Reviewerly/exHarmony huggingface dataset:
ManiacNFT : An Application for NFT Marketplace

Art is mankind's treasure, yet it is often concentrated in the hands of a few. We need a better trade mechanism and technological innovation to enable fair access to artworks. A solution to the problem can be found by using the NFT technology for the trade of artwork. NFTs make the future for the creators’ economy a little brighter, allowing artists to monetize their work by selling to eager collectors, while also collecting due royalties upon future resales of their works. This unique combination of blockchain with artworks provides a layer of security to the ownership of the artwork, and is restoring access to artworks for people from all regions.This research paper is about a decentralized marketplace application where one can buy and sell artworks in the form of NFTs digitally, providing the users with the facility to securely perform transactions of these NFTs that can be verified through Polygon blockchain.
Role of Blockchain Technology and Machine Learning in Design of Smart and Secure Warehouse Management System: A Survey

Blockchain network is defined as interconnection of many computers, and each and every computer holds the copy of the ledger. It can be observed as continuously budding chain of blocks, and blocks are interconnected with the support of hash function. Validating of new blocks is followed by a set of protocols and consensus mechanism from every node in the network. The records are kept and arranged in linear fashion chain. The main feature of the Blockchain technology is that it allows secure communication between untrusted parties without the involvement of any third party authority. Artificial intelligence, which emulates the human intelligence, is impacting heavily on the business and social media applications nowadays. Machine learning which is the subset AI, automatically learns and improve based on input data. Whereas deep learning which is subset of machine learning uses networks to identify complex patterns in data. The basic approach of machine learning is to collect and analyze the data at central location like server. But in today’s scenario the data is decentralized and emerges from multiple sources. Hence the need of distributed machine learning algorithms in many applications is required. ML can be used to make chain smarter than before. By making use of decentralized data architecture of Blockchain we can build good models of machine learning. This paper investigates the possibility of integrating Blockchain Technology and Machine learning for optimization and improvement of Warehouse operations at data and transactions levels by providing security processes needed for smart and secure warehouse system.
Detecting the software usage on a compromised system: A triage solution for digital forensics

One of the challenges of digital forensics is the high volume of investigative cases. To address this problem, researchers have proposed various triage methods. Detecting the applications that have run on the compromised system under inspection can be an excellent triage method that gives the investigator an overview of the system. In this paper, we construct the signature of software usage on a system using file path artifacts. We propose a software signature detection engine (SSDE) to identify the usage of the software on the system under investigation. The SSDE consists of two subsystems: the signature construction subsystem, which builds the software signature using the TF-IDF weighting scheme, and the signature detection subsystem, which identifies the executed set of software on the target system. We consider several parameters with different values in the design of SSDEs, leading to more than 500 SSDE models. We test the SSDE models against 14 pseudo-real systems from the M57 Patents scenario and evaluate their performance. The experimental results show that about 38% of SSDE models achieve near-perfect Precision, and about 18% of them achieve near-perfect Recall. We introduce the top models and determine which parameter values lead to the superior models. Besides, we compare the SSDE models with some doc2vec-based models. The results show that SSDE models have higher average Precision, slightly lower average Recall, and much less computational time.
Density Operator Formulation for a Supersymmetric Harmonic Oscillator: Vector Coherent State Construction and Statistical Properties

Motivated by our recent work published in [23], we achieve, in this paper, a matrix formulation of the density operator to construct a two-component vector coherent state representation for a supersymmetric harmonic oscillator. We investigate and discuss the main relevant statistical properties. We use the completeness relation to perform the thermodynamic analysis in the diagonal P-representation of the density operator.
Cross-lingual sentence embedding for mining low-resources parallel sentences

Mining low-resource multilingual parallel sentences would be useful in natural language processing tasks. A large number of high-quality parallel sentences provide necessary data support for tasks such as building bilingual parallel corpora and cross-language information retrieval. In this paper, we present an approach to mine Thai-English parallel sentences in huge documents using cross-lingual sentence embedding. To evaluate the approach, we used two extensive bilingual corpora, which provide golden scores. On TED and Tanzil set, our approach improves nearly 0.73 points in AUC and reaches 96.5%. In the task of mining BUCC parallel corpus, our approach uses less time and space but gets an F1 score similar to the LaBSE model, which has reached state-of-the-art on BUCC. Our model not only solves the problem of sentence alignment with insufficient resources but also uses less time.
Improving lateral safety distance-based on feature detection and PRM for unmanned vehicle path planning

Most of the existing path-planning algorithms do not consider lateral safe distance requirements in practical applications. Hence, in this study, a new path point selection algorithm is proposed for path planning. The algorithm first used the Harris and Line Segment Detector(LSD) algorithms to detect and obtain the corner and edge information of obstacles. A vertical line was provided to the edge of the surrounding obstacles along each corner successively. In this process, the narrow impassable area in the map was filtered and removed by setting a safety threshold, and the foot of the vertical coordinates were simultaneously obtained. The corresponding midpoint coordinates were solved by using the corner coordinates and the foot of the corresponding perpendicular coordinates. The midpoint coordinates were used as candidate points to generate path points. These candidate points are screened and relaxed using the Probabilistic Roadmaps(PRM) algorithm to obtain the series of path points required. Finally, the path was planned according to these path points and smoothed using the Quadratic polynomial interpolation method(QPMI). Through simulation experiments, the method proposed in this study can solve a unique path without randomness under given conditions, and the probability of a collision in practical applications was reduced.
Research on automatic music generation with multi-track based on melody constraints

Aiming at the problems that the melody generated by the existing multi track music generation model is not pleasant and the harmony between tracks is not enough, a multi track music generation model with TransformerX-SpanBERT sequence conditions is proposed. The module is a Generative Adversarial Network structure. Since the generated music has multiple tracks, first, use TransformerXL as the generator and SpanBERT as the discriminator to generate piano track music, and the other tracks have one generator for each track. Therefore, there are multiple generators and one discriminator. First, piano track music is as input, let each track generator to generate their own music. Then, the common discriminator is used to judge the music quality generated by each track and the harmony between tracks. Finally, the global attention mechanism is used to pay attention to the music characteristics to ensure the overall effect of music. The objective evaluation index of music and the subjective score of listeners are used to verify the model and compare it withTransformer-GAN and MuseGAN Multi-track sequential symbol music, the results show that the music generated by the model in this paper has the advantages of more harmonious tracks and better melody, rhythm and coherence, therefore, it can be applied to practical music creation.
Research on the combination of blockchain and training of deep neural network model

As deep neural network models become more and more complex and the advent of massive amounts of data, distributed computing is needed to support the training process of the network model. Blockchain is a new distributed infrastructure and computing paradigm. Training the deep neural network model by converging the computing power of each node through blockchain technology will make the underlying consensus algorithm of the blockchain more practical and at the same time solve the problem of insufficient deep learning computing power. It studies the combination of blockchain and training of deep neural network model. It designs the consensus mechanism of the blockchain, uses the distributed training process of the deep neural network model as the proof of work of the nodes, encourages each node to contribute computing power through the incentive mechanism, and records the important data generated during the computing process in the blockchain.
Human climbing detection algorithm based on multi-level HRNET and LSTM network

A multi-level HRNet-based algorithm for height-climbing behavior detection is proposed to address the problem of low recognition accuracy of existing height-climbing behavior detection algorithms. Firstly, the multi-level HRNet network is used as the human pose estimation module to obtain the 2D joint point data of the human body. Then the LSTM-based classification network is constructed, and the initial 2D joint point information is fed into the classification network for height-climbing action recognition of human pose. Finally, the target keypoint similarity OKS is used to evaluate the accuracy of keypoint recognition. Experiments were conducted on the COCO2017 dataset, and the accuracy of the proposed multi-level HRNet network for pose estimation in this paper reached 87.6%. Experiments on the human pose climbing action on the own dataset show that the accuracy of human pose climbing action detection of the multi-level HRNet and LSMT network proposed in this paper reaches 96.4%.
Image segmentation based on improved density peak clustering

Density peaks clustering(DPC) has been applied in the field of image segmentation. However, DPC only considers the global spatial information, but not the local spatial information. When a point is incorrectly allocated, the rest points will have a chain reaction, resulting in being divided into the wrong cluster. Therefore, we propose an image segmentation method based on superpixel and improved DPC. This method can fully consider local spatial information and is more robust. Firstly, an improved DPC is proposed to improve the clustering accuracy, and a new image segmentation algorithm is proposed by combining the improved DPC with superpixel. We compare this algorithm with other three algorithms in BSDS500 dataset. Four indicators of these algorithms show that the algorithm is superior to the other three algorithms.
Using phonetic and visual knowledge to improve Chinese spelling correction models

Since the birth of BERT, Bert class models have achieved good results in Chinese Spelling Correction (CSC) tasks. However, the existing CSC models do not thoroughly learn typos' phonetic and visual knowledge. We propose a pretraining method and an Error-Prone Sentence Generation (EPSC) algorithm for training to overcome the shortcomings of existing CSC models. Experiments show that our method can improve the performance of BERT and soft-masked BERT in CSC tasks.
Research on text classification algorithm based on hybrid neural network

In text classification tasks, using traditional char granularity or word granularity information to encode context information into the same parameter space cannot effectively extract enough text feature information, and the expressive ability of text semantic information is also relatively weak. In order to solve this problem, this paper adopts the embedding method of fusing graphemes and morphemes, and establishes a text classification model that combines a hybrid self-attention mechanism and RCNN. The model first divides the text into char sequences and word sequences and fuses them, and then uses the Transformer encoder to extract preliminary features. The self-attention mechanism in the Transformer can better focus on semantic and location information, and then uses RCNN to extract the deep features which fusion grapheme and morpheme information, the feature vector is calculated and output by the softmax function, and finally the classification of Chinese short texts is realized. The experimental results show that the method proposed in this paper has different degrees of improvement in the accuracy of text classification compared with a single embedding method and a single neural network.
A multiple perceptual news recommendation method based on dynamic multi-convolution kernel CNN and attention mechanism

News recommendation is one of the important tasks in recommendation tasks. The existing recommendation process is essentially a system loop feedback process. The content fed back by the recommendation system will be greatly affected by the user's click behavior, while the news recommendation model usually learns from the user's click behavior, which will lead to the system recommending news that users have clicked/liked to watch with high probability and pushing other types of news with low probability, resulting in the problem of insufficient diversity. To effectively address this type of problem, we first analyzed the news data statistically and found the bias of "viewing frequency", and then proposed a multi-perceptual news recommendation method based on dynamic multi-convolution kernel CNN and attention mechanism. We use CNN with convolution kernels of different sizes to extract headline features in the news feature extractor. And then combine them in an attentive way. Form title features with more representations. The user feature extractor is divided into biased user feature extractor and unbiased user feature extractor. One is to learn biased user attributes and perceive user characteristics biased and the other is to learn unbiased user attributes and perceive user characteristics without prejudice. Then we use the cosine similarity loss, that is, to increase the difference between the two user characteristics through cosine similarity. The most important point is that ensure that the accuracy of the model is not reduced as much as possible while debiasing, we combine the two features in an attentive manner, and finally propose a ranking method to improve the diversity of recommendations. Extensive experiments show that our method can not only remove biases, but also achieve a high accuracy of the model.
A study of BERT-based bi-directional Tibetan-Chinese neural machine translation

Tibetan-Chinese bidirectional translation can promote the development of Tibetan-Chinese scientific and cultural exchanges as well as educational and cultural undertakings, but it has been facing the dilemma of low resource language parallel corpus scarcity. In this paper, we hope to provide a research idea for Tibetan-Chinese machine translation under low-resource conditions through the study of pre-trained language models, and to alleviate the problems existing in Tibetan-Chinese machine translation such as scarce corpus and unsatisfactory translation performance, so as to promote the development of Tibetan society. In this paper, by introducing pre-trained language models BERT and ALBERT into Tibetan-Chinese bidirectional translation, the translation effect is greatly improved by using large-scale monolingual data. The translations before and after use are also analyzed. Through the tests, the average improvement of the Chinese-Tibetan translation is 2.13 BLEU values, and the average improvement of the Tibetan-Chinese translation is 2.68 BLEU values.
Controllable identity de-identification for face image

Aiming at the problem that the face image recognition technology based on anonymization mechanism cannot provide quantifiable privacy guarantee and realize controllable privacy protection, a controllable face identity recognition method based on differential privacy mechanism and generative learning model was proposed. Based on the attentional mechanism and graph convolution network, this method designed a representation learning model to extract identity-related geometric and demographic information by exploring spatial and spatial semantic relationships between different facial regions. The differential privacy mechanism was used to protect the demographic attributes while preserving the geometric attributes of the face. Then generate adversarial network reconstruction image instead of original image release for computer vision analysis task. Experimental results show that the proposed method is superior to other models in terms of privacy and utility. It can effectively resist background knowledge attacks, reconstruction attacks, and combination attacks while realizing verifiable and controllable privacy protection of face identity and ensuring the optimal tradeoff between privacy and utility of published images.
Research on modular development of embedded software for fire control system

Nowadays, embedded software has become the core part of weapons and equipment, with poor software quality being the bottleneck that constrains the development of weapons and equipment. Applying modularization technology to improve software quality and development efficiency by means of software re-use has become an important content of engineering research.
Researches advanced in application of medical image analysis based on deep learning

Medical image analysis is an interdisciplinary field of comprehensive medical imaging and analyzing, whose goal is to recognize disease diagnosis and lesion area through the related computer vision technology. Benefiting from the continuous development of the convolutional neural networks, medical image analysis based on deep learning has become a research hot spot. In this paper, based on in-depth literature research of results and progress in recent years, we mainly analyze the domestic and foreign research status of Medical Imaging in various application fields such as detection, segmentation and registration. We further compare the performance of representative methods on common data sets, and summary the existing challenges in deep learning-based medical image analysis. Finally, we discuss the solutions to these problems and predict the future development of medical image analysis tasks.
User Experience Adaptation of Complex Game Interface for User Behaviour Modeling Using RNN

In a video game review, the main focus is the narratives, characters, graphics, and mechanics in the gameplay. Some recent research mentions the user interface only when it comes into light as a creative platform for simple interactive narratives from a technical point of view; this narrative is mainly a software tool that requires traditionally modernized inputs from the user. The user needs to interact with the navigational controls or menus in order to start a basic game play. A complex game interface as stimulus is generally considered as having a feeling of immersion that allows for visual tracking of user behavioural patterns and use it to predict the next strategy of the user using robust computational models. A number of users have limited sensory perception in a gameplay and hence rely on complex game stimulus and an adaptive model is paramount when considering behavioural expectations that place the user in a digital environment with more expressive perceptions. We developed a custom based eye tracking and 3D object detection algorithm which was utilised by recruiting users to interact with visual 3D objects and trace their eye movement behaviour to generated data. We then applied the use of recurrent neural network (RNN) for direct tracing of user behavioural activities in a sequential manner to predict their behaviour for interface adaptation. Result indicates that redundant user attributes are flexible and flawless for identifying predicted response of the user in a controlled environment. This would lead to prototypical representation of user behavioural analytics as an embedded platform in the confined digital environment. One of the limitations of the project is its inability to basically specify the 3D gaze point at the inner boundaries of the visual field. Data visualisation is strictly based on combined object flow detection. The originality of the work is its ability to redefine fixation point to a rendered cascaded 3D gaze point and space-defined saccade which is indicated by the distance between one gaze points to the other. The 3D gaze point would be well suited for fixation generalisation on 3D as well as on 2D digital oriented environment.
A personalized recommendation strategy based on emotional analysis and forgetting rule

In traditional collaborative filtering recommendation system, drawbacks such as interest drift and data sparseness always exist, which has weakened performance of the system. In this paper, a novel approach from the perspective of emotion analysis and forgotten rules was proposed to offset the influence on recommendation accuracy caused by interest migration. Firstly, user online comment has been analyzed to extract emotional words and the rated object using optimized DP algorithm. Secondly, the emotional words about the rated object have been appropriate quantified so that we can take advantage of the user emotional tendency to generate interest model. Thirdly, the Ebbinghaus forgetting curve is also introduced into the recommendation process to overcome the problem that caused by the change of user interest, which can effectively increase the accuracy and offer user better service when interest move. The simulation experiment shows that the strategy could greatly reduce MAE and effectively improve coverage.
R-YOLOv5: a lightweight rotation detector for remote sensing

Recently, detection algorithms for objects with arbitrary orientation have been greatly developed, but traditional object detection algorithms use horizontal boxes to label objects in images, which will lead to the problems such as loss of accuracy and excessive background information. In this paper, a rotation detection algorithm based on YOLOv5 is proposed, which can effectively detects the targets in the remote sensing image. It uses the CSL method to mark the oblique targets, converts the angle of the rotation boxes into a classification problem, and avoids the boundary problem. We also optimize the loss function to speed up the network convergence and adopt the NMS algorithm based on the rotation boxes, which effectively removes the redundant prediction frame. The experimental results demostrate that compared with the original YOLOv5 algorithm, the improved YOLOv5 algorithm can better locate rotation objects in remote sensing images and effectively prevent occlusion problems in dense target scenes. With a guaranteed inference speed of 24.1fps, Our method has a detection accuracy of 72.65% on dataset DOTA 1.0.