ZTE Corporation announced on March 31, 2008 that it has been ranked second in terms of CDMA equipment competitiveness by the Yankee Group in a report entitled "ZTE Shines in CDMA Market" published by the res...ZTE Corporation announced on March 31, 2008 that it has been ranked second in terms of CDMA equipment competitiveness by the Yankee Group in a report entitled "ZTE Shines in CDMA Market" published by the research firm in February this year. According to the report, ZTE is the most competitive telecom vendor in China, India, emerging Asia-Pacific market, Africa and Eastern Europe. ZTE tallied the highest score in four out of seven categories the Yankee Group used to measure the CDMA competitiveness of each vendor. The seven key evaluation factors were price, technology, existing installed base, product portfolio, integration capability, government support and company culture.展开更多
Coal pyrolysis gas from different ranks of coal was monitored on real time basis using photoionization mass spectroscopy. The molecular weight distribution of different products as a function of temperature from vario...Coal pyrolysis gas from different ranks of coal was monitored on real time basis using photoionization mass spectroscopy. The molecular weight distribution of different products as a function of temperature from various coal ranks studied was observed. It was noted that the release of different classes of compounds like phenols, alkenes, alkylated aromatics and aromatic skeletons was temperature dependent. For all the coal ranks at lower temperatures phenols were the main component, with alkenes and alkylated aromatics at slight higher temperatures and aromatic skeletons were released at the highest temperatures studied.展开更多
Established on June 26, 1986 by the consolidation of four former CAS subordinates (namely,the Office of Policy Research, Division of Management Science, Editorial Department of the Journal of Dialectics of Nature,... Established on June 26, 1986 by the consolidation of four former CAS subordinates (namely,the Office of Policy Research, Division of Management Science, Editorial Department of the Journal of Dialectics of Nature, and Section of Optimization and Overall Planning under the CAS Institute of Applied Mathematics), the Institute of Policy and Management (IPM), CAS, is characterized by interdisciplinary studies of social and natural sciences. It applies itself to theoretical, methodological and applied research into national strategies, policies and management for S&T development. It also provides consultancy services for government departments, local authorities and CAS on issues concerning S&T development, social and economic growth, S&T management and administration of enterprises.……展开更多
The ranks of cyclic and negacyclic codes over the finite chain ring R as well as their minimal generating sets are defined, and then the expression forms we presented by studying the structures of cyclic and negacycli...The ranks of cyclic and negacyclic codes over the finite chain ring R as well as their minimal generating sets are defined, and then the expression forms we presented by studying the structures of cyclic and negacyclic codes over the finite chain ring R. Through the paper, it is assumed that the length of codes n can not be divided by the characteristic of R.展开更多
In this paper, the maximal and minimal ranks of the solution to a system of matrix equations over H, the real quaternion algebra, were derived. A previous known result could be regarded as a special case of the new re...In this paper, the maximal and minimal ranks of the solution to a system of matrix equations over H, the real quaternion algebra, were derived. A previous known result could be regarded as a special case of the new result.展开更多
Data size plays a significant role in the design and the performance of data mining models.A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy.Features selection a...Data size plays a significant role in the design and the performance of data mining models.A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy.Features selection algorithms aim at selecting the best features and eliminating unnecessary ones,which in turn simplifies the structure of the data mining model as well as increases its performance.This paper introduces a robust features selection algorithm,named Features Ranking Voting Algorithm FRV.It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly;based on the feature ranks and voting algorithm.The FRV comprises of three different proposed techniques to select the minimum best feature set,the forward voting technique to select the best high ranks features,the backward voting technique,which drops the low ranks features(low importance feature),and the third technique merges the outputs from the forward and backward techniques to maximize the robustness of the selected features set.Different data mining models were built using obtained selected features sets from applying the proposed FVR on different datasets;to evaluate the success behavior of the proposed FRV.The high performance of these data mining models reflects the success of the proposed FRV algorithm.The FRV performance is compared with other features selection algorithms.It successes to develop data mining models for the Hungarian CAD dataset with Acc.of 96.8%,and with Acc.of 96%for the Z-Alizadeh Sani CAD dataset compared with 83.94%and 92.56%respectively in[48].展开更多
There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winning...There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winnings and defeats of the teams.The model used by the ICC to implement rankings is deficient in certain key respects.It ignores key factors like winning margin and strength of the opposition.Various measures of the ranking concept are presented in this research.The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics.The proposed approaches not only rank the teams on their losing/winning stats but also take into consideration the margin of winning and the quality of the opposition.Three cricket team ranking techniques are presented i.e.,(1)Cricket Team-Index(ct-index),(2)Cricket Team Rank(CTR)and(3)Weighted Cricket Team Rank(WCTR).The proposed metrics are validated through the collection of cricket dataset,extracted from Cricinfo,having instances for all the three formats of the game i.e.,T20 International(T20i),One Day International(ODI)and Test matches.The comparative analysis between the proposed and existing techniques,for all the three formats,is presented as well.展开更多
By the discussion of division in F2m[u]/〈u4〉,the minimal spanning set and the rank of a（1＋u＋u2）-constacyclic code with an arbitrary length N=2en over F2m[u]/〈u4〉 are determined based on the factorization of（x...By the discussion of division in F2m[u]/〈u4〉,the minimal spanning set and the rank of a（1＋u＋u2）-constacyclic code with an arbitrary length N=2en over F2m[u]/〈u4〉 are determined based on the factorization of（xn-1） over F2m.展开更多
The purpose of this note is to give a linear algebra algorithm to find out if a rank of a given tensor over a field F is at most k over the algebraic closure of F,where K is a given positive integer.We estimate the ar...The purpose of this note is to give a linear algebra algorithm to find out if a rank of a given tensor over a field F is at most k over the algebraic closure of F,where K is a given positive integer.We estimate the arithmetic complexity of our algorithm.展开更多
For any pair of integers g and n with g≥3 and 1≤n≤g,we build a 3-manifold with a distance-2,genus-g Heegaard splitting so that(1)it contains n pairwise disjoint and nonisotopic essential tori;(2)after it is cut ope...For any pair of integers g and n with g≥3 and 1≤n≤g,we build a 3-manifold with a distance-2,genus-g Heegaard splitting so that(1)it contains n pairwise disjoint and nonisotopic essential tori;(2)after it is cut open along these tori,one resulting piece is hyperbolic while the others are small Seifert fibered spaces;(3)it provides a substantial result to the rank versus genus problem.These generalize a result in Qiu and Zou(2019).展开更多
A sign pattern is a matrix whose entries axe from the set {＋,-,0}. A sign pattern is a generalized star sign pattern if it is combinatorial symmetric and its graph is a generalized star graph. The purpose of this pap...A sign pattern is a matrix whose entries axe from the set {＋,-,0}. A sign pattern is a generalized star sign pattern if it is combinatorial symmetric and its graph is a generalized star graph. The purpose of this paper is to obtain the bound of minimal rank of any generalized star sign pattern （possibly with nonzero diagonal entries）.展开更多
This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial ra...This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.展开更多
The encryption algorithm of finite automata (FA) public key cryptosystem is implemented by a weakly invertible finite automata (WIFA) which is composed of a nonlinear WIFA with delay 0 and a linear WIFA with delay τ....The encryption algorithm of finite automata (FA) public key cryptosystem is implemented by a weakly invertible finite automata (WIFA) which is composed of a nonlinear WIFA with delay 0 and a linear WIFA with delay τ. In this paper, we proved that such an automaton bears the same properties as the linear WIFA and the increasing ranks of the latter are key factors to affecting the former. A probabilistic algorithm is given to realize a ciphertext attack, and its complexity is analysed through the increasing ranks of the linear WIFA. The size of the parameters for safe linear WIFA is estimated.展开更多
Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation gener...Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.展开更多
Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for opti...Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.展开更多
Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively...Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.展开更多
Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with ...Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.展开更多
文摘ZTE Corporation announced on March 31, 2008 that it has been ranked second in terms of CDMA equipment competitiveness by the Yankee Group in a report entitled "ZTE Shines in CDMA Market" published by the research firm in February this year. According to the report, ZTE is the most competitive telecom vendor in China, India, emerging Asia-Pacific market, Africa and Eastern Europe. ZTE tallied the highest score in four out of seven categories the Yankee Group used to measure the CDMA competitiveness of each vendor. The seven key evaluation factors were price, technology, existing installed base, product portfolio, integration capability, government support and company culture.
文摘Coal pyrolysis gas from different ranks of coal was monitored on real time basis using photoionization mass spectroscopy. The molecular weight distribution of different products as a function of temperature from various coal ranks studied was observed. It was noted that the release of different classes of compounds like phenols, alkenes, alkylated aromatics and aromatic skeletons was temperature dependent. For all the coal ranks at lower temperatures phenols were the main component, with alkenes and alkylated aromatics at slight higher temperatures and aromatic skeletons were released at the highest temperatures studied.
文摘 Established on June 26, 1986 by the consolidation of four former CAS subordinates (namely,the Office of Policy Research, Division of Management Science, Editorial Department of the Journal of Dialectics of Nature, and Section of Optimization and Overall Planning under the CAS Institute of Applied Mathematics), the Institute of Policy and Management (IPM), CAS, is characterized by interdisciplinary studies of social and natural sciences. It applies itself to theoretical, methodological and applied research into national strategies, policies and management for S&T development. It also provides consultancy services for government departments, local authorities and CAS on issues concerning S&T development, social and economic growth, S&T management and administration of enterprises.……
基金Partly supported by the National Natural Science Foundations of China (No.60673074)key project of Ministry of Education Science and Technology’s Research (107065).
文摘The ranks of cyclic and negacyclic codes over the finite chain ring R as well as their minimal generating sets are defined, and then the expression forms we presented by studying the structures of cyclic and negacyclic codes over the finite chain ring R. Through the paper, it is assumed that the length of codes n can not be divided by the characteristic of R.
基金Project supported by the National Natural Science Foundation of China （Grant No.60672160）
文摘In this paper, the maximal and minimal ranks of the solution to a system of matrix equations over H, the real quaternion algebra, were derived. A previous known result could be regarded as a special case of the new result.
文摘Data size plays a significant role in the design and the performance of data mining models.A good feature selection algorithm reduces the problems of big data size and noise due to data redundancy.Features selection algorithms aim at selecting the best features and eliminating unnecessary ones,which in turn simplifies the structure of the data mining model as well as increases its performance.This paper introduces a robust features selection algorithm,named Features Ranking Voting Algorithm FRV.It merges the benefits of the different features selection algorithms to specify the features ranks in the dataset correctly and robustly;based on the feature ranks and voting algorithm.The FRV comprises of three different proposed techniques to select the minimum best feature set,the forward voting technique to select the best high ranks features,the backward voting technique,which drops the low ranks features(low importance feature),and the third technique merges the outputs from the forward and backward techniques to maximize the robustness of the selected features set.Different data mining models were built using obtained selected features sets from applying the proposed FVR on different datasets;to evaluate the success behavior of the proposed FRV.The high performance of these data mining models reflects the success of the proposed FRV algorithm.The FRV performance is compared with other features selection algorithms.It successes to develop data mining models for the Hungarian CAD dataset with Acc.of 96.8%,and with Acc.of 96%for the Z-Alizadeh Sani CAD dataset compared with 83.94%and 92.56%respectively in[48].
文摘There is an international cricket governing body that ranks the expertise of all the cricket playing nations,known as the International Cricket Council(ICC).The ranking system followed by the ICC relies on the winnings and defeats of the teams.The model used by the ICC to implement rankings is deficient in certain key respects.It ignores key factors like winning margin and strength of the opposition.Various measures of the ranking concept are presented in this research.The proposed methods adopt the concepts of h-Index and PageRank for presenting more comprehensive ranking metrics.The proposed approaches not only rank the teams on their losing/winning stats but also take into consideration the margin of winning and the quality of the opposition.Three cricket team ranking techniques are presented i.e.,(1)Cricket Team-Index(ct-index),(2)Cricket Team Rank(CTR)and(3)Weighted Cricket Team Rank(WCTR).The proposed metrics are validated through the collection of cricket dataset,extracted from Cricinfo,having instances for all the three formats of the game i.e.,T20 International(T20i),One Day International(ODI)and Test matches.The comparative analysis between the proposed and existing techniques,for all the three formats,is presented as well.
基金Supported by the Natural Science Foundation of Anhui Province(KJ2015A308,KJ2016A307,1408085QF116)Anhui Province Colleges Outstanding Young Talents Program(gxyq ZD2016389,[2014]181)the Natural Science Project of Anhui Xinhua University(2014Zr009)
文摘By the discussion of division in F2m[u]/〈u4〉,the minimal spanning set and the rank of a（1＋u＋u2）-constacyclic code with an arbitrary length N=2en over F2m[u]/〈u4〉 are determined based on the factorization of（xn-1） over F2m.
文摘The purpose of this note is to give a linear algebra algorithm to find out if a rank of a given tensor over a field F is at most k over the algebraic closure of F,where K is a given positive integer.We estimate the arithmetic complexity of our algorithm.
基金supported by National Natural Science Foundation of China(Grant Nos.12131009 and 12326601)Science and Technology Commission of Shanghai Municipality(Grant No.22DZ2229014)。
文摘For any pair of integers g and n with g≥3 and 1≤n≤g,we build a 3-manifold with a distance-2,genus-g Heegaard splitting so that(1)it contains n pairwise disjoint and nonisotopic essential tori;(2)after it is cut open along these tori,one resulting piece is hyperbolic while the others are small Seifert fibered spaces;(3)it provides a substantial result to the rank versus genus problem.These generalize a result in Qiu and Zou(2019).
基金the Shanxi Natural Science Foundation (20011006, 20041010)
文摘A sign pattern is a matrix whose entries axe from the set {＋,-,0}. A sign pattern is a generalized star sign pattern if it is combinatorial symmetric and its graph is a generalized star graph. The purpose of this paper is to obtain the bound of minimal rank of any generalized star sign pattern （possibly with nonzero diagonal entries）.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(No.ZCLQN25A0103)。
文摘This paper studies certain estimates for the lower bound of distance between unitary orbits of normal elements.We show that the distance between unitary orbits of normal elements of simple C^(*)-algebras of tracial rank no more than k has a lower bound.Furthermore,if k≤1 and normal elements are commuting,then the lower bound will be better.Another result establishes a connection involving the spectrum distance operator Dc between a C^(*)-algebra of stable rank one C^(*)-algebra and its hereditary C^(*)-subalgebra.
文摘目的基于“脑-肠-骨轴”初步探讨左归丸对老年性骨质疏松症(senile osteoporosis,SOP)模型小鼠海马区神经元退行性病变、肠道菌群变化及骨量丢失的作用。方法连续12周腹腔注射D-半乳糖(120 mg/kg)构建SOP模型,将小鼠随机分为空白组、模型组、左归丸高剂量组、左归丸低剂量组;予以左归丸药物干预8周,随后进行Morris水迷宫实验检测小鼠认知功能。取材后检测各组脑组织氧化应激指标,尼氏染色法检测海马尼式神经元完整性,16 S rRNA检测肠道菌群多样性,小动物X光机检测小鼠骨量丢失情况,小鼠血清检测骨代谢指标,免疫组织化学法检测骨代谢通路相关蛋白,采用spearman分析法对血清骨代谢因子、脑组织氧化应激因子与肠道菌群中的差异菌群进行关联分析。结果与Model组相比,左归丸给药组可显著缩短逃避潜伏期时间(P<0.05);显著增加海马区尼式小体数量(P<0.01);提高脑组织抗氧化酶含量(P<0.05);在门水平上,Model组小鼠肠道菌群中Bacteroidota相对丰度显著增高(P<0.01),左归丸给药组Verrucomicrobiota等菌群相对丰度显著升高(P<0.05),Bacteroidota相对丰度显著下降(P<0.05)。小鼠股骨干骺端与骨干区的骨量丢失得到改善,显著改善血清骨代谢指标(P<0.01),显著提高OPG/RANKL蛋白含量比值(P<0.01)。相关性分析显示,过氧化氢酶与肠道菌群中的Proteobacteria呈正相关(P<0.01),与Prevotellaceae_NK3B31_group呈负相关(P<0.01)。结论左归丸依据“脑-肠-骨轴”改善SOP小鼠肠道菌群丰度,进而缓解海马神经元退行性病变,调节OPG/RANK/RANKL通路缓解骨量丢失。
文摘The encryption algorithm of finite automata (FA) public key cryptosystem is implemented by a weakly invertible finite automata (WIFA) which is composed of a nonlinear WIFA with delay 0 and a linear WIFA with delay τ. In this paper, we proved that such an automaton bears the same properties as the linear WIFA and the increasing ranks of the latter are key factors to affecting the former. A probabilistic algorithm is given to realize a ciphertext attack, and its complexity is analysed through the increasing ranks of the linear WIFA. The size of the parameters for safe linear WIFA is estimated.
基金supported by the National Key Research and Development Program of China(2020AAA0109300)the Shanghai Collaborative Innovation Center of data intelligence technology(No.0232-A1-8900-24-13).
文摘Chinese abbreviations improve communicative efficiency by extracting key components from longer expressions.They are widely used in both daily communication and professional domains.However,existing abbreviation generation methods still face two major challenges.First,sequence-labeling-based approaches often neglect contextual meaning by making binary decisions at the character level,leading to abbreviations that fail to capture semantic completeness.Second,generation-basedmethods rely heavily on a single decoding process,which frequently produces correct abbreviations but ranks them lower due to inadequate semantic evaluation.To address these limitations,we propose a novel two-stage frameworkwithGeneration–Iterative Optimization forAbbreviation(GIOA).In the first stage,we design aChain-of-Thought prompting strategy and incorporate definitional and situational contexts to generate multiple abbreviation candidates.In the second stage,we introduce a Semantic Preservation Dynamic Adjustment mechanism that alternates between character-level importance estimation and semantic restoration to optimize candidate ranking.Experiments on two public benchmark datasets show that our method outperforms existing state-of-the-art approaches,achieving Hit@1 improvements of 15.15%and 13.01%,respectively,while maintaining consistent results in Hit@3.
文摘Test case prioritization and ranking play a crucial role in software testing by improving fault detection efficiency and ensuring software reliability.While prioritization selects the most relevant test cases for optimal coverage,ranking further refines their execution order to detect critical faults earlier.This study investigates machine learning techniques to enhance both prioritization and ranking,contributing to more effective and efficient testing processes.We first employ advanced feature engineering alongside ensemble models,including Gradient Boosted,Support Vector Machines,Random Forests,and Naive Bayes classifiers to optimize test case prioritization,achieving an accuracy score of 0.98847 and significantly improving the Average Percentage of Fault Detection(APFD).Subsequently,we introduce a deep Q-learning framework combined with a Genetic Algorithm(GA)to refine test case ranking within priority levels.This approach achieves a rank accuracy of 0.9172,demonstrating robust performance despite the increasing computational demands of specialized variation operators.Our findings highlight the effectiveness of stacked ensemble learning and reinforcement learning in optimizing test case prioritization and ranking.This integrated approach improves testing efficiency,reduces late-stage defects,and improves overall software stability.The study provides valuable information for AI-driven testing frameworks,paving the way for more intelligent and adaptive software quality assurance methodologies.
基金funded by Soonchunhyang University,Grant Number 20250029。
文摘Recommendation systems have become indispensable for providing tailored suggestions and capturing evolving user preferences based on interaction histories.The collaborative filtering(CF)model,which depends exclusively on user-item interactions,commonly encounters challenges,including the cold-start problem and an inability to effectively capture the sequential and temporal characteristics of user behavior.This paper introduces a personalized recommendation system that combines deep learning techniques with Bayesian Personalized Ranking(BPR)optimization to address these limitations.With the strong support of Long Short-Term Memory(LSTM)networks,we apply it to identify sequential dependencies of user behavior and then incorporate an attention mechanism to improve the prioritization of relevant items,thereby enhancing recommendations based on the hybrid feedback of the user and its interaction patterns.The proposed system is empirically evaluated using publicly available datasets from movie and music,and we evaluate the performance against standard recommendation models,including Popularity,BPR,ItemKNN,FPMC,LightGCN,GRU4Rec,NARM,SASRec,and BERT4Rec.The results demonstrate that our proposed framework consistently achieves high outcomes in terms of HitRate,NDCG,MRR,and Precision at K=100,with scores of(0.6763,0.1892,0.0796,0.0068)on MovieLens-100K,(0.6826,0.1920,0.0813,0.0068)on MovieLens-1M,and(0.7937,0.3701,0.2756,0.0078)on Last.fm.The results show an average improvement of around 15%across all metrics compared to existing sequence models,proving that our framework ranks and recommends items more accurately.
文摘Since the United Nations launched the Sustainable Development Goals(SDGs)in 2015,global implementation has steadily advanced,yet prominent challenges persist.Progress has been uneven across regions and countries,with Tajikistan representing a typical example of such disparities.Based on 81 SDG indicators for Tajikistan from 2001 to 2023,this study applied a three-level coupling network framework:at the microscale,it identified synergies and trade-offs between indicators;at the mesoscale,it examined the strength and direction of linkages within four SDG-related components(society,finance,governance,and environment);and at the global level,it focused on the overall SDG interlinkages.Spearman’s rank correlation,sliding window method,and topological properties were employed to analyze the coupling dynamics of SDGs.Results showed that over 70.00%of associations in the global SDG network were of medium-to-low intensity,alongside extremely strong ones(|r|value approached 1.00,where r is the correlation coefficient).SDG interactions were generally limited,with stable local synergy clusters in core livelihood sectors.Network modularity fluctuated,reflecting a cycle of differentiation,integration,and fragmentation,while coupling efficiency varied with the external environment.Each component exhibited distinct functional characteristics.The social component maintained high connectivity through the“poverty alleviation-education-healthcare”loop.The environmental component shifted toward coordinated eco-economic governance.The governance-related component broke interdepartmental barriers,while the financial component showed weak links between resource-based indicators and consumption/employment indicators.Tajikistan’s SDG coupling evolved through three phases:survival-oriented(2001–2012),policy integration(2013–2018),and shock adaptation(2019–2023).These phases were driven by policy changes,resource industries,governance optimization,and external factors.This study enriches the analytical framework for understanding the dynamic coupling of SDGs in mountainous resource-dependent countries and provides empirical evidence to support similar countries in formulating phase-specific SDG promotion strategies.
