Quantum-behaved Bat Algorithm (QBA) has been successfully applied as an optimal technique for dealing with a variety of optimization problems. Nevertheless, QBA suffers from similar problems as other swarm intelligent algorithms,

With the rapid increase of Internet of Things (IoT) devices and applications, the ordinary cloud computing paradigm soon becomes outdated. Fog computing paradigm extends services provided by a cloud to the edge of network in order to satisfy requirements of IoT applications such as low latency, locality awareness, low network traffic, mobility support, and so forth. Task scheduling in a Cloud-Fog environment plays a great role to assure diverse computational demands are met. However, the quest for an optimal solution for task scheduling in the such environment is exceedingly hard due to diversity of IoT applications, heterogeneity of computational resources, and multiple criteria. This study approaches the task scheduling problem with aims at improving service quality and load balancing in a merged computing system of Edge-Fog-Cloud. We propose a Multi-Objective Scheduling Algorithm (MOSA) that takes into account the job characteristics and utilization of different computational resources. The proposed solution is evaluated in comparison to other existing policies named LB, WRR, and MPSO. Numerical results show that the proposed algorithm improves the average response time while maintaining load balancing in comparison to three existing policies. Obtained results with the use of real workloads validate the outcomes.

Providing and distributing fresh water to large communities is a major global concern. In addition to its scarcity as well as to its wastage, this vital resource is being affected by challenging environmental conditions. New approaches are, therefore, urgently needed for an optimized, fair, and efficient use of fresh water. The adoption of emergent technologies is giving high hopes to reach this objective. Among these technologies, digital twin is attracting increasing attention from the academic and industrial committees. This attention is particularly motivated by its expected values to any sector, including process optimization, cost reduction, and time to market shortening. In the specific field of water management, several solutions are being proposed, especially to detect leak detection and test water assets under a variety of working constraints. These solutions are still lacking intelligence and autonomy throughout the loop of data acquisition and processing as well as asset control and service generation and delivery. To this end, we are proposing in this paper a new framework based on multi-agent systems and DT paradigm to close gaps within this loop. Our multi-agent system is responsible of running data analytics mechanisms in order to assess water consumption and generate relevant feedbacks to users using, among others, a rewarding system to select the appropriate pricing policies. It is also responsible of simulating asset operations under specific working constraints for the purpose of failure and/or defect detection.

Given a text, can we segment it into semantically coherent sections in an automatic way? Can we detect the semantic boundaries, if we know how many

eHealth is widely recognized as the application of information and communication technologies (ICT) in health. However, eHealth initiatives are still incipient in Latin America and the Caribbean, and, in many cases, restrictions in terms of access have been reported, also the necessary infrastructure, interoperability and scalability of these. This work proposes a set of critical success factors (CSFs) for the implementation of eHealth, which allow the identification of gaps, and the proposal of alternatives for the optimization of eHealth. It starts with the establishment of an eHealth domain, its scope and contributions, prior to the identification of key topics, and the establishment of every CSF, with support in guiding questions and metrics. The CSFs can facilitate the planning of projects or activities in eHealth, promoting strengths, either in ICT, management, or another of the involved topics. The CSFs must be employed with a criterion of flexibility and adequacy regarding the case in which they are applied. Finally, opportunities to evaluate and apply the CSFs in a specific context are set out.

Industrial companies are more and more interested in the use of artificial intelligence (AI) in the control and monitoring of their processes. They try to take advantage of the power of this technology in order to increase the level of automation and to build smarter machines with new capabilities of self-adaptation and self-control. Especially, the automotive industry, with their high requirements in productivity and diversity management, are eager to adapt AI concepts to their processes. However, the training of Deep Learning (DL) models requires an important effort of data preparation, providing a dataset of all possible configurations. Indeed, this dataset must be collected and then annotated. Considering the fact that automotive industry deals with a huge number of references and that it often and quickly needs to modify their products, it is very difficult, if not impossible, to gather sufficient datasets for each produced reference and to have the time to train DL models in the plants with the traditional methods. This paper presents an innovative methodology to prepare the dataset by creating virtual images instead of collecting real ones and then automatically annotating them. It will demonstrate that this method will reduce the efforts and the time of the preparation of the dataset significantly. The paper will also present how this method was deployed for the quality control of welding operations in the automotive industry.

International audience

A method of repairing process models with non-free-choice constructs is proposed based on logical Petri nets, aiming at the problem of low precision in the existing repair methods. An extended successor matrix of transitions is determined according to the distance between any two transitions. There are two types of choice-construct transitions. One is a non-free-choice construct transition, and the other is a general choice construct transition. The type of choice-construct transitions can be determined based on the extended successor matrix and the relationship between the front and back sets of transitions. The location of the deviations is calculated by an improved replaying method. Finally, a model can be repaired according to remaining-token places and missing-token places. Based on the experiments on real event logs, the method proposed in this paper has a better performance in fitness, precision, and simplicity compared with its peers.

This peer reviewed article is freely available online at https://www.cai.sk/ojs/index.php/cai/article/view/2022_6_1425 .

The danmaku text clustering is a hot topic in online video reviews. Given the problem of unsatisfactory clustering accuracy caused by short text and many new words, the danmaku text clustering algorithm based on feature extension and word-pair filtering OBTM is proposed. First, a new-word discovery algorithm based on weight optimization is proposed to retain the features of new words in the danmaku text. Then, the internal information and external knowledge of new words are used to expand the features of the danmaku text for reduced feature sparsity. Furthermore, the OBTM topic model based on word-pair filtering is designed to eliminate noise features. Finally, the Single-Pass algorithm based on cluster center iteration is proposed to obtain the clustering results of topic feature words. Experimental results show that the algorithm proposed in this paper is 13.33 %, 8.52 %, 6.25 % higher than the OBTM, Word2vec+BTM, OurE.Drift* algorithm, respectively, in terms of clustering accuracy.