Miracle-dz/UniFAR-FTUs huggingface dataset:
HLT: A Hierarchical Vulnerability Detection Model Based on Transformer

Vulnerability detection is a key and challenging problem in the field of computer security, and it is more important to detect and repair vulnerabilities before an attack. Prior work has shown the usefulness of deep learning technology in software vulnerability detection. Inspired by the previous studies, this paper proposes a Hierarchical vulnerability detection model based on LSTM and Transformer, named HLT. In this paper, the function is divided into a hierarchical sequence structure of “token-statement-function”. The expression of the sentence is learned by bidirectional LSTM at the level of tokens and the representation of functions is learned by Transformer at the level of statements. The experiment selected two datasets which are open source functions of C/C++ and use Transformer as a comparative experiment on the structure of “token-function”. The accuracy rates of the two data sets on the Transformer were 64.66% and 64.5% respectively, but on the HLT were 67.85% and 67.0 % respectively. It shows that the HLT can improve the classification accuracy on this dataset.
A Light Model for Corrosion Segmentation with Multi-Scale Information and CRF

Metal corrosion is a common yet destructive natural phenomenon that can easily cause fracture, collapse and failure of metal materials. To identify the metal corrosion area and evaluate the corrosion degree precisely and automatically, a new deep leaning-based segmentation method is proposed in this paper to tackle it. Firstly, the ShuffleNetV2 with large kernel and PReLU activation function is proposed as an encoder to extract corrosion features at different scales. Secondly, multi-scale feature information is incorporated in the decoding process to augment the feature representation ability. A Recurrent Neural Network (RNN) under a dense Conditional Random Fields (CRF) formulation is proposed to improve the pixel-wise segmentation accuracy finally considering the occurrence mechanism of metal corrosion without fixed pattern and contour information. Both encoder-decoder and RNN are integrated into a holistic framework so as to train it using a back-propagation algorithm. Extensive experiments on public metal corrosion datasets demonstrate that the proposed method gets a good trade-off between the accuracy and model complexity. Specifically, it outperforms other comparable methods with 70.43% mean IoU and 82.68% mean pixel accuracy while model parameters are only 26.22MB.
PVDF Phase Formation and Its Influence on Electrical and Structural Properties of PZT-PVDF Composites

The aim of the study is to find the role of lead zirconate titanate (PZT) in the phase formation of poly vinylidene fluoride (PVDF) and its effect on electrical and structural properties of PZT-PVDF composites (50 Vol%) with 0–3 connectivity. PZT particles of different sizes ranging from micron to nano are used. The particle size (<100 nm) were determined from TEM and the broadening of the characteristic diffraction peaks in the x-ray diffraction (XRD) pattern. Scanning electron microscopy (SEM), studies were performed to analyze the nature of ceramic particle distribution within the matrix. The percentage of polymer crystallinity was determined from differential scanning calorimetry (DSC) and found to decrease with decrease in ceramic particle size. The decrease in the crystallinity percentage is due the hindrance offered by ceramic during the crystallization of the polymer. The melting endotherms of the composites were deconvoluted and the amount of PVDF β-phase present in the composites were determined and was found to decrease with decrease in ceramic particle size. Fourier transform infrared spectroscopy (FTIR) studies were also carried out to confirm the decrease in the β-phase content with decrease in particle size. The variation in the amount of β-phase of the polymer is due to the presence of PZT which acts as a source of electric field during poling and converts other polymer phases to β-phase. The piezoelectric coefficient and remnant polarization was found to decrease with decrease in ceramic particle size and is due to the decrease in percentage of β phase in the composites.
Detection of Malware Attacks in an IoT based Networks

Many security vulnerabilities have arisen as a result of the growing use of the Internet of Things. Satori, Reaper, Amnesia, Masuta, and other malware targeting IoT devices have appeared after the earlier DDoS attacks, that crawling IoT systems. Rather than using open TELNET ports (like Mirai), these viruses used design flaws to blight IoT devices, lead to extra tough to prevent with conventional methods, like firewalls, to do so. IoT malware may be detected in complex systems (example ISP, business systems) throughout the defective period instead of during affect, as demonstrated in this research. As long as the botnet attack has already taken place, detecting it isn’t that difficult, and there are previously established procedures in both academia and research to counter them. This container circulation different parameter database, a regulation step, and an additional parameter module are all included as part of the use of machine learning methods. We do test bed tests to see how well the system performs at classifying data, and we provide the findings here.
Moving Target Defense Strategy Selection Method Based on Long-term Traffic Prediction

As the core of moving target defense, defense strategy has been the focus of research. However, the existing defense strategies of moving targets defense lack pertinence and still belong to the real-time and passive defense state. In order to realize the advance deployment of defense measures, network traffic should be accurately predicted in the long-term. In this paper, a traffic prediction model based on segment lifting wavelet transform and LSTM network is proposed (LSTM-SLWT). Furthermore, the distance between the reconstructed traffic and the predicted approximate sequence is calculated by using the dynamic time warping algorithm, and the distance is considered as the possible DDoS attack intensity. Then the genetic algorithm is used to select the mutation element variation range that can carry out effective defense. So as to verify the effectiveness of the proposed method, this paper conducted a prediction experiment on the traffic of different time lengths. Experimental results show that the prediction effect of LSTM-SLWT is better than that of LSTM and the traditional LSTM-LWT prediction model. Moreover, the effective defense strategy is selected on the basis of traffic prediction, which can reduce the defense cost while ensuring the security of the system.
Control of an Ultrafast Electric Vehicle Charger based on a Series-Parallel Modular Multilevel Converter

There is an ongoing trend of upscaling the nominal power of electric vehicle chargers to reduce the charging time of modern electric vehicles. As a result, ultra-fast electric vehicle chargers have reached megawatt power ratios for large EVs such as trucks and heavyweight vehicles. Therefore, Modular Multilevel Converters are acquiring popularity in EV charging applications. In this context, this paper proposes a novel EV charger based on a Series-Parallel Modular Multilevel Converter. In this proposal, the proposed converter is regulated to perform medium-voltage low-frequency alternating current conversion to low-voltage medium-frequency alternating current required for ultra-fast charging of electric vehicles. In addition, simulation results obtained with a 500 kW SeriesParallel Modular Multilevel Converter model using PLECS are presented to validate the proposed control and topology performance.
Research on microwave signal recognition of motor imagination brain activity based on support vector machine

In this paper, the original data of microwave signals of motor imagination brain activity is obtained through experiments. The entropy eigenvalues of the original data are extracted, and then PCA is used to reduce the dimension and optimize the characteristic data. Finally, SVM classifier and SVM-BETTER classification model are used to recognize brain activity signals. The experimental results show that the average recognition accuracy of the SVM-BETTER classifier is 0.8737, which is higher than most current research methods, It can accurately recognize the brain activity information of motor imagination.
The Implications of Using Web-Based Diary Writing Apps as Innovative Learning for Writing Enforcement

This study discusses the implications of using web-based diary writing apps among university students to enforce writing intentions during the learning process in the post-pandemic era. There were ten university students, who were selected randomly, participating in this research study. Data were gathered through a collection of diary posts in the web-based diary writing app and an online survey using Google Forms distributed among participants. Then, data analysis employed descriptive qualitative analysis. The findings reveal that the web-based diary writing app provided technological implications for its users and aided their users to be more productive in writing in enjoyable conditions. Such implications may indicate that the web-based diary writing app served as innovative learning for university students since it integrated emojis and colors, a future version of emotional communication tools. Indeed, this research study designates the positive implications of web-based diary writing for enforcing the writing habits of university students.
A Prediction Model for Minimization of Flood Effects using Machine Learning Algorithms

The most devastating hazard occurred by the disturbances of the environment are floods. These are considered as the ruinous natural disasters and their prediction is very complicated. Several researches are being conducted on advances in flood prediction models that reduce the calamity, mitigate loss of life, and reduce flood-related property damage. To reduce the complexity of physical processes in the floods, a vast predictive system, neural network techniques have made significant contributions by stipulating accurate performance and user economical solutions. This paper is depicting a clear path for the flood prediction using machine learning algorithms by Random Forest classifier and Decision Tree techniques. The observation from the report analyses that the performance of Flood prediction identifies the calamity and descripted in the confusion matrix with effective positive rates using python framework.
Innovative TPS Development Solutions to Reduce the Time, Cost, and Physical Footprint of Supporting Avionics

Two significant issues facing Naval Aviation avionics support are “No Fault Found” conditions and Original Equipment Manufacturers (OEMs) ceasing support of avionics systems. With older avionics requiring more labor hours for troubleshooting and OEMs no longer providing support to repair older avionics, the increase in turnaround time has depleted supply stock of UUTs. As a result, Naval Program Management Air (PMAs) and Fleet Support Teams (FSTs) have to endure negative cost and schedule impacts. In order to support troubleshooting and repair at the Intermediate (I)-level for the H-1 Wiring Integration Assemblies (WIAs), the Cherry Point Development Team developed a solution using a universal cable design to support testing of 43 WIAs. The design utilizes thirty-seven (37) Test Adapter Cables (TACs) to interface between the WIA and the A WTS-02 whereas the OEM design required 500 TACs. Using the universal design solution lead to a 90% reduction in the number of cables, 83% reduction in cost, and a 57% reduction in schedule. This design provides Fleet users the capability to repair and declare Ready for Issue (RFI) WIAs directly in the field. Additionally, there is no testing or troubleshooting support available for the AV-8 Air Data Computer (ADC) Chassis. Currently, the I-level utilizes a process of elimination to identify if the fault is in the ADC chassis or the Circuit Card Assemblies (CCAs). In comparison, the depot maintainers rely on a Digital Multi Meter (DMM) to perform continuity checks and perform visual checks for isolation. The maintainers follow the process until the ADC passes; otherwise, the asset is deemed non-repairable and thrown away. To reduce the hours spent on testing the ADC Chassis, the Cherry Point Development Team designed a Test Program Set (TPS) utilizing the high density circuit switching offered by Eclypse International. Instead of having fourteen (14) cables connecting the ADC Chassis to the A WTS-02, the design utilizes fourteen (14) mock CCAs, four (4) ribbon cables, and two (2) Test Adapter Cables (TACs). This innovative TPS design reduces hookup time, physical footprint and cost for the AV-8 team.
Resilience and Sustainability Function Deployment for SMART companies

The paper proposes a new tool for the integration of management systems and business performance, called Resilient and Sustainability Function Deployment - RSFD. It is an evolution of the traditional and well known tool: Quality Function Deployment-QFD. The main goal is a unique view of all key factors based on Resilience and Sustainability considerations, able to highlights the main points of weakness in an organization and to suggest possible actions of improving. Thanks to the proposed tool a company can become more SMART, meaning “intelligent” and able to understand and react quickly in difficult situations.
Evaluation of Different Francis Turbine Modelling Techniques for Real Time Digital Simulator Applications

In this paper, several well-known modelling techniques of the Francis turbine in the hydropower plants for applying in the real time digital simulator (RTDS) e.g. dSpace – SCALEXIO, are presented and compared. According to the results, selecting proper modelling technique depends on the aim and outlook of the study. It is important to mention that RTDS is the brain and main intelligent core of a powerful laboratory setup named power hardware in the loop (PHIL) system as a very useful laboratory equipment for power systems studies and testing hardware in emulated power system conditions. Furthermore, considering the increase number of variable speed pumped storage hydropower units in recent years and future plans, this paper and its result assist researchers to select a proper modelling technique for implementing the Francis pump-turbine in the RDTS to model variable speed PSH units in the power system studies.
Distributed Semi-Supervised Learning with Positive and Unlabeled Data

Due to the prejudicial interest on positive aspect and/or absent of negative training data on some applications, positive and unlabeled learning (PU learning) has attracted much attention. However, all of the existing PU learning algorithms belong to centralized processing. Yet, in many real-world scenarios, data are usually collected and stored over multiple nodes with restriction of geographic factors or memory space. Considering this, in this paper, we focus on distributed classification with positive and unlabeled data, and propose a distributed PU learning algorithm based on Semi-Supervised Support Vector Machine (dPU-S3VM). To exploit the underlying information in the unlabeled data, the manifold regularization is incorporated into the design of the cost function. Furthermore, the random feature map is used for mapping the original data to high dimensional kernel space such that the global optimization problem can be decentralized without the direct transmission of the original data. The convergence performance of the proposed dPU-S3VM algorithm is analyzed theoretically. Experimental results show that the proposed algorithm has better classification performance than the state-of-the-art PU learning algorithms on various synthetic and real-world datasets especially when the portion of labeled positive examples is small.
Effect of Tip Gap and Squealer Geometry on Detailed Heat Transfer Measurements Over a High Pressure Turbine Rotor Blade Tip

The present study explores the effects of gap height and tip geometry on heat transfer distribution over the tip surface of a HPT first-stage rotor blade. The pressure ratio (inlet total pressure to exit static pressure for the cascade) used was 1.2, and the experiments were run in a blow-down test rig with a four-blade linear cascade. A transient liquid crystal technique was used to obtain the tip heat transfer distributions. Pressure measurements were made on the blade surface and on the shroud for different tip geometries and tip gaps to characterize the leakage flow and understand the heat transfer distributions. Two different tip gap-to-blade span ratios of 1% and 2.6% are investigated for a plane tip, and a deep squealer with depth-to-blade span ratio of 0.0416. For a shallow squealer with depth-to-blade span ratio of 0.0104, only 1% gap-to-span ratio is considered. The presence of the squealer alters the tip gap flow field significantly and produces lower overall heat transfer coefficients. The effects of different partial squealer arrangements are also investigated for the shallow squealer depth. These simulate partial burning off of the squealer in real turbine blades. Results show that some partial burning of squealers may be beneficial in terms of overall reduction in heat transfer coefficients over the tip surface.
Quality Analysis of high-density polyethylene based on Intelligent Vision Detection

High-density polyethylene (HDPE) are colorless and transparent particles, which are critical raw materials of many plastic products. HDPE particles with defects would affect the quality of final products and the economic benefits of enterprises. At present, there is lack of methods to identify defective HDPE particles quickly and efficiently. To address above problems, intelligent vision detection is introduced into the quality analysis of HDPE, and a set of quality analysis and detection schemes of HDPE are designed in this paper. Firstly, for obtaining better imaging quality, analysis and selection of the background color of the detection scenario is conducted. Secondly, particle conveying and photographing sensing strategy is designed for upgrading production line. Thirdly, intelligent detection of defective particles based on YOLO is merged into the analysis system. According to the experiment results, the blue color is selected as the optimal background. The recognition accuracy reaches 99.39% with the blue background color samples, thus defect particles of HDPE could be detected and identified effectively.
Speech Enhancement Based on Multi-Objective Ensemble Learning

The performance of traditional speech enhancement methods based on deep neural network is limited by using single training objective and network structure. In this paper, we propose a speech enhancement method based on multi-objective ensemble learning. First, the traditional multi-objective learning network structure is modified to reduce the training conflict caused by excess shared parameters. Then, a multi-objective ensemble learning based speech enhancement method is established by employing the modified multi-objective deep neural network (DNN), convolutional neural network (CNN) and gate recurrent unit (GRU), which overcomes the limitation of homogeneity in base models in the traditional ensemble learning based speech enhancement network. The experimental results show that the proposed methods outperforms the traditional multi-objective learning or ensemble learning based speech enhancement methods at the scores of perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI).
Detection and prevention of Sybil attack in VANET using modified ACO

Security is a primary concern in wireless networks, such as mobile ad-hoc network, vehicular ad-hoc network. Attacks are regarded as serious security issues in a vehicular ad-hoc network. The Sybil attack is one of the prominent attacks on the network which impairs the potential application by creating the illusion of the presence of multiple vehicles in the network causes traffic congestion in the network. In this paper, we provide a methodology based on modification in Ant Colony Optimization (ACO) to detect and prevent Sybil’s attack by determining the veracity of the route. The proposed approach is also implemented in a Matrix laboratory (MATLAB) R2015a environment, where it is contrasted with other approaches and tested against a variety of network quality parameters. Wherefore, the findings from numerous environmental modifications showed that the proposed ACO algorithm for a high mobility VANET system gives a significantly better performance than other earlier proposed approaches.
Efficient Mixed-Protocol Secure Four-Party Computation with Private Robustness

Secure honest-majority four-party computation (4PC) protocol was proposed for four mutually-distrusting parties to jointly evaluate a complex function on their private inputs with private robustness by cheater identification. This capability has great potential for distributed private computation, such as distributed privacy-preserving machine learning. However, the adoption of secure 4PC is hampered mainly by the computation and communication costs of online phase of protocol. In this work we propose an efficient secure 4PC protocol with private robustness. Specifically, by transferring hash verification operation for cheating detection to the fourth party, and with the help of the mutual constraint between the four parties, the communication rounds and communication costs of joint message passing primitive can be reduced. To further reduce communication cost, this paper proposes to divide the four parties into two groups and applies the method of two-party multiplication with truncation to avoid multiplication overflow. We implement secure 4PC protocol and demonstrate the efficiency of computation and communication in MNIST multi-class classification training. It achieves $2.1\times$ reduction in computation cost and $2.5\times$ reduction in communication cost compared to prior works.
Fake Beef Detection with Machine Learning Technique

Increased demand for meat causes diverse concerns, including misuse of sales by offering fake meats. Hence, consumers must be shielded from these cheated sellers. Yet, distinguishing faked meat and quality meat is not easy for regular consumers as, at present, meat identification is done manually using visual identification of human vision. Therefore, in this study, we proposed a concept to minimize the above issue by developing a virtual expert to assist in meat inspection. After extracting and pre-processing the relevant images, the model training was accomplished with the SVM, and CNN approaches. The determination of subjection of this classification process is evaluated using F1-Score and precision. Our model evaluation for pork and beef classification utilizing 20% test data against the five classification models showed that the VGG16 produced the highest accuracy rate of 95.20% with 1200 images. Besides, the best accuracy result demonstrated as (Class, F1-Score, Precision) of (Pork, 98.00%, 98.00%) and (Beef, 98.00%, 98.00%).
Highly Compact Partial Power Converter for a Highly Efficient PV-BESS Stacked Generation System

The inherently intermittent nature of photovoltaic (PV) energy has brought increasing interest towards the integration between PV sources and Battery Energy Storage Systems (BESS). In this paper, a Series Partial Power Processing (PPP) converter based on Capacitive Power Transfer (CPT) is proposed to integrate PV and BESS in a grid-connected inverter system. The proposed converter has been simulated according to a PV string capable to provide 1430 W under full irradiance conditions, a BESS nominal voltage equal to 215 V and a solar inverter assumed to operate with a minimum voltage of 150 V and a maximum current of 10 A. Simulation tests carried out at different conditions of solar radiation and required load power aim at demonstrating the correct operation of the proposed system.