教育领域中的一个服务应用:基于SOA的学分银行系统①

The paper presents a new service application in education area, namely credit bank. Credit bank is a service provided by authorized education organizations so that customers can save, manage and exchange education credits. The paper focus on establishing a service oriented information system to support the idea of credit bank. After introducing its concept, the paper studies the requirement of credit bank from both business and technical point of view. Then it presents a practical step-by-step guideline to identify services in the system. The guideline is applied to design credit bank information system (CBIS) as a case study. The contributions of the paper are: (i) It gives a new idea about how service can be used to benefit education; (ii) It presents a useful method for service identification in system design with case study.     

教育领域中的一个服务应用：基于SOA的学分银行系统

The paper presents a new service application in education area,namely credit bank.Credit bank is a service provided by authorized education organizations so that customers can save,manage and exchange education credits.The paper focus on establishing a service oriented information system to support the idea of credit bank.After introducing its concept,the paper studies the requirement of credit bank from both business and technical point of view.Then it presents a practical step-by-step guideline to identify services in the system.The guideline is applied to design credit bank information system(CBIS)as a case study.The contributions of the paper are:(i)It gives a new idea about how service can be used to benefit education;(ii)It presents a useful method for service identification in system design with case Study.     

粗糙集在残缺数据样本中的应用

数据样本集是人工智能发展需要的主要要素,所以要求提供的数据样本集,应该是全面的、有效的集合。当所提供的数据样本集残缺不全,会影响人工智能的有效应用。针对此问题,论文提出一种基于粗糙集理论的数据样本集补全方法,能科学的、正确的、有效的补全数据样本集,为提高人工智能的决策推理,铺平了道路。     

EJB技术在银行系统信息化中的应用

针对银行系统信息化建设过程中面临的诸多问题，介绍了EJB组件技术，并基于此技术提出多层模型解决方案。此方案满足了银行对系统的可靠性、稳定性、灵活性、集中管理等性能要求。同时也一定程度上保护了银行的原有投资。     

Application of DEA in analyzing a bank’s operating performance

This article takes 117 branches of a certain bank in Taiwan in 2006 as the research subject and introduces data envelopment analysis (DEA) to evaluate the operating performances of business units of this bank to provide the reference for a bank’s managers in determining operation strategies. The result indicates that, in overall technical efficiency, the case bank has many inefficient branches distinctly; the average overall technical efficiency of branches is 54.8% and the average pure technical efficiency of branches is 67%, which is probably because of lower loan-to-deposit ratio, leading to excessive input waste. The average scale efficiency of the case bank during the sample period is 82%. The ratio of resource waste due to technical inefficiency is 45.2%, of which 55.03% is due to pure technical inefficiency.     

A deep learning based bank card detection and recognition method in complex scenes

With the widespread use of mobile Internet, mobile payment has become a part of daily life, and bank card recognition in natural scenes has become a hot topic. Although printed character recognition has achieved remarkable success in recent years, bank card recognition is not limited to traditional printed character recognition. There are two types of bank cards: unembossed bank cards, such as most debit cards which usually use printed characters, and embossed bank cards, such as most credit cards which mainly use raised characters. Recognition of raised characters is very challenging due to its own characteristics, and there is a lack of fast and good methods to handle it. To better recognize raised characters, we propose an effective method based on deep learning to detect and recognize bank cards in complex natural scenes. The method can accurately recognize the card number characters on embossed and unembossed bank cards. First, to break the limitation that YOLOv3 algorithm is usually used for object detection, we propose a novel approach that enables YOLOv3 to be used not only for bank card detection and classification, but also for character recognition. The CANNYLINES algorithm is used for rectification and the Scharr operator is introduced to locate the card number region. The proposed method can satisfy bank card detection, classification and character recognition in complex natural scenes, such as complex backgrounds, distorted card surfaces, uneven illumination, and characters with the same or similar color to the background. To further improve the recognition accuracy, a printed character recognition model based on ResNet-32 is proposed for the unembossed bank cards. According to the color and morphological characteristics of embossed bank cards, raised character recognition model combining traditional morphological methods and LeNet-5 convolutional neural network is proposed for the embossed bank cards. The experimental results on the collected bank card dataset and bank card number dataset show that our proposed method can effectively detect and identify different types of bank cards. The accuracy of the detection and classification of bank cards reaches 100%. The accuracy of the raised characters recognition on the embossed bank card is 99.31%, and the accuracy of the printed characters recognition on the unembossed bank card reaches 100%.

     

Optimized Gabor features for mass classification in mammography

Gabor filter bank has been successfully used for false positive reduction problem and the discrimination of benign and malignant masses in breast cancer detection. However, a generic Gabor filter bank is not adapted to multi-orientation and multi-scale texture micro-patterns present in the regions of interest (ROIs) of mammograms. There are two main optimization concerns: how many filters should be in a Gabor filter band and what should be their parameters. Addressing these issues, this work focuses on finding optimizing Gabor filter banks based on an incremental clustering algorithm and Particle Swarm Optimization (PSO). We employ an SVM with Gaussian kernel as a fitness function for PSO. The effect of optimized Gabor filter bank was evaluated on 1024 ROIs extracted from a Digital Database for Screening Mammography (DDSM) using four performance measures (i.e., accuracy, area under ROC curve, sensitivity and specificity) for the above mentioned mass classification problems. The results show that the proposed method enhances the performance and reduces the computational cost. Moreover, the Wilcoxon signed rank test over the significance level of 0.05 reveals that the performance difference between the optimized Gabor filter bank and non-optimized Gabor filter bank is statistically significant.     

无线传感器网络中一种新型加权Mel滤波器组

提出了一种提取车辆声音特征的新型加权Mel滤波器组进行车辆的识别.这种新型滤波器组通过赋予各离散频率不同的权重,突出车辆频谱之间差异较大频段的信息,弱化较为相似频段的信息.相比于传统的MeI滤波器组,加权Mel滤波器组的识别能力得到了显著提高.仿真和实测结果均表明,与两种常用的特征提取方法相比,加权Mel滤波器组不仅能更有效地提取不同类型车辆间的差异信息,获得更高的正确识别率,还降低了计算复杂度.     

Mel scaled M-band wavelet filter bank for speech recognition

A Mel scaled M-band wavelet filter bank structure is used to extract the robust acoustic feature for speech recognition application. The proposed filter bank can provide flexibility of frequency partition that decomposes the speech signal into the M-frequency band. To estimate the difference between Mel scaled M-band wavelet and dyadic wavelet filter bank, relative bandwidth deviation (RBD) and root mean square bandwidth deviation (RMSBD) with respect to baseline (Mel filter bank bandwidth) is calculated. Proposed filter bank gives 40.90 and 49.84% reduction for RBD and RMSBD respectively, over 24-dyadic wavelet filter bank. Feature extraction from the proposed filter bank using AMUAV corpus shows an improvement in terms of word recognition accuracy (WRA) at all SNR range (20 dB to 0 dB) over baseline (MFCC) features. For AMUAV corpus, the proposed feature shows the maximum improvement in WRA of 3.93% over baseline features and 3.90% over dyadic wavelet filter bank features. When applied to the VidTIMIT corpus, proposed features show the maximum improvement in WRA of 1.64% over baseline features and 4.43% over dyadic features.     

Resuscitating privacy-preserving mobile payment with customer in complete control

Credit/debit card payment transactions do not protect the privacy of the customer. Once the card is handed over to the merchant for payment processing, customers are “no longer in control” on how their card details and money are handled. This leads to card fraud, identity theft, and customer profiling. Therefore, for those customers who value their privacy and security of their payment transactions, this paper proposes a choice—an alternate mobile payment model called “Pre-Paid Mobile HTTPS-based Payment model”. In our proposed payment model, the customer obtains the merchant’s bank account information and then instructs his/her bank to transfer the money to the merchant’s bank account. We utilize near field communication (NFC) protocol to obtain the merchant’s bank account information into the customer’s NFC-enabled smartphone. We also use partially blind signature scheme to hide the customers’ identity from the bank. As a result, our payment model provides the customer with complete control on his/her payments and privacy protection from both the bank and the merchant. We emulated our proposed mobile payment model using Android SDK 2.1 platform and analyzed its execution time.     

Application of gap metric to model bank determination in multilinear model approach

This paper presents a gap metric based method which aims to perform the operating range decomposition and the minimum linear model bank determination of a nonlinear system when multilinear model approach is employed to design a controller for this nonlinear system. For a prescribed distance level, the minimum linear model bank determined by the proposed method can provide sufficient information for multilinear model controller design of the nonlinear system. To illustrate the usefulness of the proposed method, two examples of nonlinear systems are presented. Moreover, a mixed logical dynamical model-based MPC (MLD–MPC) controller is designed based on the minimum model bank. Simulations confirm the method for selecting linear model bank in multilinear model approach.     

Scalar memory references in pipelined multiprocessors: aperformance study

Interleaved memories are essential in pipelined computers to attain high memory bandwidth. As a memory bank is accessed, a reservation is placed on the bank for the duration of the memory cycle, which is often considerably longer than the processor cycle time. This additional parameter, namely, the bank reservation time or the bank busy time, adds to the complexity of the memory model. For Markov models, exact solutions are not feasible even without this additional parameter due to the very large state space of the Markov chain. The authors develop a Markov model which explicitly tracks the bank reservation time. Because only one processor and the requested bank are modeled, the transition probabilities are not known and have to be approximated. The performance predicted by the model is in close agreement with simulation results     

Performance linked dynamic cache tuning: A static energy reduction approach in tiled CMPs

Advancement in semiconductor technology increases power density in recent Chip Multi-Processors (CMPs) which significantly increases the leakage energy consumptions of on-chip Last Level Caches (LLCs). Performance linked dynamic tuning in LLC size is a promising option for reducing the cache leakage.This paper reduces static power consumption by dynamically shutting down or turning on cache banks based upon system performance and cache bank usage statistics. Shutting down of a cache bank remaps its future requests to another active bank, called as target bank. The proposed method is evaluated on three different implementation policies, viz (1) The system can decide to shutdown or turn-on some cache banks periodically throughout the process execution. (2) The system allows to shutdown banks initially and once the bank restarting initiates, no more shutdown is permitted further. (3) This policy resizes cache like first policy with some predefined time slices, in which cache cannot be resized.For a 4MB 4 way set associative L2 cache, experimental analysis shows 66% reduction in static energy with 29% gain in Energy Delay Product (EDP) for first strategy; for the second policy, static power is reduced by 59% with 27% savings in EDP. Finally, last policy saves 65% in static power and 30% in EDP with minimal performance penalty.     

Mining fuzzy association rules in a bank-account database

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.     

Does ease-of-use contributes to the perception of enjoyment? A case of gamification in e-banking

The success of online games encouraged the development of gamification software in e-banking. Beside the growing trend of gamification, it is important understand how bank customers face the gamified applications, particularly as enjoyment and ease-of-use. To assess the determinants that influence the adoption of gamification in e-banking, we developed a research to propose a conceptual model that illustrates the adoption of gamified business applications by bank customers, in e-banking context. We conducted two quantitative studies (A and B) to understand how bank customers represent a gamified business software and its changes (or improvements) over time. Study A was performed in 2012 (N = 183), and study B in 2015 (N = 219). Online bank customers were invited to rate the importance of variables related to: socialness, ease-of-use, usefulness, enjoyment and intention to use e-banking systems with game features and social cues. The results show that ease-of-use and enjoyment are interrelated, and both have influence in e-banking usage. This study present theoretical ground of the conceptual model, and discuss two empirical studies, aiming to analyse the ease-of-use and enjoyment influence on bank customers. These findings will contribute directly to explain of adoption hedonic business software in e-banking.     

Genetic algorithm based model for optimizing bank lending decisions

To avoid the complexity and time consumption of traditional statistical and mathematical programming, intelligent techniques have gained great attention in different financial research areas, especially in banking decisions’ optimization. However, choosing optimum bank lending decisions that maximize the bank profit in a credit crunch environment is still a big challenge. For that, this paper proposes an intelligent model based on the Genetic Algorithm (GA) to organize bank lending decisions in a highly competitive environment with a credit crunch constraint (GAMCC). GAMCC provides a framework to optimize bank objectives when constructing the loan portfolio, by maximizing the bank profit and minimizing the probability of bank default in a search for a dynamic lending decision. Compared to the state-of-the art methods, GAMCC is considered a better intelligent tool that enables banks to reduce the loan screening time by a range of 12%–50%. Moreover, it greatly increases the bank profit by a range of 3.9%–8.1%.     

A nature inspired Ying–Yang approach for intelligent decision support in bank solvency analysis

Since the collapse or failure of a bank could trigger an adverse financial repercussion and generate negative impacts, it is desirable to have an early warning system (EWS) that identifies potential bank failures or high-risk banks through the traits of financial distress. This research is aimed to construct a novel fuzzy neural CMAC as an alternative to analyze bank solvency, in which a nature inspiration motivated from the famous Chinese ancient Ying–Yang philosophy is introduced to find the optimal fuzzy sets, and truth value restriction (TVR) inference scheme is employed to derive the truth-values of the rule weights. The proposed model functions as an early warning system and is able to identify the inherent traits of financial distress based on financial covariates (features) derived from publicly available financial statements. Our experiments are conducted on a benchmark dataset of a population of 3635 US banks observed over a 21 years period. Three sets of experiments are performed – bank failure classification based on the last available financial record and prediction using financial records one and two years prior to the last available financial statements. The performance of the proposed Ying–Yang FCMAC network as a bank failure classification and early warning system is very encouraging.     

Models of access delays in multiprocessor memories

The performance of an interleaved common memory accessed uniformly by multiple processors is modeled by queuing and simulation methods. The model includes access conflicts at the bank level while assuming an ideal access network. A general scaling law is derived that indicates that memory access delays are given by the product of the bank reservation time and a function of the memory utilization, which is the average number of access requests arriving at a bank per bank reservation time. For light, uniform memory traffic. access delays are proportional to the square of the bank reservation time and to the ratio of the number of active memory access streams to the number of memory banks. With an assumption of random access patterns, an open and a closed queuing model are developed. To model pipelined access operations a new negative feedback model is introduced that includes the open and the closed models as special cases and is also well suited for modeling linked access streams. Delay dependence on bank reservation time is quadratic for light loads and linear for very heavy loads. The queuing models are validated by simulations     

面向多核NUCA共享数据竞争问题的Bank一致性技术

非一致Cache体系结构(NUCA)几乎已经成为未来片上大容量cache的发展方向。多核处理器的NUCA结构中,多个处理器核对共享数据的竞争访问,可能导致数据经常处于中部的cache Bank,增加NUCA的访问延迟。本文提出支持数据副本的Bank一致性技术,通过有选择地在NUCA中为访问的处理器核创建不同的数据副本,Bank一致性技术能够缓解多核处理器对共享数据的竞争问题。本文详细地介绍了Bank一致性协议的设计方法。最后,使用全系统模拟器对8个NPB基准测试程序进行了详细评测。实验结果表明,Bank一致性技术能够有效缓解多核处理器中共享数据的竞争访问问题。相比不支持Bank一致性技术的CMP-DNUCA结构,本文的方法能将系统IPC性能平均提升5.95%。     

聚类算法在银行客户细分中的应用

针对聚类算法在金融领域广泛应用的实际情况,基于银行客户数据集,对DBSCAN, K-means和X-means 3种聚类算法在执行效率、可扩展性、异常点检测能力等方面进行对比分析,并提出将X-means算法应用于银行业客户细分。利用X-means算法建立了一套银行客户细分模型,为银行决策者提供科学的决策支持。