¿Reemplazo demográfico en el Neolítico europeo? El punto de vista de la Genética

[ES] El trabajo realiza una aproximación a la situación actual de los estudios de ADN antiguo humano en Europa, recopilando los datos de los individuos analizados hasta 2013 (n=700), a modo de síntesis interpretativa continental y regional de los territorios para los cuales se han obtenido resultados significativos (Centroeuropa, Cornisa Cantábrica, Mediterráneo occidental, Escandinavia-Báltico-Rusia y Alpes orientales). Las hipótesis se expresan en términos de continuidad o discontinuidad genética entre los grupos humanos habitantes de un territorio, centradas en la problemática de la neolitización, en una horquilla cronocultural del Paleolítico superior a la Edad del Bronce. Los resultados se resumen en (1) una preponderancia del clado mitocondrial U para muestras preneolíticas; (2) la posibilidad de una intrusión démica en una fase inicial de la neolitización centroeuropea -tipo N1a, con pérdida posterior de ese haplogrupo mitocondrial-; (3) la evidencia del proceso neolitizador como heterogéneo y con diferente impacto a escala regional; (4) una estabilización del acervo genético europeo actual como resultado de eventos postneolíticos; y (5) las posibilidades analíticas de la genética aplicada a las poblaciones antiguas como un instrumento de gran interés, observándose la necesidad de realizar más analíticas con recorrido diacrónico.

processline: visualizing time-series data in process industry

In modern process industry, it is often difficult to analyze a manufacture process due to its umerous time-series data. Analysts wish to not only interpret the evolution of data over time in a working procedure, but also examine the changes in the whole production process through time. To meet such analytic requirements, we have developed ProcessLine, an interactive visualization tool for a large amount of time-series data in process industry. The data are displayed in a fisheye timeline. ProcessLine provides good overviews for the whole production process and details for the focused working procedure. A preliminary user study using beer industry production data has shown that the tool is effective.

基于可视化分析的交互式仪表盘系统的设计与实现

面对日益激烈的市场竞争，企业需要更快更及时地了解和掌握业务运行状况，提高快速响应市场变化的能力。仪表盘（Dashboard）是反映公司状况和商业环境并辅助商务智能和绩效管理活动的有力工具。目前仪表盘的研究和应用在信息呈现方面特别是在显示关键绩效指标信息方面得到了广泛的开展，但是对于经营决策者们决策过程中信息利用活动的认知和操纵特点重视不足，决策过程中用户使用仪表盘的交互负担过重，干扰了决策思维活动的有效进行。可视化分析（Visual Analytics）以分析推理过程的研究为理论基础，将人机交互技术和信息可视化技术紧密结合，通过各种交互技术辅助用户洞察信息中隐藏的规律与模式，进而为经营决策提供有力的支持。本文拟基于可视化分析理论，以制造企业的经营决策为切入点，将笔手势和语音多通道交互技术引入到仪表盘的设计和使用过程中，全面研究了交互式仪表盘的设计及运行界面，开发了基于可视化分析的交互式仪表盘系统，提高了仪表盘设计人员的工作效率，减轻了企业决策者进行信息分析的交互操作负担。 论文的主要工作包括以下几个方面： 1.　分析了仪表盘系统的传统研发过程及应用特点，研究了可视化分析推理的过程和面向用户目标的可视化分析任务的分类方法，在此基础上建立了交互式仪表盘系统的业务流程，介绍了系统功能体系及体系结构。 2. 针对交互式仪表盘设计界面，研究了面向任务目标分析的概念图绘制工具，给出了概念网络图布局算法；阐述了数据特点及信息领域模型，并研究了数据定制的用户界面设计；从可视结构和可视化分析任务两方面分析了具有代表性的十五种可视化隐喻的特点，并提出了基于目标任务集与仪表盘所支持任务集的相似度确定符合用户目标的可视化隐喻推荐算法。 3. 针对交互式仪表盘运行界面，提出了可视化分析过程中支持交互操作的笔手势和语音的多通道交互方式，研究了多通道输入信息融合技术；实现了基于领域信息模型的多视图关联协调技术和基于直接操纵的动态过滤技术。 4. 在上述研究工作的基础上，设计并开发了面向制造企业生产经营状况分析的交互式仪表盘原型系统，并通过具体的实例介绍了系统的应用流程，应用结果表明系统具有良好的应用效果。

ΑΝΑΛΥΣΙΣ ΠΕΡΙ ΤΑ ΣΧΗΜΑΤΑ. Restoring Aristotle’s Lost Diagrams of the Syllogistic Figures

The article examines the relevance of Aristotle’s analysis that concerns the syllogistic figures. On the assumption that Aristotle’s analytics was inspired by the method of geometric analysis, we show how Aristotle used the three terms (letters), when he formulated the three syllogistic figures. So far it has not been appropriately recognized that the three terms — the major, the middle and the minor one — were viewed by Aristotle syntactically and predicatively in the form of diagrams. Many scholars have misunderstood Aristotle in that in the second and third figure the middle term is outside and that in the second figure the major term is next to the middle one, whereas in the third figure it is further from it. By means of diagrams, we have elucidated how this perfectly accords with Aristotle's planar and graphic arrangement. In the light of these diagrams, one can appropriately capture the definition of syllogism as a predicative set of terms. Irrespective of the tricky question concerning the abbreviations that Aristotle himself used with reference to these types of predication, the reconstructed figures allow us better to comprehend the reductions of syllogism to the first figure. We assume that the figures of syllogism are analogous to the figures of categorical predication, i.e., they are specific syntactic and semantic models. Aristotle demanded certain logical and methodological competence within analytics, which reflects his great commitment and contribution to the field.

Design principles for an enterprise systems chartering method

Our research follows a design science approach to develop a method that supports the initialization of ES implementation projects – the chartering phase. This project phase is highly relevant for implementation success, but is understudied in IS research. In this paper, we derive design principles for a chartering method based on a systematic review of ES implementation literature and semi-structured expert interviews. Our analysis identifies differences in the importance of certain success factors depending on the system type. The proposed design principles are built on these factors and are linked to chartering key activities. We specifically consider system-type-specific chartering aspects for process-centric Business Intelligence & Analytics (BI&A) systems, which are an emerging class of systems at the intersection of BI&A and business process management. In summary, this paper proposes design principles for a chartering method – considering specifics of process-centric BI&A.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Association for Learning Technology Annual Survey 2014 Data and Report

This is the first report from ALT’s new Annual Survey launched in December 2014. This survey was primarily for ALT members (individual or at an organisation which is an organisational member) it could however also be filled in by others, perhaps those interested in taking out membership. The report and data highlight emerging work areas that are important to the survey respondents. Analysis of the survey responses indicates a number of areas ALT should continue to support and develop. Priorities for the membership are ‘Intelligent use of learning technology’ and ‘Research and practice’, aligned to this is the value placed by respondent’s on by communication via the ALT Newsletter/News, social media and Research in Learning Technology. The survey also reveals ‘Data and Analytics’ and ‘Open Education’ are areas where the majority of respondents are finding are becoming increasingly important. As such our community may benefit from development opportunities ALT can provide. The survey is also a reminder that ALT has an essential role in enabling members to develop research and practice in areas which might be considered as minority interest. For example whilst the majority of respondents didn't indicate areas such as ‘Digital and Open Badges’, and ‘Game Based Learning’ as important there are still members who consider these areas are very significant and becoming increasingly valuable and as such ALT will continue to better support these groups within our community. Whilst ALT has conducted previous surveys of ALT membership this is the first iteration in this form. ALT has committed to surveying the sector on an annual basis, refining the core question set but trying to preserve an opportunity for longitudinal analysis.

Informal Learning in Online Knowledge Communities: Predicting Community Response to Visitor Inquiries

Nistor, N., Dascalu, M., Stavarache, L.L., Serafin, Y., & Trausan-Matu, S. (2015). Informal Learning in Online Knowledge Communities: Predicting Community Response to Visitor Inquiries. In G. Conole, T. Klobucar, C. Rensing, J. Konert & É. Lavoué (Eds.), 10th European Conf. on Technology Enhanced Learning (pp. 447–452). Toledo, Spain: Springer.

Chapter 2: Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis

Nistor, N., Dascalu, M., Stavarache, L.L., Tarnai, C., & Trausan-Matu, S. (2015). Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis. In Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk & N.-S. Chen (Eds.), State-of-the-Art and Future Directions of Smart Learning (Vol. Lecture Notes in Educational Technology, pp. 13–17). Berlin, Germany: Springer-Verlag Singapur

Visualization of polyphonic voices inter-animation in CSCL chats

Trausan-Matu, S., & Dascalu, M. (2015). Visualization of Polyphonic Voices Inter-animation in CSCL Chats. Revista Romana de Interactiune Om-Calculator, 8(4), 305–322.

ReaderBench: Automated evaluation of collaboration based on cohesion and dialogism

Dascalu, M., Trausan-Matu, S., McNamara, D.S., & Dessus, P. (2015). ReaderBench – Automated Evaluation of Collaboration based on Cohesion and Dialogism. International Journal of Computer-Supported Collaborative Learning, 10(4), 395–423. doi: 10.1007/s11412-015-9226-y

Organizational and financial transparency in the website of the foundations: an empirical study for Spain

Transparency in nonprofit sector and foundations, as an element to enhance the confidence of stakeholders in the organization, is a fact shown by several studies in recent decades. Transparency can be considered in various fields and through different channels. In our study we focused on the analysis of the organizational and economic transparency of foundations, shown through the voluntary information on their Website. We review the theoretical previous studies published to put to the foundations within the framework of the social economy. This theoretical framework has focused on accountability that make foundations in relation to its social function and its management, especially since the most recent focus of information transparency across the Website.In this theoretical framework was made an index to quantify the voluntary information which is shown on its website. This index has been developed ad hoc for this study and applied to a group of large corporate foundations.With the application of these data are obtained two kind of results, to a descriptive level and to a inferential level.We analyzed the statistical correlation between economic transparency and organizational transparency offered in the Website through quantified variables by a multiple linear regression. This empirical analysis allows us to draw conclusions about the level of transparency offered by these organizations in relation to their organizational and financial information, as well as explain the relation between them.

Rigorous Specification and Low-Latency Implementation of Technical Market Indicators

Technical market indicators are tools used by technical an- alysts to understand trends in trading markets. Technical (market) indicators are often calculated in real-time, as trading progresses. This paper presents a mathematically- founded framework for calculating technical indicators. Our framework consists of a domain specific language for the un- ambiguous specification of technical indicators, and a run- time system based on Click, for computing the indicators. We argue that our solution enhances the ease of program- ming due to aligning our domain-specific language to the mathematical description of technical indicators, and that it enables executing programs in kernel space for decreased latency, without exposing the system to users’ programming errors.

Repackaging Prostate Cancer Support Group Research Findings: An e-KT Case Study

n the context of psychosocial oncology research, disseminating study findings to a range of knowledge “end-users” can advance the well-being of diverse patient subgroups and their families. This article details how findings drawn from a study of prostate cancer support groups were repackaged in a knowledge translation website—www.prostatecancerhelpyourself.ubc.ca—using Web 2.0 features. Detailed are five lessons learned from developing the website: the importance of pitching a winning but feasible idea, keeping a focus on interactivity and minimizing text, negotiating with the supplier, building in formal pretests or a pilot test with end-users, and completing formative evaluations based on data collected through Google™ and YouTube™ Analytics. The details are shared to guide the e-knowledge translation efforts of other psychosocial oncology researchers and clinicians.