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Dynamic programming is a general technique for solving optimization problems. It is based on the division of problems into simpler subproblems that can be computed separately. In this paper, we show that Datalog with aggregates and other nonmonotonic constructs can express classical dynamic programming optimization problems in a natural fashion, and then we discuss the important classes of queries and applications that benefit from these techniques. Index Terms - Deductive databases, nonmonotonic reasoning, aggregates, optimization.

Although "a picture is worth a thousand words," this may not be enough to get your post seen on social media. This study's main objective was to determine the best ways to characterize a photo in terms of viral marketing and public appealing. We have to obtain this dataset for this reason from the social media site such as Instagram. A total of 1.4 million hashtags were used in the 570,000 photos that we crawled. Prior to training the text generation module to produce such popular hashtags, we had to determine the components and features of the photo. We trained a multilabel image classification module using a ResNet neural network model for the first section. In order to create hashtags pertaining to their popularity, we trained a cutting-edge GPT-2 language model for the second portion. This work differs from others in that, and it initially offered a cutting-edge GPT-2 model for hashtag generation using a combination of the multilabel image classification module. The popularity issues and ways to make an Instagram post popular are also highlighted in our essay. Social science and marketing research can both be conducted on this subject. Which content can be considered popular from the perspective of consumers can be researched in the social science setting. As a marketing strategy, end users can help by offering such well-liked hashtags for social media accounts. This essay adds to the body of knowledge by demonstrating the two possible uses of popularity. Compared to the base model, our popular hashtag generating algorithm creates 11% more relevant, acceptable, and trending hashtags, according to the evaluation that was carried out. [ABSTRACT FROM AUTHOR]

This paper presents a logic language for expressing NP search and optimization problems. Specifically, first a language obtained by extending (positive) DATALOG with intuitive and efficient constructs (namely, stratified negation, constraints, and exclusive disjunction) is introduced. Next, a further restricted language only using a restricted form of disjunction to define (nondeterministically) subsets (or partitions) of relations is investigated. This language, called NP Datalog, captures the power of DATALOG¬ in expressing search and optimization problems. A system prototype implementing NP Datalog is presented. The system translates NP Datalog queries into Optimization Programming Language (OPL) programs which are executed by the ILOG OPL Development Studio. Our proposal combines easy formulation of problems, expressed by means of a declarative logic language, with the efficiency of the ILOG System. Several experiments show the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

This paper introduces active integrity constraints (AICs), an extension of integrity constraints for consistent database maintenance. An active integrity constraint is a special constraint whose body contains a conjunction of literals which must be false and whose head contains a disjunction of update actions representing actions (insertions and deletions of tuples) to be performed if the constraint is not satisfied (that is its body is true). The AlCs work in a domino-like manner as the satisfaction of one AIC may trigger the violation and therefore the activation of another one. The paper also introduces founded repairs, which are minimal sets of update actions that make the database consistent, and are specified and "supported" by active integrity constraints. The paper presents: 1) a formal declarative semantics allowing the computation of founded repairs and 2) a characterization of this semantics obtained by rewriting active integrity constraints into disjunctive logic rules, so that founded repairs can be derived from the answer sets of the derived logic program. Finally, the paper studies the computational complexity of computing founded repairs. [ABSTRACT FROM AUTHOR]

This work is a contribution to prioritized reasoning in logic programming in the presence of preference relations involving atoms. The technique, providing a new interpretation for prioritized logic programs, is inspired by the semantics of Prioritized Logic Programming and enriched with the use of structural information of preference of Answer Set Optimization Programming. Specifically, the analysis of the logic program is carried out together with the analysis of preferences in order to determine the choice order and the sets of comparable models. The new semantics is compared with other approaches known in the literature and complexity analysis is also performed, showing that, with respect to other similar approaches previously proposed, the complexity of computing preferred stable models does not increase. [ABSTRACT FROM AUTHOR]

The proliferation of information available on the world wide web and the new emerging technologies that have reduced the barriers in organizing and publishing documents, have made the support for navigation and personalization of web sites an appealing and promising task for the AI community. Making the process of retrieving relevant documents easier is one of the most challenging activities in the design of modern sites and goes beyond any particular domain. This paper proposes a new technique for Web navigation based on current algorithms used in Recommendation Systems. Our approach identifies really relevant documents adopting methodologies similar to those successfully used in current search engines. The effectiveness and the validity of our technique have been shown by several experiments. [ABSTRACT FROM AUTHOR]

We propose a novel approach that combines data mining and linear programming techniques for classifying organizational units, such as research centers. We show how our proposal of clustering organizational units based on both efficiency and input/output parameters turns out to be effective in identifying groups of similar organizational units. We also propose the replacement of an expensive efficiency measurement, based on the solution of linear programs, with a simple but more efficient formula to be exploited in the clustering process. Preliminary experimental results, obtained from an analysis of research centers in the agro‐food sector, show the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of managing inconsistent databases, i.e., databases violating integrity constraints. We propose a general logic framework for computing repairs and consistent answers over inconsistent databases. A repair for a possibly inconsistent database is a minimal set of insert and delete operations which makes the database consistent, whereas a consistent answer is a set of tuples derived from the database, satisfying all integrity constraints, in our framework, different types of rules defining general integrity constraints, repair constraints (i.e., rules defining conditions on the insertion or deletion of atoms), and prioritized constraints (i.e., rules defining priorities among updates and repairs) are considered. We propose a technique based on the rewriting of constraints into (prioritized) extended disjunctive rules with two different forms of negation (negation as failure and classical negation) The disjunctive program can be used for two different purposes: to compute "repairs" for the database and produce consistent answers, i.e., a maximal set of atoms which do not violate the constraints. We show that our technique is sound, complete (each preferred stable model defines a repair and each repair is derived from a preferred stable model), and more general than techniques previously proposed. [ABSTRACT FROM AUTHOR]

This paper presents a technique for the optimization of bound queries on disjunctive deductive databases. The optimization is based on the rewriting of the source program into an equivalent program which can be evaluated more efficiently, The proposed optimization reduces the amount of data needed to answer the query and, consequently, 1) reduces the complexity of computing a single model and, more importantly, 2) greatly reduces the number of models to be considered. Although, in this paper, we consider the application of the magic-set method, other rewriting techniques defined for special classes of queries can also be applied. To show the relevance of our technique, we have implemented a prototype of an optimizer. Several experiments have confirmed the value of the technique. [ABSTRACT FROM AUTHOR]

A great number of recent papers have investigated the possibility of introducing more effective and efficient algorithms for search engines. In traditional search engines the resulting ranking is carried out using textual information only and, as showed by several works, they are not very useful for extracting relevant information. Present research, instead, takes a new approach, called Topic Distillation, whose main task is finding relevant documents using a different similarity criterion: retrieved documents are those related to the query topic, but which do not necessarily contain the query string. Current algorithms for topic distillation first compute a base set containing all the relevant pages and then, by applying an iterative procedure, obtain the authoritative pages. In this paper, we present a different approach which computes the authoritative pages by analyzing the structure of the base set. The technique applies a statistical approach to the co-citation matrix (of the base set) to find the most co-cited pages and combines a link analysis approach with the content page evaluation. Several experiments have shown the validity of our approach. [ABSTRACT FROM AUTHOR]