Reviewerly/exHarmony huggingface dataset:
Unpacking smart education’s soft smartness variables: Leadership and human resources capacities as key participatory actors

The education system has been radically transformed by technological impetuses owed to the Fourth Industrial Revolution (4.0). Most recently, developing nations expedited smart education implementation to combat the negative effects COVID-19 has on education; thus, presenting managerial issues. A review of the literature on smart education shows that past studies focused primarily on the smart learning environment, substantially ignoring the importance of leadership and human resources capacity in the management of smart education. This study addresses the deficiency observed in the literature as it relates to the key factors that enable success in the management of smart education. The study applied a quantitative approach that derived data from a structured survey of probability simple random sampled Grenadian employees belonging to tertiary education. The Hayes multiple moderated mediation, Model 23, supported by SPSS-PROCESS Macro software was used to examine the study's model. Results showed that human resources capacity has a conditional indirect effect on smart education through the mediating variable of leadership capacity. Additionally, the moderating variables of additional investment in the 'ɑ' path and student demographics in the 'ɓ' path both displayed significant moderating effects. The findings of the study have provided several valuable insights into the theoretical and practical implications of the influence of leadership and human resources capacity in the implementation of smart education. Therefore, it is recommended, that the soft variables assessed in this paper need to be harnessed accordingly to achieve smart education.
Single document summarization using the information from documents with the same topic

The essence of extractive summarization is to measure the importance of sentences in the document. When extracting summary from a single document, it is difficult to comprehensively and effectively evaluate the importance of sentences due to the lack of information. In this paper, we propose a kind of single document summarization method using information from documents under the same topic. This method integrates the topic information from neighborhood documents and statistical information from the target document to calculate the score of sentences. Then the scoring results are used as a prior scores for each sentence in the target document. After the target document is represented by the sentence graph, the final score of the sentences are obtained by the biased random walk algorithm. Finally, the Maximal Marginal Relevance (MMR) algorithm is used to select the sentences to form summary. The experimental results on the DUC2001 and DUC2002 datasets show that the effect of extracting summary is improved by incorporating information from the documents under the same topic.
An Efficient 3D Synthetic Model Generation Pipeline for Human Pose Data Augmentation

3D modeling of articulated bodies of humans or animals and using these models for synthetic 2D and 3D pose data generation can mitigate the small data challenges faced by many critical applications such as healthcare. In this paper, we present our efficient 3D synthetic model generation (3D-SMG) pipeline used for body pose data augmentation. 3D-SMG pipeline starts with scanning point clouds from various angles around the subject using an off-the-shelf RGBD camera. We then implement a dual objective iterative closest point (ICP) algorithm that uses both color (if available) as well as geometric information from point cloud and apply a pose graph node optimization to form one single rigid body mesh. 3D-SMG also includes a series of post processing steps to obtain a smooth mesh at the end of the pipeline. The approach allows it to be applied to any articulated object such as a human body or an animal. Our experiments also show high level of accuracy in dimensions of obtained 3D meshes, when compared to the original subject. As the final step towards developing augmented pose dataset, we perform model rigging to articulate the 3D model of the subject and generate dynamic avatars within variety of context-feasible poses <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Recognition of Basketball Player’s Shooting Action Based on the Convolutional Neural Network

In the field of basketball, the formulation of the existing training plan mainly relies on the coaches’ artificial observation and personal experience, which is inevitably subjective. The application of body domain network technology in athletes’ training and recognition of athletes’ postures can help coaches to assist decision-making and greatly improve athletes’ competitive ability. The human movements reflected in basketball are more complex which need deep understanding. The accuracy of basketball players’ shooting movements recognition plays a positive and important role in basketball games and training practice. Based on the prior knowledge of the convolutional neural network study, environment light conditions change the dynamic characteristics of basketball image analysis, capture images of the basketball goal algorithm of minimum circumscribed rectangle of the object, and based on the convolutional neural network, introduce two types of prior knowledge, one kind is based on the feature matching method that defined a priori knowledge, while another kind is based on training the convolution neural network model. The test results of the network model are taken as the prior knowledge, and then, a convolutional neural network dynamic target recognition model is constructed based on the prior knowledge. The construction process of the model is organized as the basketball target image is collected under any illumination conditions, the convolutional neural network model is trained with the convolutional neural network as the input data, and the standard illumination conditions are determined according to the test results of the network model. Then, put it into the trained network model to test and get the recognition results of basketball players’ shooting movements. The research is validated with performing experiments and the results revealed the success of the study.
Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study

Responsible usage of Machine Learning (ML) systems in practice does not only require enforcing high prediction quality, but also accounting for other constraints, such as fairness, privacy, or execution time. One way to address multiple user-specified constraints on ML systems is feature selection. Yet, optimizing feature selection strategies for multiple metrics is difficult to implement and has been underrepresented in previous experimental studies. Here, we propose Declarative Feature Selection (DFS) to simplify the design and validation of ML systems satisfying diverse user-specified constraints. We benchmark and evaluate a representative series of feature selection algorithms. From our extensive experimental results, we derive concrete suggestions on when to use which strategy and show that a meta-learning-driven optimizer can accurately predict the right strategy for an ML task at hand. These results demonstrate that feature selection can help to build ML systems that meet combinations of user-specified constraints, independent of the ML methods used.
An Intelligent Internet of Medical Things with Deep Learning Based Automated Breast Cancer Detection and Classification Model

In recent decades, breast cancer (BC) is a significant cause of high mortality rate among women. The earlier identification of breast cancer helps to increase the survival rate by the use of appropriate medications. At the same time, internet of medical things (IoMT) and digital mammography finds helpful to diagnose breast cancer effectively in the beginning level itself. This paper presents an intelligent IoMT based breast cancer detection and diagnosis using deep learning model. IoMT based image acquisition process takes place to gather the digital mammogram images. The proposed model performs a set of processes namely preprocessing, K-means clustering based segmentation, local binary pattern (LBP) based feature extraction and deep neural network (DNN) based classification. The presented LBP-DNN model has the capability of effectively detecting and classifying breast cancer from mammogram images. The LBP-DNN model has been validated using MIAS database and an extensive comparative analysis is carried out to evaluate its performance. The experimental results ensured the superior performance of the LBP-DNN model with the maximum sensitivity of 71.64%, specificity of 75.87% and accuracy of 70.53%.
Optimizing the digital customer journey—Improving user experience by exploiting emotions, personas and situations for individualized user interface adaptations

Abstract This paper discusses a novel approach for exploiting emotions and situation‐aware software adaptation methods for individualizing some of the touch points of the digital customer journey and thereby optimizing customer experience and effectiveness of e‐commerce applications. Our approach uses emotion recognition, eye‐tracking, and other individual tracking methods as well as customer personas for adapting interactive web applications by accessing a flexible adaptation framework at runtime. The framework allows for individualization at runtime by applying situation‐aware adaptations. Two experimental customer studies were carried out in the e‐commerce domain in order to provide a basis for exploitable emotion‐ and persona‐related situational changes. The results of the studies were used to demonstrate the potential of our situation analytic adaptation approach with examples from a commercial beauty‐products e‐business portal.
Discovering Multi-Hardware Mobile Models via Architecture Search

Hardware-aware neural architecture designs have been predominantly focusing on optimizing model performance on single hardware and model development complexity, where another important factor, model deployment complexity, has been largely ignored. In this paper, we argue that, for applications that may be deployed on multiple hardware, having different single-hardware models across the deployed hardware makes it hard to guarantee consistent outputs across hardware and duplicates engineering work for debugging and fixing. To minimize such deployment cost, we propose an alternative solution, multi-hardware models, where a single architecture is developed for multiple hardware. With thoughtful search space design and incorporating the proposed multi-hardware metrics in neural architecture search, we discover multi-hardware models that give state-of-the-art (SoTA) performance across multiple hardware in both average and worse case scenarios. For performance on individual hardware, the single multi-hardware model yields similar or better results than SoTA performance on accelerators like GPU, DSP and EdgeTPU which was achieved by different models, while having similar performance with MobilenetV3 Large Minimalistic model on mobile CPU. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Direct linkage discovery with empirical linkage learning

Problem decomposition is an important part of many state-of-the-art Evolutionary Algorithms (EAs). The quality of the decomposition may be decisive for the EA effectiveness and efficiency. Therefore, in this paper, we focus on the recent proposition of Linkage Learning based on Local Optimization (3LO). 3LO is an empirical linkage learning (ELL) technique and is proven never to report the false linkage. False linkage is one of the possible linkage defects and occurs when linkage marks two independent genes as a dependent. Although thanks to the problem decomposition quality, the use of 3LO may lead to excellent results, its main disadvantage is its high computational cost. This disadvantage makes 3LO not applicable to state-of-the-art EAs that originally employed Statistical-based Linkage Learning (SLL) and frequently update the linkage information. Therefore, we propose the Direct Linkage Empirical Discovery technique (DLED) that preserves 3LO advantages, reduces its costs, and we prove that it is precise in recognizing the direct linkage. The concept of direct linkage, which we identify in this paper, is related to the quality of the decomposition of overlapping problems. The results show that incorporating DLED in three significantly different state-of-the-art EAs may lead to promising results.
An Adaptive Particle Filter for Target Tracking Based on Double Space-Resampling

Particle filter has been widely applied in nonlinear target tracking due to the ability to carry multiple hypothesis and relaxation of linearity/Gaussian assumption.In this paper, an adaptive double space-resampling particle filter is proposed to increase the efficiency and robustness of filtering by adjusting the sample size.The first resampling operation, adopted before the prediction of samples, generates a larger number of equal-weighted samples and some auxiliary samples to enhance the robustness of filtering.The second resampling, adopted between the prediction and updating step, decreases the sample size for weight updating which is the most time consumption part of particle filter.The particle space sampling technique is used in both space-resampling, which adjusts the sample size according to not only the weights of samples but also their spatial distribution.The efficiency of filtering is improved and the robustness of algorithm is enhanced, simultaneously.The degeneracy and sample impoverishment problems can be counteracted.Simulation and experiment contrast results demonstrate that the proposed method is robust and efficient.
How We Swipe: A Large-scale Shape-writing Dataset and Empirical Findings

Despite the prevalence of shape-writing (gesture typing, swype input, or swiping for short) as a text entry method, there are currently no public datasets available. We report a large-scale dataset that can support efforts in both empirical study of swiping as well as the development of better intelligent text entry techniques. The dataset was collected via a web-based custom virtual keyboard, involving 1,338 users who submitted 11,318 unique English words. We report aggregate-level indices on typing performance, user-related factors, as well as trajectory-level data, such as the gesture path drawn on top of the keyboard or the time lapsed between consecutively swiped keys. We find some well-known effects reported in previous studies, for example that speed and error are affected by age and language skill. We also find surprising relationships such that, on large screens, swipe trajectories are longer but people swipe faster.
Real time fall detection using infrared cameras and reflective tapes under day/night luminance

Falls are the leading cause of injuries and death in elderly individuals who live alone at home. The core service of assistive living technology is to monitor elders’ activities through wearable devices, ambient sensors, and vision systems. Vision systems are among the best solutions, as their implementation and maintenance costs are the lowest. However, current vision systems are limited in their ability to handle cluttered environments, occlusion, illumination changes throughout the day, and monitoring without illumination. To overcome these issues, this paper proposes a 24/7 monitoring system for elders that uses retroreflective tape fabricated as part of conventional clothing, monitored through low-cost infrared (IR) cameras fixed in the living environment. IR camera records video even when there are changes in illumination or zero luminance. For classification among clutter and occlusion, the tape is considered as a blob instead of a human silhouette; the orientation angle, fitted through ellipse modeling, of the blob in each frame allows classification that detects falls without pretrained data. System performance was tested using subjects in various age groups and “fall” or “non-fall” were detected with 99.01% accuracy.
Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Multi-verse Optimization Algorithm

The highest number of deaths among all types of cancers in women is caused by breast cancer. Therefore, early detection and diagnosis of breast cancer are very much needed for its treatment. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used for breast cancer diagnosis. In this paper, a segmentation method using modified hard-clustering technique with multi-verse optimizer (MVO) is proposed for the detection of breast lesion in DCE-MRI. The proposed method is termed as CMVO in this paper. First, MR images are denoised, and intensity inhomogeneities are corrected in the preprocessing steps. Then clustering technique is used in segmentation of the MR images. Finally, from the segmented images, lesions are extracted in the postprocessing step. The results of CMVO are compared with that of K-means algorithm and PSO-based hard clustering. The CMVO performs better than other methods in lesion detection in breast DCE-MRI.Kar, Bikram Si, Tapas
Differentially Private Publication of Multi-Party Sequential Data

Given a set of local sequential datasets held by multiple parties, we study the problem of publishing a synthetic dataset that preserves approximate sequentiality information of the integrated dataset while satisfying differential privacy for each local dataset. The existing solutions for publishing differentially private sequential data in the centralized setting mostly adopt tree-based approaches. Such approaches rely on different tree structures that encode sequential data's statistical information. The construction of a tree structure is normally done by recursively splitting nodes whose noisy scores (e.g., entropy or count) are larger than a given threshold. However, extending similar ideas to the multi-party setting is challenging. First, the comparison between noisy scores and a given threshold needs to be done in a distributed manner without letting the parties know the noisy scores, while satisfying differential privacy for each local dataset. Second, in the multi-party setting the large number of node splitting decisions incurs prohibitive computation costs. In addressing the above challenges, we present DPST, a distributed prediction suffix tree construction solution. In DPST, we first introduce a novel node splitting decision method that calculates the comparison result under encryption with substantially improved efficiency. Then we present a novel batch-based tree construction approach to reduce the computation costs. In order to achieve high parallel performance without incurring any extra communication cost, we introduce the conjunction and slide methods to ensure that each batch contains a stable number of carefully arranged decision tasks. Extensive experiments on real datasets demonstrate that our DPST solution offers desirable data utility with low computation and communication costs.
SUPER: SUb-Graph Parallelism for TransformERs

Transformer models have revolutionized the field of Natural Language Processing (NLP) and they achieve state-of-the-art performance in applications like machine translation, question answering, regression, and summarization. However, training Transformers is challenging because of their large memory and compute requirements. The literature contains several approaches to parallelize training, like layer parallelism and pipeline parallelism, but they are optimized to benefit out-of-core models and they don't exploit the inherent parallelism in Transformer models. Other work uses model parallelism to achieve weak scaling by increasing the model size. In this paper, we propose sub-graph parallelism that provides a significant performance improvement over pure data parallelism with a fixed number of resources, and as an additional technique for strong- and weak-scaling without increasing model capacity. Our technique accelerates the training of Transformer models and we generalize the concept to any neural network with multiple branches. We optimize the communication for sub-graph parallelism and combine it with data parallelism to scale performance up to 1024 GPUs. To decrease communication overheads, we propose a topology-aware scheme that limits inter-node communication. Finally, we empirically compare sub-graph parallelism with pure data parallelism and demonstrate its performance benefits in end-to-end training.
Multiview Graph Restricted Boltzmann Machines

Recently, the restricted Boltzmann machine (RBM) has aroused considerable interest in the multiview learning field. Although effectiveness is observed, like many existing multiview learning models, multiview RBM ignores the local manifold structure of multiview data. In this article, we first propose a novel graph RBM model, which preserves the data manifold structure and is amenable to Gibbs sampling. Then, we develop a multiview graph RBM model on the basis of the graph RBM, which performs local structural learning and multiview representation learning simultaneously. The proposed multiview model has the following merits: 1) it preserves the data manifold structure for multiview classification and 2) it performs view-consistent representation learning and view-specific representation learning simultaneously. The experimental results show that the proposed multiview model outperforms other state-of-the-art multiview classification algorithms.
A new object detection method for object deviating from center or multi object crowding

• Proposing an object detection method for object deviating from center or multi object crowding. • The functions of PA-FPN, FreeAnchor and Soft-NMS are complementary and the combination of them can get the maximum benefits for the original network. RetinaNet is a typical representative of single-stage object detection, which can solve the problem of sample imbalance. However, due to the lack of region proposal extraction process in single-stage object detection, the effect of RetinaNet in dealing with the problem that object deviating from center or multi object crowding is not good. To solve this problem, we use a variety of optimization methods for RetinaNet to improve the accuracy of object detection. Firstly, FreeAnchor is introduced on the basis of RetinaNet, which can autonomously learn to match the target category; secondly, ResNeXt50 is taken as the backbone to improve the accuracy without increasing the parameter complexity; thirdly, Bottom-up Path Augmentation module is used to enhance the transmission of location information and further optimize the recall rate; finally, soft-NMS method is used to effectively reduce the false positive detection results and improve the average accuracy of object detection. We use the MS COCO data set to verify the new model. The mAP value of the new model reached 40.8, which is 4.3 more than baseline. It shows that the optimization methods are complementary to each other, which can effectively improve the object detection accuracy while ensuring the speed.