The design of Domain-Specific Languages (DSLs) plays a key role in improving productivity and usability in software engineering tools. This paper empirically evaluates a DSL developed to optimize periodic maintenance scheduling, which is crucial for efficiently managing vehicle fleets. Optimizing this process can significantly reduce costs by ensuring that only the required maintenance is performed, preventing bottlenecks and other problems derived from inefficient resource management, especially when faced with restrictions such as limited maintenance facility capacity. Most current tools for planning maintenance, such as Computerized Maintenance Management Systems (CMMS), fail to optimize their schedules, requiring custom implementations to achieve better results and being overwhelming to use due to complex navigation and repetitive procedures. In this paper, we propose a Domain-Specific Language that simplifies the maintenance scheduling process through the centralization of all the functionality in a single program, following Model-Driven Engineering principles. This DSL enables the optimization of maintenance schedules by defining constraints directly within the language. Usability tests and user surveys demonstrate a reduction in the time and effort required for maintenance scheduling and an increased satisfaction rate compared to CMMS.

The rise of Adversarial Machine Learning (AML) attacks is presenting a significant challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address this issue, we introduce Apollon, a novel defence system that can protect IDS against AML attacks. Apollon utilizes a diverse set of classifiers to identify intrusions and employs Multi-Armed Bandits (MAB) with Thompson sampling to dynamically select the optimal classifier or ensemble of classifiers for each input. This approach enables Apollon to prevent attackers from learning the IDS behaviour and generating adversarial examples that can evade the IDS detection. We evaluate Apollon on several of the most popular and recent datasets, and show that it can successfully detect attacks without compromising its performance on traditional network traffic. Our results suggest that Apollon is a robust defence system against AML attacks in IDS.

Many facts change over time, which is a fundamental aspect of our physical environment. In the case of pandemic articles, the user is not interested in the creation date of the document but in the facts and the cause of the last pandemic. Fake news can be better combated by having a document with a temporal focus. Currently, neither the sequence of events nor the temporal focus is considered when obtaining news documents. Despite the limited number of temporal aspects in the available datasets, it is difficult to test and evaluate the temporal conclusions of the model. The goal of this work is to develop a temporal focus news article retrieval model based on co-training to advance research in semi-supervised learning. A mapping of the dataset is performed using (1) the evolving focus time of news articles and (2) the semi-supervised method based on coincidence contexts for learning low-dimensional continuous vectors for learning neural contrast embedding models generating focus time-based query in sequential news articles to facilitate temporal understanding by learning low-dimensional continuous vectors. A diverse dataset of news articles is used to evaluate the effectiveness of the proposed method. With semi-supervised learning and lexicon expansion, the result of the developed model can achieve 89%. The method performed better than previous baselines and traditional machine learning models with improvements of 12.65% and 4.7%, respectively.

Abstract Cloud computing is the on‐demand provision of computing resources over the Internet, such as cloud storage, computing power, network, and so on. Cloud computing has several advantages, including high speed, cost reduction, data security, and scalability. The main challenge in cloud environment is to balance the workloads and network traffic among the available resources to achieve maximum performance. Several methods have been proposed in the literature for effective load balancing, including heuristic, meta‐heuristic, and hybrid algorithms. The performance of these techniques has been improved by combining machine learning based Artificial Intelligence (AI) techniques and meta‐heuristic algorithms. Most of the existing load balancing techniques are not aware of the content type of user tasks. However, from the literature, the content type of the tasks can be very effective to design a balanced workload distribution system in the cloud. In this work, a novel AI‐assisted hybrid approach called Content‐aware Machine Learning based Load Balancing Scheduler (CA‐MLBS) is proposed. The scheduling system CA‐MLBS combines machine learning and meta‐heuristic algorithms to perform classification based on file type. To achieve this, a Support Vector Machine (SVM) based classifier is used to classify user tasks into different content types such as video, audio, image, and text. A metaheuristic algorithm based on Particle Swarm Optimization (PSO) is used to map users' tasks in the cloud. The proposed approach was implemented and evaluated using a renowned Cloudsim simulation kit and compared with Ant Colony Optimization File Type Format (ACOFTF) and Data Files Type Formatting (DFTF) heuristics. The results of the proposed study show that the proposed CA‐MLBS technique achieved improvements of up to 29%, 29%, and 44% in terms of makespan, response time, and throughput, respectively.

introduction Share on Introduction to the Special Issue of Recent Advances in Computational Linguistics for Asian Languages Authors: Jerry Chun-Wei Lin Western Norway University of Applied Sciences, Bergen, Norway Western Norway University of Applied Sciences, Bergen, Norway 0000-0001-8768-9709View Profile , Vicente GarcÍa DÍaz University of Oviedo, Spain University of Oviedo, Spain 0000-0003-2037-8548View Profile , Juan Antonio Morente Molinera University of Granada, Spain University of Granada, Spain 0000-0002-2729-6900View Profile Authors Info & Claims ACM Transactions on Asian and Low-Resource Language Information ProcessingVolume 22Issue 3Article No.: 62pp 1–5https://doi.org/10.1145/3588316Published:14 April 2023Publication History 0citation29DownloadsMetricsTotal Citations0Total Downloads29Last 12 Months29Last 6 weeks29 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Abstract This paper investigates the automated medical learning and proposes hybrid intelligent framework, called Hybrid Automated Medical Learning (HAML). The goal is the efficient combination of several intelligent components in order to automatically learn the medical data. Multi agents system is proposed by using distributed deep learning, and knowledge graph for learning medical data. The distributed deep learning is used for efficient learning of the different agents in the system, where the knowledge graph is used for dealing with heterogeneous medical data. To demonstrate the usefulness and accuracy of the HAML framework, intensive simulations on medical data were conducted. A wide range of experiments were conducted to verify the efficiency of the proposed system. Three case studies are discussed in this research, the first case study is related to process mining, and more precisely on the ability of HAML to detect relevant patterns from event medical data. The second case study is related to smart building, and the ability of HAML to recognize the different activities of the patients. The third one is related to medical image retrieval, and the ability of HAML to find the most relevant medical images according to the image query. The results show that the developed HAML achieves good performance compared to the most up‐to‐date medical learning models regarding both the computational and cost the quality of returned solutions.

No abstract available.

An Intrusion Detection System (IDS) is a network security system that detects, identifies, and tracks an intruder or an invader in a network. As the usage of the internet is growing every day in our society, the IDS is becoming an essential part of the network security system. Therefore, the proper research and implementation of IDSs are required. Today, with the help of improved technologies at our disposal, many solutions have been found to create many intrusion detection systems. However, it is difficult to identify the perfect solution from the vast options we have available. Hence, motivated by the need of a better security system, this paper presents a survey of different published solutions that have been developed and/or researched on the topic of intrusion detection techniques during the period from 2000 to 2019, including the accuracy of the output. With the help of this survey, an all-inclusive view of the different papers would be at one’s disposal.

Monte-Carlo methods are the basis for solving many computational problems using repeated random sampling in scenarios that may have a deterministic but very complex solution from a computational point of view. In recent years, researchers are using the same idea to solve many problems through the so-called Monte-Carlo Tree Search family of algorithms, which provide the possibility of storing and reusing previously calculated results to improve precision in the calculation of future outcomes. However, developers and researchers working in this area tend to have to carry out software developments from scratch to use their designs or improve designs previously created by other researchers. This makes it difficult to see improvements in current algorithms as it takes a lot of hard work. This work presents JGraphs, a toolset implemented in the Java programming language that will allow researchers to avoid having to reinvent the wheel when working with Monte-Carlo Tree Search. In addition, it will allow testing experiments carried out by others in a simple way, reusing previous knowledge.

Intelligent security systems have evolved enormously in the last few years. Most of these security systems use a group of physics sensors and algorithms for data analysis and communication systems to notify security alarms. Many security systems that are included in doors can detect intruders when they have already opened the door, but not while intruders are forcing upon the door. However, some security systems include preventive systems, which can detect intruders before they open the door. These preventive systems are usually based on video cameras (image processing) or in-presence sensors, which can generate many false positives, for instance, when a person is next to the door for a few seconds, even if this person is not manipulating the door. This research work proposes a novel force door detection system. The system includes a specific device for monitoring door small vibrations and movements; it analyzes these data using neural networks to detect accurately if someone is forcing upon the door. Artificial intelligence must be able to categorize data records without confusing when someone is forcing upon the door with other actions, like knocking on the door.

We propose a user-oriented language to enable users to specify interconnections between heterogeneous objects in the Internet of Things (IoT). Based on the idea of the use case specification technique in software engineering, our language provides users with a natural language like syntax to allow them to specify when or under what conditions they want which objects to be connected. To support this language, we have also developed a transformation mechanism that automatically translates users' specification into the source code. We have evaluated this language through an experiment and a survey. The main contributions of this paper are: 1) a simple natural language that enables the users to specify which objects to connect and when and 2) a transformation mechanism that automatically translates users' specifications into source code and dynamically attaches the code to relevant applications. This paper represents a first step in bringing the IoT closer to their users.

People rely on other people's opinions to make decisions, especially if they belong to their circle of trust. In addition, there are lots of websites of recognized prestige that provide people opinions about different products and services, which are read by millions of people before making a decision. That is why systems for sentiment analysis are becoming increasingly important to automatically process the information and determine feelings of users. They analyze their written words, usually conditioned by the characteristics of microblogging platforms, in which a large number of messages are published every day, providing a great source of information, impossible to be managed manually. In this work, we show a proposal to analyze the feeling that Twitter users have towards different hotels or hotel chains through a platform that could be easily adapted to other contexts. The goal is to create q a structure based on independent and interchangeable components that will make it possible to conduct studies in a more uniform, open and transparent way.

The development of service robots has gained more attention over the last years. Advanced robots have to cope with many different situations emerging at runtime, while executing complex tasks. They should be programmed as dynamically adaptive systems, capable of adapting themselves to the execution environment, including the computing, user, and physical environment. Recently, dynamic languages are becoming widely used because of the high runtime adaptability they offer. Therefore, we have analyzed the suitability of these languages to implement robotic systems with high runtime adaptability requirements, using Python as case study because of its maturity. To evaluate their suitability, we have implemented a reflective robotics framework that can be programmed in both Java and any dynamic language supported by the standard Java Scripting API. An example scenario has been developed using Python to show how its distinguishing meta-programming features have facilitated the development of runtime-adaptable robotics services. Copyright © 2012 John Wiley & Sons, Ltd.

Abstract The Model‐Driven Engineering paradigm is aimed at raising the abstraction level of Software Engineering approaches through the systematic use of models as primary artifacts, not only in software design and development, but also to understand, interact, configure, and modify the runtime behavior of software. It tries to overcome the wall between the documentation and the real state of the implementation. For that matter, our long‐term goal seeks to reach a higher degree of interoperability among available meta‐modeling technologies through bridges among technological spaces (TS bridges). The proposed system provides several ATL (ATLAS Transformation Language) transformations that enable the application of measuring operations over ATL transformation models and rules, and the generation of different complementary end‐user models, such as SVG charts and (X)HTML reports. For this work, we have evaluated a set of meta‐modeling TS bridges among UML, MOF, Ecore, KM3, and Microsoft DSL Tools. These results provide quantitative measurements of the declarative and imperative constructs of these transformations and relative quality factors as well. In addition to this, all the top‐level results extracted from the measurement of these TS bridges are merged into one unique model in order to assist in performing a comparative study among them. This comparative study suggests that it is feasible to apply automatic transformations over transformation models, i.e. meta‐transformations. In this regard, there are many open research trends towards complete management, validation, optimization, and inference of TS bridges between complementary meta‐modeling technologies. Copyright © 2010 John Wiley & Sons, Ltd.