Data-driven decision making in Critique-based recommenders: from a critique to social media data

In the last decade there have been a large number of proposals in the field of Critique-based Recommenders. Critique-based recommenders are data-driven in their nature sincethey use a conversational cyclical recommendation process to elicit user feedback. In theliterature, the proposals made differ mainly in two aspects: in the source of data and in howthis data is analyzed to extract knowledge for providing users with recommendations. Inthis paper, we propose new algorithms that address these two aspects. Firstly, we propose anew algorithm, called HOR, which integrates several data sources, such as current user pref-erences (i.e., a critique), product descriptions, previous critiquing sessions by other users,and users' opinions expressed as ratings on social media web sites. Secondly, we propose adding compatibility and weighting scores to turn user behavior into knowledge to HOR and a previous state-of-the-art approach named HGR to help both algorithms make smarter recommendations. We have evaluated our proposals in two ways: with a simulator and withreal users. A comparison of our proposals with state-of-the-art approaches shows that thenew recommendation algorithms significantly outperform previous ones

A Cognitively Inspired Clustering Approach for Critique-Based Recommenders

The purpose of recommender systems is to support humans in the purchasing decision-making process. Decision-making is a human activity based on cognitive information. In the field of recommender systems, critiquing has been widely applied as an effective approach for obtaining users' feedback on recommended products. In the last decade, there have been a large number of proposals in the field of critique-based recommenders. These proposals mainly differ in two aspects: in the source of data and in how it is mined to provide the user with recommendations. To date, no approach has mined data using an adaptive clustering algorithm to increase the recommender's performance. In this paper, we describe how we added a clustering process to a critique-based recommender, thereby adapting the recommendation process and how we defined a cognitive user preference model based on the preferences (i.e., defined by critiques) received by the user. We have developed several proposals based on clustering, whose acronyms are MCP, CUM, CUM-I, and HGR-CUM-I. We compare our proposals with two well-known state-of-the-art approaches: incremental critiquing (IC) and history-guided recommendation (HGR). The results of our experiments showed that using clustering in a critique-based recommender leads to an improvement in their recommendation efficiency, since all the proposals outperform the baseline IC algorithm. Moreover, the performance of the best proposal, HGR-CUM-I, is significantly superior to both the IC and HGR algorithms. Our results indicate that introducing clustering into the critique-based recommender is an appealing option since it enhances overall efficiency, especially with a large data set

Effective Early Stopping of Point Cloud Neural Networks

Early stopping techniques can be utilized to decrease the time cost, however currently the ultimate goal of early stopping techniques is closely related to the accuracy upgrade or the ability of the neural network to generalize better on unseen data without being large or complex in structure and not directly with its efficiency. Time efficiency is a critical factor in neural networks, especially when dealing with the segmentation of 3D point cloud data, not only because a neural network itself is computationally expensive, but also because point clouds are large and noisy data, making learning processes even more costly. In this paper, we propose a new early stopping technique based on fundamental mathematics aiming to upgrade the trade-off between the learning efficiency and accuracy of neural networks dealing with 3D point clouds. Our results show that by employing our early stopping technique in four distinct and highly utilized neural networks in segmenting 3D point clouds, the training time efficiency of the models is greatly improved, with efficiency gain values reaching up to 94\%, while the models achieving in just a few epochs approximately similar segmentation accuracy metric values like the ones that are obtained in the training of the neural networks in 200 epochs. Also, our proposal outperforms four conventional early stopping approaches in segmentation accuracy, implying a promising innovative early stopping technique in point cloud segmentation

Feature-tree labeling for case base maintenance

Case Base Maintenance (CBM) algorithms update the content of the case base with the aim of improving the case-based reasoner performance. In this paper, we introduce a novel CBM method called Feature-Tree Labeling (FTL) with the focus on increasing the general accuracy of a Case-Based Reasoning (CBR) system. The proposed FTL algorithm is designed to detect and remove noisy cases from the case base, based on value distribution of individual features in the available data. The competence of the FTL method has been compared with well-known state-ofthe-art CBM algorithms. The tests have been done on 25 datasets selected from the UCI repository. The results show that FTL obtains higher accuracy than some of the state-of-the-art methods and CBR, with a statistically significant degreePeer ReviewedPostprint (author's final draft

Design and Evaluation of Gamification Experiences in Computer Science Studies

[EN] This paper presents two gamification experiences developed in the Computer Science (CS) degree at the University of Barcelona. Usually, the CS degree suffers from low class attendance, which impacts the participation of students in in-class programming activities. Additionally, this degree includes a basic course related to human computer interaction that, despite of being valuable for the formation of students, they feel as boring and useless, far from their - heavily computer focused - interests. Then we decided to gamify a basic course of programming skills and the mentioned HCI course. We decided to use two different formats, online and physical. First, one experience was performed in “Human Factors in Computing” course of third year of studies, with 70 enrolled students. First, students attended to a 3D online theoretical class related to emotional design in “The education district” (TED) platform, which is a 3D Virtual World (VW) for educational purposes, developed by Virtway corporation. Afterwards, they participated in the ‘Game of thrones’ gamified activity where they evaluated the usability of TED. Second, we designed ‘physical’ gamified classes to increase attendance to Data Structures course, and in consequence, augment the number of programming exercises that students perform and the assistance they receive from the teacher. During the gamified sessions, the 120 enrolled students enhanced their skills in solving programming problems. The experience consisted of three kinds of challenges (with easy, medium, and hard difficulty level) solved in large, medium, and low-sized groups of students, respectively. The results of both experiences were satisfactory as evidenced by the percentage of students (>=66,7%) that rated the activities between 6 and 10 (in a 0 to 10 scale).Thanks to Virtway team, I. Dieste and M. Rodilla, and projects 2017-SGR-341, MISMISLanguage (PGC2018-096212-B-C33) and Accio COMRDI18-1-0010.Rodríguez, I.; Salamó, M.; Puig, A. (2020). Design and Evaluation of Gamification Experiences in Computer Science Studies. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):1137-1145. https://doi.org/10.4995/HEAd20.2020.11212OCS1137114530-05-202

Reputation-based maintenance in case-based reasoning

Case Base Maintenance algorithms update the contents of a case base in order to improve case-based reasoner performance. In this paper, we introduce a new case base maintenance method called Reputation-Based Maintenance (RBM) with the aim of increasing the classification accuracy of a Case-Based Reasoning system while reducing the size of its case base. The proposed RBM algorithm calculates a case property called Reputationfor each member of the case base, the value of which reflects the competence of the related case. Based on this case property, several removal policies and maintenance methods have been designed, each focusing on different aspects of the case base maintenance. The performance of the RBM method was compared with well-known state-of-the-art algorithms. The tests were performed on 30 datasets selected from the UCI repository. The results show that the RBM method in all its variations achieves greater accuracy than a baseline CBR, while some variations significantly outperform the state-of-the-art methods. We particularly highlight theRBM_ACBR algorithm, which achieves the highest accuracy among the methods in the comparison to a statistically significant degree, and the RBMcr algorithm, which increases the baseline accuracy while removing, on average, over half of the case basehis work has been partially supported by the SpanishMinistry of Science and Innovation with project MISMIS-LANGUAGE (grantnumber PGC2018-096212-B-C33), by the Catalan Agency of University andResearch Grants Management (AGAUR) (grants number 2017 SGR 341 and 2017SGR 574), by Spanish Network ‘‘Learning Machines for Singular Problems andApplications (MAPAS)’’ (TIN2017-90567-REDT, MINECO/FEDER EU) and by theEuropean Union’s Horizon 2020 research and innovation programme under theMarie Sklodowska-Curie grant agreement No. 860843Peer ReviewedPostprint (author's final draft

Comparing distributional semantic models for identifying groups of semantically related words

Distributional Semantic Models (DSM) are growing in popularity in Computational Linguistics. DSM use corpora of language use to automatically induce formal representations of word meaning. This article focuses on one of the applications of DSM: identifying groups of semantically related words. We compare two models for obtaining formal representations: a well known approach (CLUTO) and a more recently introduced one (Word2Vec). We compare the two models with respect to the PoS coherence and the semantic relatedness of the words within the obtained groups. We also proposed a way to improve the results obtained by Word2Vec through corpus preprocessing. The results show that: a) CLUTO outperformsWord2Vec in both criteria for corpora of medium size; b) The preprocessing largely improves the results for Word2Vec with respect to both criteria

Machining tool identification utilizing temporal 3D point clouds

The manufacturing domain is regarded as one of the most important engineering areas. Recently, smart manufacturing merges the use of sensors, intelligent controls, and software to manage each stage in the manufacturing lifecycle. Additionally, the increasing use of point clouds to model real products and machining tools in a virtual space facilitates the more accurate monitoring of the end-to-end production lifecycle. Thus, the conjunction of both, intelligent methods and more accurate 3D models allows the prediction of uncertainties and anomalies in the manufacturing process as well as reduces the final production costs. However, the high complexity of the geometrical structures defined by point clouds and the high accuracy required by the Quality Assurance/Quality control parameters during the process, pave the way for continuous improvements in smart manufacturing methods. This paper addresses a comprehensive analysis of machining tool identification utilizing temporal point cloud data. Specifically, we deal with the identification of machining tools from temporal 3D point clouds. To do that, we propose a process to construct and train intelligent models utilizing such data. Moreover, in our case study, we provide the research community with two labeled temporal 3D point cloud datasets, and we experiment with the pioneering PointNet neural network and three of its variants demonstrating an accuracy of 95% in the identification of the utilized machining tools in a machining process. Finally, we provide a prototype end-to-end intelligent system of machining tool identification

Polarity analisys od reviews based on the omission of asymetric sentences

In this paper, we present a novel approach to polarity analysis of product reviews which detects and removes sentences with the opposite polarity to that of the entire document (asymmetric sentences) as a previous step to identify positive and negative reviews. We postulate that asymmetric sentences are morpho-syntactically more complex than symmetric ones (sentences with the same polarity to that of the entire document) and that it is possible to improve the detection of the polarity orientation of reviews by removing asymmetric sentences from the text. To validate this hypothesis, we measured the syntactic complexity of both types of sentences in a multi-domain corpus of product reviews and contrasted three relevant data configurations based on inclusion and omission of asymmetric sentences from the reviews

A hybrid multi-start metaheuristic scheduler for astronomical observations

In this paper, we investigate Astronomical Observations Scheduling which is a type of Multi-Objective Combinatorial Optimization Problem, and detail its specific challenges and requirements and propose the Hybrid Accumulative Planner (HAP), a hybrid multi-start metaheuristic scheduler able to adapt to the different variations and demands of the problem. To illustrate the capabilities of the proposal in a real-world scenario, HAP is tested on the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (Ariel) mission of the European Space Agency (ESA), and compared with other studies on this subject including an Evolutionary Algorithm (EA) approach. The results show that the proposal outperforms the other methods in the evaluation and achieves better scientific goals than its peers. The consistency of HAP in obtaining better results on the available datasets for Ariel, with various sizes and constraints, demonstrates its competence in scalability and adaptability to different conditions of the problem.Peer ReviewedPostprint (published version