Enriching Module Dependency Graphs for Improved Software Clustering

2022 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Geeta Sikka ◽  
Harleen Kaur
Keyword(s):  
Software Clustering ◽  
Dependency Graphs

Model Verification Through Dependency Graphs

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Søren Enevoldsen ◽  
Kim Guldstrand Larsen ◽  
Jiří Srba
Keyword(s):  
Model Verification ◽  
Dependency Graphs

scholarly journals Change in the Complexity of the Studied Concepts and Theoretical Models with the Age of the Student

2021 ◽  
pp. 132-136
Author(s):  
Robert V. Maier
Keyword(s):  
Theoretical Model ◽  
Theoretical Models ◽  
Dependency Graphs

The problem of the dependence of the didactic complexity of the studied concepts and theoretical models on the age of the student (schoolchild, student) is analysed. The complexity of a concept (term) can be characterized by the number of words from a fifth-grader’s thesaurus needed to explain its meaning. To find the complexity of a theoretical model of an object (for example, an atom), it is necessary to sum up the complexities of all the words that make up the description of the model and take into account the indicator of the variety of terms. Dependency graphs were built: 1) the complexity of the most difficult terms for understanding from the year of study; 2) the complexity of various theoretical models of the atom from the year of study at school and university. In both cases, the resulting curves are ascending, like a parabola, corresponding to an increase in complexity by almost a hundred times.

Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems

2021 ◽  
Vol 15 (04) ◽  
pp. 419-439
Author(s):  
Nhat Le ◽  
A. B. Siddique ◽  
Fuad Jamour ◽  
Samet Oymak ◽  
Vagelis Hristidis
Keyword(s):  
State Of The Art ◽  
Directed Graphs ◽  
Dependency Graph ◽  
The Other ◽  
Middle Ground ◽  
User Characteristics ◽  
Dialog Systems ◽  
Dependency Graphs ◽  
Novel Structure ◽  
Art Research

Most existing commercial goal-oriented chatbots are diagram-based; i.e. they follow a rigid dialog flow to fill the slot values needed to achieve a user’s goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot’s logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in both single-domain and multi-domain settings show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.

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