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The DoD’s artificial intelligence (AI) strategy requires the delivery of transformative and disruptive capabilities that impact the “character of the future battlefield and the pace of threats” that US forces must be prepared to handle. Candidate frameworks must also address key mission areas while enabling partnerships with the private sector, academia, and global allies. To meet these challenges, a flexible, cost-effective, and scalable computing infrastructure that incorporates cutting edge technologies and complies with stringent information assurance requirements is necessary. The DoD AI strategy mandates the agile employment of innovative AI capabilities that “rapidly and iteratively” execute experimentation with new operating concepts, and leverage lessons learned in subsequent experiments. Using cloud computing, we present a flexible approach to solve complex systems problems. Promoting “rapid experimentation” and collaboration on problems such as recursive algorithm implementation, deep learning, and inference in neural networks has enabled inherent advantages over existing computing frameworks. Leveraging the cloud to implement shared responsibility security models, serverless architectures, and high-performance virtual machines, aspects of the AI lifecycle including build, deploy, and monitor have resulted in an adaptable and scalable computing framework that is not only disruptive to the current computing paradigm but also promotes enhanced and productive collaboration.

The development of Information Technology (IT) is growing very rapidly. The rapid development of IT makes internet technology the main communication tool that is in great demand by the public. With the internet technology will facilitate and speed up the search for information, information is very important in supporting the course of a company to achieve the desired goals. Online transactions are a necessity that answers the problems that arise today. In online transactions, a person can freely make purchases, sales and find things he wants to do without having to meet with buyers or sellers through laptops, computer devices, or smartphones. For this reason, the authors design a web-based musical instrument sales system so that people can easily get information about selling musical instruments online quickly and precisely both regarding products, profiles, prices, and also how to shop at online stores and can directly make sales transactions. The author makes the design of this information system using UML (Unified Modeling Language), the Waterfall method, and Agile the author implements to create a website-based E-commerce application. As a result, users can shop and view information about musical instruments.

El uso masivo de las Tecnologías de la Información y Comunicaciones ha ocasionado la interdependencia de la sociedad respecto de estas; sumado a la ausencia de controles eficientes y efectivos a nivel general, incrementan la exposición a los ataques o amenazas informáticas, a las vulnerabilidades en los activos de información de las organizaciones. En este contexto, el presente artículo propone una arquitectura de análisis de datos a través de herramientas de Big Data mediante la utilización de eventos o registros de seguridad, que permitan mejorar la identificación, integración y correlación de eventos. La metodología de la investigación soportada se caracterizó por ser exploratoria y descriptiva. Para el desarrollo de la solución propuesta se empleó las fases del procesamiento de Big Data propuesta por Labrinidis y Jagadish, que permita la identificación de amenazas de informáticas. La arquitectura tecnológica diseñada se basó en la integración de Elastic Stack y sus componentes principales (Elasticsearch, Logstash, Kibana), y tecnologías como Filebeat y Wazuh Security Detection (NIPS/HIDS), gestionando la seguridad en activos de información como equipos de comunicaciones, servidores de datos y aplicaciones, motores de bases de datos, y terminales de usuario final. Su implementación permitiría la supervisión en tiempo real e histórica de una respuesta ágil y efectiva de alertas de seguridad e informes de estado ante incidentes.

Purpose The purpose of this study is to develop a computerized system that will increase the efficiency and supervision of library activities, thereby providing easy access to library usage for librarians and library users. Design/methodology/approach The researchers use the agile methodology to develop and implement the system. PHP, JavaScript, Hypertext Markup Language, Cascading Style Sheets and SQL are the languages used to create the library management system. Findings The developed system received an overall rating of 4.42 with a descriptive rating of “Excellent.” Originality/value The automated library management system is a computerized system for the public libraries designed in the Philippine setting to make the library’s processes more efficient, reliable and portable especially in times of pandemics.

Today, continuous integration (CI) is state of the art for agile software practices. Teams can choose from a range of tools, such as Jenkins, Atlassian Bamboo, and Microsoft Azure DevOps, that facilitate CI, from build servers to deployment platforms. For pure software solutions and where hardware can be virtualized, CI is relatively easy to achieve thanks to fully automated testing. It becomes significantly harder for systems involving hardware that have to be interacted with when executing test cases.

Autonomous systems, both cyber and physical, operate in highly contested environments in which it must be assumed that adversaries are capable, agile, and informed about state‐of‐the‐art technology. In order to sustain performance in such environments, systems must be able to adapt autonomously through online machine learning ‐ that is, improve their performance quickly, even over the duration of a single engagement. In this chapter, we outline the challenges of adapting in such environments and propose a solution approach that leverages a variety of recent innovations in reinforcement learning, distributed computing, and trusted consensus algorithms such as Blockchain. We argue that multicomponent systems operating in contested environments must leverage their redundancy for learning while also remaining resilient with respect to component failures and compromises. In particular, to enable and accelerate learning, such systems will have to allow some number of components to operate sub‐optimally to achieve the right exploration‐exploitation balance needed for rapid and effective reinforcement learning. At the same time that some number of components are possibly being sacrificed due to sub‐optimal performance, the underlying mission of the system must be maintained. This leads to challenges in distributed trusted computing such as Byzantine agreement problems. We include a discussion of the role of current Blockchain technology in supporting future multiagent adaptive adversarial autonomy with the desired associated resilience.

As the most fundamental infrastructure in the current Internet, the Border Gateway Protocol (BGP) supports the inter-connectivity of different Autonomous Systems (ASs) and then the reachability can be achieved from any network in the Internet. However, due to the lack of security consideration during its original design, the BGP suffers from multiple security threats. Another challenge is that it cannot support the future sophisticated applications with deterministic routing. In this article, we propose a novel BGP management architecture, namely BGPChain, which is based on the blockchain in order to establish a secure, smart, and agile routing infrastructure for the future Internet.

Smart Garbage Monitoring System using Arduino and Blynk is an IoT-based project that is integrated with cloud computing service. It was developed specifically to help janitors and residential college representative committee in a public university in Malaysia in monitoring the level of garbage in the dustbin through Blynk application, which makes it easy to empty the dustbin and optimize the garbage collection time. This project uses ultrasonic sensor to measure the level of garbage in the dustbin and the data can be viewed through mobile application, Blynk. It will also notify users through email once the garbage bin capacity is full. Other than notification function, Smart Garbage Monitoring System also consists of automatic open and close lid, track dustbin location and humidity level. The objectives of this project are to design the IoT system that detects the level of the garbage displayed in Blynk and an LCD attached to the dustbin and to validate the user acceptance test for the system. The development of Smart Garbage Monitoring System used the System Development Life Cycle (SDLC) by implementing the agile model as the methodology. A User Acceptance Testing was conducted with 20 respondents including students from the student representatives’ body and janitors. This testing aimed to determine the acceptance of the system by conducting an evaluation. The results of the User Acceptance Test shows that most of the participants agreed and verified that the system works for them and a suitable solution for their problems. Therefore, based on the features and functionalities offered by the Smart Garbage Monitoring System, it could be concluded that this system is beneficial to all the users, especially the janitors and students of the residential college.

Many cyber physical systems have little or no cybersecurity mechanisms due to their limited computing capabilities or their history of running on isolated networks. As these systems have become interconnected and connected to corporate networks, they have become more vulnerable to cyberattacks. Providing cyber physical systems with autonomic properties will allow them to become more self-aware and react in near real time to attacks and failures. Testing these systems for their susceptibility to intelligent attacks is also needed to provide assurance of their resilience. This paper describes two approaches to providing assurances to cyber physical systems. The first approach retrofits industrial control systems with autonomic properties that will allow them to automatically detect and recover from cyberattacks and other failures through the use of microservices that reconfigure the systems dynamically during attacks or failures. The second approach uses intelligent agents in a modeling and simulation framework to test the resiliency of autonomous unmanned aerial systems. Agents are orchestrated using a range of algorithms and subjected to stressful environments to measure the efficiency and safety of their operations in a simulate multi-UAS air-traffic control problem.

Purpose The application of agility principles and methodologies on Enterprise Architecture (EA) is a promising field. This paper aims to provide an in-depth study of the Agile Enterprise Architecture (AEA) by studying EA practitioners’ perspectives to propose a foundational framework for AEA. Design/methodology/approach The authors formulate a foundational framework that defines the AEA motivators, enablers and blockers using the agile manifesto as one of the AEA enablers where a total of 156 EA stakeholders with at least one year of experience in enterprise architecture were surveyed, and a set of hypotheses was analyzed and tested based on the proposed framework. The authors also develop a quantitative method to evaluate the agility index of the EA based on the introduced framework. Findings The research results show with significance that enterprise architects perceive positively the application of the agility methodologies on the enterprise architecture. This perception is primarily affected by the enterprise size, the EA dependency and the agile methodologies awareness. The findings also indicate that AEA is primarily motivated by the Business and IT Change Ready and Responsive EA. Finally, an EA Agility Index (EAAI) was designed to assess the agility application of the EA based on the three forces, i.e. motivators, enablers and blockers. Research limitations/implications Because of the chosen research approach and the sample size, the research results may lack generalizability. Also, EAAI designed was not thoroughly tested. Practical implications The paper includes implications for the design and development of an EA Agility Index, and the need to increase the awareness of the agility methodologies to overcome the blocker of the unfamiliarity with the agile methodologies implying that the current business models in enterprise must be more aligned with the agile methodologies. Originality/value While there are efforts to develop AEA frameworks, one of the major findings of the literature review conducted is that there is evident research gap in the literature on the perception and associated factors of the EA stakeholders on having an agile enterprise architecture. This paper attempts to fill this gap.

Machine-based classification usually involves some computer programs, known as algorithms, developed using several mathematical formulations to accelerate the automated classification process. Along with the exponential increase in the size and computational complexity of the data today, such optimized, robust, agile and reliable computational algorithms are required which can efficiently carry out these conforming classification tasks. In this review paper, deterministic optimization techniques have been analysed that are efficiently employed for machine learning applications. In this review, systematic literature review approach has been adopted in which 200 research articles were downloaded from which 100 latest articles has been selected based on the most commonly employed neural networks’ techniques. Moreover, the reported neural networks techniques based on Back Propagation Neural Network (BPNN), Recurrent Neural Networks (RNNs) Algorithm and Levenberg-Marquardt (LM) with several hybridized classification algorithms based on optimization techniques have been indicated that are commonly used to optimize and benefit the classification process.

WhatsApp is currently one of the most widely used communication mediums among students in tertiary education in the form of both personal messages and group messaging chats. Currently, the practice in most Malaysian Universities is that students need to consult the faculty staff or search through the faculty’s website to obtain information related to their academic studies. However, the process of obtaining needed information can take some time ranging from minutes to hours and at times are not consistent as the queries are answered by different staff members. There are WhatsApp chat groups for students to interact with faculty staff, but the chat group can be overwhelmed by questions due to the huge number of group members hence causing important messages to be overlooked. In an attempt to overcome these challenges, we developed a FCSIT (Faculty of Computer Science and Information Technology) WhatsApp chatbot to solve these problems by offering a centralized platform of communication for both faculty staff and students to carry out information sharing through WhatsApp messages. Communication is in the form of personalized messages between the chatbot and the user thus solving the problem of overwhelming messages posed by group chats. Agile Kanban methodology was used to develop the system which promotes the use of a Kanban board to visualize the development stages. A questionnaire was distributed to lecturers, faculty staff and students to test the system and to gather opinions and suggestions from targeted users, which is the FCSIT community, in order to assess whether the system fulfils its objectives.

The role of software architecture in large-scale Agile development is important because several teams need to work together to release a single software product while helping to maximise teams’ autonomy. Governing and aligning Agile architecture across autonomous squads (i.e., teams), when using the Spotify model, is a challenge because the Spotify model lacks practices for addressing Agile architecture governance. To explore how software architecture can be governed and aligned by scaling the Spotify model, we conducted a longitudinal embedded case study in a multinational FinTech organisation. Then, we developed and evaluated an approach for architectural governance by conducting an embedded case study. The collected data was analysed using Thematic Analysis and informed by selected Grounded Theory techniques such as memoing, open coding, constant comparison, and sorting. Our approach for architectural governance comprises an organisational structure change and an architecture change management process. The benefits reported by the practitioners include devolving architectural decision-making to the operational level (i.e., Architecture Owners), enhancing architectural knowledge sharing among squads, minimising wasted effort in architectural refactoring, and other benefits. The practitioners in our case study realised an improved squad autonomy by the ability to govern and align architectural decisions. We provide two key contributions in this paper. First, we present the characteristics of our proposed architectural governance approach, its evaluation, benefits, and challenges. Second, we present how the novel Heterogeneous Tailoring model was enhanced to accommodate our architectural governance approach.

Values and principles of agile working emphasize the benefits of personal communication and open team spaces with members sitting in close proximity. This facilitates customer-oriented results and reduces the risk for failures based on misunderstandings. During the current COVID-19 pandemic, most companies reduced office times and encouraged their employees to work from home in order to protect people from infection. Direct face-to-face communication was replaced by online meetings using video conferencing, chats, and cloud-based collaboration. This significantly changed the way of working. While online-based tools for remote collaboration were familiar to many employees before, others were reluctant to use them in the past. The sudden need to utilize these tools in order to be able to participate in business at all opened such tools for people who would not have used them under normal conditions. In this article, the results of a recent study are presented, in which the impact of the pandemic on agile working is examined. Based on these results, a scenario of the future of agile working in a post COVID-19 world is derived. The study was conducted as an online survey in Germany. More than 170 people working in different job positions and from different branches and companies participated. The study reveals that the pandemic significantly changed the way of working. However, people value digital tools and were able to find methods of effective and efficient collaboration in distant work environments.

In this study, we propose a machine learning (ML) model to predict the availability of an electric vehicle (EV) providing vehicle to home (V2H) services. Electric vehicles are able to store and give back energy directly to consumers and/or the grid using V2H and/or vehicle to grid (V2G) technologies. However, there is a limited understanding of what impact vehicle availability has on the its capacity to engage in such services. Using five different vehicle usage profiles, classified by the number of trips made per week, the machine learning model proposed is used to predict the availability of an EV. An optimisation model is then used on each profile to obtain the minimum electricity bill for each profile class assuming V2H service provision. PV generation providing power to the house was also considered. The ML model had an accuracy of over 85% and R 2 value of 0.78 in predicting the location and distance travelled for the EV respectively. Final results showed that the less an EV is used for travelling, the greater its availability to participate in V2H services. Also, all categories of EV user benefited from reduced power bills when deploying V2H. An electricity cost reduction of at least 46% on average was obtained when V2H is implemented with an agile electricity price structure regardless of the level of vehicle usage.

Now that drones have evolved from bulky platforms to agile devices, a challenge is to combine multiple drones into an integrated autonomous system, offering functionality that individual drones cannot achieve. Such multidrone systems require connectivity, communication, and coordination. We discuss these building blocks along with case studies and lessons learned.

Effort estimation is an essential task in a software project as it helps to establish feasible plans for the implementation of a project. It largely influences success or failure of the project. Project planning becomes more efficient with accurate effort estimates, thus providing a number of benefits to the organization. Estimating effort in agile projects has been a challenging task for researchers. Several studies exist in that domain. While some have considered people-related factors, others have catered for project-related factors for estimating effort. Others have adopted machine learning (ML) techniques to produce an accurate estimation. This paper presents a model to estimate and predict effort in a sprint using ML techniques while considering various factors that affect a sprint. The model has been evaluated using various regression algorithms, namely linear regression, K-nearest neighbor, decision tree, polynomial kernel, radius basis function and multi-layer perception (MLP). The model has produced more reliable estimates, with low error values using MLP algorithm.

In Agile model where the software prototypes are developed frequently and also rapidly, testing becomes more critical. Generating an effective Test case for complex system is a challenging task involved in software testing. The major research challenge in this area includes the test case generation with limited resources, identifying the essential functional requirement that plays a crucial role and automation of the test case generation process. To solve this issue, a hybridized bio inspired approach is proposed to generate test cases from the user stories which accepts the business requirements as input, processed using NLP and develop functional test cases from it. The proposed algorithm is compared with other existing algorithms and the experimental results proved that the proposed algorithm is more efficient in many cases.

Due to the advancement in technology and management's needs on the go, i-CAMPUS is developed for UiTM Cawangan Melaka. The i-CAMPUS has been integrated with information and communication technology (ICT) and the Internet of Things (IoT). One of the emerging technologies in IoT that is still new in Malaysia is beacon technology with smartphone applications to serve information based on the user's contextual information. Information is currently is decentralized and disseminated among people on the campus through email and by publishing on the website. In order to allow effective information delivery, i-CAMPUS is proposed and developed using an agile development life cycle approach. The system used the Flutter environment and phpMyadmin as a database management system. Integrated with beacon and geofence technology, the i-CAMPUS user will receive notification information within the application when the mobile application sensed the signal emitted by the beacon in the form of Bluetooth Low Energy (BLE). Each beacon signal is unique, and the information delivered is specified according to the beacon. The i-CAMPUS has been evaluated in terms of functionality and usability. The result shows that i-CAMPUS is working well and enables the visitor to retrieve information anywhere at any time. For future work, the navigation function is recommended to be integrated with this application.

This paper describes the development of a framework that integrates engineering software and agile tools based on ontologies and business rules. The aim of this integration is to help project manag...

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove’s ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors., In the electronic health record, using clinical notes to identify entities such as disorders and their temporality can inform many important analyses. Here, the authors present a framework for weakly supervised entity classification using medical ontologies and expert-generated rules.

The fast development of the so-called shared economy as well as collaborative networked organization increases the call for responsive and strongly interconnected production and delivery systems. The closer connection of information systems and control systems has to be achieved, as “Industry 4.0” developed by leading industries in Europe, USA and Asia requires to set new IT models in order to support agile collaborative business processes and production control processes. Whereas the service-based organizations and technologies, such as Service-Oriented Architecture, Web 2.0, Cloud and Everything as a Service technologies as well as Business models, have been successfully deployed to support agile systems relying on service selection, composition and orchestration, only few works have addressed cloud-based control systems. Despite the flexibility provided by the Internet of Thing, the integration of Cyber-Physical systems (CPS) in production control system is limited due to their lack of interoperability. To overcome this limit and provide a smooth integration of CPS systems in collaborative production process control, we propose a service-based control model integrating sensors, controllers and actuators. To support the Industry 4.0 digital transformation, including collaborative production process design and management, our Control as a Service model relies on control ontology. Our control ontology gathers IoT devices (sensors and actuators $$\ldots $$ ) and services, including their functional and non-functional properties description. Then, a Control as a Service architecture is designed to facilitate the development of cloud control system via two steps: service pre-selection/pre-composition, event-driven and context-aware service orchestration. The measurement of three core components, data manager, event manager and context manager, shows the feasibility of application of our Control as a Service model/architecture in a supply chain temperature control use case.

Traffic Signal Control (TSC) is a fundamental task in modern intelligent transport systems. TSC is often formulated as a bi‐level optimization problem, comprised by the signal timing at the upper level and the Dynamic User Equilibrium (DUE) traffic assignment at the lower level. Since DUE is non‐convex, existing methods either formulate approximation models or adopt traffic simulators. However, approximation models may oversimplify the practical situations, while traffic simulators are usually time‐consuming. This paper formulates a vehicle‐based DUE (vDUE) model and proposes an agile method that can simultaneously maintain the computational simplicity and the traffic dynamics for the traffic assignment. Further, an agile TSC system is built by combining the vDUE at the lower level for the traffic assignment with an adaptive differential evolution algorithm at the upper level for the signal timing optimization. To enhance the effectiveness of optimization, the TSC problem formulation is also improved to make it better characterize the practical requirements. In the experiments undertaken, comparisons of different TSC methods are carried out on both real‐world and synthetic transportation networks. The experimental results validate the effectiveness of the proposed agile TSC system in various traffic situations.

Integrating information and communication technologies in teaching and learning is recognized as a relevant and promising practice. However, integration can be complex, tedious, and draining. We propose an innovative approach for enhancing teaching and learning in a simple, pleasant, and effective way: repurposing a virtual world platform to develop a simulation-based training. Minecraft Education, a virtual world platform, was used to develop a simulation-based training on agile project management. So far, 153 university students have applied the Scrum framework to execute a simulated project in Minecraft Education. Results show that students’ perceived learning is very positive and that their learning experience was stimulating and challenging. Recommendations are formulated to guide practitioners and teachers adopting and adapting a “readymade” virtual world platform for teaching and learning.

Communication plays an important role to deliver the correct information. However, the communication became challenging especially for agile software teams, which are in geographical distributed. The problem arise when there are exchanging information using unstructured communication platform, misunderstanding on the information communicated and lack of documentation. The aim of this study is to propose a text classification technique for requirements identification in text messages. In this study, we adopted the cascade and cluster classification concept of Carotene that relies on the hash tag function. It classifies the text messages into requirements types instead of job title. This technique called as high-level carotene (HLC) technique that embedded into the tool to identify the functional requirement and non-functional requirements. The result shows that most of criterias evaluated have achieved more than 85% of effectiveness in identifying both of requirement in text messaging by using this technique.

Agility gives the ability to be highly responsive to changes and make improvements in the way the agile project is documented. The just-in-time and just barely good enough documentation may miss out on important executable specifications. Interestingly, the frequently asked ‘how-to-do’ questions on the popular question answering (Q&A) websites like stack overflow are strong indicators of gaps in documentation and respondent answers can complement conventional software documentation practices. Social interaction within these QA websites generates partially structured content commonly referred to as crowd knowledge that can offer a peer-reviewed re-documentation by integrating the answers of these ‘how-to-do’ concerns. But finding the best, value-added answer to the question which can contribute toward an enriched and curated documentation is computationally difficult. Moreover, query duplicates can cause seekers to spend more time finding these best answers. As a solution, the research proffers a novel question-answering crowd documentation model (QACDoc) which is based on a socially mediated documentation mechanics involving the dynamics of community-based web. Firstly, duplicate questions are detected using Siamese neural architecture where two identical hierarchical attention networks are used to generate vectors for similarity matching. Semantic matching is done using Manhattan distance function, and a multi-layer perceptron is trained to output the predictions. Next, all respondents of semantically matched questions are grouped to form an intent-based crowd and lastly, top k-experts are identified using representative social presence features for expert ranking. The crowd documentation is then filtered to only include answers of these identified experts.

The issues of error detection in artificial neural networks are considered. They are related to conflicting opinions of experts and / or limited (imperfect) description of the subject area. Approaches to their debugging are analyzed. Ways to improve existing approaches to debugging errors such as “forget about exclusion” are shown. Possible ways of application of the received decisions for debugging of errors “intersection of critical events” are shown. A formalized definition of the error of the neural network intelligent system is proposed, taking into account the requirements for efficiency and accuracy of information presentation. The issues of the influence of incorrect organization of machine learning on the accuracy of classification of elements of the neural network intellectual system are considered. The possibility of applying network contrast methods at the preparatory stage for testing the knowledge base of the intelligent decision support system is proved. This reduces the likelihood of errors of this type for these systems. The classification and analysis of algorithms for sampling knowledge from an artificial neural network are given. It is shown that to detect these types of errors it is advisable to use a modified GLARE algorithm with the adaptation procedure. Block diagrams of algorithms for debugging the knowledge base of an intelligent decision support system using the obtained theoretical solutions are presented. The scheme of the organization of testing process on levels of detailing for integration and modular testing is offered. This approach can be used to implement testing processes of Agile methodology, in particular: Agile Modeling, Agile Unified Process, Agile Data Method, Essential Unified Process, Extreme Programming, Feature Driven Development, Getting Real, Open UP, Scrum, Kanban.

The standardization of the physical aspects of nanosatellites (also known as CubeSats) and their wide adoption in academia and industry has made the mass production and availability of off-the-shelf components possible. While this has led to a significant reduction in satellite development time, the fact remains that a considerable amount of mission development time and effort continues to be spent on flight software development. The CubeSat’s agile development environment makes it challenging to utilize the advantages of existing software frameworks. Such an adoption is not straightforward due to the added complexity characterized by a steep learning curve. A well-designed flight software architecture mitigates possible sources of failure and increases mission success rate while maintaining moderate complexity. This paper presents a novel approach to a flight software framework developed specifically for nanosatellites. The software framework is characterized by simplicity, reliability, modularity, portability, and real-time capability. The main features of the proposed framework include providing a standardized and explicit skeleton for each module to simplify their construction, offering standardized interfaces for all modules to simplify communication, and providing a collection of ready-to-use common services open for further enhancement by CubeSat software developers. The framework efficiency was demonstrated through a software developed for the MeznSat mission that was successfully launched into Low Earth Orbit in September 2020. The proposed software framework proved to simplify software development for the application developer while significantly enhancing software modularity.

Indonesia is the biggest maritime country that has big marine resources too. It needed a good strategy so the marine resource usage can be managed well. Now, it can be helped by using information technology. The purpose of this research is to create a Geographic Information System for knowing potential marine resources in Daerah Istimewa Yogyakarta Coast. The result of this paper is a Geographic Information System that will runs in Android devices. The test results show 82,93% of users already satisfied with this application. It indicates that this application is in a “Very Good” category.

Cloud computing has now become a very standardised concept in our society. However, many modern applications need a better level of security that includes saving data from internal breaches. Thus, cloud databases need effective security mechanisms to keep track of data modifications. This paper will introduce the enhanced structure of cloud relational database (RDB) based on blockchain technology (BC) named BC over cloud-RDB. Through a self-verification mechanism, it enables the client to detect and prevent erroneous RDB manipulation. We proposed two systems to improve cloud-RDB namely, agile BC-based RDB and secure BC-based RDB. Both are distributed among several cloud service providers based on the Byzantine Fault Tolerance consensus. Additionally, both rely on linking records to each other using the SHA-256. At the same time, secure BC-based RDB uses a proof-of-work consensus to make data offensive operation impossible. On the basis of performance of both systems’ and security analysis, the agile BC-based RDB is highly suggested for the high throughput database. On the other hand, the secure BC-based RDB is recommended for RDB that contains sensitive data and low throughput performance. The improved RDB is flexible and can be operated according to the data owner’s specifications.

Context: User stories have been widely accepted as artifacts to capture the user requirements in agile software development. They are short pieces of texts in a semi-structured format that express requirements. Natural language processing (NLP) techniques offer a potential advantage in user story applications. Objective: Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories. Method: The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar. Inclusion and exclusion criteria are applied to filter the search results. We also use the forward and backward snowballing techniques to obtain more comprehensive results. Results: The search results identified 718 papers published between January 2009 to December 2020. After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories. Most studies used NLP techniques to extract aspects of who, what, and why from user stories. The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories. Conclusion: NLP can help system analysts manage user stories. Implementing NLP in user stories has many opportunities and challenges. Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research. As with NLP research in general, the ability to understand a sentence’s context continues to be a challenge.

Purpose–This study aims to contribute to the fire research by developing a fire report management system for the BFP that can analyze spatiotemporal attributes of fire and 520apply Knowledge Discovery in Databases (KDD) methods to identify patterns of fire incidents in the city of Manila.Method–The proponents applied the Knowledge Discovery in Databases (KDD) methods for the processing of identifying fire patterns as well as the application of SMOTE, One-Hot Encoding, and Agile Method as Software developmental model. Result–The records obtained from the BFP headquarters in Manila had a total of 3,506 cases during the six years from 2011 to 2016. The accuracy of the Decision Tree classifier model was 95.92%.Using KDD approach, it generated decision rules fire pattern in Manila. Most fire causes fall under the 'Under Investigation' category while Residential-Commercial types of establishments in Intramuros were affected. Lastly, the fire occurred in the mornings, during Sundays when most people are in their homes and the majority of which took place in the Pandacan district.Conclusion–The application of KDD in building a predictive model to be integrated into the system was the major part of this project. The outputs generated by the system can provide material for use in more accurate fire risk assessments, more efficient allocation of fire resources and personnel, and more targeted fire awareness and prevention programs.Recommendation–Future research in this area may include other factors contributing to a higher likelihood of fire incidences such as weather conditions and other geographical attributes of fire-prone locations. Analysis of these and other relevant factors may allow the BFP to gainfurther insights into the causes of fire incidents, which will enable the agency to make the necessary adjustments and changes in their current fire prevention and risk reduction programsPractical Implication–This study provides direct implication for the Bureau of Fire Protection and community through the given insights of the fire activities and the created model of the system that determine Manila's fire patterns that help identify appropriate information about fire activities and preventive measures of fire incidents.

Sampah adalah barang bekas kebutuhan manusia. Sampah membawa dampak buruk bagi lingkungan dan kesehatan. Disadari atau tidak setiap hari terjadi penumpukan sampah organik maupun sampah non-organik. Permasalahan sampah bukan hanya diperkotaan, di Desa Rejosari Kabupaten Banyuwangi, merambahnya toko-toko modern menjual kebutuhan manusia berkemasan plastik, kain, kaleng, botol minuman, besi, kaca dan sebagainya yang tergolong dalam sampah non-organik. Banyaknya lahan kosong, tidak diberdayakannya tempat pembuangan sampah sementara, sampah di buang disembarang tempat kemudian dibakar bahkan sampah dihanyutkan ke sungai. Pengelolaan Sampah harus dilakukan secara komprehensif sejak hulu sampai hilir dimulai dari desa. Penelitian ini direncanakan suatu system penanganan sampah terintegrasi teknologi dan informasi. Dengan aplikasi ini masyarakat dapat menghasilkan uang dengan menjual sampah melalui aplikasi, tidak perlu datang ke bank sampah, akan ada pengepul sampah yang datang untuk membeli sampah mereka. Masyarakat menabung sampah atau menukarkan sampah dengan produk kerajinan atau bahan kebutuhan pokok. Aplikasi menampilkan jenis sampah yang mempunyai nilai jual, produk kerajinan ataupun kebutuhan bahan pokok yang dapat ditukar dengan sampah. Pengepul yang membeli sampah warga wajib menjual atau menabung sampah kepada Bank Sampah. Aplikasi dikembangkan dengan metode Agile Development dengan XP (Extreme Programming). Pengujian menggunakan metode black-box testing, hasilnya sytem berjalan baik sesuai dengan yang direncanakan.

Difficulty in finding the destination around a campus is a common problem faced by students, staffs and visitors of a university. Outdated signboards, confusing building architecture and layout, and bad weathers are just some of the hindrance to find location for people who are unfamiliar with the campus area. To solve the problem, virtual campus tour is chosen because it can also be embedded into a university website, or use as a stand-alone app. The virtual campus tour which can be accessed as mobile app or web app, can help users in visualizing their journey around campus because it was developed using collections of panoramic video footage, which allows user to watch in 360-degree view of selected campus area. Administrative building, academic blocks and residential college are some of the destinations included in the tour. Due to the need to create and test components of the product iteratively, Agile method was employed as the development methodology. Agile supports high integration between development and testing phase. This paper discusses the development process of the virtual campus tour, which were encapsulated in the four phases of Agile methodology, requirement phase, design phase, development phase and test feedback phase.

The sixth generation (6G) networks are expected to provide a fully connected world with terrestrial wireless and satellite communications integration. The design concept of 6G networks is to leverage artificial intelligence (Ai) to promote the intelligent and agile development of network services. intelligent services inevitably involve the processing of large amounts of data, such as storage, computing, and analysis, such that the data may be vulnerable to tampering or contamination by attackers. in this article, we propose a blockchain-based data security scheme for Ai applications in 6G networks. Specifically, we first introduce the 6G architecture (i.e., a space-air-ground-underwater integrated network). Then we discuss two Ai-enabled applications, indoor positioning and autonomous vehicle, in the context of 6G. Through a case study of an indoor navigation system, we demonstrate the effectiveness of blockchain in data security. The integration of Ai and blockchain is developed to evaluate and optimize the quality of intelligent service. Finally, we discuss several open issues about data security in the upcoming 6G networks.

Teman Bumil (Friends of Millennial Mother) is application is an application developed to help pregnant women which contains some information from running a pregnancy program to monitoring the growth and development of pregnant women. This application was first released in 2017 with the number of active users currently having a stickiness rate of ± 13% per month which in this case is proven by the interview method conducted by researchers. However, this number is still considered unstable because it is only around 13%, where the target of stickiness rate is ± 15% for the month. Several improvements have been made by adding health charts, online classes, new media features, and registration using a new method, namely OTP (One Time Password). the results of this change have not been as expected. Therefore, in this study, we propose Revamp or an update using the Flutter framework which produces an application that can be used by both the iOS and Android platforms or also called a hybrid so that it is more interactive and according to user needs. In addition, this study also uses the Agile development method with the Scrumban model, which is a combination of Scrum and Kanban. The results of this research are in the form of a Teman Bumil application that has been revamped and has also been tested for the application developed and has received a good response from users based on randomly distributed questionnaires. &nbsp, Aplikasi Teman Bumil (Teman Ibu Milenial) merupakan salah satu aplikasi yang dikembangkan untuk membantu ibu hamil yang memuat beberapa informasi mulai dari menjalankan program hamil hingga memantau tumbuh kembang dari ibu hamil. Aplikasi ini pertama kali di rilis pada tahun 2017 dengan jumlah pengguna aktif saat ini memiliki stickiness rate ± 13% per bulannya yang dalam hal ini dibuktikan dengan metode wawancara yang dilakukan oleh peneliti. Namun jumlah ini dirasa masih kurang stabil karena masih hanya sekitar 13%, dimana target dari stickiness rate ± 15% untuk perbulannya. Beberapa perbaikan telah dilakukan dengan menambahkan grafik kesehatan, kelas online, fitur baru media, dan registrasi menggunakan cara yang baru yaitu OTP (One Time Password) akan tetapi hasil dari perubahan ini belum sesuai dengan yang diharapkan. Oleh karena itu, dalam penelitian ini, kami mengusulkan Revamp atau pembaharuan menggunakan framework Flutter yang menghasilkan aplikasi yang dapat digunakan baik platform iOS dan Android atau disebut juga hybrid sehingga lebih interaktif dan sesuai dengan kebutuhan pengguna. Selain itu, dalam penelitian ini juga menggunakan metode pengembangan Agile dengan model Scrumban, yang merupakan kombinasi antara Scrum dan Kanban. Hasil dari penelitian ini adalah berupa aplikasi Teman Bumil yang sudah di revamp dan juga telah dilakukan pengujian aplikasi yang dikembangkan dan mendapatkan respon yang baik dari pengguna berdasarkan penyebaran kuesioner yang dilakukan secara random.

The primary objective of this paper is to report on the successful implementation of forensic facial approximation in a real case in the forensic context. A three-dimensional (3D) facial approximation protocol of the skull was performed with free software, applying techniques in a virtual environment that have already been consolidated in the literature. The skull was scanned with the photogrammetry technique, the digital replica was imported in the Blender software (Blender Foundation, Amsterdam) and individualized model sketches of the face were traced with the MakeHuman software (MakeHuman Org) according to the anthropological profile of the victim. The face created was imported in Blender, where it was adapted, modeled, and sculpted on the 3D skull and its soft tissue markers, using an American open-source application of the technique in the digital environment. The face created in a virtual environment was recognized and legal identification procedures were started, resulting in the more agile delivery of the disappeared body to its next of kin. It is therefore concluded that facial approximation may not be a primary method of human identification, but it can be satisfactorily applied in the forensic field as an individual recognition resource. It has great value in narrowing the search, reducing the number of alleged victims, and leading to identification tests, therefore significantly reducing the number of genetic DNA (deoxyribonucleic acid) tests-which are considered costly for the State or Federation-and consequently reducing the waiting time before delivery of the body to its family.

It is more common for multiple users to collaborate to develop a software application in a P2P collaborative working environment. In collaborative software development, the rational allocation of software development tasks is of great significance. However, heterogeneous of software development tasks, such as the value of the task, the skill required, the effort required and difficulty, increase the complexity of task allocation. This paper proposes an allocation approach of crowd intelligence software development task in which multiple individuals collaborate to complete software development tasks. The heterogeneous task allocation problem in the crowd intelligence software development system is formulated as an optimization problem. Then, the process of task allocation is modelled using the hidden Markov model. In our study, due to the insufficiency of data characteristics, we propose to construct a generator using Generative Adversarial Networks(GANs) to solve this problem. Then, the Baum-Welch algorithm is used for detailed analysis and calculation of model parameters. And on this basis, effective task allocation strategies for maximizing the total value of tasks obtained by the workers are explored through the Viterbi algorithm. Based on the Agile Manager (AM) dataset, which contains a large scale real human task allocation strategy, the model learns from human decision-making strategies that have achieved good outcomes. Based on the Agile Manager dataset, this approach is evaluated experimentally. The results show that it outperforms the artificial intelligence (AI) player in the AM game platform.

When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.

A Robotic Process Automation (RPA) enabled by Artificial intelligence (AI) has become an important field within the digitalisation of the economy. AI-driven robots and machines are forecasted to grow dramatically in the next years. AI-enabled RPA replaces the work a human would normally do by mimicking interactions with applications and provides direct access to systems using APIs. RPA has superior advantages versus human execution:24x7 execution, eternal lifetime and scalability. Process automatization is per se not a brand-new technology, however due to notable progress in AI, which RPAleverages, it has become an own solution category. RPA enables algorithmic rules without being biased. Ethical considerations intend to make AI-driven RPA more human and introduce morality into the machine learning. The Uber-Waymo trial made transparent how much AI development is influenced by human irrationality and irrational exuberances. It reveals a culture of agile software development, which prioritize releasing the latest software over testing and verification, and one that encourages shortcuts and irrationality. This also give proof that applying AI cannot ensure that irrational exuberances disappear. The reason for this irrational exuberance may have its roots in the exponential growth in computing and storage technologies predicted by Gordon Moore five decades ago. This paper develops a concept how irrational exuberances with the business case of RPA can be prevented from happening. One general approach for solutioning of the issue is to increase transparency. The paper recommends applying technology to make data more accessible and more readable on the application of artificial intelligence. With the aim of application of “transparency technology XBRL (eXtensible Business Reporting Language)” is incorporated. XBRL is part of the choice architecture on regulation by governments (Sunstein, 2013). XBRL is connected to a taxonomy. The paper develops a taxonomy for RPA to make application of artificial intelligence more transparent to the public and incorporates ethical considerations. As a business case the strongly growing RPA industry is selected. The paper focus on the way to enhance AI that aligns with human values. How can incentive be provided that AI systems themselves do not become potential objects of moral concern. The main outcome of the paper is that AI-enabled RPA reveal moral concerns however transparency technologies at the same time also offer way to mitigate such risks.

Food waste is a significant problem within public catering establishments in any normal situation. During spring 2020 the Covid-19 pandemic placed the public catering system under greater pressure, revealing weaknesses within the system and generation of food waste due to rapidly changing consumption patterns. In times of crisis, it is especially important to conserve resources and allocate existing resources to areas where they can be of most use, but this poses significant challenges. This study evaluated the potential of a forecasting model to predict guest attendance during the start and throughout the pandemic. This was done by collecting data on guest attendance in Swedish school and preschool catering establishments before and during the pandemic, and using a machine learning approach to predict future guest attendance based on historical data. Comparison of various learning methods revealed that random forest produced more accurate forecasts than a simple artificial neural network, with conditional mean absolute prediction error of 0.15 for the trained dataset. Economic savings were obtained by forecasting compared with a no-plan scenario, supporting selection of the random forest approach for effective forecasting of meal planning. Overall, the results obtained using forecasting models for meal planning in times of crisis confirmed their usefulness. Continuous use can improve estimates for the test period, due to the agile and flexible nature of these models. This is particularly important when guest attendance is unpredictable, so that production planning can be optimized to reduce food waste and contribute to a more sustainable and resilient food system.

Blockchain technologies provide means to develop services that are secure, transparent and efficient by nature. Unsurprisingly, the emerging business opportunities has gained a lot of interest that is realized in form of successful Initial Coin Offerings (ICOs) that are able to raise billions of USD through crowdfunding campaign. In this exploratory research we study 91 ICOs through content analysis in order to investigate the special characteristics of ICO crowdfunding as business models towards the possible investors. We found that ICOs can be described through (1) the model for providing incentives for investment, (2) the pricing strategy, (3) the token strategy and (4) the activities for crowd involvement in value co-creation.

Part 3: Gaming for Cybersecurity Training; International audience; Agile software development is practiced in most software development projects around the world. To explicitly consider and include security requirements as part of agile software development is referred to as ‘secure agile’. To include security will naturally require additional time and effort, with potentially reduced agility as a consequence. To maintain agility, it is important to have efficient methods to include security in the development process. In this study, we describe enhancements to Threat Poker, which is a game designed for the software development team to deal with security threats identified during the agile development project. Games can be valuable educational tools for actively engaging students and practitioners alike. An experiment with students indicates that playing Threat Poker increases security awareness and that it is a fun and simple way to discuss identified security threats and how to remove security vulnerabilities during the software development process.

Many companies have followed the trend toward exposing their business assets through open (i.e., Web) application programming interfaces (APIs). However, these firms appear to have adopted API technology largely to meet their customers’ needs and demands. The pressures on industries to develop, implement, and maintain API products and services can prevent companies from gaining a greater awareness of API development’s benefits. Firms may thus miss out on related monetary or non-monetary exploitation of their business assets. This study explored the status of the API economy and development among Finnish industries. The dataset comprised publicly available information from 226 private and public organizations representing a variety of industries, such as industrial, consumer goods, and services sectors. The current status of API readiness, types, protocols, and monetization models is presented to provide a more comprehensive overview.

PurposeCloud manufacturing (CMfg) is a networked manufacturing mode that promotes the agile, service-oriented, green and intelligent development of the manufacturing industry. Although some scholars have reviewed related studies of CMfg from multiple perspectives, these reviews are not fully systematic or well justified and fail to fully reveal the key characteristics in the development process of CMfg. The purpose of this paper is to systematically review the relevant research on CMfg via identification of key characteristics of definition, architecture, supporting technology and application of CMfg to provide critical information in decision support for the innovation and development of CMfg.Design/methodology/approachThis study systematically reviews the relevant research on CMfg across theoretical methods to technical applications by integrating quantitative and qualitative methods. Word cloud method is used to quantitatively analyse the structure and feature of different definitions of CMfg. The principle of System Science is used to explore the basic components and functions of various CMfg architectures and their common and differing characteristics. A multi-level technology framework is developed to explore the development status of CMfg supporting technologies. A multi-stage application classification is proposed to reveal the application status of CMfg.FindingsThrough literature review, this study found that CMfg architecture is currently dominated by general architectures and lacks architectures that fit the actual enterprise characteristics; CMfg supporting technology is mature in the traditional cloud computing-based technology, but it is still weak in the development of virtualization and servitization technology, service scheduling technology; CMfg application is still in the initial stage and still lacks a relatively complete system application. By analysing the development status of CMfg, this study also identified potential research directions of CMfg in information management, service composition and evaluation, system application and sustainable development and other aspects.Research limitations/implicationsThis paper predominantly focuses on journal articles and some key conference papers published in English and Chinese. Chinese articles account for more than half of the total. The reason is that CMfg was proposed by the Chinese and CMfg is suitable for the development of China's manufacturing industry because of China's intelligent manufacturing environment. It is believed that this research has reached a reliable comprehensiveness that can help scholars and practitioners establish new research directions and evaluate their work in CMfg.Originality/valuePrior literature reviews ignore the identification and analysis of key feature identification for the current development of CMfg, including common and unique feature identification of different CMfg architectures and functions, multi-layer analysis and interpretation of CMfg technology and different stage analysis of CMfg applications. This study addresses these limitations and provides a comprehensive literature review.

Online learning is one of the most practical and promising learning alternatives nowadays. Flexible time and low cost of online learning are advantages that offline learning does not have. In addition, in online learning, each student can enrol the material according to their level, interest and abilities. This study aims to build an online learning application for programming language. The method used in this study was Scrum software development. Scrum is one of the Agile method in software development. The system design using use case diagram resulted 3 actors and 8 use cases. The product backlog resulted in this study was five backlogs. The sprint composed based on product backlog result 4 sprints in total 120 days’ work. All five product backlogs were built successfully using Scrum. This system can be an alternative in learning programming language that offers the flexibility to the student, based on their ability and time.

Domain-specific architectures have emerged as a promising solution to meet growing technology demands but with this comes an urgent need to improve hardware methodologies which often have long design cycles, rely on closed source and expensive tools, and have high nonrecurring engineering costs. In this article, we describe how our work developing PyRTL, an open source Python-based Hardware Development Toolkit, has proven to be a powerful agile hardware development and analysis tool with the features to improve current methodologies. We describe how this toolkit-driven approach encourages hardware reuse using modern object-oriented programming features and present an examination of its custom intermediate representation for hardware debugging, analysis, and instrumentation. This approach has proven useful in supporting fast design iteration in a variety of domains including cryptography and machine learning.

Demanded by ever-evolving data processing algorithms, field-programmable gate arrays (FPGAs) have become essential components of modern computing systems, thanks to their reconfigurable and distributed computing capabilities. However, FPGAs are among the very few integrated chips that still require long development cycles and high human efforts, even for industrial vendors. In this article, we introduce OpenFPGA, an open-source framework that can automate and significantly accelerate the development cycle of customizable FPGA architectures. OpenFPGA allows users to customize their FPGA architectures down to circuit-level details using a high-level architecture description language and autogenerate associated Verilog netlists which can be used in a backend flow to generate production-ready layouts. A generic Verilog-to-Bitstream generator is also provided, allowing end-users to implement practical applications on any FPGAs that OpenFPGA can support. Using OpenFPGA, we demonstrate less than 24-h layout generation of two FPGA fabrics, which are based on a Stratix-like architecture built with a commercial 12-nm standard cell library and 40-nm custom cells, respectively.

For five years, OpenPiton has provided hardware designs, build and verification scripts, and other infrastructure to enable efficient, detailed research into manycores and systems-on-chip. It enables open-source hardware development through its open design and support of a plethora of open simulators and CAD tools. OpenPiton was first designed to perform cutting-edge computer architecture research at Princeton University and opening it up to the public has led to thousands of downloads and numerous academic publications spanning many subfields within computing. In this article, we share some of the lessons learned during the development of OpenPiton, provide examples of how OpenPiton has been used to efficiently test novel research ideas, and discuss how OpenPiton has evolved due to its open development and feedback from the open-source community.

O Neuroscience Experiments System (NES) é um sistema desenvolvido para gerenciar informações de experimentos de neurociência. O módulo de exportação do NES permite que o pesquisador obtenha os dados e metadados de seus experimentos em formatos interoperáveis, porém a compreensão desses dados nem sempre é simples. Seguindo os conceitos da metodologia ágil, adotamos a filosofia e as especificações técnicas do Frictionless Data com o intuito de reduzir o atrito frequentemente encontrado ao trabalhar com dados. A adoção do Frictionless Data proporcionou uma melhor compreensão dos dados pelos pesquisadores, além de permitir a criação de scripts e programas para realizar a análise dos dados coletados.