During the Software Development Lifecycle (SDLC), the first stage entails the Requirement engineering phase. In this phase, engineers gather, analyze, and specify the requirements for a software system. Requirements p...
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(纸本)9798350351507
During the Software Development Lifecycle (SDLC), the first stage entails the Requirement engineering phase. In this phase, engineers gather, analyze, and specify the requirements for a software system. Requirements playa crucial role in the SDLC as they establish the foundation for the entire system by defining the expected behaviors of the software system to be built. The resulting specifications are captured in a Software Requirement Specification (SRS) document. As part of the validation process, requirement specifications are traced. Requirement tracing involves linking the requirement to the artifacts where the customer requested the high-level requirement. Teaching proper requirements tracing can be challenging in a traditional classroom setting. It is essential to educate future software engineers on the proper process of developing an SRS document and of tracing requirements back to the originating artifact, which is also challenging due to the complexity and large scope of applying the complete requirements engineering process. Understanding how changes in customer needs can impact requirements is an imperative learning opportunity. In this work, we aim to incorporate the use of AI in the teaching of requirements tracing using Large Language Models. In this experiment, both GPT -3.5 and GPT -4 are provided the transcript of an interview between the customer and the engineering team, as well as the subsequent requirements elicited from that meeting and other customer provided artifacts. The GPTs are then instructed to determine which requirements can be traced back to the interview transcript. At the same time, the students (the requirements engineering team) conduct their own effort to trace requirements back to the original interview. The experiment was taken one step further to assess students' and the GPTs abilities to address requirements modifications. After another interview with the customer, where some needs were changed, some requirements were mod
Body fitness monitoring applications are using mobile sensors to identify human activities. Human activity identification is a challenging task because of the wide availability of human activities. This paper proposes...
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Moving object segmentation is an important and challenging task in the field of autonomous driving. This paper presents a novel and effective method that combines deep learning and geometric constraints for moving obj...
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Language models excel in linguistic processing but often face challenges with complex reasoning tasks that require real-world interaction and multi-step logic. This paper presents the Cognitive Adaptive Reasoning Arch...
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Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivo...
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Spectral compressive imaging has emerged as a powerful technique to collect the 3D spectral information as 2D *** algorithm for restoring the original 3D hyperspectral images(HSIs)from compressive measurements is pivotal in the imaging *** approaches painstakingly designed networks to directly map compressive measurements to HSIs,resulting in the lack of interpretability without exploiting the imaging *** some recent works have introduced the deep unfolding framework for explainable reconstruction,the performance of these methods is still limited by the weak information transmission between iterative *** this paper,we propose a Memory-Augmented deep Unfolding Network,termed MAUN,for explainable and accurate HSI ***,MAUN implements a novel CNN scheme to facilitate a better extrapolation step of the fast iterative shrinkage-thresholding algorithm,introducing an extra momentum incorporation step for each iteration to alleviate the information ***,to exploit the high correlation of intermediate images from neighboring iterations,we customize a cross-stage transformer(CSFormer)as the deep denoiser to simultaneously capture self-similarity from both in-stage and cross-stage features,which is the first attempt to model the long-distance dependencies between iteration *** experiments demonstrate that the proposed MAUN is superior to other state-of-the-art methods both visually and *** code is publicly available at https://***/HuQ1an/MAUN.
Mental disorders have become a major disease observed in people with a contemporary lifestyle. Similar to many physical diseases, prevention and earlier detection are critical for mental health. This paper proposes a ...
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The reliability and performance of Electric vehicles (EVs) rely on effective control of electric drives, thereby improving the overall driving experience. To efficiently control the electric drive, this paper implemen...
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Large Language Models (LLMs) are extensive aggregations of human language, designed to understand and generate sophisticated text. LLMs are becoming ubiquitous in a range of applications, from social media to code gen...
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The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it *** decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of ...
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The effectiveness of the Business Intelligence(BI)system mainly depends on the quality of knowledge it *** decision-making process is hindered,and the user’s trust is lost,if the knowledge offered is undesired or of poor quality.A Data Warehouse(DW)is a huge collection of data gathered from many sources and an important part of any BI solution to assist management in making better *** Extract,Transform,and Load(ETL)process is the backbone of a DW system,and it is responsible for moving data from source systems into the DW *** more mature the ETL process the more reliable the DW *** this paper,we propose the ETL Maturity Model(EMM)that assists organizations in achieving a high-quality ETL system and thereby enhancing the quality of knowledge *** EMM is made up of five levels of maturity i.e.,Chaotic,Acceptable,Stable,Efficient and *** level of maturity contains Key Process Areas(KPAs)that have been endorsed by industry experts and include all critical features of a good ETL *** Objectives(QOs)are defined procedures that,when implemented,resulted in a high-quality ETL *** KPA has its own set of QOs,the execution of which meets the requirements of that *** brainstorming sessions with relevant industry experts helped to enhance the *** deployed in two key projects utilizing multiple case studies to supplement the validation process and support our *** model can assist organizations in improving their current ETL process and transforming it into a more mature ETL *** model can also provide high-quality information to assist users inmaking better decisions and gaining their trust.
As the range of COVID-19 sufferers increased, many nations imposed a complete lockdown. As a result, it caused a devastating international financial disaster everywhere in the world. Technical and essential evaluation...
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