Reviewerly/exHarmony huggingface dataset:
Fallen People Detection Capabilities Using Assistive Robot

One of the main problems in the elderly population and for people with functional disabilities is falling when they are not supervised. Therefore, there is a need for monitoring systems with fall detection functionality. Mobile robots are a good solution for keeping the person in sight when compared to static-view sensors. Mobile-patrol robots can be used for a group of people and systems are less intrusive than ones based on mobile robots. In this paper, we propose a novel vision-based solution for fall detection based on a mobile-patrol robot that can correct its position in case of doubt. The overall approach can be formulated as an end-to-end solution based on two stages: person detection and fall classification. Deep learning-based computer vision is used for person detection and fall classification is done by using a learning-based Support Vector Machine (SVM) classifier. This approach mainly fulfills the following design requirements—simple to apply, adaptable, high performance, independent of person size, clothes, or the environment, low cost and real-time computing. Important to highlight is the ability to distinguish between a simple resting position and a real fall scene. One of the main contributions of this paper is the input feature vector to the SVM-based classifier. We evaluated the robustness of the approach using a realistic public dataset proposed in this paper called the Fallen Person Dataset (FPDS), with 2062 images and 1072 falls. The results obtained from different experiments indicate that the system has a high success rate in fall classification (precision of 100% and recall of 99.74%). Training the algorithm using our Fallen Person Dataset (FPDS) and testing it with other datasets showed that the algorithm is independent of the camera setup.
Improving Augmented Human Intelligence to Distinguish Burkitt Lymphoma From Diffuse Large B-Cell Lymphoma Cases

Abstract Objectives To assess and improve the assistive role of a deep, densely connected convolutional neural network (CNN) to hematopathologists in differentiating histologic images of Burkitt lymphoma (BL) from diffuse large B-cell lymphoma (DLBCL). Methods A total of 10,818 images from BL (n = 34) and DLBCL (n = 36) cases were used to either train or apply different CNNs. Networks differed by number of training images and pixels of images, absence of color, pixel and staining augmentation, and depth of the network, among other parameters. Results Cases classified correctly were 17 of 18 (94%), nine with 100% of images correct by the best performing network showing a receiver operating characteristic curve analysis area under the curve 0.92 for both DLBCL and BL. The best performing CNN used all available training images, two random subcrops per image of 448 × 448 pixels, random H&amp;E staining image augmentation, random horizontal flipping of images, random alteration of contrast, reduction on validation error plateau of 15 epochs, block size of six, batch size of 32, and depth of 22. Other networks and decreasing training images had poorer performance. Conclusions CNNs are promising augmented human intelligence tools for differentiating a subset of BL and DLBCL cases.
Topic Modeling Technique for Text Mining Over Biomedical Text Corpora Through Hybrid Inverse Documents Frequency and Fuzzy K-Means Clustering

Text data plays an imperative role in the biomedical domain.As patient's data comprises of a huge amount of text documents in a non-standardized format.In order to obtain the relevant data, the text documents pose a lot of challenging issues for data processing.Topic modeling is one of the popular techniques for information retrieval based on themes from the biomedical documents.In topic modeling discovering the precise topics from the biomedical documents is a challenging task.Furthermore, in biomedical text documents, the redundancy puts a negative impact on the quality of text mining as well.Therefore, the rapid growth of unstructured documents entails machine learning techniques for topic modeling capable of discovering precise topics.In this paper, we proposed a topic modeling technique for text mining through hybrid inverse document frequency and machine learning fuzzy k-means clustering algorithm.The proposed technique ameliorates the redundancy issue and discovers precise topics from the biomedical text documents.The proposed technique generates local and global term frequencies through the bag-of-words (BOW) model.The global term weighting is calculated through the proposed hybrid inverse documents frequency and Local term weighting is computed with term frequency.The robust principal component analysis is used to remove the negative impact of higher dimensionality on the global term weights.Afterward, the classification and clustering for text mining are performed with a probability of topics in the documents.The classification is performed through discriminant analysis classifier whereas the clustering is done through the k-means clustering.The performance of clustering is evaluated with Calinsiki-Har-abasz (CH) index internal validation method.The proposed toping modeling technique is evaluated on six standard datasets namely Ohsumed, MuchMore Springer Corpus, GENIA corpus, Bioxtext, tweets and WSJ redundant corpus for experimentation.The proposed topic modeling technique exhibits high performance on classification and clustering in text mining compared to baseline topic models like FLSA, LDA, and LSA.Moreover, the execution time of the proposed topic modeling technique remains stable for different numbers of topics.
Markerless Outdoor Human Motion Capture Using Multiple Autonomous Micro Aerial Vehicles

Capturing human motion in natural scenarios means moving motion capture out of the lab and into the wild. Typical approaches rely on fixed, calibrated, cameras and reflective markers on the body, significantly limiting the motions that can be captured. To make motion capture truly unconstrained, we describe the first fully autonomous outdoor capture system based on flying vehicles. We use multiple micro-aerial-vehicles(MAVs), each equipped with a monocular RGB camera, an IMU, and a GPS receiver module. These detect the person, optimize their position, and localize themselves approximately. We then develop a markerless motion capture method that is suitable for this challenging scenario with a distant subject, viewed from above, with approximately calibrated and moving cameras. We combine multiple state-of-the-art 2D joint detectors with a 3D human body model and a powerful prior on human pose. We jointly optimize for 3D body pose and camera pose to robustly fit the 2D measurements. To our knowledge, this is the first successful demonstration of outdoor, full-body, markerless motion capture from autonomous flying vehicles.
Investigating Cross-Lingual Alignment Methods for Contextualized Embeddings with Token-Level Evaluation

In this paper, we present a thorough investigation on methods that align pre-trained contextualized embeddings into shared crosslingual context-aware embedding space, providing strong reference benchmarks for future context-aware crosslingual models.We propose a novel and challenging task, Bilingual Token-level Sense Retrieval (BTSR).It specifically evaluates the accurate alignment of words with the same meaning in crosslingual non-parallel contexts, currently not evaluated by existing tasks such as Bilingual Contextual Word Similarity and Sentence Retrieval.We show how the proposed BTSR task highlights the merits of different alignment methods.In particular, we find that using context average type-level alignment is effective in transferring monolingual contextualized embeddings cross-lingually especially in non-parallel contexts, and at the same time improves the monolingual space.Furthermore, aligning independently trained models yields better performance than aligning multilingual embeddings with shared vocabulary.
Scale-Equivariant Steerable Networks

The effectiveness of Convolutional Neural Networks (CNNs) has been substantially attributed to their built-in property of translation equivariance. However, CNNs do not have embedded mechanisms to handle other types of transformations. In this work, we pay attention to scale changes, which regularly appear in various tasks due to the changing distances between the objects and the camera. First, we introduce the general theory for building scale-equivariant convolutional networks with steerable filters. We develop scale-convolution and generalize other common blocks to be scale-equivariant. We demonstrate the computational efficiency and numerical stability of the proposed method. We compare the proposed models to the previously developed methods for scale equivariance and local scale invariance. We demonstrate state-of-the-art results on MNIST-scale dataset. Finally, we demonstrate that the proposed scale-equivariant convolutions show remarkable gains on STL-10 when used as drop-in replacements for non-equivariant convolutional layers.
Gender equality and ICT in the context of formal education: A systematic review

This article focuses on gender equality as one of the most relevant objectives of the educational system to alleviate gender violence and combat stereotypes. At the same time, ICTs have become a very important educational tool in a digital society like the one where we live. Around these two topics we conducted this research whose purpose is, from the scientific production of the last six years, to analyze the educational practices in formal contexts that deal with gender equality and ICTs (infant, primary, secondary and higher education). The research design is a systematic review, based on the PRISMA statement and the PICoS strategy, using the 2013-2019 search period. After a screening process of a total of 90 documents, 18 papers were found that cover both study topics (gender and ICTs). A content analysis supported by semantic networks was performed, using Atlas.ti v.8. Among the main results, we highlight that most of the good practices in the different educational levels are related to the use of web 2.0. and STEM competences. Finally, we recommend the design of proposals that work on gender through ICTs, with the “smart classroom" as an interesting suggestion that is part of the emerging pedagogies. Este artículo centra su interés en la igualdad de género, como uno de los objetivos más relevantes del sistema educativo para paliar la violencia de género y combatir los estereotipos. Paralelamente, las TIC se han convertido en una herramienta educativa muy importante en una sociedad digital como la que vivimos. En torno a estos dos tópicos llevamos a cabo esta investigación cuyo propósito es, a partir de la producción científica de los últimos seis años, analizar las prácticas educativas en contextos formales que trabajen la igualdad de género y las TIC (educación infantil, primaria, secundaria y superior). El diseño de investigación es una revisión sistemática, sustentada en la declaración PRISMA y la estrategia PICoS y usando el periodo de búsqueda 2013-2019. Tras el proceso de filtrado de un total de 90 documentos, se han encontrado 18 trabajos que contemplan ambos tópicos de estudio (género y TIC). Se ha realizado un análisis de contenidos apoyado en redes semánticas, usando para ello Atlas.ti v.8. Entre los principales resultados, destacamos que la mayoría de buenas prácticas en los diferentes niveles educativos se relacionan con la utilización de la web 2.0. y con las competencias STEM. Por último, se recomienda el diseño de propuestas que trabajen el género a través de las TIC, siendo la «smart classroom» una sugerencia de interés que forma parte de las pedagogías emergentes.
Deep Category-Level and Regularized Hashing With Global Semantic Similarity Learning

The hashing technique has been extensively used in large-scale image retrieval applications due to its low storage and fast computing speed. Most existing deep hashing approaches cannot fully consider the global semantic similarity and category-level semantic information, which result in the insufficient utilization of the global semantic similarity for hash codes learning and the semantic information loss of hash codes. To tackle these issues, we propose a novel deep hashing approach with triplet labels, namely, deep category-level and regularized hashing (DCRH), to leverage the global semantic similarity of deep feature and category-level semantic information to enhance the semantic similarity of hash codes. There are four contributions in this article. First, we design a novel global semantic similarity constraint about the deep feature to make the anchor deep feature more similar to the positive deep feature than to the negative deep feature. Second, we leverage label information to enhance category-level semantics of hash codes for hash codes learning. Third, we develop a new triplet construction module to select good image triplets for effective hash functions learning. Finally, we propose a new triplet regularized loss (Reg-L) term, which can force binary-like codes to approximate binary codes and eventually minimize the information loss between binary-like codes and binary codes. Extensive experimental results in three image retrieval benchmark datasets show that the proposed DCRH approach achieves superior performance over other state-of-the-art hashing approaches.
A BERT-BiLSTM-CRF Model for Chinese Electronic Medical Records Named Entity Recognition

Named entity recognition is a fundamental task in natural language processing and many studies have done about it in recent decades. Previous word representation methods represent words as a single vector of multiple dimensions, which ignore the ambiguity of the character in Chinese. To solve this problem, we apply a BERT-BiLSTM-CRF model to Chinese electronic medical records named entity recognition in this paper. This model enhances the semantic representation of words by using BERT pre-trained language model, then we combine a BiLSTM network with CRF layer, and the word vector is used as the input for training. To evaluate the performance, we compare this model with several baseline models in CCKS 2017 datasets. Experimental results demonstrate that the BERT-BiLSTM-CRF model could achieve a better performance than the other baseline models.
“How did you get to this number?” Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative study

Abstract Objective Predictive analytics are potentially powerful tools, but to improve healthcare delivery, they must be carefully integrated into healthcare organizations. Our objective was to identify facilitators, challenges, and recommendations for implementing a novel predictive algorithm which aims to prospectively identify patients with high preventable utilization to proactively involve them in preventative interventions. Materials and Methods In preparation for implementing the predictive algorithm in 3 organizations, we interviewed 3 stakeholder groups: health systems operations (eg, chief medical officers, department chairs), informatics personnel, and potential end users (eg, physicians, nurses, social workers). We applied thematic analysis to derive key themes and categorize them into the dimensions of Sittig and Singh’s original sociotechnical model for studying health information technology in complex adaptive healthcare systems. Recruiting and analysis were conducted iteratively until thematic saturation was achieved. Results Forty-nine interviews were conducted in 3 healthcare organizations. Technical components of the implementation (hardware and software) raised fewer concerns than alignment with sociotechnical factors. Stakeholders wanted decision support based on the algorithm to be clear and actionable and incorporated into current workflows. However, how to make this disease-independent classification tool actionable was perceived as a challenge, and appropriate patient interventions informed by the algorithm appeared likely to require substantial external and institutional resources. Stakeholders also described the criticality of trust, credibility, and interpretability of the predictive algorithm. Conclusions Although predictive analytics can classify patients with high accuracy, they cannot advance healthcare processes and outcomes without careful implementation that takes into account the sociotechnical system. Key stakeholders have strong perceptions about facilitators and challenges to shape successful implementation.
Rationalized Sine Cosine Optimization With Efficient Searching Patterns

Even with the advantages of the sine cosine algorithm (SCA) in solving multimodal problems, there are some shortcomings for this method. We observe that the random patterns utilized in SCA cause an increasing attraction toward local optima. This study developed a rationalized version of this technique to deal with several representative benchmark cases with different dimensions. The improved algorithm combines the chaotic local search mechanism and Lévy flight operator with the core trends of SCA. The new variant is named as CLSCA. The Lévy flight with long jumps is adopted to boost the exploratory tendencies of the algorithm, while the chaotic local search mechanism is used as a local search for the destination point, which helps to further enhance the exploitation capability of SCA. Therefore, a suitable equilibrium between the exploration and exploitation can be kept in the CLSCA by two embedded patterns. To investigate the effectiveness and strength of the developed method, the CLSCA was tested on many benchmark functions, including different types of tasks such as single modal, multi-modal, hybrid, and composition functions. We compare the CLSCA with well-known optimizers, like particle swarm optimization (PSO) algorithm, grey wolf optimizer (GWO), SCA with differential evolution (SCADE), opposition-based SCA (OBSCA), fuzzy self-tuning PSO (FST_PSO), chaotic salp swarm algorithm (CSSA), and Chaotic whale optimizer (CWOA). Numerical experimental results demonstrate that the exploratory and exploitative properties of the classical SCA are clearly improved. The experimental results also show that our improved CLSCA is a better technique for different kinds of optimization tasks.
Dynamic Heterogeneous Graph Embedding Using Hierarchical Attentions

Graph embedding has attracted many research interests. Existing works mainly focus on static homogeneous/heterogeneous networks or dynamic homogeneous networks. However, dynamic heterogeneous networks are more ubiquitous in reality, e.g. social network, e-commerce network, citation network, etc. There is still a lack of research on dynamic heterogeneous graph embedding. In this paper, we propose a novel dynamic heterogeneous graph embedding method using hierarchical attentions (DyHAN) that learns node embeddings leveraging both structural heterogeneity and temporal evolution. We evaluate our method on three real-world datasets. The results show that DyHAN outperforms various state-of-the-art baselines in terms of link prediction task.
Joint Contextual Modeling for ASR Correction and Language Understanding

The quality of automatic speech recognition (ASR) is critical to Dialogue Systems as ASR errors propagate to and directly impact downstream tasks such as language understanding (LU). In this paper, we propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with LU to improve the performance of both tasks simultaneously. To measure the effectiveness of this approach we used a public benchmark, the 2nd Dialogue State Tracking (DSTC2) corpus. As a baseline approach, we trained task specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generative Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots. i) We further trained ranker models using GPT and Hierarchical CNN-RNN models with discriminatory losses to detect the best output given n-best hypotheses. We extended these ranker models to first select the best ASR output and then identify the dialogue act and slots in an end to end fashion. ii) We also proposed a novel joint ASR error correction and LU model, a word confusion pointer network (WCN-Ptr) with multihead self attention on top, which consumes the word confusions populated from the n-best. We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
A calibration strategy for vision-guided robot assembly system of large cabin

Vision measurement technology is increasingly utilized in aerospace assembly due to its high precision, low cost and convenience. However, it remains challenges to calibrate the visual measuring equipment and the global assembly station due to complex shape, large volume and high precision requirement. This paper proposes a calibration strategy for large cabin assembly system (LCSS) with vision guidance, focusing on calibration of binocular vision, small base target (SBT) and global station. A combined large plane target (CLPT) calibration method with optimized homography matrix is proposed. Then a profiling copy SBT is developed and the special calibration method based on common point and common cylinder is proposed. The LCSS is globally calibrated at last. Numerical simulations are implemented to test the calculation method and the noise effect. Also, real data experiments of binocular vision calibration and global calibration of LCSS are developed to verify the proposed method.
A Super-Learner Ensemble of Deep Networks for Vehicle-Type Classification

Automatic vehicle-type classification plays an imperative role in the development of efficient Intelligent Transportation Systems (ITS).In this paper, a super-learner ensemble is proposed for the vehicle-type classification problem.A densely connected single-split super learner is utilized to exploit the strengths and diminish the weaknesses of the individual base learners ResNet50, Xception, and DenseNet.The super learner aims to learn fusion weights in a data-adaptive manner to obtain the optimal combination of the base learners.The proposed method is simple, robust, and enhances the discrimination capabilities among the similarly-looking classes without requiring any hand-crafted features or logical reasoning.The proposed method is evaluated using two of the most challenging publicly available traffic surveillance datasets: the MIOvision Traffic Camera Dataset (MIO-TCD) and the Beijing Institute of Technology's (BIT) vehicle classification dataset.Three variants of the super learner ensemble: RXD-CV-CW, RXD-CV-CW-NCW and Augmented-RXD, were examined on the MIO-TCD dataset with variations in applying class weights and data augmentation during training.RXD-CV-CW-NCW and Augmented-RXD share the third place among the published state-of-the-art methods reported in the MIO-TCD classification challenge.Augmented-RXD generalizes to the classes in common between the two datasets without degrading its performance on the MIO-TCD dataset.Both variants achieved an overall accuracy of 97.94%, and a Cohen Kappa score of 96.78%.In addition, the super-learner variants that we trained on the BIT-Vehicle dataset images achieved overall accuracies of up to 97.62%.