As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering ...As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering community.Previous literature studies have proposed numerousmodels for the classification of security requirements.However,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning algorithms.Moreover,most of the researchers focus only on the classification of requirements with security keywords.They did not consider other nonfunctional requirements(NFR)directly or indirectly related to security.This has been identified as a significant research gap in security requirements engineering.The major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security requirements.We use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering community.The proposed methodology consists of two steps.In the first step,we analyze all the nonfunctional requirements and their relation with security requirements.We found 10 NFRs that have a strong relationship with security requirements.In the second step,we categorize those NFRs in the security requirements category.Our proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)models.Moreover,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security categories.The performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,respectively.The proposed study shows an enhancement in metrics values compared to the previous literature studies.This is a proof of concept for systematizing the evaluation of security recognition in software systems during the early phases of software development.展开更多
China's perfume market has been experiencing significant growth.The “2024 China Perfume and Fragrance White Paper”,jointly published by Eternal Group,DSM-Firmenich,and Ipsos in September 2024,reveals that the ma...China's perfume market has been experiencing significant growth.The “2024 China Perfume and Fragrance White Paper”,jointly published by Eternal Group,DSM-Firmenich,and Ipsos in September 2024,reveals that the market surged from 11.4 billion yuan in 2018 to 22.9 billion yuan in 2023,achieving a compound annual growth rate(CAGR) of 15%.With projections estimating a rise to 44 billion yuan by 2028 at a 14% CAGR,the sector's long-term growth prospects remain strong.展开更多
The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education....The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.展开更多
Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communi...Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communication requirements and challenges encountered in Meituan UAV’s daily oper-ations,this article first introduces the operational sce-narios within current drone logistics networks and an-alyzes the related communication requirements.Then,the current communication solution and its inherent bottlenecks are elaborated.Finally,this paper explores emerging technologies and examines their application prospects in drone logistics networks.展开更多
Underhand cut-and-fill mining has been widely used in underground mining operations,especially when the rock mass or orebody is of poor quality or prone to rockburst due to high stress.In such cases,mining workers sho...Underhand cut-and-fill mining has been widely used in underground mining operations,especially when the rock mass or orebody is of poor quality or prone to rockburst due to high stress.In such cases,mining workers should carry out all production activities under the cemented backfill roof or sill mat instead of a highly fractured and unstable rock roof or a strong rock roof with a high potential of rockburst.Therefore,the stability and required strength of the sill mat are critical issues for mining engineers.In 1991,Mitchell considered that sill mat could fail by caving,sliding,rotation,and flexure.Mitchell also proposed an analytical solution to determine the minimum required strength of the sill mat for each type of failure based on two stiff or immobile rock walls.However,recent publications using numerical modeling and field measurements indicate that the compressive stresses in the sill mat induced by rock wall closure due to a stope excavation beneath the sill mat can be significant.It is thus highly necessary to investigate the required strength of the sill mat by considering rock wall closure.In this study,the crushing failure of sill mat due to rock wall closure generated by underground excavation and a new failure mode called"crushing and caving”is revealed by numerical modeling.An analytical solution corresponding to each failure mode is then developed to estimate the minimum required cohesion(cmin)of the sill mat.A criterion is also proposed to determine if the sill mat fails by crushing or crushing-and-caving failure.The proposed analytical solution does not involve any correction coefficients.The validity of the proposed analytical solution is demonstrated by numerical modeling.The proposed analytical solution can thus be employed to predict the cmin of sill mat subjected to wall closure generated by underlying stope excavation.展开更多
As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to...As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to difficulties in achieving flexible coverage.In this paper,we introduce transmissive reconfigurable intelligent surfaces(RISs)as intelligent passive auxiliary devices into indoor scenes,replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs.Specifically,we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multiuser downlink communication systems.This involves considering more realistic indoor congestion modeling and near-field propagation characteristics.The goal of our optimization is to minimize the total transmit power at the access point(AP)for different user service requirements,including quality-of-service(QoS)and wireless power transfer(WPT).Due to the nonconvex nature of the optimization problem,adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming.Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios.Furthermore,the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.展开更多
Artificial Intelligence,in general,and particularly Natural language Processing(NLP)has made unprecedented progress recently in many areas of life,automating and enabling a lot of activities such as speech recognition...Artificial Intelligence,in general,and particularly Natural language Processing(NLP)has made unprecedented progress recently in many areas of life,automating and enabling a lot of activities such as speech recognition,language translations,search engines,and text-generations,among others.Software engineering and Software Development Life Cycle(SDLC)is also not left out.Indeed,one of the most critical starting points of SDLC is the requirement engineering stage which,traditionally,has been dominated by business analysts.Unfortunately,these analysts have always done the job not just in a monotonous way,but also in an error-prone,tedious,and inefficient manner,thus leading to poorly crafted works with lots of requirement creep and sometimes technical debts.This work,which is the first iteration in a series,looks at how this crucial initial stage could not just be automated but also improved using the latest techniques in Artificial Intelligence and NLP.Using the popular and available PROMISE dataset,the emphasis,for this first part,is on improving requirement engineering,particularly the classification of Functional and Non-functional Requirements.Transformer-powered BERT(Bidirectional Encoder Representations from Transformers)Large Language Model(LLM)was adopted with validation performances of 0.93,0.88,and 0.88.The experimental results showed that Base-BERT LLM,its distilled counterpart,Distil-BERT,and its domain-specific version,Code-BERT,can be reliable in these tasks.We believe that our findings could encourage the adoption of LLM,such as BERT,in Requirement Engineering(RE)-related tasks like the FR/NFR classification.This kind of insight can help RE researchers as well as industry practitioners in their future work.展开更多
The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human re...The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.展开更多
The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure costs.Rece...The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure costs.Recently,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs.By disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network automation.However,this openness introduces new security challenges compared to traditional RANs.Many existing studies overlook these security requirements of the O-RAN networks.To gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications.We then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities effectively.By providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.展开更多
Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured...Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.展开更多
Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As re...Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.展开更多
In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge grap...In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.展开更多
Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the...Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.展开更多
BACKGROUND Autoimmunity has emerged as a probable disease modifier in patients with clinically diagnosed type 2 diabetes mellitus(T2DM),that is,patients who have insulin resistance,obesity,and other cardiovascular ris...BACKGROUND Autoimmunity has emerged as a probable disease modifier in patients with clinically diagnosed type 2 diabetes mellitus(T2DM),that is,patients who have insulin resistance,obesity,and other cardiovascular risk factors,suggesting that the presence of glutamic acid decarboxylase(anti-GAD65),islet antigen 2(anti-IA2),and zinc transporter 8(anti-Zn8T)antibodies could have deleterious effects on beta cell function,causing failure and earlier requirement for insulin treatment.AIM To evaluate anti-GAD65,anti-IA2 and anti-Zn8T as predictors of early insulin requirement in adolescents with a clinical diagnosis of T2DM.METHODS This was a case–control study in patients with clinically diagnosed with T2DM(68 cases and 64 controls with and without early insulin dependence respectively),male and female,aged 12–18 years.Somatometry,blood pressure,glucose,insulin,C-peptide,glycated hemoglobin A1c,and lipid profiles were assessed.ELISA was used to measure anti-GAD65,anti-IA2,and anti-Zn8T antibodies.Descriptive statistics,Pearson'sχ2 test,Student's t test,and logistic regression was performed.P<0.05 was considered statistically significant.RESULTS There were 132 patients(53.8%female),with a mean age was 15.9±1.3 years,and there was a disease evolution time of 4.49±0.88 years.The presence of anti-GAD65,anti-IA2,and anti-Zn8T positivity was found in 29.5%,18.2%,and 15.9%,respectively.Dividing the groups by early or no insulin dependence showed that the group with insulin had a higher frequency of antibody positivity:anti-GAD65 odds ratio(OR):2.42(1.112–5.303,P=0.026);anti-IA2:OR:1.55(0.859–2.818,P=0.105);and anti-Zn8T:OR:7.32(2.039–26.279,P=0.002).CONCLUSION Anti-GAD65 positivity was high in our study.Anti-GAD65 and anti-Zn8T positivity showed a significantly depleted beta cell reserve phenotype,leading to an increased risk of early insulin dependence.展开更多
The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challen...The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.展开更多
Carp is a temperate freshwater fish native to Asia,distributed in all regions of the world except Australia and South America.With the improvement of comprehensive and healthy breeding technology of carp,the unit yiel...Carp is a temperate freshwater fish native to Asia,distributed in all regions of the world except Australia and South America.With the improvement of comprehensive and healthy breeding technology of carp,the unit yield has been greatly increased mainly due to the exten-sive use of compound feed.In this study,the nutritional requirements of carp were summarized from the aspects of protein,amino acids,fat,carbohydrate,calcium and phosphorus,vitamins and taurine.This study provides a certain theoretical reference for scientific formula of carp feed.展开更多
The purpose of this study is to coordinate the alignment between the nursing curriculum and hospital clinical competencies,identify the reasons for the gaps,evaluate the impact of these gaps on the nursing profession,...The purpose of this study is to coordinate the alignment between the nursing curriculum and hospital clinical competencies,identify the reasons for the gaps,evaluate the impact of these gaps on the nursing profession,and propose strategies to bridge these gaps.This study will help strengthen nursing education,improve nursing students’skills,and help students adapt to complex clinical environments.展开更多
To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred ...To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.展开更多
While wormholes are just as good a prediction of Einstein’s theory as black holes, they are subject to severe restrictions from quantum field theory. To allow for the possibility of interstellar travel, a macroscopic...While wormholes are just as good a prediction of Einstein’s theory as black holes, they are subject to severe restrictions from quantum field theory. To allow for the possibility of interstellar travel, a macroscopic wormhole would need to maintain sufficiently low radial tidal forces. It is proposed in this paper that the assumption of zero tidal forces, i.e., the limiting case, is sufficient for overcoming the restrictions from quantum field theory. The feasibility of this approach is subsequently discussed by 1) introducing the additional conditions needed to ensure that the radial tidal forces can indeed be sufficiently low and 2) by viewing traversable wormholes as emergent phenomena, thereby increasing the likelihood of their existence.展开更多
Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing capacity.Despite the popularity of ML tech...Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing capacity.Despite the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering(RE)activities to solve the problems that occur in RE activities.The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023.The authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this period.Forty-five research studies were selected based on our exclusion and inclusion criteria.The results show that the scientific community used 57 algorithms.Among those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random Forest.The results show that researchers used these algorithms in eight major RE activities.Those activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural language.Our selected research studies used 32 private and 41 public data sources.The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software Engineering,and iTrust Electronic Health Care System.展开更多
基金The authors of this study extend their appreciation to the Researchers Supporting Project number(RSPD2025R544),King Saud University,Riyadh,Saudia Arabia.
文摘As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering community.Previous literature studies have proposed numerousmodels for the classification of security requirements.However,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning algorithms.Moreover,most of the researchers focus only on the classification of requirements with security keywords.They did not consider other nonfunctional requirements(NFR)directly or indirectly related to security.This has been identified as a significant research gap in security requirements engineering.The major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security requirements.We use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering community.The proposed methodology consists of two steps.In the first step,we analyze all the nonfunctional requirements and their relation with security requirements.We found 10 NFRs that have a strong relationship with security requirements.In the second step,we categorize those NFRs in the security requirements category.Our proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)models.Moreover,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security categories.The performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,respectively.The proposed study shows an enhancement in metrics values compared to the previous literature studies.This is a proof of concept for systematizing the evaluation of security recognition in software systems during the early phases of software development.
文摘China's perfume market has been experiencing significant growth.The “2024 China Perfume and Fragrance White Paper”,jointly published by Eternal Group,DSM-Firmenich,and Ipsos in September 2024,reveals that the market surged from 11.4 billion yuan in 2018 to 22.9 billion yuan in 2023,achieving a compound annual growth rate(CAGR) of 15%.With projections estimating a rise to 44 billion yuan by 2028 at a 14% CAGR,the sector's long-term growth prospects remain strong.
基金supported in part by the Teaching Reform Project of Chongqing University of Posts and Telecommunications,China under Grant No.XJG23234Chongqing Municipal Higher Education Teaching Reform Research Project under Grant No.203399the Doctoral Direct Train Project of Chongqing Science and Technology Bureau under Grant No.CSTB2022BSXM-JSX0007。
文摘The advent of large language models(LLMs)has made knowledge acquisition and content creation increasingly easier and cheaper,which in turn redefines learning and urges transformation in software engineering education.To do so,there is a need to understand the impact of LLMs on software engineering education.In this paper,we conducted a preliminary case study on three software requirements engineering classes where students are allowed to use LLMs to assist in their projects.Based on the students’experience,performance,and feedback from a survey conducted at the end of the courses,we characterized the challenges and benefits of applying LLMs in software engineering education.This research contributes to the ongoing discourse on the integration of LLMs in education,emphasizing both their prominent potential and the need for balanced,mindful usage.
基金supported by Shenzhen Science and Technology Program(KJZD20230923115210021)。
文摘Communications system has a signifi-cant impact on both operational safety and logisti-cal efficiency within low-altitude drone logistics net-works.Aiming at providing a systematic investiga-tion of real-world communication requirements and challenges encountered in Meituan UAV’s daily oper-ations,this article first introduces the operational sce-narios within current drone logistics networks and an-alyzes the related communication requirements.Then,the current communication solution and its inherent bottlenecks are elaborated.Finally,this paper explores emerging technologies and examines their application prospects in drone logistics networks.
基金financial support from the Young Scientist Project of the National Key Research and Development Program of China(Grant No.2021YFC2900600)Beijing Nova Program(Grant No.20220484057)+1 种基金The authors acknowledge the financial support from the Natural Sciences and Engineering Research Council of Canada(Grant No.RGPIN-2018-06902)industrial partners of the Research Institute on Mines and the Environment(RIME UQAT-Polytechnique:https://irme.ca/en/).
文摘Underhand cut-and-fill mining has been widely used in underground mining operations,especially when the rock mass or orebody is of poor quality or prone to rockburst due to high stress.In such cases,mining workers should carry out all production activities under the cemented backfill roof or sill mat instead of a highly fractured and unstable rock roof or a strong rock roof with a high potential of rockburst.Therefore,the stability and required strength of the sill mat are critical issues for mining engineers.In 1991,Mitchell considered that sill mat could fail by caving,sliding,rotation,and flexure.Mitchell also proposed an analytical solution to determine the minimum required strength of the sill mat for each type of failure based on two stiff or immobile rock walls.However,recent publications using numerical modeling and field measurements indicate that the compressive stresses in the sill mat induced by rock wall closure due to a stope excavation beneath the sill mat can be significant.It is thus highly necessary to investigate the required strength of the sill mat by considering rock wall closure.In this study,the crushing failure of sill mat due to rock wall closure generated by underground excavation and a new failure mode called"crushing and caving”is revealed by numerical modeling.An analytical solution corresponding to each failure mode is then developed to estimate the minimum required cohesion(cmin)of the sill mat.A criterion is also proposed to determine if the sill mat fails by crushing or crushing-and-caving failure.The proposed analytical solution does not involve any correction coefficients.The validity of the proposed analytical solution is demonstrated by numerical modeling.The proposed analytical solution can thus be employed to predict the cmin of sill mat subjected to wall closure generated by underlying stope excavation.
基金supported in part by the Natural Science Basic Research Plan in Shaanxi Province under Grant 2024JC-ZDXM-36in part by the Key Research and Development Program of Shaanxi Province under Grant 2023-YBGY-255+2 种基金in part by the Excellent Youth Science Foundation of Xi’an University of Science and Technology under Grant 2019YQ3-13in part by the Xi’an Key Laboratory of Network Convergence Communications under Grant 2022NCC-K102in part by the Fundamental Research Funds for the Central Universities under Grant QTZX23029。
文摘As emerging services continue to be explored,indoor communications geared towards different user requirements will face severe challenges such as larger penetration losses and more critical multipath issues,leading to difficulties in achieving flexible coverage.In this paper,we introduce transmissive reconfigurable intelligent surfaces(RISs)as intelligent passive auxiliary devices into indoor scenes,replacing conventional ultra-dense small cell and relay forwarding approaches to address these issues at low deployment and operation costs.Specifically,we study the optimization design of active and passive beamforming for the transmissive RISs-aided indoor multiuser downlink communication systems.This involves considering more realistic indoor congestion modeling and near-field propagation characteristics.The goal of our optimization is to minimize the total transmit power at the access point(AP)for different user service requirements,including quality-of-service(QoS)and wireless power transfer(WPT).Due to the nonconvex nature of the optimization problem,adaptive penalty coefficients are imported to solve it alternatively with closed-form solutions for both active and passive beamforming.Simulation results demonstrate that the use of transmissive RISs is indeed an efficient way to achieve flexible coverage in indoor scenarios.Furthermore,the proposed optimization algorithm has been proven to be effective and robust in achieving energy-saving transmission.
文摘Artificial Intelligence,in general,and particularly Natural language Processing(NLP)has made unprecedented progress recently in many areas of life,automating and enabling a lot of activities such as speech recognition,language translations,search engines,and text-generations,among others.Software engineering and Software Development Life Cycle(SDLC)is also not left out.Indeed,one of the most critical starting points of SDLC is the requirement engineering stage which,traditionally,has been dominated by business analysts.Unfortunately,these analysts have always done the job not just in a monotonous way,but also in an error-prone,tedious,and inefficient manner,thus leading to poorly crafted works with lots of requirement creep and sometimes technical debts.This work,which is the first iteration in a series,looks at how this crucial initial stage could not just be automated but also improved using the latest techniques in Artificial Intelligence and NLP.Using the popular and available PROMISE dataset,the emphasis,for this first part,is on improving requirement engineering,particularly the classification of Functional and Non-functional Requirements.Transformer-powered BERT(Bidirectional Encoder Representations from Transformers)Large Language Model(LLM)was adopted with validation performances of 0.93,0.88,and 0.88.The experimental results showed that Base-BERT LLM,its distilled counterpart,Distil-BERT,and its domain-specific version,Code-BERT,can be reliable in these tasks.We believe that our findings could encourage the adoption of LLM,such as BERT,in Requirement Engineering(RE)-related tasks like the FR/NFR classification.This kind of insight can help RE researchers as well as industry practitioners in their future work.
基金This work is supported by EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab,Prince Sultan University,Kingdom of Saudi Arabia,by paying the APC.
文摘The software development process mostly depends on accurately identifying both essential and optional features.Initially,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional requirements.To address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human effort.However,existing techniques often struggle with complex instructions and large-scale projects.In our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer trees.Experimental results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT dataset.Both datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse scenarios.These findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.
基金supported by the Research Program funded by the SeoulTech(Seoul National University of Science and Technology).
文摘The emergence of various technologies such as terahertz communications,Reconfigurable Intelligent Surfaces(RIS),and AI-powered communication services will burden network operators with rising infrastructure costs.Recently,the Open Radio Access Network(O-RAN)has been introduced as a solution for growing financial and operational burdens in Beyond 5G(B5G)and 6G networks.O-RAN promotes openness and intelligence to overcome the limitations of traditional RANs.By disaggregating conventional Base Band Units(BBUs)into O-RAN Distributed Units(O-DU)and O-RAN Centralized Units(O-CU),O-RAN offers greater flexibility for upgrades and network automation.However,this openness introduces new security challenges compared to traditional RANs.Many existing studies overlook these security requirements of the O-RAN networks.To gain deeper insights into the O-RAN system and security,this paper first provides an overview of the general O-RAN architecture and its diverse use cases relevant to B5G and 6G applications.We then delve into specifications of O-RAN security threats and requirements,aiming to mitigate security vulnerabilities effectively.By providing a comprehensive understanding of O-RAN architecture,use cases,and security considerations,thisworkserves as a valuable resource for future research in O-RAN and its security.
基金supported by the National Natural Science Foundation of China(71690233,71901214)。
文摘Based on the characteristics of high-end products,crowd-sourcing user stories can be seen as an effective means of gathering requirements,involving a large user base and generating a substantial amount of unstructured feedback.The key challenge lies in transforming abstract user needs into specific ones,requiring integration and analysis.Therefore,we propose a topic mining-based approach to categorize,summarize,and rank product requirements from user stories.Specifically,after determining the number of story categories based on py LDAvis,we initially classify“I want to”phrases within user stories.Subsequently,classic topic models are applied to each category to generate their names,defining each post-classification user story category as a requirement.Furthermore,a weighted ranking function is devised to calculate the importance of each requirement.Finally,we validate the effectiveness and feasibility of the proposed method using 2966 crowd-sourced user stories related to smart home systems.
文摘Software testing is a critical phase due to misconceptions about ambiguities in the requirements during specification,which affect the testing process.Therefore,it is difficult to identify all faults in software.As requirement changes continuously,it increases the irrelevancy and redundancy during testing.Due to these challenges;fault detection capability decreases and there arises a need to improve the testing process,which is based on changes in requirements specification.In this research,we have developed a model to resolve testing challenges through requirement prioritization and prediction in an agile-based environment.The research objective is to identify the most relevant and meaningful requirements through semantic analysis for correct change analysis.Then compute the similarity of requirements through case-based reasoning,which predicted the requirements for reuse and restricted to error-based requirements.Afterward,the apriori algorithm mapped out requirement frequency to select relevant test cases based on frequently reused or not reused test cases to increase the fault detection rate.Furthermore,the proposed model was evaluated by conducting experiments.The results showed that requirement redundancy and irrelevancy improved due to semantic analysis,which correctly predicted the requirements,increasing the fault detection rate and resulting in high user satisfaction.The predicted requirements are mapped into test cases,increasing the fault detection rate after changes to achieve higher user satisfaction.Therefore,the model improves the redundancy and irrelevancy of requirements by more than 90%compared to other clustering methods and the analytical hierarchical process,achieving an 80%fault detection rate at an earlier stage.Hence,it provides guidelines for practitioners and researchers in the modern era.In the future,we will provide the working prototype of this model for proof of concept.
基金supported by the National Key Laboratory for Comp lex Systems Simulation Foundation (6142006190301)。
文摘In the context of big data, many large-scale knowledge graphs have emerged to effectively organize the explosive growth of web data on the Internet. To select suitable knowledge graphs for use from many knowledge graphs, quality assessment is particularly important. As an important thing of quality assessment, completeness assessment generally refers to the ratio of the current data volume to the total data volume.When evaluating the completeness of a knowledge graph, it is often necessary to refine the completeness dimension by setting different completeness metrics to produce more complete and understandable evaluation results for the knowledge graph.However, lack of awareness of requirements is the most problematic quality issue. In the actual evaluation process, the existing completeness metrics need to consider the actual application. Therefore, to accurately recommend suitable knowledge graphs to many users, it is particularly important to develop relevant measurement metrics and formulate measurement schemes for completeness. In this paper, we will first clarify the concept of completeness, establish each metric of completeness, and finally design a measurement proposal for the completeness of knowledge graphs.
基金supported in part by the National Natural Science Foundation of China (62072248, 62072247)the Jiangsu Agriculture Science and Technology Innovation Fund (CX(21)3060)。
文摘Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security issues.These issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.
基金Supported by Mexican Federal Funds HIM,No.2018/068 SSA152.
文摘BACKGROUND Autoimmunity has emerged as a probable disease modifier in patients with clinically diagnosed type 2 diabetes mellitus(T2DM),that is,patients who have insulin resistance,obesity,and other cardiovascular risk factors,suggesting that the presence of glutamic acid decarboxylase(anti-GAD65),islet antigen 2(anti-IA2),and zinc transporter 8(anti-Zn8T)antibodies could have deleterious effects on beta cell function,causing failure and earlier requirement for insulin treatment.AIM To evaluate anti-GAD65,anti-IA2 and anti-Zn8T as predictors of early insulin requirement in adolescents with a clinical diagnosis of T2DM.METHODS This was a case–control study in patients with clinically diagnosed with T2DM(68 cases and 64 controls with and without early insulin dependence respectively),male and female,aged 12–18 years.Somatometry,blood pressure,glucose,insulin,C-peptide,glycated hemoglobin A1c,and lipid profiles were assessed.ELISA was used to measure anti-GAD65,anti-IA2,and anti-Zn8T antibodies.Descriptive statistics,Pearson'sχ2 test,Student's t test,and logistic regression was performed.P<0.05 was considered statistically significant.RESULTS There were 132 patients(53.8%female),with a mean age was 15.9±1.3 years,and there was a disease evolution time of 4.49±0.88 years.The presence of anti-GAD65,anti-IA2,and anti-Zn8T positivity was found in 29.5%,18.2%,and 15.9%,respectively.Dividing the groups by early or no insulin dependence showed that the group with insulin had a higher frequency of antibody positivity:anti-GAD65 odds ratio(OR):2.42(1.112–5.303,P=0.026);anti-IA2:OR:1.55(0.859–2.818,P=0.105);and anti-Zn8T:OR:7.32(2.039–26.279,P=0.002).CONCLUSION Anti-GAD65 positivity was high in our study.Anti-GAD65 and anti-Zn8T positivity showed a significantly depleted beta cell reserve phenotype,leading to an increased risk of early insulin dependence.
文摘The weapon and equipment operational requirement analysis(WEORA) is a necessary condition to win a future war,among which the acquisition of knowledge about weapons and equipment is a great challenge. The main challenge is that the existing weapons and equipment data fails to carry out structured knowledge representation, and knowledge navigation based on natural language cannot efficiently support the WEORA. To solve above problem, this research proposes a method based on question answering(QA) of weapons and equipment knowledge graph(WEKG) to construct and navigate the knowledge related to weapons and equipment in the WEORA. This method firstly constructs the WEKG, and builds a neutral network-based QA system over the WEKG by means of semantic parsing for knowledge navigation. Finally, the method is evaluated and a chatbot on the QA system is developed for the WEORA. Our proposed method has good performance in the accuracy and efficiency of searching target knowledge, and can well assist the WEORA.
文摘Carp is a temperate freshwater fish native to Asia,distributed in all regions of the world except Australia and South America.With the improvement of comprehensive and healthy breeding technology of carp,the unit yield has been greatly increased mainly due to the exten-sive use of compound feed.In this study,the nutritional requirements of carp were summarized from the aspects of protein,amino acids,fat,carbohydrate,calcium and phosphorus,vitamins and taurine.This study provides a certain theoretical reference for scientific formula of carp feed.
文摘The purpose of this study is to coordinate the alignment between the nursing curriculum and hospital clinical competencies,identify the reasons for the gaps,evaluate the impact of these gaps on the nursing profession,and propose strategies to bridge these gaps.This study will help strengthen nursing education,improve nursing students’skills,and help students adapt to complex clinical environments.
文摘To effectively evaluate the fuzziness of the market environment in product planning,a customer requirements analysis method based on multiple preference information is proposed.Firstly,decision-makers use a preferred information form to evaluate the importance of each customer requirement.Secondly,a transfer function is employed to unify various forms of preference information into a fuzzy complementary judgment matrix.The ranking vector is then calculated using row and normalization methods,and the initial importance of customer requirements is obtained by aggregating the weights of decision members.Finally,the correction coefficients of initial importance and each demand are synthesized,and the importance of customer requirements is determined through normalization.The development example of the PE jaw crusher demonstrates the effectiveness and feasibility of the proposed method.
文摘While wormholes are just as good a prediction of Einstein’s theory as black holes, they are subject to severe restrictions from quantum field theory. To allow for the possibility of interstellar travel, a macroscopic wormhole would need to maintain sufficiently low radial tidal forces. It is proposed in this paper that the assumption of zero tidal forces, i.e., the limiting case, is sufficient for overcoming the restrictions from quantum field theory. The feasibility of this approach is subsequently discussed by 1) introducing the additional conditions needed to ensure that the radial tidal forces can indeed be sufficiently low and 2) by viewing traversable wormholes as emergent phenomena, thereby increasing the likelihood of their existence.
基金Research Center of the College of Computer and Information Sciences,King Saud University,Grant/Award Number:RSPD2024R947King Saud University。
文摘Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing capacity.Despite the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement Engineering(RE)activities to solve the problems that occur in RE activities.The authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–2023.The authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this period.Forty-five research studies were selected based on our exclusion and inclusion criteria.The results show that the scientific community used 57 algorithms.Among those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random Forest.The results show that researchers used these algorithms in eight major RE activities.Those activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural language.Our selected research studies used 32 private and 41 public data sources.The most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in Software Engineering,and iTrust Electronic Health Care System.
