Classification and Comprehension of Software Requirements Using Ensemble Learning

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.

An Investigation on Open-RAN Specifications:Use Cases,Security Threats,Requirements,Discussions

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.

Requirements ranking based on crowd-sourcing high-end product USs

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.

Habitat preferences and nest site selection by sympatric populations of Middle Spotted Woodpecker(Dendrocoptes medius) and Syrian Woodpecker(Dendrocopos syriacus) in natural mountain forests of Southwest Iran

We examined habitat preferences and nesting requirements of sympatric populations of Middle Spotted Woodpecker(Dendrocoptes medius) and Syrian Woodpecker(Dendrocopos syriacus).We carried out our study in 2015–2018 in natural mountain forests of Southwest Iran.We compared selected features of nesting,territory,and outside territory tree stands of the studied woodpeckers.The Middle Spotted Woodpecker occupied only oak forests,but the Syrian Woodpecker inhabited heterogenic forests that included the preferred tree of this species,the Mount Atlas Mastic.We recorded that in the breeding territories of the Middle Spotted Woodpecker,a greater area covered by tree crowns,as well as a larger number of trees,and a larger trunk basal area were observed in comparison to the territories occupied by the more plastic Syrian Woodpecker.Different habitat preferences demonstrated by both species could be a result of the selection of tree stands that provide the necessary food resources for each woodpecker species.Adaptation of Syrian Woodpecker to use heterogenic forest stands including tree species that produce fruits and as Mount Atlas Mastic trees,which likely allowed this species to colonise in Asia and Europe non-forest tree stands as orchards or gardens.Our results showed that poor tree condition and large tree trunk dimensions had a positive impact on the selection of nesting sites by both species.The presence of trees with large trunk dimensions was associated with multiple years of use of woodpecker breeding sites in the studied forests.Maintaining habitats in suitable condition for both studied woodpeckers can be achieved by preserving natural forests in the mountain regions of Iran.

Towards Improving the Quality of Requirement and Testing Process in Agile Software Development:An Empirical Study

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.

Peculiarity of transcriptional and H3K27me3 dynamics during peach bud dormancy

Bud dormancy facilitates the survival of meristems under harsh environmental conditions.To elucidate how molecular responses to chilling accumulation controlling dormancy in peach buds,chromatin immunoprecipitation sequencing to identify the H3K27me3 modifications and RNA sequencing of two peach cultivars with pronounced differences in chilling requirement were carried out,the results showed that genes associated with abscisic acid and gibberellic acid signal pathways play key roles in dormancy regulation.The results demonstrated that peach flower bud differentiation occurred continuously in both cultivars during chilling accumulation,which was correlated with the transcript abundance of key genes involved in phytohormone metabolism and flower bud development under adverse conditions.The more increased strength in high chillingrequirement cultivar along with the chilling accumulation at the genome-wide level.The function of the dormancy-associated MADS-box gene PpDAM6 was identified,which is involved in leaf bud break in peach and flower development in transgenic Nicotiana tabacum(NC89).In addition,PpDAM6 was positively regulated by PpCBF,and the genes of putative dormancy-related and associated with metabolic pathways were proposed.Taken together,these results constituted a theoretical basis for elucidating the regulation of peach bud dormancy transition.

How to implement a knowledge graph completeness assessment with the guidance of user requirements

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.

Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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.

Autoantibodies against beta cells to predict early insulin requirements in pediatric patients with clinically diagnosed type 2 diabetes

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.

Operational requirements analysis method based on question answering of WEKG

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.

Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

Malware attacks on Windows machines pose significant cybersecurity threats,necessitating effective detection and prevention mechanisms.Supervised machine learning classifiers have emerged as promising tools for malware detection.However,there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection.Addressing this gap can provide valuable insights for enhancing cybersecurity strategies.While numerous studies have explored malware detection using machine learning techniques,there is a lack of systematic comparison of supervised classifiers for Windows malware detection.Understanding the relative effectiveness of these classifiers can inform the selection of optimal detection methods and improve overall security measures.This study aims to bridge the research gap by conducting a comparative analysis of supervised machine learning classifiers for detecting malware on Windows systems.The objectives include Investigating the performance of various classifiers,such as Gaussian Naïve Bayes,K Nearest Neighbors(KNN),Stochastic Gradient Descent Classifier(SGDC),and Decision Tree,in detecting Windows malware.Evaluating the accuracy,efficiency,and suitability of each classifier for real-world malware detection scenarios.Identifying the strengths and limitations of different classifiers to provide insights for cybersecurity practitioners and researchers.Offering recommendations for selecting the most effective classifier for Windows malware detection based on empirical evidence.The study employs a structured methodology consisting of several phases:exploratory data analysis,data preprocessing,model training,and evaluation.Exploratory data analysis involves understanding the dataset’s characteristics and identifying preprocessing requirements.Data preprocessing includes cleaning,feature encoding,dimensionality reduction,and optimization to prepare the data for training.Model training utilizes various supervised classifiers,and their performance is evaluated using metrics such as accuracy,precision,recall,and F1 score.The study’s outcomes comprise a comparative analysis of supervised machine learning classifiers for Windows malware detection.Results reveal the effectiveness and efficiency of each classifier in detecting different types of malware.Additionally,insights into their strengths and limitations provide practical guidance for enhancing cybersecurity defenses.Overall,this research contributes to advancing malware detection techniques and bolstering the security posture of Windows systems against evolving cyber threats.

Research Progress on Nutritional Requirements of Carp

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.

Omni-Channel Retailing and Firm Performance from a Customer Lifetime Value Perspective

In the digital era,retailers are keen to find out whether omni-channel retailing helps improve long-term firm performance.In this paper,we employ machine learning techniques on a large consumption data set in order to measure customer lifetime value(CLV)as the basis for determining long-term firm performance,and we provide an empirical analysis of the relationship between omni-channel retailing and CLV.The results suggest that omni-channel retailing may effectively enhance CLV.Further analysis reveals that this process is influenced by heterogeneous consumer requirements and that significant differences exist in the extent to which the omni-channel transition may influence CLV depending on consumer preferences for diversity of commodities,sensitivity to the cost of contract performance,and sensitivity to warehousing costs.Hence,retailers should provide consumers with a complete portfolio of goods and services based on target consumers’heterogeneous requirements in order to increase omni-channel efficiency.

A Study on Aligning Nursing Curriculum with Hospital Clinical Competency Requirements

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.

Analysis of the Brand Visual Needs System from the Perspective of Contemporary Market Demands

Brand visual design is not only an essential bridge for companies to convey their ideas and values but also a key factor in shaping the brand image and enhancing market competitiveness.However,a pervasive concern has arisen in society that many recent graduates in brand design and visual design cannot immediately meet the demands of the design industry.Despite attempts by scholars to reform courses and teaching philosophies,there are still significant shortcomings and gaps.Therefore,based on market orientation and supply-demand concepts,this study collected in-depth recruitment demands for brand design from 74 companies and conducted systematic summarization and analysis.It synthesized a demand model consisting of three major modules and 55 content points required by companies for brand design students.Based on these demands,adjustments and plans were made to the curriculum content,aiming to construct a teaching system that not only meets market demands but also enhances students’comprehensive qualities.The goal is to cultivate more outstanding talents capable of quickly adapting to and excelling in brand design work.

Macroscopic Traversable Wormholes: Minimum Requirements

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.

The Customer Requirements Analysis Method of Engineering Products Based on Multiple Preference Information

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.

Analysis of the Spirit of Struggle in the New Era Based on General Secretary Xi Jinping’s New Year’s Greetings in the Past Decade

In order to realize the national rejuvenation,General Secretary Xi Jinping attaches great importance to the spiritual strength of struggle,and has repeatedly advocated that all people should exalt the spirit of strive on different occasions.According to his New Year’s greetings since 2014,Xi Jinping has illustrated the connotation of the spirit of enterprising,the necessity of carrying forward and the requirements for practicing the spirit of struggle from different perspectives,pointing out that all Chinese must keep in mind both domestic and international situations,strive in unity,work creative and industrious with the enthusiasm of seizing every minute,catch the opportunity and solve the problem,make substantial progress on the new journey to construct China into a great modern socialist country in all respects.

气象观测场在汽车试验场中的应用研究

汽车试验场作为汽车开展道路测试的重要场所,用于验证汽车产品的品质以及可靠性。除了场地道路外,气象条件作为汽车道路测试的重要一环,在《GB/T12534-1990汽车道路试验方法通则》中也有明确要求,如:试验时应是无雨无雾天气,相对湿度小于95%,气温0-40℃,风速不大于3m/s。同时气象条件也作为试验场道路管控的重要依据,实时风速、雨量、能见度等信息为场地管理者发布限速、限行、封场等通知提供必要参考依据,直接影响道路测试安全管控的及时性。因此,文章从气象观测场的建设、气象服务、异常天气道路管控等方面开展气象观测场在汽车试验场中的应用研究。

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.