Prediction of chronic kidney disease stages by renal ultrasound imaging

ABSTRACT

To detect chronic kidney disease (CKD) at earlier stages, diagnosis through non-invasive ultrasonographic imaging techniques provides an auxiliary clinical approach for at-risk CKD patients. We have established a detection method based on imaging processing techniques and machine learning approaches for the diagnosis of different CKD stages. Decisive area-proportional and textural features and support-vector-machine techniques were applied for efficient and effective analyses. Several clustered collections of CKD patients were evaluated and compared according to the estimated glomerular filtration rates. Based on the findings of evolving changes from ultrasound images, the proposed approach could be used as complementary evidences to help differentiate between different clinical diagnoses.     

Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

ABSTRACT

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.     

基于矩特征和面积特征的二值图像检索方法

在商标图像的检索中,仅凭图像单一的特征很难达到满意的检索效果,文章提出了利用图像矩特征与面积特征相结合的方法进行二值商标图像的检索。实验表明该方法能有效减少虚警和漏检,提高检索的精度。     

Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data

Huang et al. (J Bus Econ Stat 32:237–244, 2014) first proposed a Pearson Chi-Square based feature screening procedure tailored to multi-classification problem with ultrahigh dimensional categorical covariates, which is a common problem in practice but has seldom been discussed in the literature. However, their work establishes the sure screening property only in a limited setting. Moreover, the p value based adjustments when the number of categories involved by each covariate is different do not work well in several practical situations. In this paper, we propose an adjusted Pearson Chi-Square feature screening procedure and a modified method for tuning parameter selection. Theoretically, we establish the sure screening property of the proposed method in general settings. Empirically, the proposed method is more successful than Pearson Chi-Square feature screening in handling non-equal numbers of covariate categories in finite samples. Results of three simulation studies and one real data analysis are presented. Our work together with Huang et al. (J Bus Econ Stat 32:237–244, 2014) establishes a solid theoretical foundation and empirical evidence for the family of Pearson Chi-Square based feature screening methods.     

基于几何距离可分性判据的文本特征提取

不同类的模式可以被区分熏因为它们所属类别在特征空间中的类域是不同的区域。显然这些区域重叠的部分越小或者完全没有重叠,类别的可分性越好,因此可以用几何距离或离差测度来构造类别可分性判据。为了处理方便和节省资源,而又不影响识别的正确率,需要一定的方法提取对分类识别最有效、数目最少的特征。文章从模式识别工程的角度出发,运用基于几何距离可分性判据对通信类文本进行了特征提取,并用提取后的特征向量对训练样本以外的文本进行识别,得到了较好的效果。     

Public Governance and School Performance

Abstract

According to the New Public Governance paradigm, this paper analyses the institutional and organizational conditions that can lead to an improvement in student learning (a typical co-produced outcome) by acting on the promotion of genuine collaborative relationships. The distinctive features of Italian distributed governance and the challenges for school management are discussed by examining TIMSS and INVALSI data. The results show that collaborative public management supports schools in improving student learning, confirming the research hypothesis for primary schools (IV grade) and schools that operate in socially and economically poor contexts.     

体验式教学在古诗教学中的运用

古诗含蓄隽永,意味深长,教学有一定的难度,运用体验式教学能够省时高效,本文从配乐诵读、情境想象、生活实践、主体感受等方面具体阐述古诗教学中体验式方法的运用。     

LSPR detection of extracellular vesicles using a silver-PDMS nano-composite platform suitable for sensor networks

ABSTRACT

A novel method for the detection of exosomes in body fluids and cell cultures has been developed. The method is an optical method, based on LSPR of silver nanoparticles. The method was extended to a microfluidic environment, amenable to be integrated in a sensor network. However, for clinical applications, their isolation, detection and quantification methods in bio-fluids are challenging. Herein, we present a simple label-free technique to capture and detect EVs, by using a synthetic peptide, called Vn96. To quantify EVs, a LSPR detection technique was used. This work is an attempt to adapt a biosensing method to the future requirements of Industry 4.0.     

轨道刨削损伤的特征检测

轨道刨削是超高速滑动下轨道表面的一种损伤形式。刨削的出现降低系统发射效率、缩短轨道寿命,减小射弹方向精度。对实验室内轨道发射后的轨道进行宏观形貌和微观检测分析,观察到刨槽内表面具有光泽的熔融外貌,而前后外表面出现少许烧蚀等现象。从以上观察结果总结得到刨削产生的特征,为下一步刨削机理和抑制手段的研究确定方向。     

Flexibility and Rigidity in Crisis Management and Learning at Swedish Public Organizations

Abstract

To date the relationship between crises, organizational crisis management, and learning has been understudied. In an effort to broaden theoretical understandings of the relation between crisis and learning, this article analyses the crisis management and learning processes of two public organizations during a sequence of two failures. A framework of rigidity versus flexibility in response is utilized in the analysis. The findings are discussed in relation to their implications for the nexus between crisis and learning. The study concludes by raising four hypotheses for further research.     

围绕品牌核心价值的产品设计研究

区别于视觉识别(VI),提出了通过围绕品牌核心价值的产品设计来塑造和提升品牌识别的观点,然后分析了围绕品牌核心价值进行产品设计的意义。本文给出了三种围绕品牌核心价值进行产品设计的方法:一是塑造显性的产品造型特征;二是塑造隐性的产品造型特征;三是围绕品牌核心价值的功能设计。最后提出了对概念设计结果进行风格检验的方法,只有通过检验的设计才可根据成本和其它限制因素进入到下阶段产品开发,否则便应立刻停止当前的开发进程,及时调整设计策略。     

iSDS: a self-configurable software-defined storage system for enterprise

ABSTRACT

Storage is one of the most important aspects of IT infrastructure for various enterprises. But, enterprises are interested in more than just data storage; they are interested in such things as more reliable data protection, higher performance and reduced resource consumption. Traditional enterprise-grade storage satisfies these requirements at high cost. It is because traditional enterprise-grade storage is usually designed and constructed by customised field-programmable gate array to achieve high-end functionality. However, in this ever-changing environment, enterprises request storage with more flexible deployment and at lower cost. Moreover, the rise of new application fields, such as social media, big data, video streaming service etc., makes operational tasks for administrators more complex. In this article, a new storage system called intelligent software-defined storage (iSDS), based on software-defined storage, is described. More specifically, this approach advocates using software to replace features provided by traditional customised chips. To alleviate the management burden, it also advocates applying machine learning to automatically configure storage to meet dynamic requirements of workloads running on storage. This article focuses on the analysis feature of iSDS cluster by detailing its architecture and design.     

Self-reflexive networks: Theory · topology · applications

We present a new type of Artificial Neural Networks: the Self-Reflexive Networks. We utter the theoretical presuppositions; their dynamics is analogous to the one ascribed to autopoietic systems: self-referentiality, unsupervised learning and unintentionally cooperative and contractual activities of their own units. We also hypothesize a new concept of perception. We present the basic equations of Self-Reflexive Networks, new concepts as the one of dynamic target, of Re-entry with dedicated and fixed connections, of Meta-Units. Therefore, we experiment a specific type of Self-Reflexive Networks, the Monodedicated, within the interpretation of a toy-DB and we have hinted at other already made experimentations, experimentations in process and planned experimentations. From the applicative work that we present a few specifics and novelties of this type of Neural Networks emerge:

1. the capability of answering to complex, strange, wrong or not precise questions, through the same algorithms through which the learning phase took place.
2. the capability of spontaneously transforming their own learning inaccuracy in analogic capability and original self-organization capability.
3. the capability of spontaneously integrate the models that it experienced in different moments in an achronical hyper-model.
4. the capability of behaving as it had explored a decisions graph of large dimensions, both deeply and in extension. With the consequence of behaving as an Addressing Memory forself-dynamic Contents.
5. the capability of always learning, rapidly and anyway, besides the complexity of the learning patterns.
6. the capability of answering simultaneously from different points of view, behaving, in this case, as a network that builds more similarity models for each vector-stimulus that it receives.
7. the capability of adjusting in a biunivocal way, each question to the consulting DB and each DB to the question that are submitted. The consequence of this fact is the continuous creation of new answering models.
8. the capability of building during the learning phase, a weights matrix that provides a subconceptual representation of the bi-directional relations between each couple of input variables.
9. the capability, through the Metaunits, to integrate in a unitary typology, nodes with different saturation speed and, therefore, with different memory: in fact, while the SR units are short memory nodes, since each new stumulus zeros the previous stimulus, the Metaunits memorize the SR different stimulus during time, functioning as an average length memory. This fact should confirm that the avarage length memory is of a different level from the immediate memory and that it is based only uponrelation among perceptive stimulus which are distributed in parallel and in sequence. In this context the weights matrix constitute the SR long term memory. And in this sense it will be opportune to think at a methodic through which the Metaunits can influence during time, the same weights matrix. In any case, in the SR there areservice nodes orfilter nodes andlearning nodes as if they were weights (the Metaunits).

     

A two-step simulation procedure to analyze the exercise features of American options

Abstract In this contribution we propose a two-step simulation procedure that enables to compute the exercise features of American options and analyze the properties of the optimal exercise times and exercise probabilities. The first step of the procedure is based on the calculation of an accurate approximation of the optimal exercise boundary. In particular, we use a smoothed binomial method which effectively reduces the fluctuating behavior of a discrete boundary. In the second step the boundary is used to define a stopping rule which is embodied in a Monte Carlo simulation method. A broad experimental analysis is carried out in order to test the procedure and study the behavior of the exercise features. Mathematics Subject Classification (2000): 60G40, 60J60, 65C20 Journal of Economic Literature Classification: G13     

Strategies for Internationalizing the Business Curriculum in the Asia Pacific

SUMMARY

With the rapid economic growth of the East Asian countries, there has been an increasing interest in the education and skill development strategies used in the region. One of the critical features of Singapore's human resource development strategy is the emphasis on regionalization and internationalization of the academic curriculum. The trend of this internationalization may be attributable to three major factors: Singapore government's regionalization policy, distance learning programs offered by foreign universities, and the overseas assignment of Singaporean managers as expatriates. This paper presents an overview of the internationalization of the business curriculum at the graduate and undergraduate level in Singapore, particularly at the Nanyang Technological University (NTU). The Nanyang Business School, although a young institution, is rapidly moving towards the internationalization process in various areas. At present its student body is drawn from more than 20 countries. About a third of its business faculty come from about 19 countries. At the undergraduate level, students are encouraged to study foreign languages, and required to take the international business course irrespective of their functional specialization. In addition, most functional areas of specialization offer at least one international course as an elective choice. Some of the students also complete their required professional attachment abroad. At the graduate level, the unique feature of it's MBA program is the compulsory International Business Study Mission. The newly introduced MBA specialization in International Business adds another dimension. The school is now focusing on extensive research efforts in the area of regional-ization and globalization of business through its several research centers. The paper also identifies a few concerns such as the rapid changes in the pedagogy due to the overall systemic changes in the curriculum from the traditional British education model and the availability of suitable case studies.     

Predictive analysis and modelling football results using machine learning approach for English Premier League

The introduction of artificial intelligence has given us the ability to build predictive systems with unprecedented accuracy. Machine learning is being used in virtually all areas in one way or another, due to its extreme effectiveness. One such area where predictive systems have gained a lot of popularity is the prediction of football match results. This paper demonstrates our work on the building of a generalized predictive model for predicting the results of the English Premier League. Using feature engineering and exploratory data analysis, we create a feature set for determining the most important factors for predicting the results of a football match, and consequently create a highly accurate predictive system using machine learning. We demonstrate the strong dependence of our models’ performances on important features. Our best model using gradient boosting achieved a performance of 0.2156 on the ranked probability score (RPS) metric for game weeks 6 to 38 for the English Premier League aggregated over two seasons (2014–2015 and 2015–2016), whereas the betting organizations that we consider (Bet365 and Pinnacle Sports) obtained an RPS value of 0.2012 for the same period. Since a lower RPS value represents a higher predictive accuracy, our model was not able to outperform the bookmaker’s predictions, despite obtaining promising results.     

An approach to define semantics for BPM systems interoperability

This article proposes defining semantics for Business Process Management systems interoperability through the ontology of Electronic Business Documents (EBD) used to interchange the information required to perform cross-organizational processes. The semantic model generated allows aligning enterprise’s business processes to support cross-organizational processes by matching the business ontology of each business partner with the EBD ontology. The result is a flexible software architecture that allows dynamically defining cross-organizational business processes by reusing the EBD ontology.

For developing the semantic model, a method is presented, which is based on a strategy for discovering entity features whose interpretation depends on the context, and representing them for enriching the ontology. The proposed method complements ontology learning techniques that can not infer semantic features not represented in data sources. In order to improve the representation of these entity features, the method proposes using widely accepted ontologies, for representing time entities and relations, physical quantities, measurement units, official country names, and currencies and funds, among others. When the ontologies reuse is not possible, the method proposes identifying whether that feature is simple or complex, and defines a strategy to be followed. An empirical validation of the approach has been performed through a case study.     

培养环艺类学生空间想象力的教学方法探索

针对环艺类学生在工程制图课程学习中,空间思维能力普遍较弱的情况,根据这类学生有绘画特长的特点,提出了以基本立体为导向、利用实物的教学方法。实践表明改进后的教学方法更有利于提高教学质量。     

A Novel Compact Cat Swarm Optimization Based on Differential Method

ABSTRACT

A compact cat swarm optimization scheme (cCSO) is proposed in this paper, which is designed to solve application domains plagued with limited memory and less-computation power, as a member of cat swarm optimization algorithms (CSO), it composes of two sub-modes, i.e., tracing and seeking modes, so it keeps the same search logic of CSO. On the other hand, cCSO inherits the main feature of compact algorithms, a normal probabilistic model is used to represent the population of solutions instead of processing an actual population, which ensures the cCSO to have the modest memory requirement. The updating vector for the probabilistic model provides a clear moving direction for cats in next step. A cat without historical position and velocity is applied in the algorithm. When the cat is in seeking mode, it employs a differential operator to update the cat’s position, which makes it possible for the cat to have multiple searching directions. Experimental results show that cCSO has pretty performance compared with respect to some population-based testing benchmarks. And it also shows superior performance in convergence rate to some compact optimization algorithms. The case study of gray image segmentation proves that it suits for solving the optimization problem by limited hardware.