Instance-Based Hyper-Tableaux for Coherent Logic

We consider a fragment of first-order logic known as coherent logic or geometric logic. The essential difference to standard clausal form is that there may be existentially quantified variables in the positive literals of a clause, and only constants and variables are allowed as terms. Coherent logic is interesting because many problems naturally fall into the fragment. Furthermore, the simple term structure might allow for efficient implementations. We propose a calculus for this fragment that extends the `next-generation' hyper-tableaux calculus of Baumgartner, and prove it sound and complete. To our knowledge, this is the first instance-based method that works on a richer input than clause normal form

A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks

Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks or updated versions of Recurrent Neural Networks (RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in handling long dependencies between input sequence elements and enable parallel processing. As a result, transformer-based models have attracted substantial interest among researchers in the field of artificial intelligence. This can be attributed to their immense potential and remarkable achievements, not only in Natural Language Processing (NLP) tasks but also in a wide range of domains, including computer vision, audio and speech processing, healthcare, and the Internet of Things (IoT). Although several survey papers have been published highlighting the transformer's contributions in specific fields, architectural differences, or performance evaluations, there is still a significant absence of a comprehensive survey paper encompassing its major applications across various domains. Therefore, we undertook the task of filling this gap by conducting an extensive survey of proposed transformer models from 2017 to 2022. Our survey encompasses the identification of the top five application domains for transformer-based models, namely: NLP, Computer Vision, Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze the impact of highly influential transformer-based models in these domains and subsequently classify them based on their respective tasks using a proposed taxonomy. Our aim is to shed light on the existing potential and future possibilities of transformers for enthusiastic researchers, thus contributing to the broader understanding of this groundbreaking technology

Logical concepts in cryptography

This thesis is about a breadth-first exploration of logical concepts in cryptography and their linguistic abstraction and model-theoretic combination in a comprehensive logical system, called CPL (for Cryptographic Protocol Logic). We focus on two fundamental aspects of cryptography. Namely, the security of communication (as opposed to security of storage) and cryptographic protocols (as opposed to cryptographic operators). The primary logical concepts explored are the following: the modal concepts of belief, knowledge, norms, provability, space, and time. The distinguishing feature of CPL is that it unifies and refines a variety of existing approaches. This feature is the result of our wholistic conception of property-based (modal logics) and model-based (process algebra) formalisms

Improving Retrieval of Information from the Internet

To improve the quality of the search result returned by the internet which makes users have to look through a huge amount of links for the real answers, we utilized the high quality links Google produces and the Information Retrieval technology to implement a Question Answering (QA) system. This system analyzes and downloads the text contents from the relevant web pages Google searches based on the users\u27 questions to build a dynamic knowledge collection; retrieves the relevant passages from the collection and sends the ranked passages back. The users can further refine their questions in the query refinement step for the better answers. A novel search strategy was designed to detect the semantic connections between the question and the documents. This answer retrieval also involves the TF-IDF algorithm and Vector Space Model for the document indexing. We have modified the original Cosine Coefficient Similarity Measurement to rank the candidate answers