Robust Assembly Assistance Using Informed Tree Search with Markov Chains

Manual work accounts for one of the largest workgroups in the European manufacturing sector, and improving the training capacity, quality, and speed brings significant competitive benefits to companies. In this context, this paper presents an informed tree search on top of a Markov chain that suggests possible next assembly steps as a key component of an innovative assembly training station for manual operations. The goal of the next step suggestions is to provide support to inexperienced workers or to assist experienced workers by providing choices for the next assembly step in an automated manner without the involvement of a human trainer on site. Data stemming from 179 experiment participants, 111 factory workers, and 68 students, were used to evaluate different prediction methods. From our analysis, Markov chains fail in new scenarios and, therefore, by using an informed tree search to predict the possible next assembly step in such situations, the prediction capability of the hybrid algorithm increases significantly while providing robust solutions to unseen scenarios. The proposed method proved to be the most efficient for next assembly step prediction among all the evaluated predictors and, thus, the most suitable method for an adaptive assembly support system such as for manual operations in industry.

E. M. Forster’s A Passage to India: A Tragedy of Cultural Conflicts and Distorted Interracial Relationships

E.M. Forster’s A Passage to India, hereafter (API), offers us an opportunity to realize the mentality of the white imperialist and the grotesque picture of the colonizer-colonized relationship; this picture implies multiple facets of racial vituperations, brutality, and prejudice perpetrated on Indians in the colonial period. In the novel, Forster explores the colonizers’ racist attitudes, and he brings out the racial and interracial conflicts as well as the cultural and ethnic traumas between the colonizer and the colonized. This study is primarily concerned with exploring the cultural clashes and the problematic, deformed interracial relationships, established between the Indians and the Anglo-Indians in a colonial context. The analytical approach and the Postcolonial Theory will be adopted throughout the paper as a framework. A postcolonial reading of the novel debunks the colonizer’s racist ideology and reveals various motifs of partitions, fences, interracial conflicts and gulfs. The article reveals that the different racial, cultural, and social backgrounds of the English and Indian communities create bitter differences and significant gaps that cannot be bridged. The study concludes that the ramifications of the interracial clashes and racial intolerance have a vehement impact on both the colonized and the colonizer alike; however, mutual and interracial love, respect, and understanding are robust solutions that can relatively open the ideological closure of racism, lessen the racial tensions and thus bring people of different racial backgrounds together.

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

Accurate lesion segmentation is critical in stroke rehabilitation research for the quantification of lesion burden and accurate image processing. Current automated lesion segmentation methods for T1-weighted (T1w) MRIs, commonly used in rehabilitation research, lack accuracy and reliability. Manual segmentation remains the gold standard, but it is time-consuming, subjective, and requires significant neuroanatomical expertise. We previously released a large, open-source dataset of stroke T1w MRIs and manually segmented lesion masks (ATLAS v1.2, N=304) to encourage the development of better algorithms. However, many methods developed with ATLAS v1.2 report low accuracy, are not publicly accessible or are improperly validated, limiting their utility to the field. Here we present ATLAS v2.0 (N=955), a larger dataset of T1w stroke MRIs and manually segmented lesion masks that includes both training (public) and test (hidden) data. Algorithm development using this larger sample should lead to more robust solutions, and the hidden test data allows for unbiased performance evaluation via segmentation challenges. We anticipate that ATLAS v2.0 will lead to improved algorithms, facilitating large-scale stroke rehabilitation research.

Preface to the Special Issue on “Applications of Fuzzy Optimization and Fuzzy Decision Making”

During the last decades, fuzzy optimization and fuzzy decision making have gained significant attention, aiming to provide robust solutions for problems in making decisions and achieving complex optimization characterized by non-probabilistic uncertainty, vagueness, ambiguity and hesitation [...]

Dragonfly Algorithm and Its Hybrids: A Survey on Performance, Objectives and Applications

Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.

Co-creation: A New Pathway for Solving Dysfunctionalities in Governance Systems?

Although governance systems play a crucial role in securing an accountable public sector, they can grow overly resource demanding, cause problematic distortion of welfare tasks and crowd out motivation among employees. This study contributes to existing literature by conceptualizing co-creation as a pathway for solving dysfunctionalities in governance systems and explores the prospects of such an approach. Based on a case study of the development of a municipal supervision system, the study outlines the characteristics of co-creating governance systems. The results points to co-creation as a promising, although resource demanding, pathway for finding robust solutions to dysfunctionalities in governance systems.

An Improved Nesting Algorithm for Irregular Patterns

In industrial environments, nesting consists in cutting or extracting pieces from a material sheet, with the purpose of minimizing the surface of the sheet used. This problem is present in different types of industries, such as shipping, aeronautics, woodworking, footwear, and so on. In this work, the aim is to find an acceptable solution to solve complex nesting problems. The research developed is oriented to sacrifice accuracy for speed so as to obtain robust solutions in less computational time. To achieve this, a greedy method and a genetic algorithm have been implemented, being the latter responsible for generating a sequence for the placement of the pieces, where each piece is placed in its current optimal position with the help of a representation system for both the pieces and the material sheet.

Toward Robust Monitoring of Malicious Outbreaks

Recently, diffusion processes in social networks have attracted increasing attention within computer science, marketing science, social sciences, and political science. Although the majority of existing works focus on maximizing the reach of desirable diffusion processes, we are interested in deploying a group of monitors to detect malicious diffusion processes such as the spread of computer worms. In this work, we introduce and study the [Formula: see text]-Monitoring Game} on networks. Our game is composed of two parties an attacker and a defender. The attacker can launch an attack by distributing a limited number of seeds (i.e., virus) to the network. Under our [Formula: see text]-Monitoring Game, we say an attack is successful if and only if the following two conditions are satisfied: (1) the outbreak/propagation reaches at least α individuals without intervention, and (2) it has not been detected before reaching β individuals. Typically, we require that β is no larger than α in order to compensate the reaction delays after the outbreak has been detected. On the other end, the defender’s ultimate goal is to deploy a set of monitors in the network that can minimize attacker’s success ratio in the worst-case. (We also extend the basic model by considering a noisy diffusion model, where the propagation probabilities on each edge could vary within an interval.) Our work is built upon recent work in security games, our adversarial setting provides robust solutions in practice. Summary of Contribution: Although the diffusion processes in social networks have been extensively studied, most existing works aim at maximizing the reach of desirable diffusion processes. We are interested in deploying a group of monitors to detect malicious diffusion processes, such as the spread of computer worms. To capture the impact of model uncertainty, we consider a noisy diffusion model in which the propagation probabilities on each edge could vary within an interval. Our work is built upon recent work in security games; our adversarial setting leads to robust solutions in practice.