Reviewerly/exHarmony huggingface dataset:
Textured Mesh Quality Assessment: Large-scale Dataset and Deep Learning-based Quality Metric

Over the past decade, three-dimensional (3D) graphics have become highly detailed to mimic the real world, exploding their size and complexity. Certain applications and device constraints necessitate their simplification and/or lossy compression, which can degrade their visual quality. Thus, to ensure the best Quality of Experience, it is important to evaluate the visual quality to accurately drive the compression and find the right compromise between visual quality and data size. In this work, we focus on subjective and objective quality assessment of textured 3D meshes. We first establish a large-scale dataset, which includes 55 source models quantitatively characterized in terms of geometric, color, and semantic complexity, and corrupted by combinations of five types of compression-based distortions applied on the geometry, texture mapping, and texture image of the meshes. This dataset contains over 343k distorted stimuli. We propose an approach to select a challenging subset of 3,000 stimuli for which we collected 148,929 quality judgments from over 4,500 participants in a large-scale crowdsourced subjective experiment. Leveraging our subject-rated dataset, a learning-based quality metric for 3D graphics was proposed. Our metric demonstrates state-of-the-art results on our dataset of textured meshes and on a dataset of distorted meshes with vertex colors. Finally, we present an application of our metric and dataset to explore the influence of distortion interactions and content characteristics on the perceived quality of compressed textured meshes.
Multitask Deep Learning for Human Activity, Speed, and Body Weight Estimation Using Commercial Smart Insoles

Healthcare professionals and individual users use wearable devices equipped with various sensors for healthcare management.Recently, the joint usage of artificial intelligence and these wearable sensors has played an essential role in healthcare management by providing a wide range of applications such as fitness tracking, gym activity monitoring, patient rehabilitation monitoring, and disease detection.These tasks eventually aim to enhance personal well-being and better manage the user's physical health by monitoring different activity types and body weight changes.Here, we present an efficient multi-task learning framework based on commercial smart insoles that can solve three tasks related to physical health management: activity classification, speed estimation, and body weight estimation.Our multi-task framework converts the sensor data from the smart insole to a recurrence plot, which shows significant performance improvement compared to processing the raw time series data.In addition, we utilized a modified MobileNetV2 as our backbone network, which has a total parameter of less than 100K and a computational budget of 0.34G of multiply-accumulate operations.Furthermore, we collected a vast dataset from 72 users carrying out 16 experiments, which contains the largest number of people for multi-task learning purposes using smart insoles.Extensive experiments show that the proposed multi-task learning framework is extremely efficient while outperforming or leading to comparable performance against single-task models.
Conceptual Foundations of Digital Transformation of Libraries

The article presents the brief description of the problematic situation in the field of digital transformation (DT) of libraries in Russia. The author considers the DT policy and the strategy. The DT goals and methods are beyond the scope of this article. The paper shows various definitions of DT. The author proposes conceptual model of DT of libraries and defines the main components of this multifaceted process. The article identifies socio-cultural consequences of democratization and informatization of society as environmental factors. The author formulates the management principles of digital transformation of libraries. The paper notes the aspects of DT management of libraries: socio-economic, socio-cultural, technical, legal, socio-psychological. The article allocates the objects of DT of libraries: resources (material, financial, personnel), integrated into the infrastructure of library activities; functional processes and technologies used; products and services provided; communication ties (both with the external environment and within the system) and specialists competencies. The priority directions of DT of libraries are: increasing the availability of library services for citizens and organizations; bringing the legislative and regulatory framework in line with the goals of DT of libraries and the needs of development of the system of government and public management of library activities; expanding the range of digital information services; searching for new principles of differentiated evaluation of the end results of library activities, the formation of competitive library model, the development of classification of quality indicators; gradual transition to sectoral management based on data mining. The author presents the scheme of DT conceptual model of libraries in the Russian Federation.
Automatic Core-Developer Identification on GitHub: A Validation Study

Many open-source software projects are self-organized and do not maintain official lists with information on developer roles. So, knowing which developers take core and maintainer roles is, despite being relevant, often tacit knowledge. We propose a method to automatically identify core developers based on role permissions of privileged events triggered in GitHub issues and pull requests. In an empirical study on 25/GitHub projects, (1) we validate the set of automatically identified core developers with a sample of project-reported developer lists, and (2) we use our set of identified core developers to assess the accuracy of state-of-the-art unsupervised developer classification methods. Our results indicate that the set of core developers, which we extracted from privileged issue events, is sound and the accuracy of state-of-the-art unsupervised classification methods depends mainly on the data source (commit data versus issue data) rather than the network-construction method (directed versus undirected, etc.). In perspective, our results shall guide research and practice to choose appropriate unsupervised classification methods, and our method can help create reliable ground-truth data for training supervised classification methods.
CHAD: Charlotte Anomaly Dataset

In recent years, we have seen a significant interest in data-driven deep learning approaches for video anomaly detection, where an algorithm must determine if specific frames of a video contain abnormal behaviors. However, video anomaly detection is particularly context-specific, and the availability of representative datasets heavily limits real-world accuracy. Additionally, the metrics currently reported by most state-of-the-art methods often do not reflect how well the model will perform in real-world scenarios. In this article, we present the Charlotte Anomaly Dataset (CHAD). CHAD is a high-resolution, multi-camera anomaly dataset in a commercial parking lot setting. In addition to frame-level anomaly labels, CHAD is the first anomaly dataset to include bounding box, identity, and pose annotations for each actor. This is especially beneficial for skeleton-based anomaly detection, which is useful for its lower computational demand in real-world settings. CHAD is also the first anomaly dataset to contain multiple views of the same scene. With four camera views and over 1.15 million frames, CHAD is the largest fully annotated anomaly detection dataset including person annotations, collected from continuous video streams from stationary cameras for smart video surveillance applications. To demonstrate the efficacy of CHAD for training and evaluation, we benchmark two state-of-the-art skeleton-based anomaly detection algorithms on CHAD and provide comprehensive analysis, including both quantitative results and qualitative examination. The dataset is available at https://github.com/TeCSAR-UNCC/CHAD .
Learning domain-invariant representation for generalizing face forgery detection

With the improvement of face forgery techniques, people can easily generate various forgery face images, which brings serious challenges to public confidence. Numerous face forgery detection methods have been proposed, few of them can gain satisfying performance when training and testing forgery face images are generated by different forgery methods. To address this problem, we propose a domain-invariant representation learning (DIRL) method, which include feature distribution discrepancy minimization (FDDM) and optimal classier distance minimization (OCDM). FDDM adopts the idea of joint disentanglement and generative adversarial training to separate the irrelevant information and constrains the generator to reduce the discrepancy in feature distribution between source and target domains to obtain domain-shared features. OCDM uses the meta-learning method obtain a domain-invariant representation by minimizing the optimal classier distance between the domain-shared features of source and target domains. With FDDM and OCDM, we can learn an effective domain-invariant representation space in which there exists an ideal classifier equivalent to the optimal source domain classifier and the optimal target domain classifier, and the minimum classifier error can be achieved in both the source and target domains. Ablation experiments on the FaceForensics++ dataset show that compared to the baseline, our method can bring a decrease of 3.56% to 8.56% on Half Total Error Rate (HTER) when the test and training sets are generated by different face forgery methods. And the performance comparison experiments with the state-of-the-art methods also show that our method outperforms other methods.
High-precision dynamic three-dimensional shape measurement of specular surfaces based on deep learning

In order to solve the difficulty of traditional phase measuring deflectometry (PMD) in considering precision and speed, an orthogonal encoding PMD method based on deep learning is presented in this paper. We demonstrate for, what we believe to be, the first time that deep learning techniques can be combined with dynamic-PMD and can be used to reconstruct high-precision 3D shapes of specular surfaces from single-frame distorted orthogonal fringe patterns, enabling high-quality dynamic measurement of specular objects. The experimental results prove that the phase and shape information measured by the proposed method has high accuracy, almost reaching the results obtained by the ten-step phase-shifting method. And the proposed method also has excellent performance in dynamic experiments, which is of great significance to the development of optical measurement and fabrication areas.
Feeling of Control for Virtual Object Manipulation in Handheld AR

One essential feature of handheld augmented reality (AR) is the ability to manipulate virtual objects employing different interaction techniques, such as touch-based, mid-air and tangible interactions. A critical factor for user experience evaluation of AR systems is users' feeling of control. However, little was known in literature about feeling of control for virtual object manipulation in handheld AR. This study explores users' feeling of control among three interaction techniques via self-report and task performance. The findings reveal that users perceived a stronger feeling of control when applying tangible interaction technique to manipulate virtual objects than other techniques in handheld AR.
Phenotyping in distributed data networks: selecting the right codes for the right patients.

Observational data can be used to conduct drug surveillance and effectiveness studies, investigate treatment pathways, and predict patient outcomes. Such studies require developing executable algorithms to find patients of interest or phenotype algorithms. Creating reliable and comprehensive phenotype algorithms in data networks is especially hard as differences in patient representation and data heterogeneity must be considered. In this paper, we discuss a process for creating a comprehensive concept set and a recommender system we built to facilitate it. PHenotype Observed Entity Baseline Endorsements (PHOEBE) uses the data on code utilization across 22 electronic health record and claims datasets mapped to the Observational Health Data Sciences and Informatics (OHDSI) Common Data Model from the 6 countries to recommend semantically and lexically similar codes. Coupled with Cohort Diagnostics, it is now used in major network OHDSI studies. When used to create patient cohorts, PHOEBE identifies more patients and captures them earlier in the course of the disease.
Facilitating Federated Genomic Data Analysis by Identifying Record Correlations while Ensuring Privacy.

With the reduction of sequencing costs and the pervasiveness of computing devices, genomic data collection is continually growing. However, data collection is highly fragmented and the data is still siloed across different repositories. Analyzing all of this data would be transformative for genomics research. However, the data is sensitive, and therefore cannot be easily centralized. Furthermore, there may be correlations in the data, which if not detected, can impact the analysis. In this paper, we take the first step towards identifying correlated records across multiple data repositories in a privacy-preserving manner. The proposed framework, based on random shuffling, synthetic record generation, and local differential privacy, allows a trade-off of accuracy and computational efficiency. An extensive evaluation on real genomic data from the OpenSNP dataset shows that the proposed solution is efficient and effective.
How Media Hype Affects Our Physics Teaching: A Case Study on Quantum Computing

Popular media is an unspoken yet ever-present element of the physics landscape and a tool we can utilize in our teaching. It is also well-understood that students enter the physics classroom with a host of conceptions learned from the world at large. It stands to reason, then, to suspect that media coverage may be a major contributing factor to students' views on physical phenomena and the nature of science - one whose influence will only grow amid the 21st century digital age. Yet the role of the media in shaping physics teaching and learning has remained largely unexplored in the physics education research (PER) literature so far. Here, we explore the phenomenon of media hype from a theoretical and practical perspective: how media rhetoric of current topics in science and technology evolves, and how it affects students and instructors. We argue that media hype of cutting-edge science can be a double-edged sword for educators, with the same amped-up rhetoric that motivates students to enter the classroom tending to result in inflated preconceptions of what the science and technology can actually do. We draw on examples related to teaching quantum computing as a case study, though the findings we present should generalize to other topics garnering significant media attention - from exoplanets to graphene to batteries for electric vehicles. We conclude with a set of practical recommendations for physics teachers at all levels who wish to be more cognizant of the role exposure to popular media has on students and to tailor our teaching accordingly.
Efficient Detection and Tracking of Human Using 3D LiDAR Sensor

Light Detection and Ranging (LiDAR) technology is now becoming the main tool in many applications such as autonomous driving and human–robot collaboration. Point-cloud-based 3D object detection is becoming popular and widely accepted in the industry and everyday life due to its effectiveness for cameras in challenging environments. In this paper, we present a modular approach to detect, track and classify persons using a 3D LiDAR sensor. It combines multiple principles: a robust implementation for object segmentation, a classifier with local geometric descriptors, and a tracking solution. Moreover, we achieve a real-time solution in a low-performance machine by reducing the number of points to be processed by obtaining and predicting regions of interest via movement detection and motion prediction without any previous knowledge of the environment. Furthermore, our prototype is able to successfully detect and track persons consistently even in challenging cases due to limitations on the sensor field of view or extreme pose changes such as crouching, jumping, and stretching. Lastly, the proposed solution is tested and evaluated in multiple real 3D LiDAR sensor recordings taken in an indoor environment. The results show great potential, with particularly high confidence in positive classifications of the human body as compared to state-of-the-art approaches.
ChampKit: A framework for rapid evaluation of deep neural networks for patch-based histopathology classification

Histopathology is the gold standard for diagnosis of many cancers. Recent advances in computer vision, specifically deep learning, have facilitated the analysis of histopathology images for many tasks, including the detection of immune cells and microsatellite instability. However, it remains difficult to identify optimal models and training configurations for different histopathology classification tasks due to the abundance of available architectures and the lack of systematic evaluations. Our objective in this work is to present a software tool that addresses this need and enables robust, systematic evaluation of neural network models for patch classification in histology in a light-weight, easy-to-use package for both algorithm developers and biomedical researchers. Here we present ChampKit (Comprehensive Histopathology Assessment of Model Predictions toolKit): an extensible, fully reproducible evaluation toolkit that is a one-stop-shop to train and evaluate deep neural networks for patch classification. ChampKit curates a broad range of public datasets. It enables training and evaluation of models supported by timm directly from the command line, without the need for users to write any code. External models are enabled through a straightforward API and minimal coding. As a result, Champkit facilitates the evaluation of existing and new models and deep learning architectures on pathology datasets, making it more accessible to the broader scientific community. To demonstrate the utility of ChampKit, we establish baseline performance for a subset of possible models that could be employed with ChampKit, focusing on several popular deep learning models, namely ResNet18, ResNet50, and R26-ViT, a hybrid vision transformer. In addition, we compare each model trained either from random weight initialization or with transfer learning from ImageNet pretrained models. For ResNet18, we also consider transfer learning from a self-supervised pretrained model. The main result of this paper is the ChampKit software. Using ChampKit, we were able to systemically evaluate multiple neural networks across six datasets. We observed mixed results when evaluating the benefits of pretraining versus random intialization, with no clear benefit except in the low data regime, where transfer learning was found to be beneficial. Surprisingly, we found that transfer learning from self-supervised weights rarely improved performance, which is counter to other areas of computer vision. Choosing the right model for a given digital pathology dataset is nontrivial. ChampKit provides a valuable tool to fill this gap by enabling the evaluation of hundreds of existing (or user-defined) deep learning models across a variety of pathology tasks. Source code and data for the tool are freely accessible at https://github.com/SBU-BMI/champkit.
Decoding the projective transverse field Ising model

The competition between noncommuting projective measurements in discrete quantum circuits can give rise to entanglement transitions. It separates a regime where initially stored quantum information survives the time evolution from a regime where the measurements destroy the quantum information. Here we study one such system---the projective transverse field Ising model---with a focus on its capabilities as a quantum error correction code. The idea is to interpret one type of measurement as an error and the other type as a syndrome measurement. We demonstrate that there is a finite threshold below which quantum information encoded in an initially entangled state can be retrieved reliably. In particular, we implement the maximum likelihood decoder to demonstrate that the error correction threshold is distinct from the entanglement transition. This implies that there is a finite regime where quantum information is protected by the projective dynamics, but cannot be retrieved by using syndrome measurements.
Machine learning in detection and classification of leukemia using C-NMC_Leukemia

Abstract A significant issue in the field of illness diagnostics is the early detection and diagnosis of leukemia, that is, the accurate distinction of malignant leukocytes with minimal costs in the early stages of the disease. Flow cytometer equipment is few, and the methods used at laboratory diagnostic centers are laborious despite the high prevalence of leukemia. The present systematic review was carried out to review the works intending to identify and categories leukemia by utilizing machine learning. It was motivated by the potential of machine learning (machine learning (ML)) in disease diagnosis. Leukemia is a blood-forming tissues cancer that affects the bone marrow and lymphatic system. It can be treated more effectively if it is detected early. This work developed a new classification model for blood microscopic pictures that distinguishes between leukemia-free and leukemia-affected images. The general proposed method in this paper consists of three main steps which are: (i) Image_Preprocessing, (ii) Feature Extraction, and (iii) Classification. An optimized CNN (OCNN) is used for classification. OCNN is utilized to detect and classify the photo as "normal" or "abnormal". Fuzzy optimization is used to optimize the hyperparameters of CNN. It is a quite beneficial to use fuzzy logic in the optimization of CNN. As illustrated from results it is shown that, with the using of OCNN classifier and after the optimization of the hyperparameters of the CNN, it achieved the best results due to the enhancement of the performance of the CNN. The OCNN has achieved 99.99% accuracy with C-NMC_Leukemia dataset.
The linguistic organization of grammatical text complexity: comparing the empirical adequacy of theory-based models

Abstract Although there is a long tradition of research analyzing the grammatical complexity of texts (in both linguistics and applied linguistics), there is surprisingly little consensus on the nature of complexity. Many studies have disregarded syntactic (and structural) distinctions in their analyses of grammatical text complexity, treating it instead as if it were a single unified construct. However, other corpus-based studies indicate that different grammatical complexity features pattern in fundamentally different ways. The present study employs methods that are informed by structural equation modeling to test the goodness-of-fit of four models that can be motivated from previous research and linguistic theory: a model treating all complexity features as a single dimension, a model distinguishing among three major structural types of complexity features, a model distinguishing among three major syntactic functions of complexity features, and a model distinguishing among nine combinations of structural type and syntactic functions. The findings show that text complexity is clearly a multi-dimensional construct. Both structural and syntactic distinctions are important. Syntactic distinctions are actually more important than structural distinctions, although the combination of the two best accounts for the ways in which complexity features pattern in texts from different registers.