Miracle-dz/UniFAR-FTUs huggingface dataset:
Vulnerability Discovery Modelling With Vulnerability Severity

Web browsers are primary targets of attacks because of their extensive uses and the fact that they interact with sensitive data. Vulnerabilities present in a web browser can pose serious risk to millions of users. Thus, it is pertinent to address these vulnerabilities to provide adequate protection for personally identifiable information. Research done in the past has showed that few vulnerability discovery models (VDMs) highlight the characterization of vulnerability discovery process. In these models, severity which is one of the most crucial properties has not been considered. Vulnerabilities can be categorized into different levels based on their severity. The discovery process of each kind of vulnerabilities is different from the other. Hence, it is essential to incorporate the severity of the vulnerabilities during the modelling of the vulnerability discovery process. This paper proposes a model to assess the vulnerabilities present in the software quantitatively with consideration for the severity of the vulnerabilities. It is possible to apply the proposed model to approximate the number of vulnerabilities along with vulnerability discovery rate, future occurrence of vulnerabilities, risk analysis, etc. Vulnerability data obtained from one of the major web browsers (Google Chrome) is deployed to examine goodness-of-fit and predictive capability of the proposed model. Experimental results justify the fact that the model proposed herein can estimate the required information better than the existing VDMs.
Hierarchical Coordinated Control of Electric Vehicle Parking Lot in Distribution System

Uncoordinated charging of EVs may result in overloading, increased losses and voltage deviations in distribution system. In this context, coordinated charging of EVs by parking lot operator (PLO) is a potential solution these problems. PLO coordinates charging of EVs during low electricity prices to avail monitory benefits. However, charging of EVs during low electricity prices results in unusual peaks which may adversely affect the operation of distribution system. Therefore, coordinated scheduling of EVs by PLO considering operational constraints of distribution system is essential. To this end, this study proposes a hierarchical framework of coordination between PLO and distribution system operator (DSO) for scheduling of EVs. In the hierarchy, PLO coordinates the charging demand of EVs to minimizing the charging cost. Thereafter, optimal operation of distribution system is considered at DSO level with EVs load demand obtained from PLO. If the operational constraints of distribution system are violated, DSO determines the load boundaries for EVs load and communicates to PLO. Finally, PLO allocates the charging demand to EVs considering the load boundaries suggested by DSO. The proposed methodology is validated on 33-bus radial distribution system. Simulation results indicate that proposed approach results in efficient operation of distribution system while minimizing charging cost to PLO.
Measurement Technique for the Permanent Magnet Rotor Thermal Time Constant Determination

In this paper, an innovative measurement technique that allows the determination of the permanent magnet thermal time constant of permanent magnet synchronous machines is proposed. The novelty of the proposed procedure consists of the determination of the permanent magnet thermal time constant without the test being influenced by the other rotor parts such as lamination, shaft, etc. Therefore, it can be applied to any permanent magnet synchronous machine type, assuming general validity. The proposed experimental setup consists of controlling the currents of the machine under test while an external electric drive sets its speed. Experimental results for two permanent magnet synchronous machine types are presented, demonstrating the feasibility of the proposed procedure.
Cybersecurity Named Entity Recognition Using Multi-Modal Ensemble Learning

Cybersecurity named entity recognition is an important part of threat information extraction from large-scale unstructured text collection in many cybersecurity applications. Most existing security entity recognition studies and systems use regular matching strategy or machine learning algorithms. Due to the peculiarity and complexity of security named entity, these models ignore the characteristic of security data and the correlation of entities. Therefore, through the in-depth study of security entity characteristic, we propose a novel security named entity recognition model based on regular expressions and known-entity dictionary as well as conditional random fields (CRF) combined with four feature templates. This model is named RDF-CRF. The rule-based expressions can match security entities with good accuracy in simpler situations, the known-entity dictionary can extract common and specific security entity, and the CRF-based extractor leverages the identified entities by rule-based and dictionary-based extractors to further improve the recognition performance. In order to demonstrate the effectiveness of our proposed model, extensive experiments are performed on a security text dataset collected from public security webs. The experimental results show that can achieve better performance than state-of-the-art methods.
Model-Free Predictive Current Control of Power Converters Based on Ultra-Local Model

This paper presents a model-free predictive current controller (MFPCC) based on ultra-local model for three-phase voltage source PWM rectifier (VSR), which has an inherent rapid dynamic response as a result of the MPC controller, as well as robust control performance due to use of the ultra-local model. The proposed approach, which utilizes model-free predictive current controllers, does not require any plant identification while ensuring the stability and the robustness of the control synthesis. Simulation and experimental results on a 3-phase/150V VSR indicate that, with a simple control structure, insensitivity to power supply fluctuations and to large load variations is ensured.
Comparative analysis based on the switching frequency of modulation techniques for MMC applications

The Modular Multilevel Converter (MMC) is a consolidated attractive technology taking great importance in medium / high power applications compared to other topologies of multilevel converters. One of the main lines of research is on voltage modulation techniques and its influence on the converter performance. Specifically for a relatively high number of submodules (SMs) that may depend on the specific application type. In this paper, a brief overview of the MMC basic operation is presented and the modulation techniques based on switching frequency as the main parameter are studied. Also, three different modulation techniques are studied to later be performed in Matlab/Simulink, in simulation is considering two cases of MMC systems with 5 and 20 SMs/arm, in order to comparing the impact of modulation technique on MMC performance.
Fine-Grained Action Recognition in Assembly Work Scenes by Drawing Attention to the Hands

The analysis of assembly working scenes is an important tool for improving work efficiency and detecting worker error. Many factories choose to analyze videos of employees working by human observation. It is more efficient, however, to approach this as an action segmentation task, but this is difficult due to the fine-grained actions. In this paper, we focus on featuring the workers hands in action segmentation to describe the detailed moves in assembly work scenes and utilize the tool or product information that the worker is using. We propose two methods to draw attention to the hand from each image in the video: the first is by cutting out the workers hand image and extracting features, and the second is by using an attention module specialized to extract hand features by the training of a regression network. For both methods we combine different types of features—image features and pose features—which are both important information in fine-grained actions. Due to the lack of action datasets that focus on assembly work scenes, we create a new assembly work dataset for our experiments, and the results of those experiments show that our method is effective for analyzing fine-grained action segmentation tasks.
A 16-bit Pressure Sensing Interface Integrating a 460 fJ/conv Incremental Sigma Delta ADC for Medical Devices

This work presents an incremental 2nd-order sigma delta using a single operational transconductance amplifier. This design provides a good trade-off between time conversion, high bit resolution, power consumption and area utilization for a pressure sensing reading interface. The modulator, designed in 180 nm CMOS technology, has been optimized for low area utilization, occupying only 0.058 mm2. Post-layout simulations were performed showing a resolution of 150 μV while consuming 120 μW and 460 fJ/conv. The system has a resolution of 16 bits, a bandwidth of 2 kHz, an oversampling ratio of 512 and a sampling frequency of 2 MHz.
A High-Precision Bandgap Reference With a V-Curve Correction Circuit

In this study, a precision bandgap reference with a v-curve correction (VCC) circuit is presented. The proposed VCC circuit generates a correction voltage to reduce the temperature drift of the reference voltage and achieves a low temperature coefficient (TC) in a wide temperature range. The proposed bandgap reference was designed and fabricated using a standard TSMC 0.18- $\mu \text{m}$ 1P6M CMOS technology with an active area of 0.0139 mm2. The measured results show that the proposed bandgap reference achieves a TC of 1.9–5.28 ppm/°C over a temperature range of −40°C to 140 °C at a supply voltage of 1.8 V. In addition, the circuit demonstrated a line regulation of 0.033 %/V for supply voltages of 1.2 – 1.8 V at room temperature.
Static Analysis of Android Malware Detection using Deep Learning

Android Malware is very common these days as applications are not created by trusted sources. People enter their personal data, save cards and much more, thinking these apps are going to keep them fit or help remind them to do certain essential works which we tend to forget in this busy routine of life. In such cases, detecting the malware before even installing an application would be of great help to us. It could possibly even stop a few crimes. In this paper, we propose to use the fully connected deep learning model for detection of Android malware. Key features of the proposed work include detection of Android malware even before installation, the name of the Android malware, version packages with proven extremely high accuracy of about 94.65%. This model also learns all features from all combinations of features. It includes extensive research and testing to achieve very high accuracy.
Dynamic Model of Pressure Propagation from Artery into Cuff During Monitoring of Cardiovascular System

Nowadays the development of systems for noninvasive home monitoring of blood pressure (BP) and other hemodynamic parameters taking into account their values, providing early diagnosis of arterial hypertension (AH) and critical states of the cardiovascular system (CVS) is relevant. When implementing such systems, occlusive inflatable cuffs are widely used. The oscillations formed inside them have quite a small amplitude of 1-2 mm Hg. This is one of the reasons for the emergence of various kinds of errors. Therefore, obtaining accurate values of systolic and diastolic pressures remains a serious problem for oscillatory methods with an inflatable cuff.This article presents a mathematical model that allows to take into account individual functional dependencies, linking the change in blood pressure in the artery with the form of oscillations in the hydro-cuff as a generalized transfer function, the coefficients of which can be established experimentally. This will allow measuring the hemodynamic parameters with high accuracy and assessing the critical state of the cardiovascular system reliably.
Automatic Rebar Counting using Image Processing and Machine Learning

In this paper, an automatic rebar counting system based on image processing and machine learning techniques is proposed. The system makes use of several image processing techniques including Canny edge detection, Circle Hough Transform (CHT) calculation and a machine learning system to accurately identify the number of individual rebar in a given bundle under various lighting conditions. This work includes a study of a number of different machine learning algorithms including decision tree, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), traditional neural network and Convolutional Neural Network (CNN). The proposed system is able to transfer the original object detection problem into a more easily solvable image classification problem and is hence achieve an overall accuracy of 95.99% in the presence of reasonable lighting conditions.
Predictive Analysis of Business Processes Using Neural Networks with Attention Mechanism

The analysis of ongoing processes is an important task in business process management. This is not surprising, since (e.g.) being able to predict future events in processes enables companies to intervene at an early stage if deviations from a desired workflow are likely to occur. Subsequently, errors and associated financial losses can be prevented or avoided. A common basis for being able to predict future events is a sequence of previous events that are typically stored in a socalled event log. In this work we present a neural network with attention mechanism, which is trained using publicly available event logs (e.g. BPI Challenge 2013). Furthermore, we elaborate the proposed model with an extensive dataset of events of a worldwide known German software company. In addition to promising results (e.g.) with regard to n-gram models, the training time of the proposed model is shorter than that of typical reference models such as neural networks with a long short-term memory architecture (LSTM).
A Deep Neural Network Approach for Mobile Edge Computation Offloading

Mobile computation offloading, which sending part or all of the computing tasks of the SMDs (smartphone mobile devices) to the server side with richer resources, effectively breaking the resource limitation of the mobile devices. The offloading decision algorithm plays a core role in the mobile computation offloading technology. Existing research work mainly studies the computational offloading problem based on graphs, trees, and integer linear programming. We attribute a computation offloading problem to a combinatorial optimization problem and design a deep learning neural network architecture to solve it. The experimental results show that the proposed approach achieves the close effect of the optimal solution
Human Activity Recognition using RGB-D Sensors

Computer Vision (CV) is a very alive area of research. The CV has all around multiple applications. One application of CV Ambient Assisted Living (AAL). AAL is getting very much importance day by day because of its extraordinary social values. In this paper we were considering AAL we focused on human activity recognition for that we are using knight depth camera for capturing 3-D skeleton data. This paper is about human activity recognition by using knight depth camera. This camera will capture 3dimensional skeleton data. [1] Human Activity Recognition deals with a bag of multiple frame sequences with the image caption. Many methodologies have been proposed previously. In this paper, Convolutional Neural Network is used which gives the accuracy 0.85 on SBU Kinect Interaction Dataset.
Exploratory Community Detection: Finding Communities in Unknown Networks

Community detection amounts to one of the key methods in handling social networks with the aim of capturing global patterns of a network. This paper focuses on a situation where the network is unknown, which would render existing algorithms unusable. We propose exploratory community detection which aims to detect communities by utilizing samples taken from diffusion process over the network. For this problem, we propose a neural-based algorithm that develops a matrix representation of the network structure. This matrix is then the input of a spectral clustering algorithm to reveal communities in the network. We perform experiments on real-world and synthetic data sets with simulated diffusion samples.The results reveal that our algorithm has strong empirical performance.
Network Simplification-Based Cluster Coordinated Optimization Method for Distributed PVs With Inadequate Measurement

High penetration of distributed photovoltaics (PVs) has caused severe voltage violation problem in distribution networks. In view of the actual situations, this paper proposes a coordinated optimization method for distributed PVs clusters considering inadequate measurement data. Based on pivotal measurement data, each cluster firstly uses network simplification method to obtain a simplified network with complete measurement information. And then each cluster implements coordinated optimization with adjacent clusters to minimize its network losses, PVs generation losses and voltage violation amount of downstream cluster. The coordinated optimization includes two stages, i.e. evaluating voltage regulation capability of intra-cluster reactive power resources and implementing cluster autonomous optimization control. These two stages can use the reactive power resources of upstream clusters to solve the voltage violation of downstream clusters, which avoids unnecessary generation losses of distributed PVs. The effectiveness and accuracy of the proposed method are demonstrated via simulation tests on a real 10kV feeder in China and modified IEEE 123-bus system. Compared with distributed optimization method, the proposed coordinated optimization requires much less calculation time and communication data between clusters.
Collaborative Vehicular Edge Computing Towards Greener ITS

In order to achieve a greener intelligent transport system (ITS), an efficient collaboration between vehicles is required to manage computation task processing with low latency. In this paper, we propose a collaborative edge computing scheme for vehicular Internet-of-things towards a greener ITS. The proposed scheme uses some vehicles as edge nodes, which are responsible for finding task processor nodes on behalf of a task requester node by considering the end-to-end task response time. The proposed scheme employs a two-stage approach where the first stage enables an efficient networking and computing architecture by forming vehicle clusters based on the edge architecture, and the second stage optimizes offloading tasks based on the architecture. We use realistic computer simulations to compare the proposed scheme with existing baselines, and show its superiority in terms of task offloading performance.
Computational Power Evaluation for Energy-Constrained Wireless Communications Systems

Estimating the power consumption and computational complexity of various digital signal processing (DSP) algorithms used in wireless communications systems is critical to assess the feasibility of implementing such algorithms in hardware, and for designing energy-constrained communications systems. Therefore, this paper presents a novel approach, based on practical system measurements using field programmable gate array (FPGA) and application-specific integrated circuit (ASIC), to evaluate the power consumption and the associated computational complexity of the most common mathematical operations performed within various DSP algorithms. Using the proposed approach, a new metric is developed for mapping the computational complexity to the computational power consumed by the mathematical operation in wireless transceivers. This allows combining the commonly used computational complexity metrics that are typically computed for each mathematical operation separately. Consequently, a single unified metric can be used to describe the entire algorithm. Therefore, the comparison and trade-offs between different algorithms become easier and more informative. The developed approach is used to evaluate the computational power of several DSP algorithms used in wireless communications systems, and perform thorough computational complexity comparisons. The obtained results reveal that computational complexity comparisons using different mathematical operations can be highly misleading in several scenarios. The power consumption evaluation of the considered DSP algorithms show that some algorithms may require a prohibitively high power, which makes such algorithms unsuitable for power-constrained wireless communications systems. The results also show that the proposed methodology can be adopted for various hardware implementation, however, some calibration might be required based on the adopted platform.
Charge-Aware DRAM Refresh Reduction with Value Transformation

As the memory capacity in a system has been growing, refresh operations consume increasing ratios of the total DRAM power. To reduce the power consumption of such refresh operations, this paper proposes a novel value-aware refresh reduction technique called ZERO - REFRESH which exploits zero values in memory contents. A DRAM cell can retain the discharged state without refresh operations, and ZERO - REFRESH skips refresh operations on rows with all discharged cells. For abundant unallocated memory pages in typical systems, the operating system fills them with zeros to clean the contents. For those idle pages, ZERO - REFRESH can eliminate refresh operations in an OS-transparent way without any new interface to DRAM. However, for allocated memory pages, memory contents may not have many consecutive zero values to match the refresh granularity of DRAM. To increase the frequency of zero values and to arrange them to match the refresh granularity, ZERO - REFRESH transforms the value of memory blocks to the base and delta values, inspired by the prior BDI (Base-Delta-Immediate) compression technique. Once values are converted, bits are transposed to be stored as consecutive discharged bits at the refresh granularity. Such value transformation and rearrangement can make the memory contents friendly to refresh reduction based on discharged cells. The experimental results based on simulation show that the DRAM refresh operations are reduced by 37% on average for a set of benchmark applications, if the entire memory is allocated for the applications. If the memory usage statistics collected from three data center traces are applied, the DRAM refresh operations can be reduced by 46%, 57%, and 83% respectively for the three scenarios.
Multi-Step Load Demand Forecasting Using Neural Network

The accuracy of load demand forecasting plays a vital role in economic operation and planning in the power sector. Therefore, many techniques and approaches have been proposed in the literature for forecasting. However, there is still an essential need to develop more accurate load forecast method. In this paper, three different strategies of Multi-Step-Ahead Load Forecasting (MSALF), i.e. Direct Strategy (DS), Recursive Strategy (RS) and DirRec Strategy (Direct-Recursive Strategy or DRS) have been used for electricity load demand forecasting by using the Artificial Neural Network (ANN) with Levenberg-Marquardt (LM) training algorithm. The performance evaluation for three different strategies of MSALF has been analysed on two different substations of NE-ISO data sets. Each data sets is analysed for four different cases. The performance of the DRS is better than DS and RS.