Usable Privacy Mechanisms in Home Security Camera Systems

IoT is the interconnection of People and things. When our home is connected to IoT devices it is referred to as smart home. The idea behind smart home is to make life easier such that there is little human intervention. The IoT devices in our smart home exchange data for storage and processing. This exchange of data leads to users concerns on data security and privacy. In this work, we implemented home security camera systems in such a way that the data is encrypted first before being sent to the cloud in a very simplified and almost automatic encryption process. This implementation was done putting in mind usability. A questionnaire was used to gather results on users’ perception about the system. The user study conducted yielded positive result

Twitter opinion about leaders

Mutual respect between leaders and followers is a key prerequisite to success. The opinion of followers in challenging this leadership is just as great as it has been portrayed by the uprisings in North Africa and the Middle East tagged as the “Twitter or Social media revolution”. The sudden eruption of activities in the area of opinion mining, which deals with the computational analysis of opinion, sentiment, and subjectivity in text, has thus occurred as a means of responding directly to the surge of interest that deals with opinions and use of information technologies to seek out and understand the opinions of others. This study focused on identifying a set of suitable features and an appropriate classifier that can be used for detecting and classification of opinions about leaders in tweets. Words, unigram, bigram and negation features were used alongside Naïve Bayes (NB) and Support Vector Machine (SVM) learning algorithms. The results show that using NB with unigrams can indicate opinions about leaders of up to 91.41% accuracy and can therefore be used to suggest ways to improve a leader’s reputation as well as predicting potential candidates in political election

Regularization Effects in Deep Learning Architecture

This research examines the impact of three widely utilized regularization approaches -- data augmentation, weight decay, and dropout --on mitigating overfitting, as well as various amalgamations of these methods. Employing a Convolutional Neural Network (CNN), the study assesses the performance of these strategies using two distinct datasets: a flower dataset and the CIFAR-10 dataset. The findings reveal that dropout outperforms weight decay and augmentation on both datasets. Additionally, a hybrid of dropout and augmentation surpasses other method combinations in effectiveness. Significantly, integrating weight decay with dropout and augmentation yields the best performance among all tested method blends. Analyses were conducted in relation to dataset size and convergence time (measured in epochs). Dropout consistently showed superior performance across all dataset sizes, while the combination of dropout and augmentation was the most effective across all sizes, and the triad of weight decay, dropout, and augmentation excelled over other combinations. The epoch-based analysis indicated that the effectiveness of certain techniques scaled with dataset size, with varying results