Miracle-dz/UniFAR-FTUs huggingface dataset:
Water Level Prediction at TICH-BUI river in Vietnam Using Support Vector Regression

In this paper, the support vector regression model is used to predict water levels at a downstream station of the Tich-Bui river basin. The study investigated the effects of rainfall data collected from eight gauging stations and water levels at the downstream station for the performance forecast. The model was set up to forecast water levels at the downstream station before 6-lead-hour, 12-lead-hour, 18-lead-hour and 24-lead-hour. Although the model does not require data on the climate, terrain but the forecast results are accurate. In the case of a water level forecast before 6 hours and 12 hours, the Nash coefficient gives a value of over 98.81% and the RMSE value is less than 0.20 m. This results suggest that the support vector regression model, which the authors use to accurately predict water levels in real time, can be used to warn of floods in Vietnam's rivers.
Rhyming Knowledge-Aware Deep Neural Network for Chinese Poetry Generation

Analyzing and capturing the spirit in the historic Tang Dynasty poems for creating a machine that can compose new poetry is a difficult but fun challenge. In this research, we propose a rhyming knowledge-aware deep neural network for Chinese poetry generation. The model fuses rhyming knowledge that represents phonological tones into a long short-term memory (LSTM) model. This work will help us understand more about what kind of mechanism within the neural network contributes to different styles of the generated poems. The experimental results demonstrate that the proposed method is able to guide the style of those poems towards higher phonological compliance, fluency, coherence, and meaningfulness, as evaluated by human experts. We believe that future research can adopt our approach to further integrate more knowledge such as sentiments, POS, and even stylistic patterns found in poems by famous poets into poem generation.
Segmentation based Non-learning Product Detection for Product Recognition on Store Shelves

The arrangement of products on store shelves can refer to commercial contracts, sale achievement, and customer satisfaction. At present, clerks check the arrangement manually, which spends time, costs human resource significantly, and can disturb shopping customers. Although automatic methods via computer vision (often incorporating machine learning) can solve the issue, the existing methods need single product template images for detection, facing the difficult collection of master images and the frequent upgrade of products. In this paper, we propose to detect products on store shelves by segmenting the shelves horizontally and vertically without template images and machine learning. The horizontal segmentation is based on clapboard detection via casting lateral gradient votes. The vertical segmentation contains the linear region of interest (ROI) optimization and shadow detection by longitudinal gradient grouping. In experiments, we compare our method with the only existing non-template method, and our method outperforms the existing method.
A Wearable Device of Salivation Detection and Improvement for Elderly at High Risk for Dysphagia

Targeting older adults with high risks of dysphagia. This study introduced a wearable device for saliva detection and improvement in salivation. Said device could detect its users” long-term salivation situations and determine/issue the following when salivation occurred: whether the users salivated in their left or right cheeks and voice prompts reminding the users to swallow their saliva. In this way, excessive accumulation of saliva in the oral of the older adults is avoided. , resulting in coughing and even pneumonia.
Detection of Optimal Puncture Position in OVUM Images for Artificial Insemination

This paper aims to determine the optimal puncture position of ovum by evaluating rupture membrane of cytoplasm. We employed 139 ovum images on the Piezo-ICSI (Intracytoplasmic sperm injection). In it, grayscale images before puncture and their actual puncture position were obtained from the movie file (Rupture:31, No Rupture:108), and Local Binary Pattern (LBP) feature is calculated at analysis area around the puncture position. LBP feature dimensions are reduced, and data are classified by hierarchical clustering method using feature of three dimensions. As a result, the data classified into two clusters (Clusters A and B). Cluster A has 7 Ruptures and 50 No Ruptures, Cluster B has 24 Ruptures and 58 No Ruptures. Then, the sensitivity is 0.77. Therefore, it is possible to evaluate rupture membrane of cytoplasm from shape feature of membrane. The optimal puncture position could be determined by the features.
Assessment of the impact of progressive carbon taxation strategies on Supply Chain’s strategic decisions and performances

The carbon tax legislation has been widely advocated as a cost-efficient law to push companies to reduce their carbon emission through their supply chain’s (SC) activities. Based on a carbon emission reduction’s target, different carbon taxation strategies have been implemented around the world. Generally, governments launch the carbon tax with a relatively low carbon price and plan to increase it incrementally over the years by adopting various strategies in a way to attend their emission reduction objective. The majority of developed mathematical models within the green supply chain design (GSCD) under carbon tax legislation have ignored the dynamic aspect of this legislation. Within this paper, we aim to contribute to the literature by studying the impact of the dynamic carbon tax legislations on the strategic decisions of a SC, its economic and environmental performances. Within this optic, we study the technology investment problem under different progressive strategies of carbon taxation, mainly a linear, a convex and a concave carbon taxes functions as well as a constant carbon tax law. Our objective is to assess and compare the efficiency of these progressive strategies for the purpose of assisting companies that are greening their SCs, in finding a compromise between reducing their carbon emissions and increasing their profits.
Polynomial Reduction of TSP to Freely Open-loop TSP

Travelling Salesman Problem (TSP) is one of the earliest combinatorial problem that is identified to be NP-hard problem. It is a problem that seeks to find the shortest possible route in a graph problem which passes through all nodes only once and return to the starting point. A variant of TSP is the Freely Open-loop TSP (FOTSP) which seeks to find the shortest route in the graph without having to return to starting point and with no specific starting or end node. In this paper, a reduction of polynomial complexity for TSP problem into FOTSP is presented and vice versa. This reduction proves that FOTSP is also NP-complete just as TSP.
Robust H-Infinity Control for Magnetic Levitation System

This paper studies Quancer Magnetic levitation plant and proposed a robust control for the system using H-infinity approach. The performance and robustness of the proposed method was compared with the existing Proportional Integral Velocity (PIV) + Feed-forward (FF) control designed by the manufacturer. The performance analysis of the existing PIV+FF controller was made to build the desired specification template. A dynamic state feedback controller satisfying H-infinity performance, that is numerically solved using Riccati approach was designed. Simulation results obtained, shows superior robustness to uncertainties of the proposed control over the existing control with sufficient tracking performance.
Fraud Detection in Credit Card and Application of VAT Clustering Algorithm: A Review

The evolution of secured and reliable internet facilities have greatly influence the advancement in the global e-commerce, making online transaction more efficient and even more promising for the future decades. The uses of credit card become more popular for online purchases due to the ease and availability of internets. Financial fraud pattern also changes and increases rapidly with the development of modern technology which conversely increases the level of fraudulent transactions in credit card resulting in huge losses. However, several credit card fraud detection techniques have been developed to address the problem. In this paper, different fraud detection method have been reviewed and classified based on the approaches used. Also, the major limitation and reasons why most methods are not very efficient are also presented. Furthermore, this paper proposed the application of VAT algorithm as a clustering algorithm to improve on the limitations of credit card detection methods.
Phase Retrieval via Wirtinger Flow Algorithm and Its Variants

Almost three-quarters of the underling information in the light wave field is embodied in the phase. However, the early optical detectors can only record the intensity or amplitude of the light wave field and cannot directly extract the phase information of the light wave field. Therefore, it is necessary to use the measured amplitude or strength to reconstruct the phase information of the object, this problem is denoted phase retrieval. Phase retrieval is a matter of cardinal significance in signal processing and machine learning. The phase retrieval by convex optimization algorithm is ideal but the computational complexity is high. In 2015, Candès proposed a very effective non-convex optimization algorithm-Wirtinger flow algorithm which used spectral initialization to get a better initial value and then gradient iteration to get a promised recovery effect. Subsequently, in line with the idea, a large number of variants are devised, such as: Wirtinger flow(WF), Truncated Wirtinger Flow (TWF), Truncated Amplitude Flow (TAF), Reshaped Wirtinger Flow (RWF), Incremental Truncated Wirtinger Flow (ITWF), Incremental Reshaped Wirtinger Flow (IRWF), Robust Wirtinger Flow (Robust-WF), Sparse Wirtinger Flow (SWF), Median-TWF, Median-RWF, Generalized Wirtinger Flow (GWF), Accelerated Wirtinger Flow (AWF), Thresholded Wirtinger Flow Revisited (THWFR), Thresholded Wirtinger Flow (THWF), Reweighted Wirtinger Flow (REWF), Wirtinger Flow Method With Optimal Stepsize (WFOS), Stochastic Truncated Wirtinger Flow Algorithm (STWF), Stochastic Truncated Amplitude Flow (STAF), Reweighted Amplitude Flow (RAF), Compressive Reweighted Amplitude Flow (CRAF), SPARse Truncated Amplitude flow (SPARTA) and Sparse Wirtinger Flow Algorithm with Optimal Stepsize (SWFOS), etc. This paper analyzes and summarizes these algorithms according to their characteristics such as: initialization method, step size, iteration times, sample complexity, computational complexity, etc., so that readers can intuitively and clearly see the characteristics of each algorithm. Finally, we provide the website of the source code of some algorithms, facilitate to access and use it for readers.
A Matrix-Analytic Method for Evaluating Performance of Security-Check System with Two-Type Inspection Queues*

the significant increasing of terrorist attacks, the homeland security has become more and more important, but the enhanced security-check process has caused serious impacts on the transportation delay and inconvenience for passengers. This paper studies two types of inspection channels in a security-check system, which is formulated as a queueing model with a state-dependent Quasi-Birth-and-Death (QBD) process. A matrix-analytic method is proposed in the paper to determine the steady-state probability for the studied security-check queueing system. We also develop a solving algorithm for computing the average queue length and waiting time by applying the embedded Markov chain. The presented computing method and queueing analysis could be applied in evaluating the system performance of security checkpoints.
Generalized Filter-bank Features for Robust Speech Recognition Against Reverberation

Traditionally, automatic speech recognition (ASR) uses a Hidden Markov Model with Gaussian Mixture Model (HMM-GMM) as acoustic model and hand-designed features such as Mel-frequency Cepstral Coefficient (MFCC) as acoustic features. It is usually assumed that the features are uncorrelated, making it possible to use diagonal covariances for the GMM. The assumption generally holds due to the use of Discrete Cosine Transformation (DCT) that de-correlates the speech spectra. However, DCT could cause some information loss, such as correlations between the feature components. Current ASR systems, which is based on Deep Neural Network (DNN) show to be better especially in reverberant conditions when more primitive features, such as filter-bank (FBANK), are used. This might be because DNN is better in modeling non-linear relations between the components of the features. But the use of short-time processing in FBANK may cause the lost of long-term correlations in a speech pattern. To tackle this, we propose a new feature, q-FBANK, which is a generalization of FBANK. The results on artificially reverberant speech show that the proposed features achieve better performance than MFCC and FBANK on DNN-HMM systems where an average error reduction up to 39.73% and 13.5% were achieved respectively.
A Multi-Step Attack Detection Model Based on Alerts of Smart Grid Monitoring System

Smart Grid Monitoring System(SGMS) is an important means to protect the security of smart grid. The high volumes of alerts generated by SGMS often confuse managers. Automatically handling alerts and extracting attack events is a critical issue for smart grid. Most of the existing security event analysis methods are designed for Internet, which will not be directly applicable to the power grid for high reliability and low attack tolerance requirements. In this paper, a multi-step attack detection model based on alerts of SGMS is proposed. In this model, an alert graph is constructed by IP correlation, and then transformed into candidate attack chains after being aggregated. Consequently, the candidate preliminary attack chains are pruned and denoised by negative causal correlation and non-cascading events. Finally, attack chains and visual attack graphs are formed. Our proposal model needs a little of priori knowledge while automatically extracting multi-step attack events and demonstrating the trajectories among IPs. The experimental results show the model performs well on China Grid data and DARPA 2000 data set.
Robust Control Approach for Novel Stabilized Platform with Series Elastic Actuator

The stabilized platforms are commonly used in mobile systems to stabilize the payload instruments. This paper proposes a novel stabilized platform with series elastic actuator (SEA), which has advantage over traditional stabilized platform in peak acceleration suppression. The novel stabilized platform suppresses disturbance via passive elasticity and active servo control. A robust control scheme is applied to enhance system performance against disturbance by keeping the platform in the state of force balance. The scheme is integrated with time delay estimation (TDE) feedforward and robust integral of signal of error (RISE) feedback. The performance of proposed method is validated on the self-built testbed. Experiment results indicate that the disturbance suppression performance with the proposed control scheme (TDE-RISE) is evidently better than that of the traditional proportional-integral-derivative (PID) control strategy, showing promising application prospects.
The use of non-synchronous automatic reclosing in power plants of large industrial enterprises with a complex network configuration

One of the main trends in industrial energy is the use of various sources of distributed generation. This is due to the low cost of its own generated electricity and increased reliability of electricity supply to industrial consumers. However, the introduction of objects of distributed generation leads to a significant complication of operating conditions. In particular, it is not excluded the output to the separate work with the power system. This paper focuses on the restoration of synchronous parallel operation with the power grid. Two types of non-synchronous automatic reclosing were investigated: fast-reclosing and switching on at different frequencies. An algorithm has been developed for the operation of non-synchronous automatic reclosing under conditions of distributed generation objects. The conditions under which it is advisable to use this type of automation in industrial power plants are determined. A software package for calculating transient electromechanical processes has been developed, which allows investigating the modes of resynchronization of generators with a network. Studies were conducted on the example of an industrial power plant of complex configuration. Calculations showed that the success of resynchronization depends on the network configuration, unbalance of power in the node and power of the generators. The developed algorithm allows determining the possibility of using asynchronous automatic reclosing at a power plant and developing recommendations for its use.
Attention alignment by linear space projection for video features extraction

Video feature extraction is the basis of research on video data. At present, many models based on convolution neural networks for features extracting need to stack deeper network layers to improve the robustness, which means that the amount of parameters and computation is greatly increased while the practicability of such models is constrained. In this paper, a global projection self-attention mechanism is proposed to empower shallow models generate robust video representations. Compared to Non-local [17] and Squeeze Excitation [18] networks, we adopt more succinct but sufficient context modeling strategy to establish complete global interaction space, introduce a lighter transform block for attention coefficients predicting, which improves the running efficiency of attention models greatly. We further embed our attention module to several modern networks including ResNet[3], $\mathbf{R}(\mathbf{2+1})\mathbf{D}[\mathbf{9}]$ and I3D[7], in that ResNet-18 and $\mathbf{R}(\mathbf{2+1})\mathbf{D}-\mathbf{18}$ meet considerably promotion upon its original version by more than 18.9 percent on UCF101 dataset, this result implies huge potential of proposed attention blocks.
Multi-Class Brain Age Discrimination Using Machine Learning Algorithm

Resting-state functional connectivity analyses have revealed a significant effect on the inter-regional interactions in brain. The brain age prediction based on resting-state functional magnetic resonance imaging has been proved as biomarkers to characterize the typical brain development and neuropsychiatric disorders. The brain age prediction model based on functional connectivity measurements derived from resting-state functional magnetic resonance imaging has received a lots of interest in recent years due to its great success in age prediction. However, some of the recent studies rely on experienced neuroscientist experts to select appropriate connectivity features in order to build a robust model for prediction while the others just selected the features based on trial-and-error test. Besides, the subjects used in this studies omitted some subjects that can be divided into two groups with less similarity which may confused the prediction model. In this study, we proposed a multi-class age categories discrimination method with the connectivity features selected via K-means clustering with no prior knowledge provided. The experimental results show that with K-means selected features the proposed model better discriminate multi-class age categories.
Design of A High Torque Density Motor Based on Halbach Array Integral Ring Rotor

At present, further reducing the mass of permanent magnet brushless dc motor has become a new demand for the development of humanoid robots. In order to achieve this goal, this paper presents a new design idea of motor. Compared with the motor currently used in robots, the new method will reduce the mass of the motor and improve the torque density as well, while ensuring the same torque output. This design method mainly uses HALBACH array for excitation. Through finite element analysis, the distribution rules of magnetic field, back electromotive force, torque and cogging torque of the motor are obtained. Experimental result shows that the actual torque of the motor reaches the goal of our design, and the rotor weight is reduced by 30%.
Numerical Simulation of the Processes of Microwave Heating of Dielectrics in Traveling Wave Chambers

This paper presents the results of numerical simulation of dielectric heating in a microwave traveling wave chamber based on an irregular rectangular waveguide. These chambers, which are used primarily for heating and drying dielectrics, make it possible to achieve increased heating uniformity, which is significantly affected by the dielectric properties of heated objects. The influence of the dielectric properties of the heated object on the temperature field distribution in it is analyzed. The comparison of the results of numerical simulation with the results of a full-scale experiment is given.
Action Recognition Using Attention-Joints Graph Convolutional Neural Networks

Human skeleton contains significant information about actions, therefore, it is quite intuitive to incorporate skeletons in human action recognition. Human skeleton resembles to a graph where body joints and bones mimic to graph nodes and edges. This resemblance of human skeleton to graph structure is the main motivation to apply graph convolutional neural network for human action recognition. Results show that the discriminant contribution of different joints is not equal for different actions. Therefore, we propose to use attention-joints that correspond to joints significantly contributing to the specific actions. Features corresponding to only these attention-joints are computed and assigned as node features of the graph. In our method, node features (also termed as attention-joint features) include the i) distances of attention-joints from the center-of-gravity of human body, ii) distances between adjacent attention-joints and iii) joints flow features. The proposed method gives a simple but more efficient representation of skeleton sequences by concatenating more relative distances and relative coordinates to other joints. The proposed methodology has been evaluated on single image Stanford 40-Actions dataset, as well as on temporal skeleton-based action recognition PKU-MDD and NTU-RGBD datasets. Results show that this framework outperforms existing state-of-the-art methods.
Estimation of Nigrescens Palm Oil Ripeness using Contrast and Skewness from 680 nm Image

This paper aim of this study is to assess the oil palm ripeness using a specific wavelength as a light source, 680 nm, and simple image processing technique. This paper proposes a simple and low computation technique to estimate the oil palm FFB maturity using 680 nm LED as the light source and digital camera to capture the image. There are some parameters that can be used to detect oil palm FFB maturities, such as chlorophyll concentration and fruitlet detachment. In this research, the 680 nm image contrast is used to detect the chlorophyll due to it correlated with the chlorophyll absorbance. The accuracy of this approach technique is 64.94 %. The second approach is using texture analysis, skewness, to differentiate the detached and undetached fruitlets in oil palm FFB. The accuracy of this technique is 68.83 %. The last technique is combining image contrast and skewness. The result of this approach gives accuracy by 68.83 %. From the results, skewness and combination of contrast and skewness give the same accuracy, 68.83 %. It can be concluded that the estimation of oil palm FFB maturity can be done by using an only skewness value of the 680 nm image. The result is in-between another research results in oil palm FFB maturity grading. The proposed technique can be implemented using microcomputer. It will reduce the implementation cost and can be used to build portable systems.