A Defense of Frank Jackson\u27s Two-Dimensional Analysis of the Necessary A Posteriori from Scott Soames\u27 Anti-Two-Dimensionalist Attacks

In his Reference and Description, Scott Soames raises a number of objections against the two-dimensionalists and their analysis of the necessary a posteriori. Frank Jackson is just one of several two-dimensionalists whose analysis of the necessary a posteriori comes under attack from Scott Soames. This paper is meant as a defense of Frank Jackson from the objections of Scott Soames. It begins with a summary of traditional descriptivism and Saul Kripke\u27s anti-descriptivist objections to that view, followed by a summary of Jackson\u27s two-dimensional analysis of the necessary a posteriori. The paper then provides a summary of Scott Soames\u27 objections to Frank Jackson\u27s two-dimensional program, followed by responses to those objections

Obligations of The State: State Behavior and The Occurrence of State Success

The start of the twenty-first century has coincided with the emergence of the concept of state failure as a major humanitarian and security concern. While the occurrence of state failure has received much attention, there has been little agreement on the actual definition and conceptualization of state failure. This study intends to aid in the improvement on the discourse of state failure by providing a new approach on failed states that analyzes shifts in state fragility. In this new approach, this study focuses on the occurrence of state success and explain how states succeed by focusing on the fulfillment of the different classes of obligations states are expected to complete under the modern interpretation of the nation-state. To show utility of this study’s conceptualization on the driving factors behind state success and state failure, a multinomial logit analysis is conducted that tests possible determinants of success and failure against occurrences of transitions for states based off their level or fragility risk. Findings from the analysis reveal a strong causal relationship between the completion of state obligations and occurrences of state success. A noteworthy finding from the analysis is the very influential relationship between the providing of human developmental public goods, including education and health care, and the occurrence of state success

Real-Time Face Detection and Recognition

The face has become a popular biometric for identification due to the wide range of features and difficulty in manipulation of the metric. In order to work towards a robust facial recognition system, this work contains a foundation for using the face as a recognition metric. First, faces are detected from still images using a Viola-Jones object detection algorithm. Then, Eigenfaces is applied to the detected faces. The system was tested on face databases as well as real-time feed from a web camera

How Facial Features Convey Attention in Stationary Environments

Awareness detection technologies have been gaining traction in a variety of enterprises; most often used for driver fatigue detection, recent research has shifted towards using computer vision technologies to analyze user attention in environments such as online classrooms. This paper aims to extend previous research on distraction detection by analyzing which visual features contribute most to predicting awareness and fatigue. We utilized the open-source facial analysis toolkit OpenFace in order to analyze visual data of subjects at varying levels of attentiveness. Then, using a Support-Vector Machine (SVM) we created several prediction models for user attention and identified the Histogram of Oriented Gradients (HOG) and Action Units to be the greatest predictors of the features we tested. We also compared the performance of this SVM to deep learning approaches that utilize Convolutional and/or Recurrent neural networks (CNNs and CRNNs). Interestingly, CRNNs did not appear to perform significantly better than their CNN counterparts. While deep learning methods achieved greater prediction accuracy, SVMs utilized less resources and, using certain parameters, were able to approach the performance of deep learning methods

Fast Sobel Edge Detection Using Parallel Pipeline-based Architecture on FPGA

Implementing image processing algorithms on FPGA has recently become more popular since it provides high speed in comparison with software-based approaches. In this paper, we have presented fast pipeline-based architecture for one of the most popular edge detection algorithms called Sobel edge detection. The objective of our work is to present two fast pipeline-based architectures for Sobel edge detection on FPGA benefiting one and two way parallelism. We used Verilog language to implement our designs and we synthesized each one for Cyclone IV FPGA. Experimental results show that our pipeline-based architectures perform edge detection process more than 379 and 751 times faster than software-based approach using MATLAB

LVLane: Deep Learning for Lane Detection and Classification in Challenging Conditions

Lane detection plays a pivotal role in the field of autonomous vehicles and advanced driving assistant systems (ADAS). Over the years, numerous algorithms have emerged, spanning from rudimentary image processing techniques to sophisticated deep neural networks. The performance of deep learning-based models is highly dependent on the quality of their training data. Consequently, these models often experience a decline in performance when confronted with challenging scenarios such as extreme lighting conditions, partially visible lane markings, and sparse lane markings like Botts' dots. To address this, we present an end-to-end lane detection and classification system based on deep learning methodologies. In our study, we introduce a unique dataset meticulously curated to encompass scenarios that pose significant challenges for state-of-the-art (SOTA) models. Through fine-tuning selected models, we aim to achieve enhanced localization accuracy. Moreover, we propose a CNN-based classification branch, seamlessly integrated with the detector, facilitating the identification of distinct lane types. This architecture enables informed lane-changing decisions and empowers more resilient ADAS capabilities. We also investigate the effect of using mixed precision training and testing on different models and batch sizes. Experimental evaluations conducted on the widely-used TuSimple dataset, Caltech lane dataset, and our LVLane dataset demonstrate the effectiveness of our model in accurately detecting and classifying lanes amidst challenging scenarios. Our method achieves state-of-the-art classification results on the TuSimple dataset. The code of the work will be published upon the acceptance of the paper.Comment: 8 page