Reviewerly/exHarmony huggingface dataset:
A Blockchain-Based Platform for Lawyers and Clients

Blockchain technology has the capacity to store structured data and records securely, which makes it an ideal system to store legal documents and information. It is highly secure, making it difficult to tamper with or manipulate records once added. This ensures the integrity of the data stored in the blockchain system. Also, it is now possible to model all or parts of legal agreements using code (smart contracts), decreasing the cost and friction of creating, securing, and generating binding legal agreements. That is why we should develop a blockchain-based legal system for lawyers and their potential clients to provide a secure, immutable, and transparent record of the agreement. This allows parties to trust that the terms of the agreement are being followed without having to rely on a third-party intermediary. This paper answers why the platform is built and the key problems to be solved. We are trying to build a system to help power next generation “smart” legal agreements.
Machine Learning based Pan-cancer Diagnosis Prediction and Treatment Recommendation using DNA Methylation Markers

Cancer is currently the leading cause of death around the world, with about 19 million new cases and nearly 10 million deaths every year. DNA methylation markers can accurately determine the tissue source of tumors, which is of great significance for selecting the correct tumor treatment and improving the survival expectation of patients. In this study, we constructed machine learning models to predict the potential of DNA methylation markers for Pan-cancer diagnosis, survival prediction and treatment recommendation. Our results show that the model which is based on multi-layer perceptron could classify 33 cancer types and normal tissue using 114 markers with an overall accuracy of 90.7%, while its precision is 88.7%. Based on DNA methylation data, we constructed an effective survival prediction model using survival information (P < 0.001). By combining DNA methylation data with clinical information in a machine learning parseable format, we were able to predict the treatment recommendation with 98.7% accuracy. Due to this result was 22.7% more accurate than the accuracy without DNA methylation data, it suggests that DNA methylation plays a significant role in the treatment commendation of cancers.
Research on Intelligent Text Detection Based on Embedded OCR

With the rapid development of the industrial sector in the era, special equipment plays a crucial role in various industries. The nameplate of special equipment is an important component that identifies the device's identity and performance, holding significant significance for equipment management and maintenance. However, in the face of the inefficiency and high labor costs associated with traditional manual identification methods, which cannot meet the growing demands for recognition, this design proposes an embedded OCR text recognition system capable of achieving pattern selection, real-time monitoring, nameplate recognition, cloud data uploading, cloud monitoring, and warning tracking functions. It relies on modules such as STM32103ZET6, Alibaba Cloud IoT platform, Raspberry Pi 4B+ and a series of related modules, including the CMOS high-definition camera, are utilized in the system. A super lightweight OCR recognition system specifically designed for nameplate text recognition is employed. The overall model size of the system is below 5MB, and it achieves an overall recognition rate of over 84%.
Applications of Space-Air-Ground Integrated Monitoring Technology in Resource and Environment Management

In order to deeply understand and promote the application of space-air-ground integrated monitoring technology in natural resources and environmental management, this paper summarizes the main principles, implementation paths, application scenarios, existing problems and future development trends of integrated space-air-ground monitoring technology, focusing on the application in environmental monitoring, geological disaster prediction and early warning, resource monitoring, emergency rescue management, etc. It can provide reference for related research.
A Method for Generating Small Sample Data and Evaluating the Effectiveness of Combat Weapons Based on Conditional Adversarial Nets

A neural network has received attention in weapon performance evaluation, one-dimensional combat data in the standard sample generation method, poor sample feature extraction generation, and the network training process could be more convenient. This paper proposes a small sample data generation based on CGAN and weapon combat effectiveness evaluation method - CMGAN algorithm. Based on the advantages of conditional confrontation networks in image recognition, the sample data of one-dimensional combat weapons are projected into two-dimensional space. A two-dimensional convolution neural network is used to extract sample features. Then further optimize and screen the generated samples, and finally, use the generated virtual samples for GA-BP (genetic algorithm back-propagation) neural network effectiveness evaluation of combat weapons. The results show that the improved CMGAN algorithm can generate higher-quality data. It effectively improves the accuracy of combat weapon effectiveness assessment and network training speed and has good engineering application feasibility.
Innovation and Development of Smart Library from the Perspective of Metaverse

The rise of metaverse technology provides a new idea and method for the development of smart libraries. Metaverse technology empowers the construction of smart libraries and integrates with it. This paper expounds the technical construction, service structure and practical path of the smart library from the perspective of metaverse.
Research on 3D Target State Estimation Technology of Helicopter Fire Control System

In view of the inaccuracy of target state estimation in helicopter high maneuvering flight and the fact that a single model can not cover all maneuvering forms of the target, considering the timeliness and accuracy of the solution of the fire control system, the target model selected by the Interacting Multiple Model must be simple enough. An interacting multiple model algorithm based on constant velocity, constant acceleration and coordinated turning motion model are proposed in this paper. The algorithm is combined with the smoothing algorithm to improve the reliability and accuracy of maneuvering target state estimation, and the extended Kalman filter is used to improve the adaptive ability and robustness of the algorithm. The simulation results show that the three-dimensional target state estimation technology based on interacting multiple model smoothing algorithm improves the accuracy and reliability of target state estimation and has good practical application value.
Application of Artificial Intelligence Technology in the Design of Intelligent Networked Vehicle Talent Training System

Smart connected cars are becoming the most important “wind vane” in the development of the global automotive industry, driving the industry to develop towards intelligence and connectedness. The purpose of this paper is to study the training system of intelligent networked vehicle technology personnel based on artificial intelligence technology. It discusses the necessity and importance of AI technology in the development of technical talent training for smart connected cars, analyses the talent needs of smart connected car-related enterprises and the application of AI technology in talent training, and uses advanced computer technology and management technology to build a “smart connected car technical talent training system” through web technology. “The system of talent training mode management system is introduced. The architecture of the talent training mode management system and the division of system roles are introduced, and the system but the application effect is analysed, which works well and cultivates students' vocational skills of inspection and maintenance of intelligent network-connected vehicles.
Design of Digital Platform of Rural Tourism Cooperatives Based on SOA

Rural tourism needs digital technology to stimulate the development of rural characteristic industries and realize rural revitalization boosted by China's digital strategy. Digital platform of rural tourism cooperatives is an online system that uses web technology to improve the management and provide external information services. The digital platform design adopts SOA architecture to facilitate cross platform and interoperability, which mainly includes access layer, application interface layer, management layer, service layer and data infrastructure layer. The integration of a loosely coupled heterogeneous environment can be realized by using web services, and the data functions are encapsulated in interfaces conforming to OGC standards, so as to build a service-oriented platform framework integrating shared service providers, users and managers. The functional module consists of digital management, operation and service with providing service organization and operation based on unified registration. The platform gives play to the positive role of digital means in targeted poverty alleviation and promoting the high-quality development of rural tourism.
Image Thermal Radiation Restoration Method Based on Gaussian Double Ring Filter

Traditional image restoration methods only study the restoration method based on the image itself, without considering the imaging physical process and the radiation characteristics of the object. Therefore, traditional methods have poor effects on image thermal radiation removal. Therefore, this paper proposes a joint frequency space time domain denoising and restoration algorithm that combines an improved Gaussian ring filter with smooth filtering, allowing the filter to freely adjust the radius, shape, size, and position of the ring according to different image conditions, in order to achieve better restoration effects. After processing the image using the algorithm proposed in this article, the signal-to-noise ratio is significantly improved, and the restored image retains more edge and detail information, effectively improving the resolution of the target image.
How to Decrease Defects of Smart Contracts Security

Blockchain is an open, transparent, accurate and reliable distributed ledger technology that integrates various technologies such as cryptography, consensus mechanism, and smart contracts, and adopts a peer-to-peer network. It provides a new method for building a trust system. Smart contracts are self-executing computer programs that run on a blockchain network. They enable users to build applications that can enable secure, immutable, and automated transactions without the need for a third-party intermediary. Smart contracts can help users store information, execute transactions, and manage assets safely, efficiently, and credibly on the blockchain. However, there are still security issues in the smart contract itself. This article discusses the security issues of smart contracts, and puts forward suggestions for contract security.
Research on Chinese-Lao Neural Machine Translation Based on Multi-Pivot

The performance of Neural machine translation relies on a large number of the parallel corpus. However, For low-resource language, the single language pivot-based methods still difficult to meet the model training needs. To this end, multiple pivot languages are integrated into the Neural machine translation model. This thesis introduces English and Thai corpus into the Chinese-Lao Neural machine translation, the aim is to research the influence on the performance model of pivot language similarity and improve the quality of neural machine translation of Chinese-Lao. The experimental results show that the multi-pivot method could help to improve the quality of neural machine translation of low-resource language, and the influence of corpus size and quality on the results is more than that of language similarity. The multi-Pivot Multilingual Neural Machine Translation model has the highest performance advantage by sharing parameter migration across multiple pivot languages.
Candidate Generation for Entity Linking on Military Equipment

In the intelligent document analysis and application of military news texts, due to the polysemous and synonymous of military weapons and equipment entities, the computer cannot distinguish the ambiguity, so the weapon and equipment entities cannot be directly associated with the knowledge graph. To address this issue, entity linking is employed with Candidate Generation being a crucial step. To effectively enhance the efficiency of entity linking, this paper proposes a military equipment Candidate Generation method based on multi-feature fusion. Given the few text datasets and a large amount of labeling in the military domain, this paper adopts the method of transfer learning to train the model on the general domain dataset and then transfer it to the military domain dataset. The Candidate Generation task of entity linking is completed by integrating the character, pinyin, and English features of weapon equipment entities. The experimental results show that the Candidate Generation task Recall of the military equipment entity linking system reaches 95.85%.
BP Neural Network and Computer Virtual Reality Technology in Piano Performance Practice

As an ancient musical instrument, piano plays an indispensable role in people's daily life. With the development of science and technology and the improvement of living standards, people's aesthetic demand for it also increases. And virtual reality technology can provide users with a new three-dimensional and realistic visual and auditory experience. This paper will introduce a new digital music playing method based on the combination of BP neural network and computer virtual reality system, and how to use MATLAB software to realize piano simulation in the future work. The test results show that, The piano performance system based on BP neural network algorithm and computer virtual reality technology can process more than 80% of the piano score data efficiently, and the maximum number of piano pieces stored can reach 6574k. Based on the above test results, it can be seen that the system can meet user requirements.
A Syntactic Complexity Analysis of Revised Composition through Artificial Intelligence-based Question-answering Systems

With the rapid development of AI technology, it has been increasingly applied in educational practice. The ChatGPT system, released on November 30, 2022, can provide personalized feedback for learners' learning needs in the education field, such as revising students' writing. This paper focuses on the syntactic complexity of college English argumentative essay writing. The researcher compared the English writing of the students' TEM-4 test with the essays that have been revised by ChatGPT. The results show significant differences between the revised and the students' original essays in terms of language production length, overall sentence complexity, number of coordinating structures, number of dependent structures, and phrase complexity. The writing revised by ChatGPT better match the characteristics of high-level argumentative writing, which can be used as a powerful support for students' essay writing practice. Hopefully, this study can provide a reference for college English writing teaching. The study's conditions were set as unlimited online editing of argumentative essays, and L2 syntactic complexity is influenced by the writing environment and genre, so the effects may not generalize to other conditions.
Hand Gesture Recognition for Traffic Signals Based on Improved Lightweight Convolutional Neural Network

Gesture recognition is a crucial aspect of the development of intelligent transportation systems. In this paper, we propose a novel method for recognizing gestures of artificial traffic signals on embedded devices using a lightweight convolutional neural network (CNN). Our approach adopts GhostNetV2 as the base model and improves it by incorporating the MobileNeXt Hourglass block, resulting in GhostNetV2-A, which effectively reduces the size of the convolutional layers and enhances the device's operational capability under limited conditions. In terms of hardware, we replaced the original SPI flash with a 32GB storage card, used a smaller display screen, and added a user button to address the limitations of OpenMV4 Plus and improve its usability. Finally, we deployed the model on the device, and its performance was evaluated in a self-built experimental environment, achieving an average accuracy of 81.3%. The proposed method achieves an average recognition rate of about 10FPS on improved embedded devices. This method provides a practical and efficient solution for real-time gesture recognition of artificial traffic signals on embedded devices, with potential applications in various traffic scenarios.
Diagnostic Model of Umbilical Artery Blood Flow Using NC-PSO-SVM

The pathological diagnosis and analysis of umbilical artery blood flow signals are very important for prenatal fetal monitoring and fetal health status detection. In this study, we proposed Nonlinear Characteristics (NC)-Particle Swarm Optimization (PSO)-Support Vector Machines (SVM)intelligent diagnosis model of umbilical blood flow to solve the problem of low accuracy of pathological diagnosis of early umbilical blood flow caused by human experience. Firstly, the umbilical artery blood flow SVM diagnostic model was constructed with the Peak valley ratio of umbilical blood flow velocity (S/D), Pulsation Index (PI) and Resistance Index (RI) as the characteristic parameters, and the umbilical artery blood flow PSO-SVM diagnostic model was constructed with the non-linear characteristic matrix as the characteristic parameters. On this basis, according to the non-linear characteristics of the umbilical blood flow time signal, the NC-PSO-SVM intelligent diagnostic model is constructed with the PSO algorithm and SVM. The average accuracy of the 20 times of the diagnostic model is 77.5%, The results showed that the accuracy of NC-PSO-SVM intelligent diagnosis model was 9% higher than that of SVM diagnosis model and 5% higher than that of PSO-SVM diagnosis model due to the anti-noise and nonlinear expressiveness of chaotic sequences.
Research on the Defense Mechanism of Audio Adversarial Attack

Automatic speaker verification (ASV) technology has become a core feature of intelligent voice devices. ASV systems are highly vulnerable to audio adversarial attacks, in which an attacker adds perturbations to the original audio to generate an adversarial sample; the human ear cannot distinguish between them but will deceive the ASV system, causing the system to fail to identify the real speaker. The existing defense means such as random masking are less effective against the FakeBob attack. Therefore, this paper proposes a method to eliminate the artificially added perturbations in the original audio using the high-frequency component of masked audio, combined with a multi-headed attention model for audio recovery, and re-input into the ASV system for verification after recovery. The experimental results show that the accuracy of using the high-frequency component of masked audio combined with the multi-head attention model to recover the adversarial audio to the original audio reaches 95.9%, which can effectively improve the robustness of the ASV system.
Research on Obstacle Avoidance Method for Unmanned Clusters Based on Artificial Potential Field

With the development of artificial intelligence and computer technology, autonomous collision avoidance has become the foundation of unmanned clusters. This article constructs gravity calculation formulas, repulsion calculation formulas, and unmanned cluster maneuvering parameter calculation methods based on artificial potential field theory. Combining unmanned cluster obstacle avoidance control and formation maintenance, an unmanned cluster obstacle avoidance process is designed.
Research on a Fast Image Stitching Method Based on Improved SURF Algorithm

Aiming at the problem of general real-time performance of SURF algorithm in image stitching, this paper proposes an improved SURF algorithm, which improves the speed of the algorithm by replacing the SURF descriptor with the rBRIEF descriptor for the description of the feature points, and uses the Hamming distance instead of the Euclidean distance for the matching of the feature points, which optimizes the accuracy of the algorithm's matching and further improves the algorithm's efficiency at the same time. Finally, the RANSAC algorithm is utilized for precise matching to eliminate the outlier points that are incorrectly matched. The simulation experiment results show that compared with the traditional SURF algorithm, this paper's algorithm improves the matching correctness by 5.6% over the classical SURF algorithm, and improves the operation speed by 44.4%.
Liability Associated with AI Systems under the Bulgarian Criminal Code: Requirements, Approaches and Proposals

The article aims to develop further the analysis on the criminal liability associated with AI entities under the Bulgarian Criminal law. The first part discusses the main concepts and requirements regarding the elaboration of regulation of criminal liability associated with the development, deployment, and use of AI systems. The second part offers some approaches and proposals for such regulation, accompanied by relevant examples. Finally, conclusions are drawn on the possibilities to adequately address the criminal liability associated with AI entities under the Bulgarian criminal law.
Switch-Time neurons. Definition and types

Neural networks are used in forecasting or predicting given output variable based on information provided by a set of input variables. There are also other types of models used for the same purpose: linear regression models, non-linear regression-based models, polynomial approximation etc.While linear and non-linear regression models are parametric models, neural networks, like polynomial approximation models, are semi-parametric. Neural networks models, in contrast with polynomial semi-parametric models, don’t provide closed-form solution, so only approximate solution can be provided by the neural networks.In this article, we consider the Switch-time neuron based on the so called Switch-time (ST) distribution [4]. More specifically, we consider two types of Switch-time neurons: one-side Switch-time neuron (ST neuron) and double-side Switch-time neuron (DST neuron) and present R code for simulating different examples of both types of neurons.As the concept neuron is close to the concept regression, we may by analogy consider Switch-time (stopit) regression [5] of both types.
Application of Fuzzy Logic for Detection of Financial-Customs Violations

This paper proposes the application of Fuzzy Logic as a tool to detect customs and financial violations. The fuzzy rules are developed by the method of multi-subject multicriteria expertise. Synthetic data generated using Gretel online environment is used for testing. The resulting fuzzy rule system was tested with real data from the Customs Agency, and the results obtained showed very high accuracy.