An integrated decision making approach for ERP system selection

Enterprise resource planning (ERP) systems have gained major prominence by enabling companies to streamline their operations, leverage and integrate business data process. In order to implement an ERP project successfully, it is necessary to select an ERP system which can be aligned with the needs of the company. Thus, a robust decision making approach for ERP software selection requires both company needs and characteristics of the ERP system and their interactions to be taken into account. This paper develops a novel decision framework for ERP software selection based on quality function deployment (QFD), fuzzy linear regression and zero–one goal programming. The proposed framework enables both company demands and ERP system characteristics to be considered, and provides the means for incorporating not only the relationships between company demands and ERP system characteristics but also the interactions between ERP system characteristics through adopting the QFD principles. The presented methodology appears as a sound investment decision making tool for ERP systems as well as other information systems. The potential use of the proposed decision framework is illustrated through an application.     

Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity

Accurate cropland information is of paramount importance for crop monitoring. This study compares five existing cropland mapping methodologies over five contrasting Joint Experiment for Crop Assessment and Monitoring (JECAM) sites of medium to large average field size using the time series of 7-day 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) mean composites (red and near-infrared channels). Different strategies were devised to assess the accuracy of the classification methods: confusion matrices and derived accuracy indicators with and without equalizing class proportions, assessing the pairwise difference error rates and accounting for the spatial resolution bias. The robustness of the accuracy with respect to a reduction of the quantity of calibration data available was also assessed by a bootstrap approach in which the amount of training data was systematically reduced. Methods reached overall accuracies ranging from 85% to 95%, which demonstrates the ability of 250 m imagery to resolve fields down to 20 ha. Despite significantly different error rates, the site effect was found to persistently dominate the method effect. This was confirmed even after removing the share of the classification due to the spatial resolution of the satellite data (from 10% to 30%). This underlines the effect of other agrosystems characteristics such as cloudiness, crop diversity, and calendar on the ability to perform accurately. All methods have potential for large area cropland mapping as they provided accurate results with 20% of the calibration data, e.g. 2% of the study area in Ukraine. To better address the global cropland diversity, results advocate movement towards a set of cropland classification methods that could be applied regionally according to their respective performance in specific landscapes.     

Exploring educational immersive videogames: an empirical study with a 3D multimodal interaction prototype

ABSTRACT

Gestural interaction devices emerged and originated various studies on multimodal human–computer interaction to improve user experience (UX). However, there is a knowledge gap regarding the use of these devices to enhance learning. We present an exploratory study which analysed the UX with a multimodal immersive videogame prototype, based on a Portuguese historical/cultural episode. Evaluation tests took place in high school environments and public videogaming events. Two users would be present simultaneously in the same virtual reality (VR) environment: one as the helmsman aboard Vasco da Gama’s fifteenth-century Portuguese ship and the other as the mythical Adamastor stone giant at the Cape of Good Hope. The helmsman player wore a VR headset to explore the environment, whereas the giant player used body motion to control the giant, and observed results on a screen, with no headset. This allowed a preliminary characterisation of UX, identifying challenges and potential use of these devices in multi-user virtual learning contexts. We also discuss the combined use of such devices, towards future development of similar systems, and its implications on learning improvement through multimodal human–computer interaction.     

Multi-contact vertical ladder climbing with an HRP-2 humanoid

We describe the research and the integration methods we developed to make the HRP-2 humanoid robot climb vertical industrial-norm ladders. We use our multi-contact planner and multi-objective closed-loop control formulated as a QP (quadratic program). First, a set of contacts to climb the ladder is planned off-line (automatically or by the user). These contacts are provided as an input for a finite state machine. The latter builds supplementary tasks that account for geometric uncertainties and specific grasps procedures to be added to the QP controller. The latter provides instant desired states in terms of joint accelerations and contact forces to be tracked by the embedded low-level motor controllers. Our trials revealed that hardware changes are necessary, and parts of software must be made more robust. Yet, we confirmed that HRP-2 has the kinematic and power capabilities to climb real industrial ladders, such as those found in nuclear power plants and large scale manufacturing factories (e.g. aircraft, shipyard) and construction sites.     

Examining the predictive relationship between personality and emotion traits and students’ agent-directed emotions: towards emotionally-adaptive agent-based learning environments

The current study examined the relationships between learners’ (\(N = 123\)) personality traits, the emotions they typically experience while studying (trait studying emotions), and the emotions they reported experiencing as a result of interacting with four pedagogical agents (agent-directed emotions) in MetaTutor, an advanced multi-agent learning environment. Overall, significant relationships between a subset of trait emotions (trait anger, trait anxiety) and personality traits (agreeableness, conscientiousness, and neuroticism) were found for four agent-directed emotions (enjoyment, pride, boredom, and neutral) though the relationships differed between pedagogical agents. These results demonstrate that some trait emotions and personality traits can be used to predict learners’ emotions directed toward specific pedagogical agents (with different roles). Results provide suggestions for adapting pedagogical agents to support learners’ (with certain characteristics; e.g., high in neuroticism or agreeableness) experience of adaptive emotions (e.g., enjoyment) and minimize their experience on non-adaptive emotions (e.g., boredom). Such an approach presents a scalable and easily implementable method for creating emotionally-adaptive, agent-based learning environments, and improving learner-pedagogical agent interactions in order to support learning.     

A method to select a successful interoperability solution through a simulation approach

Enterprise applications and software systems need to be interoperable in order to achieve seamless business across organizational boundaries and thus realize virtual networked organizations. Our proposition can be considered as an interoperability project selection approach and is based on three steps: (1) Modelling both collaborative business processes and potential related interoperability projects; (2) Evaluating the accessibility of each project regarding the current state of the organization; (3) Simulating each project and assessing the associated performance. These results are finally projected on a comparison matrix used as a decision support to select the most appropriate interoperability solution. An application case extracted from the French aerospace sector demonstrates the applicability and the benefits of the proposition.     

The EU data protection reform and the challenges of big data: remaining uncertainties and ways forward

As the first broad reform of the EU data protection legislation is being achieved, and notwithstanding EU institutions’ confident discourse, scepticism remains about the reform’s ability to safeguard the fundamental right to data protection in the face of evolving data processing techniques underlying so-called big data. Yet, one might wonder whether the cause for this difficulty should be ascribed mainly to technological progresses that the law finds it hard to deal with or rather to the policy choices embedded in the legal reform itself. Indeed, a new data protection enforcement model is being adopted, which relies heavily on risk assessment and management by the data controllers themselves. Likewise, data protection authorities see their supervisory role significantly weakened. These developments and the underlying rationality are discussed. Given the limitations of the risk-based approach as currently devised, we suggest that it be reappraised in consideration of risk regulatory experiences in other domains.     

Blind digital watermarking in PDF documents using Spread Transform Dither Modulation

In this paper, a blind digital watermarking scheme for Portable Document Format (PDF) documents is proposed. The proposed method is based on a variant Quantization Index Modulation (QIM) method called Spread Transform Dither Modulation (STDM). Each bit of the secret message is embedded into a group of characters, more specifically in their x-coordinate values. The method exhibits experiments of two opposite objectives: transparency and robustness, and is motivated to present an acceptable distortion value that shows sufficient robustness under high density noises attacks while preserving sufficient transparency.