Hybrid brain computer interfaces (hBCIs) have emerged as a possible path to integrated brain-computer interaction in current history. hBCI, is a device formed by the amalgamation of control signal and bio-signal combination of control signal with one or more bio-signals that enhances system performance and usability. Assistive technology, spelling, and gaming are some of the primary areas where hBCI can be used. Among these, hBCI spellers are the popular application that has opened up a slew of new possibilities of communication for paralyzed individuals. The goal of this review is to provide prospect on current state of hBCI spellers covering types of hBCI, signal processing methods and evaluation with emphasis on feature extraction techniques and classification methods used by these studies. The authors anticipate that this analysis will serve as a foundation for ongoing studies on hBCI spellers.

In this paper, we propose a novel technique for training Generative Adversarial Networks (GANs) using autoencoders. GANs, in recent years, have emerged as one of the most popular generative models. Despite their success, there are several challenges in maintaining the trade-off between diversity and quality of the generated distribution. Our idea stems from the fact that deeper layers of an autoencoder contain high-level feature representation of the input data distribution. Reusing these layers provides GAN with information about the representative characteristics of real data and hence can guide its adversarial training. We call our model Guided GAN since the autoencoder (guiding network) provides a direction to train the GAN (generative network). Guided GAN also minimizes both the forward and reverse Kullback-Leibler (KL) divergence in a single model, exploiting the complementary statistical properties of the two. We conduct extensive experiments and use various metrics for assessing the quality, diversity of generated images and convergence of the model. Our model is evaluated on two standard datasets: CIFAR-10 and CelebA demonstrating either superior or competitive performance compared to baseline GANs, especially in the earlier training stages. Our guided training procedure has been tested on different baseline GANs without any changes to their hyper-parameter configuration or architecture.

In the era of MOOCs, online exams are taken by millions of candidates, where scoring short answers is an integral part. It becomes intractable to evaluate them by human graders. Thus, a generic automated system capable of grading these responses should be designed and deployed. In this paper, we present a fast, scalable, and accurate approach towards automated Short Answer Scoring (SAS). We propose and explain the design and development of a system for SAS, namely AutoSAS. Given a question along with its graded samples, AutoSAS can learn to grade that prompt successfully. This paper further lays down the features such as lexical diversity, Word2Vec, prompt, and content overlap that plays a pivotal role in building our proposed model. We also present a methodology for indicating the factors responsible for scoring an answer. The trained model is evaluated on an extensively used public dataset, namely Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS shows state-of-the-art performance and achieves better results by over 8% in some of the question prompts as measured by Quadratic Weighted Kappa (QWK), showing performance comparable to humans.

Lipreading is the process of understanding and interpreting speech by observing a speaker’s lip movements. In the past, most of the work in lipreading has been limited to classifying silent videos to a fixed number of text classes. However, this limits the applications of the lipreading since human language cannot be bound to a fixed set of words or languages. The aim of this work is to reconstruct intelligible acoustic speech signals from silent videos from various poses of a person which Lipper has never seen before. Lipper, therefore is a vocabulary and language agnostic, speaker independent and a near real-time model that deals with a variety of poses of a speaker. The model leverages silent video feeds from multiple cameras recording a subject to generate intelligent speech of a speaker. It uses a deep learning based STCNN+BiGRU architecture to achieve this goal. We evaluate speech reconstruction for speaker independent scenarios and demonstrate the speech output by overlaying the audios reconstructed by Lipper on the corresponding videos.

Due to the multifarious challenges that emerge when developing an artificial intelligent (AI) agent that can compete with human players, the classic game of Othello has received a lot of attention from the Computational-Intelligence community. This paper proposes an AI agent that learns a winning strategy for the game of Othello using the eXtended Classifier System (XCS) algorithm which is a popular variant of the Learning Classifier System (LCS) algorithm. Othello has been a favourite in the study of AI due to its simple set of rules, low branching factor and well defined strategic concepts. The LCS system consists of a rule-set which is made to evolve using a combination of Reinforcement Learning (RL) and Genetic Algorithm (GA) such that the evolved rule-set learns an optimal action for each board state. A 6×6 Othello board will be used for this experiment in order to evaluate the applicability of the proposed agent in learning a winning game-playing strategy. The performance of the proposed agent was evaluated against three categories of agents: minimax, human and random agent. The XCS agent was able to outperform the above-mentioned agents showing the effectiveness of rule-based evolutionary learning in Othello. This work demonstrates the possibility of using the XCS algorithm in other strategy-based combinatorial games.

Gene Expression Programming, a popular evolutionary paradigm, has acquired great attention from researchers in the domain of mathematical modeling. In view of its insufficiencies arising due to premature convergence, this paper presents an Augmented Gene Expression Programming (AGEP) algorithm. Improvements suggested over classical GEP mechanism are (1) Opposition Based Learning to initialize the population of individuals to speed up convergence, (2) A diversifying clonal selection algorithm to eliminate bias towards fitter individuals, and (3) A population upliftment step to counter stagnancy over generations. A set of experiments related to function finding was conducted using AGEP and the results show a prominent improvement by AGEP over its classical counterpart, GEP and an improved version from authoritative literature (Niche technology of Outbreeding Fusion-OFN-GEP). The results have been used to reason that AGEP gives more accurate solutions at a better convergence rate.

Computing with Words (CW) is a concept which solves problems when input is provided in form of natural language. CW is at its initial stages and is not at its full potential Medicine is a pivotal field and CW has barely been explored here. This paper concentrates on how to use CW methods to overcome challenging problems in medicine. It especially focuses on finding a solution to help Dementia affected people whose symptoms are not curable. The latter part lists down some more ways in which CW can affect areas in medicine.

Recent studies have confirmed that the problem of female deficit in India is the result of sex selective abortion, negligence of girls, infanticides and foeticide, preference of son for the preservation for clan, gender bias and emergence of new technologies in medical field. This paper addresses this critical problem of gender inequality in India using fuzzy cognitive maps. Fuzzy cognitive maps model the world as a collection of classes and causal relation between classes. Authors have attempted to explore the relationships and the relative impacts among the causes; that fuels this crucial problem.

This paper presents a combination of two soft computing techniques: Neutrosophic cognitive maps and Genetic algorithm for modeling of medical disease diagnosis. Earlier, medical decision support system was proposed by many researchers where cognitive maps along with genetic algorithm were experimented. The hybrid model of fuzzy cognitive maps with genetic algorithm was implemented to handle situations where decisions are not clearly distinct. In real world situation data is not always consistent so, authors proposed a new hybrid model of Neutrosophic Cognitive Maps with Genetic Algorithms to handle indeterminacy. The proposed model will provide distinct diagnosis of disease in medical decision support system.

Real world problems have been effectively modeled using fuzzy logic that gives suitable representation of real-world data/information and enables reasoning that is approximate in nature. It is quite uncommon that the inputs captured by the fuzzy models are 100% complete and determinate. Though, humans can take intelligent decisions in such situations but fuzzy models require complete information. Incompleteness and indeterminacy in the data can arise from inherent non-linearity, time-varying nature of the process to be controlled, large unpredictable environmental disturbances, degrading sensors or other difficulties in obtaining precise and reliable measurements. Neutrosophic logic is an extended and general framework for measuring the truth, indeterminacy and falsehood-ness of the information. It is effective in representing different attributes of information like inaccuracy, incompleteness and ambiguous, thus giving fair estimate about the reliability of information. This paper suggests extending the capabilities of fuzzy representation and reasoning system by introducing Neutrosophic representation of the data and Neutrosophic reasoning system.