TASK DEFINITION: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
PROBLEM: Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft k-means scores. At initial stages of the learning procedure, the representations extracted from the auto-encoder may not be very discriminative for latter clustering. We address this issue by adopting a boosted discriminative distribution, where high score assignments are highlighted and low score ones are de-emphasized. With the gradually boosted discrimination, clustering assignment scores are discriminated and cluster purities are enlarged. Experiments on several vision benchmark datasets show that our methods can achieve a state-of-the-art performance.

SOLUTION: Discriminatively Boosted Image Clustering with Fully Convolutional Auto-Encoders

PROBLEM: Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features.Feature selection is the problem of selecting informative features among all features. Materialsand Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results:We obtained an accuracy of 80.63% using reduced 11 feature set.The proposed method compare to the accuracy of 5 machine learning feature selections.The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks

SOLUTION: Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease

PROBLEM: A key problem in sensor networks is to decide which sensors<lb>to query when, in order to obtain the most useful information<lb>(e.g., for performing accurate prediction), subject to con-<lb>straints (e.g., on power and bandwidth). In many applications<lb>the utility function is not known a priori, must be learned<lb>from data, and can even change over time. Furthermore for<lb>large sensor networks solving a centralized optimization prob-<lb>lem to select sensors is not feasible, and thus we seek a fully<lb>distributed solution. In this paper, we present Distributed<lb>Online Greedy (DOG), an efficient, distributed algorithm for<lb>repeatedly selecting sensors online, only receiving feedback<lb>about the utility of the selected sensors. We prove very strong theoretical no-regret guarantees that apply whenever the (un-<lb>known) utility function satisfies a natural diminishing returns<lb>property called submodularity. Our algorithm has extremely<lb>low communication requirements, and scales well to large sensor deployments. We extend DOG to allow observation-<lb>dependent sensor selection. We empirically demonstrate the<lb>effectiveness of our algorithm on several real-world sensing<lb>tasks.

SOLUTION:
Online Distributed Sensor Selection