Reviewerly/exHarmony huggingface dataset:
Multi-view Human Action Recognition by Cell Summary Descriptor and Decision Fusion

Multi-view human action recognition (MVHAR) is essential for many applications including smart video surveillance in shopping malls, airports, railway stations and other public places, as well as the ambient assisted living systems. Currently, a lot of methods realized MVHAR by constructing high dimensional features or by complex calculation process, which causes the recognition speed difficult to meet the needs of real-time application system. To address the problem, a cell summary (CS) descriptor is constructed for each frame by dividing each frame into several cells and further dividing each cell into several radial bins and then extracting spatiotemporal features from each radial bin. Then a video is represented by a sequence of dimension-reduced CS descriptors. The CS descriptor is not only discriminative but also low computational cost. A probabilistic classifier is learned for each view of each action category, and then action classification is carried out independently in each view. A probability estimation based decision fusion algorithm is proposed to make a final decision. Experimental results on the two publically available multi-view human action datasets MuHAVi-MAS-14 and IXMAS show that the proposed approach is superior to state-of-the-art methods in recognition accuracy and moreover, the approach is so computationally efficient that it is appropriate for real-time applications.
A New Method of Traffic Conflict Data Collection and Processing for Traffic Safety Evaluation

At present, the most common method of collecting traffic conflict data was the site observation method. Data processing methods were mainly manually observed by site or video. To overcome the shortcomings of traditional data collection and processing methods, video recognition was collected by UAV (Unmanned Aerial Vehicle) flying at high altitude and conflict were automatically detect by video recognition software in this paper. Compared with traditional data collection methods, UAV has a good high-altitude field of view, does not have shooting angle and occlusion problems, and has a large shooting range, which has obvious advantages. The video recognition program consists of the following parts: image reading and calibration, vehicle identification, vehicle tracking. Video recognition got continuous high-precision vehicle trajectory data, subsequent processing to obtain continuous vehicle conflict data. Finally, we can use these data to evaluate road traffic safety. The results show that this method can effectively evaluate road traffic safety.
3D MR Image Segmentation Algorithm based on Supervoxel and Kernel FCM Algorithm

Image segmentation is the basis of medical image processing. The proposed method can segment 3D volume data by supervoxel and kernel FCM algorithm. Firstly, the extended SLIC method is used to divide the 3D image into supervoxels. Then the supervoxels are descripted by statistical feature and segmented by kernel FCM algorithm. The experimental results on a publically available brain MR dataset show the effectiveness and superior performance of the proposed method.
Pairwise-Constrained Deep Document Clustering

While in standard clustering no side information is used, users might be interested in providing additional information to influence the clustering. In case of document clustering, additional information can take the form of pairwise constraints where a user provides additional information about pairs of documents as must-link and cannot-link constraints (indicating respectively whether the documents in the pair are coming from the same cluster or not). In this paper, we propose a novel deep document clustering framework which can employ pairwise constraints while learning document representations to obtain better tailored results. Indeed, in our proposed framework, data representations (obtained through an autoencoder) and cluster representatives are learned through back propagation in a joint way. Devising a fully differentiable deep clustering framework with the ability of using pairwise constraints is the main contribution of this paper. Experiments conducted on 5 public datasets show the gain in clustering performance which the resulting approach can yield.
Expansion of Sentiment Lexicon Based on Label Propagation

Sentiment lexicons play a vital role in the field of sentiment classification. The existing sentiment lexicon has problems such as limited coverage and poor adaptability in the field. Building an sentiment lexicon with large coverage and strong adaptability in the field has become a challenge in this field. This paper proposes a new method for selecting seed words. Firstly, the seed words are manually selected based on the general lexicon. Then, the word vectors are trained on the corpus by the seed words selected artificially. Finally, the expanded seed words are obtained. Obtaining sentiment polarity by calculating the similarity between the seed words and the candidate sentiment words, constructing propagation map and propagation matrix to construct sentiment lexicon. The experimental results show that the method can obtain higher accuracy and better robustness compared with the baseline method.
Some topics in English newsmagazines in 2019, with special reference to mineral resources, building stones, and a revised classification of igneous rocks

Information collected from geological newsmagazines in 2019 is reviewed. First topic is mineral resources such as mineral commodity summary in USA and mineralogy of borate salts, garnet, and sphene. Second topic is building stone such as porphyry tuff in Germany and Australian stones in London. Third, the improved IUGS igneous rocks' classification is reviewed. It is devised to avoid hybrid names and show information of some accessary minerals. Finally, the educational program in South Devon, England, is introduced.
An Efficient Combination Between Epipolar Geometry and Perspective Transform for Moving Object Detection

An efficient moving object detection (MOD) method for monocular camera with ego-motion is proposed in this paper. In general, there are keypoint-based foreground detection methods that find out outliers using epipolar geometry of two camera coordinate systems in consecutive frames. Determining of regions of moving objects from sparse keypoints is especially challenging. In this paper, we propose an optical flow noise removal and an efficient sparse-to-dense motion probability map. Subtraction method using perspective transform is applied to detect candidate regions of objects. Moving objects are detected by combining subtraction method with the proposed motion probability map. For stable detection, we use the Kanade-LucasTomasi (KLT) feature tracker for keypoint tracking and Kernelized Correlation Filter (KCF) tracking algorithms for bounding boxes. Through the simulations, we show that our proposed probability map is effective to detect various sizes of objects.
Accelerated Stochastic Variational Inference

Variational inference (VI) is the method from machine learning, which approximates probability densities through optimization. In this paper, we propose a stochastic optimization algorithm, called randomized stochastic accelerated natural gradient (RSANG), which uses the unbiased estimates of natural gradient that utilizes the Riemannian geometry of the approximation space at each iteration for variational inference problems. The convergence rate of proposed algorithm is proven to be faster than SGD theoretically. Based on RSANG algorithm, we develop accelerated stochastic variational inference, a scalable algorithm for approximating posterior distributions for a general class of conjugate-exponential models. The convergence rate of accelerated stochastic variational inference is proven to be faster than stochastic variational inference theoretically. We also demonstrate that the proposed method improves substantially over the non-accelerated methods in simulated examples.
Use of artificial neural networks for predicting water separation in water oil emulsion

An accurate prediction of the dehydration of a crude oil is necessary for design, selection and petroleum operations. The aim of this research was to predict the separation of water using the FFANF artificial neural network. Factors studied include the effect of mixing time, mixing speed, emulsification temperature and shearing rate. The models of water separation were developed using experimental laboratory data. The results showed that the expected relationships had a perfect correlation with the tests achieved at R = 0.99992 and the top validation performance at 143.3434 and RC = 0.98. The results also showed that the model obtained was efficient in comparison with the conventional ANN in predicting water separation with an overall improvement of 39.
Retrieval of Ontological Knowledge from Unstructured Text

In this article, we examined the issue of automatic ontology formation process from unstructured text data. To understand the ontology of the domain, ontology should be expressed in terms of information tables and ontology graphs. Ontology graph consists of taxonomic and non-taxonomic relations. Non-taxonomic relations are easier to understand to non-expert users. Extracting non-taxonomic relations from ontology is a challenge. In order to improve ontology of the domain, appropriate machine learning classifier needs to be investigated for feature classification.
Intelligent Selection Recommendation Algorithm in the “Double” Mechanism

Abstract Aiming at the phenomenon of blind selection during the course selection of college students under the credit system, the paper puts forward the most preferred course selection recommendation algorithm and the recommended course selection algorithm based on comprehensive evaluation of curriculum. The most preferred course selection recommendation algorithm uses 0-1 integer programming model to give the minimal courses and the maximum credits; the comprehensive evaluation of curriculum based on considering the students’ learning ability and learning interest gives he curriculum evaluation coefficient, students according to the curriculum evaluation coefficient can objectively and accurately select elective courses for their own development ability. These two elective courses conform to the actual situation of students’ elective courses, and can effectively guide students to select courses.
The Future Approach to Simplify the Cloud-Service Market Using a Standardized Description Language

The market of cloud services has been growing continuously during the last years. However, the market is not transparent. There is a complex pool of different cloud services potential users must understand, before they decide to acquire specific products. Additionally, each cloud provider uses its own name for a cloud service. The question that will be answered in the following research paper is: How can cloud services be described in a standardized way? To achieve the goal of answering this question, a basic form of statistical analysis has been chosen. Summing up the results, it is possible to standardize the description of cloud-based IT-services. Therefore, it is only necessary to find the right adjective/verb and object combination for a specific service. Using this new description methodology, the complex market could be structured in the future. Further research can combine the enhancements of the description language as well as the question: How could this new standard be transferred into; and accepted by the market.
Overlapping Histograms for 3D Data Classification

3D object scanning and recognition is becoming more widely available with the rapid advancement of technology. In this paper, we developed a 3 Point Histogram Comparison algorithm for improving 3D image classification accuracy. In our research, we used the Structure Sensor by Occipital, an inexpensive alternative to state-of-the-art scanners. We also built and refined an extensive 3D object reference library. Results show that we improved the accuracy in about 10-15% in comparison to previous methods, as well as shorten the computation time significantly.
Fast Graph Convolutional Recurrent Neural Networks

This paper proposes a Fast Graph Convolutional Neural Network (FGRNN) architecture to predict sequences with an underlying graph structure. The proposed architecture addresses the limitations of the standard recurrent neural network (RNN), namely, vanishing and exploding gradients, causing numerical instabilities during training. State-of-the-art architectures that combine gated RNN architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with graph convolutions are known to improve the numerical stability during the training phase, but at the expense of the model size involving a large number of training parameters. FGRNN addresses this problem by adding a weighted residual connection with only two extra training parameters as compared to the standard RNN. Numerical experiments on the real 3D point cloud dataset corroborates the proposed architecture.
A Deep Neural Network Classifier Based on Belief Theory

Classification is a machine learning technique that is used to find the membership of an object to a given set of classes or groups. Neural network (NN) classifier always suffers some issues while examining outliers and data points from nearby classes. In this paper, we present a new hyper-credal neural network classifier based on belief theory. This method is based on credal classification technique introduced in Dempster-Shafer Theory (DST). It allows a data point to belong not only to a specific class but also to a meta-class or an ignorant class based on its mass. The sample which lies in an overlapping region is accurately classified in this method to a meta-class which corresponds to the union of the overlapping classes. Therefore this approach reduces the classification error at the price of precision. But this decrease in precision is acceptable in applications such as medical, defence related applications where a wrong decision would cost more than avoiding some correct decisions. The results and analysis of different databases are given to illustrate the potential of this approach. This idea of hyper-credal classification is extended to convolutional neural network (CNN) classifiers also.
Effectiveness of Macromedia Flash digital module

Abstract In the 21st century, digital media is very important in life. With the development of technology, teachers must be able to develop multimedia-based learning systems. Modules are teaching materials that can be arranged in certain forms and can help learning activities. Modules can be made in various forms, one of them is a digital module. Macromedia Flash is an application for creating animations that are widely used by animators. The purpose of this study was to determine the effectiveness of the digital Macromedia Flash module in mathematics learning. This study uses descriptive statistical analysis. Based on the results of the author’s research, it can be concluded that the Macromedia Flash digital module is very effective on mathematics learning. Digital modules can increase student’s motivation on mathematics learning and increase the activity of students in the learning process.
Parallel Feature Pyramid Network for Image Denoising

Image denoising is a classical and essential task in consumer electronics equipped with cameras. Recently, the convolutional neural network (CNN)-based denoising methods have been widely studied. These methods adopt single-scale features to separate image structures from the noisy observation. Single-scale features, however, have limitation in covering the full characteristics of image structures at different scales. In this paper, we propose a novel denoising network that makes use of the multi-scale feature pyramid where each feature map represents the characteristics of image structure at different scales. We then combine these multi-scale features to obtain the contextual information and utilize it to effectively generate clear denoised results. Experimental results show that our network achieves superior performance to other conventional methods.
Optimal Piecewise Approximations for Model Interpretation

Recent literature interprets the predictions of "black-box" machine learning models (Neural Networks, Random Forests, etc.) by approximating these models in terms of simpler models such as piecewise linear or piecewise constant models. Existing literature does not provide guarantees on whether these approximations reflect the nature of the predictive model well, which can result in misleading interpretations. We provide a tractable dynamic programming algorithm that partitions the feature space into subsets and assigns a local model (constant/linear model) to provide piecewise constant/piecewise linear interpretations of an arbitrary predictive model. When approximation loss (between the interpretation and the predictive model) is measured in terms of mean squared error, our approximation is optimal; for more general loss functions, our interpretation is approximately optimal. Therefore, in both cases it probably approximately correctly (PAC) learns the predictive model. Experiments with real and synthetic data show that it provides significant improvements (in terms of mean squared error) over competing approaches. We also show real use cases to establish the utility of the proposed approach over competing approaches.
Systematic survey of convolutional neural network in satellite image classification for geological mapping

Convolutional Neural networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in computer vision tasks such as image recognition and classification. Notwithstanding, not much has been seen in its application to satellite image classification for geoscience purpose. This paper aims to provide a systematic review of the concept of satellite image classification using the state-of-the-art CNN, highlighting the recent developments in improving its accuracy and performance. Adequate knowledge and usage of this technique will enhance the speed and accuracy of satellite image classification tasks and subsequently generation of geological or topographic maps. These maps will equip the stakeholders in the mining, environment, educational, political, national security and national planning with more reliable and faster access to geospatial information needed for knowledge- driven decision support.
Non-Search Mathematical Programming Algorithm for Limit Analysis

Abstract In order to improve the ability of no-search mathematical programming under limit analysis, and guide big data to optimize classification, a non-search mathematical programming model based on limit analysis model is proposed. The non-search mathematical programming of hyperbolic differential equations in the limit analysis model is analyzed, which provides a mathematical theoretical basis for solving the stability control problem of the system. The hyperbolic differential equation is constructed and the characteristic decomposition of the linear programming model is carried out by using adaptive limit analysis method. The searching delay two-degree-of-freedom control method is used at the load balancing point to adaptively optimize the searching delay parameters of the limit analysis model. In this paper, the hyperbolic differential equations in the limit analysis model are obtained, and the theorems of non-search mathematical programming are given. The mathematical analysis shows that the hyperbolic differential equations in the limit analysis model have the characteristics of non-search mathematical programming. The given theorem of non-search mathematical programming is reliable, and the characteristic solutions of differential equations are stable convergence, which can guide the stability control and improve the control accuracy and reliability. The simulation results show that the algorithm has better convergence and shorter time cost under the limit analysis.
STUDENTS’ UNDERSTANDING ON PROVERB: A STUDY OF LEARNING IN SCHOOL

A proverb which can be maxim, parable, fame, contains deep implicit meanings. The meaning contains in proverb implies values to become life guidance.  The purpose of this study was to determine the ability of students to understand proverbs and students’ perceptions on proverb learning. Descriptive method was used in this study. The variables were ability to understand proverbs and perceptions on proverb learning. The research respondents were 59 students of Junior high school of Srijaya Negara Palembang. Test and questionnaires were used in this study. The results showed that the average score of the students’ comprehension tests was 41.53. There were four types of percentage levels of respondents’ understanding, which were very good, sufficient, poor, and very poor. Students’ perceptions of knowledge and learning of proverbs in schools, proverb knowledge, proverbial teaching material, and proverb learning strategies were quite adequate. Therefore, it is needed to promote proverb learning in schools.
On the Translations of Extended Logic Programs, Two-Valued Logic Programs and Normal Logic Programs

To discuss the relationships of several logic programs, we introduce some classes of logic programs - extended logic programs (EPams), two-valued logic programs (TPams) and normal logic programs (LPams). We construct the translations between EPams and TPams, the translations between EPams and LPams. The results will pave the way to discuss some other relationships of them, such as expressive ability.
Perceptual CTU Level Bit Allocation for AVS2

With developed CTU (Coding Tree Unit) bit allocation algorithms, the video compression’s performance improved significantly. However, without the consideration of the human visual system (HVS) characteristics for most algorithms, they fail to obtain subjectively optimal results. In order to improve the image’s subjective quality of video coding, in this study, we propose a perception-aware CTU level bit allocation algorithm. A perceptual distortion metric with spatial and temporal masking effects is presented firstly, and the rate-perceptual distortion constraint function is then established to obtain the optimal quantization step for each CTU. Experiments show that the proposed algorithm achieves better subjective quality with negligible affection on rate control on RD17.0 of AVS2. In addition, we demonstrate the well compatibility of the algorithm with video coding, which means it can also be extended to other standards including H.265 etc.
Towards a Multi-chain Future of Proof-of-Space

Proof-of-Space provides an intriguing alternative for consensus protocol of permissionless blockchains due to its recyclable nature and the potential to support multiple chains simultaneously. However, a direct shared proof of the same storage, which was adopted in the existing multi-chain schemes based on Proof-of-Space, could give rise to newborn attack on new chain launching. To fix this gap, we propose an innovative framework of single-chain Proof-of-Space and further present a novel multi-chain scheme which can resist newborn attack effectively by elaborately combining shared proof and chain-specific proof of storage. Moreover, we analyze the security of the multi-chain scheme and prove that it is incentive-compatible. This means that participants in such multi-chain system can achieve their greatest utility with our proposed strategy of storage resource partition.