A reindexing based approach towards mapping of DAG with affine schedules onto parallel embedded systems

We address the problem of optimally mapping uniform DAGs to systolic arrays, given an affine timing function. We introduce an automatic allocation method based on a preprocessing by reindexing that transforms the initial DAG into a new one that enables the well known projection method to minimize the number of processors along a number of directions. We demonstrate its superiority to other methods, and establish the space-optimality of the proposed method. We also show an upper bound on the number of processors that corresponds to the best space complexity that both the projection method, and the so-called grouping method can give for the initial DAG. We also describe how the new allocation method can be implemented in tools.     

E-health support services based on service-oriented architecture

This paper discusses an experimental scenario for an e-health monitoring system (EHMS) that uses a service-oriented architecture (SOA) as a model for deploying discovering, integrating, implementing, managing, and invoking e-health services. Such a model could help the healthcare industry to develop cost efficient and dependable healthcare services.     

基于SBM模型的中国省际全要素能源效率和污染排放效率研究

本文使用处理非期望产出的SBM模型，利用2001．2010年省际面板数据计算中国30省份的能源效率和污染排放效率及影响因素。研究结果表明，中国整体的能源效率和排放效率均在低位运行，有下滑趋势，且排放效率表现相对低落。随着时间推移，各省份和全国的能源效率和排放效率差距也有扩大趋势。整个研究期间，北京、上海、广东、海南等四地的能源效率和排放效率值均为1，实现了资源节约、环境友好的双赢局面，其他地区在节能减排上仍存在程度不一的改善空间。Tobit模型的回归结果表明，第二产业比重越高，越不利于提高各省能源效率和排放效率；政府影响力对各省能源效率和排放效率的影响为负值；贸易依存度、人口密度对各省能源效率和排放效率有正向显著影响。最后，本文提出了有关政策建议。     

省级气象资料传输监控平台的设计及实现

气象资料是开展气象预报、气象服务的根基。针对省级气象资料传输切换到CIMISS（China Integrated Meteorological Information Service System）系统后市县级缺乏有效资料监控手段的问题，在分析气象资料传输流程的基础上，本文给出气象资料传输监控平台的总体设计方案，提出利用时间单元偏移的方法来进行监控日志采集，完成数据库设计方案以及平台展示方案设计。监控平台采用MVC框架以及HTML、AJAX等技术实现气象资料的传输监控和质量统计等功能。平台面向省市县三级用户，为改进省级气象资料的传输质量提供可靠的技术保障。     

Development of online diary and self-management system on e-Healthcare for asthmatic children in Taiwan

Objective

Many regional programs of the countries educate asthmatic children and their families to manage healthcare data. This study aims to establish a Web-based self-management system, eAsthmaCare, to promote the electronic healthcare (e-Healthcare) services for the asthmatic children in Taiwan. The platform can perform real time online functionality based upon a five-tier infrastructure with mutually supportive components to acquire asthma diaries, quality of life assessments and health educations.

Methods

We have designed five multi-disciplinary portions on the interactive interface functioned with the analytical diagrams: (1) online asthma diary, (2) remote asthma assessment, (3) instantaneous asthma alert, (4) diagrammatical clinic support, and (5) asthma health education. The Internet-based asthma diary and assessment program was developed for patients to process self-management healthcare at home. In addition, the online analytical charts can help healthcare professionals to evaluate multi-domain health information of patients immediately.

Results

eAsthmaCare was developed by Java™ Servlet/JSP technology upon Apache Tomcat™ web server and Oracle™ database. Forty-one voluntary asthmatic children (and their parents) were intervened to examine the proposed system. Seven domains of satisfiability assessment by using the system were applied for approving the development. The average scores were scaled in the acceptable range for each domain to ensure feasibility of the proposed system.

Conclusion

The study revealed the details of system infrastructure and developed functions that can help asthmatic children in self-management for healthcare to enhance communications between patients and hospital professionals.     

普适医疗信息管理与服务的关键技术与挑战

随着区域医疗、移动医疗、转化医学等新兴技术的应用和发展,电子病历、电子健康档案及转化基因等数据都呈现出爆炸式增长.为了将医疗服务信任问题渗透到人与医疗信息系统可信交互中,提供以个性化、一体化、协同化与知识化为特征的现代医疗服务,必须有效管理和分析这些海量数据资源,达到医疗信息与服务普适共享、广大民众普遍受惠的目的.文中以社会计算为背景,提出一种面向现代医疗服务模式的普适医疗信息管理与服务体系PMIMSS,讨论了普适医疗面临的关键技术问题,包括医疗信息共享与集成、医疗知识发现与服务、医疗服务质量评价、个性化医疗服务推荐,以及基于人与医疗信息系统交互的可信机制等,并总结了当前的研究现状和进展,详细分析了存在的挑战性问题,提出了未来的研究方向.     

Convergence agent model for developing u-healthcare systems

Ubiquitous networking, the interlinking of computers, consumer electronics, automobiles, home appliances, medical equipment, transportation systems, animals and plants through an overlapping of electronic spaces and physical spaces, is expected to provide an environment that enhances our lives through improved convenience, efficiency and safety in all areas.Today’s healthcare industry emphasizes safety, efficiency, patient-oriented approach, timeliness and balance. u-healthcare makes it possible to safely deliver appropriate services from any location at any time. To explain the emergence of u-healthcare, one must invoke, first of all, progress in IT and medical technology, and then the desire and willingness on the part of health care institutions to adopt the new service concept and increased health care demand. Digitalization of information, introduction of broadband communication and leaps made in healthcare technology in recent years have provided the technological capacity necessary for the achievement of u-healthcare.Based on recently reached theoretical results, integrating u-healthcare environments in virtual organizations, we proposed a methodology for the design and implementation of u-healthcare, linking the distributed mobile agents with medical entities into a convergence and collaborative environment. Despite the challenges in implementing and deploying u-healthcare, the advantages of ubiquitous healthcare are enabled by our smart model for the soft computing endowed ubiquitous. The strength of our approach is that it relates to u-healthcare a system which consists of its dual character emerging from the synergetic interaction between ubiquitous computing techniques and the mobile devices in convergence manner. We have developed a set of initial healthcare agent services on the mobile collaboration framework. We evaluate the functionality and effectiveness of our convergence mobile agent, whether the platform can manage services based on the model and whether the results of u-healthcare services discovery could satisfy user’s requirements with example cases.     

A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level

Due to its vital role in healthcare system, performance evaluation of hospitals is indispensable. In addition, hospitals try to achieve desired and efficient conditions by careful planning based on their present facilities. Several studies have been conducted on hospital evaluation, but nearly none of them has taken into consideration the difference in the nature of performance in respect of hospitals’ managerial construction, funding, and type of services provided by them. Furthermore, hospitals’ outputs have not been estimated in respect of cause and effect relationships between inputs and outputs for achieving efficient conditions. In the present study, first, a new approach for hospital evaluation is presented according to the differences in the nature of their performances while categorized in groups. Then, optimal outputs for each hospital in its own group are dealt with using results obtained from multi-group data envelopment analysis and the method of fuzzy cognitive map. The activation Hebbian learning (AHL) algorithm is adapted to the concept of efficiency and is conducted to estimate the outputs of inefficient hospitals. In the present study, 27 hospitals located in the provincial capitals in northwest of Iran are categorized in four groups including general governmental hospitals, specialty governmental hospitals, private hospitals, and social security hospitals. Afterward, optimal outputs are estimated for the inefficient hospitals by using the proposed modified AHL algorithm. The results indicate that when the hospitals have been evaluated in groups, efficiency scores of hospitals have changed. Also, given the cause and effect relationships between inputs and outputs in each group can help to decision and policy makers to estimate the optimal outputs that have caused inefficient hospitals become efficient.

     

iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization

Nowadays, crowd-sourced review websites provide decision support for various aspects of daily life, including shopping, local services, healthcare, etc. However, one of the most important challenges for existing healthcare review websites is the lack of personalized and professionalized guidelines for users to choose medical services. In this paper, we develop a novel healthcare recommendation system called iDoctor, which is based on hybrid matrix factorization methods. iDoctor differs from previous work in the following aspects: (1) emotional offset of user reviews can be unveiled by sentiment analysis and be utilized to revise original user ratings; (2) user preference and doctor feature are extracted by Latent Dirichlet Allocation and incorporated into conventional matrix factorization. We compare iDoctor with previous healthcare recommendation methods using real datasets. The experimental results show that iDoctor provides a higher predication rating and increases the accuracy of healthcare recommendation significantly.     

Image retrieval based on incremental subspace learning

Many problems in information processing involve some form of dimensionality reduction, such as face recognition, image/text retrieval, data visualization, etc. The typical linear dimensionality reduction algorithms include principal component analysis (PCA), random projection, locality-preserving projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In this paper, we propose a semi-supervised subspace learning algorithm for image retrieval. In relevance feedback-driven image retrieval system, the user-provided information can be used to better describe the intrinsic semantic relationships between images. Our algorithm is fundamentally based on LPP which can incorporate user's relevance feedbacks. As the user's feedbacks are accumulated, we can ultimately obtain a semantic subspace in which different semantic classes can be best separated and the retrieval performance can be enhanced. We compared our proposed algorithm to PCA and the standard LPP. Experimental results on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.     

A complete FDH efficiency analysis of a diffused production network: the case of the Italian driver and vehicle agency

In a multiple input–output setting, we offer an example of a full‐blown efficiency analysis based on the free disposal hull assumption of a non‐convex technology. On a cross section of 92 units, for the time period 2006–2007, we measure technical and allocative efficiency, cost efficiency, as well as returns to scale and technical progress. Furthermore, by introducing a new cost‐efficiency measure that is corrected for differences in the scale of operations (average cost efficiency), we are able to obtain an estimate of cost reductions ensuing from network restructuring based on optimal scale sizes. A detailed regional analysis of results is presented. Differences in efficiency scores, ascertained across space and time, are assessed by means of non‐parametric inference testing. The input ratio is found not to be statistically correlated with cost‐efficiency scores. Local provision of public services at a provincial level results in a considerable amount of average cost inefficiency.     

ILP turns 20

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.     

Multi-focal nematode image stack classification using a projection-based multi-linear method

In digital multi-focal images (DMIs), morphological and topological information for a transparent specimen can be captured in the form of a stack of high-quality images. We propose to use projection methods such as coefficient of variation projection to exploit the entire information of a given DMI stack using its projection images from different directions. Besides, multiple features extracted from the projection images along different directions are combined by using canonical correlation analysis. Because DMI stacks represent the effect of different factors—texture, the directions of projection, different instances within the same class and different classes of objects, we embed the projection method within a multi-linear analysis framework to propose a multiple direction projection-based multi-linear classification approach. The experimental results on the nematode data show that our proposed classifier can achieve very reliable recognition rate (98.5%) on a real-life database, even we only use the texture feature instead of the combination of texture and shape features as in a previous work.     

An evidential fusion approach for activity recognition in ambient intelligence environments

With the growing emergence of ambient intelligence, ubiquitous computing, sensor networks and wireless networking technologies, “ubiquitous networked robotics” is becoming an active research domain of intelligent autonomous systems. It targets new innovative applications in which robotic systems will become part of these networks of artifacts to provide novel capabilities and various assistive services anywhere and anytime, such as healthcare and monitoring services for elderly in Ambient Assisted Living (AAL) environments. Situation recognition, in general, and activity recognition, in particular, provide an added value on the contextual information that can help the ubiquitous networked robot to autonomously provide the best service that meet the needs of the elderly. Dempster–Shafer theory of evidence and its derivatives are an efficient tool to handle uncertainty and incompleteness in smart homes and ubiquitous computing environments. However, their combination rules yield counter-intuitive results in high conflicting activities. In this paper, we propose a new approach to support conflict resolution in activity recognition in AAL environments. This approach is based on a new mapping for conflict evidential fusion to increase the efficiency and accuracy of activity recognition. It gives intuitive interpretation for combining multiple sources in all conflicting situations. The proposed approach, evaluated on a real world smart home dataset, achieves 78% of accuracy in activity recognition. The obtained results outperform those obtained with the existing combination rules.     

Transportation and Regional Economic Development: Analysis of Spatial Spillovers in China Provincial Regions

The role of spillover effect of transportation endowment on regional economic development is analyzed in this paper. We adopt the reduced form from the Solow growth model to estimate spillover effect from transportation. The panel database in use incorporates provincial gross regional product (GRP), labor and capital supply, and transportation investment information from 1985 to 2012 in China. The results confirm positive and significant spillover effect in Chinese provinces. In this paper, non-homogeneous spillover effects are captured in the empirical regression by the use of spatial weighing methods based on provincial economics and similarity as well as geographic connection. Highly positive spillovers are observed between economically similar provinces. However, for those under-developed provinces, high network connectivity often results in low or negative spillovers. The mobility and migration of production factors are believed to be the sources of the negative spillovers, while the industrial reallocation and market expansion contribute to the positive spillovers.     

Cooperative-Competitive Healthcare Service Negotiation

Service negotiation is a complex activity, especially in complex domains such as healthcare. The provision of healthcare services typically involves the coordination of several professionals with different skills and locations. There is usually negotiation between healthcare service providers as different services have specific constraints, variables, and features (scheduling, waiting lists, availability of resources, etc.), which may conflict with each other. While automating the negotiation processes by using software can improve the effciency and quality of healthcare services, most of the existing negotiation automations are positional bargaining in nature, and are not suitable for complex scenarios in healthcare services. This paper proposes a cooperative-competitive negotiation model that enables negotiating parties to share their knowledge and work toward optimal solutions. In this model, patients and healthcare providers work together to develop a patient-centered treatment plan. We further automate the new negotiation model with software agents.     

Internet of Things in Healthcare: Architecture,Applications, Challenges,and Solutions

Healthcare, the largest global industry, is undergoing significant transformations with the genesis of a new technology known as the Internet of Things (IoT). Many healthcare leaders are investing more money for transforming their services to harness the benefits provided by IoT, thereby paving the way for the Internet of Medical Things (IoMT), an extensive collection of medical sensors and associated infrastructure. IoMT has many benefits like providing remote healthcare by monitoring health vitals of patients at a distant place, providing healthcare services to elderly people, and monitoring a large group of people in a region or country for detection and prevention of epidemics. This paper provides a review of IoT in the healthcare domain by first describing the enabling technologies for delivering smart healthcare, followed by some of the key applications of IoT in healthcare. Next, a fog-based architecture consisting of three layers for IoT-based healthcare applications is proposed. Finally, we focus on some of the open challenges of IoT in healthcare, like fault tolerance, interoperability, latency, energy efficiency, and availability. Existing solutions for these challenges are also discussed.     

夜间灯光数据估算中国省域碳排放与国际碳数据库分配的碳排放比较

快速准确获取省域碳排放数据是实时制定差异化碳减排政策的前提。基于DMSP/OLS和NPP-VIIRS夜间灯光数据，采用统计数据比较法提取1997~2017年中国大陆各省域（不包括西藏）建成区的夜间灯光总值（用TDN表示），并利用1997~2014年各省域的TDN值与同期核算的碳排放量建立各省域碳排放预测模型。然后，以2015~2017年的TDN值为自变量估算中国各省域的碳排放量；同时，利用熵值法和碳排放分配模型将四大国际权威数据库（IEA、EIA、EDGAR和CEADs）发布的中国碳排放量分配至各省；最后，将估算结果与四大典型碳数据库分配的省域碳排放值进行比较。研究表明：估算的省域碳排放量与分配的省域碳排放量大体一致，平均绝对百分比误差（MAPE）为6.45%~9.12%，并且基于夜间灯光数据估算的省域碳排放量与IEA和EIA数据库分配的碳排放量更为接近；各省域估算的碳排放量与分配的碳排放量均落在1∶1线附近；单个省域的MAPE值变化在0.68%~14.85%，且多数省域的MAPE值均在10.0%以内。上述结果证明，基于夜间灯光数据通过提取TDN值估算省域碳排放量具有可行性和准确性。