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香港六合彩希望这里是没人打扰的地方,也盼着香港六合彩的目光能早上那样擦出火花,但又禁不住避开。也许那一瞬间放电太猛,青春的电能还不能像成人那样厚积薄发。如果问子允那种感觉究竟什么滋味,香港六合彩一定说不清,香港六合彩即使再想弄明白也不好向周晨晨问。香港六合彩在吃饭的时候边骂张腾也边设想过,如果没有张腾的那个喷嚏,后面会怎样。眼前的周晨晨已经跟那会的香港六合彩不一样,子允更设想不了。午后才有的温暖阳光正灿烂地洒进教室,子允既陶醉着周晨晨本人,也欣赏着周晨晨在桌子上移动的身影,顿时产生单见恨晚的感触。那怎么行?周晨晨像故意留给香港六合彩这种机会,等到子允深吸一口气才问,同时向门口望了一眼。子允以为说惩罚的事,忙道,没关系,大丈夫不在乎。我说的是保护身体更重要。子允这才反应过来香港六合彩说的是不要睡太晚,可见香港六合彩根本不在意自己抄作业。香港六合彩还没来得及说感动的话,周晨晨从书包里拿出护膝问,你的腿还需要吗?子允一愣,不好意思地笑道,需要,需要,太需要了。周晨晨捂嘴一笑,然后问张腾怎么了?子允想了想说,香港六合彩有病,欠揍的……忙又打住,在女生面前说揍人太鲁莽。子允认为,女人不喜欢看残酷战役的场面,只喜欢听战争的捷报,只喜欢凯旋的英雄。这时有同学进门,子允慌忙收起护膝离开。《Y滋味》一天结束了一天结束了,平时总想回家睡觉的子允今天却不急着回家,甚至不愿提起家这个词。但家终归是家,不回,去哪?子允在门外转了一圈又一圈,再不能迟了才硬着头皮掏钥匙开门。爸,妈。我回来了。嗯,才回来啊。爸爸坐在沙发上看报纸,能感觉香港六合彩心情不错。妈妈在厨房做饭,见子允回来,边在围裙上擦手边过来问为什么回来迟了。子允本想趁妈妈询问的机会把老师请家长的事说出来,早说晚说都逃不掉,但又没胆量。香港六合彩不敢,也不忍心破坏家里温馨的气氛。今天在学校没事吧?妈妈随意问了句向厨房走去,没被围裙遮着的后面显现出不错的身材。母亲虽然也是中年之人,但基础不差,平时也挺注意保养,跟年轻女子比起来只是多了几条又浅又细的皱纹,在局部地区涂厚一点化妆品就能掩饰,而香港六合彩的美丽冻人却是年轻女子没法比。爱显棒的子允现在都还穿着全棉加毛的外套和衬着棉毛裤的牛仔裤,做母亲的倒好
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Faculty
• Jacek Brzezinski• Robin Burke• Clark Elliott• Steven Lytinen• Craig Miller• Bamshad Mobasher
• Ashley Morris • Joseph Phillips• Daniela Raicu• Noriko Tomuro• Peter Wiemer-
Hastings
Research Areas/Projects (1)
• Intelligent Information Retrieval / Filtering– Web navigation (Miller)– WebACE (Mobasher)– ARCH (Mobasher, Lytinen, Miller)– FAQFinder (Tomuro, Lytinen)– Recommender systems (Burke)
• Intelligent Tutoring Systems, Cognitive Modeling– Miller– Wiemer-Hastings– Elliott
Research Areas/Projects (2)• Natural Language Processing
– Unification grammar and parsing (Tomuro, Lytinen)
– WordNet (Tomuro)– Latent Semantic Analysis (Wiemer-
Hastings)• Fuzzy GIS
– Morris• AI in Games
– Brzezinski
Robin Burke
• Recommender systems– Knowledge-based recommendation– Hybrid recommender systems– Interactive recommendation
• Applications in– Electronic Commerce: Intelligent product
catalogs– Digital Libraries: Intelligent multi-dimensional
browsing
Clark Elliott
• Emotion and Speech
– Natural Language Generation
– Natural Language Understanding
Steve Lytinen
• FAQFinder– with Noriko Tomuro– A natural language-based browser of
Frequently Asked Questions (FAQ) files • A Unification-based Natural Language Parser
– with Noriko Tomuro– Efficient parsing algorithms for a very
expressive grammar formalism called Unification Grammar
• ARCH– with Mobasher, Miller, Burke and Sieg– Document retrieval using concept hierearchy
Craig Miller
• User modeling to evaluate interfaces– in collaboration with NASA Ames
research labs – Modeling of navigation patterns/behavior
of web users– Evaluation of web site usability from a user's
perspective• Cognitive models of human learning
– A rule-based category-learning system that produces behavior consistent with human behavior
– Computational model of students interacting with an educational program (electrostatic physics)
Bamshad Mobasher
• Research Interests– Data mining and knowledge discovery
on the Web (Web Mining)– Intelligent agents for information retrieval / filtering – Agents for electronic commerce and automated
contracting• Projects
– Automatic Web Personalization based on Web Usage Mining
– MAGNET: Multi-agent distributed environment for automated contracting and supply-chain management
– WebACE: a client-side Web agent for document retrieval and categorization
Ashley Morris
• Using Fuzziness in Geographic Information Systems (GIS)
– Able to better store and represent spatial objects
• Fuzziness in data modeling
• Fuzzy learning systems
• http://morris2k.cti.depaul.edu/gis/FOOSBALL2.html
Joseph Phillips
• Computational Scientific Discovery– The field borrows from Philosophy of
Science, Machine Learning and Knowledge Discovery in Databases (KDD).
– Representing scientific knowledge – Automating scientific reasoning – Updating scientific models given data in databases – Visualizing models – Developing model building and preferencing criteria,
and defining heuristic functions over scientific models.
Daniela Raicu
• Content-based image retrieval
• Computer vision
• Data mining and knowledge discovery
• Machine learning
• Pattern recognition
Noriko Tomuro
• A Unification-based Natural Language Parser– with Steve Lytinen– Efficient parsing algorithms for a very expressive
grammar formalism called Unification Grammar• Computational Semantic Lexicon
– WordNet as the broad-coverage lexical resource• FAQFinder
– with Steve Lytinen– A natural language-based browser of Frequently
Asked Questions (FAQ) files
Peter Wiemer-Hastings• Research Interests
– Natural Language Understanding– Cognitive Modeling – Artificial Intelligence in Education
• Projects (more info at http://reed.cs.depaul.edu/peterwh)– SLSA: Hybrid symbolic and vector-based
natural language understanding– StoryStation: Helps children write better by
giving feedback from multiple agents– RMT: Research Methods Tutor, currently
used by DePaul Psychology students
Classes (CSC)• 3/457 (F) Expert Systems
– Learn how to make a rule-based system, and some theory
• 3/458 (Sp) Symbolic Programming– Learn Lisp and Prolog, basic AI langs
• 3/480 (W) Foundations of AI– Search, logic, inference, agents
• 578 (F) Machine Learning– ML and Neural Networks
• 587 (W) Cognitive Science– Computer models of cognitive tasks
Other Classes• DS/IS 575 (W) Intelligent Information
Retrieval– How to pull important info out of the web or
some other large collection• CSC 594 (Spr) Topics in AI
– This term: Topics in Knowledge Management (Burke)
• ITS 427 (Spr) Information Processing Models of Learning– Learn about how people learn
• ITS 580 (?) Artificial Intelligence in Learning Environments– Intelligence in Education
Questions?