Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry

Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization of industrial equipment, which generates extensive amounts of data. This information can be processed into useful knowledge with the use of machine learning algorithms. However, before the algorithms can effectively be applied, the data must go through an exploratory phase: assessing the meaning of the features and to which degree they are redundant. In this paper, we present the findings of the analysis conducted on a real-world dataset from a metallurgic company. A number of data analysis and feature selection methods are employed, uncovering several relationships, which are systematized in a rule-based model, and reducing the feature space from an initial 47-feature dataset to a 32-feature dataset.     

Towards model governance in predictive toxicology

Efficient management of toxicity information as an enterprise asset is increasingly important for the chemical, pharmaceutical, cosmetics and food industries. Many organisations focus on better information organisation and reuse, in an attempt to reduce the costs of testing and manufacturing in the product development phase. Toxicity information is extracted not only from toxicity data but also from predictive models. Accurate and appropriately shared models can bring a number of benefits if we are able to make effective use of existing expertise. Although usage of existing models may provide high-impact insights into the relationships between chemical attributes and specific toxicological effects, they can also be a source of risk for incorrect decisions. Thus, there is a need to provide a framework for efficient model management. To address this gap, this paper introduces a concept of model governance, that is based upon data governance principles. We extend the data governance processes by adding procedures that allow the evaluation of model use and governance for enterprise purposes. The core aspect of model governance is model representation. We propose six rules that form the basis of a model representation schema, called Minimum Information About a QSAR Model Representation (MIAQMR). As a proof-of-concept of our model governance framework we develop a web application called Model and Data Farm (MADFARM), in which models are described by the MIAQMR-ML markup language.     

外包的起源、发展及研究现状综述

对外包（outsourcing）的定义和内涵进行了分析与概括,总结了外包的起源和发展趋势,系统阐述了目前外包研究的主要基本观点及研究方向,对相关研究成果进行了综述。     

外包服务与管理复合型人才培养模式初探

随着外包产业的迅速发展,外包管理人才特别是＂服务＋管理＂复合型人才的缺口日益突出。为满足我国社会这一需求,研究和探索复合型人才的培养途径成为当务之急。本文从培养目标设置、教学要求设置、课程体系设置、师资队伍建设等几方面入手,对加强培养＂软件技术＋流程管理＂复合人才的培养模式进行了初步探讨。     

A value-based perspective on supporting and hindering factors for research data management

Research data management (RDM) is an important prerequisite for a substantial and sustainable contribution to knowledge. There is a pressing need to examine why researchers hesitate to store, annotate, share and manage their research data. To model underlying psychological factors influencing researchers’ refusal to conduct RDM, the social exchange theory is extended with elements from prospect theory. Thus, it allows psychological insights into researchers’ decision-making, and illustrates the role of cost and benefit evaluations under uncertainty. Data management policies of a major funding agency were presented to a homogeneous group of researchers from the Information Systems community in Germany. The findings show that many researchers see a high value in RDM but are still held back by uncertainty. While the benefits seem to outweigh the costs, we ascertain the uncertainty factors which hinder researchers’ intention from conducting RDM in the future. The perceived fear of losing control over one's data is identified as a major hindering factor, while the fear of losing one's unique value did not prevail. The study provides novel insights for executives, administrators, and developers in higher education institutions, which are especially important for furthering RDM implementation strategies, as well as for system development.     

研发外包促进欠发达地区产业转型升级路径研究——以青海省为例

研发外包作为一种开放式的研发形态,是外包领域的高级形态,研发基础薄弱的欠发达地区可以借助研发外包推动产业转型升级。从理论上分析借助研发外包的产业技术链升级的3种路径,结合青海省盐湖化工产业、沙棘果产业等案例分析具体措施,并对欠发达地区实施研发外包提出加大研发投入、在研发资源发达地区设立产业信息中心、委托高校和科研院所及其他企业等开展研发外包、与外部研发资源建立联合攻关虚拟组织等相关对策建议。     

广东省电子废弃物规范管理协同机制

为弥补目前广东省电子废弃物回收处理体系的不足,运用协同学的理论和方法,提出广东电子废弃物规范管理协同机制的概念,并分析其协同机理和能够获取的协同效应。该机制能够实现广东电子废弃物回收处理的各主体、全过程、多方位的集成管理。     

Human resources for Big Data professions: A systematic classification of job roles and required skill sets

The rapid expansion of Big Data Analytics is forcing companies to rethink their Human Resource (HR) needs. However, at the same time, it is unclear which types of job roles and skills constitute this area. To this end, this study pursues to drive clarity across the heterogeneous nature of skills required in Big Data professions, by analyzing a large amount of real-world job posts published online. More precisely we: 1) identify four Big Data ‘job families’; 2) recognize nine homogeneous groups of Big Data skills (skill sets) that are being demanded by companies; 3) characterize each job family with the appropriate level of competence required within each Big Data skill set. We propose a novel, semi-automated, fully replicable, analytical methodology based on a combination of machine learning algorithms and expert judgement. Our analysis leverages a significant amount of online job posts, obtained through web scraping, to generate an intelligible classification of job roles and skill sets. The results can support business leaders and HR managers in establishing clear strategies for the acquisition and the development of the right skills needed to leverage Big Data at best. Moreover, the structured classification of job families and skill sets will help establish a common dictionary to be used by HR recruiters and education providers, so that supply and demand can more effectively meet in the job marketplace.     

Real-time big data processing for anomaly detection: A Survey

The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed.     

浅析高校学生社团管理与建设新途径研究

在高校学生社团的蓬勃发展中,存在着社团管理与建设中的隐患。本文以情感管理模式为新途径研究的切入点,精心设计了如何在管理社团的过程中实践该模式的实证研究。针对管理社团中的难点,提供有建设性的措施,并付诸于实践,最终达到了良性的效果。不仅实现了情感管理模式的最终目标,开展了素质教育活动,而且还增强了学生的团队合作精神、社会适应能力,提高了他们的就业核心竞争力。     

A data science and open source software approach to analytics for strategic sourcing

Data science has emerged as a significant capability upon which firms compete. Although many data scientists and the high-performing companies that employ them seem to have developed robust methods to employ data sciences practices to achieve competitive advantages, there have been few attempts at defining and explaining how and why data science helps firms to achieve desired outcomes. In this paper, we describe how data science, which combines computer programming, domain knowledge, and analytic skillsets to scientifically extract insights from data, can be used to help meet the growing demand of analytic needs across an organization’s value chain. This is done through the illustration of an applied data science initiative to a strategic sourcing problem via the use of open-source technology. In doing so, we contribute to the growing data science literature by demonstrating the application of unique data science capabilities. Moreover, the paper provides a tutorial on how to use a specific R package along with an actual case in which that package use used.     

促进企业安全管理良性循环的问题驱动模型

为提高企业安全管理成效,促进企业安全管理体系持续改进和发展,引入循证安全管理思想,从企业安全生产管理实践的角度出发,分析企业安全管理的基本内涵以及发现和提出问题的重要性;进而对在安全生产管理实践中发现和提出问题的一个主体及三大要素进行分析,建立“基于问题发现型驱动”的企业安全管理良性循环的理论模型。以期给企业安全生产管理人员指出具体的实践方法,有效改善安全管理工作不到位、形式主义的状况,促进企业安全管理系统的良性循环。     

A reversible and secure patient information hiding system for IoT driven e-health

Internet of things (IoT) coupled with mobile cloud computing has made a paradigm shift in the service sector. IoT-assisted mobile cloud based e-healthcare services are making giant strides and are likely to change the conventional ways of healthcare service delivery. Though numerous approaches for preventing unauthorized access to information exchanged between a mobile phone and cloud platform do exist, but there is no security mechanism to prevent unauthorized access by the cloud administrators. With an aim to ensure security of client data such as Electronic Patient Records (EPR), we propose a novel high-capacity and reversible data hiding approach for securely embedding EPR within the medical images using Optimal Pixel Repetition (OPR). OPR converts every pixel of the input image to a 2 × 2 block to facilitate reversibility by ensuring all the pixels in a 2 × 2 block to have different values. Since a 2 × 2 block is comprised of 4-pixel elements, which could be arranged in sixteen possible ways; we generate a lookup table corresponding to sixteen possible positions of pixels. EPR hiding in each block is achieved by permuting the pixels of a block according to the four-bit word of secret data, resulting in a histogram invariant stego image. The histogram invariance improves the robustness of the proposed scheme to statistical attacks. A stego image is said to hide embedded data securely, when it provides better imperceptivity for an appreciably high payload. Thus, while using information embedding approach for securing client data on a mobile-cloud platform, high imperceptivity is a desirable feature. Experimental results show that average PSNR obtained is 42 dB for payload 1.25 bpp by our scheme, showing its effectiveness for preventing unauthorized access to client’s sensitive data.     

智力资本报告:大学评价与管理的新工具

智力资本报告是知识经济时代管理无形资产的一种战略工具,可以满足大学管理和评价智力资本的需求。分析大学智力资本的构成以及智力资本报告在欧洲的发展,阐述大学智力资本报告的主要内容,并就我国大学智力资本报告提出相关建议。     

浅谈中小型图书馆管理模式的创新路径

21世纪是一个依靠知识来加快发展的时代,在信息技术高速发展的今天,图书馆如何去抓住机遇,迎接挑战,建造一个具有自动化、网络化、高度社会化、信息化的图书馆,创新是一个重要的举措,也是目前中小型图书馆面临的最大的难题,本文主要针对技工学校图书馆管理模式进行阐述。     

福清市土地开发与管理决策支持系统设计及应用

以福清市土地开发与管理决策支持系统为例,提出了基于数据分析和功能分解的综合应用“3S”空间信息技术的土地管理决策信息系统设计范式和途径。建立了系统的实体－关系的数据分析模型和相应的数据流和数据库逻辑和物理结构,并利用设计的系统实现了福清市上迳乡土地变化动态J土地开发管理;地理信息系统;决策支     

高校后勤甲乙方管理效能提升之管见

我国多数高校后勤实施甲乙方管理体制,处理好后勤中甲乙方关系,为高校教育科研提供必要的基础保障非常重要.本文简要分析了高校甲乙方管理运行中存在的问题,并对提高甲乙方后勤管理运作效能提出了明确甲乙双方的地位和职责、使命和责任,科学界定双方的职责权限,坚持姓“教”,遵循经济效益和社会效益兼顾原则,建立科学的后勤监督考核体系,增加培训投入,提升管理能力等建议.     

A framework for social well-being and skills management at the workplace

Employees are the human capital that contributes to the success of high-performance and sustainable organizations. In the digital economy, organizations need to be competitive and differentiate them self through the knowledge they detained. This requires the adoption of innovative solutions that valorize and recognize workers who invest their time to keep up-to-date with areas of knowledge that are relevant to the organization. Indeed, organizations are investing in retaining skilled workers, in particular senior workers whose knowledge and experience are a valuable asset that should be passed to younger co-workers. One way to address these concerns is to create conditions to promote a feeling of employee’s wellness in their work environment.In this paper, the two perception of well-being at the workplace (i.e. physical and social) are combined with gamification techniques and analyzed how it might help employees to acquire the expertise (“soft and hard-skills”) they need to improve their curriculum. This is particularly relevant for older adults to whom the feeling of fulfilment and recognition is much appreciated. The case study relates to the Active@Work project. The proposed solution incorporates an “intelligent behavior” to keep the user aware of their well-being status, triggering notifications to mitigate the risk of fatigue or stress at work. The solution also provides support for workers to engage in new and rewarding activities, where his/her knowledge and experience is considered as a relevant contribution, promoting in this way employee recognition and social well-being at the workplace. Team management and tutoring of younger co-workers by older adults will be, together with a Skill Development Tool, some of the innovative features within the project.     

A case analysis of semantic technologies for R&D intermediation information management

It is evident that companies and research centres can find a myriad of instruments to fund their R&D (Research & Development) activities. Due to the dynamics of the information management in these circumstances there is a need for intermediary businesses intervention. Consequently, intermediary companies are thus involved in a complex process that needs to be managed and controlled. Within this scenario an Information Technology (IT) platform, named RDi-Advise, was developed by EgeoIT using semantic technologies to support R&D process decisions. This case analysis describes the development and implementation of the platform including the lessons learned during the activity period.