Reviewerly/exHarmony huggingface dataset:
SP-GNN: Learning structure and position information from graphs

Graph neural network (GNN) is a powerful model for learning from graph data. However, existing GNNs may have limited expressive power, especially in terms of capturing adequate structural and positional information of input graphs. Structure properties and node position information are unique to graph-structured data, but few GNNs are capable of capturing them. This paper proposes Structure- and Position-aware Graph Neural Networks (SP-GNN), a new class of GNNs offering generic and expressive power of graph data. SP-GNN enhances the expressive power of GNN architectures by incorporating a near-isometric proximity-aware position encoder and a scalable structure encoder. Further, given a GNN learning task, SP-GNN can be used to analyze positional and structural awareness of GNN tasks using the corresponding embeddings computed by the encoders. The awareness scores can guide fusion strategies of the extracted positional and structural information with raw features for better performance of GNNs on downstream tasks. We conduct extensive experiments using SP-GNN on various graph datasets and observe significant improvement in classification over existing GNN models.
An EffcientNet-encoder U-Net Joint Residual Refinement Module with Tversky–Kahneman Baroni–Urbani–Buser loss for biomedical image Segmentation

Quantitative analysis on biomedical images has been on increasing demand nowadays and for modern computer vision approaches. While recently advanced procedures have been enforced, there is still necessity in optimizing network architecture and loss functions. Inspired by the pretrained EfficientNet-B4 and the refinement module in boundary-aware problems, we propose a new two-stage network which is called EffcientNet-encoder U-Net Joint Residual Refinement Module and we create a novel loss function called the Tversky–Kahneman Baroni–Urbani–Buser loss function. The loss function is built on the basement of the Baroni–Urbani–Buser coefficient and the Jaccard–Tanimoto coefficient and reformulated in the Tversky–Kahneman probability-weighting function. We have evaluated our algorithm on the four popular datasets: the 2018 Data Science Bowl Cell Nucleus Segmentation dataset, the Brain Tumor LGG Segmentation dataset, the Skin Lesion ISIC 2018 dataset and the MRI cardiac ACDC dataset. Several comparisons have proved that our proposed approach is noticeably promising and some of the segmentation results provide new state-of-the-art results. The code is available at https://github.com/tswizzle141/An-EffcientNet-encoder-U-Net-Joint-Residual-Refinement-Module-with-TK-BUB-Loss.
An energy‐efficient method of resource allocation based on request prediction in multiple cloud data centers

Summary To meet the ever‐increasing requirements of the applications, cloud service providers have further built and managed multiple cloud data centers in multiple regions across geographies with many physical machines (PMs). However, most of the existing resource allocation algorithms are developed for a single cloud data center, which normally cannot efficiently handle the load burst occasions where a single cloud data center may not be enough to satisfy the demands burst of applications. Therefore, it is necessary to consider how to efficiently manage multiple cloud data centers while meeting application requirements and reducing energy consumption. This paper first systematically analyzes multiple cloud data centers and energy consumption models. Then, an energy‐efficient method of Resource Allocation based on Request Prediction in multiple cloud data centers (RARP) is proposed. The RARP method constructs a resource allocation framework based on request prediction in multiple cloud data centers, which anticipates the application request volume in advance. At the same time, the RARP method allocates VMs and PMs based on the principle of minimum remaining resources available to achieve minimum usage of PMs, thus minimizing energy consumption to complete application requests. Extensive experiments are conducted on the proposed RARP method through the simulation platform CloudSim. Finally, the experimental test results show that the accuracy of request detection and the energy consumption of cloud data centers are significantly better than those of the comparison algorithms.
Developing the data management component of an academic discipline program for an educational management information system

Objectives . The need to apply methods and models to support the educational process at universities including the formation and management of academic discipline programs (ADPs) is determined by the growing need for the active implementation of various automation tools including integrated information systems, which arise in response to a number of regulatory and legal factors. Such social factors result in the significant increase in the volume and categories of information circulating within business processes of an educational organization, as well as the expansion of the requirements for ensuring the protection, storage, and transmission of information. In recent years, the Government of the Russian Federation has approved the national “Digital Economy” and “Education” projects (including the Federal Project “Digital Educational Environment”) emphasizing the growing role of informatization and digitalization processes in education. In this connection, an obvious discrepancy arises between the theoretical characteristics of information flows existing in educational organizations and the methods of its collection, processing, storage, analysis, and application used in practice. One of the most important conceptual components of the educational process in higher education institutions is the ADP, which organizes the relationship between various components of the educational process: curriculum, competencies, training areas, learning technologies, and methods for conducting the control check of students’ knowledge. The labor-intensive and variable nature of ADP development and implementation requires the introduction of information technologies. Thus, the aim of the present work is to analyze the volume and structure of institutional educational programs in order to identify the necessary software requirements. Methods . The classification of learning management systems according to various criteria, key requirements for academic disciplines, and ADP structure is considered. Results . An analysis of links between the ADP and key entities of the educational process is presented. The functionality of the self-developed ADP module for implementing at RTU MIREA is aimed at providing interconnection, transparency, and availability of links between academic discipline parameters and its sections. Conclusions . Introducing the ADP module allows reducing the time spent on developing the program by providing universal templates of academic disciplines, along with the possibility of autofilling the academic discipline parameters and tracking the current status of ADPs, as well as increasing the level of awareness of participants in the educational process.
Survey on KNN Methods in Data Science

The k-nearest neighbors (KNN) algorithm remains a useful and widely applied approach. In the recent years, we have seen many advances in KNN methods, but few research works give a holistic account of all aspects of KNN and the progress made. This paper is a brief survey on modern KNN methods and their role in data science. Furthermore, we survey: the challenges, how they are approached in the literature, the impact of the distance metric, several KNN variations, as well as query methods.
A causal-based approach to explain, predict and prevent failures in robotic tasks

Robots working in human environments need to adapt to unexpected changes to avoid failures. This is an open and complex challenge that requires robots to timely predict and identify the causes of failures in order to prevent them. In this paper, we present a causal-based method that will enable robots to predict when errors are likely to occur and prevent them from happening by executing a corrective action. Our proposed method is able to predict immediate failures and also failures that will occur in the future. The latter type of failure is very challenging, and we call them timely-shifted action failures (e.g., the current action was successful but will negatively affect the success of future actions). First, our method detects the cause–effect relationships between task executions and their consequences by learning a causal Bayesian network (BN). The obtained model is transferred from simulated data to real scenarios to demonstrate the robustness and generalization of the obtained models. Based on the causal BN, the robot can predict if and why the executed action will succeed or not in its current state. Then, we introduce a novel method that finds the closest success state through a contrastive Breadth-First-Search if the current action was predicted to fail. We evaluate our approach for the problem of stacking cubes in two cases; (a) single stacks (stacking one cube) and; (b) multiple stacks (stacking three cubes). In the single-stack case, our method was able to reduce the error rate by 97%. We also show that our approach can scale to capture various actions in one model, allowing us to measure the impact of an imprecise stack of the first cube on the stacking success of the third cube. For these complex situations, our model was able to prevent around 95% of the stacking errors. Thus, demonstrating that our method is able to explain, predict, and prevent execution failures, which even scales to complex scenarios that require an understanding of how the action history impacts future actions.
Emotion classification of Indonesian Tweets using Bidirectional LSTM

Abstract Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of a single-layer bidirectional long short-term memory model over a two-layer stacked bidirectional long short-term memory model. This research also found that a single-layer bidirectional long short-term memory recurrent neural network met the performance of a state-of-the-art logistic regression model with supplemental closed-source features from a study by Saputri et al. [8] when classifying the emotion of Indonesian tweets.
Text FCG: Fusing Contextual Information via Graph Learning for text classification

Text classification as a fundamental task in Natural Language Processing (NLP). Graph neural networks can better handle the large amount of information in text, and effective and fast graph models for text classification have received much attention. Besides, most methods are transductive learning, which means they cannot handle the documents with new words and relations. To tackle these problems, we propose a novel method for Text Classification by Fusing Contextual Information via Graph Neural Networks (TextFCG). Concretely, we first construct a single graph for all words in each text and label the edges by fusing its various contextual relations. Our text graph contains different information of documents and enhances the connectivity of graph by introducing more typed edges, which improves the learning effect of GNN. Then, based on GNN and gated recurrent unit (GRU), our model can interact the local words with global text information and enhance the sequential representation of nodes. Moreover, we focus on contextual features from the text itself. Extensive experiments on several benchmark datasets and detailed analysis prove the effectiveness of our proposed method on the text classification task.
Ensemble-based genetic algorithm explainer with automized image segmentation: A case study on melanoma detection dataset

Explainable Artificial Intelligence (XAI) makes AI understandable to the human user particularly when the model is complex and opaque. Local Interpretable Model-agnostic Explanations (LIME) has an image explainer package that is used to explain deep learning models. The image explainer of LIME needs some parameters to be manually tuned by the expert in advance, including the number of top features to be seen and the number of superpixels in the segmented input image. This parameter tuning is a time-consuming task. Hence, with the aim of developing an image explainer that automizes image segmentation, this paper proposes Ensemble-based Genetic Algorithm Explainer (EGAE) for melanoma cancer detection that automatically detects and presents the informative sections of the image to the user. EGAE has three phases. First, the sparsity of chromosomes in GAs is determined heuristically. Then, multiple GAs are executed consecutively. However, the difference between these GAs are in different number of superpixels in the input image that result in different chromosome lengths. Finally, the results of GAs are ensembled using consensus and majority votings. This paper also introduces how Euclidean distance can be used to calculate the distance between the actual explanation (delineated by experts) and the calculated explanation (computed by the explainer) for accuracy measurement. Experimental results on a melanoma dataset show that EGAE automatically detects informative lesions, and it also improves the accuracy of explanation in comparison with LIME efficiently. The python codes for EGAE, the ground truths delineated by clinicians, and the melanoma detection dataset are available at https://github.com/KhaosResearch/EGAE.
Retrieval of Data from the Database of a BCT–Voting System

Voting is one of the critical processes of modern democracy. It gives freedom to the people to choose a representative leader to implement their ideas and promote policies that benefit them. Elections can also be audited to ensure each vote is counted correctly. However, corruption: ballot forgery, coercion, etc can cause the public to lose trust in this system. Thus, BCT-Voting [8] - a fully secure and decentralized blockchain technology-based e-voting system (DApp) - was proposed as a voting system that can increase transparency and public trust. This system will provide a secure form of electronic voting which will allow for remote voting. The proposed BCT-Voting system has been deployed on the Ethereum framework. It includes five modules – nomination process, voter identity, vote tampering, donation module, and final counting of votes. However, the data retrieval and sorting from the Ethereum blockchain is a complex process because the personal identity of voters is not known. Therefore, in this paper, we propose an Ethereum Query Language (EQL) to query data from the smart contract of the BCT-Voting system. The query method allows voters or The Federal Election Commission (FEC) to retrieve the required information from the Ethereum chain such as which account voted, when, and for who while still maintaining the privacy of the voter. This method of querying provides fast and minimally labor-intensive audits of elections. It includes recounting ballots and efficiently tracking the history of casted votes to ensure that votes were counted and handled correctly. In this system finding duplicate and fraudulent voters will be easy and it will increase the efficiency and transparency of the electoral process.
Did You Get That? Predicting Learners’ Comprehension of a Video Lecture from Visualizations of Their Gaze Data

In online lectures, unlike in face-to-face lectures, teachers lack access to (nonverbal) cues to check if their students are still "with them" and comprehend the lecture. The increasing availability of low-cost eye-trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students' level of "with-me-ness" (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (1) gaze measures of "with-me-ness" are related to comprehension, (2) people not trained in eye-tracking can predict students' comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye-tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students' comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students' gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students' posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners' comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students' learning.
Towards artificial intelligence-based learning health system for population-level mortality prediction using electrocardiograms

The feasibility and value of linking electrocardiogram (ECG) data to longitudinal population-level administrative health data to facilitate the development of a learning healthcare system has not been fully explored. We developed ECG-based machine learning models to predict risk of mortality among patients presenting to an emergency department or hospital for any reason. Using the 12-lead ECG traces and measurements from 1,605,268 ECGs from 748,773 healthcare episodes of 244,077 patients (2007-2020) in Alberta, Canada, we developed and validated ResNet-based Deep Learning (DL) and gradient boosting-based XGBoost (XGB) models to predict 30-day, 1-year, and 5-year mortality. The models for 30-day, 1-year, and 5-year mortality were trained on 146,173, 141,072, and 111,020 patients and evaluated on 97,144, 89,379, and 55,650 patients, respectively. In the evaluation cohort, 7.6%, 17.3%, and 32.9% patients died by 30-days, 1-year, and 5-years, respectively. ResNet models based on ECG traces alone had good-to-excellent performance with area under receiver operating characteristic curve (AUROC) of 0.843 (95% CI: 0.838-0.848), 0.812 (0.808-0.816), and 0.798 (0.792-0.803) for 30-day, 1-year and 5-year prediction, respectively; and were superior to XGB models based on ECG measurements with AUROC of 0.782 (0.776-0.789), 0.784 (0.780-0.788), and 0.746 (0.740-0.751). This study demonstrates the validity of ECG-based DL mortality prediction models at the population-level that can be leveraged for prognostication at point of care.
Boost Correlation Features with 3D-MiIoU-Based Camera-LiDAR Fusion for MODT in Autonomous Driving

Three-dimensional (3D) object tracking is critical in 3D computer vision. It has applications in autonomous driving, robotics, and human–computer interaction. However, methods for using multimodal information among objects to increase multi-object detection and tracking (MOT) accuracy remain a critical focus of research. Therefore, we present a multimodal MOT framework for autonomous driving boost correlation multi-object detection and tracking (BcMODT) in this research study to provide more trustworthy features and correlation scores for real-time detection tracking using both camera and LiDAR measurement data. Specifically, we propose an end-to-end deep neural network using 2D and 3D data for joint object detection and association. A new 3D mixed IoU (3D-MiIoU) computational module is also developed to acquire more precise geometric affinity by increasing the aspect ratio and length-to-height ratio between linked frames. Meanwhile, a boost correlation feature (BcF) module is proposed for the affinity calculation of the appearance of similar objects, which comprises an appearance affinity calculation module for similar objects in adjacent frames that are calculated directly using the feature distance and feature direction’s similarity. The KITTI tracking benchmark shows that our method outperforms other methods with respect to tracking accuracy.
3D GAN Inversion with Pose Optimization

With the recent advances in NeRF-based 3D aware GANs quality, projecting an image into the latent space of these 3D-aware GANs has a natural advantage over 2D GAN inversion: not only does it allow multi-view consistent editing of the projected image, but it also enables 3D reconstruction and novel view synthesis when given only a single image. However, the explicit viewpoint control acts as a main hindrance in the 3D GAN inversion process, as both camera pose and latent code have to be optimized simultaneously to reconstruct the given image. Most works that explore the latent space of the 3D-aware GANs rely on ground-truth camera viewpoint or deformable 3D model, thus limiting their applicability. In this work, we introduce a generalizable 3D GAN inversion method that infers camera viewpoint and latent code simultaneously to enable multi-view consistent semantic image editing. The key to our approach is to leverage pre-trained estimators for better initialization and utilize the pixel-wise depth calculated from NeRF parameters to better reconstruct the given image. We conduct extensive experiments on image reconstruction and editing both quantitatively and qualitatively, and further compare our results with 2D GAN-based editing to demonstrate the advantages of utilizing the latent space of 3D GANs. Additional results and visualizations are available at https://3dgan-inversion.github.io/.
UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval

Recognizing soft-biometric pedestrian attributes is essential in video surveillance and fashion retrieval. Recent works show promising results on single datasets. Nevertheless, the generalization ability of these methods under different attribute distributions, viewpoints, varying illumination, and low resolutions remains rarely understood due to strong biases and varying attributes in current datasets. To close this gap and support a systematic investigation, we present UPAR, the Unified Person Attribute Recognition Dataset. It is based on four well-known person attribute recognition datasets: PA100K, PETA, RAPv2, and Market1501. We unify those datasets by providing 3,3M additional annotations to harmonize 40 important binary attributes over 12 attribute categories across the datasets. We thus enable research on generalizable pedestrian attribute recognition as well as attribute-based person retrieval for the first time. Due to the vast variance of the image distribution, pedestrian pose, scale, and occlusion, existing approaches are greatly challenged both in terms of accuracy and efficiency. Furthermore, we develop a strong baseline for PAR and attribute-based person retrieval based on a thorough analysis of regularization methods. Our models achieve state-of-the-art performance in cross-domain and specialization settings on PA100k, PETA, RAPv2, Market1501-Attributes, and UPAR. We believe UPAR and our strong baseline will contribute to the artificial intelligence community and promote research on large-scale, generalizable attribute recognition systems. The dataset is available here: https://github.com/speckean/upar_dataset