Overview of the Multi-Task Mutual Learning Technique: A Comparative Analysis of Different Models for Sentiment Analysis and Topic Detection

openThis research aims to provide a clearer overview of a new technique called Multi-task Mutual Learning in the field of Natural Language Processing, specifically in sentiment analysis and topic detection. The objective is to understand whether employing different models within this technique may impact its performance. With the growing collection of natural language-based data, private companies, public organizations, and various entities are increasingly seeking to extract information from this vast amount of data, which can be in the form of audio, text, or video. This underscores the need to study systems that can analyze this data effectively and do so in the shortest possible time, providing a competitive advantage in the private sector and a social analysis of the current historical moment in the public domain. The method employed is Mutual Learning, and within this technique, we analyzed specific models, including Variational Autoencoder, Dirichlet Variational Autoencoder, Recurrent Neural Network, and Bidirectional Encoder Representation from Transformer. These methods were executed with two datasets: YELP, containing reviews of commercial activities, and IMDB, containing reviews of films. The main findings highlight the complexity of the model, the computational power required, and the customization of the model according to specific needs.This research aims to provide a clearer overview of a new technique called Multi-task Mutual Learning in the field of Natural Language Processing, specifically in sentiment analysis and topic detection. The objective is to understand whether employing different models within this technique may impact its performance. With the growing collection of natural language-based data, private companies, public organizations, and various entities are increasingly seeking to extract information from this vast amount of data, which can be in the form of audio, text, or video. This underscores the need to study systems that can analyze this data effectively and do so in the shortest possible time, providing a competitive advantage in the private sector and a social analysis of the current historical moment in the public domain. The method employed is Mutual Learning, and within this technique, we analyzed specific models, including Variational Autoencoder, Dirichlet Variational Autoencoder, Recurrent Neural Network, and Bidirectional Encoder Representation from Transformer. These methods were executed with two datasets: YELP, containing reviews of commercial activities, and IMDB, containing reviews of films. The main findings highlight the complexity of the model, the computational power required, and the customization of the model according to specific needs

Incorporating Decision Nodes into Conditional Simple Temporal Networks

A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network (STN) to include special time-points, called observation time-points. In a CSTN, the agent executing the network controls the execution of every time-point. However, each observation time-point has a unique propositional letter associated with it and, when the agent executes that time-point, the environment assigns a truth value to the corresponding letter. Thus, the agent observes but, does not control the assignment of truth values. A CSTN is dynamically consistent (DC) if there exists a strategy for executing its time-points such that all relevant constraints will be satisfied no matter which truth values the environment assigns to the propositional letters. Alternatively, in a Labeled Simple Temporal Network (Labeled STN) - also called a Temporal Plan with Choice - the agent executing the network controls the assignment of values to the so-called choice variables. Furthermore, the agent can make those assignments at any time. For this reason, a Labeled STN is equivalent to a Disjunctive Temporal Network. This paper incorporates both of the above extensions by augmenting a CSTN to include not only observation time-points but also decision time-points. A decision time-point is like an observation time-point in that it has an associated propositional letter whose value is determined when the decision time-point is executed. It differs in that the agent - not the environment - selects that value. The resulting network is called a CSTN with Decisions (CSTND). This paper shows that a CSTND generalizes both CSTNs and Labeled STNs, and proves that the problem of determining whether any given CSTND is dynamically consistent is PSPACE-complete. It also presents algorithms that address two sub-classes of CSTNDs: (1) those that contain only decision time-points; and (2) those in which all decisions are made before execution begins

Access Controlled Temporal Networks

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Conceptual modeling of flexible temporal workflows

Workflow technology has emerged as one of the leading technologies in modeling, redesigning, and executing business processes. The management of temporal aspects in the definition of a workflow process has been considered only recently in the literature. Currently available workflow management systems (WfMS) and research prototypes offer a very limited support for the definition, detection, and management of temporal constraints over business processes. In this paper, we propose a new advanced workflow conceptual model for expressing time constraints in business processes and we present a general technique to check different levels of temporal consistency for workflow schemata at process design time: since a time constraint can be satisfied in different ways, we propose a classification of temporal workflows according to the way time constraints are satisfied. Such classification can be used to successfully manage flexible workflows at run time

Conditional Simple Temporal Networks with Uncertainty and Resources

Conditional simple temporal networks with uncertainty (CSTNUs) allow for the representation of temporal plans subject to both conditional constraints and uncertain durations. Dynamic controllability (DC) of CSTNUs ensures the existence of an execution strategy able to execute the network in real time (ie scheduling the time points under control) depending on how these two uncontrollable parts behave. However, CSTNUs do not deal with resources. In this paper, we define conditional simple temporal networks with uncertainty and resources (CSTNURs) by injecting resources and runtime resource constraints (RRCs) into the specification. Resources are mandatory for executing the time points and their availability is represented through temporal expressions, whereas RRCs restrict resource availability by further temporal constraints among resources. We provide a fully-automated encoding to translate any CSTNUR into an equivalent timed game automaton in polynomial time for a sound and complete DC-checking

Incorporating decision nodes into conditional simple temporal networks

A Conditional Simple Temporal Network (CSTN) augments a Simple Temporal Network (STN) to include special time-points, called observation time-points. In a CSTN, the agent executing the network controls the execution of every time-point. However, each observation time-point has a unique propositional letter associated with it and, when the agent executes that time-point, the environment assigns a truth value to the corresponding letter. Thus, the agent observes, but does not control the assignment of truth values. A CSTN is dynamically consistent (DC) if there exists a strategy for executing its time-points such that all relevant constraints will be satisfied no matter which truth values the environment assigns to the propositional letters. Alternatively, in a Labeled Simple Temporal Network (Labeled STN) - Also called a Temporal Plan with Choice - The agent executing the network controls the assignment of values to the socalled choice variables. Furthermore, the agent can make those assignments at any time. For this reason, a Labeled STN is equivalent to a Disjunctive Temporal Network. This paper incorporates both of the above extensions by augmenting a CSTN to include not only observation time-points but also decision time-points. A decision time-point is like an observation time-point in that it has an associated propositional letter whose value is determined when the decision time-point is executed. It differs in that the agent - not the environment - selects that value. The resulting network is called a CSTN with Decisions (CSTND). This paper shows that a CSTND generalizes both CSTNs and Labeled STNs, and proves that the problem of determining whether any given CSTND is dynamically consistent is PSPACE-complete. It also presents algorithms that address two sub-classes of CSTNDs: (1) those that contain only decision time-points; and (2) those in which all decisions are made before execution begins