Reviewerly/exHarmony huggingface dataset:
Cross-Lingual Cross-Modal Consolidation for Effective Multilingual Video Corpus Moment Retrieval

Existing multilingual video corpus moment retrieval (mVCMR) methods are mainly based on a two-stream structure.The visual stream utilizes the visual content in the video to estimate the query-visual similarity, and the subtitle stream exploits the query-subtitle similarity.The final query-video similarity ensembles similarities from two streams.In our work, we propose a simple and effective strategy termed as Cross-lingual Cross-modal Consolidation (C 3 ) to improve mVCMR accuracy.We adopt the ensemble similarity as the teacher to guide the training of each stream, leading to a more powerful ensemble similarity.Meanwhile, we use the teacher for a specific language to guide the student for another language to exploit the complementary knowledge across languages.Extensive experiments on mTVR dataset demonstrate the effectiveness of our C 3 method.
Signatures of Nontrivial Pairing in the Quantum Walk of Two-Component Bosons

Nearest neighbor bosons possessing only on-site interactions do not form on-site bound pairs in their quantum walk due to fermionization. We obtain signatures of nontrivial on-site pairing in the quantum walk of strongly interacting two component bosons in a one dimensional lattice. By considering an initial state with particles from different components located at the nearest-neighbor sites in the central region of the lattice, we show that in the dynamical evolution of the system, competing intra- and intercomponent on-site repulsion leads to the formation of on-site intercomponent bound states. We find that when the total number of particles is three, an intercomponent pair is favored in the limit of equal intra- and intercomponent interaction strengths. However, when two bosons from each species are considered, intercomponent pairs and trimer are favored depending on the ratios of the intra- and intercomponent interactions. In both cases, we find that the quantum walks exhibit a reentrant behavior as a function of intercomponent interaction.
Development and Validation of Machine Models Using Natural Language Processing to Classify Substances Involved in Overdose Deaths

Overdose is one of the leading causes of death in the US; however, surveillance data lag considerably from medical examiner determination of the death to reporting in national surveillance reports.To automate the classification of deaths related to substances in medical examiner data using natural language processing (NLP) and machine learning (ML).Diagnostic study comparing different natural language processing and machine learning algorithms to identify substances related to overdose in 10 health jurisdictions in the US from January 1, 2020, to December 31, 2020. Unstructured text from 35 433 medical examiner and coroners' death records was examined.Text from each case was manually classified to a substance that was related to the death. Three feature representation methods were used and compared: text frequency-inverse document frequency (TF-IDF), global vectors for word representations (GloVe), and concept unique identifier (CUI) embeddings. Several ML algorithms were trained and best models were selected based on F-scores. The best models were tested on a hold-out test set and results were reported with 95% CIs.Text data from death certificates were classified as any opioid, fentanyl, alcohol, cocaine, methamphetamine, heroin, prescription opioid, and an aggregate of other substances. Diagnostic metrics and 95% CIs were calculated for each combination of feature extraction method and machine learning classifier.Of 35 433 death records analyzed (decedent median age, 58 years [IQR, 41-72 years]; 24 449 [69%] were male), the most common substances related to deaths included any opioid (5739 [16%]), fentanyl (4758 [13%]), alcohol (2866 [8%]), cocaine (2247 [6%]), methamphetamine (1876 [5%]), heroin (1613 [5%]), prescription opioids (1197 [3%]), and any benzodiazepine (1076 [3%]). The CUI embeddings had similar or better diagnostic metrics compared with word embeddings and TF-IDF for all substances except alcohol. ML classifiers had perfect or near perfect performance in classifying deaths related to any opioids, heroin, fentanyl, prescription opioids, methamphetamine, cocaine, and alcohol. Classification of benzodiazepines was suboptimal using all 3 feature extraction methods.In this diagnostic study, NLP/ML algorithms demonstrated excellent diagnostic performance at classifying substances related to overdoses. These algorithms should be integrated into workflows to decrease the lag time in reporting overdose surveillance data.
Dual-Geometric Space Embedding Model for Two-View Knowledge Graphs

Two-view knowledge graphs (KGs) jointly represent two components: an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. As such, these KGs contain heterogeneous structures that are hierarchical, from the ontology-view, and cyclical, from the instance-view. Despite these various structures in KGs, most recent works on embedding KGs assume that the entire KG belongs to only one of the two views but not both simultaneously. For works that seek to put both views of the KG together, the instance and ontology views are assumed to belong to the same geometric space, such as all nodes embedded in the same Euclidean space or non-Euclidean product space, an assumption no longer reasonable for two-view KGs where different portions of the graph exhibit different structures. To address this issue, we define and construct a dual-geometric space embedding model (DGS) that models two-view KGs using a complex non-Euclidean geometric space, by embedding different portions of the KG in different geometric spaces. DGS utilizes the spherical space, hyperbolic space, and their intersecting space in a unified framework for learning embeddings. Furthermore, for the spherical space, we propose novel closed spherical space operators that directly operate in the spherical space without the need for mapping to an approximate tangent space. Experiments on public datasets show that DGS significantly outperforms previous state-of-the-art baseline models on KG completion tasks, demonstrating its ability to better model heterogeneous structures in KGs.
An Improved Circular Fringe Fourier Transform Profilometry

Circular fringe projection profilometry (CFPP), as a branch of carrier fringe projection profilometry, has attracted research interest in recent years. Circular fringe Fourier transform profilometry (CFFTP) has been used to measure out-of-plane objects quickly because the absolute phase can be obtained by employing fewer fringes. However, the existing CFFTP method needs to solve a quadratic equation to calculate the pixel displacement amount related to the height of the object, in which the root-seeking process may get into trouble due to the phase error and the non-uniform period of reference fringe. In this paper, an improved CFFTP method based on a non-telecentric model is presented. The calculation of displacement amount is performed by solving a linear equation instead of a quadratic equation after introducing an extra projection of circular fringe with circular center translation. In addition, Gerchberg iteration is employed to eliminate phase error of the region close to the circular center, and the plane calibration technique is used to eliminate system error by establishing a displacement-to-height look-up table. The mathematical model and theoretical analysis are presented. Simulations and experiments have demonstrated the effectiveness of the proposed method.
Security-enhanced multiple-image encryption based on quick response codes and modified double random phase encoding in the fractional Fourier transform domain

A security-enhanced multiple-image encryption method is proposed based on quick response (QR) codes and modified double random phase encoding (DRPE) in the fractional Fourier transform (FrFT) domain in this paper, where each plaintext is first converted into QR code, and then each QR code is employed to generate the corresponding binary key for decryption with the help of random binary plaintext (RBP). Subsequently, the used RBP is encrypted into noise-like ciphertext by using the modified DRPE in the FrFT domain. In the modified DRPE method, the first random phase mask is activated by the initial FrFT with chaotic phase, and the wavelength of light and the fractional orders as well as the focal lengths of lenses are all used as digital keys to expand the key space. Moreover, the sensitivities of these digital keys are extremely high because the digital keys are closely mapped with the initial values of the chaotic system in the encryption process, which contributes to an extremely high security of the multiple-image encryption method. Furthermore, the high feasibility and strong robustness of the proposed security-enhanced multiple-image encryption method are also demonstrated by using computational simulations.
An Image Retrieval Framework Design Analysis Using Saliency Structure and Color Difference Histogram

This paper focuses on retrieving plant leaf images based on different features that can be useful in the plant industry. Various images and their features can be used to identify the type of leaf and its disease. For this purpose, a well-organized computer-assisted plant image retrieval approach is required that can use a hybrid combination of the color and shape attributes of leaf images for plant disease identification and botanical gardening in the agriculture sector. In this research work, an innovative framework is proposed for the retrieval of leaf images that uses a hybrid combination of color and shape features to improve retrieval accuracy. For the color features, the Color Difference Histograms (CDH) descriptor is used while shape features are determined using the Saliency Structure Histogram (SSH) descriptor. To extract the various properties of leaves, Hue and Saturation Value (HSV) color space features and First Order Statistical Features (FOSF) features are computed in CDH and SSH descriptors, respectively. After that, the HSV and FOSF features of leaf images are concatenated. The concatenated features of database images are compared with the query image in terms of the Euclidean distance and a threshold value of Euclidean distance is taken for retrieval of images. The best results are obtained at the threshold value of 80% of the maximum Euclidean distance. The system’s effectiveness is also evaluated with different performance metrics like precision, recall, and F-measure, and their values come out to be respectively 1.00, 0.96, and 0.97, which is better than individual feature descriptors.
Examining the Use of Non-fungible Tokens (NFTs) as a Trading Mechanism for the Metaverse

The notion of a metaverse seems hard to define but encourages the impression that it can be considered as a new virtual metaphysical landscape that somehow goes beyond our geographical locations and understanding (i.e., independent of time and space). Based on virtual reality, augmented reality, and blockchain, it is envisioned as an independent but extended world that is planned to be a digital virtuality entrenched not only in our old habits such as gaming and entertainment but also in virtual asset trade. In particular, trading is a pillar of the virtual economy, and auction houses will be crucial for Metaverse trading. This exploratory study examines the possibility of using an auction environment to improve the trading capabilities in a virtual universe. We investigate the cases of creating a virtual auction house with the potential of social trading of virtual assets with crypto coins and bartering. To this end, we built a virtual auction house and tested it initially using a set of scenarios. Our preliminary findings suggest that creating a virtual trading environment would be beneficial as an environment for buying and selling virtual assets and exploring their consequences.
Comparative studies of deep learning segmentation models for left ventricle segmentation

One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exerts significant impact on the understanding of the normal anatomy of the heart, as well as the ability to distinguish the aberrant or diseased structure of the heart. Therefore, LV segmentation is an important and critical task in medical practice, and automated LV segmentation is a pressing need. The deep learning models have been utilized in research for automatic LV segmentation. In this work, three cutting-edge convolutional neural network architectures (SegNet, Fully Convolutional Network, and Mask R-CNN) are designed and implemented to segment the LV. In addition, an echocardiography image dataset is generated, and the amount of training data is gradually increased to measure segmentation performance using evaluation metrics. The pixel's accuracy, precision, recall, specificity, Jaccard index, and dice similarity coefficients are applied to evaluate the three models. The Mask R-CNN model outperformed the other two models in these evaluation metrics. As a result, the Mask R-CNN model is used in this study to examine the effect of training data. For 4,000 images, the network achieved 92.21% DSC value, 85.55% Jaccard index, 98.76% mean accuracy, 96.81% recall, 93.15% precision, and 96.58% specificity value. Relatively, the Mask R-CNN outperformed other architectures, and the performance achieves stability when the model is trained using more than 4,000 training images.
Utilization of Blockchain in E-Voting System

This paper is based on a voting system that's widely used in elections known as electronic voting machines and has the potential to conduct the process securely by blockchain technology. The prime aim of this system for ensuring security, integrity as well as transparency. Voter privacy is one of the prime factors in the electronic voting portal that presents a blockchain-based voting system to overcome several drawbacks of current voting methods by proposing a simple, reliable, fast and inexpensive e-voting system. The election data can be arranged in a chain with several blocks with the best security level for this process that employs for any election to ensure flawless counting.
Contrast Aware Image Dehazing using Generative Adversarial Network

One of the most demanding aspects of computer vision is image dehazing. This paper presents a learning-based method to generate a dehazed image from a hazy input image using a densely connected end-to-end encoder-decoder-based GAN (Generative Adversarial Network) to facilitate feature extraction from images and their utilization. We added a Contrast-aware channel attention module to enhance and utilize the features extracted for generating more natural appealing dehazed images with better lightness computation, edges and structure detection. We used a traditional patch-based CNN type discriminator for pushing the generator to yield realistic results with regularized contrast and lower deformation of images. Moreover, we used cosine distance, a non-euclidean distance, and a weighted loss function to improve the perceptual quality of our output. Due to the unavailability of ground truth in real-world foggy images, we synthesized hazy images using a transmission map of the clear images for our dataset. On both synthetic and real-world foggy pictures, the results of our proposed model compete well with existing state-of-the-art techniques.
Match-Prompt

Text matching is a fundamental technique in both information retrieval and natural language processing. Text matching tasks share the same paradigm that determines the relationship between two given texts. The relationships vary from task to task, e.g. relevance in document retrieval, semantic alignment in paraphrase identification and answerable judgment in question answering. However, the essential signals for text matching remain in a finite scope, i.e. exact matching, semantic matching, and inference matching. Ideally, a good text matching model can learn to capture and aggregate these signals for different matching tasks to achieve competitive performance, while recent state-of-the-art text matching models, e.g. Pre-trained Language Models (PLMs), are hard to generalize. It is because the end-to-end supervised learning on task-specific dataset makes model overemphasize the data sample bias and task-specific signals instead of the essential matching signals, which ruins the generalization of model to different tasks. To overcome this problem, we adopt a specialization-generalization training strategy and refer to it as Match-Prompt. In specialization stage, descriptions of different matching tasks are mapped to only a few prompt tokens. In generalization stage, text matching model explores the essential matching signals by being trained on diverse multiple matching tasks. High diverse matching tasks avoid model fitting the data sample bias on a specific task, so that model can focus on learning the essential matching signals. Meanwhile, the prompt tokens obtained in the first step are added to the corresponding tasks to help the model distinguish different task-specific matching signals, as well as to form the basis prompt tokens for a new matching task. In this paper, we consider five common text matching tasks including document retrieval, open-domain question answering, retrieval-based dialogue, paraphrase identification, and natural language inference. Experimental results on eighteen public datasets show that Match-Prompt can improve multi-task generalization capability of PLMs in text matching and yield better in-domain multi-task, out-of-domain multi-task and new task adaptation performance than multi-task and task-specific models trained by previous fine-tuning paradigm.
Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models

The simulation of solar radiation is of great significance to the sustainable development of energy, engineering, and many other fields. The Himawari series of satellites has the characteristics of high temporal, spatial resolution, which helps to solve the problem of insufficient ground radiation observation in China. However, the accuracy of this data needs to be further improved. Thus, four machine learning models with 13 ground and satellite-based input combinations were used to simulate daily solar radiation. The results showed that the simulation accuracy of the model based on a combination of meteorological data from different sources was significantly improved compared with the model based on single-source data. The RMSE was 32.4% and 44.6% lower than those of the model based on the ground meteorological stations data and the model based on the satellite data, respectively. SVM13 model showed the optimal simulation performance compared with other models, and its RMSE and R2 were 1.732 MJ m−2 day−1 and 0.939 in each climate region, respectively. Overall, we conclude that the SVM13 model is the most suitable model, and the model with a complex combination of more meteorological factors as input has higher simulation accuracy than the model with a relatively simple input combination.
Computerized adaptive testing in educational electronic environment of maritime higher education institutions

The article is devoted to the organization of modern learning process, namely the use of innovative technologies – computerized adaptive testing in educational electronic environment of maritime higher education institutions. The example of educational electronic environment is presented in the article on LMS Moodle. The provided new technological and methodological opportunities are a priority in the developed methods of control and testing of knowledge, skills and abilities of students. Comparative characteristic of using computerized adaptive testing in educational electronic environment is given in the article according to different criteria: the role of tests in the learning process; methods of training; equipment; presence of the problems in educational process; level of its control and learning outcomes. The paper also presents examples of activities to form communicative competency of future maritime professionals. Types of adaptive tests are listed in the paper. The research activities were done by second year cadets of ship engineering department of Maritime College of Kherson State Maritime Academy. The experiment was devoted to the formation of communicative competence with the help of electronic environment of maritime higher education institution. The results of experiment proved positive impact of computerized adaptive testing on communicative competence of future ship engineers. Further investigation of adaptive testing can also be done for learning system of maritime education establishments using simulation technologies of virtual, augmented and mixed realities.
Impact of Annotation Noise on Histopathology Nucleus Segmentation

Abstract Deep learning is often used for automated diagnosis support in biomedical image processing scenarios. Annotated datasets are essential for the supervised training of deep neural networks. The problem of consistent and noise-free annotation remains for experts such as pathologists. The variability within an annotator (intra) and the variability between annotators (inter) are current challenges. In clinical practice or biology, instance segmentation is a common task, but a comprehensive and quantitative study regarding the impact of noisy annotations lacks. In this paper, we present a concept to categorize and simulate various types of annotation noise as well as an evaluation of the impact on deep learning pipelines. Thereby, we use the multi-organ histology image dataset MoNuSeg to discuss the influence of annotator variability. We provide annotation recommendations for clinicians to achieve high-quality automated diagnostic algorithms.
Integrated testing system of information competence components of future translators

The article deals with the diagnosis of the formation of the information competence components of translators through testing. The use of testing to determine the level of formation of the information-thematic component of the information competence of translators is demonstrated. It has been established that one of the ways to form the information-thematic component of information competence in the aspect of studying terminology can be the use of thematic networks. The development of a thematic network is shown on the example of the thematic network “Electrical equipment”. The stages of test control, which are consistent with the logic of the organization of the educational process and the process of forming the information competence of the future translator according to the scheme of the developed thematic network, have been determined. These stages are the current, thematic, modular, final testing. The main types of test tasks are defined, the combination of which allows diagnosing the level of formation of the information-thematic component of students’ information competence. Criteria and principles for the selection of test tasks for each of the testing stages are proposed. The ratio of test tasks of different types and complexity at the determined testing stages has been developed. The results of an experimental study on the diagnosis of the formation of the informationthematic component of the information competence of future translators by applying the developed integrative testing system using the Moodle platform are presented.