Extensions to rank-based prototype selection in k-Nearest Neighbour classification

The k-nearest neighbour rule is commonly considered for classification tasks given its straightforward implementation and good performance in many applications. However, its efficiency represents an obstacle in real-case scenarios because the classification requires computing a distance to every single prototype of the training set. Prototype Selection (PS) is a typical approach to alleviate this problem, which focuses on reducing the size of the training set by selecting the most interesting prototypes. In this context, rank methods have been postulated as a good solution: following some heuristics, these methods perform an ordering of the prototypes according to their relevance in the classification task, which is then used to select the most relevant ones. This work presents a significant improvement of existing rank methods by proposing two extensions: (i) a greater robustness against noise at label level by considering the parameter ‘k’ of the classification in the selection process; and (ii) a new parameter-free rule to select the prototypes once they have been ordered. The experiments performed in different scenarios and datasets demonstrate the goodness of these extensions. Also, it is empirically proved that the new full approach is competitive with respect to existing PS algorithms.This work is supported by the Spanish Ministry HISPAMUS project TIN2017-86576-R, partially funded by the EU

Improving kNN multi-label classification in Prototype Selection scenarios using class proposals

Prototype Selection (PS) algorithms allow a faster Nearest Neighbor classification by keeping only the most profitable prototypes of the training set. In turn, these schemes typically lower the performance accuracy. In this work a new strategy for multi-label classifications tasks is proposed to solve this accuracy drop without the need of using all the training set. For that, given a new instance, the PS algorithm is used as a fast recommender system which retrieves the most likely classes. Then, the actual classification is performed only considering the prototypes from the initial training set belonging to the suggested classes. Results show that this strategy provides a large set of trade-off solutions which fills the gap between PS-based classification efficiency and conventional kNN accuracy. Furthermore, this scheme is not only able to, at best, reach the performance of conventional kNN with barely a third of distances computed, but it does also outperform the latter in noisy scenarios, proving to be a much more robust approach.This work was partially supported by the Spanish Ministerio de Educación, Cultura y Deporte through FPU Fellowship (AP2012–0939), the Spanish Ministerio de Economía y Competitividad through Project TIMuL (TIN2013-48152-C2-1-R), Consejería de Educación de la Comunidad Valenciana through Project PROMETEO/2012/017 and Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through FPU Program (UAFPU2014–5883)

Chained Orchestrator Algorithm for RAN-Slicing Resource Management: A Contribution to Ultra-Reliable 6G Communications

The exponentially growing trend of Internet-connected devices and the development of new applications have led to an increase in demands and data rates flowing over cellular networks. If this continues to have the same tendency, the classification of 5G services must evolve to encompass emerging communications. The advent of the 6G Communications concept takes this into account and raises a new classification of services. In addition, an increase in network specifications was established. To meet these new requirements, enabling technologies are used to augment and manage Radio Access Network (RAN) resources. One of the most important mechanisms is the logical segmentation of the RAN, i.e. RAN-Slicing. In this study, we explored the problem of resource allocation in a RAN-Slicing environment for 6G ecosystems in depth, with a focus on network reliability. We also propose a chained orchestrator algorithm for dynamic resource management that includes estimation techniques, inter-slice resource sharing and intra-slice resource assignment. These mechanisms are applied to new types of services in the future generation of cellular networks to improve the network latency, capacity and reliability. The numerical results show a reduction in blocked connections of 38.46% for eURLLC type services, 21.87% for feMBB services, 12.5% for umMTC, 11.86% for ELDP and 11.76% for LDHMC.Spanish National Program of Research, Development, Innovation, under Grant RTI2018-102002-A-I00Junta de Extremadura under Project IB18003 and Grant GR2109

An overview of ensemble and feature learning in few-shot image classification using siamese networks

Siamese Neural Networks (SNNs) constitute one of the most representative approaches for addressing Few-Shot Image Classification. These schemes comprise a set of Convolutional Neural Network (CNN) models whose weights are shared across the network, which results in fewer parameters to train and less tendency to overfit. This fact eventually leads to better convergence capabilities than standard neural models when considering scarce amounts of data. Based on a contrastive principle, the SNN scheme jointly trains these inner CNN models to map the input image data to an embedded representation that may be later exploited for the recognition process. However, in spite of their extensive use in the related literature, the representation capabilities of SNN schemes have neither been thoroughly assessed nor combined with other strategies for boosting their classification performance. Within this context, this work experimentally studies the capabilities of SNN architectures for obtaining a suitable embedded representation in scenarios with a severe data scarcity, assesses the use of train data augmentation for improving the feature learning process, introduces the use of transfer learning techniques for further exploiting the embedded representations obtained by the model, and uses test data augmentation for boosting the performance capabilities of the SNN scheme by mimicking an ensemble learning process. The results obtained with different image corpora report that the combination of the commented techniques achieves classification rates ranging from 69% to 78% with just 5 to 20 prototypes per class whereas the CNN baseline considered is unable to converge. Furthermore, upon the convergence of the baseline model with the sufficient amount of data, still the adequate use of the studied techniques improves the accuracy in figures from 4% to 9%.First author is supported by the “Programa I+D+i de la Generalitat Valenciana” through grant APOSTD/2020/256. This research work was partially funded by the Spanish “Ministerio de Ciencia e Innovación” and the European Union “NextGenerationEU/PRTR” programmes through project DOREMI (TED2021-132103A-I00). Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

A Study of Prototype Selection Algorithms for Nearest Neighbour in Class-Imbalanced Problems

Prototype Selection methods aim at improving the efficiency of the Nearest Neighbour classifier by selecting a set of representative examples of the training set. These techniques have been studied in situations in which the classes at issue are balanced, which is not representative of real-world data. Since class imbalance affects the classification performance, data-level balancing approaches that artificially create or remove data from the set have been proposed. In this work, we study the performance of a set of prototype selection algorithms in imbalanced and algorithmically-balanced contexts using data-driven approaches. Results show that the initial class balance remarkably influences the overall performance of prototype selection, being generally the best performances found when data is algorithmically balanced before the selection stage.Work partially supported by the Spanish Ministerio de Economía y Competitividad through Project TIMuL (No. TIN2013-48152-C2-1-R supported by EU FEDER funds), the Spanish Ministerio de Educación, Cultura y Deporte through FPU program (AP2012–0939) and the Vicerrectorado de Investigación, Desarrollo e Innovación de la Universidad de Alicante through FPU program (UAFPU2014–5883)

Statistical semi-supervised system for grading multiple peer-reviewed open-ended works

In the education context, open-ended works generally entail a series of benefits as the possibility of develop original ideas and a more productive learning process to the student rather than closed-answer activities. Nevertheless, such works suppose a significant correction workload to the teacher in contrast to the latter ones that can be self-corrected. Furthermore, such workload turns to be intractable with large groups of students. In order to maintain the advantages of open-ended works with a reasonable amount of correction effort, this article proposes a novel methodology: students perform the corrections using a rubric (closed Likert scale) as a guideline in a peer-review fashion; then, their markings are automatically analyzed with statistical tools to detect possible biased scorings; finally, in the event the statistical analysis detects a biased case, the teacher is required to intervene to manually correct the assignment. This methodology has been tested on two different assignments with two heterogeneous groups of people to assess the robustness and reliability of the proposal. As a result, we obtain values over 95% in the confidence of the intra-class correlation test (ICC) between the grades computed by our proposal and those directly resulting from the manual correction of the teacher. These figures confirm that the evaluation obtained with the proposed methodology is statistically similar to that of the manual correction of the teacher with a remarkable decrease in terms of effort.This work has been supported by the Vicerrectorado de Calidad e Innovación Educativa-Instituto de Ciencias de la Educación of the Universidad de Alicante (2016-17 edition) through the Programa de Redes-I3CE de investigación en docencia universitaria (ref. 3690)

Identifying Student Profiles Within Online Judge Systems Using Explainable Artificial Intelligence

Online Judge (OJ) systems are typically considered within programming-related courses as they yield fast and objective assessments of the code developed by the students. Such an evaluation generally provides a single decision based on a rubric, most commonly whether the submission successfully accomplished the assignment. Nevertheless, since in an educational context such information may be deemed insufficient, it would be beneficial for both the student and the instructor to receive additional feedback about the overall development of the task. This work aims to tackle this limitation by considering the further exploitation of the information gathered by the OJ and automatically inferring feedback for both the student and the instructor. More precisely, we consider the use of learning-based schemes—particularly, Multi-Instance Learning and classical Machine Learning formulations—to model student behaviour. Besides, Explainable Artificial Intelligence is contemplated to provide human-understandable feedback. The proposal has been evaluated considering a case of study comprising 2,500 submissions from roughly 90 different students from a programming-related course in a Computer Science degree. The results obtained validate the proposal: the model is capable of significantly predicting the user outcome (either passing or failing the assignment) solely based on the behavioural pattern inferred by the submissions provided to the OJ. Moreover, the proposal is able to identify prone-to-fail student groups and profiles as well as other relevant information, which eventually serves as feedback to both the student and the instructor.This work has been partially funded by the “Programa Redes-I3CE de investigacion en docencia universitaria del Instituto de Ciencias de la Educacion (REDES-I3CE-2020-5069)” of the University of Alicante. The third author is supported by grant APOSTD/2020/256 from “Programa I+D+I de la Generalitat Valenciana”

Smith Predictor with Inverted Decoupling for Square Multivariable Time Delay Systems

Versión del autorThis paper presents a new methodology to design multivariable Smith predictor for n×n processes with multiple time delays based on the centralized inverted decoupling structure. The controller elements are calculated in order to achieve good reference tracking and decoupling response. Independently of the system size, very simple general expressions for the controller elements are obtained. The realizability conditions are provided and the particular case of processes with all of its elements as first order plus time delay systems is discussed in more detail. A diagonal filter is added to the proposed control structure in order to improve the disturbance rejection without modifying the nominal set-point response and to obtain a stable output prediction in unstable plants. The effectiveness of the method is illustrated through different simulation examples in comparison with other works