Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fractional Differential Fidelity-based PDE Model for Image Denoising

At present, there are many models for denoising images, such as the Perona - Malik model (PM) and the Gaussian filtering model that we often mention. These models can achieve good denoising effects in experiments, but in actual research, they are all imperfect. Based on this, the effective model for image denoising is proposed, which combines fractional-order fidelity and global gray degree fidelity. The combination of these two fidelity terms can measure the similarity of gray value changes between images, while it can remove the staircase effect and strengthen the texture information of the image. Experiments show that the model is superior to the model based on gradient fidelity in removing image noise.
Semi-Supervised Subspace Clustering via Non-Negative Low-Rank Representation for Hyperspectral Images

Hyperspectral images (HSIs) own inherent complexity, so, clustering for HSIs is a very challenging task. In this paper, we utilized a semi-supervised subspace clustering method based on non-negative low-rank representation (NNLRR) algorithm for HSI clustering. Firstly, NNLRR used Gaussian fields and harmonic functions into the low-rank representation (LRR) model. Secondly, NNLRR guided the affinity matrix construction by the supervision information. Next, finding a non-negative low-rank matrix, the matrix represents each sample by some other linear combination points, and the affinity matrix is obtained by the matrix. Then, accomplishing the affinity matrix construction and subspace clustering simultaneously. Thanks for the unification of the two steps, we can guarantee the overall optimum. Experimental results on classical data set show that, the algorithm is effective for hyperspectral image clustering.
Towards Semantically Enhanced Digital Twins

Digital twins (DTs) are a powerful mechanism for representing complex industrial assets such as oil platforms as digital models. These models can facilitate temporal analyses and computer simulations of assets. In order to enable this, DTs should be able to capture characteristics of an asset as specified by the manufacturer, its state during the run time, as well as how the asset interacts with other assets in a complex system. We argue that semantic technologies and in particular semantic models or ontologies is promising modelling paradigm for DTs. Semantic models allow to capture complex systems in an intuitive fashion, can be written in standardised ontology languages, and come with a wide range of off-the-shelf systems to design, maintain, query, and navigate semantic models. In this work we report our preliminary results on developing a system that would support semantic-based DTs. In particular, we plan to augment the PI System developed by OSIsoft with ontologies and show how the resulting solution can help in simplifying analytical and machine learning routines for DTs.
The New Family of Low-Voltage, Hyper-Speed Arcless, Hybrid, DC Circuit Breakers for Urban Traction Vehicles and Related Industrial Applications

The paper presents a new family of low voltage direct current, hybrid circuit breakers for urban traction vehicles (DCH-T) that also can be used in related industrial applications. A hyper-speed current breaking operation is performed by the means of a serial hybrid system, additionally protected by a fuse and a surge arrester. This process is arcless under any conditions. DCH-T circuit breakers are characterized by unique constructional and operational features and superior short-circuit parameters that are unreachable for classical magnetic blow-out devices. They effectively reduce short-circuit currents and the thermal and electrodynamic consequences of this phenomenon. They can protect all types of semiconductor converters used in electric traction vehicle drives. Moreover, they operate selectively in relation to the substation circuit breakers. Their contact life (even under short-circuit conditions) is equal to their mechanical life. Despite all the breaker standard functions, they offer special functions, such as cooperation with an on-board vehicle computer or a traffic control system. They are environmentally neutral and they can meaningfully improve passenger safety. Chosen thermal problems of DCH-T breakers has been modeled with the aid of simulation software and analyzed. DCH-T short-circuit parameters have been verified experimentally and the results are presented herein.
Role of Columnar Defect Size in Angular Dependent Flux Pinning Properties of YBCO Thin Films

The effect of artificially produced BaHfO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula>, BaZrO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula>, and BaSnO<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> nanocolumns within the YBa<inline-formula><tex-math notation="LaTeX">$_2$</tex-math></inline-formula>Cu<inline-formula><tex-math notation="LaTeX">$_3$</tex-math></inline-formula>O<inline-formula><tex-math notation="LaTeX">$_{6+{\mathrm{x}}}$</tex-math></inline-formula> (YBCO) lattice is investigated in terms of flux pinning and vortex dynamics by studying the properties of the angular dependent critical current density. Based on the differences in the growth mechanisms of discussed dopant species, the superconducting properties such as the critical current density, the accommodation field, and the effective pinning force have been observed to be greatly dependent on the size and the distribution of the nanocolumns. Therefore, the mechanisms of the collective individual and multivortex pinning have been observed being in a critical role when interpreting the in-field critical current anisotropy in YBCO films with artificial pinning centers.
Bidirectional Switching Characteristics Analysis Based on SiC MOSFET

Compared with Si MOSFET, the SiC MOSFET has the advantages of high temperature resistance, high voltage, high switching frequency and low switching loss. Bidirectional switches are widely used in matrix converters, T-type three-level and VIENNA rectifiers, however, the bidirectional switching characteristics based on SiC MOSFET are still less discussed. In order to explore the bidirectional switching characteristic of SiC MOSFET, the equivalent circuit of SiC MOSFET is first established, and a static model including trans-conductance, threshold voltage and pin resistance, and a dynamic model including junction capacitance and body diode are constructed. The forward and reverse conduction and turn-off characteristics of the SiC MOSFET bidirectional switch are deduced and analyzed. The reason for bidirectional switching oscillation and voltage spike are discussed in combination with the experiment. The characteristic law of SiC MOSFET and its bidirectional switch are verified through using simulation and experimental results.
Multivariate Sleep Stage Classification using Hybrid Self-Attentive Deep Learning Networks

Recently, significant efforts have been made to explore comprehensive sleep monitoring to prevent sleep-related disorders. Multivariate sleep stage classification has garnered great interest among researchers in health informatics. In this paper, we propose HybridAtt, a unified hybrid self-attentive deep learning network, to classify sleep stages from multivariate polysomnography (PSG) records. HybridAtt is an end-to-end model that explicitly captures the complex correlations among biomedical channels and the dynamic relationships over time. By constructing a new multi-view convolutional representation module, HybridAtt is able to extract hidden features from both channel-specific and global views of the heterogeneous PSG inputs. In order to enhance feature representation, a new fusion-based attention mechanism is also proposed to integrate the complementary information carried by each feature view. To evaluate the performance of our model, we carry out experiments on a benchmark PSG dataset. Experimental results show that the proposed HybridAtt model achieves better performance compared to ten baseline methods, demonstrating the effectiveness of HybridAtt in the task of sleep stage classification.
Serverless Big Data Processing using Matrix Multiplication as Example

Serverless computing, or Function-as-a-Service (FaaS), is emerging as a popular alternative model to on-demand cloud computing. Function services are executed by a FaaS provider; a client no longer uses cloud infrastructure directly as in traditional cloud consumption. Is serverless computing a feasible and beneficial approach to big data processing, regarding performance, scalability, and cost effectiveness? In this paper, we explore this research question using matrix multiplication as example. We define requirements for the design of serverless big data applications, present a prototype for matrix multiplication using FaaS, and discuss and synthesize insights from results of extensive experimentation. We show that serverless big data processing can lower operational and infrastructure costs without compromising system qualities; serverless computing can even outperform cluster-based distributed compute frameworks regarding performance and scalability.
Recognition of Common Pests in Agriculture and Forestry Based on Convolutional Neural Networks

In order to achieve rapid recognition of the common pests in agriculture and forestry a new method based on deep convolution neural network was proposed. Firstly, the pest dataset is built by the search engines and manual photographed with image processing algorithm. Then an 11 layers VGG-A neural network is used to recognize common pests in agriculture and forestry. In addition, the structure of the network is simplified by optimized parameters such as the number of convolution kernels and the fully convolutional layer width. Dropout is utilized to reduce the problem of overfitting. The experimental results demonstrate that the accuracy of the optimized model reaches 99.02%, which is 4.04 percentage points higher than the network structure before optimization.
A novel technique based on modified genetic algorithm for the synthesis of thinned planar antenna array with low peak side lobe level over desired scan volume

In this paper, a novel algorithm is devised to reduce the peak side lobe level (PSLL) of the thinned planar antenna (TPA) array at antenna boresight as well as over the desired scan volume without occurrence of grating lobes when the beam is electronically scanned away from the boresight. Randomized symmetrical thinning is incorporated in the proposed algorithm to find the best combination of 'ON' and 'OFF' elements. Modified binary coded genetic algorithm (MBC-GA) is employed in the present study to solve this optimization problem. A 12×12-element TPA array is numerically examined to access the performance of proposed method. Results obtained through this study show that array under consideration renders maximum possible reduction in PSLL and also demonstrate the absence of grating lobes when the beam is scanned to 20 deg. and 40 deg. away from the boresight.
FPGA Implementation of Face Recognition System Based on Convolution Neural Network

This paper presents a FPGA implementation of face recognition system based on convolution neural network. The hardware architectures for convolution layers, pooling layers, full-connected layer and softmax layer are designed. In each convolution layer, the parallelisms among convolution kernels and among feature maps are explored, and the convolution operations are carried out in parallel. The pooling operations for different feature maps are also computed in parallel. The Verilog HDL modules for the architectures are designed, simulated and synthesized to FPGA. The result shows that the architectures designed in this paper are correct and effective.
Twitter Sentiment Analysis, 3-Way Classification: Positive, Negative or Neutral?

People face with the huge amount of information on each day with the advent of big data era. The data amount stored and processed by Facebook, Twitter and other big social networks store (e.g. Instagram) is massive in those days. Online social networks provide great opportunity for propagation of almost any type of information. It’s actually much much easier to disseminate an idea/knowledge than previous times. Naturally, this creates information validity and immediate curiosity about mass evaluation problem in general. In this regard, sentimental polarity detection in social media (e.g.Classification of a tweet as negative or positive or neutral) is highly valuable for certain institutions, organizations. The study’s main focus is to classify negative, positive and neutral approaches of three (3) annotated twitter datasets. Effect of oversampling, unigram features and other features on overall and class-based accuracy ratios is worked on the datasets. Baseline is reached in dataset-2 experiments. 88% overall accuracy was observed in dataset-1 experiments which outperforms the prior art.Unigram features has shown significant effect on overall accuracy, class-based accuracy balance.
Prediction of PM2.5 Concentration Based on NDFA-LSSVM Model

At present, there are widespread air pollution problems in most parts of China, the accurate prediction of atmospheric pollutant concentration has become a hot issue for people to study. This paper proposes the NDFA-LSS VM model to predict the concentration of PM2.5. The hyper-parameter of Least Square Support Vector Machine ($LS$ SVM) were optimized by using the New Dynamic Firefly Algorithm (NDFA) to establish a PM2.5 concentration prediction model NDFA-LSSVM. The air quality data of monitoring stations at Chaoyang Agricultural Exhibition Hall District was used as source data to compare the performance of the optimized model with LSSVM model and General Regression Neural Network (GRNN) model. The experimental results show that the NDFA-LSSVM model proposed in this paper effectively improves the prediction accuracy of PM2.5 concentration.
Protein Regulating Networks underlying Multiple Actions against Cancer Delivered by Ginseng

Ginseng is a traditional Chinese medicine (TCM) with multiple constituents and therapeutic actions. These actions have been adapted in herb formulae against cancers. Yet the underlying mechanisms are not clear. In this study, started with proteins regulated by ginsenoside rb1/re/rg1, ginseng’s multi constituents, bioinformatics analysis towards protein interacting networks were deployed. As a result, three protein interacting networks regulated by ginsenosides against cancers were constructed. These networks target fundamental processes e.g., metabolism, immune response and mitosis. Via regulating these fundamental processes, ginseng delivers its multiple actions against cancers. This study proposes a novel method to explore underlying mechanisms of multiple actions of Chinese herbal medicines with multiple constituents.
Semi-Supervised Dictionary Learning Based on Atom Graph Regularization

In this paper, we propose a novel unified optimization framework for semi-supervised dictionary learning, which optimizes a graph Laplacian component and the dictionary simultaneously. In the framework, the graph Laplacian is defined on the atoms and the corresponding sparse codings. Since the atoms are more concise and representative than the original training samples, the constructed graph Laplacian can not only effectively capture the manifold structure of training samples, but also be more robust to noise and outliers. Moreover, the dictionary and the graph Laplacian can facilitate each other during the learning iterations. We derive an efficient algorithm by combining the block coordinate descent method with the alternating direction method of multipliers to solve the unified optimization problem. Extensive experimental evaluation on several challenging datasets demonstrates the superior performance of the proposed method.
Balancing Control Through Post-Optimization of Contact Forces

In this work we present a novel method to address the balancing problem for torque controlled legged robots through post-optimization of contact forces. The main concept consists in treating a legged robot as a fully actuated fixed-base system in order to compute the desired joint torques according to a fixed-based torque controller. The under-actuated component of the obtained torques is then mapped into contact forces through an optimal distribution problem. Besides extending previous work to the floating-base case, the proposed method has the notable advantage of avoiding the specification of a desired momentum of rotation, in addition to a reduced number of decision variables compared to full-inverse dynamics methods. The effectiveness of our approach has been validated in simulation using two different humanoid platforms: the CEN-TAURO and the COMAN+ robots, both recently developed at Istituto Italiano di Tecnologia (IIT). Preliminary experimental results on COMAN+ are also presented.
Single Flip-Chip Packaged Dielectric Resonator Antenna for CMOS Terahertz Antenna Array Gain Enhancement

A single dielectric resonator antenna (DRA) capable of enhancing the antenna gain of each element of a <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> terahertz (THz) antenna array realized in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS technology is proposed in this paper. The DRA implemented in a low-cost integrated-passive-device technology is flip-chip packaged onto the CMOS antenna array chip through low-loss gold bumps. By designing the DRA to work at the higher order mode of TE<inline-formula> <tex-math notation="LaTeX">$_{3,\delta,9}$ </tex-math></inline-formula>, only a single DRA, instead of conventionally needing four DRAs, is required to simultaneously improve the antenna gain of each element of the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> antenna array. This not only simplifies the assembly process, but it can also reduce the assembly cost. Moreover, the DRA can provide great antenna gain enhancement because of being made of high-resistivity silicon material and higher order mode operation. The simulated antenna gain of each on-chip patch antenna of the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> CMOS antenna array can be increased from 0.1 to 8.6 dBi at 339 GHz as the DRA is added. To characterize the proposed DRA, four identical power detectors (PDs) are designed and integrated with each element of the <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> THz antenna array. By measuring the voltage responsivity of each PD output, the characteristics of each antenna of the antenna array with the proposed DRA, including the gain enhancement level and radiation pattern, can be acquired. The measurement results match well with the simulated ones, verifying the proposed DRA operation principle. The four PDs with the proposed DRA are also successfully employed to demonstrate a THz imaging system at 340 GHz. To the best of our knowledge, the proposed DRA is the one with the highest order operation mode at THz frequencies reported thus far.
Classification of Various Daily Activities using Convolution Neural Network and Smartwatch

In the traditional human activity recognition field, human activity has been classified into two categories: exercise type and exercise posture. However, as the internet of things technology and wearable devices have been developed and become popular, in order to provide useful services, it is necessary to classify daily activities as well as existing activities. In this paper, we propose a novel classification model that classifies human activities into 11 different categories including activities that are highly active and less active in daily life. We collect data with an off-the-shelf smartwatch and use a deep learning model with a convolution neural network for the classification. An extensive evaluation shows that various daily human activities can be classified with 97.19% accuracy.
Automatic Construction of Sentiment Lexicon by Analyzing SMS Bigdata

SMS messages are expected to be different in vocabularies other than those used in ordinary written sentences. We explored an automatic construction of a sentiment lexicon through the extraction of sentiment words by analyzing SMS big data and general-purpose text corpus. Sentiment terms are extracted through POS tagging of the sentences, and the high frequency words in the corpus are compared. As a result, the use of adjectives is more frequent than that of verbs in SMS sentences, and the words of sentiment expression are frequently used. Using these characteristics, we proposed a method to automatically construct a sentiment word dictionary.
Machine Learning Approaches to Predict Functional Upper Extremity Use in Individuals with Stroke

The majority of stroke survivors suffer from residual functional deficits in the Upper Extremity (UE) that limits the patients’ ability to incorporate the hemiparetic UE into daily function such as reaching and grasping objects. Correctly assessing the degree to which individuals with stroke use their UE would be important for rehabilitation. However, previous work shows that many stroke patients, when observed in the laboratory, can appear to use the paretic arm with adequate ability, yet do not use the arm at home with the expected regularity. The main purposes of this study are to use various machine learning techniques to examine associations between laboratory-based measures of limb functioning and actual home use. The UE kinematics during reaching and grasping tasks were measured in the laboratory, while the use of the paretic arm in the home environment was assessed with portable accelerometry and the Motor Activity Log (MAL). The study identified a subset of the biomechanical features that predicted well the paretic arm use at home. Arm use was also well predicted with clinical evaluations of impairment using Fugl-Meyer and Action Research Arm Test (ARAT) scores. However, combining the clinical and a subset of biomechanical features yielded the best model to predict UE use in individuals with stroke.
Q-Learning with Naïve Bayes Approach Towards More Engaging Game Agents

One of the goals of modern game programming is adapting the life-like characteristics and concepts into games. This approach is adopted to offer game agents that exhibit more engaging behavior. Methods that prioritize reward maximization cause the game agent to go into same patterns and lead to repetitive gaming experience, as well as reduced playability. In order to prevent such repetitive patterns, we explore a behavior algorithm based on Q-learning with a Naïve Bayes approach. The algorithm is validated in a formal user study in contrast to a benchmark. The results of the study demonstrate that the algorithm outperforms the benchmark and the game agent becomes more engaging as the amount of gameplay data, from which the algorithm learns, increases.
cnnCNV: A Sensitive and Efficient Method for Detecting Copy Number Variation based on Convolutional Neural Networks

Along with the promotion and application of genetic techniques, desiring to improve the efficiency of calling copy number variation (CNV) in next-generation sequencing analysis technics (NGS) data turns into a requirement. A single existing method is insufficient for calling all potential CNVs. In this paper, we present cnnCNV, a convolutional neural network (CNV) based framework for calling CNV. Firstly, cnnCNV merges the output of existing CNV calling tools as candidates; secondly, generates images of each candidate region from aligned reads based on multiple detection theories; finally, a trained model can be used to classify candidates into true and false. Our approach was tested on simulated data comparing with existing tools, including Breakdancer, ControlFREEC, CNVnator, Delly and readDepth. Results show that cnnCNV improve precision and sensitivity of CNV calling remarkably. Pseudo-code of image generation can be download at: The pseudo-code of image creation can be downloaded at: https://github.com/StudMs/cnnCNV.