Miracle-dz/UniFAR-FTUs huggingface dataset:
An Entity-Level Sentiment Analysis of Financial Text Based on Pre-Trained Language Model

With the rapid development of the Internet and global finance, more and more financial data is accumulated, while a majority of them are unstructured. Within the unstructured financial data, sentiment information is the most attractive and commonly desired to fetch for the financial participants, because sentiment can be an indicator to reflect the state of the financial market, which means the market can be predicted to some extent through sentiment analysis of the financial text on the Internet. Although sentiment analysis has been studied at the document level, sentence level, and aspect level, it may not provide enough information on what the financial participants want to know. Therefore, this paper aims at entity-level sentiment analysis of financial text based on the pre-trained language model and the improvement of granularity of the financial information. Bidirectional Encoder Representations from Transformers is adopted to illustrate the efficacy of pre-trained language model-based financial sentiment analysis. Experiments are conducted with a labeled test set and a train set with 8,000 and 10,000 pieces of financial network data, respectively. The experiment results show that at least 95% accuracy can be achieved for determining negative information, while at least 93% accuracy can be achieved for fetching financial entity that the negative information correlates to.
Fault Detection and Classification of Machinery Bearing Under Variable Operating Conditions Based on Wavelet Transform and CNN

The rolling bearing, one of the most critical components of wind turbines, is subject to variable operating conditions because of the unsteadiness of environmental aspects. The development of an efficient technique for predicting and classifying rolling bearing faults is a critical task. Condition-based maintenance (CBM) techniques are used to plan the maintenance procedure depending on the actual state of the asset. The convolutional neural network (CNN) structure was built using a neural architecture search (NAS) approach based on reinforcement learning. The time-series data of vibration signals are preprocessed using the continuous wavelet transform (CWT) before delivering to the CNN. The results confirmed that the proposed approach would automatically learn and discover distinct features from vibration signals, as well as identify various rolling bearing health conditions.
Reliable, Efficient Large-File Delivery over Lossy, Unidirectional Links

Many mission-specific network architectures inhibit or delay bidirectional connectivity among hosts. Such constraints are common in Delay-Tolerant Networks, cyber-physical systems, and secured networks, where crucial network protocols - principally the Transmission Control Protocol (TCP) - cannot be used. In this paper, we present a process based upon Low-Density Parity Check (LDPC) codes for efficiently and reliably bridging TCP connections across network segments whose architecture enforces one-way data flows and prevents Automatic Repeat Request (ARQ) prompts. We demonstrate its performance, measured by the time required to complete a file transaction, as comparable to scp (Secure Copy), a common file transfer utility.
Wheelchair Automatic Docking Method for Body-Separated Nursing Bed Based on Grid Map

To improve the mode and precision of wheelchair/nursing-bed automatic docking, a novel central embedded wheelchair/nursing-bed automatic docking method based on grid map is proposed. Firstly, Laplace operator and Iterative Closest Point (ICP) algorithm are used to filter and match Lidar point cloud, and the linear features of V-shaped artificial landmark are fitted by Split-merge method and least square method. Then Extended Kalman Filter (EKF) is used to fuse Inertial Measurement Unit (IMU) and odometer data to realize the localization of the bed and wheelchair. Meanwhile, the grid map is used for path planning. Based on the center-line of the two rear wheels and the angular bisector of V-shaped artificial landmark, the wheelchair pose is adjusted in real-time to ensure that the wheelchair gradually approaches the bed along the angular bisector of V-shaped artificial landmark. The yaw angle is reduced by using the improved Proportion Integration Differentiation (PID). 9 sets of experimental data, ie. (x, y, $\theta $ ) were collected at different starting positions during the docking process. The results show that the yaw angle of the wheelchair during the docking process is controlled within 2.5°, and the distance deviation between the final position and the ideal position of the wheelchair after docking is controlled within 0.02m. In the case of light interference with different luminous fluxes, the docking can still maintain good performance. The proposed docking algorithm has the robust performance of rapid response and low steady error, which can greatly improves the self-care ability of the bedridden elderly, and reduces the labor intensity of the nursing staff.
Triple Coaxial-Half-Slot Antenna Scheme With Deep Learning-Based Temperature Prediction for Hepatic Microwave Ablation: Finite Element Analysis and In Vitro Experiment

This research proposes a triple-antenna scheme of three coaxial half slot antennas (CHSA) for minimally invasive hepatic microwave ablation (MWA). In the MWA treatment, the antennas were positioned around the cancer tissue in equilateral triangle formation, with the half-slot side of the antennas directed at the triangle center to concentrate the microwave energy. Finite element (FE) simulations and in vitro experiments with swine liver were carried out to determine the temperatures and coagulation zone under different distances between the three antennas: 10, 20, and 30 mm. Besides, a regression-based deep learning algorithm was utilized to predict temperatures at the midpoint of the triple-CHSA arrangement without inserting a temperature sensor into the targeted tissue. The algorithm relied on temperatures at a site adjacent to the targeted cancer tissue to predict the midpoint temperatures. The triple-CHSA scheme could achieve a large coagulation zone with temperatures of 60 °C or higher. The FE simulation results showed that the largest coagulation volumes achieved were 18.425, 29.312, and 63.507 cm3 for the between-antenna distances of 10, 20, and 30 mm, respectively. The largest coagulation volumes of in vitro experiments were 18.437, 29.317, and 63.504 cm3 for the corresponding between-antenna distances. The prediction accuracy of the deep learning algorithm were 99.98 % for mean square error, 98.75 % for mean absolute error, and 99.22 % for mean absolute percentage error. The novelty of the research lies in the use of CHSA surrounding the cancer tissue in equilateral triangle formation for hepatic MWA, as opposed to the conventional hepatic MWA procedure which requires inserting antennas into the targeted tissue. Another research novelty is the use of regression-based deep learning algorithm to predict the temperature of liver tissue at the midpoint of the triple-CHSA arrangement.
A Deep Learning Model for Information Loss Prevention From Multi-Page Digital Documents

World Wide Web has redefined almost all the business models in the past twenty-five to thirty years. IoT, Big Data, AI are some of the comparatively recent technologies which brought in a revolution in the digitization and management of data. Along with the revolution arose the need for data security and consumer privacy protection, primarily concerning financial institutions. The data breach of Equifax in 2017 and personal information leaks from Facebook in 2021 led to general skepticism among the customers of large corporations. The GLBA, 1999, also known as the Financial Modernization Act, was implemented by US federal law to enforce the financial institutions to protect their private information. Built upon the GLBA, guidelines are paved by FTC for all financial institutions of the United States of America, including TI companies. In this paper, an ANN-based content classification technique using MLP architecture in combination with n-gram TF-IDF feature descriptor is proposed to detect and protect the customers’ sensitive information of a reputed TI company securing it’s one of the digital image-document stores. The proposed technique is compared with other state-of-the-art strategies. Data samples from the digital document store of the company have been taken into consideration in the study, and the prediction accuracy metrics obtained are found to be substantially better and within the acceptable range defined by the organization’s information security monitoring team.
Sequence Adaptation Adversarial Network for Remaining Useful Life Prediction Using Small Data Set

Data-driven intelligent method has shown superior performance in remaining useful life (RUL) prediction. However, the model training is difficult due to the limited degradation data. To address the challenges of small data set, a Sequence Adaptation Adversarial Network (SAAN) is proposed in this paper. SAAN could expand training data with auxiliary set by sequence domain adaption. We verify the proposed method with C-MAPSS dataset. By comparing with the literature methods, results show SAAN could significantly improve the accuracy of RUL prediction under small data set, and also keeps a competitive performance on sequence life prediction.
An Emotion Recognition Based Assistant for Vehicles

Lots of researches have been done on automated vehicles. Moreover, there are some researches done on improving the safety of vehicles. Here most researches on safety areas are done by using electronic and hardware components. As examples lane keep assistant, auto parking, and city break assist can be introduced. However, there are only a limited number of software-related components are using in present vehicles. For this, the main example is vehicle assistants. However, present vehicle assistants can perform for drivers or passengers commands and perform some specific tasks. This could be able to minimize driver stickiness and improve driving pleasure in some ways. However, this proposed assistant is a novel approach when comparing to the present vehicle assistants. It can identify drivers’ emotions and perform according to them. The main purpose of this research is to minimize accidents in the cause of the distraction of the driver. Moreover, this proposed assistant can catch drivers’ facial expressions like drowsiness, sadness, anger, and perform according to those expressions and keep drivers concentration on the road.
Intrusion Detection Method of Industrial Control Network Based on Lightgbm

Aiming at the requirements of high accuracy and high real-time for industrial control network intrusion detection, combined with the characteristics of high accuracy of supervised learning machine learning algorithm under label data set, an intrusion detection method for industrial control network based on lightgbm is proposed. And bayesian optimization algorithm is used to optimize the parameters of lightgbm model to solve the difficult problem of parameter adjustment; finally, this algorithm is compared with other traditional methods in the standard industrial control network data set. The simulation structure shows that the proposed method has higher detection accuracy and efficiency than the traditional method.
Graphene Based Functional Hybrid Nanostructures: Preparation, Properties and Applications

The intent of this chapter is to provide a basic overview of recent advances in graphene based hybrid nanostructures including their preparation, properties and potential applications in various field. The development of graphene based functional materials, has shown their tremendous interest in areas of science, engineering and technology. These materials include graphene supported inorganic nanomaterials and films, graphene-metal decorated nanostructures, Core/shell structures of nanocarbon-graphene and graphene doped polymer hybrid nanocomposites etc. They have been prepared by various methods like chemical vapor deposition of hydrocarbon on metal surface, liquid phase exfoliation of graphite, chemical reduction of GO, silver mirror reaction, catalysis, in-situ hydroxylation and sono sol-gel route, respectively. The attractive properties of graphene and their derivatives filled with metal nanoparticles (e.g. Au, Ag, Pd, Pt, Ni, and Cu) have made them ideal templates. Graphene and their derivatives have also been decorated with various semiconductor nanomaterials (e.g. metal oxides and dioxides, metal sulfides). These metal decorated graphene nanostructures can be useful as functional hybrid nanomaterials in electronics, optics, and energy based products like solar cells, fuel cells, Li-ion batteries and supercapacitors, ion exchange and molecular adsorption.
DRNet: A Deep Neural Network With Multi-Layer Residual Blocks Improves Image Denoising

Recently, with the broad applications of deep learning technology in image denoising, many deep neural networks based on the residual block (ResBlock) have been proposed to improve the performance. This paper presents DC-ResBlock, a ResBlock with an extra dilated convolution, to replace standard ResBlock for image denoising. Our study shows that the deep neural network with DC-ResBlocks, named DRNet, can achieve a very competitive result. We experiment with other residual blocks by adding Rectified Linear Unit, batch normalization, and Dilated Convolution to the standard ResBlock in several different ways. Ablation study shows that DC-Resblock has the best denoising performance. To evaluate the robustness of proposed DRNet, we make a statistical measure using many different random seeds instead of just a single seed, as used by many previous studies. DRNet performs well in denoising gray and color images with additive white Gaussian noise. Furthermore, because of its good performance on the Smartphone Image Denoising Dataset, DRNet is hopeful to be applied in practical tasks. The code of DRNet is accessible at https://github.com/JiaHongZ/DRNet.
AVMSN: An Audio-Visual Two Stream Crowd Counting Framework Under Low-Quality Conditions

Crowd counting is considered as the essential computer vision application that uses the convolutional neural network to model the crowd density as the regression task. However, the vision-based models are hard to extract the feature under low-quality conditions. As we know, visual and audio are used widely as media platforms for human beings to touch the physical change of the world. The cross-modal information gives us an alternative method of solving the crowd counting task. In this case, in order to solve this problem, a model named the Audio-Visual Multi-Scale Network (AVMSN) is established to model the unconstrained visual and audio sources for completing the crowd counting task in this paper. Based on the Feature extraction and Multi-modal fusion module, in order to handle the objects of various sizes in the crowd scene, the Sample Convolutional Blocks are adopted by the AVMSN as the multi-scale Vision-end branch in the Feature extraction module to calculate the weighted-visual feature. Besides, the audio, which is the temporal domain transformed into the spectrogram information and the audio feature is learned by the audio-VGG network. Finally, the weighted-visual and audio features are fused by the Multi-modal fusion module, which adopts the cascade fusion architecture to calculate the estimated density map. The experimental results show the proposed AVMSN achieves a lower mean absolute error than other state-of-art crowd counting models under the low-quality conditions.
Intelligent Portfolio Theory and Trading in Commodity Futures

This paper presents a specific form of the Intelligent Portfolio Theory for trading in commodity futures markets. The theory is based on the recognition of the reality that any single trading strategy is bounded in rationality, so it is unable to remain profitable consistently over time; thus an intelligent portfolio consists of a multi-market portfolio of which capital allocation on each market is managed by a multi-strategy portfolio. In Chinese commodity futures markets, 3 futures are selected; and 2 trading strategies are developed and applied on each of the selected futures. This specific intelligent portfolio trading system is tested on historical intraday data, and the result exhibits superior performance with the least maximum drawdown and the biggest reward-to-risk ratio.
Design and Analysis of Optimal Recipe Prediction Model Based on Deep Learning for Advanced Composite Material Injection Molding

Manufacturing recipe management is essential for product quality and efficient production in the injection molding manufacturing process. If the recipe setting is not optimized, various problems such as the cause of equipment failure, decrease in production efficiency, and increase in defect rate may occur. The injection molding manufacturing recipe is influenced by a combination of factors of various manufacturing conditions. In particular, with the recent development of the electric vehicle market, differences from general injection molding technologies exist in manufacturing electric vehicle battery cases using injection molding technology. It includes technology that adds advanced composite materials to the surface of the battery case and to make products that are durable against high heat and pressure. Derivation of injection molding recipes using high-tech composite materials has more complex factors affecting product production than general injection molding, so it is difficult to expect products of uniform quality if recipes are set based on the empirical know-how of existing experts. The production efficiency is also not constant, causing problems. For the above reasons, this paper proposes a model that can derive an optimal recipe for injection molding recipes using advanced composite materials using the MLP algorithm.
NMR Studies of Membranes and Whole Cells

The proton magnetic resonance ('H NMR) spectrum of whole cells is a complex, composite spectrum with a myriad ofdipolar-broadened ("broadline") and non-dipolar-broadened ("high resolution") contributions. Methods of separating and characterizing the different types of components will be described. The results [1] will be applied to the R13762 rat mammary adenocarcinoma cell line and the significance of the results in relation to previous studies [2] of the metastatic capacity of cancer cells will be discussed. A crucial factor in the ability to make reliable assignments of the main features of the spectra is the special character of the superlorentzian line [3, 4] arising from lipid molecules in the liquid crystalline state of the cell membranes.
Multi-Channel Temporal Graph Convolutional Network for Stock Return Prediction

Stock return prediction can help investors make better investment decisions and trends of country's economics. However, most of methods for stock return prediction are based on time-series models, treating the stocks as independent from each other. Inter-relations among stocks' time series are out of consideration. In this work, a Multi-Channel Temporal Graph Convolutional Neural Network (MCT-GCN) is proposed to optimize stock movement prediction. Experiments show that its performance is greater than benchmark algorithms, LSTM in the S&P 500.
Efficient Local Navigation Approach for Autonomous Driving Vehicles

This paper presents an efficient and practical approach for a car navigation system (CVM-Car) based on the velocity space optimization paradigm. The method calculates the velocity control commands to keep the car in the lane while avoiding the obstacles detected by the proximity sensors. The car has to follow a road path consisting of a sequence of lanelets. This approach is a lower-level reactive control that combines the pure pursuit method to obtain a reference curvature and a reactive control algorithm that keeps the vehicle in the center of the lane’ s free space while avoiding obstacles that can partially block it. CVM-Car formulates local obstacle avoidance as a constrained optimization problem in the velocity space of the car. In addition to the vehicle dynamics and obstacles constraints included by the curvature method, car-shape and non-holonomic restrictions are considered in the CVM-Car velocity space. The method has been applied to an autonomous vehicle prototype.
Short Term Passenger Flow Forecast of Metro Based on Inbound Passenger Plow and Deep Learning

Based on the suitable artificial intelligence model, this paper establishes the short-term passenger flow forecasting model of subway, studies the correlation characteristics that affect the short-term passenger flow forecasting of subway, and combines with the existing results, selects five related forecasting variables through data analysis, and uses copula correlation coefficient and Granger causality correlation analysis to process a large number of swipe card data. The weather factors, holidays, peak, the number of relevant stations and the number of outbound factors, are analyzed. By extracting the features that affect the accuracy of passenger flow forecast, using multiple constraints and taking the 15 minute interval as the time granularity, a passenger flow forecast method based on the combination of inbound passenger flow feature fusion and neural network prediction model (LSTM) is proposed. It not only improves the accuracy of passenger flow prediction, but also provides the basis for management decision-making, and provides reference information for people's travel.
Detection COVID-19 using Machine Learning from Blood Tests

Coronavirus (COVID-19) is an infectious disease that spreads around the world lately. It is a worldwide pandemic that affects mainly the lungs. It leads to huge risks as blood clotting, heart attack, and most of the time leading to death if it is not detected and cured early. The purpose of this research is to detect COVID-19 in its early stages to minimize the number of deaths caused by this disease. Discovering COVID-19 in early stages minimize the economy loss according to its drastic effect in manpower production. For the first time, detecting COVID19 occurrence using blood tests which are easy to collect and at the same time not expensive. We generated our own dataset which was used in detecting COVID-19. The dataset includes 134 cases from different genders, different ages and separated between positive and negative cases. The dataset contains blood tests which are (CBC, CRP, D-Dimer, S-ferritin, ALT,LDH). By these blood tests, doctors can know if people are infected by COVID-19 or not. Our approach used Machine Learning technique with different classifiers such as Random Forest (RF), Support Vector Machine (SVM) and Naive Bayes. The accuracy achieved were 76% for RF, 88% for SVM and 85% for Naive Bayes. SVM classifier achieved the best accuracy and was used in our model. According to the American Center of Disease Control and prevention (CDC), the accuracy of PCR is 80%, and it is expense relative to the blood tests, and using our model we achieved a better results than the PCR.