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Temporarily closed open access journal abstracted/indexed in DBLP, CrossRef, EBSCO Discovery Services, and WorldCat. It focuses on artificial intelligence including smart manufacturing, smart cities and smart medical services and more, with no publishing fees.

Editor(s)-in-Chief: Emil Vassev

A large class of software-intensive systems, including those for industrial automation, consumer electronics, airplanes, automobiles, medical devices, and civic infrastructure, must interact with the physical world. More advanced systems, such as unmanned autonomous systems, don’t just interact but also perceive important structural and dynamic aspects of their operational environment.

To become interactive, an autonomous system must be aware of its physical environment and whereabouts, as well as its current internal status. This ability helps software-intensive systems sense, draw inferences, and react by exhibiting self-adaptation. As software is used for more pervasive and critical applications, support for self-adaptation is increasingly seen as necessary in avoiding costly disruptions for repair, maintenance and evolution of systems.

A common understanding about the process of self-adaptation is the ability of a system to autonomously monitor its behavior and eventually modify the same according to changes in the operational environment or in the system itself. A good example of self-adaptive systems can be addressed to contemporary robotics systems that rely on the most recent advances in automation and robotic technologies to promote autonomy and self-adaptation to robotized systems.

The paradigm of self-adaptive systems is closely related to AI, which makes the research and development of such systems extremely challenging and demanding new approaches that can efficiently tackle the problems of expressing autonomy requirements, designing and implementing self-adaptive features, and efficiently testing self-adaptive behavior.

  • AI for wireless communications
  • AI for signal processing
  • AI for image processing
  • AI for medical/biological analysis, visualization and diagnosis
  • AI for robotics
  • AI for audio, language and speech processing
  • AI for big data mining and analysis
  • AI for emotion analysis
  • AI for self-piloting automobile
  • Applications in neuroscience
  • Computer imaging, vision and graphics
  • Machine learning
  • Deep learning
  • Deep reinforcement learning
  • Pattern recognition
  • Automated reasoning and inference
  • Natural language processing
  • Knowledge and data engineering

Special Issues Editor: Jorge De-j. Lozoya-santos (University of Monterrey, Mexico)

Bashar Nuseibeh (The Open University, UK and Lero, the Irish Software Engineering Research Center, University of Limerick)

Christopher Rouff (Johns Hopkins Applied Physics Laboratory)

Danny Weyns (Linnaeus University)

Diana Spears (Swarmotics LLC, Laramie, Wyoming, USA)

Franco Zambonelli (UNIMORE, Italy)

Genaina Rodrigues (University of Brasilia)

Giacomo Cabri (UNIMORE, Italy)

Imrich Chlamtac (CREATE-NET Research Consortium, University of Trento, Italy)

James Windsor (ESTEC, European Space Agency, Netherlands)

Michael O'Neill (UCD, Ireland)

Mike Hinchey (Lero, the Irish Software Engineering Research Center, University of Limerick)

Richard Antony (University of Greenwich)

Simon Dobson (University of St Andrews)


  • Activity Monitoring System for Independent Elderly Living

    Appears in:
    sas 16(5): e2

    Authors:
    Douglas Dow, Joseph Santacroce, Vanessa Restrepo, Timothy Walters, Rosemary Espinal

    Published:
    24th May 2016

    Abstract:
    Some elderly people prefer to live independently and not in group settings, such as nursing homes. As age increases, health risks for independent living increase, including incapacitation. The longer one remains in an incapacitated state before being detected and receiving help, the higher the risk of permanent impairment or death. Systems that monitor activity within a residence may allow an elderly person to live independently, since the system would detect a lack of activity and would make alerts for help to arrive if one were to enter an incapacitated state. The objectives of this project were to develop and test modules of an activity monitoring system for a bathroom, the toiletry restroom of a residence. Prototype modules have been developed based on a Raspberry Pi, sensors for motion, contact switches to determine opening and closing of doors and cabinets, water valve sensors, and a temperature and humidity sensor. LEDs in the prototype serve the role of alerts or actuators, to be developed further. The prototype system did read from each sensor and controlled LEDs that modeled actuators. The received data was stored in Google Spreadsheet with data presented in graphs. The system shows promise but further development and testing are required toward application.

  • Sensitivity based selection of inputs and delays for NARX models

    Appears in:
    sas 16(5): e3

    Authors:
    Martin Macas, Fabio Moretti

    Published:
    24th May 2016

    Abstract:
    In this paper, an extension of sensitivity based pruning (SBP) method for Nonlinear AutoRegressive models with eXogenous inputs (NARX) model is presented. Besides the inputs, input and output delays are simultaneously pruned in terms of the backward elimination. The concept is based on replacement of some regressors by their mean value, which corresponds to the removal of influence of the particular regressors from the network. The method is demonstrated on two datasets. Firstly, one artificial generator is used to test if the method is able to find an optimal set of inputs and delays. Further, the method is used for prediction of gas consumption of a simulated heating for an office building. It is shown that the SBP significantly reduces the complexity of the NARX network without any significant performance degradation. Moreover, it is hypothesized than SBP can be more important for NARX than for simple feedforward neural network, because NARX is more prone to overfitting and has problems with stability.

  • A Self-Organizing Map Architecture for Arm Reaching Based on Limit Cycle Attractors

    Appears in:
    sas 16(5): e1

    Authors:
    Rodolphe Gentili, James Reggia, Di-Wei Huang

    Published:
    24th May 2016

    Abstract:
    Creating and studying neurocognitive architectures is an active and increasing focus of research efforts. Based on our recent research that uses neural activity limit cycles in self-organizing maps (SOMs) to represent external stimuli, this study explores the use of such limit cycle attractors in a neurocognitive architecture for an open-loop arm reaching task. The goal is to learn to produce a static motor command for arm joints that moves the manipulator to a target spatial location, while the internal neural activity remains oscillatory. Unlike with static SOMs, stabilizing output based on changing neural activity becomes an important issue. Our architecture is also characterized by simple and forgiving timing requirements, meaning that the time of gating among neural components can be set relatively arbitrarily due to the repetitiveness of limit cycle activity. The results indicate that our architecture generalizes to unseen data, and that the overall performance is insensitive to exact gate timing.

  • Optimal topology of gene-regulatory networks: role of the average shortest path

    Appears in:
    sas 16(7): e3

    Authors:
    Preetam Ghosh, Ahmed Abdelzaher, Michael Mayo, Thang Dinh

    Published:
    24th May 2016

    Abstract:
    Gene regulatory networks (GRNs) possess an important structural property; they are sparse and resilient, with a robust topology that affords protection against random "attacks" (e.g., gene deletions). However, such networks exhibit optimal or near-optimal topological features not present in other scale-free networks. This paper utilizes an integer linear program formulation to gauge the exact structural optimality of scale-free networks measured using the average shortest path between transcription factors and the regulated genes of a gene-regulatory network sampled from the Escherichia coli bacterium. While randomly generated versions of these networks show several cases for improvement, few subnetworks sampled from Escherichia coli's transcriptional network show optimized solutions that differ substantially from their original topology. We therefore conclude that sampled transcriptional subnetworks from Escherichia coli exhibit an "optimal" topology not present in alternative networks. Because these analyses do not consider the biology of expression dynamics and are based on topology alone, other communication systems, such as wireless networks, may benefit from a more detailed examination of the role in which the average shortest path affects system function, such as with noise or other signaling disruptions.

  • A topological approach for multivariate time series characterization: the epileptic brain

    Appears in:
    sas 16(7): e5

    Authors:
    Marco Piangerelli, Matteo Rucco, Emanuela Merelli, Daniele Toller

    Published:
    24th May 2016

    Abstract:
    In this paper we propose a methodology based on Topogical Data Analysis (TDA) for capturing when a complex system, represented by a multivariate time series, changes its internal organization. The modification of the inner organization among the entities belonging to a complex system can induce a phase transition of the entire system. In order to identify these reorganizations, we designed a new methodology that is based on the representation of time series by simplicial complexes. The topologization of multivariate time series successfully pinpoints out when a complex system evolves. Simplicial complexes are characterized by persistent homology techniques, such as the clique weight rank persistent homology and the topological invariants are used for computing a new entropy measure, the so-called weighted per- sistent entropy. With respect to the global invariants, e.g. the Betti numbers, the entropy takes into account also the topological noise and then it captures when a phase transition happens in a system. In order to verify the reliability of the methodology, we have analyzed the EEG signals of PhysioNet database and we have found numerical evidences that the methodology is able to detect the transition between the pre-ictal and ictal states.

  • Automatic Regulator for Supplemental Oxygen Therapy

    Appears in:
    sas 16(6): e4

    Authors:
    Yasameen Al Mharib, Vian Abraham, Douglas Dow, Xiaobin Le, Joseph Santacroce, Brian Donnel

    Published:
    24th May 2016

    Abstract:
    Living tissues require a sufficient supply of oxygen for survival. An insufficient supply of oxygen may result from impairments of the respiration or oxygen transport systems, such as lung disease, heart disease, and anemia. Supplemental oxygen therapy increases the oxygen content in the lungs and blood oxygen saturation. The control of the oxygen flow is typically adjusted manually. This manual adjustment increases the risk for a mismatch between the oxygen flow rate and oxygen consumption that may change with physiological activity. An insufficient supply of oxygen increases the risk for cell damage and hypoxia. An excessive supply of oxygen risks oxygen toxicity and rapid depletion of the oxygen supply. This paper develops and tests the regulation of gas flow intake based on input of patient oxygen blood saturation. During testing of the prototype, gas pressure was regulated based on input oxygen blood saturation values, indicating control of gas flow. Further development and testing would be required for closed loop control of the system. Such a system would contribute toward improving the match of oxygen flow to physiological demands, and contribute to improving the health of many patients.

  • On the Computational Complexity of Software (Re)Modularization: Elaborations and Opportunities

    Appears in:
    sas 16(7): e4

    Authors:
    Todd Wareham

    Published:
    24th May 2016

    Abstract:
    Software system modularization and remodularization are key challenges in software engineering. All previous research has assumed that these problems are computationally intractable and hence focused on heuristic methods such as hill-climbing, evolutionary algorithms, and simulated annealing that are fast but do not guarantee to produce solutions of optimal or even near-optimal quality. However, this intractability has never been formally established. In this paper, we give the first proofs of the $NP$-hardness of software modularization and remodularization relative to several models of module-internal connectivity. We also review three popular algorithmic approaches for producing provably optimal or near-optimal solutions efficiently and both discuss the applicability of these approaches in and list results in the literature relevant to practical software modularization and remodularization.

  • Optimization of Aircraft Landing Route and Order: An approach of Hierarchical Evolutionary Computation

    Appears in:
    sas 16(6): e5

    Authors:
    Akinori Murata, Tim Kovacs, Hiroyuki Sato, Masaya Nakata, Keiki Takadama

    Published:
    24th May 2016

    Abstract:
    This paper focuses on the aircraft landing optimization problem where both the landing routes and the landing order of aircrafts should be optimized to minimize an occupancy time of airport , and proposes its optimization method which is robust to dynamical situations such as weather condition change and other aircrafts’ landing routes change. As a difficulty of this optimization problem, appropriate landing routes of aircrafts change depending on such an environment change. To tackle this problem, this paper proposes the hierarchical evolutionary computation to solve the aircraft landing optimization problem. Specifically, our method firstly generates candidates of main landing route of all aircrafts with their own additional sub-routes, which can be applied into the main routes depending on the current environmental situation. Secondly, our method evolves the good combination of landing routes (including their sub-routes) of all aircrafts to minimize an occupancy time of airport. Through the intensive experiment on a benchmark problem, the following implications have been found: (1) our method successfully generates robust landing routes including some sub-routes，which are flexible depending on environmental situations; and (2) Our method can finds an adequate landing order which contributes to reducing the occupancy time.

  • A Population Based ACO Algorithm for the Combined Tours TSP Problem

    Appears in:
    sas 16(7): e2

    Authors:
    Lydia Lotzmann, Martin Middendorf, Martin Clauss

    Published:
    24th May 2016

    Abstract:
    In this paper we apply a Population based Ant Colony Optimization(PACO) algorithm for solving the following new version of the Traveling Salesperson problem that is called the Combined Tours TSP (CT-TSP). Given are a set of cities, for each pair of cities a cost function and an integer k. The aim is to find a set of k (cyclic) tours, i.e., each city is contained exactly once in each tour and each tour returns to its origin city, which have minimum total costs. In this paper the case of finding two tours is studied where the costs of one tour depends on the other tour. Each pair of cities has a distance and a weight which influence the costs of the tours. The weight is used to define if it is advantageous or disadvantageous when the corresponding pair of cities is contained, i.e., neighbouring, in both tours. Different heuristics that the ants of the PACO use for the construction of the tours are compared experimentally. One result is that it is (often) advantageous when the heuristic for the second tour is different from the heuristic for the first tour such that the former heuristic uses knowledge about the first tour.

  • On the Computational Complexity of Designing and Reconfiguring Component-based Software Systems

    Appears in:
    sas 16(5): e4

    Authors:
    Marieke Sweers, Todd Wareham

    Published:
    24th May 2016

    Abstract:
    Though Component-Based Development (CBD) is a popular approach to mitigating the costs of creating software systems, it is not clear to what extent CBD is preferable to other approaches to software engineering or to what extent the core component selection and adaptation activities of CBD can be implemented to operate without human intervention in an efficient and reliable manner. In this paper, we use computational complexity analysis to compare the computational characteristics of software system design and reconfiguration by de novo design, component selection, and component selection with adaptation. Our results show that none of these approaches can be implemented to operate both efficiently and reliably either in general or relative to a surprisingly large number of restrictions on software system, component, and component library structure. We also give the first restrictions under which all of these approaches can be implemented to operate both efficiently and reliably.

  • A Framework for System Event Classification and Prediction by Means of Machine Learning

    Appears in:
    sas 15(3): e3

    Authors:
    Jonas Grunert, Teerat Pitakrat, Fabian Keller, Oliver Kabierschke, Andre van Hoorn

    Downloads:
    2483

    Abstract:
    During operation, software systems produce large amounts of log events, comprising notifications of different severity from various hardware and software components. These data include important information that helps to diagnose problems in the system, e.g., post-mortem root cause analysis. Manual processing of system logs after a problem occurred is a common practice. However, it is time-consuming and error-prone. Moreover, this way, problems are diagnosed after they occurred—even though the data may already include symptoms of upcoming problems. To address these challenges, we developed the SCAPE approach for automatic system event classification and prediction, employing machine learning techniques. This paper introduces SCAPE, including a brief description of the proof-of-concept implementation. SCAPE is part of our Hora framework for online failure prediction in component-based software systems. The experimental evaluation, using a publicly available supercomputer event log, demonstrates SCAPE’s high classification accuracy and first results on applying the prediction to a real world data set.

  • A topological approach for multivariate time series characterization: the epileptic brain

    Appears in:
    sas 16(7): e5

    Authors:
    Marco Piangerelli, Matteo Rucco, Emanuela Merelli, Daniele Toller

    Downloads:
    2240

    Abstract:
    In this paper we propose a methodology based on Topogical Data Analysis (TDA) for capturing when a complex system, represented by a multivariate time series, changes its internal organization. The modification of the inner organization among the entities belonging to a complex system can induce a phase transition of the entire system. In order to identify these reorganizations, we designed a new methodology that is based on the representation of time series by simplicial complexes. The topologization of multivariate time series successfully pinpoints out when a complex system evolves. Simplicial complexes are characterized by persistent homology techniques, such as the clique weight rank persistent homology and the topological invariants are used for computing a new entropy measure, the so-called weighted per- sistent entropy. With respect to the global invariants, e.g. the Betti numbers, the entropy takes into account also the topological noise and then it captures when a phase transition happens in a system. In order to verify the reliability of the methodology, we have analyzed the EEG signals of PhysioNet database and we have found numerical evidences that the methodology is able to detect the transition between the pre-ictal and ictal states.

  • The Development of Intelligent Service System for Machine Tool Industry

    Appears in:
    sas 15(4): e4

    Authors:
    Hung-Sheng Chiu, Yueh-Ling Lin, Chih-Chieh Lin

    Downloads:
    2188

    Abstract:
    Developing efficient services for Taiwan’s machine-tool industry is important because of the intelligent service enhancement for industrial networks and systems integration. The purpose of this study is to provide innovative service via remote cloud computing for machine tool solutions. The intelligent system integrates both hardware and software functions of machine tools. A platform was developed for machinery information convergence and intelligent service system. To complete the standards for the proposed system, the Taiwan Machine Tool Connect (TMTC) standard was developed to provide over 80% data acquisition support for current CNC machines and PLC controllers. Compare the TMTC standard with the MTConnect standard, the TMTC can provide faster and lower traffic on multi-trip data exchange to accelerate the interpretation and increase 50% transmission speed. The proposed system was demonstrated by applying it to the aerospace and automotive industries. The system performance was verified by proximal system functional test, platform verification, work and service process feasibility assessment, and key performance evaluation for machine tool solutions. The results successfully connect 302 machines with ServBox to achieve real-time machine monitoring and enhance productivity. Finally, the proposed system can provide machinery data analysis, intelligent service, and applications for machine tool solutions and increase production values for Taiwan’s machine-tool industry.

  • Ascending and Descending Walking Recognition using Smartphone

    Appears in:
    sas 15(6): e1

    Authors:
    Sang Vu, Khoa Truong, Toshiyuki Kondo, Shiro Yano

    Downloads:
    2119

    Abstract:
    In the recent decades, activity recognition for human behavior monitoring has been one of the major interesting research subjects. This paper investigates fractal dimension (FD) as an effective feature for human activity recognition. The target activities are descending and ascending stairs, which are performed by six male participants. To testify the validity of feature, the classification methods are k-nearest neighbors (kNN) and support vector machine (SVM). The best result achieved in this results is using SVM classifier and obtaining 95.30% for walking downstairs and 88.56% for walking upstairs with window of 4s and equalized data. Another aim of this paper is to examine the impact of database on the classification results. The finding in the paper provides evidence that the classification accuracy is affected insignificantly by the ratio of descending and ascending data.

  • Evaluating Unsupervised Fault Detection in Self-Healing Systems Using Stochastic Primitives

    Appears in:
    sas 15(1): e3

    Authors:
    Chris Schneider, Simon Dobson, Adam Barker

    Downloads:
    1913

    Abstract:
    Autonomous fault detection represents one approach for reducing operational costs in large-scale computing environments. However, little empirical evidence exists regarding the implementation or comparison of such methodologies, or offers proof that such approaches reduce costs. This paper compares the effectiveness of several types of stochastic primitives using unsupervised learning to heuristically determine the root causes of faults. The results suggest that self-healing systems frameworks leveraging these techniques can reliably and autonomously determine the source of an anomaly within as little as five minutes. This finding lays the foundation for determining the potential these approaches have for reducing operational costs and ultimately concludes with new avenues for exploring anomaly prediction.

  • Docking autonomous robots in passive docks with Infrared sensors and QR codes

    Appears in:
    sas 15(2): e2

    Authors:
    Adriaan Zeeman, Roberto Quilez, Julien Vandaele, Nathalie Mitton

    Downloads:
    1906

    Abstract:
    In this paper, we describe an inexpensive and easy to deploy docking solution in passive charging docks for autonomous mobile robots. The objective is to achieve long-term autonomous robots within an experiment test-bed. We propose to combine the use of QR codes as landmarks and Infrared distance sensors. The relative size of the lateral edges of the visual pattern is used to position the robot in relation with the dock. Infrared distance sensors are then used to perform different approaching strategies depending on the distance. Experiments show that the proposed solution is fully operational and robust. Not to rely exclusively on visual pattern recognition avoids potential errors induced by camera calibration. Additionally, as a positive side effect, the use of Infrared sensors allows the robot to avoid obstacles while docking. The finality of such an approach is to integrate these robots into the FIT IoT Lab experimental testbed which allows any experimenter to book wireless resources such as wireless sensors remotely and to test their own code. Wifibots holding wireless sensors will be integrated as additional reservable resources of the platform to enlarge the set of possible experimentations with mobile entities.

  • A Tool for Visualizing Buffer Overflow with Detecting Return Address Overwriting

    Appears in:
    sas 16(5): e5

    Authors:
    Isao Sasano

    Downloads:
    1793

    Abstract:
    Buffer overflow is a serious problem when the software is run as a server on the internet. Especially when the return addresses are overwritten intentionally, the control flow may be changed as the attacker intends. Although there have been proposed several ways to protect attacks that utilize the buffer overflow, the number of the errors owing to the buffer overflow have been increasing gradually. This paper presents a tool that visualizes the buffer overflow when executing programs in C language, especially when the return addresses are overwritten. The functionality is mainly targeted at beginners of C programming who do not recognize the attacks, which we expect makes the number of errors decrease in the future.

  • A Population Based ACO Algorithm for the Combined Tours TSP Problem

    Appears in:
    sas 16(7): e2

    Authors:
    Lydia Lotzmann, Martin Middendorf, Martin Clauss

    Downloads:
    1511

    Abstract:
    In this paper we apply a Population based Ant Colony Optimization(PACO) algorithm for solving the following new version of the Traveling Salesperson problem that is called the Combined Tours TSP (CT-TSP). Given are a set of cities, for each pair of cities a cost function and an integer k. The aim is to find a set of k (cyclic) tours, i.e., each city is contained exactly once in each tour and each tour returns to its origin city, which have minimum total costs. In this paper the case of finding two tours is studied where the costs of one tour depends on the other tour. Each pair of cities has a distance and a weight which influence the costs of the tours. The weight is used to define if it is advantageous or disadvantageous when the corresponding pair of cities is contained, i.e., neighbouring, in both tours. Different heuristics that the ants of the PACO use for the construction of the tours are compared experimentally. One result is that it is (often) advantageous when the heuristic for the second tour is different from the heuristic for the first tour such that the former heuristic uses knowledge about the first tour.

  • Intelligence of reconstructed biomolecular motor system

    Appears in:
    sas 16(8): e4

    Authors:
    Arif Rashedul Kabir, Akira Kakugo, Daisuke Inoue

    Downloads:
    1506

    Abstract:
    Collective motion is a fascinating example of coordinated behavior of self-propelled objects, which is often associated with the formation of large scale patterns. Nowadays, in vitro gliding assay is being considered a model system to experimentally investigate various aspects of group behavior and pattern formation by self-propelled objects. In this work, we have demonstrated the collective motion of kinesin driven microtubules by regulating mutual interaction among the gliding microtubules, by employing depletion force among them. Proper regulation of the mutual interaction among the gliding microtubules through employment of the depletion force was found to allow the exhibition of collective motion and stream pattern formation by microtubules. We also discuss how collectively moving microtubule on kinesin coated elastomer substrate response to external stimuli such as mechanical stresses.

  • Infrared dynamic hand gesture recognition based on Gabor feature and sparse representation

    Appears in:
    sas 15(4): e1

    Authors:
    Lei Shang, Haixia Zhang, Zhi Liu

    Downloads:
    1457

    Abstract:
    Dynamic hand gesture recognition is a subject which has been investigated almost from the beginning of using terminals to interact with the computer central unit. We present a method for dynamic hand gesture recognition with image source acquired by a single IR camera. First of all, the hand images are captured by one infrared camera, which are light independent, and not be limited by skin color. Second, due to the dynamic hand gesture sets contain more than one frame, the data dimension is very large and the adjacent frames have limited difference. We use the twofold selections to choose the key frames from the gesture image sets. The Gabor feature is used to describe the locality variation. And the Sparse representation based classification (SRC) is used for recognition. Experimental results on dynamic hand gesture images with variations of rotation and translation demonstrate the good performance of our method.
Publisher
EAI
ISSN
2410-4051
Number of Volumes
2
Last Published
2016-12-28
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