Reviewerly/exHarmony huggingface dataset:
Comparative Analysis of OpenPose, PoseNet, and MoveNet Models for Pose Estimation in Mobile Devices

Pose estimation is a significant strategy that has been actively researched in various fields. For example, the strategy has been adopted for motion capture in moviemaking, and character control in video games. It can also be applied to implement the user interfaces of mobile devices through human poses. Therefore, this paper compares and analyzes four popular pose estimation models, namely, OpenPose, PoseNet, MoveNet Lightning, and MoveNet Thunder, using pre-classified images. The results show that MoveNet Lightning was the fastest, and OpenPose was the slowest among the four models. But OpenPose was the only model capable of estimating the poses of multiple persons. The accuracies of OpenPose, PoseNet, MoveNet Lightning, and MoveNet Thunder were 86.2%, 97.6%, 75.1%, and 80.6%, respectively.
Differential Evolution-Based Feature Selection: A Niching-Based Multiobjective Approach

Feature selection is to reduce both the dimensionality of data and the classification error rate (i.e., increase the classification accuracy) of a learning algorithm. The two objectives are often conflicting, thus a multiobjective feature selection method can obtain a set of nondominated feature subsets. Each solution in the set has a different size and a corresponding classification error rate. However, most existing feature selection algorithms have ignored that, for a given size, there can be different feature subsets with very similar or the same accuracy. This article introduces a niching-based multiobjective feature selection method that simultaneously minimizes the number of selected features and the classification error rate. The proposed method conceives to identify: 1) a set of feature subsets with good convergence and distribution and 2) multiple feature subsets choosing the same number of features with almost the same lowest classification error rate. The contributions of this article are threefold. First, a niching and global interaction mutation operator is proposed that can produce promising feature subsets. Second, a newly developed environmental selection mechanism allows equal informative feature subsets to be stored by relaxing the Pareto-dominance relationship. Finally, the proposed subset repairing mechanism can generate better feature subsets and further remove the redundant features. The proposed method is compared against seven multiobjective feature selection algorithms on 19 datasets, including both binary and multiclass classification tasks. The results show that the proposed method can evolve a rich and diverse set of nondominated solutions for different feature selection tasks, and their availability helps in understanding the relationships between features.
Artificial intelligence and machine learning overview in pathology &amp; laboratory medicine: A general review of data preprocessing and basic supervised concepts

Machine learning (ML) is becoming an integral aspect of several domains in medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such tools and are unprepared for their inevitable integration. To bridge this knowledge gap, we present an overview of key elements within this emerging data science discipline. First, we will cover general, well-established concepts within ML, such as data type concepts, data preprocessing methods, and ML study design. We will describe common supervised and unsupervised learning algorithms and their associated common machine learning terms (provided within a comprehensive glossary of terms that are discussed within this review). Overall, this review will offer a broad overview of the key concepts and algorithms in machine learning, with a focus on pathology and laboratory medicine. The objective is to provide an updated useful reference for those new to this field or those who require a refresher.
The Information Lens: An Intelligent System for Information Sharing and Coordination

This chapter describes a prototype system, called the Information Lens, that focuses on one aspect of this problem: how to help people share the many diverse kinds of qualitative information that are communicated via electronic messaging systems. It shows that general capabilities that help with information sharing can be used to support a variety of more specific coordination processes such as task tracking and meeting scheduling. One of the key problems when any group of people cooperates to solve problems or make decisions is how to share information. The Lens system is based on a set of semi-structured messages. For each message type, the system includes a template with a number of fields or slots for holding information. To the construction and use of message templates the templates are arranged in a network so that all subtypes of a given template inherit the field names and property values from the parent template.
A Discrete Multiobjective Particle Swarm Optimizer for Automated Assembly of Parallel Cognitive Diagnosis Tests

Parallel test assembly has long been an important yet challenging topic in educational assessment. Cognitive diagnosis models (CDMs) are a new class of assessment models and have drawn increasing attention for being able to measure examinees' ability in detail. However, few studies have been devoted to the parallel test assembly problem in CDMs (CDM-PTA). To fill the gap, this paper models CDM-PTA as a subset-based bi-objective combinatorial optimization problem. Given an item bank, it aims to find a required number of tests that achieve optimal but balanced diagnostic performance, while satisfying important practical requests in the aspects of test length, item type distribution, and overlapping proportion. A set-based multiobjective particle swarm optimizer based on decomposition (S-MOPSO/D) is proposed to solve the problem. To coordinate with the property of CDM-PTA, S-MOPSO/D utilizes an assignment-based representation scheme and a constructive learning strategy. Through this, promising solutions can be built efficiently based on useful assignment patterns learned from personal and collective search experience on neighboring scalar problems. A heuristic constraint handling strategy is also developed to further enhance the search efficiency. Experimental results in comparison with three representative approaches validate that the proposed algorithm is effective and efficient.
Secondary pre-service teachers’ perceptions of technological pedagogical content knowledge (TPACK): What do they really think?

Meaningful integration of digital technology into learning and teaching is ill-structured, complex, and messy. Inherent in the complexity is the interaction between the different domains of teacher knowledge. The multifaceted problem is further compounded by the diversity of learners and technology in today's dynamic classroom contexts. Pre-service teachers often feel ill-prepared to plan for effective technology integration in their classrooms. Technological pedagogical content knowledge (TPACK) has provided educators with a theoretical framework to unpack the complexity of technology integration. It sits at the heart of three interrelated components: content knowledge, pedagogical knowledge, and technological knowledge. These knowledge areas interact, support, and constrain each other. This study investigated secondary pre-service teachers’ perceptions of TPACK. Data were collected through an online survey and interviews. Following a brief introduction to TPACK, this article explores secondary pre-service teachers’ perceptions of TPACK and its components, along with their professional learning needs for TPACK development. Implications for teacher education programs are also provided.
Deep Memory Network for Cross-Modal Retrieval

With the explosive growth of multimedia data on the Internet, cross-modal retrieval has attracted a great deal of attention in both computer vision and multimedia communities. However, this task is challenging due to the heterogeneity gap between different modalities. Current approaches typically involve a common representation learning process that maps data from different modalities into a common space by linear or nonlinear embedding. Yet, most of them only handle the dual-modal situation and generalize poorly to complex cases that involve multiple modalities. In addition, they often require expensive fine-grained alignment of training data among diverse modalities. In this paper, we address these with a novel cross-modal memory network (CMMN), in which memory contents across modalities are simultaneously learned from end to end without the need of exact alignment. We further account for the diversity across multiple modalities using the strategy of adversarial learning. Extensive experimental results on several large-scale datasets demonstrate that the proposed CMMN approach achieves state-of-the-art performance in the task of cross-modal retrieval.
Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches

In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evaluation of clinically relevant image features that might reflect mitotic cells in histological images is time-consuming and error prone, due to the heterogeneous physical characteristics of mitotic cells. Computer-assisted automated detection of mitotic cells could overcome these limitations of manual analysis and act as a useful tool for pathologists to make cancer diagnoses efficiently and accurately. Here, we propose a new approach for mitotic cell detection in breast histological images that uses a deep convolution neural network (CNN) with wavelet decomposed image patches. In this approach, raw image patches of 81 × 81 pixels are decomposed to patches of 21 × 21 pixels using Haar wavelet and subsequently used in developing a deep CNN model for automated detection of mitotic cells. The decomposition step reduces convolution time for mitotic cell detection relative to the use of raw image patches in conventional CNN models. The proposed deep network was tested using the MITOS (ICPR2012) and MITOS-ATYPIA-14 breast cancer histological datasets and shown to outperform existing algorithms for mitotic cell detection. Overall, our method improves the performance and reduces the computational burden of conventional deep CNN approaches for mitotic cell detection.
Structural Instability of Driven Josephson Circuits Prevented by an Inductive Shunt

Superconducting circuits are a versatile platform to implement a multitude of Hamiltonians which perform quantum computation, simulation and sensing tasks. A key ingredient for realizing a desired Hamiltonian is the irradiation of the circuit by a strong drive. These strong drives provide an in-situ control of couplings, which cannot be obtained by near-equilibrium Hamiltonians. However, as shown in this paper, out-of-equilibrium systems are easily plagued by complex dynamics leading to instabilities. Predicting and preventing these instabilities is crucial, both from a fundamental and application perspective. We propose an inductively shunted transmon as the elementary circuit optimized for strong parametric drives. Developing a novel numerical approach that avoids the built-in limitations of perturbative analysis, we demonstrate that adding the inductive shunt significantly extends the range of pump powers over which the circuit behaves in a stable manner.
Transfer Clustering Ensemble Selection

Clustering ensemble (CE) takes multiple clustering solutions into consideration in order to effectively improve the accuracy and robustness of the final result. To reduce redundancy as well as noise, a CE selection (CES) step is added to further enhance performance. Quality and diversity are two important metrics of CES. However, most of the CES strategies adopt heuristic selection methods or a threshold parameter setting to achieve tradeoff between quality and diversity. In this paper, we propose a transfer CES (TCES) algorithm which makes use of the relationship between quality and diversity in a source dataset, and transfers it into a target dataset based on three objective functions. Furthermore, a multiobjective self-evolutionary process is designed to optimize these three objective functions. Finally, we construct a transfer CE framework (TCE-TCES) based on TCES to obtain better clustering results. The experimental results on 12 transfer clustering tasks obtained from the 20newsgroups dataset show that TCE-TCES can find a better tradeoff between quality and diversity, as well as obtaining more desirable clustering results.
Evaluating Rewards for Question Generation Models

Tom Hosking, Sebastian Riedel. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Spatio–Temporal Image Representation of 3D Skeletal Movements for View-Invariant Action Recognition with Deep Convolutional Neural Networks

Designing motion representations for 3D human action recognition from skeleton sequences is an important yet challenging task. An effective representation should be robust to noise, invariant to viewpoint changes and result in a good performance with low-computational demand. Two main challenges in this task include how to efficiently represent spatio–temporal patterns of skeletal movements and how to learn their discriminative features for classification tasks. This paper presents a novel skeleton-based representation and a deep learning framework for 3D action recognition using RGB-D sensors. We propose to build an action map called SPMF (Skeleton Posture-Motion Feature), which is a compact image representation built from skeleton poses and their motions. An Adaptive Histogram Equalization (AHE) algorithm is then applied on the SPMF to enhance their local patterns and form an enhanced action map, namely Enhanced-SPMF. For learning and classification tasks, we exploit Deep Convolutional Neural Networks based on the DenseNet architecture to learn directly an end-to-end mapping between input skeleton sequences and their action labels via the Enhanced-SPMFs. The proposed method is evaluated on four challenging benchmark datasets, including both individual actions, interactions, multiview and large-scale datasets. The experimental results demonstrate that the proposed method outperforms previous state-of-the-art approaches on all benchmark tasks, whilst requiring low computational time for training and inference.
Incremental Fuzzy C-Regression Clustering From Streaming Data for Local-Model-Network Identification

In this paper, a new approach to evolving fuzzy model identification from streaming data is given. The structure of the model is given as a local model network in Takagi–Sugeno form, and the partitioning of the input–output space is based on metrics in which these local models are defined as prototypes of the clusters. This means that the clusters and the local models share the same parameters; therefore, the number of parameters of the evolving system is much lower in comparison to similar systems of comparable complexity, and the problems of parameter identifiability are not a particular issue. The algorithm adds the local models in an incremental fashion and recursively adapts the local model parameters. The proposed algorithm is tested on three examples to demonstrate the main features. The first example is a simple simulated example with intersecting clusters; the second is a very well-known benchmark that treats the Mackey–Glass time series; the third is an example that shows the classification of the data from a laser rangefinder. These examples show the great potential of the proposed approach in certain applications.
A Cascade Ensemble Learning Model for Human Activity Recognition with Smartphones

Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer’s classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient.
A Fault Detection System for a Geothermal Heat Exchanger Sensor Based on Intelligent Techniques

This paper proposes a methodology for dealing with an issue of crucial practical importance in real engineering systems such as fault detection and recovery of a sensor. The main goal is to define a strategy to identify a malfunctioning sensor and to establish the correct measurement value in those cases. As study case, we use the data collected from a geothermal heat exchanger installed as part of the heat pump installation in a bioclimatic house. The sensor behaviour is modeled by using six different machine learning techniques: Random decision forests, gradient boosting, extremely randomized trees, adaptive boosting, k-nearest neighbors, and shallow neural networks. The achieved results suggest that this methodology is a very satisfactory solution for this kind of systems.
Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation

Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text.We show that a naïve method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditional frame-level speech features.Specifically, we generate phoneme labels for speech frames and average consecutive frames with the same label to create shorter, higher-level source sequences for translation.We see improvements of up to 5 BLEU on both our high and low resource language pairs, with a reduction in training time of 60%.Our improvements hold across multiple data sizes and two language pairs.
Global Optimization under Length Constraint for Neural Text Summarization

We propose a global optimization method under length constraint (GOLC) for neural text summarization models. GOLC increases the probabilities of generating summaries that have high evaluation scores, ROUGE in this paper, within a desired length. We compared GOLC with two optimization methods, a maximum log-likelihood and a minimum risk training, on CNN/Daily Mail and a Japanese single document summarization data set of The Mainichi Shimbun Newspapers. The experimental results show that a state-of-the-art neural summarization model optimized with GOLC generates fewer overlength summaries while maintaining the fastest processing speed; only 6.70% overlength summaries on CNN/Daily and 7.8% on long summary of Mainichi, compared to the approximately 20% to 50% on CNN/Daily Mail and 10% to 30% on Mainichi with the other optimization methods. We also demonstrate the importance of the generation of in-length summaries for post-editing with the dataset Mainich that is created with strict length constraints. The ex- perimental results show approximately 30% to 40% improved post-editing time by use of in-length summaries.
A comprehensive study on the use of artificial neural networks in wearable fall detection systems

As a result of the challenge posed by demographic aging and the important personal and medical costs associated to falls of elderly people, the study of automatic Fall Detection Systems (FDS) has gained significant research attention during the last decade. Due to the intrinsic difficulty of identifying falls with a deterministic or a simple threshold-based method, Artificial Neural Networks (ANNs) and other machine learning strategies have been largely employed by the literature as the detection algorithms. This paper thoroughly revises and analyzes those papers in the literature that have proposed a FDS that is partly or entirely based on the use of an ANN. The study, which is focused on wearable devices (as they normally impose higher battery and computing restrictions), discusses the most recent trends in the application of neural architectures to the problem of automatic detection. In this regard, the analysis reveals that the related literature has normally emphasized the maximization of the detection performance, while disregarding some methodological, operative implementation-related issues of utmost importance for the assessment of the actual feasibility of these applications. In particular, the investigation shows that cross validating the detection methods with public benchmarking datasets is gaining importance in recent works. Conversely, the practical problems related to the use of wearables (consumption of power and computing resources, configuration and limitations of the sensing units and the wireless transmissions, etc.) are still normally disregarded.
Bridging Semantic Gaps between Natural Languages and APIs with Word Embedding

Developers increasingly rely on text matching tools to analyze the relation between natural language words and APIs. However, semantic gaps, namely textual mismatches between words and APIs, negatively affect these tools. Previous studies have transformed words or APIs into low-dimensional vectors for matching; however, inaccurate results were obtained due to the failure of modeling words and APIs simultaneously. To resolve this problem, two main challenges are to be addressed: the acquisition of massive words and APIs for mining and the alignment of words and APIs for modeling. Therefore, this study proposes Word2API to effectively estimate relatedness of words and APIs. Word2API collects millions of commonly used words and APIs from code repositories to address the acquisition challenge. Then, a shuffling strategy is used to transform related words and APIs into tuples to address the alignment challenge. Using these tuples, Word2API models words and APIs simultaneously. Word2API outperforms baselines by 10-49.6 percent of relatedness estimation in terms of precision and NDCG. Word2API is also effective on solving typical software tasks, e.g., query expansion and API documents linking. A simple system with Word2API-expanded queries recommends up to 21.4 percent more related APIs for developers. Meanwhile, Word2API improves comparison algorithms by 7.9-17.4 percent in linking questions in Question&Answer communities to API documents.