Reviewerly/exHarmony huggingface dataset:
How Many Students and Items Are Optimal for Teaching Level Evaluation of College Teachers? Evidence from Generalizability Theory and Lagrange Multiplier

Budget and cost are two of the problems that cannot be ignored when conducting a measure study. Based on the application of generalizability theory, combined with Lagrange multiplier, this paper explores how many students and items are optimal for teaching level evaluation of college teachers under budget constraints to maintain the sustainable development of higher education. A total of 397 students are required to evaluate 10 teachers’ teaching level using the Teaching Level Evaluation Questionnaire for College Teachers, and we make different generalizability designs (i.e., (s:t) × i, (s:t) × (i:v) and (s:t) × (i:v) × o) for the collected data. The study unifies the Lagrange multiplier formula, derives the optimal sample size formula of different designs under budget constraints in generalizability theory, and calculates the optimal sample size for teaching level evaluation of college teachers in different designs with the estimated variance components. Results indicate that: (1) the unified formula of Lagrange multiplier has a stronger robustness and can be applied to different study designs under budget constraints in generalizability theory; (2) the occasion has a great effect on teaching level evaluation for college teachers; (3) the (s:t) × (i:v) × o design has a high efficiency in estimating the optimal sample size of teaching level evaluation for college teachers; (4) the design of (s:t) × (i:v) × o is the optimal generalizability design of teaching level evaluation for college teachers under budget constraints in generalizability theory; and (5) under budget constraints of teaching level evaluation for college teachers in generalizability theory, the optimal sample size of students is 31 for each teacher and the optimal sample size of items is 7 for each dimension.
A Topology Based Automatic Registration Method for Infrared and Polarized Coupled Imaging

In multi-source camera collaborative imaging research, it is known that the differences in size and resolution of the sensor chip, the angle of view and field of view when imaging, and the imaging characteristics of optical systems between cameras, makes image registration a topic that can never be avoided in data analysis and post-processing. Additionally, lacking common features between multi-source images means that the accurate registration of multi-modal images can only be completed manually. Aiming at the registration problem of the polarization parameter image and infrared image, this study takes advantage of the invariant feature of the imaging target topology and introduces the image texture-based segmentation method to obtain the target topology structure. Subsequently, the registration control points are extracted based on the target topology skeleton, which can break through the limitation of feature differences, improve the robustness of the algorithm to target transformation, and realize the automatic registration of multi-source images.
Technology-facilitated violence: A conceptual review

This article provides a conceptual review of the term ‘technology-facilitated violence’. In the last decade, discussion of technology-facilitated violence has become commonplace in criminological and social scientific discourses. Yet, scholars have not settled on what this term means or the kind of relationship between technology and violence it infers. Addressing this ambiguity, we review how scholars have conceptualised technology-facilitated violence, evaluate the adequacy of those conceptualisations, and develop strategies to improve them. To do so, we bring the philosophy of technology into conversation with the scholarship on technology-facilitated violence to identify the latent theories of technology that underpin existing definitions of technology-facilitated violence. Then, synthesising insights from these two fields of scholarship, we generate a new definition of technology-facilitated violence that builds on the strengths of existing definitions while avoiding their key limitations. This new definition and the conceptual review that informs it should improve scholarly understandings of technology-facilitated violence and help design better strategies to address its harms. Hence, we conclude by emphasising the importance of this kind of conceptual and synthetic work and the value it offers scholars concerned with improving both theory and practice.
Complementarity is the king: Multi-modal and multi-grained hierarchical semantic enhancement network for cross-modal retrieval

Cross-modal retrieval takes a query of one modality to retrieve relevant results from another modality, and its key issue lies in how to learn the cross-modal similarity. Note that the complete semantic information of a specific concept is widely scattered over the multi-modal and multi-grained data, and it cannot be thoroughly captured by most existing methods to learn the cross-modal similarity accurately. Therefore, we propose a Multi-modal and Multi-grained Hierarchical Semantic Enhancement network (M2HSE), which contains two stages to obtain more complete semantic information by fusing the complementarity in multi-modal and multi-grained data. In stage 1, two classes of cross-modal similarity (primary similarity and auxiliary similarity) are calculated more comprehensively in two subnetworks. Especially, the primary similarities from two subnetworks are fused to perform the cross-modal retrieval, while the auxiliary similarity provides a valuable complement for the primary similarity. In stage 2, the multi-spring balance loss is proposed to optimize the cross-modal similarity more flexibly. Utilizing this loss, the most representative samples are selected to establish the multi-spring balance system, which adaptively optimizes the cross-modal similarities until reaching the equilibrium state. Extensive experiments conducted on public benchmark datasets clearly prove the effectiveness of our proposed method and show its competitive performance with the state-of-the-arts.
Cryptographic obfuscation for smart contracts: Trustless bitcoin bridge and more

Privacy protection for smart contracts is currently inadequate. Existing solutions for privacy-preserving smart contracts either support only a limited class of smart contracts or rely on noncryptographic assumptions. We propose a cryptographic obfuscation scheme for smart contracts based on existing blockchain mechanisms, standard cryptographic assumptions, and witness encryption. In the proposed scheme, an obfuscated smart contract does not reveal its algorithm and hardcoded secrets and preserves encrypted states. Any user can provide it with encrypted inputs and allow an untrusted third party to execute it. Although multiparty computation (MPC) among dynamically changing users is necessary, its privacy is protected if at least one user is honest. If the MPC does not finish within a period of time, anyone can cancel and restart it. The proposed scheme also supports decentralized obfuscation where even the participants of the obfuscation process cannot learn secrets in the obfuscated smart contract unless all of them are malicious. As its applications, we present a new trustless bitcoin bridge mechanism that exposes no secret key and privacy-preserving anti-money laundering built into smart contracts.
Automated Camera Exposure Control for Accuracy-Enhanced Stereo-Digital Image Correlation Measurement

An automated camera exposure control method, which allows a two-camera stereo-digital image correlation (stereo-DIC) system to capture high-quality speckle image pairs, is presented for accuracy-enhanced stereo-DIC measurement. By using this method, the two synchronized cameras can automatically determine the optimal camera exposure and ideal average grayscale for capturing the optimal reference image pair in the reference state. Furthermore, high-quality deformed image pairs can be recorded during the test by adaptively adjusting the camera exposure in case of serious ambient light variations. Validation tests, including varying illumination tests and translation tests, were performed to verify the effectiveness and robustness of this method. Experimental results indicate that the proposed method overperforms the existing stereo-DIC technique with empirically determined fixed camera exposure time. The practicality of the proposed automated camera exposure control method was verified using real high-temperature experiments.
A Method for Detecting Feature-Sparse Regions and Matching Enhancement

Image matching is a key research issue in the intelligent processing of remote sensing images. Due to the large phase differences or apparent differences in ground features between unmanned aerial vehicle imagery and satellite imagery, as well as the large number of sparsely textured areas, image matching between the two types of imagery is very difficult. Tackling the difficult problem of matching unmanned aerial vehicle imagery and satellite imagery, a feature sparse region detection and matching enhancement algorithm (SD-ME) is proposed in this study. First, the SuperGlue algorithm was used to initially match the two images, and feature-sparse region detection was performed with the help of the image features and initial matching results, with the detected feature sparse areas stored in a linked list one by one. Then, according to the order of storage, feature re-extraction was performed on the feature-sparse areas individually, and an adaptive threshold feature screening algorithm was proposed to filter and screen the re-extracted features. This retains only high-confidence features in the region and improves the reliability of matching enhancement results. Finally, local features with high scores that were re-extracted in the feature-sparse areas were aggregated and input to the SuperGlue network for matching, and thus, reliable matching enhancement results were obtained. The experiment selected four pairs of un-manned aerial vehicle imagery and satellite imagery that were difficult to match and compared the SD-ME algorithm with the SIFT, ContextDesc, and SuperGlue algorithms. The results revealed that the SD-ME algorithm was far superior to other algorithms in terms of the number of correct matching points, the accuracy of matching points, and the uniformity of distribution of matching points. The number of correctly matched points in each image pair increased by an average of 95.52% compared to SuperGlue. The SD-ME algorithm can effectively improve the matching quality between unmanned aerial vehicle imagery and satellite imagery and has practical value in the fields of image registration and change detection.
Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network

Fine-needle aspiration cytology (FNAC) is regarded as one of the most important preoperative diagnostic tests for thyroid nodules. However, the traditional diagnostic process of FNAC is time-consuming, and its accuracy is highly related to the experience of the cytopathologist. Computer-aided diagnostic (CAD) systems are rapidly evolving to provide objective diagnostic recommendations. So far, most studies have used fixed-size patches and usually hand-select patches for model training. In this study, we develop a CAD system to address these challenges. In order to be consistent with the diagnostic working mode of cytopathologists, the system is mainly composed of two task modules: the detecting module that is responsible for detecting the regions of interest (ROIs) from the whole slide image of the FNAC, and the classification module that identifies ROIs having positive lesions. The system can then output the top-k ROIs with the highest positive probabilities for the cytopathologists to review. In order to obtain the overall good performance of the system, we compared different object detection and classification models, and used a combination of the YOLOV4 and EfficientNet networks in our system.
Visual-GRoup AFFEct Recognition (V-GRAFFER): A Unified Application for Real-Time Group Concentration Estimation in E-Lectures

This paper presents the most recent version of V-GRAFFER, a novel system that we have been developing for Visual GRoup AFFEct Recognition research. This version includes new algorithms and features, as well as a new application extension for using and evaluating the new features. Specifically, we present novel methods to collect facial samples from other e-lecture applications. We use screen captures of lectures, which we track and connect with samples during the duration of e-educational events. We also developed and evaluated three new algorithms for drawing conclusions on group concentration states. As V-GRAFFER required such complex functionalities to be combined together, many corresponding microservices have been developed. The current version of V-GRAFFER allows drawing real-time conclusions using the input samples collected from the use of any tutoring system, which in turn leads to real-time feedback and allows adjustment of the course material.
Adaptive H-infinity extended Kalman filtering for a navigation system in presence of high uncertainties

The optimal performance of the Kalman filters is highly dependent on the measurement and process noise characteristics, making the whole system unable to achieve the desired estimation in the presence of non-Gaussian mean noise distribution and high initial uncertainties. Recently, the H-infinity filter, as a robust algorithm, has been broadly used, as it is not being dependent on the pre-knowledge of the noise nature; however, making a balance between high robustness and estimation accuracy is a challenging issue. Hence, to overcome this problem, a new adaptive H-infinity extended Kalman filter (AHEKF) was designed in this paper, which benefits from both high robustness and precision. The suggested algorithm contains two adaptive sections to achieve high accuracy as well as controlling the effects of time-varying noise characteristics, high initial uncertainties, and abnormal data that can degrade the accuracy of state estimation in an integrated navigation system. The presented algorithm was used to integrate data from two independent sensors data. The simulation results for an inertial navigation system (INS)/global positioning system (GPS) sensor fusion are presented and compared with the standard H-infinity filter, extended Kalman filter (EKF), and unscented Kalman filter (UKF) to show the effectiveness of the proposed algorithm. Evaluations demonstrate that the AHEKF achieves over 50% higher accuracy and robustness, and over 2.5 times faster convergence of estimation errors than the standard H-infinity filter.
Beyond Design Thinking: Exploring Design Modes for Children’s Medical Care Service and Experience

Children's Medical Center established a design-driven collaboration to improve children's medical care service and experience.By introducing and analysing this seven-year exploration, We found that following the traditional problem-solving was not a good solution to the communication problem between children patients and doctors.So we established a new design model of an IPbased communication system for children patients, the parents and the doctors, with sense-making at its core.With the design practice and reflection, this paper attempts to understand and reframe the design modes in the context of designing for children's medical care service and experience, providing space for further discussion and practice.
Small object detection using retinanet with hybrid anchor box hyper tuning using interface of Bayesian mathematics

AbstractAbstractIn recent years object detection system has been improved by many folds due to many novel deep learning models. Deep learning has outperformed the existing traditional computer vision techniques. In recent many deep learning models uses the concept of anchor box, the model proposes various anchor boxes on the images. The models generally use a classification model and a regression models, the regression model is used to predict the position of next possible anchor box and the classification is used to validate the anchor box. The hyper tuning of these models are generally based on the anchor box specifications, many researchers have used an optimized anchor box dimensions which is obtained for a specific dataset, due to which the accuracy increases drastically but the model are not scalable on any other data set. We propose a new hybrid anchor box optimization technique by using a variant of Bayesian optimization and sub sampling for small object detection using retina net model with resnet backbone. Our hybrid model is scalable over various datasets, the model is used on visdrone dataset and the result shows a 3.7% improvement in MAP result.Subject Classification: 62H30Keywords: Anchor boxBayesian mathematicsDeep learningObject detectionRetinanet
Effective negative triplet sampling for knowledge graph embedding

AbstractAbstractKnowledge graphs contain only positive triplet facts, whereas the negative triplets need to be generated precisely to train the embedding models. Early Uniform and Bernoulli sampling are applied but suffer's from the zero loss problems during training, affecting the performance of embedding models. Recently, generative adversarial technic attended the dynamic negative sampling and obtained better performance by vanishing zero loss but on the adverse side of increasing the model complexity and training parameter. However, NSCaching balances the performance and complexity, generating a single negative triplet sample for each positive triplet that focuses on vanishing gradients. This paper addressed the zero loss training problem due to the low-scored negative triplet by proposing the extended version of NSCaching, to generate the high-scored negative triplet utilized to increase the training performance. The proposed method experimented with semantic matching knowledge graph embedding models on the benchmark datasets, where the results show the success on all evaluation metrics.Subject Classification: Primary 68T35Secondary 68T05Keywords: Knowledge graphRepresentation learningEmbeddingNegative samplingLink prediction
Semi-supervised graph learning framework for apicomplexan parasite classification

Apicomplexan parasites cause diseases including malaria, toxoplasmosis, and babesiosis, affecting large parts of the world and hampering economic development to a considerable severity. Early recognition of pathogenic parasites can effectively constrain the spread of these infectious diseases. Recently, deep-learning-based computer-aided parasite recognition systems provide many practical tools for automated apicomplexan parasite recognition. However, existing research works are limited by the complicated and expensive manual annotating workload in supervised deep learning methods, which require many experts’ manually annotated samples. Moreover, they also ignore the significance of correlations among different parasite cells, which retain helpful information for identification. Therefore, this paper is the first to employ a semi-supervised learning strategy to reduce the manual labeling workload and introduce a hybrid graph learning algorithm into parasite recognition. Our approach has a superior parasite recognition capability with semi-supervised techniques and the graph-based model. Specifically, this research proposes a Semi-Supervised Graph Learning (SSGL) framework, composing CNN (Convolutional Neural Networks) feature embedding, learnable graph building, and graph feature learning components. To verify the performance of the SSGL model, the paper applies multiple evaluation indicators to appraise our model, and the approach can achieve high accuracy of 91.75%, AUC of 91.83%, the sensitivity of 91.75% and specificity of 97.25% with only a small amount of labeled data (20%). In conclusion, SSGL model provides high parasite recognition capability with minimal labels, greatly reduces the workload of relevant experts, and promotes the popularization of computer-aided parasite diagnosis tools.
A framework based on Natural Language Processing and Machine Learning for the classification of the severity of road accidents from reports

Road safety analysis is typically performed by domain experts on the basis of the information contained in accident reports. The main challenges are the difficulty of considering a large number of reports in textual form and the subjectivity of the expert judgments contained in reports. This work develops a framework based on the combination of Natural Language Processing (NLP) and Machine Learning (ML) for the automatic classification of accidents with the final aim of assisting experts in performing road safety analyses. Two different models for the representation of the textual reports (Hierarchical Dirichlet Processes (HDPs) and Doc2vec) and three ML-based classifiers (Artificial Neural Networks (ANNs), Decision Trees (DTs) and Random Forests (RFs)) are compared. The framework is applied to a repository of road accident reports provided by the US National Highway Traffic Safety Administration. The best trade-off between accuracy of the classification and explainability of the obtained results is achieved by combining HDP topic modeling and RF classification.
An End-to-End Robust Video Steganography Model Based on a Multi-Scale Neural Network

The purpose of video steganography is to hide messages in the video file and prevent them from being detected, and finally the secret message can be extracted completely at the receiver. In this paper, an end-to-end video steganography based on GAN and multi-scale deep learning network is proposed, which consists of the encoder, decoder and discriminator. However, in the transmission process, videos will inevitably be encoded. Thus, a noise layer is introduced between the encoder and the decoder, which makes the model able to resist popular video compressions. Experimental results show that the proposed end-to-end steganography has achieved high visual quality, large embedding capacity, and strong robustness. Moreover, the proposed method performances better compared to the latest end-to-end video steganography.
Topic optimization–incorporated collaborative recommendation for social tagging

Purpose With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging. Design/methodology/approach Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics. Findings Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging. Originality/value With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.