Reviewerly/exHarmony huggingface dataset:
Conditions and evaluation of the effectiveness of spiritual and moral development of students of educational organizations of the MIA of Russia

The relevance of the research is based on the need to increase the prestige of humanitarian knowledge, which is important for ideological, personal growth and the formation of a comprehensively educated person. The article demonstrates the value of the interpersonal connections in solving the indicated problem. From the point of spiritual and moral growth the interpersonal relationships allow to exchange values and mindsets between the future professionals. In the course of communication teachers and students exchange their values. Mere academic knowledge is not enough: in order to learn, to solve arising conflicts students need choice criterions, and precisely here, in search for reasons, lies the spiritual growth. The exploration of the values, spiritual and moral context of the humanitarian studies by improving teaching methods, involving interactive technologies, involving real life situations of moral conflict, choice is of great importance. The article presents the results of a study that highlights the conditions for the effectiveness of the spiritual and moral growth of students in the process of mastering the humanitarian subjects. The analysis helped author to identify four criteria for evaluating the effectiveness of the spiritual and moral development of students in the process of mastering the humanitarian subjects. These include personal, cognitive, motivational-value, activity approach criteria. The study is based on the comparative analysis and mathematical processing of data using two methods: a questionnaire for identifying self-esteem «Spiritual and moral development of personality» and a projective method «Unfinished sentences» applied at the final stage of the research.
Construction of bilingual knowledge graph based on meteorological simulation

Abstract Geographical simulation is the core function of virtual geographic environments (VGEs). Providing knowledge support has become a key issue in current research. The literature involves massive knowledge, especially for meteorological simulation, an important component of the geographic environment. However, this knowledge is noisy, unstructured, and difficult to share and reuse. Based on weather research and forecasting models, this study constructs a cross‐language knowledge graph to manage difficult‐to‐handle knowledge and to make complex knowledge structured and more complete. First, we propose a cross‐language knowledge graph construction framework. Second, the bidirectional long short‐term memory‐conditional random fields and translation‐based multilingual knowledge graph embedding model are used to extract knowledge and align cross‐lingual entities. Finally, the bilingual knowledge graph is visually stored with a graph database. The framework and method can provide efficient queries and analyses for the meteorological field, while helping to provide a knowledge base for knowledge‐driven VGEs.
Trusted Data Storage Architecture for National Infrastructure

National infrastructure is a material engineering facility that provides public services for social production and residents' lives, and a large-scale complex device or system is used to ensure normal social and economic activities. Due to the problems of difficult data collection, long project period, complex data, poor security, difficult traceability and data intercommunication, the archives management of most national infrastructure is still in the pre-information era. To solve these problems, this paper proposes a trusted data storage architecture for national infrastructure based on blockchain. This consists of real-time collection of national infrastructure construction data through sensors and other Internet of Things devices, conversion of heterogeneous data source data into a unified format according to specific business flows, and timely storage of data in the blockchain to ensure data security and persistence. Knowledge extraction of data stored in the chain and the data of multiple regions or fields are jointly modeled through federal learning. The parameters and results are stored in the chain, and the information of each node is shared to solve the problem of data intercommunication.
HMD-Net: A Vehicle Hazmat Marker Detection Benchmark

Vehicles carrying hazardous material (hazmat) are severe threats to the safety of highway transportation, and a model that can automatically recognize hazmat markers installed or attached on vehicles is essential for intelligent management systems. However, there is still no public dataset for benchmarking the task of hazmat marker detection. To this end, this paper releases a large-scale vehicle hazmat marker dataset named VisInt-VHM, which includes 10,000 images with a total of 20,023 hazmat markers captured under different environmental conditions from a real-world highway. Meanwhile, we provide an compact hazmat marker detection network named HMD-Net, which utilizes a revised lightweight backbone and is further compressed by channel pruning. As a consequence, the trained-model can be efficiently deployed on a resource-restricted edge device. Experimental results demonstrate that compared with some established methods such as YOLOv3, YOLOv4, their lightweight versions and popular lightweight models, HMD-Net can achieve a better trade-off between the detection accuracy and the inference speed.
A Computational Performance Study of Unsupervised Data Clustering Algorithms on GPU

Classification task is a very popular preprocessing step in different research fields. Its main role is to separate the different components of an object or dataset into homogeneous regions or groups based on the similarity of properties and features. Among the most popular clustering algorithms we cite fuzzy C-means (FCM) and K-means. In this these iterative techniques, a distance metric between each actual dataset point and the estimated centroids is calculated at each iteration. In this paper, we implement four algorithms; sequential FCM, sequential K-means and their 2 parallel versions. A comparative computational performance study between the sequential and the parallel version is presented. This study, will focus on the execution time of the parallel and sequential implementations in addition to the speed up of the parallel version with respect to the sequential one. The experimental tests were conducted on a randomly generated number dataset. The parallel versions were implemented on a SIMD architecture of Nvidia GPU. The influence of the variation of the data size and the number of clusters on the execution time was analyzed and interpreted.
A new composite lognormal-Pareto type II regression model to analyze household budget data via particle swarm optimization

When data exhibits heavy-tailed behavior, traditional regression approaches might be inadequate or inappropriate to model the data. In such data analyses, composite models, which are built by piecing together two or more weighted distributions at specified threshold(s), are alternative models. When data contain covariate information, composite regression models can be used. In the existing literature, there is not much work done on this topic. The only study is Gan and Valdez (N Am Actuar J 22(4):554–573, 2018) paper. In this study, a novel Lognormal-Pareto type II composite regression model is proposed. Particle swarm optimization (PSO) is performed to obtain model parameters of the proposed model. The proposed model is applied to model monthly consumption expenditure and affecting factors. The data is obtained from the National Household Budget Survey, which is conducted annually by the Turkish Statistical Institute (TurkStat). Since the sampling design of the Household Budget Survey is stratified two-stage cluster sampling, the parameters are estimated under weighted data by updating the proposed model and PSO. Additionally, the proposed regression model performance is compared with Lognormal, Lomax, Gamma, and Gamma–Pareto type II regression models. The results demonstrate that the proposed model provides an improved fit to data.
Action-dependent bidirectional contrastive predictive coding for neural belief representations

The key to solving the complex, partially observable Markov decision process (POMDP) with high-dimensional observations lies in explicit belief representations. However, the existing methods generally adopted the black-box model for shaping beliefs, which is inefficient and lacks interpretability. Due to this reason, the action-dependent bidirectional contrastive predictive coding (BCPC|Action) is proposed in this paper, in which the observation features are extracted efficiently through self-supervised contrastive learning. Owing to the bottleneck belief constraints in the bidirectional model, the upper bound of prediction errors is effectively reduced. Besides, the forward prediction is optimized by the guidance of an easier trainable backward prediction; thus, the bidirectional match regularization (BMR) could be derived for stabilizing the training process. More importantly, the interpretability of the learned belief representation is thoroughly explored based on the gradient truncation. Simulation results verify the effectiveness of the presented method; apart from achieving highly accurate belief tracking, the state uncertainties could be characterized reasonably, which provides a guarantee for solving the POMDP optimal policy for downstream tasks.
A Mining Strategy for Minimizing Waiting Time in Blockchains for Time-Sensitive Applications

Blockchain, as proposed in Bitcoin, focuses on securing financial transactions. However, in recent years, the use of blockchain has expanded to a wide range of networks and application domains. This includes time-sensitive applications which need transactions to be processed fast enough to meet delay requirements. Reducing the transaction waiting time in the mining process is key to the successful adoption of blockchain in such applications. In this paper, we propose a mining strategy that is aimed at minimizing the average waiting time per transaction by ensuring a certain minimum required block size, based on the average transaction arrival rate, mining service rate, and maximum block size. We derive an expression for the average transaction waiting time of the proposed mining strategy and determine the optimal mining rule. Numerical results show that the average waiting time per transaction can be reduced by up to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>15</mn> <mi>%</mi> </math> using the proposed mining strategy compared to the traditional strategy in which miners immediately start the next mining round using the transactions already waiting in the pool.
Automatic screening of mild cognitive impairment and Alzheimer’s disease by means of posterior-thresholding hesitation representation

Dementia is a chronic or progressive clinical syndrome, characterized by the deterioration of problem-solving skills, memory and language. In Mild Cognitive Impairment (MCI), which is often considered to be the prodromal stage of dementia, there is also a subtle deterioration of these cognitive functions; however, it does not affect the patients’ ability to carry out simple everyday activities. The timely identification of MCI could provide more effective therapeutic interventions to delay progression, and to postpone the possible conversion to dementia. Since language changes in MCI are present even before the manifestation of other distinctive cognitive symptoms, a non-invasive way of early automatic screening could be the use of speech analysis. Earlier, our research team developed a set of temporal speech parameters that mainly focus on the amount of silence and hesitation, and demonstrated its applicability for MCI detection. However, for the automatic extraction of these attributes, the execution of a full Automatic Speech Recognition (ASR) process is necessary. In this study we propose a simpler feature extraction approach, which still quantifies the amount of silence and hesitation in the speech of the subject, but does not require the application of a full ASR system. We experimentally demonstrate that this approach, operating directly on the frame-level output of a HMM/DNN hybrid acoustic model, is capable of extracting attributes as useful as the ASR-based temporal parameter extraction workflow was able to. That is, on our corpus consisting of 25 healthy controls, 25 MCI and 25 mild AD subjects, we achieve a (three-class) classification accuracy of 70.7%, an F-measure score of 89.6 and a mean AUC score of 0.804. We also show that this approach can be applied on simpler, context-independent acoustic states with only a slight degradation of MCI and mild Alzheimer’s detection performance. Lastly, we investigate the usefulness of the three speaker tasks which are present in our recording protocol.
Design of optimized interval type-2 fuzzy logic controller based on the continuity, monotonicity, and smoothness properties for a cart-pole inverted pendulum system

In this study, an interval type-2 fuzzy logic controller design method is proposed and validated on the real-time under-actuated nonlinear inverted pendulum system for swing-up and stabilization control problems. The swing-up algorithm is designed to give a faster response and the stabilization controller is designed based on continuity, monotonicity, and smoothness theorems for worst and best cases using the Mamdani fuzzy inference system in order to show the effectiveness of the proposed method. Outstanding type reduction methods are analyzed for the stabilization controller based on the design approach to determine the best type reduction algorithm for the effective and robust control performance. Real-time experiments are conducted to investigate the capability of the proposed controller in terms of adaptation performance and robustness ability. The controller also is able to handle structured and unstructured uncertainties such as measurement noise, external payload, undesirable internal/external disturbances, and parameter uncertainties. The results show that the proposed method clearly improves the control performance of the system in six experimental tasks conducted.
Soft one-class extreme learning machine for turboshaft engine fault detection

One-class extreme learning machine (OC-ELM) is a very common one-class classification algorithm. In the process of constructing the OC-ELM, a part of training samples will be removed by the algorithm, which leads to inconsistency between its constraint condition and decision function, and is also known as the problem of hard margin flaw. In order to solve this problem, a soft one-class extreme learning machine (SOC-ELM) is proposed by assigning an appropriate margin to each training sample. Experimental results on benchmark data sets show that SOC-ELM has a strong classification performance. When the SOC-ELM is used for fault detection of a turboshaft engine, it can achieve good detection effectiveness and has strong robustness.
A compositional approach to in-situ evaluation of polymetallic deposits. A case study at Sungun Cu−Mo deposit, NW Iran

Mineral reserve evaluation is based on compositional data, reporting in units such as percentages, mg/kg or ppm. The nature of such data implies that the values cannot vary independently and geostatistical modeling of their raw form could be misleading. In this study we propose a compositional approach —based on orthonormal isometric logratio (olr) coordinates— to simulate the in-situ value (in US Dollars) at Sungun Cu − Mo deposit in NW Iran. A dataset of 1422 surface samples, analyzed for Ag, As, Au, Cu, Mo, Sb, Pb and Zn is used. This surface covers the current mine pit and is sampled prior to extraction, as a detailed exploration phase. We assumed the sampling surface as an extraction mine bench and separated 34% of the original samples for validation. Comparing with a noncompositional approach, it is prominent that in all extraction categories and for all Cu and Mo cut-off grades, the compositional values are closer to the real ones. Comparison of in-situ value maps revealed that the compositional approach is better able to delineate intense local variability; especially in high grade parts of the deposit. This superiority is also evident in Euclidean and Aitchison distance-distance diagrams of the real and simulated values. It is concluded that, in contrast with known geostatistical methods, the proposed olr-based method delivers more accurate models and, consequently, an improved and more realistic vision of the deposit value is achieved.
Analyzing generalized planning under nondeterminism

In automated planning, there has been a recent interest in solving a class of problems, where a single solution applies for multiple, possibly infinitely many, instances. This necessitates a generalized notion of plans, such as plans with loops. However, the correctness of such plans is non-trivial to define, making it difficult to provide a clear specification of what we should be looking for. In an influential paper, Levesque proposed a formal specification for analyzing the correctness of such plans. He motivated a logical characterization within the situation calculus that included binary sensing actions. This characterization argued that from each state considered possible initially, the plan should terminate while satisfying the goal. Increasingly, classical plan structures are being applied to stochastic environments such as robotics applications. This raises the question as to what the specification for correctness should look like, since Levesque's account makes the assumption that actions are deterministic. In this work, we aim to generalize Levesque's account to handle actions with nondeterministic outcomes, which may also be accorded probabilities. By appealing to an extension of the situation calculus to handle probabilistic nondeterminism, we will show that Levesque's definition, as well as a notion of goal achievability proposed by Lin and Levesque, have limited appeal under stochastic nondeterminism. In essence, they correspond to one correct execution, which is unlikely to be adequate. Rather, we propose to delineate between goal satisfaction and termination leading to a range of correctness criteria. To better study these criteria, and to position the results in a broader context while still allowing for the generality of the situation calculus, we consider an abstract framework to study the correctness of plans with loops, in domains that are possibly unbounded, and/or stochastic, and/or continuous. Within that framework, we then prove numerous relationships between the criteria, including some impossibility results for categorically satisfying goals. Finally, we show that these notions provide a more granular view than those discussed in the literature, such as strong planning and strong cyclic planning.
Deep active contours using locally controlled distance vector flow

Active contours model (ACM) has been extensively used in computer vision and image processing. In recent studies, convolutional neural networks (CNNs) have been combined with ACM replacing the user in the process of contour evolution and image segmentation to eliminate limitations associated with ACM dependence on energy functional parameters and initialization. However, prior studies did not aim for automatic initialization, which is addressed in this article. In addition to manual initialization, current methods are highly sensitive to the initial location and fail to delineate borders accurately. We propose a fully automatic image segmentation method to address problems of manual initialization, insufficient capture range, and poor convergence to boundaries, in addition to the problem of assignment of energy functional parameters. We train two CNNs, one of which generating ACM weighting parameters and the other generating a ground truth mask to extract distance transform (DT) and an initialization circle. DT is used to form a vector field pointing from each pixel of the image towards the closest ground truth boundary point. Vector magnitudes are equal to the Euclidean distance between each pixel and the closest ground truth boundary point. We evaluate our method on four publicly available datasets, including two building instance segmentation datasets, i.e., Vaihingen and Bing huts, and two mammography image datasets, INBreast and DDSM-BCRP. Our approach achieves state-of-the-art results in mean Intersection over Union (mIoU), Dice similarity coefficient and Boundary F-score (BoundF) with the values of 92.33%, 92.44%, and 86.57% for Vaihingen dataset, and 87.12%, 86.86%, and 66.91% for Bing huts dataset. We obtained the Dice similarity coefficient values of 94.23% and 90.89% for the INBreast and DDSM-BCRP, respectively.
Audit as You Go: A Smart Contract-Based Outsourced Data Integrity Auditing Scheme for Multiauditor Scenarios with One Person, One Vote

The data outsourcing services provided by cloud storage have greatly reduced the headache of data management for users, but the issue of remote data integrity poses further security concerns and computing burdens. The introduction of a third-party auditor (TPA) frees data owners from the auditing burden and alleviates disputes over the audit results between data owners and cloud storage providers. However, malicious cloud servers may collude with TPAs to deceive users for financial profits. Hiring multiple auditors in a single audit assignment appears to be a method to address the above problem, but the ensuing voting issues need to be further explored. In this paper, we proposed a smart contract-based outsourced data integrity auditing scheme for multiauditor scenarios. Unlike some existing schemes using reputation like factors as their voting weights, auditors in our scheme vote equally and audit as they go, without any maintenance. This mechanism not only frees auditors from trivia not related to the auditing but also avoids the drawbacks of centralization associated with over-high voting weights. The challenge used to check the integrity of the outsourced data is jointly generated by each involved auditor. Any collusion would be detected as long as there exists more than one honest auditor in the audit. We implement and deploy the scheme as Ethereum smart contracts. With the help of blockchain, the entire auditing process is public and transparent. Both the generated data and the obtained results are persisted with immutability, which ensures the traceability of all historical audits. The comprehensive theoretical and experimental analyses demonstrate that our scheme meets the claimed targets with high efficiency and low gas costs.