Reviewerly/exHarmony huggingface dataset:
Blockchain-based cryptography model to preserve privacy for smart contracts

As a computer programmer, a smart contract encapsulates the contractual features of the parties’ transactions through code. Some blockchain technology platforms, such as Hyperledger Fabric, IBM Blockchain and Ethereum, currently allow smart contracts. Smart contracts on these platforms are kept on the blockchain and are not under the control of any party involved. They can’t be tampered with after they’ve been digitally signed and placed on the blockchain. The use of smart contracts in blockchains is critical. It is the authors’ primary goal with this research to provide an alternative to standard contracts by allowing users to modify data stored in the chain. Methodology for designing smart contracts is presented in this article, along with an example of how it has used modifications to the data stored in the chain. In this article, we present a methodology for the design of smart contracts and we also illustrate its application with a case study.
Application of Fractal Dimension Technique on a Badland Topography in Tapi Basin, Deccan Trap Region, India

A fractal is a fragmented geometric shape which is characterized by scale invariance under contractions or dilations. Fractal is statistically a self-similar body, which implies that some aspect of a process or phenomena is invariant under scale-changing transformation. Fractal dimension is applied in geomorphology in wide range of topics, such as tectonics, coastal configuration, river basin geometry, landslides, soil studies, karst features, etc. With the availability of high-resolution digital elevation data and operating GIS tools, further new interests have arisen in the technique. In the present study, fractal dimension has been applied to an alluvial badland topography along the banks of Tapi River, in the Deccan Trap region, India. The area is characterized by semi-arid climate. A newly developed software ‘Viz-Morphotec’ was used to calculate fractal dimension (D) for the entire area of badlands which yielded that ‘D’ values between 2.9 and above clearly coincide with the location of badlands in the basin. Two sample catchments were selected for determining fractal properties of these badlands and to understand microprocesses operating in this topography. Fractal dimensions were calculated at three levels, namely, linear, perimeter and surface. Variograms were computed for both the catchments also. Results indicate a multifractal topography, where two or more processes are operating in the landscape. The curves of the variograms indicate possible influence of diffusional and erosional processes operating on the topography or could be a result of tectonics or changes in the climatic conditions that are still manifested in the landscape. Hence, these badland areas indicate multifractal topography where more than one process are operating within it. The results also reveal that rivers are actively eroding, and linear erosion is predominant in the whole region.
Secure Transaction Framework based on Encrypted One-time Password and Multi-factor

This paper presents a secure transaction framework for money transactions. A qualitative study is taken into consideration as the goal of the research. The proposed framework ensures a secure money transaction and is based on multi-factor Encrypting One Time Password (EOTP) that have a strong authentication method and overcome the weakness found in One Time Password (OTP) and EOTP methods. The proposed framework is analyzed against different attacks encountered by OTP and EOTP and is found to be effective and secure.
Applying the BBRP01 Technique Used To Secure The Data

In this digital era, the current state of the data is critical to the lives of citizens, as data is remaining as the primary factor determine the survival of a person in the world. In particular, data is more readily available on social media platforms. Despite the hype, the data lacks adequate securing by allowing the hackers to readily access it. To overcome this challenge, this research work propose a novel technique called BBRP01 to solve this problem. Generally, there are four stages to this approach. In this 1st stage applies the T-test method; 2nd stage applies the odd equation; 3rd stage represent even equation; and final stage applies the perfect number operations. Further, the BBRP01 technique provides advanced security when compared to ChaCha method.
A Fine-tuning-based Adversarial Network for Member Privacy Preserving

With the development of machine learning, the issue of privacy leakage has attracted much attention. Member inference attack is an attack method that threatens the privacy of training datasets. It uses the model’s behavior to infer whether the input user record belongs to the training datasets, and then get the user’s private information according to the purpose of the model. This paper studies the member inference attack under the black box model. We design a defense mechanism to make the learning model and the inference attack model learn from each other, and use the gains from the attack model to fine-tune the last layer’s parameters of the learning model. The fine-tuned learning model can reduce the gains from the membership inference attack with less loss of prediction accuracy. We use different datasets to evaluate the defense mechanism on deep neural networks. The results show that when the training accuracy and test accuracy of the learning model convergence are similar, the learning model only losses about 1% of the prediction accuracy, which the accuracy of the member inference attack drops by a maximum of around 20%.
Arbitrary Style Transfer via Learning to Paint in the Feature Domain

Style transfer is enjoyed by people in multimedia fields, like photography. However, arbitrary style transfer still faces the challenge of fully migrating the style while maintaining content consistency. To solve this, we propose a novel arbitrary style transfer algorithm named LPFD. We firstly propose the point of view of Learning to Paint in the Feature Domain (VLPFD). Then, according to the VLPFD, the Parameter Generation Network and Painting Network is designed to stylize the content feature map. Thanks to the VLPFD, the parameters of the Painting Network are significantly reduced, which considerably alleviates the burden of the Parameter Generation Network. The experimental results demonstrate that our algorithm can achieve more satisfactory results than the state-of-the-art arbitrary style transfer algorithms. Besides, LPFD can run in real-time.
Enhancing Nearest Centroid with Coverage Principle for Classification Problem

Motivated by the network coverage of the transmitter, this research proposes a novel coverage-based method to improve the Nearest Centroid&#x2019;s class prediction by replacing the centroid with radius coverage as the reference in measuring distance. This novel approach, called Nearest Coverage, was tested using a breast cancer dataset to demonstrate its efficacy. The results indicate that this new classifier approach is promising more accuracy than the Nearest Centroid using appropriate coverage configurations. This method has the advantage of being straightforward and applicable to a wide variety of classification problems.
Robust Query-by-example Spoken Term Detection for Unknown Words Using Speech Retrieval-oriented E2E ASR Modeling

Query-by-example spoken term detection (STD) systems can make good use of automatic speech recognition (ASR), especially when the error rate is low. However, ASR suffers from the out-of-vocabulary (OOV) problem. The OOV problem in the ASR stage has a significant impact on the performance of STD for speech retrieval and can generate false retrieval for query words. In recent studies, End-to-end (E2E) ASR systems have achieved competitive performance compared to traditional DNN-HMM ASR systems. It has also been shown that E2E ASR system can reduce the impact of the OOV problem by using characters or sub-words as the output unit during recognition. In this paper, we propose an improved method using E2E ASR modeling adapted to a speech retrieval task, based on the STD method that considers acoustic similarity at the sub-phone level. Experimental results using the NTCIR-12 SpokenQuery&Doc-2 task show that the STD method using E2E ASR improves retrieval performance over the STD method using DNN-HMM ASR. This is attributed the fact that E2E ASR was able to reduce the OOV problem for spoken documents and spoken queries.
Overview of information visualization for business under the background of big data

In the era of data explosion, the data is growing exponentially. Information visualization maps abstract data into visual effects, and helps people to understand the market and make efficient decisions in an intuitive way, which has attracted more and more attention.This paper focuses on the field of information visualization, studies the theoretical basis of visualization models, research methods and design principles, expounds commercial application cases of information visualization, and presents the challenges and opportunities of the information visualization.
Automatic detection of Temples in Colour Images by using LBP

The automatic exposure of Temple in colour images is a difficult task due to a lack of samples to deduce the variations in colour digital picture testing. In the field of image processing, object recognition is a harsh but useful dilemma. This paper has presented a computer vision-based scheme for detecting temples to consider a lot of effort had been put in on animals, people, bikes, cars, and so on. As a result, we decide to focus on temple detection, which is based on a self-created dataset; there will be no testing of temple exposure in photos. The demonstration or contour of the multiplicity of buildings is indistinguishable, making image processing-based temple identification difficult. As a result, this research work proposes a novel approach to deal with the building structure on temple discovery in digital photographs by using Local Binary Pattern [LBP]. This paper includes a few landscapes that have a similar contour in digital images of temples, such as building classification. Even though equal shapes are initiated in building structures, the proposed work representing shape features is used to find a temple in photographs.
Research on Text Classification Method of Distribution Network Terminal Debugging Job Based on Hierarchical Comprehensive Context

Since a lot of historical operation description texts in the distribution network terminal automation system, and there is a lack of classification efficiency and accuracy between manual classification and traditional text classification methods. In order to realize the effective classification of job text information, a neural network classification model based on hierarchical comprehensive context is constructed by using the features of debugging job text of distribution network terminal. By combining LSTM network with temporal convolution network and combining residual and attention mechanism, context information is extracted and integrated with original input. Hierarchical extraction of content and deep semantic features and long-term history fuzzy information recording are realized. The experiment on the text data set of distribution network terminal debugging operation shows that this method has better optimization effect than CNN and TCN model under the selected evaluation indexes. The model can extract the defect content and sequence information more comprehensively, realize the accurate automatic classification of debugging text, effectively improve the intelligent level of distribution network terminal debugging, and promote the application of text processing technology in power grid.
Globally Optimal and Scalable Video Image Stitching for Robotic Visual Inspection of Electric Generators

Large-scale image stitching of temporal video data acquired by a small robot can facilitate the inspection process of electric generators, reduce the inspection time, and improve the reliability. However, the image data poses a number of challenges due to the small field of view, lack of distinct texture, specular highlights, and other image artifacts. We introduce a novel image stitching method, which generates composite images of generator wedges from temporal videos using intensity-based registration and non-linear blending. In contrast to previous intensity-based registration approaches, our global method simultaneously exploits the information of all image frames of a video and directly determines the global image translations. We propose a suitable energy function and employ a graph-based method for globally optimal minimization in linear runtime. Regularization is used to exploit physical knowledge about the application domain which improves the robustness. We have applied our approach to temporal video data of rotor wedges and performed a comparison with previous methods. We found that our method yields superior results.
Enhancing recommendation competence in nearest neighbour models

Collaborative Filtering approaches are viewed as essential tools to suggest products to users based on historical knowledge. Widely adopted user based Collaborative Filtering approaches rely on ratings provided by similar users of the target user to generate appropriate recommendations. The pair-wise user similarity is based on set of common rated items between users and identifying similar users is a challenging task when the set is small. To improve the recommendation quality in low correlated data, User Trait Model and Bayesian Global Agreement model are suggested in this work. The proposed models assign global agreement score to users. A linear function of any baseline user similarity and global agreement score of users is defined as a new similarity measure. From inception, recommendation approaches are keen on improving accuracy of recommended items and downplays the diversity of recommendations, which results in poor user satisfaction. The proposed models focus on improving accuracy and diversity of recommendations. Experiments are conducted on three benchmark data sets and the results are compared with other user based CF approaches suggested in the literature. The experimental results indicate that the proposed approaches outperform other user based CF approaches based on the evaluation metrics namely, MAE, RMSE, F1 for prediction accuracy, MN, ILD for recommendation diversity and Coverage for the extent of recommendations.
DMVO: A Multi-motion Visual Odometry for Dynamic Environments

Most existing algorithms solve the visual odometry problem in dynamic scenes by removing dynamic objects. However, some scenarios such as autonomous driving, visual surveillance and virtual reality, require explicit motion information of the objects to help making decisions and understanding scenes. In this paper, we propose DMVO (Dymamic Multi-motion Visual Odometry), a robust and effective visual odometry algorithm that estimate camera pose as well as motion of objects in dynamic scenes using information from stereo cameras. DMVO combines disparity and depth estimation networks to obtain high-precision depth information, which improves the accuracy of the system. Local pose graph and global factor graph of dynamic and static structure are constructed for optimization. The DMVO system is tested on an indoor Oxford multi-motion dataset and an outdoor KITTI tracking dataset. The results show that our system achieves improved performance compared with the state-of-the-art algorithms.
Canonical Segmentation Using Affix Characters as a Unit on Transformer for Javanese Language

Morphological segmentation for agglutinative languages is the process of getting stems and affixes. Morphological segmentation is a necessary process in various NLP applications such as machine translation, question answering, and speech recognition. Several neural morphological segmentation studies have used the sequence of characters as input to encoder-decoder. However, this can not provide linguistic information. We propose affix characters as a unit to provide affixes feature on Transformer encoder-decoder. We use the Javanese word corpus which consists of affixed, canonical affixed, and non-affixed words. For affixed words, our proposed method obtains 11.2 times higher point of accuracy than the Sequence of Characters. For canonical affixed words, we get 21.9 times higher point of accuracy than the baseline method. The results also show that the use of different affix symbols, which are &#x201C;&#x0025;&#x0025;&#x201D;, &#x201C;&#x0023;&#x0023;&#x201D;, and &#x201C;@@&#x201D; for each type of affix improve accuracy in affix recognition.
Detecting Racist and Bad Words Using Text Mining in Social Media

The use of social media has grown rapidly in the last decades. Many of these social media platforms, i.e., Twitter, do not screen or remove potentially offensive/ harmful tweets. It can cause discomfort for the users, especially if minors encounter these words while they are surfing the media. We came up with a profanity detection model with text mining that uses bag of words algorithm. Then we applied four different classifiers to see which one gets the highest accuracy score. The results show a high accuracy performance, with Random Forest classification being the highest with 97.5%.
ASL Tutor Using Deep Learning

Sign Language is used by people with speech impairment and it improves the individual ability to efficiently communicate with the world. Although Sign Language has become conventional lately, the main drawback persists that there is still a gap between the disabled and the enabled, which is blatantly ignored due to its given insignificance. The goal of this work is to create a vision-based application that helps in tutoring the American Sign Language (ASL) to the common crowd and thus shortening this communication gap. The proposed object detection model takes an image input and extracts the temporal features which are then processed by a custom YOLO-Tiny architecture model. This approach uses the Object Detection techniques for this application where most of the research is done using Image Classification techniques. In the end, the user’s actions are tested by the model based on how similar the action is to the relevant gesture.
Strengthening Data Security Using Multi-level Cryptography Algorithm

Information security incorporates information encryption, hashing, tokenization, and key administration rehearses that ensure information over all applications and stages. The purpose of this paper is to strengthen data security using multi-level cryptography algorithm. A half-breed calculation is a calculation that joins at least two different calculations that take care of a similar issue, either picking one (contingent upon the information) or exchanging between them through the span of the calculation. This is commonly done to consolidate wanted highlights of each, with the goal that the general calculation is superior to the individual parts. The average values are calculated here for time and memory parameters by combining three algorithms which are multi-prime RSA, DES, and Vernam Cipher. RSA calculation is a well-known exponentiation in a limited field over whole numbers including prime numbers. The Data Encryption Standard is a symmetric-key calculation for encryption of computerized information. Despite the fact that its short key length of 56 bits makes it excessively unreliable for applications, it has been profoundly powerful in the headway of cryptography. In present day wording, Vernam Cipher is an even stream cipher where the plaintext is joined with an arbitrary stream of information (the “keystream”) of a similar range, to create the cipher text, utilizing XOR. Subsequent to consolidating these three calculations, the framework will be somewhat rugged. The results have been represented after using a different number of primes with this hybrid algorithm. The preferences and thinking for utilizing this approach have additionally been recorded in this paper.
Blockchain-Based Cooperative Incentive System for Emergency Road Right Transferring

Rescue vehicles arriving at the accident site on time are essential for road rescue. The problem is heavy traffic causes difficulties in ensuring a quick rescue. Unintentionally or intentionally, the vehicles ahead do not necessarily give way. An informative and incentive mechanism to encourage more people to transfer road rights in emergencies is required to solve the problem. We have proposed a blockchain-based cooperative incentive system consisting of a program to ask drivers for road priority combines a incentive smart contract to reward those who transfer road rights to vehicles during emergencies.
Algorithm to Control Primary Codec Under the Influence of Interference in Communication Channel

An algorithm to control primary codec under the influence of interference in communication channel is proposed. The effect of errors in communication channel on the quality of reconstructed speech signals at the output of known types of primary codecs is analyzed. Recommendations to use algorithms for primary coding of speech signals in transmission systems from the viewpoint of ensuring the highest possible quality of reconstructed speech with slight change in transmission rate are given. An algorithm for joint control of primary and noise-immune codecs is proposed, providing a smoother change in channel rate when interference environment in communication channel changes.
Study on the evaluation method of diffuse radiation models——taking 5 typical cities in China as examples

Diffuse solar radiation models play an extremely important role in solar photothermal utilization, resource assessment and energy consumption simulation, etc. The accuracy of these diffuse solar radiation models usually need to be evaluated by various statistical parameters. Among these statistical parameters, the Global Performance Index (GPI) has been extensively employed in recent years because of its comprehensiveness and wide applicability. This paper takes five cities in China as representatives of 5 typical climate regions, and 12 solar scattered radiation models are fitted with the meteorological data of 5 cities. Based on the comparative analysis of the existing GPI calculation methods, this paper points out that there are some defects in the existing GPI, and modifies the existing GPI based on the comprehensive consideration of statistical parameters, normalization preprocessing of statistical parameters, unified evaluation direction of parameters, weight redistribution of statistical parameters, and adjustment of extreme coefficient. 12 types of new GPI are established in this paper, and the performance of diffuse solar radiation models are compared based on these GPI. The rationality of GPI corrective measures is analyzed by means of the method reasonable index (MRI). The results show that the GPI calculation method (N10) which takes five corrective measures has the best performance, and the accuracy of the existing GPI can be improved by 13.33 to 65%.