Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Recognition Method of Web Application Categories Based on Multi-layer Simhash Algorithm

Traditional Web application category recognition is implemented by fingerprint rule matching, which is difficult to extract fingerprint rules and has limited coverage. At present, many improved identification methods semi-automatically extract fingerprints through certain rules and identify Web application categories through clustering or classification algorithms, but still rely on fingerprint rules and human intervention, and the time complexity of classification is too high to process a large amount of data. This paper proposes Multi-layer Simhash Algorithm and combines DBSCAN clustering to realize intelligent identification of Web application types, pioneering the complete automation of fingerprint identification of Web applications. This method has the function of discovering unknown Web applications and predicting unknown application types, and solves the problems of fingerprint rule extraction and manual dependence of Web applications. This paper through the TF-IDF algorithm to extract the Web page text key words and weight, Then, Multi-layer Simhash Algorithm is used to transform text feature words and weights into binary characteristic hash value, at last, the hamming distance between the input Web page and the characteristic hash value of the known category is compared with the radius of the base class, which determines the category of the input Web application. The experimental results show that the accuracy of Web application category recognition and prediction is more than 97% and 93% respectively.
Nanofibrous Scaffolds for Diabetic Wound Healing

Chronic wounds are one of the secondary health complications that develop in individuals who have poorly managed diabetes mellitus. This is often associated with delays in the wound healing process, resulting from long-term uncontrolled blood glucose levels. As such, an appropriate therapeutic approach would be maintaining blood glucose concentration within normal ranges, but this can be quite challenging to achieve. Consequently, diabetic ulcers usually require special medical care to prevent complications such as sepsis, amputation, and deformities, which often develop in these patients. Although several conventional wound dressings, such as hydrogels, gauze, films, and foams, are employed in the treatment of such chronic wounds, nanofibrous scaffolds have gained the attention of researchers because of their flexibility, ability to load a variety of bioactive compounds as single entities or combinations, and large surface area to volume ratio, which provides a biomimetic environment for cell proliferation relative to conventional dressings. Here, we present the current trends on the versatility of nanofibrous scaffolds as novel platforms for the incorporation of bioactive agents suitable for the enhancement of diabetic wound healing.
DFCNI: Researching on Data-centric Framework of Cloud Network Integration for Telecom Operators

With the rapid development of network technology and cloud computing, the data of the telecommunications industry has shown massive growth. Traditionally, telecom operators have built integrated information services with cloud and network infrastructure as the core. But data remains isolated and underutilized. The overall big data architecture and infrastructure that oriented data value circulation and integrated multiple data, technologies and resources have not really been established. So we proposes a DFCNI: Data-centric Framework of Cloud Network Integration for Telecom Operators, which can provide an innovative infrastructure for telecom operators' big data industry. We first analyzed the technical architecture, deployment scheme and key technologies of DFCNI. This architecture changes the traditional cloud resource concentration mode, and makes full use of the resource capabilities of the cloud and network through distributed computing, resource migration, virtualization and other technologies. And then we introduced a typical application scenario of DFCNI- -Smart City, Solving the problem of multi-party data fusion in this scenario under the premise of ensuring data privacy. Finally, we summarized three advantages of DFCNI and proposed future research directions including data right confirmation technology and data circulation guarantee technology based on Blockchain.
Wigner-Ville distribution applied to cardiac motion estimation

Some heart abnormalities like ischemia and infarction of myocardium can produce significant changes in cardiac wall motion and decrease severely the cardiac function. For this reason the cardiac motion estimation and quantification have been the focus of many investigations. The aim of this work is to study cardiac motion by means a dense velocity vector field based on the computation of the Wigner-Ville distribution, a common transformation used in the spatiotemporal-frequency (STF) analysis. The major motivation for considering the use of STF comes from observations in mammalian vision. This paper describes a method to obtain a 3D-frequency spectrum, based on the computation of the Wigner-Ville distribution, and a procedure to determine, automatically, the velocity of pixels in a temporal series of images. The method is also applied to synthetic images and real images obtained from gated-SPECT.
Identification of Hot data and Caching strategy for Industrial Big Data Based on Temperature Model

In storage systems, there are heat differences between data. Traditional algorithms such as LRU are limited by specific data structures. These methods cannot be well applied to industrial big data storage systems. Methods based on “temperature” are usually limited by static parameters and it is unable to adapt to dynamic load. Based on Newton's law of cooling, we proposed an identification model called AdjustDTM, with adjustable parameters. Our method identifies the hot and cold by assigning the attribute “temperature” to the data. Then, the model can dynamically adjust the parameters according to accessing interval and frequency. Our model can also preheat the correlative data. Finally, The experimental results showed that the hit rate of AdjustDTM is higher than other strategies.
Classing and Extracting Information from Radar Images

GPR techniques had been successfully using for many years to examine structures and materials from inside. They are based on the possibility of sending electromagnetic pulses and registering the echo time and the amplitude of the signals eventually back-scattered by the interface between propagation media of difierent dielectric constants. The result consists of some radar images (the so-called radargrammes), where incidental targets generate distinctive hyperbolic plots or discontinuities, as an indirect evidence of either changes in the properties, nature and composition of the medium or the presence of internal breaks. The present note aims at expounding some tests and preliminary results drawn by the employment of speciflc algorithms for automatically interpreting, classing and extracting explicit information from radargrammes, once these have been traced from raw radar data by an ad hoc application designed in the MatLab framework.
Parameter Robust Deadbeat Predictive Current Control for Open-Winding Surface Permanent Magnet Synchronous Motor Drives

Deadbeat predictive current control (DPCC) of open-winding surface permanent magnet synchronous motor (OW-SPMSM) has excellent dynamic and steady-state performance, but it depends heavily on the accuracy of motor model parameters (resistance, inductance, and flux linkage). To eliminate the influence of parameter mismatch and retain the fast dynamic performance of the traditional DPCC, a parameter robust DPCC for OW-SPMSM drives is proposed in this article. First, the incremental prediction model of OW-SPMSM is introduced, which eliminates the flux linkage parameters in traditional DPCC. Second, the parameter sensitivity analysis shows that the mismatch of resistance parameter hardly affects the current prediction errors of incremental prediction model, which are mainly caused by the mismatch of inductance parameters. Finally, to extract the actual inductance information, an inductance extraction strategy is proposed. Experimental results show that the current control performance of the proposed robust DPCC is not influenced by any motor parameter mismatch, which always has smaller current tracking errors and total harmonic distortion (THD) than the traditional DPCC. Moreover, compared with the existing robust DPCC with observers, the proposed DPCC has better dynamic response performance.
Multiscale snapshot imaging spectrometer with large FOV and fast speed

A novel snapshot imaging spectrometer with large field-of-view (FOV) up to 100° is achieved by taking the advantages of a multiscale fore-optics and a compact Offner imaging spectrograph. Based on the diffraction imaging theory, the multiscale fore-optics composed of a monocentric spherical lens and multi-channel microlens array is designed, over which panchromatic images with small FOV are of uniform image quality. And identical imaging spectrographs with a dimension less than 30 cubic millimeters and with a high spectral resolution of about 2nm are designed correspondingly. The presented imaging spectrometer works at the visible wavelength range which is from 400nm to 780nm. It is of a fast speed about F/2.4 and a compact configuration of only 200mm×300mm×300mm in dimension. But the smile and keystone distortions are negligible.
ABox Enrichment Through Semantic Data Recognition in Industrial Internet of Things

As the internet of things networks tend to grow bigger and bigger, the need for interoperability across systems is increasing. However, due to privacy, security, and segmentation across companies, the current practice is to create always new data silos, leading to a discontinuity in the unity of data. To address this, ontologies are seeing increasing usage in the industrial world. Nevertheless, the problem of linking the data back to the ontology is still present, which is often done manually.This paper introduces an approach to simplify this task by providing a semi-supervised tool using semantic data type detection and deep learning. One of the features of the proposed method is the ability to train the model without the need to keep every dataset stored together.
Attack Resilient Cloud-Based Control Systems for Industry 4.0

In recent years, since the cloud can provide tremendous advantages regarding storage and computing resources, the industry has been motivated to move industrial control systems to the cloud. However, the cloud also introduces significant security challenges since moving control systems to the cloud can enable attackers to infiltrate the system and establish an attack that can lead to damages and disruptions with potentially catastrophic consequences. Therefore, some security measures are necessary to detect these attacks in a timely manner and mitigate their impact. In this paper, we propose a security framework for cloud control systems that makes them resilient against attacks. This framework includes three steps: attack detection, attack isolation, and attack mitigation. We validate our proposed framework on a real testbed and evaluate its capability by subjecting it to a set of attacks. We show that our proposed solution can detect an attack in a timely manner and keep the plant stable, with high performance during the attack.
Improvement of Oxidative Stability of Fish Oil-in-Water Emulsions through Partitioning of Sesamol at the Interface

The susceptibility of polyunsaturated fatty acids to oxidation severely limits their application in functional emulsified foods. In this study, the effect of sesamol concentration on the physicochemical properties of WPI-stabilized fish oil emulsions was investigated, focusing on the relationship between sesamol–WPI interactions and interfacial behavior. The results relating to particle size, zeta-potential, microstructure, and appearance showed that 0.09% (w/v) sesamol promoted the formation of small oil droplets and inhibited oil droplet aggregation. Furthermore, the addition of sesamol significantly reduced the formation of hydrogen peroxide, generation of secondary reaction products during storage, and degree of protein oxidation in the emulsions. Molecular docking and isothermal titration calorimetry showed that the interaction between sesamol and β-LG was mainly mediated by hydrogen bonds and hydrophobic interactions. Our results show that sesamol binds to interfacial proteins mainly through hydrogen bonding, and increasing the interfacial sesamol content reduces the interfacial tension and improves the physical and oxidative stability of the emulsion.
A Multi-source Signal Fusion Network with Feature Refinement Capability for Hob Spindle Fault Diagnosis

In the fault diagnosis of rotating machinery, the effect of multi-sensor signal analysis based on convolution neural network (CNN) is remarkable. However, despite the powerful feature extraction capability of CNN, due to the interference of noise and other irrelevant features, it cannot stably focus on important features when dealing with multi-sensor signals in complex working conditions. The interpretability of the internal feature extraction process of the neural network is poor. A multi-source signal fusion network with feature refinement module (FRM-CNN) is proposed to optimize the fault diagnosis of the hob spindle. The feature refinement module (FRM) enhances the end-to-end model's attention to crucial implicit features, allowing the fault diagnosis process to be completely free of additional noise reduction preprocessing. Hob spindle experiments verify the generalization ability of the model for real-time fault diagnosis under variable working conditions. The effectiveness of feature extraction is intuitively demonstrated through the feature output of crucial layers. FRM-CNN achieves higher and stable accuracy than other typical models.
Chlorophyll Meter: A Precision Agricultural Decision-Making Tool for Nutrient Supply in Durum Wheat (Triticum turgidum L.) Cultivation under Drought Conditions

How crop biodiversity adapts to drought conditions and enhances grain yield became the most important issue facing agronomists and plant breeders at the turn of the century. Variations in genetic response, inadequacy of nutrients in the soil, and insufficient access to nutrients are factors that aggravate drought stressors. The development of screening tools for identifying drought tolerance is important in the deployment of durum wheat varieties suited to drought-prone environments. An experiment was conducted to evaluate durum wheat varieties under a range of nutrient supplies in naturally imposed drought conditions. The treatments consisting of two nitrogen regimes (i.e., control and 60 kg ha−1), four durum wheat varieties, and three types of nutrients (control, sulfur, and zinc) that were arranged in a split-split plot design with three replications. Both foliar-based sulfur and zinc fertilization were employed at the flag leaf stage, at a rate of 4 and 3-L ha−1, respectively. The results showed a significant (p < 0.05) genetic variation in chlorophyll concentration, grain protein content, tillering potential, and leaf area index. Varieties that contained better leaf chlorophyll content had improved grain yield by about 8.33% under 60 kg/ha nitrogen. A combined application of nitrogen and zinc at flag leaf stage significantly improved grain yield of Duragold by about 21.3%. Leaf chlorophyll content was found to be a more important trait than spikes per m2 to discriminate durum wheat varieties. Foliar application of sulfur increased the grain yield of drought-stressed plants by about 12.23%. Grain yield and protein content were strongly correlated with late-season SPAD readings. Significant (p < 0.05) correlation coefficients were obtained between normalized difference vegetation index, leaf area index, grain yield, and protein content with late-season chlorophyll content, revealing the importance of chlorophyll content in studying and identifying drought-tolerant varieties.
Subspace Trajectory Piecewise-Linear Model Order Reduction for Nonlinear Circuits

Despite the efficiency of trajectory piecewise-linear (TPWL) model order reduction (MOR) for nonlinear circuits, it needs large amount of expansion points for large-scale nonlinear circuits. This will inevitably increase the model size as well as the simulation time of the resulting reduced macromodels. In this paper, subspace TPWL-MOR approach is developed for the model order reduction of nonlinear circuits. By breaking the high-dimensional state space into several subspaces with much lower dimensions, the subspace TPWL-MOR has very promising advantages of reducing the number of expansion points as well as increasing the effective region of the reduced-order model in the state space. As a result, the model size and the accuracy of the TWPL model can be greatly improved. The numerical results have shown dramatic reduction in the model size as well as the improvement in accuracy by using the subspace TPWL-MOR compared with the conventional TPWL-MOR approach.
Focus on New Test Cases in Continuous Integration Testing based on Reinforcement Learning

In software regression testing, newly added test cases are more likely to fail, and therefore, should be prioritized for execution. In software regression testing for continuous integration, reinforcement learning-based approaches are promising and the RETECS (Reinforced Test Case Prioritization and Selection) framework is a successful application case. RETECS uses an agent composed of a neural network to predict the priority of test cases, and the agent needs to learn from historical information to make improvements. However, the newly added test cases have no historical execution information, thus using RETECS to predict their priority is more like ‘random’. In this paper, we focus on new test cases for continuous integration testing, and on the basis of the RETECS framework, we first propose a priority assignment method for new test cases to ensure that they can be executed first. Secondly, continuous integration is a fast iterative integration method where new test cases have strong fault detection capability within the latest periods. Therefore, we further propose an additional reward method for new test cases. Finally, based on the full lifecycle management, the ‘new’ additional rewards need to be terminated within a certain period, and this paper implements an empirical study. We conducted 30 iterations of the experiment on 12 datasets and our best results were 19.24%, 10.67%, and 34.05 positions better compared to the best parameter combination in RETECS for the NAPFD (Normalized Average Percentage of Faults Detected), RECALL and TTF (Test to Fail) metrics, respectively.
A Multi-Objective Evolutionary Algorithm for Wireless Sensor Networks Node Deployment

Wireless sensor networks (WSNs) play an important role in the computer field. The optimal node deployment of wireless sensor networks is the key problem of wireless sensor networks. It has an important impact on the performance of wireless sensor networks. But it is complex to establish a balance between conflicting optimization factors. In this paper, we derive an effective sensing radius in specular enclosed space (CESR) from Elfes sensing model as the foundation of mutation operator. It has been utilized to the proposed multi-objective evolutionary algorithm for WSNs node deployment based on MOEA/P framework, MOEA/P-PD. In deployment, considering various objectives and preferences, such as the number and location of sensor nodes, expected network coverage fraction and connectivity. The results of experiments show that the algorithm can effectively solve the multi-objective optimization problem in WSNs node deployment.
Analyses and Parallel Optimization Exploration of the Coupled Atmosphere-Ocean Wave Numerical Forecasting Model

The governing equations in atmospheric and ocean computational fluid dynamics models are specific forms of Navier-Stokes equations. Due to the complexity of the model itself, it is difficult to find the optimal parallel solution method. By coupling the atmospheric-ocean numerical model and simulating the interaction between the atmospheric-ocean fluids, the accuracy and prediction ability of the prediction model can be improved, but a more complex numerical calculation is needed. Based on the newly developed Model for Prediction Across Scales atmosphere model (MPAS-atmosphere, MPAS-A) and NOAA/EMC WAVEWATCH III (NWW3) wave model, we use the C-Coupler2 coupler to realize the construction of the global atmosphere-wave coupling numerical forecasting model. Due to the urgent need for operational timeliness of forecast, the parallel performance of the operating system has been analyzed. The parallel I/O, compiling options, parallel partitioning, and other optimizations were tested after the system deployment on the Lenovo cluster of the National Marine Environmental Forecasting Center. After optimization, a well-balanced performance is obtained, and computing and I/O resources are reasonably utilized, thus laying a foundation for the real-time coupling forecasting operation system of global across scales high-resolution atmospheric and ocean wave model.
APT Attribution for Malware Based on Time Series Shapelets

To discover and defend against APT attacks more efficiently, we need to conduct binary analysis and source tracing research on APT malicious codes. This paper attributes APT groups for malicious codes from the perspective of binary similarity. First, we innovatively select the local features of the binary functions for classification and apply time series mining techniques to the mining of sequences of basic blocks (called paths). The Shapelet model selects path shapelets, which are path fragments that can best represent paths and are used to distinguish paths. Path shapelets can provide path-level interpretability for classification. Second, we use API calls to filter functions and generate paths of interest to reduce resource consumption. To evaluate the proposed method, we collect APT malicious codes based on publicly available threat intelligence reports. Our method filters 92.82% of functions and generates an average of 1.37 paths per function. The classification effect has obvious advantages over other methods.