Reviewerly/exHarmony huggingface dataset:
Object tracking and counting in a zone using YOLOv4, DeepSORT and TensorFlow

The main objective of this research work is to solve multiple object tracking problems in a given frame, wherein the proposed model intends to identify and track various objects. The problem has been solved in three stages viz. detecting, identifying, and tracking the object in a particular zone, i.e., but it is observed that something more could be done in this field, mostly the MOT-A score was not up to the mark; hence the proposed research work utilized Kalman filters for obtaining enhanced results and compared the obtained MOT-A metric with previous works, and the results were good. Object detection and recognition occur via the YOLO algorithm, which enables us to classify the objects into 80 classes. Then, Motion Prediction and feature generation occur in which an estimation model is created, and Kalman filters are used to model these states for capturing moving objects in the frame. Finally, tracking takes place with Kalman filters in the previous frame, and newly detected objects are placed in the current frame, after which an association is made for new detection. All this is done via the DeepSORT algorithm, which is essentially a Deep association metric with the SORT algorithm. Here, Kalman filters are used as they improved the accuracy of the proposed model and yielded better results. On the same lines, YOLO is used to perform object detection and recognition at the same time. It is also a detector, which by applying a single neural network, it can predict the bounding boxes and perform multi-class classification. This problem can have various applications, especially in traffic management. It can also prevent people from gathering during COVID times and raising an alert for all the authorities. Hence, this is a multidisciplinary approach wherein the work of object detection is being used in various fields like crowd assembling, Surveillance, Animal management in Zoo/Biodiversity parks as well as in the case of traffic systems as well. The real motivation behind this work was using the state-of-the-art technology to solve the modern-day problems. Manually human vigilance in large areas is a utopian task and especially when surveillance and security is big threat out there. Adding to it is the COVID Pandemic which has claimed millions of lives and yet vaccination is a still a dream yet to be realized. Hence, cluster identification problem is considered and also the ways to solve this challenge using the state-of-the-art technology. Once it has been achieved, it is initiated further to find more applications of this technology in various other domains and found out how it is applied in various use cases in case of crowd gathering at a single point and how this is a similar problem in case of animal gathering at a point and how forest rangers can solve the problem in a more efficient way.
Class-Imbalanced Voice Pathology Detection and Classification Using Fuzzy Cluster Oversampling Method

The Massachusetts Eye and Ear Infirmary (MEEI) database is an international-standard training database for voice pathology detection (VPD) systems. However, there is a class-imbalanced distribution in normal and pathological voice samples and different types of pathological voice samples in the MEEI database. This study aimed to develop a VPD system that uses the fuzzy clustering synthetic minority oversampling technique algorithm (FC-SMOTE) to automatically detect and classify four types of pathological voices in a multi-class imbalanced database. The proposed FC-SMOTE algorithm processes the initial class-imbalanced dataset. A set of machine learning models was evaluated and validated using the resulting class-balanced dataset as an input. The effectiveness of the VPD system with FC-SMOTE was further verified by an external validation set and another pathological voice database (Saarbruecken Voice Database (SVD)). The experimental results show that, in the multi-classification of pathological voice for the class-imbalanced dataset, the method we propose can significantly improve the diagnostic accuracy. Meanwhile, FC-SMOTE outperforms the traditional imbalanced data oversampling algorithms, and it is preferred for imbalanced voice diagnosis in practical applications.
Machine Learning in Measurement Part 1: Error Contribution and Terminology Confusion

Like any science and engineering field, Instrumentation and Measurement (I&M) is currently experiencing the impact of the recent rise of Applied AI and in particular Machine Learning (ML) [1]. But I&M and ML use terminology that sometimes sound or look similar, though they might only have a marginal relationship or even be false friends. Therefore, understanding the terminology used by both communities and how they do and do not relate to one another is of crucial importance to understand the influences of ML in an I&M system. In addition, while I&M experts are well aware of the importance of measurement uncertainty, the concept has been understudied in the ML context. In this article, we will give an overview of ML's contribution to measurement error, and how to avoid confusion with the said terminology, to better understand the application of ML in measurement. Then, in Part 2 [2], we use that understanding and terminology to show how to quantify the uncertainty introduced by ML in a measurement system. This is of particular importance for measurement in the age of big data because we need to evaluate the trustworthiness of the available data and their impact on the derived conclusions and decision-making [3].
GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

This paper develops the GalaXC algorithm for Extreme Classification, where the task is to annotate a document with the most relevant subset of labels from an extremely large label set. Extreme classification has been successfully applied to several real world web-scale applications such as web search, product recommendation, query rewriting, etc. GalaXC identifies two critical deficiencies in leading extreme classification algorithms. First, existing approaches generally assume that documents and labels reside in disjoint sets, even though in several applications, labels and documents cohabit the same space. Second, several approaches, albeit scalable, do not utilize various forms of metadata offered by applications, such as label text and label correlations. To remedy these, GalaXC presents a framework that enables collaborative learning over joint document-label graphs at massive scales, in a way that naturally allows various auxiliary sources of information, including label metadata, to be incorporated. GalaXC also introduces a novel label-wise attention mechanism to meld high-capacity extreme classifiers with its framework. An efficient end-to-end implementation of GalaXC is presented that could be trained on a dataset with 50M labels and 97M training documents in less than 100 hours on 4 × V100 GPUs. This allowed GalaXC to not only scale to applications with several millions of labels, but also be up to 18% more accurate than leading deep extreme classifiers, while being upto 2-50 × faster to train and 10 × faster to predict on benchmark datasets. GalaXC is particularly well-suited to warm-start scenarios where predictions need to be made on data points with partially revealed label sets, and was found to be up to 25% more accurate than extreme classification algorithms specifically designed for warm start settings. In A/B tests conducted on the Bing search engine, GalaXC could improve the Click Yield (CY) and coverage by 1.52% and 1.11% respectively. Code for GalaXC is available at https://github.com/Extreme-classification/GalaXC
Information technology and the optimisation of experience – The role of mobile devices and social media in human-nature interactions

Information technologies have seeped their way into every aspect of our lives, mediating interactions between ourselves and our environments. They are becoming an important part of human-nature interactions, with smartphones, their apps and social media offering new ways to plan, navigate and share experiences. This article explores the changes that these mobile media technologies bring to human-nature interactions, focusing on the outdoor practices of experienced outdoor users. Drawing on observational and interview data gathered in the Scottish Highlands, we analysed hillwalkers', mountain bikers' and nature photographers' interactions with mobile media technology. Using social practice theory and the idea of technologies as 'scripts', we found that the increased availability of information reportedly enhanced access to, confidence in and knowledge about outdoor practices. Participants negotiated the use of devices within social norms of good practice, but generally showed enthusiasm for the ever-increasing access to information. The easy access to information and the ability to share one's performance, inscripted in the technology, guided the participants to optimise their experience. Paradoxically, this optimisation seemed to reduce the likelihood of encountering unanticipated situations that would have made their experience memorable, something our participants had previusly identified as an important aspect of their outdoor activities. Our findings illustrate the value of an in-depth empirical understanding of lived experiences, revealing how interactions between technological scripts, personal agency and social norms amplify some aspects of human-nature interactions while attenuating others. Although incremental, these changes fundamentally alter the character of our experience of nature.
Overview of Lifelogging: Current Challenges and Advances

Lifelogging is the process of digital tracking of person’s daily experiences for a variety of purposes. In recent years, lifelogging has become an increasingly popular area of research due to not only the growing demands from many applications, such as wellbeing, entertainment, healthcare systems, and intelligent environments, but also to the advances in device technologies that offer the promise to record and store large volumes of personal data using inexpensive tools. However, getting insights from egocentric experience using huge deluge of unlabeled and unstructured data continues to pose major challenges. A large number of research have been conducted in recent years to cover these challenges but there is still a lack of studies that provide a comprehensive survey of the available literature, while most of the existing lifelogging surveys generally focus on only one aspect. This review highlights the advances of state-of-the-art in lifelogging from different angles, including its research history, current applications, activity recognition techniques, moment retrieval, storytelling, privacy and security issues, as well as challenges and future research trends.
Sentiment Analysis Using Multi-Head Attention Capsules With Multi-Channel CNN and Bidirectional GRU

Existing text sentiment analysis methods mostly rely on a large number of language knowledge and sentiment resources. This paper proposes the Multi-channel convolution and bidirectional GRU multi-head attention capsule (AT-MC-BiGRU-Capsule), which uses vector neurons to replace scalar neurons to model text emotions, and uses capsules to characterize text emotions. In addition, traditional methods cannot extract the multi-level features of text sequence well. Multi-head attention can encode the dependencies between words, capture sentiment words in text, and using Convolutional Neural Network (CNN) and Bidirectional gated recurrent unit network (Bi-GRU) to extract local features and global semantic features of text respectively, the global average pooling layer is introduced to obtain the multi-level feature representation of the text sequence more comprehensively. This paper selects three English datasets and one Chinese dataset in the general corpus of sentiment classification to conduct experiments, and achieves better results than other baseline models.
Dynamic Pseudo-Label Generation for Weakly Supervised Object Detection in Remote Sensing Images

In recent years, fully supervised object detection methods in remote sensing images with good performance have been developed. However, this approach requires a large number of instance-level annotated samples that are relatively expensive to acquire. Therefore, weakly supervised learning using only image-level annotations has attracted much attention. Most of the weakly supervised object detection methods are based on multi-instance learning methods, and their performance depends on the process of scoring the candidate region proposals during training. In this process, the use of only image-level labels for supervision usually cannot obtain optimal results due to the lack of location information of the object. To address the above problem, a dynamic sample pseudo-label generation framework is proposed to generate pseudo-labels for each proposal without additional annotations. First, we propose the pseudo-label generation algorithm (PLG) to generate the category labels of the proposal by using the localization information of the object. Specifically, we propose to use the pixel average of the object’s localization map in the proposal as the proposal category confidence and calculate the pseudo-label by comparing the proposal category confidence with the preset threshold. In addition, an effective adaptive threshold selection strategy is designed to eliminate the effect of different category shape differences in computing sample pseudo-labels. Comparative experiments on the NWPU VHR-10 dataset demonstrate that our method can significantly improve the detection performance compared to existing methods.
Deep Mixture Generative Autoencoders

Variational autoencoders (VAEs) are one of the most popular unsupervised generative models that rely on learning latent representations of data. In this article, we extend the classical concept of Gaussian mixtures into the deep variational framework by proposing a mixture of VAEs (MVAE). Each component in the MVAE model is implemented by a variational encoder and has an associated subdecoder. The separation between the latent spaces modeled by different encoders is enforced using the d -variable Hilbert-Schmidt independence criterion (dHSIC). Each component would capture different data variational features. We also propose a mechanism for finding the appropriate number of VAE components for a given task, leading to an optimal architecture. The differentiable categorical Gumbel-softmax distribution is used in order to generate dropout masking parameters within the end-to-end backpropagation training framework. Extensive experiments show that the proposed MVAE model can learn a rich latent data representation and is able to discover additional underlying data representation factors.
Bridging the Gap Between Geophysics and Geology With Generative Adversarial Networks

Inverse mapping from geophysics to geology is a difficult problem due to the inherent uncertainty of geophysical data and the spatially heterogeneous patterns (structure) in geology. We improve conditional facies simulation based on GANs (GANSim), a new Earth model simulation method based on generative adversarial networks (GANs), to bridge the gap between remotely sensed geophysical information and geology, by introducing a specially designed loss function and an input architecture for probability maps representing geophysical interpretation. After training, the GANSim is then used to produce multiple geological facies models conditioned to the input geophysics-interpreted probability maps alone or together with input well observations and global features. By evaluation, the generated facies models are realistic, diversified, and consistent with all input conditions. We demonstrate that the GAN learns the implicit geological pattern knowledge from the training data set and the knowledge of conditioning to inputs from human-defined explicit functions. Given the commonality of remotely sensed geophysical information, sparse measurements, and global features, GANSim should be applicable to many problems of geosciences.
Memory Residual Regression Autoencoder for Bearing Fault Detection

Anomaly detection is the cornerstone for the health management of rolling element bearings. The unsupervised learning model for anomaly detection driven only by normal data has received increasing attention in recent years. In this article, an innovative deep-learning-based model, namely, memory residual regression autoencoder (MRRAE), is developed to improve the accuracy of anomaly detection in bearing condition monitoring. The memory module and autoregressive estimator are applied to calculate the probability density distribution of the latent memory residual representation. The reconstruction errors and surprisal values of the proposed model are used to detect the abnormal condition of bearing. To verify the superiority of the proposed method in anomaly detection, two sets of run-to-failure experimental data set gathered from the laboratories are studied and analyzed. The result demonstrates that the proposed MRRAE model achieves superior performance compared with several conventional and deep-learning-based anomaly detection methods. Furthermore, the proposed method pays close attention to the special structure of bearing vibration signal and provides a new way for explaining the decision-making processes of deep neural networks.
Bayesian networks in healthcare: What is preventing their adoption?

There has been much research effort expended toward the use of Bayesian networks (BNs) in medical decision-making. However, because of the gap between developing an accurate BN and demonstrating its clinical usefulness, this has not resulted in any widespread BN adoption in clinical practice. This paper investigates this problem with the aim of finding an explanation and ways to address the problem through a comprehensive literature review of articles describing BNs in healthcare. Based on the literature collection that has been systematically narrowed down from 3810 to 116 most relevant articles, this paper analyses the benefits, barriers and facilitating factors (BBF) for implementing BN-based systems in healthcare using the ITPOSMO-BBF framework. A key finding is that works in the literature rarely consider barriers and even when these were identified they were not connected to facilitating factors. The main finding is that the barriers can be grouped into: (1) data inadequacies; (2) clinicians’ resistance to new technologies; (3) lack of clinical credibility; (4) failure to demonstrate clinical impact; (5) absence of an acceptable predictive performance; and (6) absence of evidence for model’s generalisability. The facilitating factors can be grouped into: (1) data collection improvements; (2) software and technological improvements; (3) having interpretable and easy to use BN-based systems; (4) clinical involvement in the development or review of the model; (5) investigation of model’s clinical impact; (6) internal validation of the model’s performance; and (7) external validation of the model. These groupings form a strong basis for a generic framework that could be used for formulating strategies for ensuring BN-based clinical decision-support system adoption in frontline care settings. The output of this review is expected to enhance the dialogue among researchers by providing a deeper understanding for the neglected issue of BN adoption in practice and promoting efforts for implementing BN-based systems.
HOOPS: Human-in-the-Loop Graph Reasoning for Conversational Recommendation

There is increasing recognition of the need for human-centered AI that learns from human feedback. However, most current AI systems focus more on the model design, but less on human participation as part of the pipeline. In this work, we propose a Human-in-the-Loop (HitL) graph reasoning paradigm and develop a corresponding dataset named HOOPS for the task of KG-driven conversational recommendation. Specifically, we first construct a KG interpreting diverse user behaviors and identify pertinent attribute entities for each user--item pair. Then we simulate the conversational turns reflecting the human decision making process of choosing suitable items tracing the KG structures transparently. We also provide a benchmark method with reported performance on the dataset to ascertain the feasibility of HitL graph reasoning for recommendation using our developed dataset, and show that it provides novel opportunities for the research community.
Adaptive Network Pruning for Wireless Federated Learning

In this letter, we apply the model compression, i.e., network pruning, into wireless federated learning (FL) system to mitigate the local computation and communication bottlenecks. Firstly, the convergence rate and learning latency of the FL system are mathematically analyzed. Then, an optimization problem is formulated to maximize the convergence rate while guaranteeing the learning latency via jointly optimizing the pruning ratio and spectrum allocation. Finally, the experimental results show that the proposed learning scheme can improve the performance of the wireless FL as compared with other conventional schemes.
Effective Cloud Resource Utilisation in Cloud ERP Decision-Making Process for Industry 4.0 in the United States

Cloud enterprise resource planning (C-ERP) represents an evolution of traditional ERP, which also offers the advantages of cloud computing (CC) such as ease of use and resource elasticity. This article presents the opportunities and challenges of the C-ERP adoption for industry 4.0 in the United States as well as the factors that boost or hinder such a decision. The quantitative research method is used to gather the predictor factors and correlation amongst them. An online survey questionnaire received 109 responses, mainly decision-makers and professionals from the US consumer goods industry. Statistical analysis has been carried out to rank the different levels of influence in the C-ERP adoption decision. The predictor’s complexity and regulatory compliance positively influence C-ERP private service deployment, whereas technology readiness is a good predictor of community service deployment. This paper also proposes a decision support system (DSS), tailored to industry 4.0, and aimed at assisting decision-makers in adopting C-ERP as an effective resource for decision-making. The DSS is built upon the predictors using the analytic hierarchy process (AHP) and it supports decision-makers in the selection of services and deployment models for C-ERP as a resource.
Interpretation of Swedish Sign Language Using Convolutional Neural Networks and Transfer Learning

Abstract The automatic interpretation of sign languages is a challenging task, as it requires the usage of high-level vision and high-level motion processing systems for providing accurate image perception. In this paper, we use Convolutional Neural Networks (CNNs) and transfer learning to make computers able to interpret signs of the Swedish Sign Language (SSL) hand alphabet. Our model consists of the implementation of a pre-trained InceptionV3 network, and the usage of the mini-batch gradient descent optimization algorithm. We rely on transfer learning during the pre-training of the model and its data. The final accuracy of the model, based on 8 study subjects and 9400 images, is 85%. Our results indicate that the usage of CNNs is a promising approach to interpret sign languages, and transfer learning can be used to achieve high testing accuracy despite using a small training dataset. Furthermore, we describe the implementation details of our model to interpret signs as a user-friendly web application.