| 1. |
- Pesämaa, Ossi, et al.
(författare)
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How a learning orientation affects drivers of innovativeness and performance in service delivery
- 2013
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Ingår i: Journal of engineering and technology management. - : Elsevier BV. - 0923-4748 .- 1879-1719. ; 30:2, s. 169-187
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Tidskriftsartikel (refereegranskat)abstract
- Relying on organizational innovativeness for long-term growth and profitability can be difficult, time consuming, and expensive. In the context of service delivery of 395 strategic business units (SBU) in Israel's healthcare industry, this paper examines the role of a learning-orientation as a moderator in an integrative model of organizational innovativeness. We find moderation of the impacts of risk-taking, creativity, competitor benchmarking orientation, and environmental opportunities on innovativeness. Moreover, we find the influence on performance pronounced for high learning-oriented SBUs. The paper shows that learning orientation should be considered for understanding effective innovativeness work for competitive service delivery.
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| 2. |
- Lundberg, Jenny, 1976-, et al.
(författare)
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Early Signs of Diabetes Explored from an Engineering Perspective
- 2019
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Ingår i: Smart Industry & Smart Education. - Cham : Springer. - 9783319956787 - 9783319956770 ; , s. 22-31
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Konferensbidrag (refereegranskat)abstract
- Undetected diabetes is a global issue, estimated to over 200 million persons affected. Engineering opportunities in capturing early signs of diabetes has a potential due to the complexity to interpret early signs and link it to diabetes. Persons with untreated diabetes are doubled in risk of getting cardiovascular diseases and may also suffer other consequent diseases. In Sweden, approximately 450 thousand have diabetes where 80-90% are of type 2 with 1/4 unaware of it, i.e. approx. 100 thousand. Screening approaches, searching specifically for diabetes in persons not showing symptoms has been initiated with positive results. However, some general drawbacks of screening such as false sense of security are an issue. In this publication, we focus upon in home measurements and empowering of the individual in identifying early signs of diabetes. The methods in this publication are to gather data, evaluate and give suggestion if clinical test to confirm or reject diabetes. In home measurements, education process with companies for innovation possibilities.
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| 3. |
- Lundberg, Jenny, et al.
(författare)
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An Approach towards using Agent in Multi-Agent Systems to streamline emergency services
- 2008
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Ingår i: Proceedings of 5th International Conference on Information Technology and Applications. - Cairns, Austrailia. - 9780980326727
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Konferensbidrag (refereegranskat)abstract
- In emergency services, the evaluation of a situation is performed, manually, which includes synchronizing the services for each situation. However, the work requires divergent and decisive decisions resolved from several points of views, which would benefit from automating parts of the work. Using computer systems in life-critical domains can nvolve careful consideration of the work practice and of the situation components. In this paper, we propose using agents, intelligent agents and meta-agents, to streamline emergency services. The intelligent agents respond to both static and dynamic input in a flexible and structured way and perform required actions. From these actions, the meta-agents are created in which the divisions of dynamic and static information have an impact on the structure of the calculations but also on the outcome produced by the meta-agents. As an example application, we provide a scenario of agents and meta-agents in multi-agent systems, where the meta-agents hold systemic properties. The scenario has a strong grounding in the emergency service domain, in which we highlight coordination issues related to the multi-agents and the meta-agents.
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| 4. |
- Kim, Jinhan, et al.
(författare)
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Guiding Deep Learning System Testing Using Surprise Adequacy
- 2019
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Ingår i: Proceedings - International Conference on Software Engineering. - : IEEE. - 0270-5257. ; 2019-May, s. 1039-1049, s. 1039-1049
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Konferensbidrag (refereegranskat)abstract
- Deep Learning (DL) systems are rapidly being adopted in safety and security critical domains, urgently calling for ways to test their correctness and robustness. Testing of DL systems has traditionally relied on manual collection and labelling of data. Recently, a number of coverage criteria based on neuron activation values have been proposed. These criteria essentially count the number of neurons whose activation during the execution of a DL system satisfied certain properties, such as being above predefined thresholds. However, existing coverage criteria are not sufficiently fine grained to capture subtle behaviours exhibited by DL systems. Moreover, evaluations have focused on showing correlation between adversarial examples and proposed criteria rather than evaluating and guiding their use for actual testing of DL systems. We propose a novel test adequacy criterion for testing of DL systems, called Surprise Adequacy for Deep Learning Systems (SADL), which is based on the behaviour of DL systems with respect to their training data. We measure the surprise of an input as the difference in DL system's behaviour between the input and the training data (i.e., what was learnt during training), and subsequently develop this as an adequacy criterion: a good test input should be sufficiently but not overtly surprising compared to training data. Empirical evaluation using a range of DL systems from simple image classifiers to autonomous driving car platforms shows that systematic sampling of inputs based on their surprise can improve classification accuracy of DL systems against adversarial examples by up to 77.5% via retraining.
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| 5. |
- Jiang, Yuning, 1993-, et al.
(författare)
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A Semantic Framework With Humans in the Loop for Vulnerability-Assessment in Cyber-Physical Production Systems
- 2020
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Ingår i: Risks and Security of Internet and Systems. - Cham : Springer. - 9783030415679 - 9783030415686 ; , s. 128-143
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Konferensbidrag (refereegranskat)abstract
- Criticalmanufacturingprocessesinsmartnetworkedsystems such as Cyber-Physical Production Systems (CPPSs) typically require guaranteed quality-of-service performances, which is supported by cyber- security management. Currently, most existing vulnerability-assessment techniques mostly rely on only the security department due to limited communication between di↵erent working groups. This poses a limitation to the security management of CPPSs, as malicious operations may use new exploits that occur between successive analysis milestones or across departmental managerial boundaries. Thus, it is important to study and analyse CPPS networks’ security, in terms of vulnerability analysis that accounts for humans in the production process loop, to prevent potential threats to infiltrate through cross-layer gaps and to reduce the magnitude of their impact. We propose a semantic framework that supports the col- laboration between di↵erent actors in the production process, to improve situation awareness for cyberthreats prevention. Stakeholders with dif- ferent expertise are contributing to vulnerability assessment, which can be further combined with attack-scenario analysis to provide more prac- tical analysis. In doing so, we show through a case study evaluation how our proposed framework leverages crucial relationships between vulner- abilities, threats and attacks, in order to narrow further the risk-window induced by discoverable vulnerabilities.
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| 6. |
- Kebande, Victor R., 1985-, et al.
(författare)
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A blockchain-based multi-factor authentication model for a cloud-enabled internet of vehicles
- 2021
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 21:18
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Tidskriftsartikel (refereegranskat)abstract
- Continuous and emerging advances in Information and Communication Technology (ICT) have enabled Internet-of-Things (IoT)-to-Cloud applications to be induced by data pipelines and Edge Intelligence-based architectures. Advanced vehicular networks greatly benefit from these architectures due to the implicit functionalities that are focused on realizing the Internet of Vehicle (IoV) vision. However, IoV is susceptible to attacks, where adversaries can easily exploit existing vulnerabilities. Several attacks may succeed due to inadequate or ineffective authentication techniques. Hence, there is a timely need for hardening the authentication process through cutting-edge access control mechanisms. This paper proposes a Blockchain-based Multi-Factor authentication model that uses an embedded Digital Signature (MFBC_eDS) for vehicular clouds and Cloud-enabled IoV. Our proposed MFBC_eDS model consists of a scheme that integrates the Security Assertion Mark-up Language (SAML) to the Single Sign-On (SSO) capabilities for a connected edge to cloud ecosystem. MFBC_eDS draws an essential comparison with the baseline authentication scheme suggested by Karla and Sood. Based on the foundations of Karla and Sood’s scheme, an embedded Probabilistic Polynomial-Time Algorithm (ePPTA) and an additional Hash function for the Pi generated during Karla and Sood’s authentication were proposed and discussed. The preliminary analysis of the proposition shows that the approach is more suitable to counter major adversarial attacks in an IoV-centered environment based on the Dolev–Yao adversarial model while satisfying aspects of the Confidentiality, Integrity, and Availability (CIA) triad. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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| 7. |
- Petersen, Kai, et al.
(författare)
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Choosing Component Origins for Software Intensive Systems: In-House, COTS, OSS or Outsourcing?-A Case Survey
- 2018
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Ingår i: IEEE Transactions on Software Engineering. - : IEEE Computer Society. - 0098-5589 .- 1939-3520. ; 44:3, s. 237-261
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Tidskriftsartikel (refereegranskat)abstract
- The choice of which software component to use influences the success of a software system. Only a few empirical studies investigate how the choice of components is conducted in industrial practice. This is important to understand to tailor research solutions to the needs of the industry. Existing studies focus on the choice for off-the-shelf (OTS) components. It is, however, also important to understand the implications of the choice of alternative component sourcing options (CSOs), such as outsourcing versus the use of OTS. Previous research has shown that the choice has major implications on the development process as well as on the ability to evolve the system. The objective of this study is to explore how decision making took place in industry to choose among CSOs. Overall, 22 industrial cases have been studied through a case survey. The results show that the solutions specifically for CSO decisions are deterministic and based on optimization approaches. The non-deterministic solutions proposed for architectural group decision making appear to suit the CSO decision making in industry better. Interestingly, the final decision was perceived negatively in nine cases and positively in seven cases, while in the remaining cases it was perceived as neither positive nor negative.
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| 8. |
- Tuzun, Eray, et al.
(författare)
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Ground-Truth Deficiencies in Software Engineering : When Codifying the Past Can Be Counterproductive
- 2022
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Ingår i: IEEE Software. - : IEEE Computer Society. - 0740-7459 .- 1937-4194. ; 39:3, s. 85-95
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Tidskriftsartikel (refereegranskat)abstract
- In software engineering, the objective function of human decision makers might be influenced by many factors. Relying on historical data as the ground truth may give rise to systems that automate software engineering decisions by mimicking past suboptimal behavior. We describe the problem and offer some strategies. ©IEEE.
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| 9. |
- Ali, Nauman bin, et al.
(författare)
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The impact of a proposal for innovation measurement in the software industry
- 2020
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Ingår i: International Symposium on Empirical Software Engineering and Measurement. - New York, NY, USA : IEEE Computer Society. - 1949-3789 .- 1949-3770. - 9781450375801
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Konferensbidrag (refereegranskat)abstract
- Background: Measuring an organization's capability to innovate and assessing its innovation output and performance is a challenging task. Previously, a comprehensive model and a suite of measurements to support this task were proposed. Aims: In the current paper, seven years since the publication of the paper titled Towards innovation measurement in the software industry, we have reflected on the impact of thework. Method:We have mainly relied on quantitative and qualitative analysis of the citations of the paper using an established classification schema. Results: We found that the article has had a significant scientific impact (indicated by the number of citations), i.e., (1) cited in literature from both software engineering and other fields, (2) cited in grey literature and peerreviewed literature, and (3) substantial citations in literature not published in the English language. However, we consider a majority of the citations in the peer-reviewed literature (75 out of 116) as neutral, i.e., they have not used the innovation measurement paper in any substantial way. All in all, 38 out of 116 have used, modified or based their work on the definitions, measurements or the model proposed in the article. This analysis revealed a significant weakness of the citing work, i.e., among the citing papers, we found only two explicit comparisons to the innovation measurement proposal, and we found no papers that identify weaknesses of said proposal. Conclusions: This work highlights the need for being cautious of relying solely on the number of citations for understanding impact, and the need for further improving and supporting the peer-review process to identify unwarranted citations in papers. © 2020 IEEE Computer Society. All rights reserved.
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| 10. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a (Formula presented.) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model ((Formula presented.) _RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model (Formula presented.) _RF achieved the highest accuracy of 94.59%. The proposed model (Formula presented.) _RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model (Formula presented.) _RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module ((Formula presented.)).
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