Reviewerly/exHarmony huggingface dataset:
Online-learning organization methodology as component of it technologies at students of technical universities

The article describes the application of innovative web-quest technologies as online methodical component. It was advisable to apply the Web-quest for technology as a tool for innovation and the introduction of ICT. Consequently, all the methodological developments were developed in the pro-historical course “PCP PO” in the online mode, i.e. a special Internet resource was developed. The relevance of using Web-quest technology: provides users with complete, reliable and easy to read information resources; creates a convenient interface for accessing information resources; Formation of such key factors as specialists' ability to acquire new knowledge and skills, creative activity in decision making, initiative, professional competence; Improving the effectiveness of the educational process.
A Metaheuristic Approach for Image Segmentation Using Genetic Algorithm

Euclidian distance between any two observations in M × N matrix is computed using genetic algorithm. RGB image is classified using K-means clustering, and Euclidian distance matrix is calculated involving fitness function. Higher limit for terminating the program are assigned for both normal (500) as well as for higher accuracy values (2000). Clusters are assigned in such a way that closest distance can be obtained from the distance matrix, which is optimum. When image element becomes equals to cluster number, it is replaced. PSNR value is much higher for segmented image compared to the data obtained from thresholding, which clearly speaks in favor of genetic algorithm. Cluster value and image matrix can be changed with the objective to reduce the fitness function.
Analysis of Groundwater Quality in Dusun Salam and Randuacir, Argomulyo District

Abstract Groundwater quality is very much influenced by soil type, soil content, and water flow pattern in the soil. Dusun Salem and Randuacir are hamlets not far from the final garbage dump in Salatiga. This research was conducted to determine the condition of groundwater in Salam and Randuacir hamlets, Randuacir Village, Argomulyo District. The samples were taken as many as 19 wells and the sampling was done randomly by disproportional stratification. To determine water quality, the data from the measurement results of heavy metals [Copper (Cu 2+ ), Manganese (Mn 2+ ), and Zinc (Zn 2+ ], chemical and bacteriological parameters of well water are compared with drinking water quality standards according to Government Regulation No. 82 of 2001 concerning Water Quality Management and Water Pollution Control. Meanwhile, the physical parameters are compared with the Regulation of the Minister of Health of the Republic of Indonesia Number 32 of 2017 concerning Environmental Health Quality Standards and Water Health Requirements for Hygiene, Swimming Pools, Solus Per Aqua, and Public Baths. To determine the level of groundwater pollution, the Pollution Index (IP) is used according to the Decree of the State Minister for the Environment No. 115 of 2003. Based on the results of the study, it can be concluded that: 1) Of the 19 wells in Salam and Randuacir hamlets, there are 3 wells (15.79 %) contaminated with Cu 2+ , Mn 2+ , and Zn 2+ , 2 wells (10.53%) were contaminated with Cu 2+ and Zn 2+ , 6 wells (31.58%) were contaminated with Mn 2+ and Zn 2+ , and the remaining 8 wells (42.10%) were contaminated with Zn 2+ ; 2) Based on the pollution index (IP) of the 19 wells studied that are within a radius of less than 1 km and more than 1 km, all wells are lightly polluted (100%).
Attentive Autoencoders for Multifaceted Preference Learning in One-class Collaborative Filtering

Most existing One-Class Collaborative Filtering (OC-CF) algorithms estimate a user's preference as a latent vector by encoding their historical interactions. However, users often show diverse interests, which significantly increases the learning difficulty. In order to capture multifaceted user preferences, existing recommender systems either increase the encoding complexity or extend the latent representation dimension. Unfortunately, these changes inevitably lead to increased training difficulty and exacerbate scalability issues. In this paper, we propose a novel and efficient CF framework called Attentive Multimodal AutoRec (AMA) that explicitly tracks multiple facets of user preferences. Specifically, we extend the Autoencoding-based recommender AutoRec to learn user preferences with multimodal latent representations, where each mode captures one facet of a user's preferences. By leveraging the attention mechanism, each observed interaction can have different contributions to the preference facets. Through extensive experiments on three realworld datasets, we show that AMA is competitive with state-of-the-art models under the OC-CF setting. Also, we demonstrate how the proposed model improves interpretability by providing explanations using the attention mechanism.
RETRACTED: Research on Innovation and Entrepreneurship Education for Accounting Majors in Higher Vocational Colleges based on the Analysis of Big Data from the Perspective of Resource Synergy

Abstract With the continuous development of social science and computer technology, the state has invested more and more in the reform of educational system. China is in a great historical period of developing an innovative economy and building an innovative country. Innovative economy is a closely cooperative society, which relies on talents and innovation. High-level innovation and entrepreneurship talents are indispensable supporting conditions in China’s current major development strategy. Firstly, this paper analyses Zahra-George’s process model of innovation and entrepreneurship. Secondly, this paper analyses the typical ways of educational resources synergy on innovation and entrepreneurship for accounting majors in Higher Vocational colleges according to the analysis of big data. Then, this paper constructs the influencing factors model of the accounting majors’innovation and entrepreneurship ability development. Finally, some suggestions are put forward.
Creating Awareness About Health and Hygiene During Menstrual Cycle Among Indian Adolescent Girls Using Virtual Reality

India is an especially confusing country portrayed by diversified culture, fluctuated social convictions, segment and geographic profiles with various religion. India is growing fast and moving upwardly with innovation and making a worldwide market for other people. Yet, there are various social taboos which are not talked about publicly. One such issue is the menstruation cycle in teenage girls. Menstruation cycle in teenage girls is one of the taboos which needs to be addressed in India. Numerous myths are blindly followed due to unawareness about hygiene leading to unhealthy environment and practices. As a number of individuals are shy to discuss this subject openly in the community, Virtual Reality (VR) can prove to be a beneficial medium to convey these social communication campaigns. VR can give a very personal experience to users to understand this topic. A teenage girl can see the natural changes in her body and comprehend the period cycle herself glimpsing inside the VR without taking any other individual to help. This paper aims to probe into understanding the scenario of an adolescent girl in the rural areas and come up with the proposal of effective use of VR to create better awareness on menstruation cycle management. This paper offers a perspective of the concepts and how to integrate a VR product to enhance the understanding of social awareness communication in rural areas.
An Improved Local Path Planning Algorithm Based on Kinematics Analysis of The Skid-Steered Wheeled Vehicle

Path planning research takes a significant position in the field of driverless driving, especially local path planning is a key point to ensure the safety of driverless vehicles. At present, the local path planning algorithm is mainly designed for the ackermann vehicle. However, due to the different steering principles, the local path planning algorithm based on the Ackerman steering principle is not suitable for the skid-steered wheeled vehicle. The local path planning algorithm does not consider the kinematics of the skid-steered wheeled vehicle. In this paper, the skid-steered wheeled vehicle is simplified to a single-axle model and the steering kinematics characteristic is analyzed. The classical Dynamic Window Approach (DWA) is improved based on the analysis of the kinematics characteristic of the skid-steered wheeled vehicle with the shortest passing time as the goal. The improved algorithm combines the available velocities of the right and left wheels at the next moment, and finally chooses the combination that takes the shortest time to avoid the obstacle. The planned local path can meet the steering requirements of the skid-steered wheeled vehicle. The improved algorithm is simulated in MATLAB, and the time passed in the map of 10m range is 42.1s.
WITHDRAWN: S2OPE security: Shuffle standard onetime padding encryption for improving secured data storage in decentralized cloud environment

Blockchain is one of the most promising and service resourceful technologies for cloud architecture security in development. Specifically for Check transactions in data storage, access control, and key management in a Distributed Database System (DDS). By the consequences, the Blockchain is very proactivefor access control by verified integrity proofing system when it comes to using encrypted data structures. Due to this access control probably affect by various attacks because of key leakage concerns by improper security. In this paper, we propose a shuffle standard onetime padding encryption (S2OPE) for improving secured data storage in a decentralized cloud environment to resolve the security problems. To strengthen the security level we have proposed a hierarchical security system which is more secure than any of the single level encryption type. Initially, we make a shuffle standard cryptography key policy with optimized Key Digital Signature Policy (KDSP), besides, one-time padding makes legitimate key for integrity proofing to improve the access control by which the cloud data can only be accessed by legitimate users. Our algorithm is fast and secure in both directions to base on owner policy to request access to the files. If some data is lost, there is a hierarchical possibility of decoding technique, the data is very difficult to decrypt to improve the integrity of the security.
Automatic Landmark-Guided Face Image Generation for Anime Characters Using C$$^2$$GAN

Recently, comics with animations, called motion comics, has appeared. However, due to the time and effort required to create the animations, only a few famous comics have been converted to motion comics. In this study, we propose a method for generating landmark-based face images of anime characters using C\(^2\)GAN with the aim of automatically generating animation of facial expressions, where C\(^2\)GAN proposed by Hao et al. is a framework for generating keypoint-guided image. In addition, this paper explains how to create datasets almost automatically from anime videos. In the experiment, we firstly trained C\(^2\)GAN with dataset created from anime videos, and then we tried to improve the performance by changing the representation of facial landmarks.
Stereo Matching of VHR Remote Sensing Images via Bidirectional Pyramid Network

Stereo matching, an essential step in 3D reconstruction, still faces unignorable problems due to the very high resolution and complex structures of remote sensing images. Especially in occluded areas of high buildings and untextured areas of waters and woods, precise disparity estimation has become a difficult but important task. In this paper, we propose a novel method based on the pyramid stereo matching network to solve the aforementioned problems. Inspired by the classical optical flow estimation framework, we adopt the forward-backward consistency assumption to improve the accuracy. Moreover, we improve the construction of cost volume since the traditional deep-learning networks only work well for positive disparities and the disparity ranges in remote sensing images vary a lot. The proposed network is compared with two baselines. The experimental results show that our proposed method outperforms two baselines in terms of average endpoint error (EPE) and the fraction of erroneous pixels(D1), and the improvements in occluded areas are significant.
Concepts, strategies, and challenges of data deduplication

Data deduplication (DD) approaches are used to eliminate redundant data from the existing data. It means that DD helps for the effective utilization of storage space and then reduces accessing time of data. It is regarded as a propitious approach to manage duplicate data. DD originally permits the uploading of exclusive data copy to the storage, whereas the succeeding copies (duplicates) are rendered with pointers to the genuine amassed duplicates. Nevertheless, numerous DD methods were posited and utilized; no particular best solution was developed to manage all sorts of redundancies. Every DD approach was created with dissimilar designs in addition to DD time-centered on performance together with overhead. Presume that the datasets have numerous duplicates for a file. In this scenario, the DD relates files devoid of observing at their content for a quick running time. Nevertheless, for similar files (not identical), DD approaches look within the files for verifying which portion of the file contents are existent (same) in the formerly saved data for effectually saving the storage space. Here various prevailing DD approaches are organized centered on granularity, deduplication’s location, and deduplication time. This work commences by clarifying the effective detection of redundancy utilizing hashing (chunk index) and bloom filters. After that, it illustrates how every DD approach functions.
Using fuzzy C-means Clustering to Identify Heavy Metal Polluted Soil in a Certain Area of Shanghai

Abstract Fuzzy clustering is an important branch of fuzzy pattern recognition, which has been widely used in many fields such as data mining, image processing, big data analysis and so on. Among many fuzzy clustering algorithms, fuzzy c-means algorithm is the most widely used. In this paper, the fuzzy c-means clustering is applied to the identification of polluted soil. To solve the problem of determining the optimal number of clusters for this method, the method we choose is obtaining an initial clustering result firstly and then merging. Based on the important characteristic of fuzzy c-means method that the objective function of fuzzy c-means clustering decreases rapidly as the number of clusters increases, and the rate become slow after exceeding the optimal number of clusters, we choose the clustering result whose objective function is reduced to a certain degree as the initial clustering result. Then use the parameter estimation method in statistics to estimate the distance between classes, determine the reasonable range of distance between classes, combine the initial classification results, and finally perform the final clustering results according to the clustering validity function. Evaluation and experiments prove the feasibility and effectiveness of the scheme.
Fine-Grained Categorization From RGB-D Images

In the field of computer vision, fine-grained visual categorization has attracted a lot of attention and made great progress due to convolutional neural networks and a large number of publicly available datasets. With next-generation sensing technology, RGB-D cameras can provide high-quality synchronized RGB and depth images for solving many computer vision problems. Although RGB-D cameras have been used in the context of multi-view object category detection and scene understanding, they have not been widely used in fine-grained classification. In this paper, we introduce a multiview RGB-D dataset RGBD-FG for fine-grained categorization. Currently, the dataset contains 93 051 RGB-D images covering 19 super-categories and 50 sub-categories of common vegetables and fruit, and is organized in a hierarchical manner. We provide extensive experimental results to establish state-of-the-art benchmarks for our dataset, illustrating its diversity and scope for improvement through future work. We also propose a novel modality-specific multimodal network called FS-Multimodal network, which can solve two limitations of multimodal networks trained based on fine-tuning techniques: over-fitting and lack of effective depth-specific features. We hope that our study lays the foundations for fine-grained categorization of RGB-D data.
Lower bounds on the failure probability of unambiguous discrimination

We present a systematic approach for obtaining lower bounds on the failure probability of unambiguous state discrimination by introducing an ancillary degree of freedom. Specifically, the lower bounds are obtained by introducing a suitable homogeneous function of the off-diagonal entries of the reduced matrix in the ancillary degree of freedom. A series of lower bounds on the failure probability of unambiguous discrimination of both pure states and mixed states are derived using the ${l}_{r}$-norm of the off-diagonal entries. Our method adds insights into the problem of unambiguous state discrimination. In particular, since we have related the lower bound with off-diagonal entries of density matrices, e.g., the ${l}_{1}$-norm coherence, our work suggests that unambiguous state discrimination may provide further applications in quantifying coherence, and vise versa.
Game Theoretic Storage Outsourcing in the Mobile Blockchain Mining Network

Besides the computation limitation, the requirement of storing the entire blockchain is another challenge for blockchain mining in mobile environments, and thus has hindered the development of blockchain-powered mobile applications. Storage outsourcing to a cloud service provider (CSP) is a viable solution. An individual miner can store his blockchain in the cloud and then validate transactions by querying the CSP. However, validation outsourcing to a remote CSP incurs delay and damages a miner's winning probability in the mining competitions. To shorten such an unwanted delay, miners can also cache the unspent transaction output (UTXO) set in a nearby edge service provider (ESP) for fast transaction validations, which definitely brings extra costs. In this paper, we consider a two-layer outsourcing paradigm to solve storage shortage for mobile miners. Due to the delay-cost tradeoff when selecting service providers, we can model interactions among miners as a non-cooperative game and formulate a Nash equilibrium problem to investigate the effects of outsourcing on miners' utilities. We also study the access probability of UTXOs with different generation times. This will guide miners on how to select unspent transaction outputs if they decide only to cache the partial UTXO set in the edge. We further extend our game by modeling multiple mining rounds as a one-shot game to see how the cache update frequency affects miners' strategies. Numerical evaluation is conducted to show the feasibility of storage outsourcing and to validate the proposed models and theoretical results.
An Effective Statistical Test Suite for Pseudorandom number Generators in Digital Signatures and Security Robustness Evaluation including a Wavelet Test for Randomness

The need for random and pseudorandom numbers arises in many cryptographic applications including digital signatures management. For instance, common cryptosystems employ keys that must be generated in a random fashion. Many cryptographic protocols also require random or pseudorandom inputs at various points, e.g., for auxiliary quantities used in generating digital signatures, or for generating challenges in authentication protocols. A suitable set of statistical tests for randomness of message digests (MD5, SHA) and digital signatures DSA algorithms is outlined and evaluated in this paper based mainly on the revisions of National Institute of Standards and Technology (NIST) procedures in detecting deviations of a binary sequence from randomness. Although such an analysis exists for AES standard there is no similar study for message digests or digital signatures. The contribution of this paper lies in the attempt to fill in this gap from an educational point of view, providing relevant plots of the derived sequences, as well as to introduce a novel Discrete Wavelet Transform Test for Randomness.
Image Self-Recovery Based on Authentication Feature Extraction

This paper proposes a novel image self-recovery scheme based on authentication feature extraction. The Authentication Feature Extraction method is proposed to calculate the authentication information. The Set Partitioning in Hierarchical Trees encoding algorithm is employed to calculate the recovery information. Moreover, in order to retrieve the damaged information caused by tampering, we propose to map each block into another position and generate the mapped-recovery information accordingly. In this way, a double assurance of recovery information can be provided. Experimental results show the superior performance of the proposed scheme in terms of image self-recovery. Comparison with the state-of-the-art works demonstrate that the proposed scheme shows efficiency in strong capability for image recovery, and effectiveness of attack resistance.
Uncertainty Quantification in Medical Image Segmentation

In medical images, the observer's manual description of different structures is very different, and it spans a wide range of various structures and pathologies. This variability (which is a characteristic of biological issues, imaging modality and expert annotators) has not been fully considered in the design of computer algorithms for medical image quantification. So far, few people predict the uncertainty of medical image segmentation. In this paper, we designed a V-shaped network to quantify the uncertainty in prostate MRI image segmentation. We have embedded a feature pyramid attention module in the backbone network, which can extract high-level semantic context information at different scales and provide a pixel-level attention to the decoder. At the same time, the module will not bring a large computational burden. In our experiments, we tested the performance of the proposed method on 55 clinical subjects.
The Fusion of Neural Architecture Search and Destruction and Construction Learning

Object classification is a classic problem in the field of pattern recognition. The traditional deep neural networks have been able to achieve good results on some classification problems, however, there are still many difficulties to be overcome in the fine-grained identification task, whose performance are still baffled by practical problems. In this paper, we introduce neural architecture search (NAS) to search the appropriate network according to the specific data set, which do not need more engineering work to adjust parameters for the optimized performance. We further combine the Destruction and Construction Learning (DCL) network and the NAS-based network for pollen recognition. To this end, we use a fusion algorithm to implement the combination of different networks, and won the pollen recognition competition held at the international pattern recognition Conference (ICPR) 2020.
Research on the Application of Computer in the Construction of Higher Mathematics Network Teaching Mode Platform

Abstract With the continuous development of computer technology, digital technology continues to affect the teaching mode. For colleges and universities, advanced mathematics is almost a compulsory course for college students. This article relies on computer technology, aims at the construction of advanced mathematics network teaching mode platform, analyzes the feasibility of mixed teaching mode, explores the "micro-learning + advanced mathematics" mode, and is task-driven Use situational teaching as a means to improve the quality of higher mathematics teaching.