Experimental evaluation of the impact of b-learning methodologies on engineering students in Spain

The aim of this article is to highlight the importance of an active learning methodology in engineering degrees in Spain. We present the outcomes of an experimental study carried out during the academic years 2007/2008 and 2008/2009 with engineering students at the University of Salamanca (Spain). In the present research, as we have done in previous ones, we have selected a subject which is common to the four degrees under consideration: Computer Science. This study explores in greater depth the validity of experimental designs coming from educational research and the impact of innovative teaching methodologies. The hypothesis that impulses this research is formulated to ascertain that the learning level and the satisfaction of students will be higher after the implementation of new teaching methodologies (based on constructive learning, collaborative work, and blended learning resources), than in more traditional teaching contexts. The obtained results partially confirm this hypothesis. The ultimate purpose of this work is that of providing evidence that contributes to the improvement of education and teaching methods for a better performance of students in engineering.     

Lights and shadows in Evolutionary Deep Learning: Taxonomy,critical methodological analysis,cases of study,learned lessons,recommendations and challenges

Much has been said about the fusion of bio-inspired optimization algorithms and Deep Learning models for several purposes: from the discovery of network topologies and hyperparametric configurations with improved performance for a given task, to the optimization of the model’s parameters as a replacement for gradient-based solvers. Indeed, the literature is rich in proposals showcasing the application of assorted nature-inspired approaches for these tasks. In this work we comprehensively review and critically examine contributions made so far based on three axes, each addressing a fundamental question in this research avenue: (a) optimization and taxonomy (Why?), including a historical perspective, definitions of optimization problems in Deep Learning, and a taxonomy associated with an in-depth analysis of the literature, (b) critical methodological analysis (How?), which together with two case studies, allows us to address learned lessons and recommendations for good practices following the analysis of the literature, and (c) challenges and new directions of research (What can be done, and what for?). In summary, three axes – optimization and taxonomy, critical analysis, and challenges – which outline a complete vision of a merger of two technologies drawing up an exciting future for this area of fusion research.     

Artificial intelligence and healthcare: Forecasting of medical bookings through multi-source time-series fusion

Nowadays, Artificial intelligence (AI), combined with the digitalization of healthcare, can lead to substantial improvements in Patient Care, Disease Management, Hospital Administration, and supply chain effectiveness. Among predictive analytics tools, time series forecasting represents a central task to support healthcare management in terms of bookings and medical services predictions. In this context, the development of flexible frameworks to provide robust and reliable predictions became a central point in this healthcare innovation process. This paper presents and discusses a multi-source time series fusion and forecasting framework relying on Deep Learning. By combining weather, air-quality and medical bookings time series through a feature compression stage which preserves temporal patterns, the prediction is provided through a flexible ensemble technique based on machine learning models and a hybrid neural network. The proposed system is able to predict the number of bookings related to a specific medical examination for a 7-days horizon period. To assess the proposed approach’s effectiveness, we rely on time series extracted from a real dataset of administrative e-health records provided by the Campania Region health department, in Italy.     

Selective body biasing for post-silicon tuning of sub-threshold designs: A semi-infinite programming approach with Incremental Hypercubic Sampling

Sub-threshold designs have become a popular option in many energy constrained applications. However, a major bottleneck for these designs is the challenge in attaining timing closure. Most of the paths in sub-threshold designs can become critical paths due to the purely random process variation on threshold voltage, which exponentially impacts the gate delay. In order to address timing violations caused by process variation, post-silicon tuning is widely used through body biasing technology, which incurs heavy power and area overhead. Therefore, it is imperative to select only a small group of the gates with body biasing for post-silicon-tuning. In this paper, we first formulate this problem as a linear semi-infinite programming (LSIP). Then an efficient algorithm based on the novel concept of Incremental Hypercubic Sampling (IHCS), specially tailored to the problem structure, is proposed along with the convergence analysis. Compared with the state-of-the-art approach based on adaptive filtering, experimental results on industrial designs using 65 nm sub-threshold library demonstrate that our proposed IHCS approach can improve the pass rate by up to 7.3× with a speed up to 4.1×, using the same number of body biasing gates with about the same power consumption.     

Adaptive visual target detection and tracking using weakly supervised incremental appearance learning and RGM-PHD tracker

Multiple visual target tracking is a challenging problem due to various uncertainties including occlusion, miss-detection and noisy measurement. Most tracking approaches utilize an object-specific detector, pre-trained on many labeled images, to provide suitable measurements for their tracking system. In this paper, we use a simple background subtraction detector which only needs the background image to localize targets independent of their shape or type. In order to cope with the uncertainties resulted by the detector, we propose an adaptive appearance model and develop an incremental appearance learning algorithm to learn the target appearances in time. The proposed method employs the background information and our defined keypoints’ miss-matched history to adapt the target appearances within different frames. Furthermore, we combine Refined Gaussian Mixture Probability Hypothesis Density (RGM-PHD) tracker with the detectors to keep target trajectories and handle uncertainties. The experiments conducted on several video datasets show the effectiveness of our proposed method.     

Learning fuzzy controllers in mobile robotics with embedded preprocessing

The automatic design of controllers for mobile robots usually requires two stages. In the first stage, sensorial data are preprocessed or transformed into high level and meaningful values of variables which are usually defined from expert knowledge. In the second stage, a machine learning technique is applied to obtain a controller that maps these high level variables to the control commands that are actually sent to the robot. This paper describes an algorithm that is able to embed the preprocessing stage into the learning stage in order to get controllers directly starting from sensorial raw data with no expert knowledge involved. Due to the high dimensionality of the sensorial data, this approach uses Quantified Fuzzy Rules (QFRs), that are able to transform low-level input variables into high-level input variables, reducing the dimensionality through summarization. The proposed learning algorithm, called Iterative Quantified Fuzzy Rule Learning (IQFRL), is based on genetic programming. IQFRL is able to learn rules with different structures, and can manage linguistic variables with multiple granularities. The algorithm has been tested with the implementation of the wall-following behavior both in several realistic simulated environments with different complexity and on a Pioneer 3-AT robot in two real environments. Results have been compared with several well-known learning algorithms combined with different data preprocessing techniques, showing that IQFRL exhibits a better and statistically significant performance. Moreover, three real world applications for which IQFRL plays a central role are also presented: path and object tracking with static and moving obstacles avoidance.     

Controlling the slides: Does clicking help adults learn?

When utilizing screen media as an educational platform, maintaining control over one's experience may lead to more successful learning outcomes. In the current work, adults learned four new action sequences, each via a different slideshow type. The computer advanced slides automatically, but each version had a different pausing mechanism: (1) free pause (viewers could click the mouse at any point to pause the show), (2) subgoal pause (show paused after subgoals, viewer clicked to continue), (3) timed pause (show paused every 20 slides, viewer clicked to continue), and (4) no pause (no viewer interaction). Participants completed a written memory test, live performance test, cognitive load measures, and satisfaction measures. Results indicated that memory recall was significantly lower in the no pause version when compared to the versions with pause capability. Also, over half of participants reported that the no pause version was their least favorite format to learn from. Conversely, over half of participants selected the free pause as their favorite slideshow format, and participants reported that they felt most in control of the free pause version. These reports occurred in spite of only one-quarter of all participants actually using the click-to-pause feature in the free pause slideshow. Perhaps the mindset of being in control, rather than the pausing itself, increased likeability of the program. This research has implications for program design and education, pointing to flexible pacing features being helpful in enhancing users' enjoyment of the program and ability to extract novel information.     

Learning machines: Rationale and application in ground-level ozone prediction

Multilayer perceptron (MLP) and support vector machine (SVM), two popular learning machines, are increasingly being used as alternatives to classical statistical models for ground-level ozone prediction. However, employing learning machines without sufficient awareness about their limitations can lead to unsatisfactory results in modeling the ozone evolving mechanism, especially during ozone formation episodes. With the spirit of literature review and justification, this paper discusses, with respect to the concerning of ozone prediction, the recently developed algorithms/technologies for treating the most prominent model-performance-degradation limitations. MLP has the “black-box” property, i.e., it hardly provides physical explanation for the trained model, overfitting and local minima problems, and SVM has parameters identification and class imbalance problems. This commentary article aims to stress that the underlying philosophy of using learning machines is by no means as trivial as simply fitting models to the data because it causes difficulties, controversies or unresolved problems. This article also aims to serve as a reference point for further technical readings for experts in relevant fields.