Reviewerly/exHarmony huggingface dataset:
Extended vertical lists for temporal pattern mining from multivariate time series

Abstract In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level‐wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.
DualDiv

In recommender systems, item diversification and explainable recommendations improve users' satisfaction. Unlike traditional explainable recommendations that display a single explanation for each item, explainable hybrid recommendations display multiple explanations for each item and are, therefore, more beneficial for users. When multiple explanations are displayed, one problem is that similar sets of explanation styles (ESs) such as user-based, item-based, and popularity-based may be displayed for similar items. Although item diversification has been studied well, the question of how to diversify the ESs remains underexplored. In this paper, we propose a method for diversifying ESs and a framework, called DualDiv, that recommends items by diversifying both the items and the ESs. Our experimental results show that DualDiv can increase the diversity of the items and the ESs without largely reducing the recommendation accuracy.
AttriNet

Human activity recognition (HAR) is essential to many context-aware applications in mobile and ubiquitous computing. A human's physical activity can be decomposed into a sequence of simple actions or body movements, corresponding to what we denote as mid-level features. Such mid-level features ("leg up," 'leg down," "leg still,"...), which we contrast to high-level activities ("walking," "sitting,"...) and low-level features (raw sensor readings), can be developed manually. While proven to be effective, this manual approach is not scalable and relies heavily on human domain expertise. In this paper, we address this limitation by proposing a machine learning method, AttriNet, based on deep belief networks. Our AttriNet method automatically constructs mid-level features and outperforms baseline approaches. Interestingly, we show in experiments that some of the features learned by AttriNet highly correlate with manually defined features. This result demonstrates the potential of using deep learning techniques for learning mid-level features that are semantically meaningful, as a replacement to handcrafted features. Generally, this empirical finding provides an improved understanding of deep learning methods for HAR.
Comparison-based learning of relational categories (you’ll never guess).

In accord with structural alignment theory, same-category comparison opportunities within a classification learning task should promote relational category acquisition. However, a straightforward merging of the classification paradigm with copresentation of same-category item pairs does not yield an advantage relative to an equal number of single-item exposures. In 3 experiments, we explore the hypothesis that the traditional classification learning mode (guess-and-correct) and comparison have a previously unforeseen incompatibility. In Experiment 1, we test this hypothesis by contrasting classification with supervised observational learning (passive study of labeled examples) under 3 presentation formats: same-category pairs, mixed pairs, and single-item. We find an observational advantage with same-category pairs and produce the elusive advantage over single-item exposures. In Experiment 2, we assess the generality of the learning mode effect by testing both same- and different-category comparison. The observational advantage replicates and extends to different-category comparison-although, we do not find a significant difference between the 2 types of comparison. In Experiment 3, relative to the classification mode, we find enhanced performance in an intermediate learning mode between classification and observation in which participants are instructed to make a covert category guess (without making an actual response) before seeing the correct category label. Implications and interpretations-including our interpretation that the performance emphasis inherent in classification learning undermines the benefits that arise from comparison opportunities-are discussed. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Evaluating Model Predictive Performance: A Medicare Fraud Detection Case Study

Evaluating a machine learning model's predictive performance is vital for establishing the practical usability in real-world applications. The use of separate training and test datasets, and cross-validation are common when evaluating machine learning models. The former uses two distinct datasets, whereas cross-validation splits a single dataset into smaller training and test subsets. In real-world production applications, it is critical to establish a model's usefulness by validating it on completely new input data, and not just using the crossvalidation results on a single historical dataset. In this paper, we present results for both evaluation methods, to include performance comparisons. In order to provide meaningful comparative analyses between methods, we perform real-world fraud detection experiments using 2013 to 2016 Medicare durable medical equipment claims data. This Medicare dataset is split into training (2013 to 2015 individual years) and test (2016 only). Using this Medicare case study, we assess the fraud detection performance, across three learners, for both model evaluation methods. We find that using the separate training and test sets generally outperforms cross-validation, indicating a better real-world model performance evaluation. Even so, cross-validation has comparable, but conservative, fraud detection results.
Electronic Mobile Devices, Transformative Pedagogy and Learning: Higher Education and Changing Times in Kenya

Abstract The Fourth Industrial requires new concepts and skills to be acquired by learners at various levels. This has led to a new breed of learners joining our education institutions at various levels with prerequisite knowledge on technology which can be harnessed for learning and teaching purposes. This scenario has generated a lot of interest and more research on how first the process of knowledge acquisition is changing. There is excited attention among researchers, educators, education commentators and stakeholders on this aspect of learning. The new generations of techno savvy leaners joining institutions are a challenge to the old pedagogy of teaching. These young generations are creating a digital gap between the old ways of teaching and learning to new approaches in education. The nature of the generation change needs to bring some reforms in education especially in higher education in Kenya where electronic mobile devices penetration has escalated for the last ten years. This research paper, highlights issues around use of electronic mobile devices for knowledge acquisition purposes which if well utilized can aid the education systems in most African Countries in terms of access, affordability, ease of use and leaners wide interaction and independent study. The old pedagogy of the teacher being the absolute source of information is being transformed by the use of an array of emerging devices like Smartphone, ipads, laptops and others which are readily available to the leaner’s. The study was carried out among undergraduate and postgraduate students at the University of Nairobi, Kenya between 2016 to 2018.The researchers found out that, electronic mobile devices are with the students for ease of use. The study also found out that, some of leaners in higher institutions of learning are not well informed on how to utilize the mobile devices for learning purposes hence most of the time they are used for other purposes not related to learning. In general, most countries in Africa have tried to invest on internet connectivity, ICT Infrastructure, capacity building among others structure which can also be leveraged for Education needs.
Clustering Clinical Data in R

We are currently witnessing a paradigm shift from evidence-based medicine to precision medicine, which has been made possible by the enormous development of technology. The advances in data mining algorithms will allow us to integrate trans-omics with clinical data, contributing to our understanding of pathological mechanisms and massively impacting on the clinical sciences. Cluster analysis is one of the main data mining techniques and allows for the exploration of data patterns that the human mind cannot capture. This chapter focuses on the cluster analysis of clinical data, using the statistical software, R. We outline the cluster analysis process, underlining some clinical data characteristics. Starting with the data preprocessing step, we then discuss the advantages and disadvantages of the most commonly used clustering algorithms and point to examples of their applications in clinical work. Finally, we briefly discuss how to perform validation of clusters. Throughout the chapter we highlight R packages suitable for each computational step of cluster analysis.
An Improvised Classification Model for Predicting Delirium.

With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, there is still room for improvement in every aspect. In this paper, we are demonstrating a method that improves the performance of a delirium prediction model using random forest in combination with logistic regression.
Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain

This paper describes the contribution by participants from Umea University, Sweden, in collaboration with the University of Bern, Switzerland, for the Medical Domain Visual Question Answering chall ...
Improved Causal Discovery from Longitudinal Data Using a Mixture of DAGs

Causal processes in biomedicine may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs), where the joint distribution in a population follows a DAG at any single point in time but potentially different DAGs across time. We also introduce an algorithm called Causal Inference over Mixtures that uses longitudinal data to infer a graph summarizing the causal relations generated from a mixture of DAGs. Experiments demonstrate improved performance compared to prior approaches.
Factors affecting male and female Indonesian EFL students’ writing anxiety

This study is aimed at investigating the writing anxiety levels of foreign language learners and factors affecting the foreign language writing anxiety levels on the learners based on the gender variable. It applies close-ended questionnaires as instruments to collect the data, adopted from Cheng (2004) about Second Language Writing Anxiety Inventory (SLWAI), and Causes of Writing Anxiety Inventory (CWAI) questionnaire adopted from Younas (2014). The results of the study show that Indonesian English learners suffered a mediocre level of English writing anxiety, especially cognitive anxiety as preference. In relation to gender, female students tend to have significantly higher anxiety level than male in terms of writing English compositions. The study reports that both male and female learners suffered from anxiety due to lack of linguistic competence.
An Individual Local Mean-based 2DPCA for Face Recognition under Illumination Effects

Principal component analysis (PCA) is a classical technique in pattern recognition and computer vision. It is one of the most successful techniques for face recognition. The PCA consists of two main steps including (I) covariance matrix calculation and (II) eigenvector and eigenvalue extraction. In case of face recognition, the input image is converted to the vector form before forwarding to covariance matrix computation. Then, the matrix is used to extract the eigenvector and eigenvalue. Two-dimensional PCA (2DPCA) is introduced to reduce high-dimensional problems. The illumination effect problems in the face recognition is still needed to be resolved. In order to improve and solve the problems, this paper proposes an individual local mean-based 2DPCA (ILM-2DPCA), which replaces a single local mean in 2DPCA method. The individual local mean can provide more appropriate mean to each image, which can reduce the illumination effect effectively. The experimental study is set up on dataset Yale face database B+. The results indicate that the proposed method outperforms, based on the accuracy rate, all the baseline methods which are 2DPCA, I-2DPCA, Bi2DPCA and 2D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PCA.
Model Synchronization: A Formal Framework for the Management of Heterogeneous Models

In this article, we present the conceptual foundations and implementation principles of model synchronization, a formal framework for the management of heterogeneous models. The proposed approach relies on S2ML (System Structure Modeling Language) as a pivot language. We show, by means of a case study, that model synchronization can be used to ensure the consistency between system architecture models designed with Capella and safety models written in AltaRica 3.0.
Character recognition of modern Japanese official documents using CNN for imbalanced learning data

The documents of the government-general of Taiwan recorded from 1895 to 1945 contain the whole of Japanese official documents before the end of the WW2, and have great historic value. The characters in the documents, however, are illegible because they were written by hand with a brush. It is labor-intensive work for historians or scholars to understand the documents. We propose a method for character recognition of these documents by using a convolutional neural network and also conduct to solve the problem of imbalanced learning data. Experimental results show that the top-1 and the top10 accuracies were 89.48% and 98.10%, respectively.
Using N-gram Analysis to Map Intertextual Networks in Old English Verse

Old English poems are difficult to date, largely anonymous, and written in a formulaic idiom, making it difficult to establish authorship and intertextual relationships among various poems. This essay employs computational stylometry—specifically, n-gram analysis—and network analysis to address this problem. It proposes methods for screening out chance n-grams and for employing n-grams of various lengths within a single analysis to show that, in Old English poetry, formulaic diction exists at the level of idiolect, sociolect, and language. Specifically, the signed poems of Cynewulf evince idiolectal characteristics; and along with Andreas, Christ III, The Phoenix, and Guthlac A and B, they form a poetic subtradition (sociolect).
Turning Silver into Gold: Error-Focused Corpus Reannotation with Active Learning

While high quality gold standard annotated corpora are crucial for most tasks in natural language processing, many annotated corpora published in recent years, created by annotators or tools, contains noisy annotations.These corpora can be viewed as more silver than gold standards, even if they are used in evaluation campaigns or to compare systems' performances.As upgrading a silver corpus to gold level is still a challenge, we explore the application of active learning techniques to detect errors using four datasets designed for document classification and part-of-speech tagging.Our results show that the proposed method for the seeding step improves the chance of finding incorrect annotations by a factor of 2.73 when compared to random selection, a 14.71% increase from the baseline methods.Our query method provides an increase in the error detection precision on average by a factor of 1.78 against random selection, an increase of 61.82% compared to other query approaches.
De la tecnología blockchain a la economía del token

The aim of this research is to bring the reader closer to several concepts that are becoming more common day by day, and which find themselves in unstoppable development, such as blockchain technology, tokens, ICO (Initial Coin Offerings) or DAO (Decentralized Autonomous Organizations). Thus, the process of «tokenization», based on abstractly representing a value through the blockchain, is a transcendental innovation in areas such as the financial or the corporate spheres, where ICO —introducing a new business financing channel through the online sale of cryptographic assets— or DAO —entities managed in a decentralized way through smart contracts by tokens holders— come onto the scene. Likewise, their undeniable utilities will be enhanced, but the practical problems faced by these technological developments will also be analyzed, being the main one the regulatory uncertainty.
An Open Source Vision Pipeline Approach for RoboCup Humanoid Soccer

We are proposing an Open Source ROS vision pipeline for the RoboCup Soccer context. It is written in Python and offers sufficient precision while running with an adequate frame rate on the hardware of kid-sized humanoid robots to allow a fluent course of the game. Fully Convolutional Neural Networks (FCNNs) are used to detect balls while conventional methods are applied to detect robots, obstacles, goalposts, the field boundary, and field markings. The system is evaluated using an integrated evaluator and debug framework. Due to the usage of standardized ROS messages, it can be easily integrated into other teams’ code bases.
Differentiable learning of numerical rules in knowledge graphs

Rules over a knowledge graph (KG) capture interpretable patterns in data and can be used for KG cleaning and completion. Inspired by the TensorLog differentiable logic framework, which compiles rule inference into a sequence of differentiable operations, recently a method called Neural LP has been proposed for learning the parameters as well as the structure of rules. However, it is limited with respect to the treatment of numerical features like age, weight or scientific measurements. We address this limitation by extending Neural LP to learn rules with numerical values, e.g., “People younger than 18 typically live with their parents”. We demonstrate how dynamic programming and cumulative sum operations can be exploited to ensure efficiency of such extension. Our novel approach allows us to extract more expressive rules with aggregates, which are of higher quality and yield more accurate predictions compared to rules learned by the state-of-the-art methods, as shown by our experiments on synthetic and real-world datasets.
Sliced Cramer Synaptic Consolidation for Preserving Deeply Learned Representations

Deep neural networks suffer from the inability to preserve the learned data representation (i.e., catastrophic forgetting) in domains where the input data distribution is non-stationary, and it changes during training. Various selective synaptic plasticity approaches have been recently proposed to preserve network parameters, which are crucial for previously learned tasks while learning new tasks. We explore such selective synaptic plasticity approaches through a unifying lens of memory replay and show the close relationship between methods like Elastic Weight Consolidation (EWC) and Memory-Aware-Synapses (MAS). We then propose a fundamentally different class of preservation methods that aim at preserving the distribution of internal neural representations for previous tasks while learning a new one. We propose the sliced Cram\'{e}r distance as a suitable choice for such preservation and evaluate our Sliced Cramer Preservation (SCP) algorithm through extensive empirical investigations on various network architectures in both supervised and unsupervised learning settings. We show that SCP consistently utilizes the learning capacity of the network better than online-EWC and MAS methods on various incremental learning tasks.
Combinatorial search for selecting the structure of models of dynamical systems with equation discovery

Automated modeling aims at the induction of mathematical models, both their structure and parameter values, from time-series measurements of observed system variables. In this paper, we address the task of model structure selection, i.e., selecting an optimal structure from a user-specified finite set of alternative model structures, using various approaches to combinatorial search. We propose a mapping of the set of candidate model structures to a fixed-length, vector representation allowing the use of an arbitrary search algorithm as a solver of the structure selection task. We perform a comparative analysis of the performance of thirteen variants of several search algorithms, ranging from ones with high intensification, i.e., focus on neighborhood of the best candidate solutions, to ones with high diversification, i.e., focus on covering the entire search space. The empirical analysis involves eight tasks of reconstructing known models of dynamical systems from synthetic and measured data. The results of the analysis show that search algorithms involving moderate diversification methods have superior performance on the structure selection task. The empirical analysis also reveals that this finding is related to specific properties of the search space of candidate model structures.
Identity-Based Blind Multisignature From Lattices

Blind multisignature (BMS), first introduced by Horster et al, constitutes a crucial primitive that allows a user to generate a signature of a message from multiple signers, while the signers cannot obtain any information about the message. With these useful properties, blind multisignature is suitable for electronic payments and electronic voting. However, most of the current BMS schemes may be attacked by quantum computers in the future because they are based on traditional number theories, such as discrete logarithm assumption and large integer factor assumption. In this work, we first formalize the notion and the sound security models of the identity-based blind multisignature scheme (IDBMS). Then we present an instantiation based on lattices, along with rigorous proofs of the blindness and unforgeability under the lattice hard assumption (short integer solution, SIS), which is considered to remain secure under quantum computer attacks. To the best of our knowledge, it is the first identity-based quantum-resistant scheme that has the advantages of blind signature and multisignature.