The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent a...
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We propose PYHSCRF, a novel tagger for domain-specific named entity recognition that only requires a few seed terms, in addition to unannotated corpora, and thus permits the iterative and incremental design of named e...
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Chatbots have been increasingly playing a greater role in English as a foreign language education, offering learners the opportunity to practise with a conversational agent at any time and in different contexts. To gr...
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Motivated by the great success of classical generative models in machine learning, enthusiastic exploration of their quantum version has recently started. To depart on this journey, it is important to develop a releva...
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Motivated by the great success of classical generative models in machine learning, enthusiastic exploration of their quantum version has recently started. To depart on this journey, it is important to develop a relevant metric to evaluate the quality of quantum generative models; in the classical case, one such example is the (classical) inception score (cIS). In this paper, as a natural extension of cIS, we propose the quantum inception score (qIS) for quantum generators. Importantly, qIS relates the quality to the Holevo information of the quantum channel that classifies a given dataset. In this context, we show several properties of qIS. First, qIS is greater than or equal to the corresponding cIS, which is defined through projection measurements on the system output. Second, the difference between qIS and cIS arises from the presence of quantum coherence, as characterized by the resource theory of asymmetry. Third, when a set of entangled generators is prepared, there exists a classifying process leading to the further enhancement of qIS. Fourth, we harness the quantum fluctuation theorem to characterize the physical limitation of qIS. Finally, we apply qIS to assess the quality of the one-dimensional spin chain model as a quantum generative model, with the quantum convolutional neural network as a quantum classifier, for the phase classification problem in the quantum many-body physics.
Reinforcement learning (RL) is a computational approach that trains agents in decision-making through iterative interaction with their environment. Despite RL's success in various sectors, like robotics, autonomou...
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This paper presents a conceptual framework for designing relational agents (RAs) in healthcare contexts, developed through the findings from multiple user studies on RAs about their acceptance, efficacy, and usability...
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Image Scenario classification is widespread for many IoT applications. Classifying scenario helps in making proper decisions. The study aims at classifying six different scenarios using a deep neural network algorithm...
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The paper considers some models of a two-level hierarchical system for different-degree awareness of the center and its subsystems. The authors investigate control procedures through the distribution of resources, the...
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The presented model of an economic system is the generalization of the Arrow-Debreu model for the dynamics case. The authors use the description techniques to develop an optimal enterprise by taking into account the d...
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