Reviewerly/exHarmony huggingface dataset:
Finetuning Language Model for Person Description Search in Thai

Person description search is matching a textual description of a person with the image of the same person. This is a multimodal image-text task, where the model generally has two branches: image and text. The objective is for these two branches to embed their respective input into a joint space, where the embeddings should be near each other if the image and text pair is a match, and far apart if they are not. The image branch can simply use pretrained vision models off-the-shelf without any modification, because “person” is a common class in large image datasets. For the text branch on the other hand, person descriptions are not part of the dataset commonly used to train large language models (LM). Recent deep learning language models are based on the transformer architecture, which are commonly trained using large text corpus using masked language model loss. In this paper we propose finetuning the transformer-based LM in an unsupervised manner using the person description text before supervised training on the actual task. The result shows that unsupervised LM finetuning is beneficial for Thai person description search.
Thai Sentiment Analysis and Visualization of Movie Reviews From Social Media

Nowadays, online social media platforms such as Facebook are where many people follow and share opinions about stories, products, and services. Facebook Page is one of the Facebook online communities where posted information is monitored by both Facebook Page admins and members. However, with so many Facebook Pages and comments today, it can be very challenging to find and compare opinions on a particular issue from the same and between Facebook Pages. As a result, consumers lose time to search and may not receive different perspectives of information enough to make an informed decision. This research, therefore, presents a system to help users collect, analyze and visualize opinions from both the same and other Facebook Pages to make a comparison of aggregated sentiment analysis from Facebook Pages more convenient by using a case study of sentiment analysis of movie reviews in Thai. In addition, a preliminary assessment of the proposed system was undertaken and the findings are reported in this study.
Optimization of Deep Neural Network Models Based on JTRT Technique

The accuracy and performance requirements of deep neural network models have increased a lot due to the application of deep learning and embedded platforms, and the complexity of the network models is increasing exponentially, significantly increasing the computational volume and time of network model inference. The limited computational resources of embedded platforms eventually lead to the problem of sluggish response and high latency when the network models are deployed. To accelerate the network model inference process and reduce the impact of latency on the research, we propose to use the JTRT technique to accelerate the inference of deep neural network models. To verify the capability of the JTRT technique, we apply it to various neural network models. The experimental results show that the JTRT technique can compress various network models and perform accelerated inference using semi-precision techniques to achieve optimization acceleration of the network models. The most considerable improvement is achieved for the ResNet framework, with a 9.42-fold speedup, and the acceleration performance of JTRT is better for deeper network models.
Research on Quantum Machine Learning Technology Based on Regenerating Kernel Hilbert Space

In the era of big data, software algorithms are improving rapidly, and machine learning algorithms are also widely used. In doing so, transmitting signals from quantum devices will also contribute to research on machine learning technology. Traditional machine learning networks face the problem of low efficiency and slow response when processing large amounts of complex data. Quantum computing technology offers supercomputing power to compensate for this shortcoming. Based on the advanced development of quantum machine learning and quantum computing technology, this paper combines machine learning technology. Integrate the regenerated kernel Hilbert space to complete the composition of the architecture, effectively improving the computational performance of machine learning.
Simulation of E-business User Interest Mining Based on Cloud Computing

E-business websites provide users with more and more choices. At the same time, under the massive commodity information, users often lose themselves and cannot find the commodities they need. In the increasingly fierce competition of e-business, personalized recommendation system is increasingly sought after by enterprises and has become an important field of e-business research. In this paper, the technology of big data processing based on cloud computing is used to study the implementation technology of e-business intelligent recommendation algorithm and the comprehensive evaluation method of recommendation system in big data environment, and an e-business user interest mining model is constructed to meet the personalized intelligent recommendation requirements of e-business in big data environment. The results show that the error of the proposed user data mining algorithm is small, and the accuracy rate is high, reaching 96.85%. It is of great theoretical significance and economic value to study the intelligent recommendation system of e-business based on cloud computing technology to improve the effectiveness, scale and real-time of personalized recommendation system of e-business and enhance the level of personalized precision marketing.
DyMAP: Defeating SGX Controlled Channel Attacks with Dynamic Memory Access Patterns

SGX controlled channel attacks is a method to steal the data inside the enclave. To defeat it, this paper present a novel method to develop the software with a dynamic memory access pattern which is called as DyMAP which is abbreviation of Dynamic Memory Access Patterns. DyMAP is able to dynamically assign control data at runtime, so that the software change its control flow every time it runs, thus breaking the static mapping relationship between control flow and data flow, which greatly increases the difficulty of SGX side channel analysis. Our tests shows that DyMAP is capable of defeating SGX controlled channel attacks with a acceptable overhead.
Big Data among Multiple Institutions Based on DAG Blockchain

The transparency and reliability, tamper resistance, traceability, etc. of blockchain technology enable it to protect privacy and share information. The purpose of this paper is to study the secure sharing method of big data among multiple institutions based on the DAG blockchain. This paper analyzes the development of big data and the importance of big data security sharing, as well as the shortcomings of existing security sharing schemes, introduces some theoretical basic knowledge used in the design of this paper, and introduces the big data security sharing scheme based on DAG blockchain. It includes three parts: the scheme architecture of GCP-ABE, the design of DAG block structure and the design of attribute encryption algorithm. The processing steps of encrypted files are described, and the computational cost of the GCP-ABE scheme is compared with the existing big data security sharing schemes through simulation experiments to prove that the scheme in this paper has good performance and functional advantages.
Customer Churn Prediction in Bank Based on Different Machine Learning Models

With the rise of internet finance, the competition in the banking industry has become increasingly fierce. Preventing the loss of customers and retaining old customers has become an important concern of major banks. Firstly, according to an existing open dataset, this paper makes a descriptive statistical analysis of each feature, and uses Logistic, Random Forest and Scv models to predict customer churn, such as AUC curves, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Error (MSE). The better-performing models listed above are then chosen. Finally, Svm is selected as the best performance model, and according to descriptive statistics and feature importance, some suggestions are put forward for banks to retain customers.
Method of grounding grid detection signal filtering

This paper studies a filtering method for grounding grid detection signals, which includes: burying three-electrode sensors near the grounding grid; establishing a wavelet transform filtering model, substituting the collected data into the model, and performing wavelet multi-scale analysis on the detection data, determine the type of electromagnetic interference existing in the detection data; select the appropriate threshold value and substitute it into the wavelet transform filtering model, reconstruct the detection data, and obtain the detection data after filtering the electromagnetic interference; establish a Kalman filter model, and process the The detected data is substituted into the Kalman filter model for analysis; the type of Gaussian white noise existing in the detected data is judged; the appropriate adaptive factor is selected to further construct an adaptive Kalman filter model, and the Gaussian white noise existing in the detected data will be Filtering is performed to finally obtain detection data with completely filtered interference, which solves the problem of low accuracy of detection data in the prior art.
Research on Fast Seismic Intensity Evaluation Method Based on Convolution Neural Network

The rapid determination of earthquake intensity after an earthquake is of great significance for disaster relief and disaster reduction. The traditional earthquake intensity assessment is determined by on-site manual investigation, which usually takes a long time, and it often takes a longer time to complete the strong earthquake with a larger impact range. In this paper, we try to use computer method to replace manual work, that is, convolutional neural network (CNN) in depth learning. According to the characteristics of post earthquake building damage and the specifications of seismic intensity scale of Chinese earthquakes, we build a proper CNN model, train and test the model with marked data set samples, and build a rapid seismic intensity evaluation platform with high timeliness. Finally, this paper points out the problems of this method in the evaluation, and looks forward to the future research and application of fast seismic intensity evaluation method based on convolutional neural network.
Blockchain Data Privacy Protection Method Based on Intelligent Collaborative Computing

With the increasing maturity of blockchain technology, more and more systems use blockchain as the underlying architecture. And the ever-increasing number of loT devices also generate massive amounts of data. There is no doubt that these data will generate greater data value when they are collaboratively processed and shared. Considering that these data belong to different entities or individuals, using secure multi-party computing to design a data processing and sharing scheme can well take into account the two requirements of data security and privacy protection. The main purpose of this paper is to conduct research on blockchain data privacy protection methods based on intelligent collaborative computing. Based on Paillier algorithm, this paper optimizes its decryption process and reduces the decryption time of the homomorphic encryption process. In the experiment of testing the algorithm scheme, the experimental results show that the key generation time of various key lengths is tested for the proposed scheme, and the results show that the key generation time increases almost linearly with the increase of the key length.
Prediction and Data Analysis of Price Based on PSO with Extreme Learning Machine Algorithm and Particle Swarm Optimization

The changes in the trading market are affected by many factors, and traditional forecasting methods are more and more difficult to meet people's needs. In order to improve the accuracy of prediction, this paper proposes an extreme learning machine algorithm based on particle swarm optimization for prediction and analysis. First, the fluctuation data of previous years are collected, and the closing price of the stock is determined as an experiment and simulated to establish a learning sample, and then the learning sample is input. Based on the online extreme learning machine model optimized by particle swarm optimization, the prediction model is established. The results show that the optimized extreme learning machine algorithm, compared with models such as ELM, PSO-BP and DE-ELM, has better surface prediction accuracy and fitting effect, and can more accurately describe the change trend, which is more efficient than other methods.
IS-Net: Improved Ship Detector Based on RetinaNet

Ship detection has important practical significance in military and livelihood fields, and the rapid identification and accurate positioning of ships help monitor maritime traffic, thus ensuring maritime navigation safety and safeguarding national maritime rights and interests. However, in practical scenarios, the arrangement of ships mostly has the characteristics of dense and different directions, which makes the horizontal frame detection no longer applicable to ship detection. Therefore, in this paper, we use an improved rotating frame detection network based on RetinaNet, named IS-net. In order to fully extract ship features to improve detection accuracy, the ConvNext network is used as the backbone network. In addition, we use Kullback-Leibler Divergence (KLD) as the regression loss. KLD (and its derivative) can dynamically adjust the parameter gradient according to the characteristics of the object, which makes the regression effect more accurate. In this paper, we use the well-known HRSC2016 dataset, and a large number of ablation experiments show that IS-net has excellent detection accuracy.
An Automated Workflow for Generation of Neural Networks for Embedded FPGAs on IoT

With the increasing popularity of artificial neural networks to solve a variety of issues for edge computing devices in the Internet of Things and the resource-intensive nature of these algorithms, there is a demand for hardware accelerators to optimize them. However, developing this type of device is usually locked behind specialized programming expertise and long design cycles. We propose an automatic generation workflow that, from a trained model, writes code for a hardware accelerator that optimizes the execution of the neural network that can be synthesizable in an FPGA. A proof of concept workflow with limited function compatibility was then created and an accelerator that is synthesizable in a small, low power FPGA is generated. It is then tested against a personal computer CPU, and significant speed up and energy efficiency gains are obtained.
Development of a Blockchain and IoT-based Platform for Animal Surveillance

Digital technologies such as Internet of Things are widely used in the veterinary area, including applications in animal health and welfare. The use of blockchain in this area is in the beginning, but many benefits may be achieved. This paper presents a proof of concept implementation of an animal surveillance system using blockchain and IoT. The architecture consists of a sensor-fog-cloud integration, including blockchain and smart contracts. The proof of concept used real horse temperature measurements, sending this data to a blockchain implemented with the Hyperledger Fabric platform. In the implementation and testing of the system, it was possible to observe that blockchain may improve the availability, immutability, security, and reliability of the data related to the application.
Extend quantum homomorphic encryption scheme based on Brown state

Quantum fully homomorphic encryption (QFHE) is a kind of secure method which allows random evaluation computations performed by each server on the cipher states and do not decrypt them. In order to extend the QFHE scheme for three-participant, an extend QFHE scheme based on brown state is proposed in this paper. Firstly, the conception of extend means we use a state which are familiar in quantum secret sharing procedure to extend the former QFHE scheme with three-part. Secondly, universal quantum circuit (UQC) enables random quantum transformations on the cipher states which promise the security, and the key-updating algorithms are also secure because of the former UQC. Finally, due to the property of QFHE, we do not need any security check to make it efficient for the secret sharing procedure.
Gesture Recognition Based on Improved AlexNet

Gesture recognition is one of the research hotspots in the field of computer science and technology today, aiming to establish a natural and harmonious human-computer interaction environment and a colorful interaction mode. The application range of gesture recognition is very wide: gesture recognition can not only help deaf people to solve interaction problems, but also be applied in various fields such as daily life, education, business office, etc. Meanwhile, gesture recognition technology is also needed in the more popular metaverse recently. Handcrafted methods for vision-based gesture recognition usually involve multiple stages of professional processing, such as traditional handcrafted-based feature extraction methods, which are generally used to specialize in specific tasks with insufficient generalization capability. For this reason, this paper proposes an improved AlexNet-based gesture recognition algorithm by stacking four 3×3 convolutional kernels instead of the 11×11 convolutional kernels in AlexNet. In addition, the number of training data is increased and its diversity is enriched by preprocessing such as random cropping and random level flipping to further improve the generalization ability of the algorithm. The improved algorithm proposed in this paper further enhances the feature extraction capability of the network, and the experiments are conducted on multiple datasets, i.e., one American Sign Language dataset and two NUS gesture datasets. The average accuracy of the improved algorithm is 95.3%, which is better than the original AlexNet network as well as other deep learning algorithms.
Design and Implementation of Rumor Governance System Based on Blockchain

Nowadays, the network rumor is constantly breeding and spreading. It becomes a big difficulty to trace the source and refute the rumors with high efficiency. For this, we designed and implemented an intelligent rumor detection, source tracing and accurate rumor refuting system. This system has three main functions. First, the system provides credible evidence for the tracing and accountability of rumors with the immutable nature of blockchain. Second, we propose a new intelligent rumor detection model that does not involve manual work, that is, by looking for opposite semantic pairs. Third, a new accurate rumor refuting scheme is proposed, which can help break through the information cocoons. The system provides a standardized and effective management platform for the collaborative governance of online rumors in the new era and the improvement of the environment for online public opinion. It also facilitates the development of the Clear Action and the crackdown on online rumors.
A high performance fraud detection strategy prediction model

Fraud is one of the most important problems facing the financial sector. It's very expensive. Because the model based on machine learning can scan huge transaction data sets, detect abnormal activities, and identify all cases that may be vulnerable to fraud. Therefore, we choose to build a model based on LightGBM to predict whether the transaction is fraudulent. For the transaction data set conducted by European cardholders within two days in September 2013, the factor of unequal data labels is very important. We use random undersampling to balance the sample. Then the sample is segmented and trained. The results show that our model has an accuracy of more than 99.5%. Compared with popular classification models, our model has higher performance. The solution can detect the default rate or fraud tendency of each potential customer and transaction, and provide key alerts and insights for financial institutions.
Augmented CycleGAN With an Improved Loss Function For Generative method of Security X-Ray Images in Security For knives

In recent years, as the research field of deep neural networks has become popular, more and more researchers are focusing on the automatic detection of dangerous goods in security inspection X-ray images. However, sample imbalance in the datasets is one of the difficulties in this research. In particular, the number of knives is small, resulting in data imbalance. To solve the above problem, we put forward the double-cycle consistency loss function and an enhanced CycleGAN image generation method, which can transform natural images of dangerous cutters into security inspection X-ray images as an enhancement of the dataset data. Not only can that enrich the diversity of samples in the public dataset and the diversity of poses, but also solve the problems of unbalanced data samples. Finally, we compare and evaluate the images generated by our model with the CycleGAN model, and confirm that our method can generate high-quality security inspection X-ray images with better effect than the CycleGAN model. We also use the FID index to evaluate the quality of images generated by the model, which proves that our model has good performance.
A distributed ABAC access control scheme based on blockchain

The blockchain has the characteristics of decentralization, trustworthiness and difficult to tamper with. Combining the blockchain with access control can solve the problems such as single point of failure and opaque authority judgment in traditional centralized access control systems. The access control system of the system pays more attention to the framework design, ignoring the influence of the uncertainty of the entity's behavior after authentication on the blockchain network. Based on this, we propose a distributed access control system based on blockchain. Based on the ABAC model, the policy decision point, policy management point, and attribute authority are completed through smart contracts deployed by nodes in the blockchain network. The main function. The experimental results show that the scheme is feasible and effective, realizes decentralization, transparent authority judgment, and supports dynamic and fine-grained access control system.
STC-Net: Fusing Swin Transformer and Convolution Neural Network for 2D Medical Image Segmentation

Convolutional Neural Network (CNN) has made significant development in intelligent medical image analysis. However, due to the limitations of convolution, it cannot well learn a global and long sequence of semantic information, which also limits its performance in clinical medical segmentation. To solve the above problems, STC-Net proposed in this paper combines the global image information processed by Swin Transformer with the low-level detail features processed by CNN. To fuse the two kinds of information, a fusion module with channel attention mechanism and spatial attention mechanism (CSM) is proposed in the information fusion part of STC-Net. In the channel attention part, local channel information interaction is realized by the Fast 1D convolution method, while the spatial attention part focuses on important spatial location, scale and other information. The CSM can focus on this important information and suppress the non-target segmentation information. The results show that STC-Net achieves good performance in brain tumor segmentation, polyp segmentation and skin lesion segmentation.