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移动互联网与心理健康相关应用

CCPL实验室开发了基于Android平台的一系列应用，划分为以下三个系统：

• 移动心理测评系统 中科心解：该系统基于经典的心理学测量工具，结合现代信息科学的技术手段，向用户科学地揭示心灵奥秘，包括了人格、心理健康等多个主题。借助本系统，用户在不需要接受任何专业人士指导的情况下就能够实现对自身心理特征的专业性剖析与解读。整个使用过程十分方便、快捷，特别适合于非专业心理学爱好者使用。欢迎使用，点击此处下载。

• 自助心理健康调节系统 抑郁心导：通过对手机心理测评与传统纸笔、网络心理测评的比较，基于认知行为疗法（CBT）理论，利用专业的测评量表帮助人们了解自身的心理健康状况，并在手机平台上开展心理自助调节。欢迎使用，点击此处下载。

• 移动认知训练系统 抑制/转换/刷新 ：用户通过该系统提供的难易程度不同的认知训练，可以对应的提高对特定刺激的抑制能力、对多个任务或认知操作之间相互转换的控制能力以及记忆刷新能力。 欢迎使用， 点击此处Pad版应用“抑制”下载；Pad版应用“转换”下载；[http://ccpl.psych.ac.cn/mobile/Running_memory.apk Pad版应用 “抑制

WebMind 浏览器

WebMind浏览器是WSI实验室开发的个性化的浏览器。该浏览器集成了意图预测与推荐和心理预测模型，模型的主要功能是通过分析您最近会话内的浏览动作来预测您的信息需求以及心理状态，并根据此意图和心理来生成推荐信息。点击右上角的两个图片分别可以获取当前你的心理状态以及系统为您生成的推荐页面。欢迎使用，点击此处下载，点击此处下载使用指南，如有问题请联系：webmind.wsi@gmail.com

WebIC --- An Effective Complete-Web Recommender System

WebIC is an IE-Based browser which is expected to predict IC-Pages for web users ... can also be used for annotation ...

Association Rule(Apriori)

This is my implementation of Apriori in:

R. Agrawal, R. Srikant, Fast Algorithms for Mining Association Rules, Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, Sept. 1994. Expanded version available as IBM Research Report RJ9839, June 1994.

Sequential Pattern(WAP)

This is my implementation of:

J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu `` Mining Access Patterns Efficiently from Web Logs (PDF), Proc. 2000 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD'00), Kyoto, Japan, April 2000.

DMTalker

DMTalker is a software for Mandarin sentence prosodic rules mining and Text-To-Speech, and it is implemented on Borland C++Builder 3.0.

The pitch models which are now being used in Mandarin Text-To-Speech are extracted by linguistics experts, and they are described qualitatively and with low precise. To acquire more accurate prosodic rules, data mining is employed to extract pitch models from actual speech, and the result is encouraging. The preprocessing of the prosodic rules data mining includes text analysis, time-wrapping, moving average, and normalization, and typical pitch series have been extracted by clustering analysis on the preprocessed pitch arrays for discretion. Decision tree(C4.5) and neural network have been used for extracting pitch variation patterns from preprocessed train data.