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.

Ex Input:
Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. In this paper, inspired by features of human recognition process and their biological mechanisms, a new integrated and dynamic framework is proposed to mimic the semantic extraction, concept formation and feature re-selection in human visual processing. The main contributions of the proposed model are as follows: (1) Semantic feature extraction: Local semantic features are learnt from episodic features that are extracted from raw images through a deep neural network; (2) Integrated concept formation: Concepts are formed with local semantic information and structural information learnt through network. (3) Feature re-selection: When ambiguity is detected during recognition process, distinctive features according to the difference between ambiguous candidates are re-selected for recognition. Experimental results on hand-written digits and facial shape dataset show that, compared with other methods, the new proposed model exhibits higher robustness and precision for visual recognition, especially in the condition when input samples are smantic ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science.

Ex Output:
A Novel Biologically Mechanism-Based Visual Cognition Model –Automatic Extraction of Semantics, Formation of Integrated Concepts and Re-selection Features for Ambiguity


Ex Input:
A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space. We present an ensemble method that combines embeddings produced by GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013) with structured knowledge from the semantic networks ConceptNet (Speer and Havasi, 2012) and PPDB (Ganitkevitch et al., 2013), merging their information into a common representation with a large, multilingual vocabulary. The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations. Its score of ρ = .596 on an evaluation of rare words (Luong et al., 2013) is 16% higher than the previous best known system.

Ex Output:
An Ensemble Method to Produce High-Quality Word Embeddings


Ex Input:
*Rajdeep Borgohain Department of Computer Science and Engineering, Dibrugarh University Institute of Engineering and Technology, Dibrugarh, Assam Email: rajdeepgohain@gmail.com Sugata Sanyal School of Technology and Computer Science, Tata Ins titute of Fundamental Research, Mumbai, India Email: sanyals@gmail.com *Corresponding Author -------------------------------------------------------------------ABSTRACT-----------------------------------------------------------------The use of Artificial Intelligence is finding prominence not only in core computer areas, but also in cross disciplinary areas including medical diagnosis. In this paper, we present a rule based Expert System used in diagnosis of Cerebral Palsy. The expert system takes user input and depending on the symptoms of the patient, diagnoses if the patient is suffering from Cerebral Palsy. The Expert System also classifies the Cerebral Palsy as mil d, moderate or severe based on the presented symptoms.

Ex Output:

Rule Based Expert System for Cerebral Palsy Diagnosis