14,712 research outputs found

    Ways of Applying Artificial Intelligence in Software Engineering

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    As Artificial Intelligence (AI) techniques have become more powerful and easier to use they are increasingly deployed as key components of modern software systems. While this enables new functionality and often allows better adaptation to user needs it also creates additional problems for software engineers and exposes companies to new risks. Some work has been done to better understand the interaction between Software Engineering and AI but we lack methods to classify ways of applying AI in software systems and to analyse and understand the risks this poses. Only by doing so can we devise tools and solutions to help mitigate them. This paper presents the AI in SE Application Levels (AI-SEAL) taxonomy that categorises applications according to their point of AI application, the type of AI technology used and the automation level allowed. We show the usefulness of this taxonomy by classifying 15 papers from previous editions of the RAISE workshop. Results show that the taxonomy allows classification of distinct AI applications and provides insights concerning the risks associated with them. We argue that this will be important for companies in deciding how to apply AI in their software applications and to create strategies for its use

    The relationship between the urica and correctional treatment in a sample of violent male offenders

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    The usefulness of the University of Rhode Island Change Assessment scale (URICA) in identifying treatment progress in violent adult offenders was evaluated in this archival study. The 198 men in the study participated in a 21-week treatment program at a Canadian federal institution. On average, individuals were 31 years old with four prior violent convictions. Most offenders were Aboriginal (53%). Study variables included self-report questionnaires (e.g., URICA, Criminal Sentiments Scale-Modified, State-Trait Anger Expression Inventory), staff ratings of treatment participation (Group Behaviour Checklist [GBC]), and risk measures (Security Reclassification Scale, Violence Risk Scale [VRS], Psychopathy Checklist-Revised). Post-treatment institutional misconduct information was available for 193 individuals and recidivism data was collected for the 50 individuals who were released to the community. The psychometric properties of the URICA for this sample were similar to those found in past research. Cluster analyses of pre- and post-treatment URICA data produced five-cluster solutions. These cluster profiles were consistent with previous research and rank-ordered to reflect increasing readiness for change. Profile rankings correlated significantly with anger problems and antisocial attitudes at pre- and post-treatment. GBC scores for individuals in less advanced profiles "peaked" at treatment week 15 and then decreased, whereas those in more advanced profiles improved throughout treatment. Differences in GBC scores between these two profile groups may have been delayed until the second half of treatment due to the increasing difficulty of treatment material. Profile rankings were not correlated with risk measures and correlated minimally with institutional misconduct/recidivism. Profile rankings correlated significantly with stage membership (from the VRS) at pre- but not post-treatment; the different time frames involved in scoring the URICA and VRS resulted in the URICA being more susceptible to fluctuations in mood or environment at post-treatment. When comparing the strength of the correlations between profile rankings and VRS stages with other variables, the VRS stages had significantly stronger correlations with risk measures. Overall, the URICA was useful in identifying treatment progress, and the URICA's strength was in identifying short-term change rather than long-term change, which was consistent with past research

    Machine learning approaches using freshwater gene expression profiles to predict seawater performance in Atlantic Salmon

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    Atlantic salmon (Salmo salar) has an anadromous life cycle, spending the first part of its life in freshwater before migrating to seawater. Smoltification is the process where Atlantic salmon undergo several morphological, physiological and behavioral changes preparing for transition to marine environment. A major challenge in the Norwegian salmon farming industry is the high mortality (12-14%), after release of smolt into seawater. One reason is suboptimal smolt production, resulting in a state where salmon are not well adopted for life in seawater. It is therefore important to optimize smolt production protocols and develop better ways to assess seawater-readiness to ensure higher survival, growth and reduce welfare issues. Traditionally, the increased expression of the saltwater isoform nkaα1b and nkcc1a cotransporter, and a reduction in expression of the freshwater isoform nkaα1b in the gills are used as predictive markers for seawater-readiness in the salmon farming industry. The current study aimed to use Random Forest to build predictive models for growth in seawater based on gill transcriptome data from fish given different light manipulation during smolt production. The results showed poor predictive ability towards seawater growth, although superior to simple correlation with single gene expression levels. We also found that photoperiodic history had effect on the Random Forest predictions, where the Random Forest model from fish exposed to continuous light (24:0) was much better at predicting SW growth than any of the models from the fish exposed to short photoperiods (8:16 and 12:12). We extracted most influential genes for each Random Forest model and found that these differed depending on the light regime used. Based on these results the salmon farming industry should apply caution when relying on traditional smolt gene-expression markers to determine the optimal time for SW transfer

    Individual Differences Versus Consumer Readiness Variables Predictive Power Over Internet Banking Adoption in South Africa

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    Self-service banking technology is gaining recognition globally in spite of its numerous challenges. Debit cards, ATMs and other Self Service Technologies (SSTs) are well received in South African market; however, customers seem cynical about Internet Banking (IB). IB consumer’s adoption patterns frameworks are tested within this paper based on a framework providing for the South African market with the best explanatory power. Thus, investigating consumer readiness (CR) and individual differences (IDs) variables as common groupings usually cited in the literature consumer variables predictive efficacy, provide better understanding of the consumers towards SSTs in South Africa.CR comprises role clarity, ability and motivation (extrinsic and intrinsic), while IDs includes inertia, technology anxiety, need of interaction, previous experience and demographics. This study purposes to identify among IDs and CR variables, which one with greater predictive power on IB adoption in South Africa.Considering this gap within the body of knowledge, in relation with IB adoption behaviour among the South African consumer is therefore the present article primary objective. Consumer’s individual differences (technology anxiety and education variables specifically) as exogenous variables, through a large sample size (n=1516), descriptive quantitative analysis, were found in context of South African market with greatest predictive power for IB adoption by comparison over consumer readiness. In South Africa particularly for marketers, these findings therefore are a set of relevant keys that can be useful in promoting IB adoption.&nbsp

    The Predictive Ability of Self-Efficacy on Recidivism Among Adult Male Offenders

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    With crime rates high and increasing numbers of offenders receiving community-based corrections sentences, factors related to risk of recidivism should be a high priority for researchers. The impact of crime on offenders, victims, and communities is costly. Traditional punishment has done little to reduce crime, particularly among repeat offenders. The purpose of this study was to examine the predictive ability of self-efficacy on recidivism, based on social cognitive theory. The research design was quantitative and nonexperimental, using regression analyses. The nonrandomized sample consisted of adult males incarcerated on felony charges at a large urban jail in the Midwest of the United States. The archival data were generated between May 2017 and the end of October 2017. Total scores on the Index of Sense of Self-Efficacy Scale was used as the independent variable data, while incidents of reincarceration were collected as dependent variable data. The findings suggest that there is a significant relationship between the two variables, in which participants’ ISSES scores significantly predicted recidivism when self-efficacy was measured in total score and recidivism was measured as time. Potential for positive social change lies in the reduction in victimization, decreased financial and emotional cost of recidivism, and increased public safety through the development of interventions aimed at decreasing recidivism by increasing self-efficacy

    Naval Aviation Squadron Risk Analysis Predictive Bayesian Network Modeling Using Maintenance Climate Assessment Survey Results

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    Associated risks in flying have resulted in injury or death to aircrew and passengers, and damage or destruction of the aircraft and its surroundings. Although the Naval Aviation\u27s flight mishap rate declined over the past 60 years, the proportion of human error causal factors has stayed relatively constant at about 80%. Efforts to reduce human errors have focused attention on understanding the aircrew and maintenance actions occurring in complex systems. One such tool has been the Naval Aviation squadrons\u27 regular participation in survey questionnaires deigned to measure respondent ratings related to personal judgments or perceptions of organizational climate for meeting the extent to which a particular squadron achieved the High Reliability Organization (HRO) criteria of achieving safe and reliable operations and maintenance practices while working in hazardous environments. Specifically, the Maintenance Climate Assessment Survey (MCAS) is completed by squadron maintainers to enable leadership to assess their unit\u27s aggregated responses against those from other squadrons. Bayesian Network Modeling and Simulation provides a potential methodology to represent the relationships of MCAS results and mishap occurrences that can be used to derive and calculate probabilities of incurring a future mishap. Model development and simulation analysis was conducted to research a causal relationship through quantitative analysis of conditional probabilities based upon observed evidence of previously occurred mishaps. This application would enable Navy and Marine Corps aviation squadron leadership to identify organizational safety risks, apply focused proactive measures to mitigate related hazards characterized by the MCAS results, and reduce organizational susceptibility to future aircraft mishaps

    A Causal Comparative Analysis of Leveraging the Business Analytical Capabilities and the Value and Competitive Advantages of Mid-level Professionals Within Higher Education

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    The purpose of this quantitative causal-comparative study is an empirical examination of the differences in business intelligence capability and the value and competitive advantage of mid-level higher education academia professionals from community colleges, four-year public, and four-year private institutions within the United States. Institutions of higher education have an overabundant amount of student data that is often inaccessible and underutilized. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and Management Information Systems/Decision Support Systems theory, using two-way ANOVA analysis, this research examined factors to understand the mastery of readiness for mid-level professionals in higher education institutions to embrace digital technologies and resources to develop a culture of digital transformation. This study applied the Business Analytics Capability Assessment survey responses from 176 mid-level higher education professionals, from community colleges, four-year private, and four-year public institutions, to understand how higher education professionals use Business Intelligence Analytics (BIA) and Big Data (BD) to improve the organization, operational business decisions, and data management strategies to provide actionable insights. This study found no significance between the type of institution that has business intelligence capability and the value and competitive advantage. A significant difference with a medium effect was identified between the Business Analytics Capability and the Value and Competitive Advantage for mid-level professionals who do and do not utilize BIA and BD resources. Therefore, this study calls for future research to understand how successful institutions have implemented BIA and BD tools and how higher education is shaped on a macro level

    A Condition Based Maintenance Approach to Forecasting B-1 Aircraft Parts

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    United States Air Force (USAF) aircraft parts forecasting techniques have remained archaic despite new advancements in data analysis. This approach resulted in a 57% accuracy rate in fiscal year 2016 for USAF managed items. Those errors combine for 5.5billionworthofinventorythatcouldhavebeenspentonothercriticalspareparts.Thisresearcheffortexploresadvancementsinconditionbasedmaintenance(CBM)anditsapplicationintherealmofforecasting.ItthenevaluatestheapplicabilityofCBMforecastmethodswithincurrentUSAFdatastructures.ThisstudyfoundlargegapsindataavailabilitythatwouldbenecessaryinarobustCBMsystem.ThePhysics−BasedModelwasusedtodemonstrateaCBMlikeforecastingapproachonB−1spareparts,andforecasterrorresultswerecomparedtoUSAFstatusquotechniques.ResultsshowedthePhysics−BasedModelunderperformedUSAFmethodsoverall,howeveritoutperformedUSAFmethodswhenforecastingpartswithasmoothorlumpydemandpattern.Finally,itwasdeterminedthatthePhysics−BasedModelcouldreduceforecastingerrorby2.465.5 billion worth of inventory that could have been spent on other critical spare parts. This research effort explores advancements in condition based maintenance (CBM) and its application in the realm of forecasting. It then evaluates the applicability of CBM forecast methods within current USAF data structures. This study found large gaps in data availability that would be necessary in a robust CBM system. The Physics-Based Model was used to demonstrate a CBM like forecasting approach on B-1 spare parts, and forecast error results were compared to USAF status quo techniques. Results showed the Physics-Based Model underperformed USAF methods overall, however it outperformed USAF methods when forecasting parts with a smooth or lumpy demand pattern. Finally, it was determined that the Physics-Based Model could reduce forecasting error by 2.46% or 12.6 million worth of parts in those categories alone for the B-1 aircraft

    Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

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    Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners

    Measuring Software Process: A Systematic Mapping Study

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    Context: Measurement is essential to reach predictable performance and high capability processes. It provides support for better understanding, evaluation, management, and control of the development process and project, as well as the resulting product. It also enables organizations to improve and predict its process’s performance, which places organizations in better positions to make appropriate decisions. Objective: This study aims to understand the measurement of the software development process, to identify studies, create a classification scheme based on the identified studies, and then to map such studies into the scheme to answer the research questions. Method: Systematic mapping is the selected research methodology for this study. Results: A total of 462 studies are included and classified into four topics with respect to their focus and into three groups based on the publishing date. Five abstractions and 64 attributes were identified, 25 methods/models and 17 contexts were distinguished. Conclusion: capability and performance were the most measured process attributes, while effort and performance were the most measured project attributes. Goal Question Metric and Capability Maturity Model Integration were the main methods and models used in the studies, whereas agile/lean development and small/medium-size enterprise were the most frequently identified research contexts.Ministerio de Economía y Competitividad TIN2013-46928-C3-3-RMinisterio de Economía y Competitividad TIN2016-76956-C3-2- RMinisterio de Economía y Competitividad TIN2015-71938-RED
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