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Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
Leveraging Social Network Data for Analytical CRM Strategies - The Introduction of Social BI.
(2012)
Adaptive logistics : information management for planning and control of small series assembly
(2007)
Bitcoin is a cryptocurrency and is considered a high-risk asset class whose price changes are difficult to predict. Current research focusses on daily price movements with a limited number of predictors. The paper at hand aims at identifying measurable indicators for Bitcoin price movements and the development of a suitable forecasting model for hourly changes. The paper provides three research contributions. First, a set of significant indicators for predicting the Bitcoin price is identified. Second, the results of a trained Long Short-term Memory (LSTM) neural network that predicts price changes on an hourly basis is presented and compared with other algorithms. Third, the results foster discussions of the applicability of neural nets for stock price predictions. In total, 47 input features for a period of over 10 months could be retrieved to train a neural net that predicts the Bitcoin price movements with an error rate of 3.52 %.
Concept, scientific research and managerial applications of Provocative Coaching, according to the „Provocative Therapy“ of Prof. Dr. Frank Farrelly (University of Wisconsin, U.S.A) in terms of an application of the Provocative Communication Style in specific situations of practical leadership, especially in the role of a coach for their subordinates.
O Perfil Reiss apresenta no resultado uma visão da estrutura dos motivos e impulsos de uma pessoa. O ponto de saída para a análise do resultado é o conhecimento de que os motivos vitais (= valores e metas de sobrevivência) formam a moldura, na qual as competências e capacidades que uma pessoa possui possam se desabrochar de forma ideal. A partir desse princípio, não existe no Perfil Reiss nenhum resultado que se possa classificar como correto ou falso, nem como bom ou mau. Uma comparação entre o resultado do Perfil Reiss e o conteúdo de uma atividade apresentada ou ambicionada dá informação sobre até que ponto a capacidade de rendimento de uma pessoa nessa posição pode vir ou virá a se desenvolver por um longo tempo.
Explorer CEOs: The effect of CEO career variety on large firms’ relative exploration orientation
(2018)
Prior studies demonstrate that firms need to make smart trade-off decisions between exploration and exploitation activities in order to increase performance. Chief executive officers (CEOs) are principal decision makers of a firm’s strategic posture. In this study, we theorize and empirically examine how relative exploration orientation of large publicly listed firms varies based on the career variety of their CEOs – that is, how diverse the professional experiences of executives were prior to them becoming CEOs. We further argue that the heterogeneity and structure of the top management team moderates the impact of CEO career variety on firms’ relative exploration orientation. Based on multisource secondary data for 318 S&P 500 firms from 2005 to 2015, we find that CEO career variety is positively associated with relative exploration orientation.
Interestingly, CEOs with high career varieties appear to be less effective in pursuing exploration, when they work with highly heterogeneous and structurally interdependent top management teams.
Many of today’s factors make software development more and more complex, such as time pressure, new technologies, IT security risks, et cetera. Thus, a good preparation of current as well as future software developers in terms of a good software engineering education becomes progressively important. As current research shows, Competence Developing Games (CDGs) and Serious Games can offer a potential solution.
This paper identifies the necessary requirements for CDGs to be conducive in principle, but especially in software engineering (SE) education. For this purpose, the current state of research was summarized in the context of a literature review. Afterwards, some of the identified requirements as well as some additional requirements were evaluated by a survey in terms of subjective relevance.