, thereby affirming the conclusion that human existence is meaningless and the universe is purposeless, the conviction about life’s meaninglessness is also reinforced by the possibility of a second death, that is, the death of the transcendent consciousness in the traditional African view. In this article, I will mount two objections to Attoe’s submission. First, I argue that Attoe’s refutation of life’s meaning may be faulted by a world view devoid of the ideas of the afterlife and immortality. Life’s meaninglessness for him should have been limited to the facts of human existence in the world, not the human inability to continue meaning after death. Second, Attoe’s conception of death as finality questions his principle of causality that suggests the reality of an endless afterlife. Attoe’s overarching submission on life’s meaninglessness gives the impression that theories of life’s meaning must be anchored on the thought of an afterlife and immortality. (shrink)

In this article, I draw attention to the value of community in John Mbiti’s philosophy using his famous axiom by reconciling the tension between the individual and community his philosophy envisages. To do this, I offer a reconstruction of Mbiti’s communitarian axiom: “I am because we are; and since we are, therefore I am.” Mbiti is considered one of the forerunners of the communitarian debate in African philosophy. His axiom, which describes his idea of Afro-communitarianism, accounts for the importance of (...) individual and community in thinking about social existence in African thought. However, Mbiti’s argument for the direct influence of the community in the formation of the individual is taken to mean the supremacy of the community over the individual. Mbiti’s concept of community has implications for the idea of personhood in that it raises the question of individual agency. This has affected the reception of Mbiti’s contribution to the idea of community in African political thought. While Mbiti’s contributions to Afro-communitarianism transcend the discourse of personhood, I argue that maximising the potential of his idea requires reconstruction and a critical analysis of his axiom. In doing that, I demonstrate that a possible relationship exists between the “I” and “we” in his axiom that represents a harmony between the community and individual and does not suggest a primacy of either but the significance of community. This analysis will enable fair engagement with Mbiti’s conception of community. (shrink)

In this article we argue that a discussion on African epistemologies must precede the quest for both the decolonisation of knowledge and curriculum in Africa. Decolonial thought in Africa is significant because it focuses, among other things, on the decolonisation of Western epistemological supremacy within the space where knowledge is produced and transferred. We contend that knowledge acquired through the process of learning must resonate with people’s lived experiences and realities. To meaningfully pursue that involves placing in focus people’s modes (...) of thought and epistemic ideas, in the interpretation of their experiences and the realities around them. This article argues that designing a decolonised curriculum that centres unique African intellectual ideas should be informed by the understanding of African epistemologies, whose principle of epistemic validation is grounded on the idea of interconnectedness in African thought. Considering a decolonial curriculum as an attempt in pursuing ‘centring’ is to align curricula with the modes of knowledge validation in African thought. (shrink)

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows its (...) assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has an accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naïve Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution. (shrink)

In this research, we focus on the usage of adversarial sampling to test for the fairness in the prediction of deep neural network model across different classes of image in a given dataset. While several framework had been proposed to ensure robustness of machine learning model against adversarial attack, some of which includes adversarial training algorithm. There is still the pitfall that adversarial training algorithm tends to cause disparity in accuracy and robustness among different group. Our research is aimed at (...) using adversarial sampling to test for fairness in the prediction of deep neural network model across different classes or categories of image in a given dataset. We successfully demonstrated a new method of ensuring fairness across various group of input in deep neural network classifier. We trained our neural network model on the original image, and without training our model on the perturbed or attacked image. When we feed the adversarial samplings to our model, it was able to predict the original category/ class of the image the adversarial sample belongs to. We also introduced and used the separation of concern concept from software engineering whereby there is an additional standalone filter layer that filters perturbed image by heavily removing the noise or attack before automatically passing it to the network for classification, we were able to have accuracy of 93.3%. Cifar-10 dataset have ten categories of dataset, and so, in order to account for fairness, we applied our hypothesis across each categories of dataset and were able to get a consistent result and accuracy. (shrink)

Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...) messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naïve Bayes (MNB) and optimized linear Support Vector Machine(SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action. (shrink)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over (...) the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness. (shrink)

Ever since the era of internet had ushered in cloud computing, there had been increase in the demand for the unlimited data available through cloud computing for data analysis, pattern recognition and technology advancement. With this also bring the problem of scalability, efficiency and security threat. This research paper focuses on how data can be dynamically mine in real time for pattern detection in a secure cloud computing environment using combination of decision tree algorithm and Random Forest over a restful (...) Application Programming Interface (API). We are able to successfully Implement data mining on cloud computing bypassing or avoiding direct interaction with data warehouse and without any terminal involve by using combination of IBM Cloud storage facility, Amazing Web Service, Application Programming Interface and Window service along with a decision tree and Random Forest algorithm for our classifier. We were able to successfully bypass direct connection with the data warehouse and cloud terminal with 94% accuracy in our model. (shrink)

The ever-evolving ways attacker continues to improve their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable (...) framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection. (shrink)

Data mining possess a significant threat to ethics, privacy, and legality, especially when we consider the fact that data mining makes it difficult for an individual or consumer (in the case of a company) to control accessibility and usage of his data. Individuals should be able to control how his/ her data in the data warehouse is being access and utilize while at the same time providing enabling environment which enforces legality, privacy and ethicality on data scientists, or data engineer (...) during data mining process. This paper review issues of legality, privacy, and ethicality in data mining, review processes of Data mining, and also proposes solution to current ethical and privacy issue in data mining. It introduces a new method which enforces data mining without infringing on the privacy of individual or consumer whose data are being used. The sole aim of this paper is to propose a new method of mining data which restricts scientists within the constraints of legality, privacy, and ethicality. (shrink)

Today, we have a mixture of young and older individuals, people with special needs, and people who can care for themselves. Over 1 billion people are estimated to be disabled; this figure corresponds to about 15% of the world's population, with 3.8% (approximately 190 million people) accounting for people aged 15 and up (Organization, 2011). The number of people with disabilities is upward due to the increase in chronic health conditions and many other things. These and other factors have made (...) the need for proper care facilities urgent in today's society. Several care facilities are built to help people with disabilities live their everyday lives and not be left out of the community. (shrink)

Unless and until our society recognizes cyber bullying for what it is, the suffering of thousands of silent victims will continue.” ~ Anna Maria Chavez. There had been series of research on cyber bullying which are unable to provide reliable solution to cyber bullying. In this research work, we were able to provide a permanent solution to this by developing a model capable of detecting and intercepting bullying incoming and outgoing messages with 92% accuracy. We also developed a chatbot automation (...) messaging system to test our model leading to the development of Artificial Intelligence powered anti-cyber bullying system using machine learning algorithm of Multinomial Naïve Bayes (MNB) and optimized linear Support Vector Machine(SVM). Our model is able to detect and intercept bullying outgoing and incoming bullying messages and take immediate action. (shrink)

To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art (...) machine learning models depends on different algorithms for training which also impact their suitability for detection of a particular type of cyberattack. In this research, we analyzed each of the current state-of-the art machine learning models for different types of cyberattack detection from the past 10 years with a major emphasis on the most recent works for comparative study to identify the knowledge gap where work is still needed to be done with regard to detection of each category of cyberattack. (shrink)

Yleensä Wienin piirin loogista empirismiä kannattaneet filosofit mielletään antirealisteiksi. Tässä artikkelissa kuitenkin argumentoidaan, että piirin johtohahmo Moritz Schlick oli eräänlainen realisti myös nykystandardien valossa. Näin ollen – niin yllättävältä kuin se kuulostaakin – positivismi ja realismi ovat yhteensovitettavissa. Schlick tosin erotti kannattamansa empiirisen realismin jyrkästi metafyysisestä realismista, jota hän piti merkityksettömänä. Schlickin realismin esittelyn lisäksi artikkelissa tarkastellaan yleisellä tasolla hänen epistemologiaansa, kielifilosofiaansa ja metafysiikan kritiikkiään. Tekstissä pohditaan myös Schlickin suosiman realismin asemaa ja relevanssia nykyisessä realismikeskustelussa.