Multiple-instance learning (MIL) is a generalization of the supervised learning problem where each training observation is a labeled bag of unlabeled instances. Several supervised ...
With the exponential growth of moving objects data to the Gigabyte range, it has become critical to develop effective techniques for indexing, updating, and querying these massive ...
Jens Dittrich, Lukas Blunschi, Marcos Antonio Vaz ...
Code coverage is a common aid in the testing process. It is generally used for marking the source code segments that were executed and, more importantly, those that were not execu...
Yoram Adler, Eitan Farchi, Moshe Klausner, Dan Pel...
In this paper, we exploit the problem of inferring images’ semantic concepts from community-contributed images and their associated noisy tags. To infer the concepts more accura...
New protocols for the data link and network layer are being proposed to address limitations of current protocols in terms of scalability, security, and manageability. High-speed r...
Lorenzo De Carli, Yi Pan, Amit Kumar, Cristian Est...