Reviewerly/exHarmony huggingface dataset:
Informative pseudo-labeling for graph neural networks with few labels

Abstract Graph neural networks (GNNs) have achieved state-of-the-art results for semi-supervised node classification on graphs. Nevertheless, the challenge of how to effectively learn GNNs with very few labels is still under-explored. As one of the prevalent semi-supervised methods, pseudo-labeling has been proposed to explicitly address the label scarcity problem. It is the process of augmenting the training set with pseudo-labeled unlabeled nodes to retrain a model in a self-training cycle. However, the existing pseudo-labeling approaches often suffer from two major drawbacks. First, these methods conservatively expand the label set by selecting only high-confidence unlabeled nodes without assessing their informativeness. Second, these methods incorporate pseudo-labels to the same loss function with genuine labels, ignoring their distinct contributions to the classification task. In this paper, we propose a novel informative pseudo-labeling framework (InfoGNN) to facilitate learning of GNNs with very few labels. Our key idea is to pseudo-label the most informative nodes that can maximally represent the local neighborhoods via mutual information maximization. To mitigate the potential label noise and class-imbalance problem arising from pseudo-labeling, we also carefully devise a generalized cross entropy with a class-balanced regularization to incorporate pseudo-labels into model retraining. Extensive experiments on six real-world graph datasets validate that our proposed approach significantly outperforms state-of-the-art baselines and competitive self-supervised methods on graphs.
Adversarial Transformers for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims at localizing objects with only image-level labels, which has better scalability and practicability than fully supervised methods. However, without pixel-level supervision, existing methods tend to generate rough localization maps, which hinders localization performance. To alleviate this problem, we propose an adversarial transformer network (ATNet), which aims to obtain a well-learned localization model with pixel-level pseudo labels. The proposed ATNet enjoys several merits. First, we design an object transformer ( G ) that can generate localization maps and pseudo labels effectively and dynamically, and a part transformer ( D ) to accurately discriminate detailed local differences between localization maps and pseudo labels. Second, we propose to train G and D via an adversarial process, where G can generate more accurate localization maps approaching pseudo labels to fool D . To the best of our knowledge, this is the first work to explore transformers with adversarial training to obtain a well-learned localization model for WSOL. Extensive experiments with four backbones on two standard benchmarks demonstrate that our ATNet achieves favorable performance against state-of-the-art WSOL methods. Besides, our adversarial training can provide higher robustness against adversarial attacks.
3D Pose Estimation and Tracking in Handball Actions Using a Monocular Camera

Player pose estimation is particularly important for sports because it provides more accurate monitoring of athlete movements and performance, recognition of player actions, analysis of techniques, and evaluation of action execution accuracy. All of these tasks are extremely demanding and challenging in sports that involve rapid movements of athletes with inconsistent speed and position changes, at varying distances from the camera with frequent occlusions, especially in team sports when there are more players on the field. A prerequisite for recognizing the player's actions on the video footage and comparing their poses during the execution of an action is the detection of the player's pose in each element of an action or technique. First, a 2D pose of the player is determined in each video frame, and converted into a 3D pose, then using the tracking method all the player poses are grouped into a sequence to construct a series of elements of a particular action. Considering that action recognition and comparison depend significantly on the accuracy of the methods used to estimate and track player pose in real-world conditions, the paper provides an overview and analysis of the methods that can be used for player pose estimation and tracking using a monocular camera, along with evaluation metrics on the example of handball scenarios. We have evaluated the applicability and robustness of 12 selected 2-stage deep learning methods for 3D pose estimation on a public and a custom dataset of handball jump shots for which they have not been trained and where never-before-seen poses may occur. Furthermore, this paper proposes methods for retargeting and smoothing the 3D sequence of poses that have experimentally shown a performance improvement for all tested models. Additionally, we evaluated the applicability and robustness of five state-of-the-art tracking methods on a public and a custom dataset of a handball training recorded with a monocular camera. The paper ends with a discussion apostrophizing the shortcomings of the pose estimation and tracking methods, reflected in the problems of locating key skeletal points and generating poses that do not follow possible human structures, which consequently reduces the overall accuracy of action recognition.
Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study

The automatic coding of clinical text documents by using the International Classification of Diseases, 10th Revision (ICD-10) can be performed for statistical analyses and reimbursements. With the development of natural language processing models, new transformer architectures with attention mechanisms have outperformed previous models. Although multicenter training may increase a model's performance and external validity, the privacy of clinical documents should be protected. We used federated learning to train a model with multicenter data, without sharing data per se.This study aims to train a classification model via federated learning for ICD-10 multilabel classification.Text data from discharge notes in electronic medical records were collected from the following three medical centers: Far Eastern Memorial Hospital, National Taiwan University Hospital, and Taipei Veterans General Hospital. After comparing the performance of different variants of bidirectional encoder representations from transformers (BERT), PubMedBERT was chosen for the word embeddings. With regard to preprocessing, the nonalphanumeric characters were retained because the model's performance decreased after the removal of these characters. To explain the outputs of our model, we added a label attention mechanism to the model architecture. The model was trained with data from each of the three hospitals separately and via federated learning. The models trained via federated learning and the models trained with local data were compared on a testing set that was composed of data from the three hospitals. The micro F1 score was used to evaluate model performance across all 3 centers.The F1 scores of PubMedBERT, RoBERTa (Robustly Optimized BERT Pretraining Approach), ClinicalBERT, and BioBERT (BERT for Biomedical Text Mining) were 0.735, 0.692, 0.711, and 0.721, respectively. The F1 score of the model that retained nonalphanumeric characters was 0.8120, whereas the F1 score after removing these characters was 0.7875-a decrease of 0.0245 (3.11%). The F1 scores on the testing set were 0.6142, 0.4472, 0.5353, and 0.2522 for the federated learning, Far Eastern Memorial Hospital, National Taiwan University Hospital, and Taipei Veterans General Hospital models, respectively. The explainable predictions were displayed with highlighted input words via the label attention architecture.Federated learning was used to train the ICD-10 classification model on multicenter clinical text while protecting data privacy. The model's performance was better than that of models that were trained locally.
LGBTQ Futures and Participatory Design

This paper presents the findings from a series of participatory design workshops with LGBTQ people living in the rural Midwestern United States. Using future workshops as a method, we seek to understand contemporary problems facing rural LGBTQ people and leverage design exercises to facilitate community members to come up with creative solutions. What we find are people grappling with the complexities of visibility, safety, and resource access in their rural communities; people who wanted to be able to use and create sociotechnical solutions that could help them navigate these complexities. Drawing on these findings, we argue for further exploration of design that experiments with the tension between visibility and safety for LGBTQ people. Further, we argue that future workshops and participatory design are well-positioned for continued work with marginalized communities, but that we need to maintain political orientations towards liberation and justice.?
Reinforced pedestrian attribute recognition with group optimization reward

Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent video surveillance. Two key challenges in PAR include complex alignment relations between images and attributes, and imbalanced data distribution. Existing approaches usually formulate PAR as a recognition task. Different from them, this paper addresses it as a decision-making task via a reinforcement learning framework, which is dubbed as Rein-PAR. Specifically, PAR is formulated as a Markov decision process (MDP) to efficiently explore semantic alignments between images and attributes. To alleviate the inter-attribute imbalance problem, we apply an Attribute Grouping Strategy (AGS) by dividing all attributes into subgroups according to their region and category information. Then we employ an agent to recognize each group of attributes, which is trained with Deep Q-learning algorithm. We also propose a Group Optimization Reward (GOR) function to alleviate the intra-attribute imbalance problem. Experimental results on the three benchmark datasets of PETA, RAP and PA100K illustrate the effectiveness and competitiveness of the proposed approach and demonstrate that the application of reinforcement learning to PAR is a valuable research direction.
FossilMorph: A new tool for fossil automatic measurement and statistical analysis

Quantifying morphological features of fossils represents a major methodology in palaeontological researches. However, an overwhelming number of fossil images trap researchers in the dilemma of laborious manual measurements which is not only time-consuming, but also prone to errors. Here we present FossilMorph, a user-friendly and open access software that offers rapid measurement and statistical analysis based on fossil images. Routine steps of image processing technologies and algorithms designed for three types of organic-walled microfossils (i.e., spheroidal type, filamentous type, and broken type) have been introduced in detail in order to illuminate the rationale of FossilMorph. In addition, two case studies on the form genera Leiosphaeridia and S iphonophycus have been provided to test the feasibility of FossilMorph on the study of micropalaeontology, especially the Proterozoic spheroidal acritarchs. Future development of FossilMorph will focus on fossils with complex morphology and massive preservation to make the software a convenient, efficient, full-featured tool for automatic measurement and statistical analysis in the field of palaeontology.
Prediction Method for Mine Earthquake in Time Sequence Based on Clustering Analysis

Under the background of the intelligent construction of a coal mine, how to efficiently extract effective information from the massive monitoring data of mine earthquakes, and improve prediction accuracy, is a research hotspot in the field of coal mine safety production. In view of this problem, more and more machine learning methods are being applied to the prediction on mine earthquakes. Considering that clustering analysis can enhance the correlation between microseism data, we propose a method whose main idea is to cluster microseism data before establishing the prediction model, and then train the model, so as to improve prediction accuracy. Specifically, microseism events on a working face are divided into clusters in advance by the Spatial Temporal-DBSCAN(ST-DBSCAN) algorithm, then a prediction model is established with Support Vector Regression (SVR) to predict the occurrence location and daily frequency of high-energy mine earthquake events. A set of engineering experiments were conducted in H Coal Mine, and the results show that the spatial-temporal clustering analysis of microseism events can indeed improve the prediction accuracy of machine learning methods on mine earthquakes.
Designing a Data Story: A Storytelling Approach to Curation, Sharing and Data Reuse in Support of Ethnographically-driven Research

In this paper, we introduce an innovative design concept for the curation of data, which we call 'Data Story'. We view this as an additional resource for data curation, aimed specifically at supporting the sharing of qualitative and ethnographic data. The Data Story concept is motivated by three elements: 1. the increased attention of funding agencies and academic institutions on Research Data Management and Open Science; 2. our own work with colleagues applying ethnographic research methods; and 3. existing literature that has identified specific challenges in this context. Ongoing issues entailed in dealing with certain contextual factors that are inherent to qualitative research reveal the extent to which we still lack technical design solutions that can support meaningful curation and sharing. Data Story provides a singular way of addressing these issues by integrating traditional data curation approaches, where research data are treated as 'objects' to be curated and preserved according to specific standards, with a more contextual, culturally-nuanced and collaborative organizing layer that can be thought of as a "Story". The concept draws on existing literature on data curation, digital storytelling and Critical Data Studies (CDS). As a possible design solution for Research Data Management and data curation, Data Story offers: 1) a collaborative workflow for data curation; 2) a story-like format that can serve as an organizing principle; 3) a means of enhancing and naturalizing curation practices through storytelling. Data Story is currently being developed for deployment and evaluation.
Fast computation of fractal dimension for 2D, 3D and 4D data

The box-counting (BC) algorithm is one of the most popular methods for calculating the fractal dimension (FD) of binary data. FD analysis has many important applications in the biomedical field, such as cancer detection from 2D computed axial tomography images, Alzheimer's disease diagnosis from magnetic resonance 3D volumetric data, and consciousness states characterization based on 4D data extracted from electroencephalography (EEG) signals, among many others. Currently, these kinds of applications use data whose size and amount can be very large, with high computation times needed to calculate the BC of the whole datasets. In this study we present a very efficient parallel implementation of the BC algorithm for its execution on Graphics Processing Units (GPU). Our algorithm can process 2D, 3D and 4D data and we tested it on two platforms with different hardware configurations. The results showed speedups of up to 92.38 × (2D), 57.27 × (3D) and 75.73 × (4D) with respect to the corresponding CPU single-thread implementations of the same algorithm. Against an OpenMP multi-thread CPU implementation, our GPU algorithm achieved speedups of up to 16.12 × (2D), 6.86 × (3D) and 7.49 × (4D). We have also compared our algorithm to a previous GPU implementation of the BC algorithm in 3D, achieving a speedup of up to 4.79 × . Finally, as a practical application of our GPU BC algorithm a study comparing the FD of 4D data extracted from the EEGs of a schizophrenia patient and a healthy subject was performed. The computation time for processing 40 4D matrices was reduced from three hours (sequential CPU) to less than three minutes with our GPU algorithm.
Social media analytical CRM: a case study in a bank

The use of the social media (SM) has become more and more widespread during the last two decades, the companies started looking for insights for how they can improve their businesses using the information accumulating therein. In this regard, it is possible to distinguish between two lines of research: those based on anonymous data and those based on customer specific data. Although obtaining customer specific SM data is a challenging task, analysis of such individual data can result in very useful insights. In this study we take up this path for the customers of a bank, analyze their tweets and develop three kinds of analytical models: clustering, sentiment analysis and product propensity. For the latter one, we also develop a version where, besides the text information, the structural information available in the bank databases are also used in the models. The result of the study is a considerably more efficient set of analytical CRM models.
A hybrid breast cancer classification algorithm based on meta-learning and artificial neural networks

The incidence of breast cancer in women has surpassed that of lung cancer as the world's leading new cancer case. Regular screening and measures become an effective way to prevent breast cancer and also provide a good foundation for later treatment. Women should receive regular checkups in the hospital after reaching a certain age. The use of computer-aided technology can improve the accuracy and efficiency of physicians' decision-making. Data pre-processing is required before data analysis, and 16 features are selected using a correlation-based feature selection method. In this paper, meta-learning and Artificial Neural Networks (ANN) are combined to create a hybrid algorithm. The proposed hybrid algorithm for predicting breast cancer was attempted to achieve 98.74% accuracy and 98.02% F1-score by creating a combination of various meta-learning models whose output was used as input features for creating ANN models. Therefore, the hybrid algorithm proposed in this paper can obtain better prediction results than a single model.
Modeling factors affecting the interests of the container terminal using fuzzy cognitive map and fuzzy DEMATEL

Purpose Because container terminals (CTs) are broker organizations, their interest has complex, various internal and external factors, the purpose of this study is to scrutiny and structure the factors affecting the interests of the CT. Design/methodology/approach In terms of purpose, this study is a developmental study that, are identified the factors related to the interests of the CT through studying the library and interviewing experts and then the degree of influence of each factor on each other by using the interview with experts of CT in Bushehr province are determined. Then, the fuzzy DEMATEL method is used to calculate the feedback loop of the indicators, and then the matrix obtained Fcmapper software and the fuzzy cognitive map (FCM) is drawn. Findings According to the FCM analysis, three important factors are centrality identified in terms of attracting capital, quality of service and efficiency. In addition, there are three factors, attracting capital, operator performance and law have high outdegree. Originality/value This research has identified 34 effective indicators of CT interests and evaluated their relationship with FCM. In the available researches, all these indicators measurement has not been evaluated together. Furthermore, fuzzy DEMATEL has been used to evaluate self-loops. Another contribution of this research is the introduction of a strategic route as a roadmap for CT managers in Bushehr province to decide on the interests of a CT.
Unsupervised video summarization using deep Non-Local video summarization networks

Video summarization is to extract effective information from videos to quickly obtain the most informative summary. Most of the existing video summarization methods use recurrent neural networks and their variants such as long and short-term memory (LSTM), to simulates the variable range time dependence between video frames. However, those methods can only process serial inputs of the video frames along with the hidden layer information from the previous time step, which affects the performance and the quality of video summarization. To tackle this issue, we present a deep non-local video summarization network (DN-VSN) for original video abstracts in this paper. Our unsupervised model treats video summarization as a sequence of decision problems. Given an input video, the probability that a video frame is selected as a part of the summary is obtained through a non-local convolutional network, and a strategy gradient algorithm of reinforcement learning is adopted for optimization in the training phase. The proposed method has been tested on four widely used datasets. The experimental results show the superiority of the proposed unsupervised model over the state-of-the-art methods.
Design and Development of Bus Boarding System for Visually Impaired Passengers

People who are visually impaired or blind are referred to as “visually impaired” or VIBP have a difficult time in finding vehicles for public transportation because of their limited vision. Traveling in real-time traffic is one of the biggest problems they face every day. Using RFID-based Bluetooth, buses and bus stops have access points installed. The proposed technique created a working prototype of a mobile application that communicates data to assist blind bus passengers in reaching their destinations. As a project outcome, we are hoping to develop an easy navigation system for people who are visually challenged.
Explainable Artificial Intelligence: Concepts and Current Progression

AI is a broad term used in computer science to mimic the human mind. Machine Learning is also a part of AI and is used in various fields of decision-making by using different algorithms. In the last few decades, AI has gained more popularity in the field of Industry, Medicine, Education, Defense, etc. Based on the algorithm AI is categorized into: Interpretable AI and Explainable AI (XAI). In interpretable AI, experts have problems in understanding the working of the model. They have no idea of working behind the algorithm so from here the term explainable AI came into existence. The term Explainable AI is coined by DARPA. This is also known as Explainable Machine Learning. The term came into existence to provide transparency in the process of decision-making by different Machine learning algorithms. In the current scenario, XAI is gaining popularity because of its transparent working. XAI is used in every field where AI can be used but with some modification or by adding some techniques of XAI as "SHAP (Shaply Additive exPlanations), DeepSHAP, DeepLIFT, CXplain, and LIME". The main goal of this chapter is to provide a brief overview of XAI by covering almost every aspect of XAI. The chapter is divided into sections, in the first section brief introduction is discussed. Related work concerning medicine is stated in Sect. 2. Later other sections will cover principles, techniques, current state of art, benefits, and, applications. This chapter infers that XAI is a great development in AI due to its transparent nature. The chapter also addressed the challenges in XAI, as well as the field's future potential.