Reviewerly/exHarmony huggingface dataset:
Group Role Assignment with a Training Plan Considering the Duration in Adaptive Collaboration

Group Role Assignment (GRA) seeks the maximum group performance by assigning roles to the most appropriate individual agents. As an extension of GRA, GRA with a Training Plan (GRATP) is proposed to address the trainingrelated collaboration problems. However, the existing models neglect the influence of changes in duration, i.e., the duration of training is a preset constant, which has limited applications. The variation of duration has a great influence on the improvement of the agent’s ability and assignment, especially in the dynamic scenario. Therefore, this paper formalizes a GRATP problem that considers the duration in dynamic scenarios, and constructs an algorithm to solve it. In the formulated problem, the training plan includes three factors: the starting time, the duration and the training programs. The total benefit is taken as the goal of the algorithm, rather than the group performance, because the agent’s ability and group performance vary over time in adaptive collaboration (AC). Moreover, the reassignment needs to be launched after training, as the original assignment may not be optimal. By utilizing GRA and its Environment—Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, the optimal training plan and reassignment are obtained by maximizing the total benefit. Experiments verify the effectiveness of the proposed algorithm.
Designing an infographic webtool for public health

Abstract Objective To create and evaluate a public health informatics tool, Florence, for communicating information to the public. Materials and Methods This user-centered design study included 3 phases: (1) an interview and survey study with public health practitioners to assess needs for creating infographics; (2) the application of assessment findings and public health-motivated design guidelines to the design and development of a public health-specific infographic design tool; and (3) a feasibility and usability study to evaluate the feasibility and usability of the tool. Results In phase 1, participants noted the importance of tailoring infographics to an audience and wanted flexible tools along with design guidance to help make fewer design decisions. In phase 2, we developed a prototype tool with: (1) layout and functionality familiar to PH users, (2) quick and intuitive ways to add and modify data in visualizations, and (3) health-focused visual elements. In phase 3, participants found Florence to be usable, providing an intuitive and straightforward experience, and that the focus on public health was useful. Discussion Based on needs assessments and existing literature, we created Florence along with public health practitioners to address their domain specific needs, ultimately leading to a tool that participants in our study deemed useful. Future research can build on our work to develop user-centered tools to meet their needs. Conclusion Infographics are important for public health communication. Creating user-centered solutions to address the unique needs of public health can support communication efforts.
名古屋大学と岐阜大学における制御工学実験授業の同時実施

This study discusses control engineering experimental classes for undergraduate students and considers how we realize the same experiment together at the same time in plural places which are far apart from one another. Such a class operation may increase importance for Covid-19 or rationalization of lab classes due to the aged society with a low birth rate. The objective of this study is to make clear the system configuration realizing highly educational classes safely. This report shows the state of a trial simultaneous execution of control engineering experimental classes in Nagoya and Gifu National Universities.
What can we learn from the convenience yield of Bitcoin? Evidence from the COVID-19 crisis

In this study, we employ the cost of carrying model to estimate the convenience yields of gold and bitcoin. Using the COVID-19 crisis as a demand shock, we show that the convenience yield of bitcoin decreases, but the convenience yield of gold increases during the pandemic. We further document an inverse relationship between bitcoin user adoption and convenience yield. Such relationship intensifies during the COVID-19 crisis and is mainly driven by the unexpected component of bitcoin's unique active addresses. Our overall findings indicate that gold rather than bitcoin attracts flight-to-safety investors in the pandemic. We provide supporting evidence for the decrease in bitcoin convenience yield, which can be explained by the sudden increase in active trading addresses that short-term speculators own most.
Online Learning Samples and Adaptive Recovery for Robust RGB-T Tracking

With the increasing diversity of visual tracking tasks, object tracking in RGB and thermal (RGB-T) modalities has received widespread interest. Most of the existing RGB-T tracking methods mainly improve tracking performance by integrating hierarchically complementary information from RGB and thermal modalities, however, they are insufficient in handling tracking failures due to the lack of re-detection capability. To address these issues, we propose a new RGB-T tracking method with online learning samples and adaptive object recovery. First, the features of RGB and thermal modalities are concatenated for robust appearance modeling. Second, a multimodal fusion strategy is designed to stably integrate reliable information of modalities and propose to use similarity to measure tracking confidence. Finally, a detector with online learning of positive and negative samples and adaptive recovery is developed to correct unreliable tracking results. Numerical results on five recent large-scale benchmark datasets demonstrate that the proposed tracker achieves competitive performance compared to other state-of-the-art methods.
Py4MER: A CTC-Based Mathematical Expression Recognition System

Mathematical expression recognition is a research field that aims to develop algorithms and systems capable of interpreting mathematical content. The recognition of MEs requires handling two-dimensional symbol relationships such as sub/superscripts, matrices and nested fractions, among others. The prevalent technology for addressing these challenges are based on encoder-decoder architectures with attention models. In this paper we propose the Py4MER system, based on Convolutional Recurrent Neural Network (CRNN) models, for the recognition and transcription of MEs into LaTeX mark-up sequences. This model is proposed as an alternative to encoder-decoder approaches, as CRNN models trained through Connectionist Temporal Classification (CTC) implicitly model the dependencies between symbols and do not suffer from under/over parsing of the input image, generating more consistent mark-up. The proposed model is evaluated on the Im2Latex-100k data set based on both textual and image-level metrics, showing a remarkable improvement from other CTC-based approaches. Recognition results are analyzed for different ME lengths and ME structures. Furthermore, a study based on the edit distance is performed, showing a considerable improvement in precision when up to 5 edit operations are considered. Finally, we show that CTC-based CRNN models can adapt to non left-to-right ordering of ME elements, warranting more research for this approach.
Attentive Neighborhood Feature Augmentation for Semi-supervised Learning

Recent state-of-the-art semi-supervised learning (SSL) methods usually use data augmentations as core components. Such methods, however, are limited to simple transformations such as the augmentations under the instance’s naive representations or the augmentations under the instance’s semantic representations. To tackle this problem, we offer a unique insight into data augmentations and propose a novel data-augmentation-based semi-supervised learning method, called Attentive Neighborhood Feature Augmentation (ANFA). The motivation of our method lies in the observation that the relationship between the given feature and its neighborhood may contribute to constructing more reliable transformations for the data, and further facilitating the classifier to distinguish the ambiguous features from the low-dense regions. Specially, we first project the labeled and unlabeled data points into an embedding space and then construct a neighbor graph that serves as a similarity measure based on the similar representations in the embedding space. Then, we employ an attention mechanism to transform the target features into augmented ones based on the neighbor graph. Finally, we formulate a novel semi-supervised loss by encouraging the predictions of the interpolations of augmented features to be consistent with the corresponding interpolations of the predictions of the target features. We carried out experiments on SVHN and CIFAR-10 benchmark datasets and the experimental results demonstrate that our method outperforms the state-of-the-art methods when the number of labeled examples is limited.
Classifying Hematoxylin and Eosin Images Using a Super-Resolution Segmentor and a Deep Ensemble Classifier

Developing an automatic and credible diagnostic system to analyze the type, stage, and level of the liver cancer from Hematoxylin and Eosin (H&E) images is a very challenging and time-consuming endeavor, even for experienced pathologists, due to the non-uniform illumination and artifacts. Albeit several Machine Learning (ML) and Deep Learning (DL) approaches are employed to increase the performance of automatic liver cancer diagnostic systems, the classification accuracy of these systems still needs significant improvement to satisfy the real-time requirement of the diagnostic situations. In this work, we present a new Ensemble Classifier (hereafter called ECNet) to classify the H&E stained liver histopathology images effectively. The proposed model employs a Dropout Extreme Learning Machine (DrpXLM) and the Enhanced Convolutional Block Attention Modules (ECBAM) based residual network. ECNet applies Voting Mechanism (VM) to integrate the decisions of individual classifiers using the average of probabilities rule. Initially, the nuclei regions in the H&E stain are segmented through Super-resolution Convolutional Networks (SrCN), and then these regions are fed into the ensemble DL network for classification. The effectiveness of the proposed model is carefully studied on real-world datasets. The results of our meticulous experiments on the Kasturba Medical College (KMC) liver dataset reveal that the proposed ECNet significantly outperforms other existing classification networks with better accuracy, sensitivity, specificity, precision, and Jaccard Similarity Score (JSS) of 96.5%, 99.4%, 89.7%, 95.7%, and 95.2%, respectively. We obtain similar results from ECNet when applied to The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset regarding accuracy (96.3%), sensitivity (97.5%), specificity (93.2%), precision (97.5%), and JSS (95.1%). More importantly, the proposed ECNet system consumes only 12.22 s for training and 1.24 s for testing. Also, we carry out the Wilcoxon statistical test to determine whether the ECNet provides a considerable improvement with respect to evaluation metrics or not. From extensive empirical analysis, we can conclude that our ECNet is the better liver cancer diagnostic model related to state-of-the-art classifiers.
Recapitulation Web 3.0: Architecture, Features and Technologies, Opportunities and Challenges

Tim Berners-Lee developed the internet at CERN in early 1990 with fundamental technologies such as HTML, URL, and HTTP which became the foundation of the web. The contemporary web we use today has been much advanced over a period of time ever since the innovation of the World Wide Web was introduced. The static web was the first version of the web, which was the read-only web. Succeeding development in web technology is web 3.0 which is a distributed and decentralized web with emerging technologies. This article emphasizes the comparison of important details with the evolution of the web. The paper also demonstrates the transactional architecture of DApps in networks and decentralized state machines. Decentralization, connectivity, the semantic web, augmented reality and artificial intelligence are the significant features of web 3.0 technology. These features are tremendously used in decision-making on critical issues, which are discussed elaborately in the article. The paper provides various technologies to implement web 3.0 efficiently. The evolution of web 3.0 brings forth opportunities and challenges. The opportunities are the ownership of the data and a personalized web browsing experience. The main concerns are security and scalability requirements for blockchain transactions. The article also laid out the challenges that can be considered for further research.
Pre-Locator Incorporating Swin-Transformer Refined Classifier for Traffic Sign Recognition

In the field of traffic sign recognition, traffic signs usually occupy very small areas in the input image. Most object detection algorithms directly reduce the original image to a specific size for the input model during the detection process, which leads to the loss of small object information. Additionally, classification tasks are more sensitive to information loss than localization tasks. This paper proposes a novel traffic sign recognition approach, in which a lightweight pre-locator network and a refined classification network are incorporated. The pre-locator network locates the sub-regions of the traffic signs from the original image, and the refined classification network performs the refinement recognition task in the sub-regions. Moreover, an innovative module (named SPP-ST) is proposed, which combines the Spatial Pyramid Pool module (SPP) and the Swin-Transformer module as a new feature extractor to learn the special spatial information of traffic sign effectively. Experimental results show that the proposed method is superior to the state-of-the-art methods (82.1 mAP achieved on 218 categories in the TT100k dataset, an improvement of 19.7 percentage points compared to the previous method). Moreover, both the result analysis and the output visualizations further demonstrate the effectiveness of our proposed method. The source code and datasets of this work are available at .
Pure Detail Feature Extraction Network for Visible-Infrared Re-Identification

Cross-modality pedestrian re-identification has important applications in the field of surveillance. Due to variations in posture, camera perspective, and camera modality, some salient pedestrian features are difficult to provide effective retrieval cues. Therefore, it becomes a challenge to design an effective strategy to extract more discriminative pedestrian detail. Although many effective methods for detailed feature extraction are proposed, there are still some shortcomings in filtering background and modality noise. To further purify the features, a pure detail feature extraction network (PDFENet) is proposed for VI-ReID. PDFENet includes three modules, adaptive detail mask generation module (ADMG), inter-detail interaction module (IDI) and cross-modality cross-entropy (CMCE). ADMG and IDI use human joints and their semantic associations to suppress background noise in features. CMCE guides the model to ignore modality noise by generating modality-shared feature labels. Specifically, ADMG generates masks for pedestrian details based on pose estimation. Masks are used to suppress background information and enhance pedestrian detail information. Besides, IDI mines the semantic relations among details to further refine the features. Finally, CMCE cross-combines classifiers and features to generate modality-shared feature labels to guide model training. Extensive ablation experiments as well as visualization results have demonstrated the effectiveness of PDFENet in eliminating background and modality noise. In addition, comparison experiments in two publicly available datasets also show the competitiveness of our approach.
Leakage Reduces Device Coherence Demands for Pulse-Level Molecular Simulations

Quantum simulation on noisy intermediate-scale quantum devices is severely limited by short qubit coherence times. A variational pulse-shaping algorithm known as ctrl-VQE was recently proposed to address this issue by eliminating the need for parameterized quantum circuits, which lead to long state preparation times. Here, we find the fastest possible pulses that prepare target molecular wave functions for a given device Hamiltonian describing coupled transmon qubits using simulations. We find that the resulting time-optimal pulses develop a bang-bang form consistent with Pontryagin's maximum principle. We further investigate how the minimal state preparation time is impacted by the number of energy levels active in the transmon simulations. We find that leakage outside the computational subspace (something that is usually considered problematic) speeds up the state preparation, further reducing device coherence-time demands. Our analysis reveals that this speedup is due to both an enlarged solution space of target wave functions and the appearance of additional channels connecting initial and target states.
View-Based Axiomatic Reasoning for PSO

Weak memory models describe the semantics of concurrent programs on modern multi-core architectures. Reasoning techniques for concurrent programs, like Owicki-Gries-style proof calculi, have to be based on such a semantics, and hence need to be freshly developed for every new memory model. Recently, a more uniform approach to reasoning has been proposed which builds correctness proofs on the basis of a number of core axioms. This allows to prove program correctness independent of memory models, and transfers proofs to specific memory models by showing these to instantiate all axioms required in a proof. The axiomatisation is built on the notion of thread views as first class elements in the semantics. In this paper, we investigate the applicability of this form of axiomatic reasoning to the Partial Store Order (PSO) memory model. As the standard semantics for PSO is not based on views, we first of all provide a view-based semantics for PSO and prove it to coincide with the standard semantics. We then show the new view-based semantics to satisfy all but one axiom. The missing axiom refers to message-passing (MP) abilities of memory models, which PSO does not guarantee. As a consequence, only proofs without usage of the MP axiom are transferable to PSO. We illustrate the reasoning technique by proving correctness of a litmus test employing a fence to ensure message passing.
INSIDE: an Ontology-based Data Integration System Applied to the Oil and Gas Sector

Context: Data integration remains a major challenge facing organizations in the information age. Despite the advances made in recent decades, new approaches have become necessary to deal with new challenges such as Big Data. Problem: Semantic heterogeneity is a significant problem faced by companies in the oil and gas sector, as it makes it difficult to exchange information with other companies. Furthermore, there is a shortage of data integration systems that use open source technologies to deal with semantics, interoperability and scalability. Solution: INSIDE - Semantic Interoperability for Engineering Data Integration - an information system based on ontologies for data integration developed for an oil and gas company. SI Theory: This work is influenced by Representation Theory, based on the idea that an information system is a faithful representation of certain phenomena in the real world. Method: Review of state of the art on system architectures for data integration and use of methodologies for elaborating ontologies that represent the knowledge base of the information system. Summary of Results: Implementation of a prototype that allows querying heterogeneous data sources using a vocabulary familiar to the user, removing ambiguities from data with semantics. Contributions and Impact in IS area: The development of a solution for data integration using open source technologies tested with real-world data from a company in the oil and gas sector that can serve as a reference for developing new applied systems to other sectors.
Past, Present, and Future of the Augmented Reality (AR)-Enhanced Interactive Techniques: A Survey

Augmented Reality (AR) is a recent technology that brings together the actual environment and computer vision and visuals. For people interested in simultaneously exploring the virtual and physical worlds, Augmented Reality (AR) has been a popular study area. This paper discusses what Augmented Reality is in detail. Furthermore, this survey discusses the use of AR in major fields and how recent technology can be improved. This paper summarizes by suggesting the use of Augmented Reality in the field of Cinema Industries.
AD-DETR: DETR with asymmetrical relation and decoupled attention in crowded scenes

&lt;abstract&gt;&lt;p&gt;Pedestrian detection in crowded scenes is widely used in computer vision. However, it still has two difficulties: 1) eliminating repeated predictions (multiple predictions corresponding to the same object); 2) false detection and missing detection due to the high scene occlusion rate and the small visible area of detected pedestrians. This paper presents a detection framework based on DETR (detection transformer) to address the above problems, and the model is called AD-DETR (asymmetrical relation detection transformer). We find that the symmetry in a DETR framework causes synchronous prediction updates and duplicate predictions. Therefore, we propose an asymmetric relationship fusion mechanism and let each query asymmetrically fuse the relative relationships of surrounding predictions to learn to eliminate duplicate predictions. Then, we propose a decoupled cross-attention head that allows the model to learn to restrict the range of attention to focus more on visible regions and regions that contribute more to confidence. The method can reduce the noise information introduced by the occluded objects to reduce the false detection rate. Meanwhile, in our proposed asymmetric relations module, we establish a way to encode the relative relation between sets of attention points and improve the baseline. Without additional annotations, combined with the deformable-DETR with Res50 as the backbone, our method can achieve an average precision of 92.6%, MR$ ^{-2} $ of 40.0% and Jaccard index of 84.4% on the challenging CrowdHuman dataset. Our method exceeds previous methods, such as Iter-E2EDet (progressive end-to-end object detection), MIP (one proposal, multiple predictions), etc. Experiments show that our method can significantly improve the performance of the query-based model for crowded scenes, and it is highly robust for the crowded scene.&lt;/p&gt;&lt;/abstract&gt;
Embedded Restricted Boltzmann Machine Approach for Adjustments of Repetitive Physical Activities Using IMU Data

Machine learning models play a crucial role in sports monitoring by effectively identifying various activities and tracking the number of repetitions during repetitive movements. However, creating models that accurately detect different types of exercises and provide feedback on movement adjustments for wearable devices remains a challenge. In this paper, we propose an alternative approach that addresses this issue by using the Restricted Boltzmann Machine (RBM) algorithm to learn, evaluate, and provide adjustment feedback based on inertial sensor data in real-time. Our experimental results show that by evaluating body segments individually, highly specialized models can be generated from a small set of movement repetitions. Moreover, these models have the capability to offer users precise recommendations on how to fine-tune the intensity, acceleration, and amplitude of the monitored segment. By using our proposed method, there is a great potential to enhance the accuracy and effectiveness of wearable devices used for sports monitoring.