Identification

In his political novel, Autumn , Ali Smith has implicitly integrated family life with political matrices of British-European relations. In this study, Autumn was investigated in light of Kenneth Burke's notions of identification, substance, symbolism, and imagination known as rhetoric of motives. Identification refers to identifying with characters having some components or substances in common. Rhetoric of motives refers to the application of terms with persuasive function that move people into action. The themes of Ali Smith's Autumn are in congruity with Kenneth Burke’s theoretical ideas represented as theme of love to explain illogical coupling of a young woman and an old man that recalls England and Europe/EU interconnection and mismatching relations. This study explored how the rhetoric and dialect of Smith that have symbolic meanings and functions is motivating through identification with characters. Burke believes that through rhetoric of motives and grammar of motives it is possible to create protagonists that people can easily identify with to persuade them into action based on their intended political views. Through rhetoric of motives by the seasonal novel via identification, symbolism, and imagination, Smith indirectly portrays the political event of Brexit (British Exit) and its consequences in British society.

Human Identification has been widely implemented to enhance the efficiency of surveillance systems, however, systems based on common CCTV (closed-circuit television) cameras are mostly incompatible with the advanced identification algorithms which aim to extract the facial features or speech of an individual for identification. Gait (i.e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i.e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance. This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i.e., probability of the match being positive) between two gait profiles. This is achieved by incorporating computer vision, digital image processing, vectorization, artificial intelligence, and multi-threading. The proposed model extracts gait profiles (from low-resolution camera feeds) by breaking down the complete gait cycle into four quarter-cycles using the variations in the width of the region-of-interest and then saves the gait profile in the form of four distinct projections (i.e., vectors) of length 20 units each, thus, summing up to 80 features for each individual’s gait profile. The focus of this study revolved around the speed-accuracy tradeoff of the proposed model where, with a limited dataset and training, the model runs at a speed of 30Hz and yields 85% accurate results on average. A Receiver Operating Characteristic Curve (ROC) is obtained for comparison of the proposed model with other machine learning models to better understand the efficiency of the system