Reviewerly/exHarmony huggingface dataset:
Similarity Calculation via Passage-Level Event Connection Graph

Recently, many information processing applications appear on the web on the demand of user requirement. Since text is one of the most popular data formats across the web, how to measure text similarity becomes the key challenge to many web applications. Web text is often used to record events, especially for news. One text often mentions multiple events, while only the core event decides its main topic. This core event should take the important position when measuring text similarity. For this reason, this paper constructs a passage-level event connection graph to model the relations among events mentioned in one text. This graph is composed of many subgraphs formed by triggers and arguments extracted sentence by sentence. The subgraphs are connected via the overlapping arguments. In term of centrality measurement, the core event can be revealed from the graph and utilized to measure text similarity. Moreover, two improvements based on vector tunning are provided to better model the relations among events. One is to find the triggers which are semantically similar. By linking them in the event connection graph, the graph can cover the relations among events more comprehensively. The other is to apply graph embedding to integrate the global information carried by the entire event connection graph into the core event to let text similarity be partially guided by the full-text content. As shown by experimental results, after measuring text similarity from a passage-level event representation perspective, our calculation acquires superior results than unsupervised methods and even comparable results with some supervised neuron-based methods. In addition, our calculation is unsupervised and can be applied in many domains free from the preparation of training data.
Modified Coral Reef Optimization Methods for Job Shop Scheduling Problems

The job shop scheduling problem (JSSP) is a fundamental operational research topic with numerous applications in the real world. Since the JSSP is an NP-hard (nondeterministic polynomial time) problem, approximation approaches are frequently used to rectify it. This study proposes a novel biologically-inspired metaheuristic method named Coral Reef Optimization in conjunction with two local search techniques, Simulated Annealing (SA) and Variable Neighborhood Search (VNS), with significant performance and finding-solutions speed enhancement. The two-hybrid algorithms’ performance is evaluated by solving JSSP of various sizes. The findings demonstrate that local search strategies significantly enhance the search efficiency of the two hybrid algorithms compared to the original algorithm. Furthermore, the comparison results with two other metaheuristic algorithms that also use the local search feature and five state-of-the-art algorithms found in the literature reveal the superior search capability of the two proposed hybrid algorithms.
Long Text Truncation Algorithm Based on Label Embedding in Text Classification

The long text classification task has become a hot research topic in the field of text classification due to its long length and redundant information. At present, the common processing methods for long text data, such as the truncation method and pooling method, are prone to the problem of too many sentences or loss of contextual semantic information. To deal with these issues, we present LTTA-LE (Long Text Truncation Algorithm Based on Label Embedding in Text Classification), which consists of three key steps. Firstly, we build a pretraining prefix template and a label word mapping prefix template to obtain the label word embedding, and we realize the joint training of long text and label words. Secondly, we calculate the cosine similarity between the label word embedding and the long text embedding, and we filter the redundant information of the long text to reduce the text length. Finally, a three-stage model training architecture is introduced to effectively improve the classification performance and generalization ability of the model. We conduct comparative experiments on three public long text datasets, and the results show that LTTA-LE has an average F1 improvement of 1.0518% over other algorithms, which proves that our method can achieve satisfactory performance.
No-Reference Quality Assessment of Transmitted Stereoscopic Videos Based on Human Visual System

Provisioning the stereoscopic 3D (S3D) video transmission services of admissible quality in a wireless environment is an immense challenge for video service providers. Unlike for 2D videos, a widely accepted No-reference objective model for assessing transmitted 3D videos that explores the Human Visual System (HVS) appropriately has not been developed yet. Distortions perceived in 2D and 3D videos are significantly different due to the sophisticated manner in which the HVS handles the dissimilarities between the two different views. In real-time video transmission, viewers only have the distorted or receiver end content of the original video acquired through the communication medium. In this paper, we propose a No-reference quality assessment method that can estimate the quality of a stereoscopic 3D video based on HVS. By evaluating perceptual aspects and correlations of visual binocular impacts in a stereoscopic movie, the approach creates a way for the objective quality measure to assess impairments similarly to a human observer who would experience the similar material. Firstly, the disparity is measured and quantified by the region-based similarity matching algorithm, and then, the magnitude of the edge difference is calculated to delimit the visually perceptible areas of an image. Finally, an objective metric is approximated by extracting these significant perceptual image features. Experimental analysis with standard S3D video datasets demonstrates the lower computational complexity for the video decoder and comparison with the state-of-the-art algorithms shows the efficiency of the proposed approach for 3D video transmission at different quantization (QP 26 and QP 32) and loss rate (1% and 3% packet loss) parameters along with the perceptual distortion features.
Image correlation technique for strain measurement of polycrystalline microstructures

Abstract An image processing technique is proposed to measure the deformation of polycrystalline materials based on correlating the grains in reference and deformed SEM images. The advantage of this technique compared to the conventional subset‐based Digital Image Correlation (DIC) is that it can be applied when speckle patterning is not efficient or when studying boundary‐related mechanics is the objective. The technique is based on correlating grains by defining their boundaries rather than just subsets of image pixels. It reveals the anisotropy inherent in the polycrystals since it allows the analysis to specify each grain separately without averaging the results. The technique is applied by detecting the approximate grain boundaries edges and then refining their location with high accuracy. The correlation is performed between points calculated from each grain in the reference and deformed images as a Point Set Registration (PSR) problem. Finally, the displacements and strains are calculated from the resulting transformation matrix. A benchmark problem was developed to discuss the error over a strain range of 0.02 to 0.2 and showed that the resulting strains are reasonably accurate. Also, an in situ experiment was conducted to demonstrate the implementation of the technique using a specimen with fine‐grained Zirconia polycrystals. The technique successfully revealed the crack tip plastic zone, and strain mismatch between grains.
Ensembles of Biologically Inspired Optimization Algorithms for Training Multilayer Perceptron Neural Networks

Artificial neural networks have proven to be effective in a wide range of fields, providing solutions to various problems. Training artificial neural networks using evolutionary algorithms is known as neuroevolution. The idea of finding not only the optimal weights and biases of a neural network but also its architecture has drawn the attention of many researchers. In this paper, we use different biologically inspired optimization algorithms to train multilayer perceptron neural networks for generating regression models. Specifically, our contribution involves analyzing and finding a strategy for combining several algorithms into a hybrid ensemble optimizer, which we apply for the optimization of a fully connected neural network. The goal is to obtain good regression models for studying and making predictions for the process of free radical polymerization of methyl methacrylate (MMA). In the first step, we use a search procedure to find the best parameter values for seven biologically inspired optimization algorithms. In the second step, we use a subset of the best-performing algorithms and improve the search capability by combining the chosen algorithms into an ensemble of optimizers. We propose three ensemble strategies that do not involve changes in the logic of optimization algorithms: hybrid cascade, hybrid single elite solution, and hybrid multiple elite solutions. The proposed strategies inherit the advantages of each individual optimizer and have faster convergence at a computational effort very similar to an individual optimizer. Our experimental results show that the hybrid multiple elite strategy ultimately produces neural networks which constitute the most dependable regression models for the aforementioned process.
Utility of Features in a Natural-Language-Processing-Based Clinical De-Identification Model Using Radiology Reports for Advanced NSCLC Patients

The de-identification of clinical reports is essential to protect the confidentiality of patients. The natural-language-processing-based named entity recognition (NER) model is a widely used technique of automatic clinical de-identification. The performance of such a machine learning model relies largely on the proper selection of features. The objective of this study was to investigate the utility of various features in a conditional-random-field (CRF)-based NER model. Natural language processing (NLP) toolkits were used to annotate the protected health information (PHI) from a total of 10,239 radiology reports that were divided into seven types. Multiple features were extracted by the toolkit and the NER models were built using these features and their combinations. A total of 10 features were extracted and the performance of the models was evaluated based on their precision, recall, and F1-score. The best-performing features were n-gram, prefix-suffix, word embedding, and word shape. These features outperformed others across all types of reports. The dataset we used was large in volume and divided into multiple types of reports. Such a diverse dataset made sure that the results were not subject to a small number of structured texts from where a machine learning model can easily learn the features. The manual de-identification of large-scale clinical reports is impractical. This study helps to identify the best-performing features for building an NER model for automatic de-identification from a wide array of features mentioned in the literature.
Multithematic Geoscience Data Integration for Identification of Mineral Potential Areas in Parts of Central Indian Precambrian Craton

The paper highlights the results of multi-thematic geoscience data integration involving geological, geophysical, geochemical and remote sensing data sets, for an area of 4000 Sq. Km in parts of Precambrian Bastar craton, Chhattisgarh. The present team has been able to identify two discrete mineral potential areas for base metal sulphides, Sn, W and REE in sheared and altered felsic volcanics in Kotra-Bhansula area and for gold in propylitically altered meta basalts in Bhursatola-Khampura area. The dataset utilized during collation and integration includes (i) high resolution aeromagnetic data (ii) 1:50000 geological data covering lithostructural and mineralisation attributes (iii) 1:50 k geochemical data for 61 elements (iv) multispectral ASTER satellite data and its derivative alteration maps. Thematic data and derivative maps were studied and interpreted for evaluation of concerned parameters for estimating their role in mineralisation. The data integration has been implemented in three steps beginning with GIS based spatial modelling using Fuzzy logic method resulting into production of mineral favourability map, followed by detailed field evaluation and characterization of the identified mineral favourability areas. Eventual identification of potential areas was based on synthesis of modelling output, field outcome and laboratory results of analyses carried out on critical samples collected from mineral favourability areas. The identified two mineral potential areas are recommended for further detailed exploration for gold, base metal, Sn, W and REE through surface and sub-surface probing.
Semantically Annotated Cooking Procedures for an Intelligent Kitchen Environment

Food preparation is one of the essential tasks in daily life and involves a large number of physical interactions between hands, utensils, ingredients, etc. The fundamental unit in the food preparation activity is the concept of a recipe. The recipe describes the cooking process—the way to make a dish in a sequential order of cooking steps. Frequently, following these steps can be an extremely complicated process, which requires coordination, monitoring and execution of multiple tasks simultaneously. This work introduces a cooking assistance system powered by Computer Vision techniques that provide the user with guidance in the accomplishment of a cooking activity in terms of a recipe and its correct execution. The system can provide the user with guidance for carrying out a recipe through the appropriate messages, which appear in a panel specifically designed for the user. Throughout the process, the system can validate the correctness of each step by (a) detection and motion estimation of the ingredients and utensils in the scene and (b) spatial arrangement of them in terms of where each one is located to another. The system was first evaluated on individual algorithmic steps and on the end-to-end execution of two recipes with promising results.
A Secure Personal Health Record Sharing System with Key Aggregate Dynamic Searchable Encryption

Recently, as interest in individualized health has increased, the Personal Health Record (PHR) has attracted a lot of attention for prognosis predictions and accurate diagnoses. Cloud servers have been used to manage the PHR system, but privacy concerns are evident since cloud servers process the entire PHR, which contains the sensitive information of patients. In addition, cloud servers centrally manage the PHR system so patients lose direct control over their own PHR and cloud servers can be an attractive target for malicious users. Therefore, ensuring the integrity and privacy of the PHR and allocating authorization to users are important issues. In this paper, we propose a secure PHR sharing system using a blockchain, InterPlanetary File System (IPFS), and smart contract to ensure PHR integrity and secure verification. To guarantee the patient’s authority over the management of his/her own PHR, as well as provide convenient access, we suggest a key aggregate dynamic searchable encryption. We prove the security of the proposed scheme through informal and formal analyses including an Automated Verification of Internet Security Protocols and Applications (AVISPA) simulation, Burrows–Abadi–Needham (BAN) logic, and security-model-based games. Furthermore, we estimate the computational costs of the proposed scheme using a Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL) and compare the results with those of previous works.
PCformer: A parallel convolutional transformer network for 360° depth estimation

Abstract 360° depth estimation has been extensively studied because 360° images provide a full field of view of the surrounding environment as well as a detailed description of the entire scene. However, most well‐studied convolutional neural networks (CNNs) for 360° depth estimation can extract local features well, but fail to capture rich global features from the panorama due to a fixed receptive field in CNNs. PCformer, a parallel convolutional transformer network that combines the benefits of CNNs and transformers, is proposed for 360° depth estimation. The transformer has the nature to model long‐range dependency and extract global features. With PCformer, both global dependency and local spatial features can be efficiently captured. To fully incorporate global and local features, a dual attention fusion module is designed. Besides, a distortion‐weighted loss function is designed to reduce the distortion in panoramas. Extensive experiments demonstrate that the proposed method achieves competitive results against the state‐of‐the‐art methods on three benchmark datasets. Additional experiments also demonstrate that the proposed model has benefits in terms of model complexity and generalisation capability.
Tensor-Based Learning for Detecting Abnormalities on Digital Mammograms

In this study, we propose a tensor-based learning model to efficiently detect abnormalities on digital mammograms. Due to the fact that the availability of medical data is limited and often restricted by GDPR (general data protection regulation) compliance, the need for more sophisticated and less data-hungry approaches is urgent. Accordingly, our proposed artificial intelligence framework utilizes the canonical polyadic decomposition to decrease the trainable parameters of the wrapped Rank-R FNN model, leading to efficient learning using small amounts of data. Our model was evaluated on the open source digital mammographic database INBreast and compared with state-of-the-art models in this domain. The experimental results show that the proposed solution performs well in comparison with the other deep learning models, such as AlexNet and SqueezeNet, achieving 90% ± 4% accuracy and an F1 score of 84% ± 5%. Additionally, our framework tends to attain more robust performance with small numbers of data and is computationally lighter for inference purposes, due to the small number of trainable parameters.
A local manipulation path replanning algorithm on deformable linear objects for collisions resulted from model deviation

Robotic manipulation on deformable linear objects asks for action path planning firstly. However, the simplified cable model used for promoting planning efficiency tends to deviate from the real shape of manipulated deformable linear objects, which will lead to collisions and the original planned manipulation path failing, which brings a block between path planning and its robotic implementation. Aimed at the planning failure problem, this paper raises a novel replanning algorithm. Firstly, the deviation and resulted collision situations are classified and the severity of deviation is assessed with a partition policy to determine the moment for performing path replanning. Then the model of the cable in collision is formulated, and a refined artificial potential field is constructed to generate a constrained collision-free path for the deformable linear object trapped in deviation and collisions. Moreover, a time-decaying policy is implemented to fuse the original planned path and the replanned path to avoid further collisions. Finally, the effects of the algorithm are verified by applying it to multiple experiment scenes constructed with the physics engine. It can be concluded that with the replanning scheme, the effector keeps the manipulated cables from severe deviation and collisions adaptively and the safety of the off-line planned path is improved.
Domain Adaptation Problem in Sketch Based Image Retrieval

In this article, we present two algorithms that discover the discriminative structures of sketches, given pairs of sketches and photos in sketch-based image retrieval (SBIR) scenarios. Unlike the existing approaches, we aim at the few-shot and domain adaptation (DA) problems, and set up a module with canonical correlation analysis (CCA) technique in our algorithms to improve retrieval performance. For single source domain settings, our first algorithm can effectively transfer a classifier trained on a known dataset to a new one. For multisource settings, our second algorithm sophisticatedly combines multisource domain data to yield a classifier on the target domain. To the best of our knowledge, these two works are the first research in SBIR field. Experiments on the Sketchy and TU-Berlin sketch benchmark datasets demonstrate the effectiveness of our algorithms and compelling performance. Compared with the state-of-the-art methods, our algorithms do not use text based semantic information, but achieve competitive results. Furthermore, experiments also turn out that feature representation by available trained deep networks has distinct advantages and combination with traditional machine learning methods brings substantial improvements against the state-of-the-art methods on image retrievals. The complete source code of the proposed algorithms will be released on gitHub: [ GitHub- ADAM0912/Domain-Adaptation-Problem-in-Sketch-Based-Image-Retrieval: The code repository for the articleDomain Adaptation Problem in Sketch Based Image Retrieval ] .
Teachers’ Experiences with and Perceptions of Virtual Manipulatives Following the COVID-19 Pandemic

While the use of virtual manipulatives (VM) is rising in classrooms, there is still limited research. regarding teacher experiences with and perceptions of virtual manipulatives. Most of the research regarding teacher perceptions of VM has focused only on short-term uses following professional development sessions and none has highlighted the experiences of teachers using them during emergency remote teaching during COVID-19. The purpose of this study was to explore teacher perceptions and. experiences with virtual manipulatives following emergency remote teaching during COVID-19. To achieve this, the researchers conducted an online survey to gather data on educator's (n = 103) experiences, perceptions, and usage of virtual manipulatives. The qualitative and quantitative data show that educators feel that VM are a valid and feasible support of mathematics instruction when physical manipulatives are not available. Results regarding usage of virtual manipulatives including frequency of use, standards taught, and types used are presented and discussed.The online version contains supplementary material available at 10.1007/s11528-022-00796-9.