Abstract
Peer Observation of Teaching has raised a lot of interest as a device for quality enhancement of teaching. While much research has focused on its models, implementation schemes and feedback to the observed, little attention has been paid to what the observer actually sees and can learn from the observation. A multidisciplinary peer observation of teaching program is described, and its data is used to identify the pedagogical aspects to which lecturers pay more attention to when observing classes. The discussion addresses the valuable learning opportunities for observers provided by this program, as well as its usefulness in disseminating, sharing and clarifying quality teaching practices. The need for further research concerning teacher-student relationships and students' engagement is also suggested.

Abstract
CloudAnchor is a multiagent e-commerce platform which offers brokerage and resource trading services to Infrastructure as a Service (IaaS) providers and consumers. The access to these services requires the prior negotiation of Service Level Agreements (SLA) between the parties. In particular, the brokerage SLA (bSLA), which is mandatory for a business to have access to the platform, specifies the brokerage fee the business will pay every time it successfully trades a resource within the platform. However, while the negotiation of the resource SLA (rSLA) includes the uptime of the service, the brokerage SLA was negotiated for an unspecified time span. Since the commercial relationship defined through the bSLA - between a business and the platform can be long lasting, it is essential for businesses to be able to renegotiate the bSLA terms, i.e., renegotiate the brokerage fee. To address this issue, we designed a bSLA renegotiation mechanism, which takes into account the duration of the bSLA as well as the past behaviour (trust) and success (transactions) of the business in the CloudAnchor platform. The results show that the implemented bSLA renegotiation mechanism privileges, first, the most reliable businesses, and, then, those with higher volume of transactions, ensuring that the most reliable businesses get the best brokerage fees and resource prices. The proposed renegotiation mechanism promotes the fulfilment of SLA by all parties and increases the satisfaction of the trustworthy businesses in the CloudAnchor platform.

Abstract
This paper describes a forgetting technique for the live update of viewer profiles based on individual sliding windows, fading and incremental matrix factorization. The individual sliding window maintains, for each viewer, a queue holding the last n viewer ratings. As new viewer events occur, they are inserted in the viewer queue, by shifting and fading the queue ratings, and the viewer latent model is faded. We explored time, rating-and-position and popularity-based fading techniques, using the latter as the base fading algorithm. This approach attempts to address the problem of dynamic viewer profile updating (volatile preferences) as well as the problem of bounded processing resources (fixed size queues). The results show that our approach outperforms previous approaches, improving the quality of the predictions.

Abstract
This study analyzes Standard & Poor’s 500 Index top 250 companies’ responses to Bloomberg’s disclosed calculations of CEO pay ratios. The results suggest that CEO pay ratios, CEO compensations and average worker compensations do not seem to be related to the decision to respond. They also indicate that many of the corporations have adopted a strategy of avoiding the issue or deflecting attention from it by either choosing not to respond or criticizing the technicalities of the calculation of the CEO pay ratios. Corporations that responded largely conceptualize and communicate the rationale for high executive compensation in performance-driven language.

Abstract
The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.

Abstract
This paper addresses the issue of learning time series forecasting models in changing environments by leveraging the predictive power of ensemble methods. Concept drift adaptation is performed in an active manner, by dynamically combining base learners according to their recent performance using a non-linear function. Diversity in the ensembles is encouraged with several strategies that include heterogeneity among learners, sampling techniques and computation of summary statistics as extra predictors. Heterogeneity is used with the goal of better coping with different dynamic regimes of the time series. The driving hypotheses of this work are that (i) heterogeneous ensembles should better fit different dynamic regimes and (ii) dynamic aggregation should allow for fast detection and adaptation to regime changes. We extend some strategies typically used in classification tasks to time series forecasting. The proposed methods are validated using Monte Carlo simulations on 16 real-world univariate time series with numerical outcome as well as an artificial series with clear regime shifts. The results provide strong empirical evidence for our hypotheses. To encourage reproducibility the proposed method is publicly available as a software package.