arXiv subjects

Paulo Alencar

Publications and source records attributed to Paulo Alencar.

At least 37 records · Page 2Linked to original sources

arXiv2021

Towards an Adaptive and Normative Multi-Agent System Metamodel and Language: Existing Approaches and Research Opportunities

Several Multi-Agent System (MAS) metamodels and languages have been proposed in the literature to support the development of agent-based applications. MAS metamodels are used to capture a collection of concepts the relevant entities and relationships in the MAS domain, which include entities such as agent, message, role, action and plan, and relationships that represent, for example, that a role is responsible for one or more tasks. In addition, to models, MAS modeling languages have also been used to support the development of MASs in a wide variety of domains, including social networking, robotics, security and smart city environments. However, there is a lack of support in these models and languages for abstractions involving norms and adaptations as well as their interactions. This paper presents a survey of some existing metamodels and languages and compares their expressiveness using abstractions related to agents, norms and adaptation. The comparison serves as a basis for the definition of a new MAS metamodeling.

cs.MA↗

arXiv2021

Machine Learning Model Development from a Software Engineering Perspective: A Systematic Literature Review

Data scientists often develop machine learning models to solve a variety of problems in the industry and academy but not without facing several challenges in terms of Model Development. The problems regarding Machine Learning Development involves the fact that such professionals do not realize that they usually perform ad-hoc practices that could be improved by the adoption of activities presented in the Software Engineering Development Lifecycle. Of course, since machine learning systems are different from traditional Software systems, some differences in their respective development processes are to be expected. In this context, this paper is an effort to investigate the challenges and practices that emerge during the development of ML models from the software engineering perspective by focusing on understanding how software developers could benefit from applying or adapting the traditional software engineering process to the Machine Learning workflow.

cs.SE↗

arXiv2021

A Reference Model for IoT Embodied Agents Controlled by Neural Networks

Embodied agents is a term used to denote intelligent agents, which are a component of devices belonging to the Internet of Things (IoT) domain. Each agent is provided with sensors and actuators to interact with the environment, and with a 'controller' that usually contains an artificial neural network (ANN). In previous publications, we introduced three software approaches to design, implement and test IoT embodied agents. In this paper, we propose a reference model based on statecharts that offers abstractions tailored to the development of IoT applications. The model represents embodied agents that are controlled by neural networks. Our model includes the ANN training process, represented as a reconfiguration step such as changing agent features or neural net connections. Our contributions include the identification of the main characteristics of IoT embodied agents, a reference model specification based on statecharts, and an illustrative application of the model to support autonomous street lights. The proposal aims to support the design and implementation of IoT applications by providing high-level design abstractions and models, thus enabling the designer to have a uniform approach to conceiving, designing and explaining such applications.

cs.AI↗

arXiv2021

A Cognitive and Machine Learning-Based Software Development Paradigm Supported by Context

Advances in the use of cognitive and machine learning (ML) enabled systems fuel the quest for novel approaches and tools to support software developers in executing their tasks. First, as software development is a complex and dynamic activity, these tasks are highly dependent on the characteristics of the software project and its context, and developers need comprehensive support in terms of information and guidance based on the task context. Second, there is a lack of methods based on conversational-guided agents that consider cognitive aspects such as paying attention and remembering. Third, there is also a lack of techniques that make use of historical implicit or tacit data to infer new knowledge about the project tasks such as related tasks, task experts, relevant information needed for task completion and warnings, and navigation aspects of the process such as what tasks to perform next and optimal task sequencing. Based on these challenges, this paper introduces a novel paradigm for human-machine software support based on context, cognitive assistance, and machine learning, and briefly describes ongoing research activities to realize this paradigm. The research takes advantage of the synergy among emergent methods provided in context-aware software processes, cognitive computing such as chatbots, and machine learning such as recommendation systems. These novel paradigms have the potential to transform the way software development currently occurs by allowing developers to receive valuable information and guidance in real-time while they are participating in projects.

cs.SE↗

arXiv2020

Exploring Context-Aware Conversational Agents in Software Development

Software development is a complex endeavor that depends on a wide variety of contextual factors involving a large amount of distributed information. This knowledge could include: technology-related tasks, software operating environments and stakeholder requirements. A major roadblock to using this knowledge in software development is that most of this information is implicit and captured in the developers' minds (tacit) or spread through volumes of documentation. Developers, as they work often have to maintain mental models of these tasks as they produce the software. As a result, context can be easily lost or forgotten and developers often use trial-and-error approaches while finishing the project. This study aims at analyzing whether supporting software developers with a chatbot during task execution can improve the overall development experience. The chatbot can assist the developers in executing different tasks based on implicit contextual information. We propose an implementation to explore the viability of using textual chatbots to assist developers automatically and proactively with software development project activities that recur.

cs.SE↗

arXiv2020

KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter Sentiments

Cryptocurrencies, such as Bitcoin, are becoming increasingly popular, having been widely used as an exchange medium in areas such as financial transaction and asset transfer verification. However, there has been a lack of solutions that can support real-time price prediction to cope with high currency volatility, handle massive heterogeneous data volumes, including social media sentiments, while supporting fault tolerance and persistence in real time, and provide real-time adaptation of learning algorithms to cope with new price and sentiment data. In this paper we introduce KryptoOracle, a novel real-time and adaptive cryptocurrency price prediction platform based on Twitter sentiments. The integrative and modular platform is based on (i) a Spark-based architecture which handles the large volume of incoming data in a persistent and fault tolerant way; (ii) an approach that supports sentiment analysis which can respond to large amounts of natural language processing queries in real time; and (iii) a predictive method grounded on online learning in which a model adapts its weights to cope with new prices and sentiments. Besides providing an architectural design, the paper also describes the KryptoOracle platform implementation and experimental evaluation. Overall, the proposed platform can help accelerate decision-making, uncover new opportunities and provide more timely insights based on the available and ever-larger financial data volume and variety.

cs.CL↗

arXiv2019

Spatial-Temporal Cluster Relations -- A Foundation for Trajectory Cluster Lifetime Analysis

Spatial-temporal data, that is information about objects that exist at a particular location and time period, are rich in value and, as a consequence, the target of so many initiative efforts. Clustering approaches aim at grouping datapoints based on similar properties for classification tasks. These approaches have been widely used in domains such as human mobility, ecology, health and astronomy. However, clustering approaches typically address only the static nature of a cluster, and do not take into consideration its dynamic aspects. A desirable approach needs to investigate relations between dynamic clusters and their elements that can be used to derive new insights about what happened to the clusters during their lifetimes. A fundamental step towards this goal is to provide a formal definition of spatial-temporal cluster relations. This report introduces, describes, and formalizes 14 novel spatial-temporal cluster relations that may occur during the existence of a cluster and involve both trajectory-cluster membership conditions and cluster-cluster comparisons. We evaluate the proposed relations with a discussion on how they are able to interpret complex cases that are difficult to be distinguished without a formal relation specification. We conclude the report by summarizing our results and describing avenues for further research.

cs.DB↗

arXiv2019

Context-Augmented Software Development Projects: Literature Review and Preliminary Framework

Software development is a complex activity which depends on diverse technologies and people's expertise. The approaches to developing software highly depend on these different characteristics, which are the context developers are subject to. This context contains massive knowledge, and not capturing it means knowledge is continuously lost. Although extensively researched, context in software development is still not explicit, nor proposed into a broader view of the context needed by software developers and tools. Therefore, developers' productivity is affected, as the ability to reuse this rich context is hampered. This paper proposes a literature review on context for software development, through nine research questions. The purpose of this study is making the discovered context explicit into an integrated view and proposing a platform to aid software development using context information. We believe supporting contextual knowledge through its representation and mining for recommendation and real-time provision can significantly improve big data software project development.

cs.SE↗

arXiv2019

Testing Self-Organizing Multiagent Systems

Multiagent Systems (MASs) involve different characteristics, such as autonomy, asynchronous and social features, which make these systems more difficult to understand. Thus, there is a lack of procedures guaranteeing that multiagent systems would behave as desired. Further complicating the situation is the fact that current agent-based approaches may also involve non-deterministic characteristics, such as learning, self-adaptation and self-organization (SASO). Nonetheless, there is a gap in the literature regarding the testing of systems with these features. This paper presents a publish-subscribe-based approach to develop test applications that facilitate the process of failure diagnosis in a self-organizing MAS. These tests are able to detect failures at the global behavior of the system or at the local properties of its parts. To illustrate the use of this approach, we developed a self-organizing MAS system based on the context of the Internet of Things (IoT), which simulates a set of smart street lights, and we performed functional ad-hoc tests. The street lights need to interact with each other in order to achieve the global goals of reducing the energy consumption and maintaining the maximum visual comfort in illuminated areas. To achieve these global behaviors, the street lights develop local behaviors automatically through a self-organizing process based on machine learning algorithms.

cs.MA↗

arXiv2018

The iEnvironment Platform: Developing an Open Science Software Platform for Integrated Environmental Monitoring and Modeling of Surface Water

This paper describes the development of iEnvironment, an open science software platform that supports monitoring and modeling of aspects of surface water. The platform supports science and engineering research, especially in the context of the creation, sharing, analysis and maintenance of big and open data. In this era of big data, iEnvironment facilitates access to open data resources and research collaboration among science and research disciplines supported by computer scientists and software developers.

cs.SE↗

arXiv2018

A Variability-Aware Design Approach to the Data Analysis Modeling Process

The massive amount of current data has led to many different forms of data analysis processes that aim to explore this data to uncover valuable insights. Methodologies to guide the development of big data science projects, including CRISP-DM and SEMMA, have been widely used in industry and academia. The data analysis modeling phase, which involves decisions on the most appropriate models to adopt, is at the core of these projects. However, from a software engineering perspective, the design and automation of activities performed in this phase are challenging. In this paper, we propose an approach to the data analysis modeling process which involves (i) the assessment of the variability inherent in the CRISP-DM data analysis modeling phase and the provision of feature models that represent this variability; (ii) the definition of a framework structural design that captures the identified variability; and (iii) evaluation of the developed framework design in terms of the possibilities for process automation. The proposed approach advances the state of the art by offering a variability-aware design solution that can enhance system flexibility, potentially leading to novel software frameworks which can significantly improve the level of automation in data analysis modeling process.

cs.SE↗

arXiv2018

The Next Generation of Metadata-Oriented Testing of Research Software

Research software refers to software development tools that accelerate discovery and simplifies access to digital infrastructures. However, although research software platforms can be built increasingly more innovative and powerful than ever before, with increasing complexity there is a greater risk of failure if unplanned for and untested program scenarios arise. As systems age and are changed by different programmers the risk of a change impacting the overall system increases. In contrast, systems that are built with less emphasis on program code and more emphasis on data that describes the application can be more readily changed and maintained by individuals who are less technically skilled but are often more familiar with the application domain. Such systems can also be tested using automatically generated advanced testing regimes.

cs.SE↗

arXiv2018

Cluster Lifecycle Analysis: Challenges, Techniques, and Framework

Novel forms of data analysis methods have emerged as a significant research direction in the transportation domain. These methods can potentially help to improve our understanding of the dynamic flows of vehicles, people, and goods. Understanding these dynamics has economic and social consequences, which can improve the quality of life locally or worldwide. Aiming at this objective, a significant amount of research has focused on clustering moving objects to address problems in many domains, including the transportation, health, and environment. However, previous research has not investigated the lifecycle of a cluster, including cluster genesis, existence, and disappearance. The representation and analysis of cluster lifecycles can create novel avenues for research, result in new insights for analyses, and allow unique forms of prediction. This technical report focuses on studying the lifecycle of clusters by investigating the relations that a cluster has with moving elements and other clusters. This technical report also proposes a big data framework that manages the identification and processing of a cluster lifecycle. The ongoing research approach will lead to new ways to perform cluster analysis and advance the state of the art by leading to new insights related to cluster lifecycle. These results can have a significant impact on transport industry data science applications in a wide variety of areas, including congestion management, resource optimization, and hotspot management.

cs.CY↗

arXiv2018

An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning

Agent-based Internet of Things (IoT) applications have recently emerged as applications that can involve sensors, wireless devices, machines and software that can exchange data and be accessed remotely. Such applications have been proposed in several domains including health care, smart cities and agriculture. However, despite their increased adoption, deploying these applications in specific settings has been very challenging because of the complex static and dynamic variability of the physical devices such as sensors and actuators, the software application behavior and the environment in which the application is embedded. In this paper, we propose a modeling approach for IoT analytics based on learning embodied agents (i.e. situated agents). The approach involves: (i) a variability model of IoT embodied agents; (ii) feedback evaluative machine learning; and (iii) reconfiguration of a group of agents in accordance with environmental context. The proposed approach advances the state of the art in that it facilitates the development of Agent-based IoT applications by explicitly capturing their complex and dynamic variabilities and supporting their self-configuration based on an context-aware and machine learning-based approach.

cs.AI↗

arXiv2018

Governance in Adaptive Normative Multiagent Systems for the Internet of Smart Things: Challenges and Future Directions

The rapidly changing environments in which companies operate to support the Internet of Things (IoT) and Autonomous Vehicles is challenging traditional Multi agent System (MAS) approaches. The requirements of these highly dynamic environments gave rise to Adaptive Normative MAS approaches. At the same time, governance is an essential and challenging feature that still needs to be addressed in adaptive normative MAS. Indeed, governance of individual and societal agent behavior in Adaptive Normative MASs is still a vague concept that has not been properly investigated, modeled and implemented. However, governance is fundamental for solving problems involving MAS coordination, organizations and institutions. In this paper, we present our ongoing research towards understanding and improving governance in Adaptive Normative MASs. We also discuss challenges and future directions that will facilitate the development of domain specific smart IoT systems with governance features.

cs.SE↗

arXiv2018

Machine Learning-based Variability Handling in IoT Agents

Agent-based IoT applications have recently been proposed in several domains, such as health care, smart cities and agriculture. Deploying these applications in specific settings has been very challenging for many reasons including the complex static and dynamic variability of the physical devices such as sensors and actuators, the software application behavior and the environment in which the application is embedded. In this paper, we propose a self-configurable IoT agent approach based on feedback-evaluative machine-learning. The approach involves: i) a variability model of IoT agents; ii) generation of sets of customized agents; iii) feedback evaluative machine learning; iv) modeling and composition of a group of IoT agents; and v) a feature-selection method based on manual and automatic feedback.

cs.MA↗

arXiv2018

Software Engineers vs. Machine Learning Algorithms: An Empirical Study Assessing Performance and Reuse Tasks

Several papers have recently contained reports on applying machine learning (ML) to the automation of software engineering (SE) tasks, such as project management, modeling and development. However, there appear to be no approaches comparing how software engineers fare against machine-learning algorithms as applied to specific software development tasks. Such a comparison is essential to gain insight into which tasks are better performed by humans and which by machine learning and how cooperative work or human-in-the-loop processes can be implemented more effectively. In this paper, we present an empirical study that compares how software engineers and machine-learning algorithms perform and reuse tasks. The empirical study involves the synthesis of the control structure of an autonomous streetlight application. Our approach consists of four steps. First, we solved the problem using machine learning to determine specific performance and reuse tasks. Second, we asked software engineers with different domain knowledge levels to provide a solution to the same tasks. Third, we compared how software engineers fare against machine-learning algorithms when accomplishing the performance and reuse tasks based on criteria such as energy consumption and safety. Finally, we analyzed the results to understand which tasks are better performed by either humans or algorithms so that they can work together more effectively. Such an understanding and the resulting human-in-the-loop approaches, which take into account the strengths and weaknesses of humans and machine-learning algorithms, are fundamental not only to provide a basis for cooperative work in support of software engineering, but also, in other areas.

cs.SE↗

arXiv2017

Developing a Spatial-Temporal Contextual and Semantic Trajectory Clustering Framework

This paper reports on ongoing research investigating more expressive approaches to spatial-temporal trajectory clustering. Spatial-temporal data is increasingly becoming universal as a result of widespread use of GPS and mobile devices, which makes mining and predictive analyses based on trajectories a critical activity in many domains. Trajectory analysis methods based on clustering techniques heavily often rely on a similarity definition to properly provide insights. However, although trajectories are currently described in terms of its two dimensions (space and time), their representation is limited in that it is not expressive enough to capture, in a combined way, the structure of space and time as well as the contextual and semantic trajectory properties. Moreover, the massive amounts of available trajectory data make trajectory mining and analyses very challenging. In this paper, we briefly discuss (i) an improved trajectory representation that takes into consideration space-time structures, context and semantic properties of trajectories; (ii) new forms of relations between the dimensions of a pair of trajectories; and (iii) big data approaches that can be used to develop a novel spatial-temporal clustering framework.

cs.DB↗