INQUIRIES IN INTELLIGENT INFORMATION SYSTEMS: NEW TRAJECTORIES AND PARADIGMS

Rapid Digital transformation drives organizations to continually revitalize their business models so organizations can excel in such aggressive global competition. Intelligent Information Systems (IIS) have enabled organizations to achieve many strategic and market leverages. Despite the increasing intelligence competencies offered by IIS, they are still limited in many cognitive functions. Elevating the cognitive competencies offered by IIS would impact the organizational strategic positions. With the advent of Deep Learning (DL), IoT, and Edge Computing, IISs has witnessed a leap in their intelligence competencies. DL has been applied to many business areas and many industries such as real estate and manufacturing. Moreover, despite the complexity of DL models, many research dedicated efforts to apply DL to limited computational devices, such as IoTs. Applying deep learning for IoTs will turn everyday devices into intelligent interactive assistants. IISs suffer from many challenges that affect their service quality, process quality, and information quality. These challenges affected, in turn, user acceptance in terms of satisfaction, use, and trust. Moreover, Information Systems (IS) has conducted very little research on IIS development and the foreseeable contribution for the new paradigms to address IIS challenges. Therefore, this research aims to investigate how the employment of new AI paradigms would enhance the overall quality and consequently user acceptance of IIS. This research employs different AI paradigms to develop two different IIS. The first system uses deep learning, edge computing, and IoT to develop scene-aware ridesharing mentoring. The first developed system enhances the efficiency, privacy, and responsiveness of current ridesharing monitoring solutions. The second system aims to enhance the real estate searching process by formulating the search problem as a Multi-criteria decision. The system also allows users to filter properties based on their degree of damage, where a deep learning network allocates damages in 12 each real estate image. The system enhances real-estate website service quality by enhancing flexibility, relevancy, and efficiency. The research contributes to the Information Systems research by developing two Design Science artifacts. Both artifacts are adding to the IS knowledge base in terms of integrating different components, measurements, and techniques coherently and logically to effectively address important issues in IIS. The research also adds to the IS environment by addressing important business requirements that current methodologies and paradigms are not fulfilled. The research also highlights that most IIS overlook important design guidelines due to the lack of relevant evaluation metrics for different business problems

Streamlining code smells: Using collective intelligence and visualization

Context. Code smells are seen as major source of technical debt and, as such, should be detected and removed. Code smells have long been catalogued with corresponding mitigating solutions called refactoring operations. However, while the latter are supported in current IDEs (e.g., Eclipse), code smells detection scaffolding has still many limitations. Researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of smells-infected code. Objective. This thesis presents a new approach to code smells detection that we have called CrowdSmelling and the results of a validation experiment for this approach. The latter is based on supervised machine learning techniques, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms, thereby lessening the subjectivity issue. Method. In the context of three consecutive years of a Software Engineering course, a total “crowd” of around a hundred teams, with an average of three members each, classified the presence of 3 code smells (Long Method, God Class, and Feature Envy) in Java source code. These classifications were the basis of the oracles used for training six machine learning algorithms. Over one hundred models were generated and evaluated to determine which machine learning algorithms had the best performance in detecting each of the aforementioned code smells. Results. Good performances were obtained for God Class detection (ROC=0.896 for Naive Bayes) and Long Method detection (ROC=0.870 for AdaBoostM1), but much lower for Feature Envy (ROC=0.570 for Random Forrest). Conclusions. Obtained results suggest that Crowdsmelling is a feasible approach for the detection of code smells, but further validation experiments are required to cover more code smells and to increase external validityContexto. Os cheiros de código são a principal causa de dívida técnica (technical debt), como tal, devem ser detectados e removidos. Os cheiros de código já foram há muito tempo catalogados juntamente com as correspondentes soluções mitigadoras chamadas operações de refabricação (refactoring). No entanto, embora estas últimas sejam suportadas nas IDEs actuais (por exemplo, Eclipse), a deteção de cheiros de código têm ainda muitas limitações. Os investigadores argumentam que a subjectividade do processo de deteção de cheiros de código é um dos principais obstáculo à mitigação do problema da qualidade do código. Objectivo. Esta tese apresenta uma nova abordagem à detecção de cheiros de código, a que chamámos CrowdSmelling, e os resultados de uma experiência de validação para esta abordagem. A nossa abordagem de CrowdSmelling baseia-se em técnicas de aprendizagem automática supervisionada, onde a sabedoria da multidão (dos programadores de software) é utilizada para calibrar colectivamente algoritmos de detecção de cheiros de código, diminuindo assim a questão da subjectividade. Método. Em três anos consecutivos, no âmbito da Unidade Curricular de Engenharia de Software, uma "multidão", num total de cerca de uma centena de equipas, com uma média de três membros cada, classificou a presença de 3 cheiros de código (Long Method, God Class, and Feature Envy) em código fonte Java. Estas classificações foram a base dos oráculos utilizados para o treino de seis algoritmos de aprendizagem automática. Mais de cem modelos foram gerados e avaliados para determinar quais os algoritmos de aprendizagem de máquinas com melhor desempenho na detecção de cada um dos cheiros de código acima mencionados. Resultados. Foram obtidos bons desempenhos na detecção do God Class (ROC=0,896 para Naive Bayes) e na detecção do Long Method (ROC=0,870 para AdaBoostM1), mas muito mais baixos para Feature Envy (ROC=0,570 para Random Forrest). Conclusões. Os resultados obtidos sugerem que o Crowdsmelling é uma abordagem viável para a detecção de cheiros de código, mas são necessárias mais experiências de validação para cobrir mais cheiros de código e para aumentar a validade externa

Videogames: the new GIS?

Videogames and GIS have more in common than might be expected. Indeed, it is suggested that videogame technology may not only be considered as a kind of GIS, but that in several important respects its world modelling capabilities out-perform those of most GIS. This chapter examines some of the key differences between videogames and GIS, explores a number of perhaps-surprising similarities between their technologies, and considers which ideas might profitably be borrowed from videogames to improve GIS functionality and usability

Psychology and the research enterprise: Moving beyond the enduring hegemony of positivism

Almost since its inception, psychology has embraced the positivist orientation of the natural sciences. The research enterprise in psychology has reinforced this through its insistence that psychological science is objective, generalisable, and value free (or neutral). Consequently, experimental designs are privileged over other forms of enquiry and alternate epistemologies, methodologies, and methods remain marginalised within the discipline. We argue that alternate methodologies, and the philosophies that underpin the research endeavour, should be included in mainstream psychology programmes so that the existing imbalance is rectified. Achieving this balance will mean that psychology will be better positioned to address applied research problems and students will graduate with the skills and knowledge that they will need in the multidisciplinary workforce they will enter. We discuss recommendations for how psychology in Australia can move towards embracing methodological and epistemological pluralism. Breen, L. & Darlaston-Jones, D. (2008). Psychology and the research enterprise: Moving beyond the enduring hegemony of positivism. Australian Journal of Psychology, 60 (S1), 107-208. doi:10.1080/0004953080238555

Wireless sensor network integrated with ros for danger avoidance in mobile robotics

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáEnvironmental awareness is a crucial task that robots must perform to navigate autonomously. Moreover, it must be well executed to make navigation safer and collisionfree. Since autonomous mobile robots are being used in dynamic scenarios where a simultaneity of events occurs, it becomes even more difficult to correctly sense and perceive the environment. This paper proposes the integration of a wireless sensor network with the Robotic Operating System – ROS – to incorporate advanced information from the environment into layered cost maps used by the robot to navigate. The system architecture was implemented, evaluated in a simulated environment, tested and validated, and the results obtained showed the effective gain in the computation of the paths and in the reduction of the computational load of the associated subsystems. With positive results in a simulated environment, the system was deployed on a robotic platform and evaluated in a real scenario, through experiments. The results obtained in the experiments prove the gain in the navigation process of the platform due to the better perception of the environment in the tested scenarios.A perceção do ambiente é uma tarefa crucial que os robôs têm de realizar para navegar de forma autónoma. Além disso, deve ser bem realizada para tornar a navegação mais segura e livre de colisões. Como os robôs móveis autónomos estão a ser empregados em cenários dinâmicos, onde ocorre uma simultaneidade de eventos, tornam-se ainda mais difícil a detecção e a percepção adequada do ambiente. Este trabalho propõe a integração de uma rede de sensores sem fios com o Sistema Operacional Robótico – ROS – para incorporar informações avançadas do ambiente em mapas de custos por camadas utilizados pelo robô para navegar. A arquitectura do sistema foi implementada, avaliada em ambiente simulado, testada e validada, e os resultados obtidos mostraram o ganho efetivo no cômputo dos percursos e na redução da carga computacional dos subsistemas associados. Com resultados positivos em ambiente simulado, o sistema foi implantado numa plataforma robótica e avaliado num cenário real, mediante experimentos. Os resultados obtidos nos experimentos comprovam o ganho no processo de navegação da plataforma devido a melhor percepção do ambiente nos cenários testados

SPARC 2019 Fake news & home truths : Salford postgraduate annual research conference book of abstracts

Welcome to the Book of Abstracts for the 2019 SPARC conference. This year we not only celebrate the work of our PGRs but also our first ever Doctoral School Best Supervisor awards, which makes this year’s conference extra special. Once again we have received a tremendous contribution from our postgraduate research community; with over 90 presenters, the conference truly showcases a vibrant, innovative and collaborative PGR community at Salford. These abstracts provide a taster of the inspiring, relevant and impactful research in progress, and provide delegates with a reference point for networking and initiating critical debate. Find an abstract that interests you, and say “Hello” to the author. Who knows what might result from your conversation? With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. To meet global challenges, high impact research needs interdisciplinary collaboration. This is recognised and rewarded by all major research funders. Engaging with the work of others and forging collaborations across subject areas is an essential skill for the next generation of researchers. Even better, our free ice cream van means that you can have those conversations while enjoying a refreshing ice lolly

Evaluation of Parametric and Nonparametric Statistical Models in Wrong-way Driving Crash Severity Prediction

Wrong-way driving (WWD) crashes result in more fatalities per crash, involve more vehicles, and cause extended road closures compared to other types of crashes. Although crashes involving wrong-way drivers are relatively few, they often lead to fatalities and serious injuries. Researchers have been using parametric statistical models to identify factors that affect WWD crash severity. However, these parametric models are generally based on several assumptions, and the results could generate numerous errors and become questionable when these assumptions are violated. On the other hand, nonparametric methods such as data mining or machine learning techniques do not use a predetermined functional form, can address the correlation problem among independent variables, display results graphically, and simplify the potential complex relationship between the variables. The main objective of this research was to demonstrate the applicability of nonparametric statistical models in successfully identifying factors affecting traffic crash severity. To achieve this goal, the performance of parametric and nonparametric statistical models in WWD crash severity prediction was evaluated. The following parametric methods were evaluated: Logistic Regression (LR), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Linear Discriminant Analysis (LDA), and Gaussian Naïve Bayes (GNB). The following nonparametric methods were evaluated: Random Forests (RF), Decision Trees (DT), and Support Vector Machine (SVM). The evaluation was based on sensitivity, specificity, and prediction accuracy. The research also demonstrated the applicability of nonparametric supervised learning algorithms on crash severity analysis by combining tree-based data mining techniques and marginal effect analysis to show the correlation between the response and the predictor variables. The analysis was based on 1,475 WWD crashes that occurred on arterial road networks from 2012-2016 in Florida. The results showed that nonparametric models provided better prediction accuracy on predicting serious injury compared to parametric models. By conducting prediction accuracy comparison, contributor variables’ marginal effect analysis, variable importance evaluation, and crash severity pattern recognition analysis, the nonparametric models have been demonstrated to be valid and proved to serve as an alternative tool in transportation safety studies. The results showed that head-on collisions, weekends, high-speed facilities, crashes involving vehicles entering from a driveway, dark-not lighted roadways, older drivers, and driver impairment are important factors that play a crucial role in WWD crash severity on non-limited access facilities. This information may assist researchers and safety engineers in identifying specific strategies to reduce the severity of WWD crashes on arterial streets. Besides unveiling the factors contributing to WWD crash severity and their relationship with each other, this research has demonstrated the potential of using data mining techniques in yielding results that are easily understandable and interpretable

Developing Cyberspace Data Understanding: Using CRISP-DM for Host-based IDS Feature Mining

Current intrusion detection systems generate a large number of specific alerts, but do not provide actionable information. Many times, these alerts must be analyzed by a network defender, a time consuming and tedious task which can occur hours or days after an attack occurs. Improved understanding of the cyberspace domain can lead to great advancements in Cyberspace situational awareness research and development. This thesis applies the Cross Industry Standard Process for Data Mining (CRISP-DM) to develop an understanding about a host system under attack. Data is generated by launching scans and exploits at a machine outfitted with a set of host-based data collectors. Through knowledge discovery, features are identified within the data collected which can be used to enhance host-based intrusion detection. By discovering relationships between the data collected and the events, human understanding of the activity is shown. This method of searching for hidden relationships between sensors greatly enhances understanding of new attacks and vulnerabilities, bolstering our ability to defend the cyberspace domain

Art and Medicine: A Collaborative Project Between Virginia Commonwealth University in Qatar and Weill Cornell Medicine in Qatar

Four faculty researchers, two from Virginia Commonwealth University in Qatar, and two from Weill Cornell Medicine in Qatar developed a one semester workshop-based course in Qatar exploring the connections between art and medicine in a contemporary context. Students (6 art / 6 medicine) were enrolled in the course. The course included presentations by clinicians, medical engineers, artists, computing engineers, an art historian, a graphic designer, a painter, and other experts from the fields of art, design, and medicine. To measure the student experience of interdisciplinarity, the faculty researchers employed a mixed methods approach involving psychometric tests and observational ethnography. Data instruments included pre- and post-course semi-structured audio interviews, pre-test / post-test psychometric instruments (Budner Scale and Torrance Tests of Creativity), observational field notes, self-reflective blogging, and videography. This book describes the course and the experience of the students. It also contains images of the interdisciplinary work they created for a culminating class exhibition. Finally, the book provides insight on how different fields in a Middle Eastern context can share critical /analytical thinking tools to refine their own professional practices

Exploring the Use of Drones for Conducting Traffic Mobility and Safety Studies

ABSTRACT Advanced traffic data collection methods, including the application of aerial sensors (drones) as traffic data collectors, can provide real-time traffic information more efficiently, effectively, and safely than traditional methods. Traffic trajectory data like vehicles’ coordinates and point timestamps are challenging to obtain at intersections using traditional field survey methods. The coordinates and timestamps crucial in calculating trajectories can be obtained using drones and their particular integrated software. Thus, this study explores the use of unmanned aerial systems (UAS), particularly tethered drones, to obtain traffic parameters for traffic mobility and safety studies at an unsignalized intersection in Tallahassee, Florida. Tethered drones provided more flexibility in heights and angles and collected data over a relatively larger space needed for the proposed approach. Turning movement counts, gap study, speed study, and Level of Service (LOS) analysis for the stated intersection were the traffic studies conducted in this research. The turning movements were counted through ArcGIS Pro. From the drone footages, the gap study followed by the LOS analysis was carried out. A speed algorithm was developed to calculate speed during a speed study. Based on the results, the intersection operates under capacity with LOS B during the time. Also, the results indicated that the through movement traffic tends to slow down as they approach the intersection while south-bound right and east-bound left-turning traffic increase their speeds as they make a turn. Accuracy assessment was done by comparing the drone footages with the results displayed in ArcGIS software. The drone’s data collection was 100% accurate in traffic movement counting and 96% accurate in traffic movement classification. The level of accuracy is sufficient compared to other advanced traffic data collection methods. In this study, safety was assessed by the surrogate safety measures (SSMs). SSMs can be the viable alternatives for locations with insufficient historical data and indicate potential future conflicts between roadway users. The surrogate measures used in this study include the Time to Collision (TTC), Deceleration-based Surrogate Safety Measure (DSSM), and Post-encroachment Time (PET). TTC and DSSM were used for rear-end conflicts, while PET was used to evaluate cross conflicts and other conflicts such as sideswipes. The number of potential conflicts obtained in a one-hour study period was around 20 per 1000 vehicles traversing the intersection. The number of potential conflicts in one non-peak hour may indicate a safety problem associated with the intersection. This study’s findings can help develop appropriate guidelines and recommendations to transportation agencies in evaluating and justifying the feasibility of using tethered drones as safer and cheaper data collection alternatives while significantly improving intersection safety and operations