Reviewerly/exHarmony huggingface dataset:
Improving test security and efficiency of computerized adaptive testing for the Force Concept Inventory

This paper presents improvements made to a computerized adaptive testing (CAT)-based version of the FCI (FCI-CAT) in regards to test security and test efficiency. First, we will discuss measures to enhance test security by controlling for item overexposure, decreasing the risk that respondents may (i) memorize the content of a pretest for use on the post-test or (ii) share information about the items with their classmates who take the assessment later. Second, we will discuss measures to enhance test efficiency, so that a shorter test length can yield a desired accuracy and precision of the measurement. Specifically, we utilized collateral information in the form of a pretest proficiency estimate of each respondent for selecting items and estimating respondent proficiency level in the post-test. To shorten the total testing time further, we also allowed the test lengths to be different for the pre- and post-test. To analyze how these improvements affect the accuracy and precision (which we measure in terms of root-mean-square error) of Cohen's d, we conducted a Monte Carlo simulation and a post hoc simulation. Then, we calculated the minimal test length of the FCI-CAT whose accuracy and precision are equivalent to that of the paper-and-pencil version of the FCI. Consequently, we obtained the following three findings: (i) By using collateral information, we can achieve the accuracy and precision of the full-length FCI with fewer items via the FCI-CAT. (ii) For a class size of 40, we can control for test security while still reducing the sum of the pre- and post-test lengths of the FCI-CAT to a total of 33 items (17 items on the pretest and 16 items on the post-test), thereby reducing the testing time to 55%. (iii) If one's goal is to maximize test efficiency, the pretest length should be slightly larger than the post-test length. On the other hand, if the goal is to maximize test security, the pretest length should be smaller and the post-test length should be larger. If one desires a balance of these two goals, it would be reasonable to choose equal pre- and post-test lengths.Received 12 October 2021Accepted 27 January 2022DOI:https://doi.org/10.1103/PhysRevPhysEducRes.18.010112Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentPhysics Education Research
ITS Based on Deep Graph Convolutional Fraud Detection Network Blockchain-Enabled Fog-Cloud

The advancement in transport applications increases at the everyday progress in technologies. Therefore, intelligent transport systems (ITS) gain a lot of progress at the different vehicle levels and in the vehicular area network. However, privacy and security at the network level are critical issues for ITS applications in the existing mechanism. In this paper, the study devises the cost-efficient and secure Serverless Blockchain Enable Task Scheduling (SBETS) ITS system and algorithm framework. The main goal is to reduce processing and security blockchian costs for ITS applications in the system. The processing cost minimizes based on the new proposed function-based price model and secures the data by a suggested deep graph convolutional neural network scheme in the network. The simulation results show that SBETS outperformed all existing ITS systems and minimized processing costs by 10% and fraud detection issues by 50% for transport applications.
Heterogeneous Semi-Asynchronous Federated Learning in Internet of Things: A Multi-Armed Bandit Approach

Federated learning (FL) has recently received significant attention in Internet of Things, due to its capability of enabling multiple clients to collaboratively train machine learning models using neural networks, without sharing their privacy-sensitive data. However, due to the heterogeneity of clients in their computation and communication capability, they might not return the training model to the server at the same time, which may result in high waiting latency at the server, especially in synchronous FL. Although asynchronous FL can reduce the waiting latency, aggregating global model in a completely asynchronous way may lead to some local models out of date, resulting in low training accuracy. To address the above issues, this paper aims to propose a novel Heterogeneous Semi-Asynchronous FL mechanism, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HSA_FL</i> . Firstly, we use a Multi-Armed Bandit (MAB) approach to identify the heterogenous communication and computation capabilities of clients, based on which, we assign different training intensities to clients. Generally, the clients with lower capabilities will be assigned with less number of local updates. In addition, instead of waiting all the clients to return their training models or immediately aggregation after getting a single local model, this paper proposes two aggregation rules, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adaptive</i> update and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fixed</i> adaptive, respectively. Finally, simulation results show that the proposed scheme can effectively reduce the training time and improve the training accuracy as compared with some benchmark algorithms.
SeeTek: Very Large-Scale Open-set Logo Recognition with Text-Aware Metric Learning

Recent advances in deep learning and computer vision have set new state of the art in logo recognition [2], [9], [36]. Logo recognition has mostly been approached as a closed-set object recognition problem and more recently as an open-set retrieval problem. Current approaches suffer from distinguishing visually similar logos, especially in open-set retrieval for very large-scale applications with thousands of brands. To address the problem, we propose a multi-task learning architecture of deep metric learning and scene text recognition. We use brand names as weak labels and enforce the model to simultaneously extract distinct visual features as well as predict brand name text. To achieve it, we collected a dataset with 3 Million logos cropped from Amazon Product Catalog images across nearly 8K brands, named PL8K. Our experiments show that adding the task of text recognition during training boosts the model’s retrieval performance both on our PL8K dataset and on five other public logo datasets.
Multidepot Drone Path Planning With Collision Avoidance

Intersections of flight paths in multidrone missions are indications of a high likelihood of in-flight drone collisions. This likelihood can be proactively minimized during path planning. This article proposes two offline collision-avoidance multidrone path-planning algorithms: 1) DETACH and 2) STEER. Large drone tasks can be divided into smaller ones and carried out by multiple drones. Each drone follows a planned flight path that is optimized to efficiently perform the task. The path planning of the set of drones can then be optimized to complete the task in a short time, with minimum energy expenditure, or with maximum waypoint coverage. Here, we focus on maximizing waypoint coverage. Different from existing schemes, our proposed offline path-planning algorithms detect and remove possible in-flight collisions. They are based on a constrained nearest-neighbor search algorithm that aims to cover a large number of waypoints per flight path. DETACH and STEER perform vector intersection check for flight path analysis, but each at different stages of path planning. We evaluate the waypoint coverage of the proposed algorithms through a novel profit model and compare their performance on a work area with different waypoint densities. Our results show that STEER covers 40% more waypoints and generates 20% more profit than DETACH in high-density waypoint scenarios.
Design Guidelines for Mammogram-Based Computer-Aided Systems Using Deep Learning Techniques

Breast cancer is the second fatal disease among cancers patients both in Canada and across the globe. However, when detected early, a patients’ survival rate can be raised. Thus, researchers and scientists have been practicing to develop Computer-Aided Detection (CADe) and Computer-Aided Diagnosis (CADx) systems. Traditional CAD systems depend on manual feature extraction, which has provided radiologists with poor detection and diagnosis tools. Nevertheless, recently, the powerful application of Convolutional Neural Networks (CNN)s as one of the deep learning-based methods has revolutionized these systems’ accuracy and development. This article proposes categorizing the current deep learning research on mammogram types based on researchers’ techniques for their empirical studies. Also, we provide an overview of different publicly available data resources and available datasets for breast imaging. This critical review of the state-of-the-art techniques is presented, which we believe can serve as a valuable source for research scientists investigating deep learning-based breast mammogram classification.
Breaking Plausibility Without Breaking Presence - Evidence For The Multi-Layer Nature Of Plausibility

A novel theoretical model recently introduced coherence and plausibility as the essential conditions of XR experiences, challenging contemporary presence-oriented concepts. This article reports on two experiments validating this model, which assumes coherence activation on three layers (cognition, perception, and sensation) as the potential sources leading to a condition of plausibility and from there to other XR qualia such as presence or body ownership. The experiments introduce and utilize breaks in plausibility (in analogy to breaks in presence): We induce incoherence on the perceptual and the cognitive layer simultaneously by a simulation of object behaviors that do not conform to the laws of physics, i.e., gravity. We show that this manipulation breaks plausibility and hence confirm that it results in the desired effects in the theorized condition space but that the breaks in plausibility did not affect presence. In addition, we show that a cognitive manipulation by a storyline framing is too weak to successfully counteract the strong bottom-up inconsistencies. Both results are in line with the predictions of the recently introduced three-layer model of coherence and plausibility, which incorporates well-known top-down and bottom-up rivalries and its theorized increased independence between plausibility and presence.
Weighted IForest and siamese GRU on small sample anomaly detection in healthcare

Background and objectiveAt present, many achievements have been made in anomaly detection of big data using deep neural network, However, in many practical application scenarios, there are still some problems, such as shortage of data, too large workload of manual data annotating and so on. MethodsThis paper proposes weighted iForest and Siamese GRU (WIF-SGRU) algorithm on small sample anomaly detection. In the data annotation stage, we propose a weighted IForest algorithm for automatic annotation of unlabeled data. In the training phase of anomaly detection model, the Siamese GRU is proposed to train the target data to obtain the anomaly model and detect the real-time anomaly of small sample data. ResultsThe proposed algorithm is verified on six public datasets (Arrhythmia, Shuttle, Staellite, Sttimage-2, Lymphography, and WBC). The experimental results show that compared with the traditional data annotation and anomaly detection algorithm, the algorithm of weighted IForest and Siamese GRU improves the accuracy and real-time performance. ConclusionsThis paper proposes a weighted IForest and Siamese GRU algorithm architecture, which provides a more accurate and efficient method for outlier detection of data. Firstly, the framework uses the improved IForest algorithm to label the label-free data, Then the Siamese GRU is optimized by the improved FDAloss function,the optimized network is used to learn the distance between data for real-time and efficient anomaly detection. Experiments show that the framework has good potential.
An analysis of classical multidimensional scaling with applications to clustering

Classical multidimensional scaling is a widely used dimension reduction technique. Yet few theoretical results characterizing its statistical performance exist. This paper provides a theoretical framework for analyzing the quality of embedded samples produced by classical multidimensional scaling. This lays a foundation for various downstream statistical analyses, and we focus on clustering noisy data. Our results provide scaling conditions on the signal-to-noise ratio under which classical multidimensional scaling followed by a distance-based clustering algorithm can recover the cluster labels of all samples. Simulation studies confirm these scaling conditions are sharp. Applications to the cancer gene-expression data, the single-cell RNA sequencing data and the natural language data lend strong support to the methodology and theory.
Prostate cancer management with lifestyle intervention: From knowledge graph to Chatbot

Abstract Background Personal lifestyle is an important cause of prostate cancer (PCa), hence establishing a corresponding knowledge graph (KG) and a chatbot is a convenient way for preventing and assessing risks. The chatbot based on a KG of PCa‐associated lifestyles will be helpful to PCa management, then save health care resources in the ageing society. Results Based on our established knowledge base, we define entities and corresponding relationships to construct the PCa‐associated lifestyles KG for visualization by importing the triples into the Neo4j graph server. The dialogue system uses the Flask framework to determine the classification of questions through entity recognition and relationship extraction and later uses the query template to search the answers from the PCa‐associated lifestyles KG. The PCa‐associated lifestyles KG contains 11 types of entities and 14 types of relationships, the total number of nodes and links is 21 546 and 66 493, respectively. Also, the entity “Lifestyle”, “Paper”, “Baseline” and “Outcome” contain multiple attributes. The established chatbot can answer 12 types of basic questions and predict the probability of a certain lifestyle resulting in a certain PCa. The chatbot is available at http://sysbio.org.cn:5000/Pca/chatbot . Conclusion A chatbot based on PCa‐associated lifestyles KG was constructed to help researchers, physicians or patients learn more about PCa lifestyle management interactively.
Optimized Breast Cancer Premature Detection Method With Computational Segmentation

Breast cancer is the most common cancer in women aged 59 to 69 years old. Studies have shown that early detection and treatment of breast cancer increases the chances of survival significantly. They also demonstrated that detecting small lesions early improves forecasting and results in a significant reduction in death cases. The most effective screening diagnostic technique in this case is mammography. However, interpretation of mammograms is difficult due to small differences in tissue densities within mammographic images. This is especially true for dense breasts, and this study suggests that screening mammography is more effective in fatty breast tissue than in dense breast tissue. This study focuses on breast cancer diagnosis as well as identifying risk factors and their assessments of breast cancer as well as premature detection of breast cancer by analyzing 3D MRI mammography methods and segmentation of mammographic images using machine learning.
Blockchain Enabled Track-and-Trace Framework for Covid-19 Immunity Certificate

The outbreak of Coronavirus/COVID-19 pandemic is no doubt the greatest worldwide challenge at this point of time. After its discovery in China in late 2019, the virus spreads fiercely through various social transmissions, causing more than 60 million confirmed cases and close to 1.9 million deaths by early 2021. On 11th March 2020, the WHO went on to declare the onset of COVID-19 pandemic. To lessen societal pressure and restore normalcy, many countries committed to vaccination have introduced the COVID-19 immunity passports, certificates, or licenses, collectively called the COVID-19 immunity certificate. This is to legally certify that the certificate bearer has been vaccinated and is immune to COVID-19. However, there exists several practical and legal obstacles to efficiently track/trace the certificates. Matter of fact, this kind of certificate is to impose restrictions on who can and who cannot participate in an official social activity. This has regrettably promoted the fabrication of the certificate and is casting doubt on the vaccination integrity. In this paper, we thus propose a novel blockchain-based system for establishing trust on the certificate bearer and to eradicate vaccination immunity certificate fraud. Our proposed system is aimed to address the issues in trackability, and traceability of the data embedded in the immunity certificate. This is to uphold the trust on the certificate bearer/holder that all necessary precautions pertaining to COVID-19 vaccination have been legally obeyed and complied with. The system can track the origin of the COVID-19 immunity certificate and monitor the vaccines process flow at each step of its manufacturing and its associated chain of distribution.
Coarse-to-fine dual-level attention for video-text cross modal retrieval

The effective representation of video features plays an important role in video vs. text cross-modal retrieval, and many researchers either use a single modal feature of the video or simply combine multi-modal features of the video. This makes the learned video features less robust. To enhance the robustness of video feature representation, we use coarse-fine-grained parallel attention model and feature fusion module to learn more effective video feature representation. Among them, coarse-grained attention learns the relationship between different feature blocks in the same modality feature and fine-grained attention applies attention to global features and strengthens the connection between points. Coarse-grained attention and Fine-grained attention complement each other. We integrate multi-head attention network into the model to expand the receptive field for features, and use the feature fusion module to further reduce the semantic gap between different video modalities. Our proposed model architecture not only strengthens the relationship between global features and local features, but also compensates the differences between different modality features in the video. Evaluation on three widely used datasets AcitivityNet-Captions, MSRVTT and LSMDC demonstrates its effectiveness.
Ultrasound image denoising using generative adversarial networks with residual dense connectivity and weighted joint loss

Ultrasound imaging has been recognized as a powerful tool in clinical diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of ultrasound images. Various denoising algorithms cannot fully reduce speckle noise and retain image features well for ultrasound imaging. The application of deep learning in ultrasound image denoising has attracted more and more attention in recent years.In the article, we propose a generative adversarial network with residual dense connectivity and weighted joint loss (GAN-RW) to avoid the limitations of traditional image denoising algorithms and surpass the most advanced performance of ultrasound image denoising. The denoising network is based on U-Net architecture which includes four encoder and four decoder modules. Each of the encoder and decoder modules is replaced with residual dense connectivity and BN to remove speckle noise. The discriminator network applies a series of convolutional layers to identify differences between the translated images and the desired modality. In the training processes, we introduce a joint loss function consisting of a weighted sum of the L1 loss function, binary cross-entropy with a logit loss function and perceptual loss function.We split the experiments into two parts. First, experiments were performed on Berkeley segmentation (BSD68) datasets corrupted by a simulated speckle. Compared with the eight existing denoising algorithms, the GAN-RW achieved the most advanced despeckling performance in terms of the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and subjective visual effect. When the noise level was 15, the average value of the GAN-RW increased by approximately 3.58% and 1.23% for PSNR and SSIM, respectively. When the noise level was 25, the average value of the GAN-RW increased by approximately 3.08% and 1.84% for PSNR and SSIM, respectively. When the noise level was 50, the average value of the GAN-RW increased by approximately 1.32% and 1.98% for PSNR and SSIM, respectively. Secondly, experiments were performed on the ultrasound images of lymph nodes, the foetal head, and the brachial plexus. The proposed method shows higher subjective visual effect when verifying on the ultrasound images. In the end, through statistical analysis, the GAN-RW achieved the highest mean rank in the Friedman test.
Evolving Long Short-Term Memory Network-Based Text Classification

Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM networks suffer from the parameter tuning problem. Generally, initial and control parameters of LSTM are selected on a trial and error basis. Therefore, in this paper, an evolving LSTM (ELSTM) network is proposed. A multiobjective genetic algorithm (MOGA) is used to optimize the architecture and weights of LSTM. The proposed model is tested on a well-known factory reports dataset. Extensive analyses are performed to evaluate the performance of the proposed ELSTM network. From the comparative analysis, it is found that the LSTM network outperforms the competitive models.
Bi-level artificial intelligence model for risk classification of acute respiratory diseases based on Chinese clinical data

The high incidence of respiratory diseases has dramatically increased the medical burden under the COVID-19 pandemic in the year 2020. It is of considerable significance to utilize a new generation of information technology to improve the artificial intelligence level of respiratory disease diagnosis.Based on the semi-structured data of Chinese Electronic Medical Records (CEMRs) from the China Hospital Pharmacovigilance System, this paper proposed a bi-level artificial intelligence model for the risk classification of acute respiratory diseases. It includes two levels. The first level is a dedicated design of the "BiLSTM+Dilated Convolution+3D Attention+CRF" deep learning model that is used for Chinese Clinical Named Entity Recognition (CCNER) to extract valuable information from the unstructured data in the CEMRs. Incorporating the transfer learning and semi-supervised learning technique into the proposed deep learning model achieves higher accuracy and efficiency in the CCNER task than the popular "Bert+BiLSTM+CRF" approach. Combining the extracted entity data with other structured data in the CEMRs, the second level is a customized XGBoost to realize the risk classification of acute respiratory diseases.The empirical study shows that the proposed model could provide practical technical support for improving diagnostic accuracy.Our study provides a proof-of-concept for implementing a hybrid artificial intelligence-based system as a tool to aid clinicians in tackling CEMR data and enhancing the diagnostic evaluation under diagnostic uncertainty.