With the acceleration of the pace of work and life, people have to face more and more pressure, which increases the possibility of suffering from depression. However, many patients may fail to get a timely diagnosis d...
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In recent years, major universities have attached great importance to digital information construction, but the survey found that there are still areas for improvement in the design of the content blocks that students...
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In recent years, major universities have attached great importance to digital information construction, but the survey found that there are still areas for improvement in the design of the content blocks that students are most concerned about, and the processing speed of database data needs to be optimized. Campus service system platform based on the big data studies the optimization method, and five optimization ideas are proposed. The first is schema optimization; the second is index optimization; the third is query performance optimization; the fourth is based on server optimization; the fifth is based on operating system optimization. Applying these optimization schemes, it is found that the speed of data processing is obviously improved, and the response time is shortened, which proves that the implementation of the system is feasible and the optimization effect is remarkable.
Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still r...
Borrowing the power of deep neural networks, deep reinforcement learning achieved big success in games, and it becomes a popular method to solve the sequential decision-making problems. However, the success is still restricted to single agent training environment. Multi-agent reinforcement learning still is a challenge problem. Although some multi-agent deep reinforcement learning methods have been proposed, they can only perform well when the number of agents is very limited. In this paper, by analyzing the dynamic changing observation space and action space of multi-agent environment, we propose a novel multi-agent deep RL method that compress the joint observation space and action space as the time goes on. The proposed method is potential for a large number of agents cooperative or competitive tasks
In this paper, we propose several opacity-preserving (bi)simulation relations for nondeterministic transition systems (NTSs) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step ...
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In the past few years, person re-identification (reID) has developed rapidly due to the success of deep convolutional neural networks. The softmax loss function is an important component for learning discriminative fe...
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In this paper we describe our systems submitted to Semeval 2018 Task 1 "Affect in Tweet" (Mohammad et al., 2018). We participated in all subtasks of English tweets, including emotion intensity classification...
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In this paper, we first utilize the word embedding that focuses on sub-word units to the Mongolian Phrase Break (PB) prediction task by using Long Short-Term Memory (LSTM) model. Mongolian is an agglutinative language...
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Existing deep models using attributes usually take global features for identity classification and attribute recognition. However, some attributes exist in local position, such as a hat and shoes, therefore global fea...
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This paper describes the system we submitted to the Task 11 in SemEval 2018, i.e., Machine Comprehension using Commonsense Knowledge. Given a passage and some questions that each have two candidate answers, this task ...
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In the speech synthesis systems, the phrase break (PB) prediction is the first and most important step. Recently, the state-of-the-art PB prediction systems mainly rely on word embeddings. However this method is not f...
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