Validating the Automated Assessment of Participation and of Collaboration in Chat Conversations

International audienceAs Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to classic educational scenarios, the need for automated tools capable of supporting tutors in the time consuming process of analyzing conversations becomes more stringent. Moreover, in order to fully explore the benefits of such scenarios, a clear demarcation must be made between participation or active involvement, and collaboration that presumes the intertwining of ideas or points of view with other participants. Therefore, starting from a cohesion-based model of the discourse, we propose two computational models for assessing collaboration and participation. The first model is based on the cohesion graph and can be perceived as a longitudinal analysis of the ongoing conversation, thus accounting for participation from a social knowledge-building perspective. In the second approach, collaboration is regarded from a dialogical perspective as the intertwining or overlap of voices pertaining to different speakers, therefore enabling a transversal analysis of subsequent discussion slices

Fostering Joint Innovation: A Global Online Platform for Ideas Sharing and Collaboration

In today's world, where moving forward hinges on innovation and working together, this article introduces a new global online platform that is all about sparking teamwork to come up with new ideas. This platform goes beyond borders and barriers between different fields, creating an exciting space where people from all over the world can swap ideas, get helpful feedback, and team up on exciting projects. What sets our platform apart is its ability to tap into the combined brainpower of a diverse bunch of users, giving people the power to come up with game-changing ideas that tackle big global problems. By making it easy for people to share ideas and promoting a culture of working together, our platform is like a buddy for innovation, boosting creativity and problem-solving on a global level. This article spills the details on what the platform aims to do, how it works, and what makes it special, emphasizing how it can kickstart creativity, ramp up problem-solving skills, and get different fields collaborating. It is not just a tool it is a whole new way of teaming up to make daily life better and build a global community of problem-solving pals.Comment: 5 pages, 5 figures, ITNG 2024 21st International Conference on Information Technology. Las Vegas, Nevada, US

Econometric Approach of the Scenarios regarding the Impact of the Consumer's Empowerment and Companies' Responsibility for Environment Sustainability on the Electricity Market Performance

Energy is a major component of the economy, both as a sector in itself and as an input factor to all other economic activities. This sector is facing major challenges regarding increasing oil prices, severity of climate change or extremely complex implications of the global financial crisis. Organized as an empirical study, based on econometric analysis supported by a rigorous literature review, the paper studies possible correlations between the performance of electricity market, renewable resource consumption, consumers' behaviour, the influence of economic environment and economic development. It also aims to encourage a new and wider research framework regarding the implications of economic policies' use on consumers' perception. The results of the study indicate that the early stage of renewable energy use and the prospect of influencing the consumer behaviour in a way to increase the market performance, through the development of strategies oriented towards sustainable energy consumption, can have a positive impact on companies’ responsibility. It is concluded that consumers' empowerment stimulates competition, raises efficiency and rethinks companies' strategies for environment sustainability

Identification of temperature profile and heat transfer on a dielectric membrane for gas sensors by `COSMOS' program simulation

The application of commercial 3-D software `COSMOS' for the design and thermal analysis of the low power consumption test structures with dielectric membrane for gas microsensors is presented. Within this work, the simulation provides the estimation of the temperature profile on the active area and the whole membrane including the four bridges and the heating efficiency in the temperature range 20-500 °C. Unravelling of the heat loss mechanisms in terms of radiation, convection, conduction by air and solid materials during heat transfer on the dielectric membrane is reported for the first time as a mean to evaluate by 3-D simulation the contribution of technological processes and lay-out design to the total heat losses

A simple model of ac hopping surface conductivity in ionic liquids

The boundary conditions proposed to discuss the charge exchange taking place in an ionic liquid in contact with non-blocking electrodes are reconsidered in a dynamic situation. Assuming that the variation of the bulk ionic current density depends linearly on the surface value of the ionic current density, the frequency dependence of the phenomenological parameter is determined. The analysis has been performed in the framework where the relaxation times are smaller than a maximum relaxation time τM, and that the response function is independent on the value of the relaxation time. Using simple physical considerations, an expression for the surface conductivity describing the ionic charge exchange at the electrode is obtained. According to our calculations, its frequency dependence is similar to that predicted for the electric conductivity in disordered materials when the mechanism is of the hopping type. From measurements of impedance spectroscopy, by the best fit of the experimental data, the temperature dependence of the hopping time, of the dc surface conductivity, and of the diffusion coefficient are derived. They are in good agreement with the theoretical predictions obtained with the random distribution of surface energy barrier. Keywords: Ionic liquids, Non-blocking electrodes, Electrical impedance spectroscopy, AC hopping surface conductivit

Software Requirements Specification of A University Class Scheduler

The University Class Scheduler (UCS) presented in this paper is a novel scheduling tool intended to be used by universities to schedule classes into classrooms. In essence, UCS allows university administrators to enter relevant college and building information, schedule the input classes (courses) into input classrooms, and create web pages that provide detailed schedule information on a semester-by-semester basis. The UCS, which performs the scheduling of classes according to a number of user-selected parameters, can be easily adapted for applications outside the academic realm. This paper presents the main aspects of the University Class Scheduler’s UML-based specification, gives details of the UCS’ current development status, and points to a series of possible extensions that we intend to investigate in the near future

MELPF version 1: Modeling Error Learning based Post-Processor Framework for Hydrologic Models Accuracy Improvement

This paper studies how to improve the accuracy of hydrologic models using machine-learning models as postprocessors and presents possibilities to reduce the workload to create an accurate hydrologic model by removing the calibration step. It is often challenging to develop an accurate hydrologic model due to the time-consuming model calibration procedure and the nonstationarity of hydrologic data. Our findings show that the errors of hydrologic models are correlated with model inputs. Thus motivated, we propose a modeling-error-learning-based post-processor framework by leveraging this correlation to improve the accuracy of a hydrologic model. The key idea is to predict the differences (errors) between the observed values and the hydrologic model predictions by using machine-learning techniques. To tackle the nonstationarity issue of hydrologic data, a moving window-based machine-learning approach is proposed to enhance the machine-learning error predictions by identifying the local stationarity of the data using a stationarity measure developed based on the Hilbert–Huang transform. Two hydrologic models, the Precipitation–Runoff Modeling System (PRMS) and the Hydrologic Modeling System (HEC-HMS), are used to evaluate the proposed framework. Two case studies are provided to exhibit the improved performance over the original model using multiple statistical metrics