NeuroBoun
NeuroBoun Tool is designed to support researchers while investigating a subject of interest within a body of work through inquiries that embody a set of queries. Researchers can perform several comparisons without losing the context of their search by obtaining the aggregated inter-related query results. The design of NeuroBoun emerged while investigating the cerebral asymmetry in general and the lateralization of subcortical structures such as the amygdala in particular.
Context sensitive article ranking with citation context analysis
In this work, we analyze citation contexts to rank articles properly for a given topic. The model proposed uses citation contexts in order to create a directed and edge-labeled citation network based on the target topic. Then we apply common ranking algorithms in order to find important articles in this newly created network. We showed that this method successfully detects a good subset of most prominent articles in a given topic. The biggest contribution of this approach is that we are able to identify important articles for a given search term even though these articles do not contain this search term.
S-BounTI
S-BounTI is a topic identification approach that identifies topics of a crowd of microblog users. It represents topics using Topico ontology which is designed to express microblog topics. S-BounTI and Topico are products of Ahmet Yildirim's PhD work under the supervision of Suzan Uskudarli, members of SosLab (Complex Systems Laboratory) in Department of Computer Engineering, Bogazici University, Istanbul, Turkey.
Parent oriented teacher selection causes language diversity
An evolutionary model for emergence of diversity in language is developed. We investigated the effects of two real life observations, namely, people prefer people that they communicate with well, and people interact with people that are physically close to each other. Clearly these groups are relatively small compared to the entire population. We restrict selection of the teachers from such small groups, called imitation sets, around parents.
S-BounTI
S-BounTI is a topic identification approach that identifies topics of a crowd of microblog users. It represents topics using Topico ontology which is designed to express microblog topics. S-BounTI and Topico are products of Ahmet Yildirim's PhD work under the supervision of Suzan Uskudarli, members of SosLab (Complex Systems Laboratory) in Department of Computer Engineering, Bogazici University, Istanbul, Turkey.
The Dose of the Threat Makes the Resistance for Cooperation
Greater memory size is unfavorable to evolutionary success when there is no threat. In contrast, the presence of an appropriate level of threat triggers the emergence of a self-protection mechanism for cooperation.
Gossip on Weighted Networks
In this work, we analyze gossip spreading on weighted networks. We try to define a new metric to classify weighted complex networks using our model. The model proposed here is based on the gossip spreading model introduced by Lind et al. on unweighted networks. The new metric is based on gossip spreading activity in the network, which is correlated with both topology and relative edge weights in the network.
Boun-TI: Extracting Topics From Twitter And Representing Them Using Wikipedia Page Titles
In this project, we have researched if Wikipedia page titles can be used to represent topics that are talked about in Twitter and proposed an approach to do that. In contrast to existing topic extraction methods that extracts topics from only one tweet, our approach extracts topics from multiple posts but express them using words, our approach expresses topics using title of Wikipedia pages.
Iterated Prisoners Dilemma with limited attention
In order to beat defection, players do not need a full memorization of each action of all opponents. There exists a critical memory capacity threshold to beat defectors.
Attention Competition with Advertisement
In this study, we investigate the impact of advertisement pressure on a cultural market where consumers have a limited attention capacity.
Accelerometer Based Calculator For Visually-Impaired People Using Mobile Devices
This study aims to find an alternative approach to classify 20 different gestures captured by iPhone 3GS’s built-in accelerometer and make high accuracy on user-independent classifications. The method is based on Dynamic Time Warping (DTW) with dynamic warping window sizes. The first experimental result, which is obtained from collected data set, gives 96.7% accuracy rate among 20 gestures with 1062 gesture data totally.