Reviewerly/exHarmony huggingface dataset:
Data-driven sequence labeling methods incorporating the long-range spatial variation of geological data for lithofacies sequence estimation

The use of geophysical well-log data for interpreting the stratigraphic lithofacies sequence is cost effective. In this study, several data-driven lithofacies sequence estimation models are developed, where long- and short-term memory (LSTM) and bidirectional LSTM (BLSTM) are applied to efficiently complement the long-range spatial variation of successive well-log and lithofacies measurements. During the development, the models using the autoregressive (AR) input variables of lithofacies are designed to incorporate the lithofacies sequence pattern into the estimation. The performances of the proposed models are comparatively validated with an artificial deep neural network (DNN)-based model that does not consider long-range variation. Accordingly, a total of six estimation models are examined: DNN, AR-DNN, LSTM, AR-LSTM, BLSTM, and AR-BLSTM. For model implementation, synthetic data and actual data acquired from the Satyr-5 well in Western Australia are used. For the synthetic data, the results indicate that the incorporation of nonstationary statistical information improves the performance of BLSTM-based models. In addition, AR-input information is effective with respect to the estimation of the vertical thickness of lithofacies. The advantage of using AR inputs can also be observed for actual data, where AR-based models perform significantly better than the other models. Quantitatively speaking, the fitness of DNN-, LSTM-, and BLSTM-based models is 81.79 %, 84.53 %, and 85.08 %, respectively, whereas that of AR-DNN-, AR-LSTM-, and AR-BLSTM-based models is 85.43 %, 85.72 %, and 86.56 %, respectively. The proposed models are expected to be useful with respect to interpreting the heterogeneity of lithofacies distribution in a cost-effective and computationally efficient way. Particularly, BLSTM-based models are widely applicable because they perform well regardless the spatial statistics of lithofacies sequences.
Local Mean k-General Nearest Neighbor Classifier

The well-known k-Nearest Neighbor classifier is a simple and flexible algorithm that has sparked wide interest in pattern classification. In spite of its straightforward implementation, the kNN is sensitive to the presence of noisy training samples and variance of the distribution. Local mean based k-nearest neighbor rule has been developed to overcome the negative effect of the noisy training sample. In this article, the local mean rule is implemented with the general nearest neighbors that are selected in a more generalized way. A new local mean based nearest neighbor classifier is proposed termed Local Mean k-General Nearest Neighbor (LMkGNN). The proposed LMkGNN classifier finds the local mean vector from general nearest neighbors of each class and classifies the test sample based on the distances between the test sample and local mean vectors. Fifteen real-world datasets from the UCI machine learning repository are used to assess and evaluate the classification performance of the proposed classifier. The performance comparison is also made with five benchmark classifiers (kNN, PNN, LMkNN, LMPNN and kGNN) in terms of the classification accuracy. Experimental results demonstrate that the proposed LMkGNN classifier performs significantly well and obtain the best classification accuracy com-pared to the five competing classifiers.
A Fuzzy-Based Support Vector Regression Framework for Crop Yield Prediction

This paper proposes a fuzzy-based support vector regression framework for crop yield prediction. To achieve its objectives, interval type-2 fuzzy logic (IT2-FL), principal component analysis (PCA) and support vector regression (SVR) algorithms are employed. The IT2-FL algorithm is used to predict the missing predictor parameter values in the original crop yield prediction dataset while PCA performs feature selection and dimensionality reduction, thereby eliminating redundant information (features) from the transformed dataset by IT2-FL. SVR, machine learning algorithm, is used for model training and testing of the reduced dataset. The performance of the SVR model is evaluated using mean square error (MSE) and root mean square error (RMSE) metrics. Results show that SVR is more accurate at predicting crop yield with an error of 0.002071 for MSE and 0.045513 for RMSE, providing robustness to outliers and minimizing generalization errors with accuracy of 99% for MSE and 95% for RMSE. This indicates that farmers can rely on predicted outcomes from the proposed framework to adopt practices that maximize crop production, improve crop yield, and sustain food sufficiency to the teeming population.
Genetic Programming with Random Binary Decomposition for Multi-Class Classification Problems

This paper introduces a new Genetic Programming (GP) based classification framework for multiclass classification problems. The proposed framework uses a binary decomposition-based GP method to extract new features to enhance the performance of classifiers in the multiclass classification task. We firstly introduce a random binary decomposition method that uses a part-vs-part strategy to decompose the multiclass problems which increase the number of binary problems that can be decomposed from a multiclass problem. Then the details of combining GP with this binary decomposition method for feature extraction are explained. Finally, we compare our method to several popular ML methods and traditional GP methods in a broad set of benchmark problems. The outcome shows the performance of classifiers is enhanced for multi-class classification tasks when combined with this technique. The effect of applying this framework to different classifiers and large real-world data set is also explored. The results suggest the effectiveness and universality of our method.
Parameter Identification of Coulomb Oscillator from Noisy Sensor Data

Coulomb oscillator is used for analyzing several real-life systems, which demand a precise modeling of the oscillation. The modeling is based on the stochastic estimation of unknown parameters of the model representing the oscillation. This paper introduces a Bayesian approach for the estimation of unknown parameters from sensor-generated noisy data. Among several Bayesian approaches, Gaussian filtering approach is most popular. A major challenge that appeared with the Gaussian filtering is intractable integral, which is approximated numerically. Several Gaussian filters have been reported by using different numerical approximation methods. This paper implements a popular Gaussian filter, named as cubature Kalman filter (CKF), for the estimation of unknown parameters. The CKF uses a third-degree spherical radial rule for the numerical approximation of the intractable integrals. Simulation results conclude a high accuracy of the CKF-based estimate of unknown parameters.
About Qualifications in the List of Specialties and Areas of Preparation for Higher Education

The article analyzes the draft order of the Ministry of Science and Higher Education of Russia «On the approval of the list of specialties and areas of training for higher education» for the names of qualifications given in the list of specialties and areas of training for higher education. The author proposes to unify the names of qualifications assigned after the completion of postgraduate studies; proposals for qualifications are also given after the completion of specialty programs.
The Application of Information Processing Theory in Higher Education

The application of Information Processing Theory (IPT) has been attempted in almost all topics of human cognitive learning. This paper aimed to design learning activities based on the information processing theory during the course offering to 153 sophomore students at the Zhao-Qing University. According to the characteristics of students' information processing ability, this paper explored teaching strategies and practices based on the model of information processing. The purpose was to motivate students to conduct meaningful learning. At the end of the course, the curriculum satisfaction questionnaires were collected associated with semi-structured interviews in November 2020. Based on the teaching evaluation data obtained from this study, this paper proposed the reflection on teaching practices in order to improve the teaching strategies based on the information processing model.
Practice and Evaluation of On-demand Distance Learning in Physical Education

Because students could not go to school due to COVID-19 infection, we gave an on-demand lecture on physical education. A questionnaire survey was conducted on the lecture, and the lecture was evaluated by students. From the results of the questionnaire survey, it was found that the students were highly satisfied with the lectures and that they also improved their physical strength. There were also efforts to discover issues on their own and resolve those issues. Future lecture improvements will be to create content that takes into consideration the diversity of students and to exchange opinions with students in on-demand lectures.
Predicting Vehicle Theft with Backpropagation Algorithm in East Java Regional Police

Vehicle theft in East Java is a crime that causes unrest and anxiety in the public. The increased rate of vehicle theft is due to several factors, including economic, environment, low education, increased unemployment, and lacking legal awareness. One way to anticipate an increased level of theft is by predicting the possibility of theft based on previous incident data. This research is focused on the design of an intelligent system to predict the locations prone to vehicle theft using Backpropagation Neural Network. The training dataset used was obtained from data on the location of vehicle thefts in the East Java Regional Police from 2015 to 2019. Backpropagation architecture model 7-10-6 was used. The best level of accuracy for the performance of this model is 100%, with an epoch of 65 iterations, cross entropy of 5.188, and an error value of 0. Prediction test was carried out on ten new theft data in 2020 and 2021. The test resulted in a quite accurate level of accuracy in predicting locations prone to theft in a short time and gave correct prediction results.
A Fuzzy Approach to Language Universals for NLP

One of the currently biggest challenges in NLP is to develop multilingual language technology. Lack of data in low-resources languages poses great difficulty to NLP researchers and limits NLP technology's availability to a small number of resource-rich languages. It has been shown that linguistic typology and the knowledge of language universals can help NLP in the development of multilingual resources. To contribute to this research area, we present a fuzzy approach to language universals. Our proposal combines a constraint-based formalism with fuzzy logic to define a fuzzy-gradient model to characterize linguistic universals. This model will allow us to evaluate linguistic universals and to define a universal grammar. This universal grammar will be integrated into an automatic technique to infer from linguistic data the particular grammar of any understudied natural language.
An algorithm of fire situation information perception using fuzzy neural network

With the development of mobile communication and information technology, many complex scenes that are difficult for front-line personnel to work have been improved with the support of new technologies. Sensors with more complete functions provide richer communication data, and technological developments such as heterogeneous networks also provide a better communication environment for the field and command center. In a more complex communication scenario such as fire, more abundant communication resources are used to conduct situational awareness on the scene. This paper proposes a fire situation information perception algorithm based on fuzzy neural network. The algorithm normalizes situation information data matrix and trains it as the input of fuzzy neural network. Finally, the fuzzy logic system theory and BP neural network are combined to obtain a fuzzy neural network with good perception of on-site situation information, which provides support for the subsequent decision-making of the command center and front-line personnel.
Data sharing scheme for 5G IoT based on blockchain

The data of the Internet of Things has an extremely important value, but based on the existing data sharing mechanism, there will be inevitable security risks, such as node failure and data leakage. In the process of using blockchain technology to achieve efficient and reliable data sharing, data will inevitably be missing, redundancy and other anomalies. We propose an anomaly detection method called MAE (Memory Autoencoder). The effectiveness of the model have been tested on different types of data. The results show that compared with mainstream models, the MAE not only improves the anomaly detection effect, but also has better versatility.
Obstacle Avoidance for Autonomous Mobile Robots in Unstructured Human Environments

This paper presents an obstacle avoidance approach for autonomous mobile robots in unstructured human environments, focusing on avoiding irregular obstacles that cannot be detected by traditional 2D laser-based obstacle detection techniques. With the aid of ultrasound sensors and RGB-D cameras, the developed approach can effectively avoid obstacles below the laser scanning plane, which can address the challenges of mobile robots crashing human's feet in unstructured human environments. Furthermore, the developed approach can reliably avoid obstacles hanging in the air, which put up severe challenges to mobile robots working in such unstructured human environments as airports. The effectiveness of the developed obstacle avoidance approach is verified by extensive experiments.
QuantileRK: Solving Large-Scale Linear Systems with Corrupted, Noisy Data

Measurement data in linear systems arising from real-world applications often suffers from both large, sparse corruptions, and widespread small-scale noise. This can render many popular solvers ineffective, as the least squares solution is far from the desired solution, and the underlying consistent system becomes harder to identify and solve. Quan-tileRK is a member of the Kaczmarz family of iterative projective methods that has been shown to converge exponentially for systems with arbitrarily large sparse corruptions. In this paper, we extend the analysis to the case where there are not only corruptions present, but also noise that may affect every data point, and prove that QuantileRK converges with the same rate up to an error threshold. We give both theoretical and experimental results demonstrating QuantileRK’s strength.
IMITA: Imitation Learning for Generalizing Cloud Orchestration

Operating large scale and feature-rich applications is becoming increasingly complex as engineers need to deploy highly configurable software releases on distributed cloud stacks while managing ever-shorter production cycles. Although recent proposals attempt to streamline cloud resources orchestration, there is still a significant challenge in making such solutions generalize to unseen cloud stacks. In other words, the behavior of application-specific Key Performance Indicators (KPIs) and resource configurations, crafted for specific stacks, may differ on heterogeneous deployments, requiring time-consuming policy adjustments. We introduce IMITA, a system that leverages imitation learning to create models by imitating an expert behavior that can be generalized seamlessly to new cloud stacks. To make a generalized model, IMITA maps expert actions taken based on the application KPI space to the space of resource utilization metrics that are universally available in cloud platforms. This mapping enables the model to trigger actions, mimicking expert behavior, upon the occurrence of similar re-source utilization footprints across deployments. We demonstrate IMITA by learning to scale-out Cassandra deployments with diverse configurations and workloads. Our results show IMITA can replicate expert actions across deployments and extrapolate to unseen environments by achieving 50 - 94% fewer false positives actions than traditional threshold-based policies while still adhering to Service-Level Objectives (SLO) and avoiding under-provisioning of resources. Moreover, since collecting data in clouds is costly, IMITA gathers data only for representative configurations to train the imitator model. This approach reduces the size of the collected data to 50%.
A Secure and Efficient Bitcoin Payment Channel Using Intel SGX

Hardware trusted execution environment (TEE) provided by Intel SGX enclave has been introduced in existing payment channel schemes as a root-of-trust to enforce faithful protocol execution so that participants do not need to monitor Bitcoin blockchain anymore. However, the security of these schemes relies totally on enclaves. Since private keys of all channel funds are kept by both payment channel participants’ enclaves, a malicious participant can steal funds from the counterparty by defeating her own enclave. To solve the above problem, we present a novel TEE-based payment channel scheme that transfers the responsibility of running enclaves from participants to a third party committee, while relieving both participants from monitoring the blockchain at the same time. Furthermore, since committee members can try to steal funds by defeating their own enclaves, we exploit the additive homomorphic property of signature keys in Elliptic Curve Cryptography to design a novel secret sharing scheme to tolerate a subset of committee members to be malicious. By using the above secret sharing scheme, private keys of the channel funds are never constructed in any committee member’s enclave, so that a malicious committee member cannot steal funds by defeating his own enclave. Finally, experiment shows our scheme can ensure payment channel funds security without efficient compromises compared with existing TEE-based payment channel schemes.
Sequence-tracker: Multiple object tracking with sequence features in severe occlusion scene

Multiple object tracking is one of the most fundamental tasks in computer vision, and it is still very challenging for real-world applications due to its severe occlusion and motion blur. Most of the existing methods solve these multiple object tracking issues by performing data association based on the deep features of the detections in consecutive frames, which only contain the spatial information of the detected objects. Therefore, the inaccuracy of data association would easily occur, especially in the severe occlusion scenes. In this paper, a novel multiple object tracking model named sequence-tracker (STracker) has been proposed, which combines both the temporal and spatial features to perform data association. We trained a sequence feature extraction network based on video pedestrian re-identification offline, fused the obtained sequence features with the depth features of the previous frame, and then implemented the Hungarian algorithm for data association. Experiments have been carried out to validate the effectiveness of the proposed algorithm and the corresponding results indicates that it can significantly improve the trajectory quality of our dataset in this paper. Remarkably, for the public detector results from MOT official website, the proposed algorithm can achieve up to 57.2% MOTA and 50.9% IDF1 on the MOT17 dataset.
Defining degree of semantic similarity using description logic tools

Establishing the semantic similarity of information is an integral part of the process of solving any information retrieval tasks, including tasks related to big data processing, discovery of semantic web services, categorization and classification of information, etc. The special functions to determine quantitative indicators of degree of semantic similarity of the information allow ranking the found information on its semantic proximity to the purpose or search request/template. Forming such measures should take into account many aspects from the meanings of the matched concepts to the specifics of the business-task in which it is done. Usually, to construct such similarity functions, semantic approaches are combined with structural ones, which provide syntactic comparison of concepts descriptions. This allows to do descriptions of the concepts more detail, and the impact of syntactic matching can be significantly reduced by using more expressive descriptive logics to represent information and by moving the focus to semantic properties. Today, DL-ontologies are the most developed tools for representing semantics, and the mechanisms of reasoning of descriptive logics (DL) provide the possibility of logical inference. Most of the estimates presented in this paper are based on basic DLs that support only the intersection constructor, but the described approaches can be applied to any DL that provides basic reasoning services. This article contains the analysis of existing approaches, models and measures based on descriptive logics. Classification of the estimation methods both on the levels of defining similarity and the matching types is proposed. The main attention is paid to establishing the similarity between concepts (conceptual level models). The task of establishing the value of similarity between instances and between concept and instance consists of finding the most specific concept for the instance / instances and evaluating the similarity between the concepts. The term of existential similarity is introduced. In this paper the examples of applying certain types of measures to evaluate the degree of semantic similarity of notions and/or knowledge based on the geometry ontology is demonstrated.
Dr Robot: the new normal is here

With the use of telemedicine increasing significantly during the COVID-19 pandemic, this article discusses the implications for healthcare facilities and the extent to which generational gaps affect adoption of new technology.
User Embodiment Comparison of Semi-Autonomous and Fully-Captured Avatar Movements in Virtual Reality

We describe in this paper a user study in the context of animated virtual human to compare the user self-perception of well known semi-autonomous Avatar and full-body tracked Avatar. We aim at highlighting the advantages and limitations of those methods during various walking phases. The participants walked inside 4 simulated environments with different obstacles. Those results are quantified through a virtual reality sickness questionnaire and a new questionnaire specialized on user perception and enjoyment of his avatar lower part. This study shows positive results for the semi-autonomous Avatar especially in a cluttered environment. The users maintain the same efficiency with both methods, they have no sickness issues after more than 45 minutes inside the simulation and they present better enjoyment results for environment with complex obstacles.
Small sample signal modulation recognition method based on transfer learning

In view of the problem that traditional deep learning requires a large amount of labeled signal data set but the measured data is difficult to meet, this paper proposes a small sample recognition method based on transfer learning (TLSSM), which transfers the recognition model from large-scale simulation data to the small sample measured data recognition model. It can also achieve better recognition performance in the case of a small number of samples. Simulation experiments show that when TLSSM has 100 samples of each type of signal in the training set, the signal recognition accuracy of 5-10dB mixed signal is as high as 90%, which is 84% smaller than the data required by traditional convolutional network. It proves that the method proposed in this paper is effective and feasible in reducing the size of the training set and improving the speed of recognition.