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Handwriting style of an individual will be usually varying from other individuals. The degree of complexity involved in recognition of a handwritten character of south Indian languages is high due to the presence of compound characters. Furthermore the complexity of interpreting handwritten text aggravates due to irregular handwriting styles. Building of datasets for training the database in such environments is tremendous and requires a lot of manual data processing and other preliminary tasks. The major contribution focuses on creation of morphable feature space for addressing handwritten recognition problem. In this paper, techniques useful for simplifying dataset preparation procedures along with a hybrid recognition approach for improvising recognition accuracies of Telugu handwritten characters are proposed. The techniques include text document segmentation, automatic cropping and pincushion and distance transformation based recognition approach. A feature vector is generated from the normalized Gabor features are extracted from pincushion and distance transform models of a character image and classified using Ada boost classifier with a recognition accuracy of 89%.

First order logic lies at the core of many methods in mathematics, philosophy, linguistics, and computer science. Although important efforts have been made to extend first order logic to the task of handling uncertainty, existing solutions are sometimes limited by the way they model uncertainty, or simply by the complexity of the problem formulation. These approaches could be strengthened by adding more flexibility in assigning probabilities (e.g., through intervals) and a more rigorous method of assigning probability/uncertainty measures. In this paper we present the basic theory of Uncertain Logic Processing (ULP), a robust framework for modeling and inference when information is available in the form of first order logic formulas subject to uncertainty. Dempster–Shafer (DS) theory provides the substrate for uncertainty modeling in the proposed ULP formulation. ULP can be tuned to preserve consistency with classical logic, allowing it to incorporate typical inference rules and properties, while preserving the strength of DS theory for representing and manipulating uncertainty.

The 2020 presidential campaign was plagued by charges of voter fraud both before and after the election took place. While past literature finds that electoral losers are most likely to express misgivings about election integrity, little else is known about the characteristics of individuals who exhibit these beliefs or how the beliefs have changed over time. Employing national surveys from 2012, 2016, 2018, and 2020, we examine the levels of pre-election expectations of fraud in the event of an electoral loss over time, as well as the individual-level correlates of beliefs in a range of election-related conspiracy theories prominent in 2020. Our analysis reveals that beliefs in election fraud are common and stable across time, and only occasionally relate to partisanship. Moreover, we find that, even accounting for the influence of partisan motivated reasoning, several psychological orientations––conspiracy thinking, anomie, dark triad personality traits, and denialism––play a unique role in promoting perceptions of voter fraud.

In this paper, a computer-based application has been proposed that facilitates speech recognition in a multi-speaker environment. It identifies the speaker, recognizes the speech and displays the text output obtained from speech to text conversion along with the identified speaker’s name tag on the computer screen. The proposed system provides three functionalities: i) Speech Recognition ii) Speaker Identification iii) Many to one transmission-reception of converted speech to text using the client-server model and multi-threading concept. The system consists of transmission and reception capable devices like computers, a computer application that connects various devices using WiFi technology and microphone to obtain speech input from the users. All the functionalities are implemented as a computer application with a user-friendly graphical user interface(GUI). The application has two scenarios: i) Near and ii) Far. In the case when the speakers are in the range of the user’s microphone, the user opts for near scenario else user opts for the far scenario. The proposed system is intended mainly for people suffering from hearing loss. Although hearing aid technology is greatly improved, it still utilizes an impaired person’s inner, outer and middle ear including hearing nerve to support natural hearing.

In delay-based TCP congestion avoidance mechanisms, a source adjusts its window size to adapt to changes in network conditions as measured through changing queueing delays. Although network calculus (NC) has been used to study window flow control and determine performance bounds, there is a lack of a bridge between NC theory and the practical issues of delay-based TCP congestion avoidance. In this paper, we use an NC-based approach to derive ideal congestion controllers for representative delay-based window flow control models with time-variant feedback delays. We show that the basic delay-based TCP congestion avoidance mechanisms in TCP Vegas, Enhanced TCP Vegas, and FAST TCP can be viewed as different approaches to approximating a certain NC controller. Moreover, we derive another NC controller that is explicitly constructed to address the throughput degradation of the current delay-based methods due to delayed acknowledgement (ACK) packets caused by network traffic in the reverse path (ACK packet) direction. The approximation of this F-model NC-based controller is shown to provide better throughput and fairness over the existing delay-based methods for a variety of network topologies in ns-2 simulations.