Bipin Indurkhya

Multi-pheromone ant Colony Optimization for Socio-cognitive Simulation Purposes

We present an application of Ant Colony Optimisation (ACO) to simulate socio-cognitive features of a population. We incorporated perspective taking ability to generate three different proportions of ant colonies: Control Sample, High Altercentricity Sample, and Low Alter-centricity Sample. We simulated their performances on the Travelling Salesman Problem and compared them with the classic ACO. Results show that all three ‘cognitively enabled’ ant colonies require less time than the classic ACO. Also, though the best solution is found by the classic ACO, the Control Sample finds almost as good a solution but much faster. This study is offered as an example to illustrate an easy way of defining inter-individual interactions based on stigmergic features of the environment.

Enhancing Particle Swarm Optimization with Socio-cognitive Inspirations

We incorporate socio-cognitively inspired metaheuristics, which we have used successfully in the ACO algorithms in our past research, into the classical particle swarm optimization algorithms. The swarm is divided into species and the particles get inspired not only by the global and local optima, but share their knowledge of the optima with neighboring agents belonging to other species. Our experimental research gathered for common benchmark functions tackled in 100 dimensions show that the metaheuristics are effective and perform better than the classic PSO. We experimented with various proportions of different species in the swarm population to find the best mix of population.

Interpreting Visual Metaphors: Asymmetry and Reversibility

In a verbal metaphor, the target and the source domains can usually be distinguished clearly, and some features of the source domain are mapped to the target domain, and not vice versa. This asymmetry of metaphor has been acknowledged in conceptual metaphor theory, as well as in interaction theory. However, the asymmetry of visual metaphor, in which concepts are depicted in images, is debated in the existing literature. The authors argue that the main reason behind this is that images lack an explicit copula (“X is Y”); so it is not always clear what a visual metaphor is about (what its target is). The authors explore the asymmetry of visual metaphors by considering a number of examples, and also by using the results of an empirical study they conducted with forty-four participants. Their study shows that, although the source and the target of visual metaphors are reversible more often than in their verbal counterparts, the transferred features usually change drastically by the reversal. This essay argues that the visualmetaphors can appear to be symmetricmore often than the verbalmetaphors because the lack of copula can turn the focus on the comparison between the source and the target, instead of the target itself. The examples demonstrate that context plays a major role in this process by identifying the source and the target of a visual metaphor.

Measuring diversity of socio-cognitively inspired ACO search

In our recent research, we implemented an enhancement of Ant Colony Optimization incorporating the socio-cognitive dimension of perspective taking. Our initial results suggested that increasing the diversity of ant population — introducing different pheromones, different species and dedicated inter-species relations — yielded better results. In this paper, we explore the diversity issue by introducing novel diversity measurement strategies for ACO. Based on these strategies we compare both classic ACO and its socio-cognitive variation.

A BLACKBOARD SYSTEM FOR GENERATING POETRY

We present a system to generate poems based on the information extracted from input text such as blog posts. Our design uses the blackboard architecture, in which independent specialized modules cooperate during the generation process by sharing a common workspace known as the blackboard. Each module is responsible for a particular task while generating poetry. Our implementation incorporates modules that retrieve information from the input text, generate new ideas, or select the best partial solutions. These distinct modules (experts) are implemented as diverse computational units that make use of lexical resources, grammar models, sentiment-analyzing tools, and languageprocessing algorithms. A control module is responsible for scheduling actions on the blackboard. We argue that the blackboard architecture is a promising way of simulating creative processes because of its flexibility and compliance with the Global Workspace Theory of mind. The main contribution of this work is the design and prototype implementation of an extensible platform for a poetry-generating system that may be further extended by incorporating new experts as well as some existing poetrygenerating systems as parts of the blackboard architecture. We claim that this design provides a powerful tool for combining many of the existing efforts in the domain of automatic poetry generation.

Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey

Large-Scale Multimedia Retrieval (LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more interdisciplinary approach is necessary to develop an LSMR system that is really meaningful for humans. To this end, this paper aims to stimulate attention to the LSMR problem from diverse research fields. By explaining basic terminologies in LSMR, we first survey several representative methods in chronological order. This reveals that due to prioritizing the generality and scalability for large-scale data, recent methods interpret semantic meanings with a completely different mechanism from humans, though such humanlike mechanisms were used in classical heuristic-based methods. Based on this, we discuss human-machine cooperation, which incorporates knowledge about human interpretation into LSMR without sacrificing the generality and scalability. In particular, we present three approaches to human-machine cooperation (cognitive, ontological, and adaptive), which are attributed to cognitive science, ontology engineering, and metacognition, respectively. We hope that this paper will create a bridge to enable researchers in different fields to communicate about the LSMR problem and lead to a ground-breaking next generation of LSMR systems. Correspondence: Correspondence concerning this article should be addressed to Kimiaki Shirahama, Pattern Recognition Group, University of Siegen, Hoelderlinstrasse 3, 57076 Siegen, Germany, or via email to kimiaki.shirahama@uni-siegen.de.

Text-independent writer identification using convolutional neural network

Abstract The text-independent approach to writer identification does not require the writer to write some predetermined text. Previous research on text-independent writer identification has been based on identifying writer-specific features designed by experts. However, in the last decade, deep learning methods have been successfully applied to learn features from data automatically. We propose here an end-to-end deep-learning method for text-independent writer identification that does not require prior identification of features. A Convolutional Neural Network (CNN) is trained initially to extract local features, which represent characteristics of individual handwriting in the whole character images and their sub-regions. Randomly sampled tuples of images from the training set are used to train the CNN and aggregate the extracted local features of images from the tuples to form global features. For every training epoch, the process of randomly sampling tuples is repeated, which is equivalent to a large number of training patterns being prepared for training the CNN for text-independent writer identification. We conducted experiments on the JEITA-HP database of offline handwritten Japanese character patterns. With 200 characters, our method achieved an accuracy of 99.97% to classify 100 writers. Even when using 50 characters for 100 writers or 100 characters for 400 writers, our method achieved accuracy levels of 92.80% or 93.82%, respectively. We conducted further experiments on the Firemaker and IAM databases of offline handwritten English text. Using only one page per writer to train, our method achieved over 91.81% accuracy to classify 900 writers. Overall, we achieved a better performance than the previously published best result based on handcrafted features and clustering algorithms, which demonstrates the effectiveness of our method for handwritten English text also.

Dance with Me! Child-Robot Interaction in the Wild

This paper presents the results of a singular experiment that has been conducted in a kindergarten in Japan. Four groups of ten children aged 3- to 5-year old interacted freely with the robot Pepper for about 20 min. In the first part of the experiment, the robot introduced itself to the children explaining a few basics. The children were then invited to touch the robot, to dance with it and finally to play with it freely while it was idle. Our experiment shows that regardless of the children’s age, they engage easily with the robot while it was talking and moving, however children of different ages have a different perception of the robot when it is idle. Younger children consider it more as a toy while older children are more likely to attribute a meaning to its idleness.

Intelligence level and the allocation of resources for creative tasks : a pupillometry study

This pupillometry study examined the relationship between intelligence and creative cognition from the resource allocation perspective. It was hypothesized that, during a creative metaphor task, individuals with higher intelligence scores would have different resource allocation patterns than individuals with lower intelligence scores. The study also examined the influence of intelligence in language and visuo-spatial domains on the resource allocation mechanism of verbal and visual creativity. The results suggested that individuals with higher intelligence scores allocated more cognitive resources for creative tasks than those with lower intelligence scores but not for non-creative tasks. The findings of this study support the view that creativity requires allocation of several cognitive faculties and may share underlying cognitive and neural mechanisms with intelligence. Domain-specific intelligence did not seem to play a significant role in the same domain, as individuals with higher scores in both domains showed similar resource allocation patterns. However, individuals with higher intelligence scores in the visuo-spatial domain generated more creative metaphorical interpretations in both verbal and visual creative metaphor tasks suggesting its importance in creative cognition.

Introduction to the special issue on machine law

We are surrounded by machines. From simple ones—AC motors and transformers—through radio receivers, TV sets, smartphones and personal computers, to sophisticated AI systems, such as self-driving cars, autonomous weapons and IBM’s Watson. The advances in technology have reshaped the world we inhabit, including our social environment. When iPhone is the girl’s best friend, our communication and decision-making is aided by complex algorithms, and various tasks so far reserved for human beings are carried out by robots, the contemporary societies are not what they used to be. Moreover, the technology is advancing at such a rapid pace that many ideas, such as companion and sex robots, which used to be a fodder for science fiction are fast becoming a reality. This is a profound challenge for any legal system. The law is there to regulate the actions of individuals so that they contribute to the functioning of large societies. It means that legal institutions should be designed in such a way as to embrace any changes and developments that reshape our communal practices. For this reason, technological progress has been a focus of lawyers’ debates since the first industrial revolution. The great discoveries of the nineteenth and twentieth centuries—car, airplane, radio, TV, computer, the Internet—have not only influenced the existing legal institutions, but have also led to the establishment of entirely new branches of law. Arguably, however, they did not revamp the very foundations of their contemporary legal systems, but served as a means for regulating interactions between human beings. Technology has been considered only as a tool used by