Reviewerly/exHarmony huggingface dataset:
Supervisory Scheme for Blockchain Privacy Protection Technique Based on Group Signature

As research on blockchain deepens, privacy protection technologies of blockchain continue to develop. However, the use of these privacy protection technologies may cause supervisory problems since tracing and censoring malicious users and their misbehaviors become difficult. In order to solve the supervisory problems, we propose a supervisory scheme based on the Linkable Spontaneous Anonymous Group Signature (LSAG) schemes. In this paper, we first proposed the overall supervisory scheme based on LSAG to achieve effective tracing of blockchain users. To prevent tracing power abuses, we then redesigned the key generation algorithm and its corresponding negotiative decryption algorithm in the scheme to allow decentralized traceability. Only when all supervisory nodes agree can a transaction be decrypted and linked to its sender. Experiments show that our scheme can effectively realize transaction accountability and also prevent the abuse of this supervisory power.
Analysis of the suitability of rice farming land as an opportunity for determining the agricultural planting calendar in Ajibarang District, Banyumas Regency, Central Java Province

Abstract Ajibarang District is one of the areas in Banyumas Regency with a high level of productivity, but the decrease in rainfall in 2017-2019 caused Ajibarang District to have difficulty in developing their agriculture so that farmers experienced crop failure. The impact that occurs from crop failure causes the land to not be cultivated and if it is cultivated, farmers plant secondary crops. It is also greatly influenced by climate change that occurs. In fact, Ajibarang Subdistrict is included in the B2 Climate Zone with an average rainfall of 1000 - 2000 mm/year which is a climate zone business. The purpose of this study was to determine the opportunities for the rice planting that is very suitable for rice farming. In addition, the Ajibarang Sub-district is also drained by a large river, namely the Serayu River and the Tajum River which are tributaries of the river. So, it is necessary to adjust the climate to be able to determine a suitable planting calendar for agricultural calendar in Ajibarang District. Method The research used is the Forward Sampling and Backward Sampling analysis methods. So, the results of this study resulted in a rice and secondary cropping calendar with three planting seasons in one year and two dry seasons in one year.
An Improved Deep Neural Network Based on a Novel Visual Attention Mechanism for Text Recognition

Text recognition from images captured by handheld mobile devices has attracted considerable research interest because of its commercial applications. The state-of-the-art printed text recognition methods are often based on attention mechanisms. However, these methods perform poorly on images captured due to poor illumination conditions, blur, noise, and low resolution. To address these unfavorable conditions, we propose a new text recognition method based on an encoder-decoder model. Particularly, we present a novel attention mechanism using a multi-scale cascade fashion combined with a channel attention gate module. Our model is also strengthened by an EfficientNet-like backbone. Extensive experiments on three popular datasets, including SROIE 2019, B-MOD, and CORD, show that our proposed method outperforms the baseline attention mechanism and achieves competitive accuracy compared to other state-ofthe-art approaches.
Research on Twitter Data Crawling and Data Visualization Analysis Based on Python

In this era of rapid development of the Internet, there will always be a large amount of new and innovative information. For Internet users, it is very tedious and impractical for them to filter out interesting and high-quality information. Therefore, a good-performance recommendation system that can intelligently screen and recommend users is necessary in the Internet era, and the essence of a good-performance recommendation system is a reliable recommendation algorithm.With the development of the Internet, the generation and storage of data are everywhere, and it is of great significance to analyze social reality responses based on user behavior data. The article mainly uses python user comments to crawl, and use python for text data mining and analysis, by using more than 100,000 sets of captured data to form multi-party visualization including line graphs, histograms, and tag clouds to promote conclusions.In that case, the automatic collection of people’s search data to determine the weight of the index solves the problem of the lack of persuasiveness of the traditional expert method of determining the weight. Finally, it helps people effectively draw conclusions from extensive and complex data.
An Improved GAIL Based on Object Detection, GRU, and Attention

Imitation Learning (IL) learns expert behavior without any reinforcement signal. Thus, it is seen as a potential alternative to Reinforcement Learning (RL) in tasks where it is not easy to design reward functions. However, most models based on IL methods cannot work well when the demonstration is high dimension, and the tasks are complex. We set one realistic-like UAV race simulation environment on AirSim Drone Racing Lab (ADRL) to study the two problems. We propose a new model improves on Generative Adversarial Imitation Learning (GAIL). An object detection network trained by the expert dataset allows the model to use high-dimensional visual inputs while alleviating the data inefficiencies of GAIL. Benefit from the recurrent structure and attention mechanism, the model can control the drone cross the gates and complete the race as if it were an expert. Compared to the primitive GAIL structure, our improved structure showed a 70.6% improvement in average successful crossing over 2000 flight training sessions. The average missed crossing decreased by 18.8% and the average collision decreased by 14.1%.
Mufin: Enriching Semantic Understanding of Sentence Embedding using Dual Tune Framework

With the advancements of Natural Language Understanding (NLU), diverse industrial applications like user intent classification, smart chatbots, sentiment analysis and question answering have be-come a primary paradigm. Transformers-based multi-lingual language models such as XLM have performed significantly well in diverse semantic understanding and classification tasks. However, fine-tuning such large pre-trained architectures is resource and compute intensive, limiting its wide adoption in enterprise environments.We present a novel efficient and light-weight frame-work based on sentence embeddings to obtain enhanced multi-lingual text representations for domain-specific NLU applications. Our framework combines the concepts of up-projection, alignment and meta-embeddings enhancing the textual semantic similarity knowledge of smaller sentence embedding architectures. Extensive experiments on diverse cross-lingual classification tasks showcase the proposed framework to be comparable to state-of-the-art large language models (in mono-lingual and zero-shot settings), even with lesser training and resource requirements.
Ponying the Slovos: A Parallel Linguistic Analysis of A Clockwork Orange in English, French, and Spanish

Anthony Burgess’s novella A Clockwork Orange has been translated over fifty times into approximately thirty different languages. A unique feature of the novella is its anti-language, Nadsat. Nadsat poses stylistic and creative challenges for translators, being composed of different categories which draw on different word-formation principles. Building on our own work in the area, in this chapter we unpack such challenges through a contrastive analysis of the English original and two of its more popular translations, the French L’Orange Mécanique and the Spanish La Naranja Mecánica. We investigate Nadsat in each translation, offering a description of the construction of Nadsat across languages, an exploration of how the French and Spanish translators handle the multiplicity of words for ‘women’ in English-Nadsat, and a critical, comparative evaluation of Leal’s and Quijada Vargas’s idiosyncratic approaches to translating Nadsat and the impact their varied approaches have on the novella. Overall, our findings show that corpus approaches can offer data-driven insights into the translation of science fiction texts. Moreover, our formal categorisation of Nadsat items offers a bottom-up, language-agnostic approach to categorising Nadsat across languages and our review of the language of women in Nadsat points to the importance of consistency in translation.
BAN: Large Scale Brand ANonymization for Creative Recommendation via Label Light Adaptation

One of the primary component in ads creative recommendation system is the brand anonymization that removes brand-specific information from ad text for legal compliance and providing ready to use template for the advertisers to customize and consume. In our previous work [1] on ads creative recommendation system, the anonymization is done via a block list created solely based on manual reviewing, which is expensive and limits in the scale of the deployment of the ads recommendation. In this work we investigate a large scale, automated approach for brand anonymization. Such a problem presents many unique and non-trivial challenges, including the domain specificity of the brand entities, the fine-granularity requirements of structured output, the tight constraint of the limited contexts, the high level of grammatical noise in the advertisement data, and the heterogeneity of information required to perform anonymization. We propose a transformer model that leverage implicit knowledge together with a label-light adaptation procedure for this task. Our model is rolled out to ads systems in Yahoo that cover billions of impression traffic per month and improved previous production system by 68.3% F1-score on token level prediction and 61.6% on ad level prediction.
A Multi-semantic Knowledge Graph Construction Scheme Suitable for Intelligent Power Customer Service

The most classic method of constructing knowledge graph is the Translate-based TransE model, which uses Euclidean distance as the measure in the score function. Each feature dimension participates in the calculation with the same weight, and its accuracy is limited when dealing with complex relations. Aiming at the defects of traditional models, a knowledge graph construction model, TransCD, based on cloud model for multi-semantic relationship is proposed to adapt to the construction of domain knowledge graph in power industry. By dividing the relationship into multiple semantics, the Gaussian mixture model of the relationship is constructed; The corresponding cloud model is constructed to obtain the linguistic value and certainty that can best express the relationship; The determinacy is taken as the weight and the weighted Euclidean distance is taken as the new score function. At the same time, combined with the TransD method, the space projection model of entities and relations is established through dynamic mapping matrix, which integrates the advantages of the two models and increases the ability of the model to deal with multiple semantic relations. The experimental results show that the new method is superior to Trans(E, H, R, D) in terms of link prediction and ternary classification tasks, and has advantages in the processing of multi-semantic professional knowledge in the power industry.
Depth Analysis of Gradient in Teaching

Gradient and divergence are the basis of electromagnetic field theory, and have been a special difficulty in mathematical theory.Understanding these concepts requires strong spatial and abstract thinking.In this paper, through the graphical and quantitative methods, the significance of gradient representation is clearly displayed in the form of graphics.
Application Model Design of Complaint Work Order of Electric Power Enterprise Based on Text Mining

In order to understand the application performance of text mining technology in power enterprise complaint work order, this paper analyzes. Firstly, the text mining technology is summarized, and the basic principle of this technology is described. Secondly, the construction method of the work order text mining model is analyzed. Finally, the validity of the model is verified by a case. The results show that text mining technology can analyze the complaint work orders of power enterprises and then classify all work orders according to the analysis results, which is conducive to manual work and plays a role in improving work efficiency. At the same time, the application of text mining technology model in this paper is effective and has a certain application value. Because of the large number of modern power users, the number of complaint work orders increases, so the traditional labor is difficult to deal with all complaint work orders efficiently, and text mining technology can break through the limitations of labor and improve the status of complaint work orders.
Design and Implementation of Medical QA System using Machine Learning Techniques

Nowadays, people rely on Traditional books or Google for every answer to their questions on a day-to-day basis, from basic information to medical queries. Till now, people are facing problems and are unable to find the accurate answer to their questions or fetch relevant results. Also, this technique is time-consuming as people have to go through many books to obtain one relevant answer or search various websites, which is a tedious task and not an efficient way in today's world where time is the top priority, yet the majority of people follow these techniques. So, to overcome this technique and solve the current problems, we have implemented a new technique in this paper. The BERT model, pre-trains deep bidirectional representations from the unlabeled text which conditions on both left and right, as a result, provides accurate answers to the user’s query when compared to the state-of-the-art model. This same model can be further implemented in other domains to obtain accurate results.
ФОРМУВАННЯ ЕМОЦІЙНОГО ІНТЕЛЕКТУ В МЕНЕДЖЕРІВ-МАГІСТРІВ В ІНСТИТУТІ ПІСЛЯДИПЛОМНОЇ ОСВІТИ ТА МЕНЕДЖМЕНТУ

The article reflects the forms and methods of work of scientific and pedagogical workers of the university on the formation of emotional intelligence in future managers. The essence of emotional intelligence and their role in the management activities of the head of the organization are revealed. Written out tips for the development of this phenomenon in the practical work of the manager
Signal Detection Method at the OFDM Receiver Based on Conditional GAN

Deep learning is an effective approach for signal detection which is a challenging issue in wireless communication systems. Some DNN deep learning methods have shortcomings, like complex structures, many training parameters, and converging difficulty, which may cause information loss and affect accuracy and reliability if the received signals pass through the continuous layers of the neural networks. To overcome those, in our paper, the signal detection part is replaced with a conditional generative adversarial (cGAN) network at the OFDM receiver, and we preprocess the received signals using LS and ZF algorithms based on model-driven as the initial input of the neural network. And the cGAN model introduces pilots as the condition to counter-train the two deep learning networks for generating signals closer to being under real channels. In addition, with an adaptive loss function (GAN Loss), the cGAN model has a certain corrective effect on optimizing the neural network for recovering data. The simulation results illustrate that the cGAN model performs better than the existing DNN models under various signal-to-noise ratios (SNR), especially facing low SNRs and short pilot sequences, that is, it has better robustness for restoring effectively the transmitted signals under real channels.
Random Forest Regression of Markov Chains for Accessible Music Generation

With the advent of machine learning, new generative algorithms have expanded the ability of computers to compose creative and meaningful music. These advances allow for a greater balance between human input and autonomy when creating original compositions. This project proposes a method of melody generation using random forest regression, which increases the accessibility of generative music models by addressing the downsides of previous approaches. The solution generalizes the concept of Markov chains while avoiding the excessive computational costs and dataset requirements associated with past models. To improve the musical quality of the outputs, the model utilizes post-processing based on various scoring metrics. A user interface combines these modules into an application that achieves the ultimate goal of creating an accessible generative music model.
A combinatorial optimization approach for multi-label associative classification

Mining associations between variables corresponding to multiple class labels (or outcomes) is prevalent in various applied domains, such as medical diagnosis, text mining, e-commence, and social behavior analysis. While most associative classification algorithms have been developed to discover association rules of binary variables for single-label classification problem, there are limited methods designed for the problem, called multi-label classification, that accounts for multi-labels. In this study, we consider the multi-label classification problem as a multi-class classification problem and formulate it as a 0–1 integer optimization model. We then leverage combinatorial optimization and association rule techniques to solve this hard problem. More specifically, we propose a ranking metric for selecting and aggregating stronger rules to form an optimal multi-label classifier. The computational results for multiple real applications show that our algorithm is able to identify significant association rules between key variables and multiple labels, and in turn achieves a competitive classification performance compared to state-of-the-art machine learning methods such as logistic regression, decision trees, and random forest. Moreover, we design a user-interface tool interfaced with the developed algorithm and demonstrate a medical diagnosis problem to predict multiple high-risk subgroups during emergency care in practice.
Model Independence

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Construction of information security risk assessment model based on static game

Game theory is a branch of modern mathematics, which is a mathematical method to study how decision-makers should make decisions in order to strive for the maximum interests in the process of competition. In this paper, from the perspective of offensive and defensive confrontation, using game theory for reference, we build a dynamic evaluation model of information system security risk based on static game model. By using heisani transformation, the uncertainty of strategic risk of offensive and defensive sides is transformed into the uncertainty of each other's type. The security risk of pure defense strategy and mixed defense strategy is analyzed quantitatively, On this basis, an information security risk assessment algorithm based on static game model is designed.
Text Sequence Recognition in Natural Scenes Based on Deep Recurrent Neural Networks

Detection and recognition of variable length text sequence in natural scene has a wide range of applications, such as license plate recognition, road sign recognition, advertising and so on. Traditional way of text information extracting and recording are manually operated, which is high cost, low efficient and inconvenient. To solve this problem, CNN, RNN and CTC networks are combined to be a union detection and recognition system. Experiment results show that this system can accurately and efficiently detect and recognize of variable length text sequence in English in real scene.
ASTER Thermal Infrared Quantitative Inversion Research on the SiO<sub>2</sub> Content of Surface Rocks in the Easten Tianshan Area of Xinjiang

The SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content information in rocks plays a very important role in the classification of igneous rock types and geological prospecting. The thermal infrared emissivity spectrum characteristics of silicate minerals can reflect their SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content well. This paper selects the rock spectrum data in the USGS and JHU spectrum library as the modeling sample, resamples the spectrum information to the ASTER thermal infrared band, and calculates the correlation coefficient between original spectrum, ratio spectrum and logarithmic spectrum of ASTER thermal infrared band and SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content. On this basis, a number of multiple linear regression models based on ASTER thermal infrared spectroscopy and SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content were established, and the accuracy of each model was evaluated. Finally, the model with the highest accuracy was selected to perform a quantitative inversion of SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content on a scene of ASTER data in the Easten Tianshan area of Xinjiang. Through comparative research, the inversion results are in good agreement with the SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content of various lithologies in the geological map. It shows that this method is feasible for inversion of SiO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> content regionally in surface rocks, and has certain guiding significance for remote sensing ore prospecting.
Action Reproducer: Virtual Reality Rehabilitation System to Reduce Fear of Walking

Walking is a daily activity for most people. Lack of opportunities or inability to walk may cause both mental and physical health problems. However, in some circumstances, such as during a global pandemic, fear of heights, or withdrawal from society (hikikomori), people tend to walk less. To overcome such issues, we developed a walking rehabilitation system, Action Reproducer, to encourage people to walk in a virtual environment, e.g., around sightseeing spots, or to train walking on a high building. The proposed system comprised a motion seat to present vestibular sensation to the waist, slider-pedal devices to provide motion sensation to the lower limbs, wearable pseudo force devices to pull sensation to the fingers, and an avatar in the virtual environment to hold the user’s hand during walking. This system can reduce user burden because the user simply sits on the motion seat and perceives multiple sensations that allow them to enjoy walking activities for long periods of time. During the virtual conference, we will conduct a live demonstration and present the details of our system with some interactive content, such as virtual traveling and walk rehabilitation between high buildings.