Even though every individual is entitled to freedom of speech, some limitations exist when this freedom is used to target and harm another individual or a group of people, as it translates to hate speech. In this stud...
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Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to d...
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Free speech is essential, but it can conflict with protecting marginalized groups from harm caused by hate speech. Social media platforms have become breeding grounds for this harmful content. While studies exist to detect hate speech, there are significant research gaps. First, most studies used text data instead of other modalities such as videos or audio. Second, most studies explored traditional machine learning algorithms. However, due to the increase in complexities of computational tasks, there is need to employ complex techniques and methodologies. Third, majority of the research studies have either been evaluated using very few evaluation metrics or not statistically evaluated at all. Lastly, due to the opaque, black-box nature of the complex classifiers, there is need to use explainability techniques. This research aims to address these gaps by detecting hate speech in English and Kiswahili languages using videos manually collected from YouTube. The videos were converted to text and used to train various classifiers. The performance of these classifiers was evaluated using various evaluation and statistical measurements. The experimental results suggest that the random forest classifier achieved the highest results for both languages across all evaluation measurements compared to all classifiers used. The results for English language were: accuracy 98%, AUC 96%, precision 99%, recall 97%, F1 98%, specificity 98% and MCC 96% while the results for Kiswahili language were: accuracy 90%, AUC 94%, precision 93%, recall 92%, F1 94%, specificity 87% and MCC 75%. These results suggest that the random forest classifier is robust, effective and efficient in detecting hate speech in any language. This also implies that the classifier is reliable in detecting hate speech and other related problems in social media. However, to understand the classifiers’ decision-making process, we used the Local Interpretable Model-agnostic Explanations (LIME) technique to explain the
With their limitless potential for highly accurate decision-making behaviors, machine learning algorithms have emerged to influence the world of information systems. Algorithms designed for structured and unstructured...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data r...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O（n） communication and storage overhead compared to prior O（n2） schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.
With cutting-edge functionality, 3D Shopping (Avatar Retail) promises to revolutionize the online shopping experience by seamlessly integrating gamification, social media, and augmented reality features. By immersing ...
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It is seen that there is a rapid increase in Internet of Things (IoT) devices, and fog computing has emerged as an efficient and viable solution to reduce latency by performing computations closer to the edge. However...
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Many challenges are faced by agricultural sector due to various dependent factors like changes in climatic conditions, variation in soil properties and farming practices. What to plant in their agricultural land is th...
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The "Automated DSM Insight Analyzer" is a groundbreaking initiative that revolutionizes the analysis of Daily Stand-Up Meetings (DSMs). Leveraging a sophisticated combination of cutting-edge technologies, in...
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Agriculture is the backbone of the economic system for any country and for ages, agriculture has been related with the production of vital food crops to satisfy the needs of consumers. Farmers must meet the changing n...
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Large Language Models (LLMs) have emerged as strategic in the promotion of natural language processing (NLP) to the extent of having machines translate languages as well as making logical deductions. The role of the o...
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