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1. chinaXiv:202107.00010 [pdf]

基于游戏行为的黑暗人格预测技术研究

吕思华; 陈雯雯; 张乙川; 朱廷劭
Subjects: Psychology >> Applied Psychology

[目的]本研究利用DOTA2游戏行为数据,实现对DOTA2玩家黑暗人格三维度的无侵入识别。[方法]本文利用Clarity 2解析包对DOTA2的游戏日志文件进行解析,提取玩家的游戏行为特征,并利用黑暗十二条量表对玩家的行为特征进行标注,采用机器学习的方法实现对黑暗人格三维度的识别。 [结果]结果发现,在马基雅维利主义、自恋和精神病态三维度上,采用高斯过程回归算法建立的模型在效度和信度上表现最佳,模型预测值与真实值之间的相关系数在0.31-0.45之间,重测信度在0.33-0.53之间。 [局限]本研究未将被试的言语行为特征纳入到建模过程中,使得游戏行为特征不够全面。 [结论]研究结果发现游戏行为数据能够帮助预测个体的黑暗人格,并且通过高斯过程回归建立的模型具有最高信效度。

submitted time 2021-07-08 Hits2749Downloads180 Comment 0

2. chinaXiv:202106.00114 [pdf]

RLEPSO:Reinforcement learning based Ensemble particle swarm optimizer

尹世远
Subjects: Computer Science >> Computer Software

Evolution is the driving force behind the evolution of biological intelligence. Learning is the driving force behind human civilization. The combination of evolution and learning can form an entire natural world. Now, reinforcement learning has shown significant effects in many places. However, Currently, researchers in the field of optimization algorithms mainly focus on evolution strategies. And there is very little research on learning. Inspired by these ideas, this paper proposes a new particle swarm optimization algorithm Reinforcement learning based Ensemble particle swarm optimizer (RLEPSO) that combines reinforcement learning. The algorithm uses reinforcement learning for pre-training in the design phase to automatically find a more effective combination of parameters for the algorithm to run better and Complete optimization tasks faster. Besides, this algorithm integrates two robust particle swarm variants. And it sets the weight parameters for different algorithms to better adapt to the solution requirements of a variety of different optimization problems, which significantly improves the robustness of the algorithm. RLEPSO makes a certain number of sub-swarms to increase the probability of finding the global optimum and increasing the diversity of particle swarms. This proposed RLEPSO is evaluated on an optimization test functions benchmark set (CEC2013) with 28 functions and compared with other eight particle swarm optimization variants, including three state-of-the-art optimization algorithms. The results show that RLEPSO has better performance and outperforms all compared algorithms.

submitted time 2021-06-29 Hits1915Downloads182 Comment 0

3. chinaXiv:202106.00111 [pdf]

Zero-knowledge Based Proof-chain: A methodology for blockchain-partial system

Yuqi Bai; Lei Luo; Shixin Liang
Subjects: Computer Science >> Computer Software

Intuitively there is drastic distinction between the “pure” decentralized block-chain systems like Defis and those that only utilizes block-chain as an enhancing technology but remains centralized with real-world business model and conventional technologies like database, application server etc. Our study explores extensively this distinction from a methodological point of view, classifies them into blockchain-complete and blockchain-partial, analyzes key features of the two types, and reveal the root cause of this distinction. We analyze the function or, in more strong words, the “ultimate purpose” of blockchain in the blockchain-partial systems, and present a conceptual model we named proof-chain that quite satisfactorily represented the general paradigm of blockchain in blockchain-partial systems. A universal tension between strength of proof-chain and privacy is then revealed and the zero-knowledge based proof-chain takes shape. Several case studies demonstrate the explaining power of our proof-chain methodology. We then apply proof-chain methodology to the analysis of the ecosystem of a collaborating group of blockchain-partial systems, representing the paradigm of public and private data domain whose border the proof-chain crosses. Finally, some derived guidelines from this methodology speaks usefulness of our methodology.

submitted time 2021-06-29 Hits2096Downloads287 Comment 0

4. chinaXiv:202106.00009 [pdf]

Complex-valued Deng Entropy

Lipeng Pan; Yong Deng
Subjects: Computer Science >> Other Disciplines of Computer Science

Complex evidence theory has been applied to several fields due to its advantages in modeling and processing uncertain information. However,to measure the uncertainty of the complex mass function is still an open issue. The main contribution of this paper is to propose a complex-valued Deng entropy. The complex-valued Deng entropy can effectively measure the uncertainty of the mass function in the complex-valued framework. Meanwhile, the complex-valued Deng entropy is a generalization of the Deng entropy and Shannon entropy. That is, the complex-valued Deng entropy can degenerate to classical Deng entropy when the complex-valued mass function degenerates to a mass function in real space. In addition, the proposed complex-valued Deng entropy can also degenerates to Shannon entropy when the complex-valued mass function degenerates to a probability distribution in real space. Some numerical examples demonstrate the compatibility and effectiveness of the complex-valued Deng entropy.

submitted time 2021-06-03 Hits2405Downloads282 Comment 0

5. chinaXiv:202106.00005 [pdf]

Complex-valued Renyi Entropy

Lipeng Pan; Yong Deng
Subjects: Computer Science >> Other Disciplines of Computer Science

Complex-valued expression models have been widely used in the application of intelligent decision systems. However, there is a lack of entropy to measure the uncertain information of the complex-valued probability distribution. Therefore, how to reasonably measure the uncertain information of the complex-valued probability distribution is a gap to be filled. In this paper, inspired by the Renyi entropy, we propose the Complex-valued Renyi entropy, which can measure uncertain information of the complex-valued probability distribution under the framework of complex numbers, and is also the first time to measure uncertain information in the complex space. The Complex-valued Renyi entropy contains the features of the classical Renyi entropy, i.e., the Complex-valued Renyi Entropy corresponds to different information functions with different parameters q. Meanwhile, the Complex-valued Renyi entropy has some properties, such as non-negativity, monotonicity, etc. Some numerical examples can demonstrate the flexibilities and reasonableness of the Complex-valued Renyi entropy.

submitted time 2021-05-31 Hits2335Downloads241 Comment 0

6. chinaXiv:202105.00090 [pdf]

基于深度学习神经网络的电池分容阶段容量预测的方法

孙瑜
Subjects: Computer Science >> Computer Application Technology

本文提出了一个使用深度学习方法预测锂离子电池分容工序的容量的解决方案。该方案从化成和分容工序中提取部分工步的物理观测值记录作为特征,训练了一个深度神经网络(Deep Neural Network, DNN)实现了电池容量的精准预测。据测试,该模型预测的电池容量与真实值相比,平均百分比绝对误差(Mean Absolute Percentage Error, MAPE)仅为0.78%。将该模型与生产线结合,可以大大缩减生产时间与能耗,降低电池生产成本。

submitted time 2021-05-29 Hits2419Downloads244 Comment 0

7. chinaXiv:202105.00077 [pdf]

二维光学刺激下的视觉感知定律

祝锐; 刘玉红; 王体春; 陈龙聪; 谢正祥
Subjects: Computer Science >> Computer Application Technology
Subjects: Engineering and technical science >> Optical Engineering

人类对刺激量的感知分为数量感知和质量感知。无论是韦伯-费克纳(Weber-Fechner)的对数感觉定律还是史蒂文斯(Stevens)的幂函数感觉定律,都是关于感觉量与一维亮度刺激之间定量关系的定律。图像属于具有二维亮度分布特征的刺激量。本文研究的是二维亮度刺激的质量的感知,即二维亮度刺激质量好坏程度的感知。好坏程度是一个模糊的心理学概念,因此我们需要用模糊数学的方法来量化图像视觉感知质量的好坏程度,即建立一个模糊隶属函数PQ来定量表示图像视觉质量的好与坏的程度。

submitted time 2021-05-24 Hits2930Downloads315 Comment 0

8. chinaXiv:202105.00070 [pdf]

Copula熵:理论和应用

马健
Subjects: Statistics >> Mathematical Statistics
Subjects: Computer Science >> Computer Application Technology
Subjects: Information Science and Systems Science >> Basic Disciplines of Information Science and Systems Science

统计独立性是统计学和机器学习领域的基础性概念,如何表示和度量统计独立性是该领域的基本问题。Copula理论提供了统计相关性表示的理论工具,而Copula熵理论则给出了度量统计独立性的概念工具。本文综述了Copula熵的理论和应用,概述了其基本概念和定理,以及估计方法。介绍了Copula熵研究的最新进展,包括其在统计学四个基本问题(结构学习、关联发现、变量选择和时序因果发现等)上的理论应用。讨论了四个理论应用之间的关系,以及其对应的深层次的相关性和因果性概念之间的联系,并将Copula熵的(条件)独立性度量框架与基于核函数和距离的相关性度量框架进行了对比。简述了Copula熵在水文学、环境气象学、认知神经学、系统生物学、老年医学和能源工程等领域的实际应用。

submitted time 2021-05-21 Hits3925Downloads523 Comment 0

9. chinaXiv:202104.00004 [pdf]

QZNs: Quantum Z-numbers

Deng, Jixiang; Deng, Yong
Subjects: Computer Science >> Integration Theory of Computer Science

Because of the efficiency of modeling fuzziness and vagueness, Z-number plays an important role in real practice. However, Z-numbers, defined in the real number field, lack the ability to process the quantum information in quantum environment. It is reasonable to generalize Z-number into its quantum counterpart. In this paper, we propose quantum Z-numbers (QZNs), which are the quantum generalization of Z-numbers. In addition, seven basic quantum fuzzy operations of QZNs and their corresponding quantum circuits are presented and illustrated by numerical examples. Moreover, based on QZNs, a novel quantum multi-attributes decision making (MADM) algorithm is proposed and applied in medical diagnosis. The results show that, with the help of quantum computation, the proposed algorithm can make diagnoses correctly and efficiently.

submitted time 2021-04-12 Hits3197Downloads461 Comment 0

10. chinaXiv:202103.00068 [pdf]

自监督图像增强及去噪

张雨
Subjects: Computer Science >> Computer Software

This paper proposes a self-supervised low light image enhancement method based on deep learning, which can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising. The method contains two deep sub-networks, an Image Contrast Enhancement Network (ICE-Net) and a Re-Enhancement and Denoising Network (RED-Net). The ICE-Net takes the low light image as input and produces a contrast enhanced image. The RED-Net takes the result of ICE-Net and the low light image as input, and can re-enhance the low light image and denoise at the same time. Both of the networks can be trained with low light images only, which is achieved by a Maximum Entropy based Retinex (ME-Retinex) model and an assumption that noises are independently distributed. In the ME-Retinex model, a new constraint on the reflectance image is introduced that the maximum channel of the reflectance image conforms to the maximum channel of the low light image and its entropy should be the largest, which converts the decomposition of reflectance and illumination in Retinex model to a non-ill-conditioned problem and allows the ICE-Net to be trained with a self-supervised way. The loss functions of RED-Net are carefully formulated to separate the noises and details during training, and they are based on the idea that, if noises are independently distributed, after the processing of smoothing filters (\eg mean filter), the gradient of the noise part should be smaller than the gradient of the detail part. It can be proved qualitatively and quantitatively through experiments that the proposed method is efficient.

submitted time 2021-03-01 Hits3659Downloads444 Comment 0

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