Metaheuristic algorithms such as ant colony optimization (ACO) and firefly (FF) have been successfully employed to solve the optimization problems such as robot motion planning in dynamic environments. The systematic plantation of rubber trees on a rectangular grid motivated us to explore application of grid search algorithms. We compared the ACO and FF algorithms in various scenarios by changing simulation parameters like density of the environment, land size, number of robots simultaneously available, and hillock plantations. In all different scenarios, we evaluated the performance of ACO and FF in terms of path length and time of execution, we found that later is outperforming the former. Regression equations are framed to establish the contributions of different parameters. Statistical significance of the results has been in favor of this hypothesis. The shortest path on a plain land is the relatively simplest scenario, while the Hamiltonian on a concave surface is arguably the most difficult. The novelty of this work lies in the very idea of an autonomous robot for the rubber tapping and then path optimization by employing soft computing techniques. This proposal of rubber harvesting robot if implemented for latex collection, has a potential to drive the rubber farming and allied businesses to scale up the economy of the coastal areas of India, say for example, Kerala.

The objective of this article is to review several automatic question generation systems and find why automatic question generation is still an attraction for researchers. The focus is mainly on the task of question generation, analysis of the approaches and evaluation of various methods of automatic question generation. Pointers for further research are included.Keywords: Automatic question generation, evaluation techniques, quality enhancers, ranking, sentence simplification.A study reveals that an average student asks over 26 questions per hour in one-on-one human tutoring sessions; in contrast, the student poses 120 questions per hour in a learning environment that forces her to ask questions in order to access any information 1 . Conversely, students learn more deeply if prompted by questions 2 . Conventionally, questions are constructed and assessed by tutors. It has been a trend for several decades that automatic question generation (AQG) system generates questions from the corpora using natural language processing.AQG systems were first developed in the 1976 (ref.3). They have been created for English language and vocabulary, medicine, education and using multimedia. The sequence of developments is as follows: learning words [4][5][6] , English 7 , grammar testing 8 , medicine 9 , academic writing 10 , literature review 11 , education 12 (henceforth, Heilman and Smith AQG is abbreviated as HSAQG), multimedia 13 , and finally a recent major development, on-line learning 14 . This article presents a review of more than 50 contributions in the domain of AQG. Types of questionsIn classroom practice, a tutor evaluates the comprehension of a learner by asking gap-fill type questions (GFQs), multiple choice questions (MCQs), factoid-based questions (FBQs) and deep learning-type questions (DLQs). Gap-fill questionsA stem is a good question or problem to be solved 15 . To identify a stem and generate a GFQ, an informative sentence is selected from a given document. The selection of information involves identification of semantic features in the entire document.Next, a key phrase or answer phrase (assume it is a noun phrase) is selected; term frequency plays an important role. A distractor (not expected to occur in the question) is a choice given to a learner. A good distractor could be a synonym of the key phrase or an important term in the domain of the key phrase. Distractors in Revup, an AQG, are selected from word2vec, a vector of words 16 . Text summarization features like length of a sentence, number of common tokens, number of noun and pronouns, and position of a sentence are generally considered 17 .

One of the most important undeciphered scripts of the ancient world is the Indus script. Earlier studies had focused on the correlations between signs in the Indus texts using various statistical and computational techniques such as N-grams or Markov chains. In the present study, K-means clustering, an unsupervised machine learning technique is used to identify clusters of similar texts without making any assumptions about its content. The technique is effective in extracting significant clusters and patterns in the script. Nine clusters are extracted from this study. The texts in each cluster share a common set of structural elements and are more similar to each other than the texts in other clusters. The clusters, as extracted from the study, reveal inherent patterns due to adjacent and non-adjacent dependencies between signs in the Indus texts. These clusters have definitive patterns in the usage of the signs but are only weakly associated to any archaeological site or medium of writing. The characteristic signature features of each cluster are identified in the study.

Four major approaches to robot motion planning in dynamic environments are discussed: probabilistic robot, probabilistic collision state (PCS), partially closed-loop receding horizon control (PCLRHC) and gross hidden Markov model (GHMM). A comparison of three mapping techniques, Kalman filter, expectation and maximization algorithm and Markov model, is presented. The PCS method is the probabilistic extension of inevitable collision state, which is found to be the safest motion planning method. The concept of open-loop and partially closed-loop receding horizon control (OLRHC and PCLRHC) is compared critically, and the algorithms are benchmarked. GHMM is the best suited method for environments with limited space and dynamic environment due to human interactions. GHMM parameters and structure are evaluated using an incremental "learn-and-predict" approach. For exploring GHMM, we simulated a cafeteria with eight tables to be served by a robot, considering three different arrangements of tables along with convex and concave obstacles, and obtained the path length and time taken for a Hamiltonian path. During the simulation, it was observed that for a given static or dynamic environment, the concavity of the obstacles is what makes the scenario a complex one.

A collision free path to a target location in a random farm is computed by employing a probabilistic roadmap (PRM) that can handle static and dynamic obstacles. The location of ripened mushrooms is an input obtained by image processing. A mushroom harvesting robot is discussed that employs inverse kinematics (IK) at the target location to compute the state of a robotic hand for holding a ripened mushroom and plucking it. Kinematic model of a two-finger dexterous hand with 3 degrees of freedom for plucking mushrooms was developed using the Denavit-Hartenberg method. Unlike previous research in mushroom harvesting, mushrooms are not planted in a grid or some pattern, but are randomly distributed. No human intervention is required at any stage of harvesting.