Reviewerly/exHarmony huggingface dataset:
The Center of the Circle Fitting Optimization Algorithm Based on the Hough Transform for Crane

The basic principle of photogrammetry is mature and widely used in engineering. For gantry cranes, the base of which is usually a cylinder, the measurement of the center of the cylinder cross section is difficult, but its coordinates have an important impact on the safety evaluation of cranes. Aiming at the problem of measuring the center of a circle, an optimization method of fitting the center of a circle based on photogrammetry and the Hough transform is proposed. In this algorithm, the effect of image point distortion on the measurement accuracy is considered, and the similarity between ideal and actual midperpendicular is compared in the Hough space. The similarity is taken as the weight of the midperpendicular, and the space coordinates of the center of the circle are fitted again. This process needs to iterate repeatedly until convergence, and the fitting accuracy of the equal weighted midperpendicular fitting algorithm and the weighted midperpendicular fitting algorithm is compared. Finally, according to the characteristics of the algorithm, a theoretical verification experiment and an engineering experiment are carried out. The experimental results show that the proposed weighted midperpendicular fitting algorithm has a better effect than the equal weighted midperpendicular fitting algorithm, which obviously improves the fitting accuracy of the center of the circle and has high engineering value. In both experiments, the relative error was less than one percent. Especially in the engineering experiments, the weighted midperpendicular algorithm improved accuracy by an order of magnitude. Therefore, the proposed algorithm significantly improves the fitting accuracy of the center of the circle and effectively solves the difficulty that the center of the circle cannot be directly measured on the construction machinery.
On the dynamic neural network toolbox design for identification, estimation and control

Purpose There are common problems in the identification of uncertain nonlinear systems, nonparametric approximation, state estimation, and automatic control. Dynamic neural network (DNN) approximation can simplify the development of all the aforementioned problems in either continuous or discrete systems. A DNN is represented by a system of differential or recurrent equations defined in the space of vector activation functions with weights and offsets that are functionally associated with the input data. Design/methodology/approach This study describes the version of the toolbox, that can be used to identify the dynamics of the black box and restore the laws underlying the system using known inputs and outputs. Depending on the completeness of the information, the toolbox allows users to change the DNN structure to suit specific tasks. Findings The toolbox consists of three main components: user layer, network manager, and network instance. The user layer provides high-level control and monitoring of system performance. The network manager serves as an intermediary between the user layer and the network instance, and allows the user layer to start and stop learning, providing an interface to indirectly access the internal data of the DNN. Research limitations/implications Control capability is limited to adjusting a small number of numerical parameters and selecting functional parameters from a predefined list. Originality/value The key feature of the toolbox is the possibility of developing an algorithmic semi-automatic selection of activation function parameters based on optimization problem solutions.
A Generative Learning Steganalysis Network against the Problem of Training-Images-Shortage

In recent years, several steganalysis neural networks have been proposed and achieved satisfactory performances. However, these deep learning methods all encounter the problem of Training-Images-Shortage (TIS). In most cases, it is difficult for steganalyses to obtain enough signals about steganography from a game opponent. In order to solve the problem of TIS for steganalysis, we propose a novel steganalysis method based on generative learning and deep residual convolutional neural networks. Comparative experiments show that the proposed architecture can achieve promising performance in response to spatial domain steganalysis despite a lack of training images.
A two rounds dynamic authenticated group key agreement protocol based on LWE

The goal of a group key agreement (GKA) protocol is to enable multiple users to compute and share a same session key over an open channel. Some GKA protocols based on the discrete logarithm problem also consider the authentication, forward security and dynamic. However, these protocols cannot satisfy post-quantum security. The existing lattice-based post-quantum dynamic GKA protocols cannot resist the known session key attacking. In this paper, we propose a two-round lattice-based dynamic authenticated GKA protocol based on the learning with errors (LWE) problem. We use random oracle prove that our protocol is secure under the standard corruption model. Our protocol requires only two rounds of communication and does not limit the number of members involved.
Deep Spatial-Temporal Neural Network for Dense Non-Rigid Structure from Motion

Dense non-rigid structure from motion (NRSfM) has long been a challenge in computer vision because of the vast number of feature points. As neural networks develop rapidly, a novel solution is emerging. However, existing methods ignore the significance of spatial–temporal data and the strong capacity of neural networks for learning. This study proposes a deep spatial–temporal NRSfM framework (DST-NRSfM) and introduces a weighted spatial constraint to further optimize the 3D reconstruction results. Layer normalization layers are applied in dense NRSfM tasks to stop gradient disappearance and hasten neural network convergence. Our DST-NRSfM framework outperforms both classical approaches and recent advancements. It achieves state-of-the-art performance across commonly used synthetic and real benchmark datasets.
A benchmark for evaluating Arabic word embedding models

Abstract Modelling the distributional semantics of such a morphologically rich language as Arabic needs to take into account its introflexive, fusional, and inflectional nature attributes that make up its combinatorial sequences and substitutional paradigms. To evaluate such word distributional models, the benchmarks that have been used thus far in Arabic have mimicked those in English. This paper reports on a benchmark that we designed to reflect linguistic patterns in both Contemporary Arabic and Classical Arabic, the first being a cover term for written and spoken Modern Standard Arabic, while the second for pre-modern Arabic. The analogy items we included in this benchmark are chosen in a transparent manner such that they would capture the major features of nouns and verbs; derivational and inflectional morphology; high-, middle-, and low-frequency patterns and lexical items; and morphosemantic, morphosyntactic, and semantic dimensions of the language. All categories included in this benchmark are carefully selected to ensure proper representation of the language. The benchmark consists of 45 roots of the trilateral, all-consonantal, and semivowel-inclusive types; six morphosemantic patterns (’af‘ala; ifta‘ala; infa‘ala; istaf‘ala; tafa‘‘ala; and tafā‘ala); five derivations (the verbal noun, active participle, and the contrasts in Masculine-Feminine; Feminine-Singular-Plural; Masculine-Singular-Plural); and morphosyntactic transformations (perfect and imperfect verbs conjugated for all pronouns); and lexical semantics (synonyms, antonyms, and hyponyms of nouns, verbs, and adjectives), as well as capital cities and currencies. All categories include an equal proportion of high-, medium-, and low-frequency items. For the purpose of validating the proposed benchmark, we developed a set of embedding models from different textual sources. Then, we tested them intrinsically using the proposed benchmark and extrinsically using two natural language processing tasks: Arabic Named Entity Recognition and Text Classification. The evaluation leads to the conclusion that the proposed benchmark is truly reflective of this morphologically rich language and discriminatory of word embeddings.
On Coevaluation Behavior and Equivalence

Coevaluation, the coinductive interpretation of standard big-step evaluation rules, is a concise form of semantics, with the same number of rules as in evaluation, which intends to simultaneously describe finite and infinite computations. However, it is known that it is only able to express an infinite computations subset, and, to date, it remains unknown exactly what this subset is. More precisely, coevaluation behavior has several unusual features: there are terms for which evaluation is infinite but that do not coevaluate, it is not deterministic in the sense that there are terms which coevaluate to any value v, and there are terms for which evaluation is infinite but that coevaluate to only one value. In this work, we describe the infinite computations subset which is able to be expressed by coevaluation. More importantly, we introduce a coevaluation extension that is well-behaved in the sense that the finite computations coevaluate exactly as in evaluation and the infinite computations coevaluate exactly as in divergence. Consequently, no unusual features are presented; in particular, this extension captures all infinite computations (not only a subset of them). In addition, as a consequence of thiswell-behavior, we present the expected equivalence between (extended) coevaluation and evaluation union divergence.
Optimal Neural Network Model for Short-Term Prediction of Confirmed Cases in the COVID-19 Pandemic

COVID-19 is one of the largest issues that humanity still has to cope with and has an impact on the daily lives of billions of people. Researchers from all around the world have made various attempts to establish accurate mathematical models of COVID-19 spread. In many branches of science, it is difficult to make accurate predictions about short time series with extremely irregular behavior. Artificial neural networks (ANNs) have lately been extensively used for such applications. Although ANNs may mimic the nonlinear behavior of short time series, they frequently struggle to handle all turbulences. Alternative methods must be used as a result. In order to reduce errors and boost forecasting confidence, a novel methodology that combines Time Delay Neural Networks is suggested in this work. Six separate datasets are used for its validation showing the number of confirmed daily COVID-19 infections in 2021 for six world countries. It is demonstrated that the method may greatly improve the individual networks’ forecasting accuracy independent of their topologies, which broadens the applicability of the approach. A series of additional predictive experiments involving state-of-the-art Extreme Learning Machine modeling were performed to quantitatively compare the accuracy of the proposed methodology with that of similar methodologies. It is shown that the forecasting accuracy of the system outperforms ELM modeling and is in the range of other state-of-the art solutions.
Weakly supervised learning based on hypergraph manifold ranking

Significant challenges still remain despite the impressive recent advances in machine learning techniques, particularly in multimedia data understanding. One of the main challenges in real-world scenarios is the nature and relation between training and test datasets. Very often, only small sets of coarse-grained labeled data are available to train models, which are expected to be applied on large datasets and fine-grained tasks. Weakly supervised learning approaches handle such constraints by maximizing useful training information in labeled and unlabeled data. In this research direction, we propose a weakly supervised approach that analyzes the dataset manifold to expand the available labeled set. A hypergraph manifold ranking algorithm is exploited to represent the contextual similarity information encoded in the unlabeled data and identify strong similarity relations, which are taken as a path to label expansion. The expanded labeled set is subsequently exploited for a more comprehensive and accurate training process. The proposed model was evaluated jointly with supervised and semi-supervised classifiers, including Graph Convolutional Networks. The experimental results on image and video datasets demonstrate significant gains and accurate results for different classifiers in diverse scenarios. • This work proposes a label expansion approach through a hypergraph manifold ranking algorithm. • The label expansion process is entirely unsupervised. • A wide experimental evaluation on weakly supervised scenarios achieved significant gains. • Different classifiers were evaluated, including recent Graph Convolutional Networks. • The method is efficient in terms of runtime and space complexity.
Discrimination of Rock Units in Karst Terrains Using Sentinel-2A Imagery

We explored the potential incorporation of Sentinel-2A imagery for rock unit determination in the Croatian karst region dominated by carbonate rocks. The various lithological units are potential sources of both stone aggregates and dimension stone, and their spatial distribution is of high importance for mineral resource management. The presented approach included the preprocessing and processing of existing analog data (geological maps), Sentinel-2A satellite images and the United States Geological Survey spectral indices, all in combination with ground truth data. Geological mapping and digital processing of legacy maps using the K-means and random forest algorithm reduced the spatial error of the geometry of geological boundaries from 100 m and 300 m to below 100 m. The possibility of discriminating individual lithological units based on spectral analysis and discriminant function analysis was also examined, providing a tool for evaluating the geological potential for mineral resources. Despite the challenges posed by the lithological homogeneity of karst terrain, the results of this study show that the use of spectral signature data derived from Sentinel-2A satellite images can be successfully implemented in such terrains for the enhancement of existing geological maps and mineral resources exploration.
Instrument Pointer Recognition Scheme Based on Improved CSL Algorithm

The traditional pointer instrument recognition scheme is implemented in three steps, which is cumbersome and inefficient. So it is difficult to apply to the industrial production of real-time monitoring. Based on the improvement of the CSL coding method and the setting of the pre-cache mechanism, an intelligent reading recognition technology of the YOLOv5 pointer instrument is proposed in this paper, which realizes the rapid positioning and reading recognition of the pointer instrument. The problem of angle interaction in rotating target detection is eliminated, the complexity of image preprocessing is avoided, and the problems of poor adaptability of Hough detection are solved in this strategy. The experimental results show that compared with the traditional algorithm, the algorithm in this paper can effectively identify the angle of the pointer instrument, has high detection efficiency and strong adaptability, and has broad application prospects.
The Core of Smart Cities: Knowledge Representation and Descriptive Framework Construction in Knowledge-Based Visual Question Answering

Visual question answering (VQA), which is an important presentation form of AI-complete task and visual Turing tests, coupled with its potential application value, attracted widespread attention from both researchers in computer vision and natural language processing. However, there are no relevant research regarding the expression and participation methods of knowledge in VQA. Considering the importance of knowledge for answering questions correctly, this paper analyzes and researches the stratification, expression and participation process of knowledge in VQA and proposes a knowledge description framework (KDF) to guide the research of knowledge-based VQA (Kb-VQA). The KDF consists of a basic theory, implementation methods and specific applications. This paper focuses on describing mathematical models at basic theoretical levels, as well as the knowledge hierarchy theories and key implementation behaviors established on this basis. In our experiment, using the statistics of VQA’s accuracy in the relevant literature, we propose a good corroboration of the research results from knowledge stratification, participation methods and expression forms in this paper.
How to Promote Low-Carbon Cities with Blockchain Technology? A Blockchain-Based Low-Carbon Development Model for Chinese Cities

Cities are the important spaces, responsible entities and action units for achieving low-carbon and high-quality economy. Blockchain technology provides a new solution for low-carbon development in cities. Based on low-carbon information sharing, low-carbon incentives, low-carbon regulations with blockchain technology, this paper endeavors to propose a low-carbon development model for Chinese cities. The results indicate that: (1) blockchain-based governments, enterprises, city dwellers, and third-party service agencies are important stakeholders in promoting urban low-carbon development; (2) the effectiveness of the blockchain platform needs the coordination between the production and consumption systems, the equal emphasis on systemicity and long-term effectiveness, and the deep integration of government regulations and market mechanism; (3) blockchain-based information sharing instruments, incentive instruments, and regulation instruments should be applied to low-carbon development in cities; (4) blockchain technology promotes low-carbon development for cities in both direct ways and indirect ways. This study can provide a reference for the urban sustainable development in China and other countries.
Security and privacy preservation using constructive hierarchical data-sharing approach in cloud environment

With the vast development in the cloud computing environment, many cloud users intend to outsource the data to a remote location and share the data with multiple users. The hierarchical model is an extensively used data organization process. It is highly complex to guarantee integrity, privacy, and confidentiality of the data and the structure of the model when the sensitive data is held in this manner. This research attempts to give a solution to hierarchically ensure the security and privacy of the information while data sharing occurs. A constructive hierarchical data sharing (CHDS) method is proposed to adopt symmetric encryption over the rooted hierarchical graph structure. The hierarchical graph model deals with incoming data features to establish the model's privacy and authenticity. Based on this model, the proposed CHDS is known to be transparent, secure, and confident in the public environment. Here, performance metrics like computational complexity, key generation, prediction accuracy, and execution time are evaluated. The outcomes provide the security of the multi-party environment without forfeiting sensible resources when the hierarchical model grows to a more significant number of siblings, edges, and vertices. The proposed CHDS gives a better trade-off when compared with various existing approaches.
Crowd Density Estimation in Spatial and Temporal Distortion Environment Using Parallel Multi-Size Receptive Fields and Stack Ensemble Meta-Learning

The estimation of crowd density is crucial for applications such as autonomous driving, visual surveillance, crowd control, public space planning, and warning visually distracted drivers prior to an accident. Having strong translational, reflective, and scale symmetry, models for estimating the density of a crowd yield an encouraging result. However, dynamic scenes with perspective distortions and rapidly changing spatial and temporal domains still present obstacles. The main reasons for this are the dynamic nature of a scene and the difficulty of representing and incorporating the feature space of objects of varying sizes into a prediction model. To overcome the aforementioned issues, this paper proposes a parallel multi-size receptive field units framework that leverages the majority of the CNN layer’s features, allowing for the representation and participation in the model prediction of the features of objects of all sizes. The proposed method utilizes features generated from lower to higher layers. As a result, different object scales can be handled at different framework depths, and various environmental densities can be estimated. However, the inclusion of the vast majority of layer features in the prediction model has a number of negative effects on the prediction’s outcome. Asymmetric non-local attention and the channel weighting module of a feature map are proposed to handle noise and background details and re-weight each channel to make it more sensitive to important features while ignoring irrelevant ones, respectively. While the output predictions of some layers have high bias and low variance, those of other layers have low bias and high variance. Using stack ensemble meta-learning, we combine individual predictions made with lower-layer features and higher-layer features to improve prediction while balancing the tradeoff between bias and variance. The UCF CC 50 dataset and the ShanghaiTech dataset have both been subjected to extensive testing. The results of the experiments indicate that the proposed method is effective for dense distributions and objects of various sizes.
Prediction of COVID-19 Cases Using Constructed Features by Grammatical Evolution

A widely used method that constructs features with the incorporation of so-called grammatical evolution is proposed here to predict the COVID-19 cases as well as the mortality rate. The method creates new artificial features from the original ones using a genetic algorithm and is guided by BNF grammar. After the artificial features are generated, the original data set is modified based on these features, an artificial neural network is applied to the modified data, and the results are reported. From the comparative experiments done, it is clear that feature construction has an advantage over other machine-learning methods for predicting pandemic elements.
Dynamically adaptive adjustment loss function biased towards few‐class learning

Convolution neural networks have been widely used in the field of computer vision, which effectively solve practical problems. However, the loss function with fixed parameters will affect the training efficiency and even lead to poor prediction accuracy. In particular, when there is a class imbalance in the data, the final result tends to favor the large-class. In detection and recognition problems, the large-class will dominate due to its quantitative advantage, and the features of few-class can be not fully learned. In order to learn few-class, batch nuclear-norm maximization is introduced to the deep neural networks, and the mechanism of the adaptive composite loss function is established to increase the diversity of the network and thus improve the accuracy of prediction. The proposed loss function is added to the crowd counting, and verified on ShanghaiTech and UCF_CC_50 datasets. Experimental results show that the proposed loss function improves the prediction accuracy and convergence speed of deep neural networks.