Logical Operator Estimation

This report proposes an estimation method to find the unknown boolean input variable of a logical operation (AND, OR, XOR). The estimators might help to assess if the association between an input and output signal is the result of simple logical operations.

A Logical Operator Oriented Face Retrieval Approach: How to Identify a Suspect Using Partial Photo Information from Different Persons?

Facial sketch recognition is one of the most commonly used method to identify a suspect when only witnesses are available, which, however, usually leads to four gaps, i.e. memory gap, communication gap, description-sketch gap, and sketch-image gap. These gaps limit its application in practice to some extent. To circumvent these gaps, this paper therefore focus on the problem: how to identify a suspect using partial photo information from different persons. Accordingly, we propose a new Logical Operation Oriented Face Retrieval (LOOFR) approach provided that partial information extracted from several different persons' photos is available. The LOOFR defines the new AND and OR operators on these partial information. For example, " eyes of person A AND mouth of person B" means retrieving the target person whose eyes and mouth are similar to that of person A and person B respectively, while "eyes of person A OR eyes of person B" means retrieving target person whose eyes are similar to both person A and B. Evidently, these logical operators cannot be directly implemented by INTERSECTION and UNION in set operations. Meanwhile, they are better for human understanding than set operators. Subsequently, we propose a two-stage LOOFR approach, in which the representations of partial information are learned in the first stage while the logical operations are processed in the second stage. As a result, the target photo of a suspect can be retrieved. Experiments show its promising results.

A Logical Operator Oriented Face Retrieval Approach: How to Identify a Suspect Using Partial Photo Information from Different Persons?

Facial sketch recognition is one of the most commonly used method to identify a suspect when only witnesses are available, which, however, usually leads to four gaps, i.e. memory gap, communication gap, description-sketch gap, and sketch-image gap. These gaps limit its application in practice to some extent. To circumvent these gaps, this paper therefore focus on the problem: how to identify a suspect using partial photo information from different persons. Accordingly, we propose a new Logical Operation Oriented Face Retrieval (LOOFR) approach provided that partial information extracted from several different persons' photos is available. The LOOFR defines the new AND and OR operators on these partial information. For example, " eyes of person A AND mouth of person B" means retrieving the target person whose eyes and mouth are similar to that of person A and person B respectively, while "eyes of person A OR eyes of person B" means retrieving target person whose eyes are similar to both person A and B. Evidently, these logical operators cannot be directly implemented by INTERSECTION and UNION in set operations. Meanwhile, they are better for human understanding than set operators. Subsequently, we propose a two-stage LOOFR approach, in which the representations of partial information are learned in the first stage while the logical operations are processed in the second stage. As a result, the target photo of a suspect can be retrieved. Experiments show its promising results.

Encryption and Decryption Technique Involving Finite Fields

The object of this paper is to produce a technique for encryption and decryption Involving Finite Fields. In this paper we consider the elements of finite fields GF(2m ) and logical operator XOR to analyze the encryption and decryption technique. Here we also uses the non-singular matrix and a key matrix.

‘And’ is not always a logical conjunction

The mental models theory has shown that the logical connectives do not always refer to the interpretation assigned to them by standard logic. Several papers authored by its proponents clearly reveal that in the cases of the conditional and disjunction. In this paper, following a methodology of analysis akin to that of the mental models theory, I try to check whether or not the same applies to conjunction, and my conclusion is that, indeed, this last connective can be linked to any of the sixteen possible interpretations that a logical operator relating two clauses can have.

A Logical Theory of Truth-Makers and Falsity-Makers

We explicate the different ways that a first-order sentence can be true (resp., false) in a model M, as formal objects, called (M-relative) truth-makers (resp., falsity-makers). M-relative truth-makers and falsity-makers are co-inductively definable, by appeal to the “atomic facts” in M, and to certain rules of verification and of falsification, collectively called rules of evaluation. Each logical operator has a rule of verification, much like an introduction rule; and a rule of falsification, much like an elimination rule. Applications of the rules (∀) and (∃) involve infinite furcation when the domain of M is infinite. But even in the infinite case, truth-makers and falsity-makers are tree-like objects whose branches are at most finitely long. A sentence φ is true (resp., false) in a model M (in the sense of Tarski) if and only if there existsπ such that π is an M-relative truth-maker (resp., falsity-maker) for φ. With “ways of being true” explicated as these logical truthmakers, one can re-conceive logical consequence between given premises and a conclusion. It obtains just in case there is a suitable method for transforming M-relative truthmakers for the premises into an M-relative truthmaker for the conclusion, whatever the model M may be.