Phases Of System Development Research Articles

Auditor Involvement in Systems Development Phases

Auditor Involvement in Systems Development Phases

Development and testing of an image transformer for explainable autonomous driving systems

Purpose Perception has been identified as the main cause underlying most autonomous vehicle related accidents. As the key technology in perception, deep learning (DL) based computer vision models are generally considered to be black boxes due to poor interpretability. These have exacerbated user distrust and further forestalled their widespread deployment in practical usage. This paper aims to develop explainable DL models for autonomous driving by jointly predicting potential driving actions with corresponding explanations. The explainable DL models can not only boost user trust in autonomy but also serve as a diagnostic approach to identify any model deficiencies or limitations during the system development phase. Design/methodology/approach This paper proposes an explainable end-to-end autonomous driving system based on “Transformer,” a state-of-the-art self-attention (SA) based model. The model maps visual features from images collected by onboard cameras to guide potential driving actions with corresponding explanations, and aims to achieve soft attention over the image’s global features. Findings The results demonstrate the efficacy of the proposed model as it exhibits superior performance (in terms of correct prediction of actions and explanations) compared to the benchmark model by a significant margin with much lower computational cost on a public data set (BDD-OIA). From the ablation studies, the proposed SA module also outperforms other attention mechanisms in feature fusion and can generate meaningful representations for downstream prediction. Originality/value In the contexts of situational awareness and driver assistance, the proposed model can perform as a driving alarm system for both human-driven vehicles and autonomous vehicles because it is capable of quickly understanding/characterizing the environment and identifying any infeasible driving actions. In addition, the extra explanation head of the proposed model provides an extra channel for sanity checks to guarantee that the model learns the ideal causal relationships. This provision is critical in the development of autonomous systems.

Model-Based Design and Safety Assessment for Crewless Autonomous Vessel

The Designing the Future of Full Autonomous Ship (DFFAS) Project conducted the crewless maritime autonomous surface ship long-distance demonstration in congested waters in March 2022. This study shows a model-based systematic design methodology and a safety evaluation method for autonomous ships conducted through this project. A reference model of ConOps (Concept of Operations) for autonomous navigation systems is proposed using the DFFAS system as an example. System Theoretic Process Analysis (STPA) is also applied at the subsystem and component levels according to the actual system development phase to extract appropriate granularity of safety requirements.

Deletion of the Sodium-Dependent Glutamate Transporter GLT-1 in Maturing Oligodendrocytes Attenuates Myelination of Callosal Axons During a Postnatal Phase of Central Nervous System Development.

The sodium-dependent glutamate transporter GLT-1 (EAAT2, SLC1A2) has been well-described as an important regulator of extracellular glutamate homeostasis in the central nervous system (CNS), a function that is performed mainly through its presence on astrocytes. There is, however, increasing evidence for the expression of GLT-1 in CNS cells other than astrocytes and in functional roles that are mediated by mechanisms downstream of glutamate uptake. In this context, GLT-1 expression has been reported for both neurons and oligodendrocytes (OLGs), and neuronal presynaptic presence of GLT-1 has been implicated in the regulation of glutamate uptake, gene expression, and mitochondrial function. Much less is currently known about the functional roles of GLT-1 expressed by OLGs. The data presented here provide first evidence that GLT-1 expressed by maturing OLGs contributes to the modulation of developmental myelination in the CNS. More specifically, using inducible and conditional knockout mice in which GLT-1 was deleted in maturing OLGs during a peak period of myelination (between 2 and 4 weeks of age) revealed hypomyelinated characteristics in the corpus callosum of preferentially male mice. These characteristics included reduced percentages of smaller diameter myelinated axons and reduced myelin thickness. Interestingly, this myelination phenotype was not found to be associated with major changes in myelin gene expression. Taken together, the data presented here demonstrate that GLT-1 expressed by maturing OLGs is involved in the modulation of the morphological aspects associated with CNS myelination in at least the corpus callosum and during a developmental window that appears of particular vulnerability in males compared to females.

AI ethical biases: normative and information systems development conceptual framework

ABSTRACT Alongside the revolutionary benefits of AI, it can cause numerous problems across the system development process. AI ecosytem players have recently started to interrogate the ethical biases implicit in AI-enabled applications and agents. The contestable nature of ethics and the complexity of AI-enabled applications has led to incoherent literature around AI ethical biases. The numerous conceptions of AI ethics and a multiplicity of ethical biases has compounded matters for researchers, practitioners, and policy makers. The current study proposes a conceptual framework to organize AI ethical biases. A narrative literature review was conducted to identify and group the biases into data biases, method biases and implementation biases. The CRISP-DM framework was used to classify the ethical biases. The emerging conceptual framework has four clusters that represents: System development phases, scope of ethical bias, exemplars, and possible solutions. The study extends the existing AI ethical frameworks and provides a unified communication artefact for practitioners.

Jurnal Penggunaan Metode Scrum Dalam Membentuk Sistem Informasi Penyimpanan Gudang Berbasis Web

Scrum method is a methodology that is included in agile software development. Scrum is considered capable of producing good quality software in accordance with the wishes of the user, can be used in large or small projects, and easy to adopt changes. Scrum activity stages include backlog, sprint backlog, daily scrum, sprint review, and sprint restropective products. Roles in Scrum include the product owner, Scrum Master, and Development Team. Scrum has stages that are structured and iterative, so that if the product in the first sprint does not adequately meet the needs, then the next sprint can be developed in accordance with the user's evaluation system. The results obtained on Scrum Method on Building Web Based Information System of Storage Inventory are proven to be able to overcome the changing requirements during the system development phase, producing products that are in accordance with the wishes of the user due to repeated reviews.

Clones in deep learning code: what, where, and why?

Deep Learning applications are becoming increasingly popular worldwide. Developers of deep learning systems like in every other context of software development strive to write more efficient code in terms of performance, complexity, and maintenance. The continuous evolution of deep learning systems imposing tighter development timelines and their increasing complexity may result in bad design decisions by the developers. Besides, due to the use of common frameworks and repetitive implementation of similar tasks, deep learning developers are likely to use the copy-paste practice leading to clones in deep learning code. Code clone is considered to be a bad software development practice since developers can inadvertently fail to properly propagate changes to all clones fragments during a maintenance activity. However, to the best of our knowledge, no study has investigated code cloning practices in deep learning development. The majority of research on deep learning systems mostly focusing on improving the dependability of the models. Given the negative impacts of clones on software quality reported in the studies on traditional systems and the inherent complexity of maintaining deep learning systems (e.g., bug fixing), it is very important to understand the characteristics and potential impacts of code clones on deep learning systems. This paper examines the frequency, distribution, and impacts of code clones and the code cloning practices in deep learning systems. To accomplish this, we use the NiCad clone detection tool to detect clones from 59 Python, 14 C#, and 6 Java based deep learning systems and an equal number of traditional software systems. We then analyze the comparative frequency and distribution of code clones in deep learning systems and the traditional ones. Further, we study the distribution of the detected code clones by applying a location based taxonomy. In addition, we study the correlation between bugs and code clones to assess the impacts of clones on the quality of the studied systems. Finally, we introduce a code clone taxonomy related to deep learning programs based on 6 DL software systems (from 59 DL systems) and identify the deep learning system development phases in which cloning has the highest risk of faults. Our results show that code cloning is a frequent practice in deep learning systems and that deep learning developers often clone code from files contain in distant repositories in the system. In addition, we found that code cloning occurs more frequently during DL model construction, model training, and data pre-processing. And that hyperparameters setting is the phase of deep learning model construction during which cloning is the riskiest, since it often leads to faults.

Digitally Sustainable Information Systems in Axiomatic Design

Nowadays, information systems are evolving towards increasingly interconnected, smart, and self-adaptive models. This transformation has led to the representation of the systems themselves in terms of natural ecosystems. Similar to the natural environment, the virtual world can be threatened by specific forms of pollution, such as illegitimate access to the system, unwanted changes to data, and loss of information, which affect the only resource it possesses, i.e., data. In order to provide proactive protection of data integrity and confidentiality, in this paper we consider the well-known principles of privacy by design and privacy by default in the design phase of system development. To this end, we propose an approach based on axiomatic design, which allows us to implement these two principles through an appropriate reinterpretation of the information axiom, in terms of privacy impact assessment. We illustrate our approach by a case study, which implements the process of managing patients in home care. However, the proposed method can be applied to processing systems that provide services. The main result achieved is to select the most digitally sustainable design solution, i.e., the one that best prevents the threats mentioned above.

A collaborative knowledge-based method for the interactive development of cabin systems in virtual reality

Progressive digitization in the development phase of systems is leading to shorter development times and lower costs. At the same time, the interactions in more complex systems are increasing and become more nested, which affects the understanding of system dependencies for humans as well as modeling these. This results in the challenge of digitizing the knowledge (rules, regulations, requirements, etc.) required to describe the system and its interrelationships. An example of such a system is the aircraft. In practice, usually, the technical design of the cabin and its systems is done separately from the preliminary aircraft design and the cabin results will be integrated late in the aircraft development process. In this paper, a proposal is given for a conceptual design method that enables a cabin systems layout based on preliminary aircraft design data (parameter set). Therefore, a central data model is developed that links cabin components to several disciplines to enable an automated layout. Here, knowledge is stored in an ontology. Linking the ontology with design rules and importing external parameters, missing information needed for preliminary design of cabin systems can be generated. The design rules are based on requirements, safety regulations as well as expert knowledge for design interpretation that has been collected and formalized. Using the ontology, an XML data structure can be instantiated which contains all information about properties, system relationships and requirements. So, the metadata and results of heterogenous domain-specific models and software tools are accessible for all experts of the layout process in a holistic manner and ensure data consistency. Using this XML data structure, a 3D virtual cabin mockup is created in which users have the possibility to interact with cabin modules and system components via controllers. This virtual development platform enables an interaction with complex product data sets like the XML file by visualizing metadata and analysis results along with the cabin geometry, making it even better comprehensible and processable for humans. So, various new cabin system designs can be iterated, evaluated, and optimized at low cost before the concepts are validated in a real prototype. For this, the virtual environment provides a platform that integrates all related disciplines, experts, research partners or the entire supply chain to improve communication among all stakeholders by directly participating and intervening in the evaluation and optimization process. Moreover, the use of VR is being investigated as a new technology in pre-design phase to exploit the potential of knowledge acquisition in immersive environments early in the development stage.

On Real-Time Hybrid Testing of Ocean Wave Energy Conversion Systems: An Experimental Study

The growing wave energy sector requires an efficient and flexible testing process for the development phase of wave energy systems. Real-time hybrid testing is a promising technique for the accelerated testing of wave energy conversion systems. This article presents an experimental study on developing a hybrid testing platform for wave energy systems at the Wallace Energy System and Renewables Facility (WESRF) at Oregon State University. The wave energy conversion system is broken down into numeric (i.e., virtual) and physical (i.e., hardware) components. The numeric component involves software components such as the control algorithm for Wave Energy Converter (WEC) and controller for the power electronic converters and numerical models for the WEC device hydrodynamics. The hardware involves an ocean wave emulator testbed, Power Take-Off (PTO) mechanism, power electronics, and instrumentation. The numeric components are implemented in a real-time target machine and are interfaced with the experimental system. A case study implementation of Nonlinear Model Predictive Control (NMPC) is presented for a single degree of freedom heaving nonlinear WEC model with a Permanent Magnet Synchronous Generator (PMSG) as a PTO system. A Field-Oriented Control (FOC) algorithm controls the PMSG-PTO generation using a three-phase Integrated Intelligent Power (IIP) module converter. A demonstration of the proposed hybrid testing setup is provided.

A Scoring Framework and Apparatus for Epilepsy Seizure Detection Using a Wearable Belt.

To develop a wearable device that can detect epilepsy seizures. In particular, due to their prevalence, attention is focused on detecting the generalized tonic-clonic seizure (GTCS) type. When a seizure is detected, an alert phone call is initiated and an alarm SMS sent to the nearest health-care provider (and/or a predesignated family member), including the patient's location as global positioning system (GPS) coordinates. A wearable belt is developed including an Arduino processor that constantly acquires data from four different sensing modalities and monitors the acquired signal patterns for abnormalities. The sensors are a heart rate sensor, electromyography sensor, blood oxygen level (oxygen saturation) sensor, and an accelerometer to detect sudden falls. Higher-than-normal threshold levels are established for each sensor's signal. If two or more signal measurements exceed the corresponding threshold value for a predetermined time interval, then the seizure alarm is triggered. Clinical trials were not pursued in this study as this is the initial phase of system development (phase 0). Instead, the instrumented belt seizure detection prototype was tested on nine healthy individuals mimicking, to some degree, seizure symptoms. A total of eighteen trials took place of which half had <2 sensor thresholds exceeded and no alarm, whereas the other half resulted in activating the alarm when two or more sensor thresholds were exceeded for at least the predetermined time interval corresponding to each of the higher-than-normal sensor readings. For each trial that triggered the alarm when a seizure was detected, the on-board GPS and global system for mobile communication (GSM) units successfully initiated an alert phone call to a predesignated number in addition to sending an SMS message, including GPS location coordinates. Continuous real-time monitoring of signals from the four different sensors allows the developed wearable belt to detect GTCS while reducing false alarms. The proposed device produces an important alarm that may save a patient's life.

Development of Electoral Process System to Leverage COVID-19 Pandemic Risks

The electoral process has had challenges in policy as a result of large number of competitive elective positions and COVID-19 pandemic risks. In mitigating these challenges, there is need to incorporate innovations of Information and Communication Technologies in the electoral process so as to have confidence in the electoral management bodies that have always been blamed for the failure of delivering credible and timely election every election cycle that have in many instances resulted into violation of the electoral laws and guidelines that have led to violence in some selected counties. In this research project the emphasis was on investigating the problems and challenges associated with the past electoral process systems that were identified as the ineffective tallying of votes and delay of the elections results in some regions that have vast geographical coverage and/or poor network infrastructure coverage. The analysis is carried on the collected facts and design of the system done that evolve prototype that would lead to improvement in provision of electoral process services. The prototype developed manages the tasks that include registration, appointments, polling, tallying, equipment, suppliers, payments and employees. In insecurity, threats are reduced by enforcement of the encryption and access of only authorized pages as a security measure for emerging threats. The spiral development methodology is adapted in the electoral process system development phases.

Toward an Understanding of How Post- Deployment User- Developer Interactions Influence System Utilization

Although initial adoption of an information system has been shown to influence system success, further value can be obtained when end-users move beyond adoption, utilizing more features of the system and integrating it into their work routines. Organizations can increase the post-deployment utilization of their systems by emphasizing continued interaction between developers and end-users. In this study, we develop a research model investigating the influence of shared understanding, faithfulness of appropriation, and consensus on spirit on post-deployment system utilization. Using a sample from a healthcare organization, we show that increased end-user postdeployment interaction with developers supports a shared understanding between the two groups, which ultimately impacts both the routinization and infusion of a system. This study provides a contribution by demonstrating the impact of developer/user interaction in the post-implementation phase of systems development.

Design and Fabrication of Printed Human Skin Model Equivalent Circuit: A Tool for Testing Biomedical Electrodes without Human Trials

Within the efforts of developing a new generation of biomedical electrodes with embedded switching logics, developing safe and simple procedures for testing these novel systems is tackled. The development and demonstration of an all‐printed flexible testbed for automated validation and testing of multi pad systems is presented. The system is based on a Human model equivalent circuit (HMEC), which, when connected to the electrical stimulation system, mirrors the electrical behavior of biomedical electrodes and their specific interface material as if they are placed on a human subject. A simulation model of the electrical stimulation system components was developed based on the experimental data, in order to optimize printed electronic components’ characteristics and design. The testbed is composed of five layers of different conductive and dielectric materials screen‐printed on a flexible poly(ethylene terephthalate) (PET) substrate. The system was prototyped with the characteristic values of the HMEC matching the average experimental data acquired from human subjects. Thus, it is demonstrated that an all printed flexible HMEC is a feasible approach to enabling the functional testing of transcutaneous electrical stimulation devices required for their fabrication, evaluation and optimization, reducing the need for tests on human subjects in the development phase of new systems.

46, XY DSD (Disorder of Sex Development) : Diagnosis dan Tatalaksananya.

Disorder of Sex Development (DSD) is a congenital disorder that occurs in the development of chromosomes, gonads, and internal or external genital organ. DSD of 46 XY is a condition where the children with XY genotype is not able to have complete virilization of external genital. Determination phase is an initial phase of reproductive system development. Disruption of this phase may potentially lead to DSD. Optimal care for children with Disorder of Sex Development requires a multidisciplinary team starting since neonatal period. Family and pregnancy history, complete physical examination and assessment of genital organ are the first step of ensuring the diagnose. Management of children with DSD are focusing on gender determination, hormone support therapy and surgical management. On the other hand, Children with XY genotype should be raised as a boy however if there is no responsive evidence in administration of androgen the children should be raised as a girl. Subsequent to prescribe the gender of the children, surgical management is a required treatment for removing unnecessary genital afterward.&#x0D; Keywords: Disorders of Sex Development, DSD, 46 XY DSD

Marketing management risks of online business: taxonomy, verification and assessment

Introduction. Digital transformation, which is characterized by the rapid pace of changes in the marketing environment, aggressive actions of competitors, high heterogeneity of consumer requests and preferences, has a significant impact on the business, namely, it requires the generation and implementation of new approaches to solving business problems, as well as achieving the set goals. In order to effectively conduct and develop an online business, it is necessary to follow new tendencies and trends in meeting the constantly growing requests, needs and preferences of online buyers, simultaneously levelling or eliminating risks that may arise in the short and long term with the greatest probability, as well as have negative effects for business. Purposes of our research are to develop the taxonomy marketing and management risks of online business by using multidimensional systematization and decomposition methods; to substantiate the scientific and methodical approach to verification and qualitative assessment of probability of the marketing and management risks for online business; to formulate recommendations regarding the formation of an intelligent IT security system for levelling and / or eliminating the risks of online business in the context of the implementation of the relevant risk management plan; to develop and test applied tools for effective risk management of online business. Methods. The symbiosis of general and specific methods is used to achieve the purposes, including: methods of dialectical cognition, generalization and systematization, abstraction, synthesis; statistical, dynamic and cluster analysis; formalization method; expert survey; calculation-analytical and comparative methods; matrix method. Results. The marketing management risks of online business in the digital transformation context are explicated and concretized. The taxonomy of marketing-management risks of online business is developed and represented like a multi-dimensional risks systematization as a result of their decomposition. The scientific and methodical approach to verification and qualitative assessment of the probability of the marketing and management online business risks is substantiated. The algorithm for the identification of the online business risks status is proposed. It reflects the consecutive stages of the qualitative risks groups assessment and determines the choice of a method for its levelling and / or elimination. The framework of the risk management plan for online business is developed. This framework takes into account triggers and consequences of risks onset. The matrix for monitoring the implementation of the risk management plan for online business is compiled. The phases of the intelligent IT security system development are substantiated. Conclusions. The proposed scientific and methodical approach and the developed applied tools are tested by the example of Ukrainian retailers’ online businesses. The testing results reflect the significance of the authors’ developments and proposals, as well as the expediency of their implementation for the successful set up and development of online business in the digital transformation context.

Socio-Technical Perspective for Electronic Tax Information System in Tanzania

Socio-technical systems theory has rarely been used by system architects in setting up computing systems. However, the role of socio-technical concepts in computing, which is becoming social in nature, has made the concepts more relevant and commercial. Tax information systems are examples of such systems because they are influenced by external variables such as the political environment, technological trends, and social environment, introducing complexity in their deployment and determining the type of e-services and their delivery to a diverse group of people. It was observed that in Tanzania there is resistance, reluctance and minimal use of electronic tax system because of insufficient end-user support and their involvement in constructing the system. Therefore, there is need to develop an electronic tax information system using socio-technical systems perspectives to ensure design of an efficient user-friendly tax administration system. The research used the qualitative approach, featuring case studies in Korea, Chile, Tanzania, and Denmark. The study used best practices from the Organization for Economic Cooperation and Development (OECD) to benchmark Tanzania Revenue Authority current practices. It was found that tax models implemented are techno-centric push models, which don’t attract its use by tax payers and requiring human intervention in its operation, hence not cost-effective. As the first and relevant phase in socio-technical system development, this paper presents the problem definition and analysis of e-Tax collection system in Tanzania.

Enhancing Cyberweapon Effectiveness Methodology With SE Modeling Techniques

A recent cyberweapons effectiveness methodology clearly provides a parallel but distinct process from that of kinetic weapons – both for defense and offense purposes. This methodology promotes consistency and improves cyberweapon system evaluation accuracy – for both offensive and defensive postures. However, integrating this cyberweapons effectiveness methodology into the design phase and operations phase of weapons systems development is still a challenge. The paper explores several systems engineering modeling techniques (e.g., SysML) and how they can be leveraged towards an enhanced effectiveness methodology. It highlights how failure mode analyses (e.g., FMEA) can facilitate cyber damage determination and target assessment, how block and parametric diagraming techniques can facilitate characterizing cyberweapons and eventually assess the effectiveness of such weapons and conversely assess vulnerabilities of systems to certain types of cyberweapons.

무기체계 연구개발단계에서의 체계적인 FMEA 실행 방법

Purpose: This paper proposes methods and procedures to effectively identify and select potential failure modes in the performance of faliure mode and effect analysis (FMEA). Specifically, a method is proposed to perform FMEA with emphasis on deriving failure mechanisms.<BR>Methods: The failure mechanism is known for a design change or improvement preventing recurrence and proactive. Therefore, it is important in designing FMEA to derive failure mechanisms and potential failure modes. This paper presents methods and procedures to derive failure mode and failure mechanism by analyzing past failure cases, usage/environmental condition parts (material), and interaction.<BR>Results: The effective design FMEA implementation procedure in the inorganic system development phase was described through the design FMEA practice case for antenna circuit card assembly and HL motor assembly among the drives used in induction weapon systems.<BR>Conclusion: The effectiveness of new identification of failure modes can be identified that the existing FMEA had not predicted by applying the proposed effective FMEA implementation methods and procedures to the weapon system development model.

Few-Shots Parallel Algorithm Portfolio Construction via Co-Evolution

Generalization, i.e., the ability of solving problem instances that are not available during the system design and development phase, is a critical goal for intelligent systems. A typical way to achieve good generalization is to learn a model from vast data. In the context of heuristic search, such a paradigm could be implemented as configuring the parameters of a parallel algorithm portfolio (PAP) based on a set of “training” problem instances, which is often referred to as PAP construction. However, compared to the traditional machine learning, PAP construction often suffers from the lack of training instances, and the obtained PAPs may fail to generalize well. This article proposes a novel competitive co-evolution scheme, named co-evolution of parameterized search (CEPS), as a remedy to this challenge. By co-evolving a configuration population and an instance population, CEPS is capable of obtaining generalizable PAPs with few training instances. The advantage of CEPS in improving generalization is analytically shown in this article. Two concrete algorithms, namely, CEPS-TSP and CEPS-VRPSPDTW, are presented for the traveling salesman problem (TSP) and the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW), respectively. The experimental results show that CEPS has led to better generalization, and even managed to find new best-known solutions for some instances.