Automation and Computer Science

This paper presents a new Web service composition method which combines the AI planning graph technique with an immune-inspired algorithm to find the optimal composition solution. Simultaneously with the planning graph construction, a matrix of semantic links is built to store the semantic links established between the services on different layers of the graph. The planning graph and the matrix of semantic links represent the main building blocks of our immune-inspired technique for finding the optimal composition solution. We use a multi-criteria function which evaluates the composition solution in terms of its QoS attributes and the quality of the semantic match between the services involved in the solution. In order to validate our approach to automatically compose Web services, we have developed an experimental framework that integrates the planning graph composition approach and the immune-inspired selection technique. We have performed our experiments on a set of Web services from two domains.

Web Service composition offers the facility to create new services satisfying a certain functionality which can not be assured by a single existent service. The full potential of Web Service composition can only be achieved if the process is automated. Services should first be annotated with semantic information in order to do automatic service composition. The semantic information is provided by ontologies. This paper presents an unsupervised approach to automatically build a domain specific taxonomy from textual descriptions. The approach is based on hierarchical self-organizing maps. The candidates for concept names are collected by mining text corpora. The term extraction process is based on recognizing linguistic patterns in a text corpus. The taxonomy has been built in the framework of the Food Trace project [20] for traceability in the domain of food industry. The learnt taxonomy represents the domain specific branches of an ontology used for semantic annotation of the web services. Taxonomy concepts are semantic descriptions of the inputs and outputs of the operations provided by a Web Service.

In this paper, the use of a logic programming framework, the event calculus , is discussed in the automated composition of web services. The web service discovery problem is beyond of the scope of the paper. Our goal is to show that the event calculus can be used for both definitions of web services composition. That is, it can be used to generate a composite process as the output of planning. It can also be used to define a generic composition and produce a user specific composition (plan) according to the user constraints. Abductive planning of event calculus is used to show that when atomic services are available, composition of services that would yield the desired effect is possible. An abductive planner implementation of the event calculus [4] is extended to be used for plan generation.

The clustering and sorting behavior of ants, as well as the foraging behavior of birds in nature represented sources of inspiration for designing clustering methods applicable in computer science. This paper investigates how biologically-inspired clustering methods can be adapted to cluster Semantic Web services aiming at the efficiency of the discovery process. The methods consider the semantic similarity between services as the main clustering criterion. To measure the semantic similarity between two services, we propose a matching method that evaluates the degree of match between the semantic description of the two services. We have tested the biologically-inspired clustering methods on the SAWSDL service retrieval test collection (SAWSDL-TC) benchmark, and we have comparatively evaluated their performance using the Dunn index and the Average-Item Cluster Similarity metric, the latter being introduced in this paper.

Biological systems exhibit self-optimization and self-organization capabilities in a decentralized way without the existence of a central coordinator. These capabilities make them a good source of inspiration for designing meta-heuristics for solving optimization problems in computer science. Inspired from biology, in this paper we propose a hybrid firefly method for selecting the optimal solution in semantic Web service composition. In our approach, the search space of the selection method is represented by an Enhanced Planning Graph structure which encodes all the Web service composition solutions for a given user request. As selection criteria we have considered the QoS attributes of the services involved in the composition as well as the semantic similarity between them. For the evaluation of the proposed selection method we have implemented an experimental prototype and carried out experiments on a scenario from the trip planning domain.

This paper presents a framework for automatic service composition which combines a composition graph model with an Ant Colony Optimization metaheuristic to find the optimal composition solution. The composition graph model encodes all the possible composition solutions that satisfy a user request. The graph will be further used as the search space for the ant-inspired selection method targeting the identification of the optimal composition solution. To identify the optimal composition solution we define a fitness function which uses the QoS attributes and the semantic quality as selection criteria. The proposed composition framework has been tested and evaluated on an extended version of the SAWSDL-TC benchmark.

Web Service composition offers the facility to create new services satisfying a certain functionality which can not be assured by a single existent service. The full potential of Web Service composition can only be achieved if the process is automated. Services should first be annotated with semantic information in order to do automatic service composition. The semantic information is provided by ontologies. This paper presents an unsupervised approach to automatically build a domain specific taxonomy from textual descriptions. The approach is based on hierarchical self-organizing maps. The candidates for concept names are collected by mining text corpora. The term extraction process is based on recognizing linguistic patterns in a text corpus. The taxonomy has been built in the framework of the Food Trace project [20] for traceability in the domain of food industry. The learnt taxonomy represents the domain specific branches of an ontology used for semantic annotation of the web services. Taxonomy concepts are semantic descriptions of the inputs and outputs of the operations provided by a Web Service.

Biological systems exhibit self-optimization and self-organization capabilities in a decentralized way without the existence of a central coordinator. These capabilities make them a good source of inspiration for designing meta-heuristics for solving optimization problems in computer science. Inspired from biology, in this paper we propose a hybrid firefly method for selecting the optimal solution in semantic Web service composition. In our approach, the search space of the selection method is represented by an Enhanced Planning Graph structure which encodes all the Web service composition solutions for a given user request. As selection criteria we have considered the QoS attributes of the services involved in the composition as well as the semantic similarity between them. For the evaluation of the proposed selection method we have implemented an experimental prototype and carried out experiments on a scenario from the trip planning domain.

The world is facing major societal challenges because of an aging population that is putting increasing pressure on the sustainability of care. While demand for care and social services is steadily increasing, the supply is constrained by the decreasing workforce. The development of smart, physical, social and age-friendly environments is identified by World Health Organization (WHO) as a key intervention point for enabling older adults, enabling them to remain as much possible in their residences, delay institutionalization, and ultimately, improve quality of life. In this study, we survey smart environments, machine learning and robot assistive technologies that can offer support for the independent living of older adults and provide age-friendly care services. We describe two examples of integrated care services that are using assistive technologies in innovative ways to assess and deliver of timely interventions for polypharmacy management and for social and cognitive activity s...

This paper presents an ant-inspired method for selecting the optimal or a near optimal solution in semantic Web service composition. The proposed method adapts and enhances the Ant Colony Optimization meta-heuristic and considers as selection criteria the QoS attributes of the services involved in the composition as well as the semantic similarity between them. To improve the performance of the proposed selection method a 1-OPT heuristic is defined which expands the search space in a controlled way so as to avoid the stagnation on local optimal solutions. The ant-inspired selection method has been evaluated on a set of scenarios having different complexities and comparatively analyzed with a cuckoo-inspired and a bee-inspired selection method.

Web Service composition offers the facility to create new services satisfying a certain functionality which can not be assured by a single existent service. The full potential of Web Service composition can only be achieved if the process is automated. Services should first be annotated with semantic information in order to do automatic service composition. The semantic information is provided by ontologies. This paper presents an unsupervised approach to automatically build a domain specific taxonomy from textual descriptions. The approach is based on hierarchical self-organizing maps. The candidates for concept names are collected by mining text corpora. The term extraction process is based on recognizing linguistic patterns in a text corpus. The taxonomy has been built in the framework of the Food Trace project [20] for traceability in the domain of food industry. The learnt taxonomy represents the domain specific branches of an ontology used for semantic annotation of the web se...

This paper presents a business ontology model for semantic annotation of Web services which consists of a core ontology and two categories of taxonomic trees: Business Service Description trees and Business Product Description trees. The Business Service Description trees contain generic business concepts, and the Business Product Description trees contain domain specific concepts. A business ontology for the Romanian language in the domain of traceability has been built according to this model in the framework of the Food-Trace project. The domain concepts of this ontology are organized into a domain taxonomy which is automatically built out of textual descriptions from Web sites of Romanian meat industry companies.

This paper addresses the problem of optimality in semantic Web service composition by proposing a hybrid nature-inspired method for selecting the optimal or near-optimal solution in semantic Web Service Composition. The method hybridizes the Honey-Bees Mating Optimization algorithm with components inspired from genetic algorithms, reinforcement learning, and tabu search. To prove the necessity of hybridization, we have analyzed comparatively the experimental results provided by our hybrid selection algorithm versus the ones obtained with the classical Honey Bees Mating Optimization algorithm and with the genetic-inspired algorithm of Canfora et al.

This paper presents a system for diabetes risk stratification that combines fuzzy logic with two bio-inspired algorithms. The developed system takes as input a set of patients described by numerical and categorical features and generates fuzzy rules to classify them into groups according to their risk of having diabetes. To take into consideration the uncertainty from the input dataset, our system combines fuzzy logic techniques with bio-inspired algorithms and hierarchical classification. The system has been evaluated on Pima Indians data from UCI Machine Learning Repository.

This paper analyzes comparatively the performance of Random Forests and Gradient Boosting algorithms in the field of forecasting the energy consumption based on historical data. The two algorithms are applied in order to forecast the energy consumption individually, and then combined together by using a Weighted Average Ensemble Method. The comparison among the achieved experimental results proves that the Weighted Average Ensemble Method provides more accurate results than each of the two algorithms applied alone.

As the population in the Western world is rapidly aging, the remote monitoring solutions integrated into the living environment of seniors have the potential to reduce the care burden helping them to self-manage problems associated with old age. The daily routine is considered a useful tool for addressing age-related problems having additional benefits for seniors like reduced stress and anxiety, increased feeling of safety and security. In this paper, we propose a solution for identifying the daily routines of seniors using the monitored activities of daily living and for inferring deviations from the routines that may require caregivers’ interventions. A Markov model-based method is defined to identify the daily routines, while entropy rate and cosine functions are used to measure and assess the similarity between the daily monitored activities in a day and the inferred routine. A distributed monitoring system was developed that uses Beacons and trilateration techniques for monito...

In this paper we propose a Long Short-Term Memory Network based method to forecast the energy consumption in public buildings, based on past measurements. Our approach consists of three main steps: data processing step, training and validation step, and finally the forecasting step. We tested our method on a data set consisting of measurements taken every half an hour from the main building of the National Archives of the United Kingdom, in Kew and as evaluation metrics we have used Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address ...