We explore the performance of multiple maxout activation variants on the big data text sentiment analysis task using convolutional neural networks. Maxout networks have gained great success in many computer vision tasks, but there is limited work on other classification tasks. Our experiments compare ReLU, LReLU, SeLU and tanh to four maxout variants. We evaluate the effectiveness of the activation functions on five datasets, including two datasets collected from the Amazon product reviews corpus, two datasets collected from the Yelp corpus, and the Sentiment140 dataset. Throughout the experiments, we found that maxout networks are slow to train compared to the traditional activation functions. We find that on average across all datasets, ReLU’s classification performance is better than any maxout activation if the number of convolutional filters is doubled. Our experiments suggest that adding more filters enhance the classification accuracy of ReLU, without affecting its comparatively low training time.

Watching movies is one of the most popular entertainments among people. Every year, a huge amount of money goes to the movie industry to release movies to the market. In this paper, we propose a multimodal model to predict the likability of movies using textual, visual and product features. With the help of these features, we capture different aspects of movies and feed them as inputs to binary and multi-class classification and regression models to predict IMDB rating of movies at early steps of production. We also propose our own dataset consisting of about 15000 movie subtitles along with their metadata and poster images. We achieve 76% and 63% weighted F-score for binary and multiclass classification respectively, and 0.7 mean square error for the regression model.

Semantic labeling of texts allows people and computing devices to more easily understand the meaning of a natural language sentence as a whole. It is very often one of the steps taken of procedures related to natural language processing. However, this step is often done manually, which is very expensive and time-consuming. When automatic labeling systems are employed, methods such as maximum entropy models are used, which receive as input features specified by specialists that also make the development of the system more expensive. In this article we present a model of the deep recurrent network that semantically annotates texts in English using as labels the top categories of an ontology. The tests showed that it is possible to obtain better results than the models that need the features to be made explicit.

The increasing use of social networks has made opinion mining an important field in the area of Natural Language Processing. The analysis of texts from the reader perspective tends to generate multi-label data since one can interpret the text using different contexts. In this paper, a new method for multi-label classification is proposed to identify reactions or emotions in texts. The new method uses data correlation to improve the class ensemble process used to create the classifiers. In addition to the new method, a new corpus of news written in Brazilian Portuguese labeled with user reactions is presented. Experiments performed with the new corpus and with two existing corpora have demonstrated that the proposed method generates statistically superior or equivalent results, requiring fewer classifiers or classes than traditional problem transformation methods.

Preference models are used by AI systems to make decisions about human desires. We explore a new systematic methodology for comparing preference models on the basis of real world data. We demonstrate this methodology on two existing preference models, evaluating their abilities to fit real world data from 30 human electoral datasets, and explain how our methodology could be easily extended to compare many more models.

A Knowledge Graph (KG), popularly used in both industry and academia, is an effective representation of knowledge. It consists of a collection of knowledge elements, each of which in turn is extracted from the web or other sources. Information extractors that use natural language processing techniques or other complex algorithms are usually noisy. That is, the vast number of knowledge elements extracted from the web may not only be associated with different confidence values but may also be inconsistent with each other. Many applications such as question answering systems that are built on top of large-scale KGs are required to reason efficiently about these confidence values and inconsistencies. In addition, they are required to incorporate ontological constraints in their reasoning. One way to do this is to extract a subgraph of a KG that is consistent with the ontological constraints and is of maximum total confidence value. Such a subgraph is referred to as the top hypothesis and is combinatorially hard to find. In this paper, we introduce an algorithmic framework for efficiently addressing the combinatorial hardness and selecting the top K hypotheses. Our approach is based on powerful algorithmic techniques recently invented in the context of the Weighted Constraint Satisfaction Problem (WCSP).

In this paper the generations of artificial neural net- works (ANN) are surveyed. The assumptions present in Gen 1 and 2 ANNs are enumerated. In the pro- cess of reformulating the Gen 2 ANN an extension was observed that could increase the biological plausibility of the model. The resulting model makes use of the neurological interneuron structures that provide inhibi- tion and input gain control in the cortical regions of the brain. The resulting interneuron neural network (INN) is applied to the well know MNIST. The INN beats an identical ANN. The application of the model is used to validate the derivation of the model and associated backpropagation.

We consider learning problems of an intuitive and concise preference model, called lexicographic preference lists (LP-lists). Given a set of examples that are pair- wise ordinal preferences over a universe of objects built of attributes of discrete values, we want to learn (1) an optimal LP-list that decides the maximum number of these examples, or (2) a near-optimal LP-list that decides as many examples as it can. To this end, we introduce a dynamic programming based algorithm and a genetic algorithm for these two learning problems, respectively. Furthermore, we empirically demonstrate that the sub-optimal models computed by the genetic algorithm very well approximate the de facto optimal models computed by our dynamic programming based algorithm, and that the genetic algorithm outperforms the existing greedy heuristic with higher accuracy predicting new preferences.

Gifted students have a higher capabilities of understanding and learning. They are characterized by a high level of attention and a high performance in the classroom. Gifted children are defined in this paper as children who have a performance higher than the average group (59.64%). In order to predict gifted students from normal students, we conducted an experiment where 17 pupils have voluntarily participated in this study. We collected different types of data (gender, age, performance, initial average in math and EEG mental states) in a web platform to learn mathematics called NetMath. Participants were invited to respond to top-level exercises on the four basic operations in decimals. We trained different machine learning algorithms to predict gifted students. Our first results show that the decision tree could predict gifted students with an accuracy of 76.88%. Using J48 trees, we noticed also that two relevant features could determine gifted children: the relaxation extracted from EEG headset and the characteristic of strong student. A strong student is defined as a student who obtained a mean higher than the group’s mean in the first step evaluation in class.

In this paper, we study the problem of forming coalitions with heterogeneous agents for allocating them to tasks. Several agents work together to complete a given task. Due to the inherent complexity of real-world tasks and limited capabilities of a particular type of a physical agent such as a robot, it is imperative to form a team consisting of different types of robots to complete the tasks. Our work in this paper proposes a distributed bipartite graph partitioning approach along with a region growing strategy for coalition formation with heterogeneous agents such as humans and/or robots for instantaneous allocation to tasks (ST-MR-IA). We also extend this approach to apply in the scenarios where the tasks might have dependencies among each other (ST-MR-TD).We have implemented the proposed algorithms within theWebots simulator. The proposed strategy allocates near-optimal (up to 98%) agent coalitions to tasks. Results also show that our proposed approach can easily handle as many as 100 agents and 10 tasks while spending an almost negligible amount of time.