Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Cyber-Physical Attack Classification with Extreme Gradient Boosting for Smart Transmission Grids

The power grids are transforming into the cyber-physical smart grid with increasing two-way communications and abundant data flows. Despite the efficiency and reliability promised by this transformation, the growing threats and incidences of cyber attacks targeting the physical power systems have exposed severe vulnerabilities. To detect and identify attack threats from the heterogeneous cyber-physical information in the smart grid, this paper proposes an ensemble learning-based attack classifier using extreme gradient boosting (XGBoost). Compare with the popular single-classifier based approaches, the XG Boost-based attack classifier can effectively learn from the multi-sourced data to pinpoint attacks from normal and fault scenarios. Considering the potential imbalance of data samples among normal, fault, and attack samples, the paper also utilizes imbalance learning technique to boost the balance of data and subsequently the performance of XGboost. The proposed classifier is evaluated on a cyber-physical power system dataset collected from a high-fidelity hardware-in-the-loop smart grid testbed. The overall classification accuracy achieves over 99%, an effective 4% increase over the state-of-the-art, while maintaining over 95% accuracy in more complex, dynamic scenarios.
Decoding of Polar Code by Machine Learning

In this paper, we proposed a block neural network (BlockNN) algorithm for polar code. We equally divide the 2n bit polar code into many small sub-blocks according to the encoding rules of polar code, then put these sub-blocks into the neural network of the same structure for processing. This decoding algorithm is non-iterative and inherently enables a high level of parallelization, while showing a competitive BER(bit error arte) performance. On the aspect of hardware implementation, this decoding structure of the neural network can be multiplexed and the computational complexity does not increase with the code length, only related to the size of the block.
A Stochastic Market-based Approach for Operation of Active Distribution Networks

This paper proposes a novel technique for operation of distribution networks with considering active network management (ANM) schemes and demand response (DR) within a joint active and reactive distribution market environment. The objective of proposed model is to maximize social welfare (SW) using market-based joint active and reactive optimal power flow (OPF). Firstly, the intermittent behavior of renewable sources (solar irradiance, wind speed) and load demands is modeled through Scenario-Tree technique. Then, a network frame is recast using mixed-integer linear programming (MILP), which is solvable using efficient off-the-shelf branch-and cut solvers. Additionaly, this work explores the impact of wind and solar power penetration on the active and reactive distribution locational marginal prices (D-LMPs) within the distribution market environment with integration of ANM schemes and DR. A realistic case study (16-bus UK generic medium voltage distribution system) is used to demonstrate the effectiveness of the proposed method.
Modeling of Wireless Sensor Nodes Airdrop in Wind Field

In recent years, more and more research and applications use The Unmanned Aerial Vehicle (UAV) airdrop sensor nodes to deploy and maintain large wireless sensor networks (WSN). However, the problem of increasing the energy consumption of the node communication and even deviating from the monitoring range caused by the deviation of the airdrop node cannot be ignored. In this paper, by studying the variation of the wind’s characteristic parameters with height and geomorphology, the wind force received by the airdrop node at any height of any landform is obtained, and then the model of the motion process of the airdrop node in the horizontal and vertical directions is established. Finally, the range of deviation caused by UAV seeding sensor nodes at any altitude under wind force is simulated. The influence of the error of air airdropping nodes is discussed, and an executable solution is proposed.
Using Image Features to Investigate Accuracy of Classification Techniques in Categorizing Visual Defect Types of Defective Yellow Soybean Seeds

Visual defects of yellow soybean seeds are one of the many factors influencing the soybean price. If those defects are found and classified correctly, the farmers or sellers can cope with those problems immediately. Therefore, this study investigated the accuracy of classification techniques using 14 image features. Those features were extracted utilizing image processing techniques. The obtained feature values were used for classification in four ways. From the classification processes using 10 techniques, the C5.0 with boosting method (trials = 80) provided up to 93.53% accuracy, with the highest accuracy being for six defect types (types 1, 3, 4, 5, 8, and 10). The results can be used as an alternative approach to identify the causes of damage in soybean seeds.
Toward Characterization of the Feasible Region of Loadability of Power Systems

The feasible region of loadability represents the allowable load range that yields the feasible operating points of power systems. In this paper, a complete characterization of the feasible region of loadability is developed. The theoretical work is then numerically verified on test systems using the exact AC power flow model. It is also shown that the current work based on the well-known DC power flow model may give inaccurate results (characterization), especially under different voltage constraints, the feasible region of loadability varies greatly. Finally, the proposed characterization of the feasible region is applied to the IEEE 118-bus test system to demonstrate its accuracy.
Using Text Annotation Tool on Cyber Security News — A Review

Cyber-attack has become one of the main concern in our everyday life and being reported throughout online news website. As thousands of news article existed, it is difficult to go through all the news which lead to a slower analyzing process. Hence, a vital text mining component known as Information Extraction (IE) is needed in order to ease the knowledge discovery process for the wide collection of the cyber security news. To make IE process better and easier, the usage of tool such as General Architecture for Text Engineering (GATE) can help a lot especially in creating annotated corpus. In this paper, we will introduce and reviewing several annotation tools that are freely available and also to discuss steps needed to create an annotated corpus for the cyber security text documents.
NEURODEGENERACIO : OREGEDES ES DEMENCIA. ELEKTRONTRANSZPORT-ZAVAR, MINT ETIOPATOGENETIKAI TENYEZO. TERAPIAS LEHETOSEGEK

The neurodegenerative disorders (Parkinson's disease, Alzheimer's dementia, Huntington's disease, cerebellar degeneration) are common medical and social problems. The late onset diseases and slow neurodegeneration is connected with excitotoxins and alteration of mitochondrial electron transport chain. In elderly, congenital and acquired defects of mitochondrial complexes cause formation of free radicals. The overstimulation of excitatory amino acid receptors interfere with the cellular energy metabolism and also forming reactive oxygen species. The impaired energy metabolism make neuronal cells vulnerable to the excitotoxic damage. In these ways, excitotoxicity may be the final common pathway of neuronal death in a variety of neurodegenerative diseases. Potential therapeutic strategies would be use receptor antagonist or drugs to bypass energetic defects.
Unsupervised Classification for Social Networks with RS Matrix

Community detection is an important research issue for understanding the structures of social networks. The existing community detection algorithms can be classified into two major classes: clustering based on modularity and spectral clustering. In this paper, we present a novel method for detecting community structures without prior information such as the number and size of communities in networks. This approach is based on a matrix called RS which enhances the cluster-properties in the data. We reveal the reason why a two-layer GCN is enough to achieve satisfied results. Our partition algorithm divides a network into different communities according to the similarity of the number of mutual neighbours among nodes, and makes use of a silhouette coefficient to determine the final unique partition. Experiments show that our community detection method can obtain accurate community structures on real-world networks, outperforming existing leading algorithms.
Detection and Classification of Three-Class Initial Dips Using Vector Phase Analysis with Dual Threshold Circles: An fNIRS Study

In this paper, we have used dual-threshold circles in the vector phase analysis to maximize classification accuracy of three class initial dip in functional near-infrared spectroscopy (fNIRS)-based brain-computer interface (BCI). fNIRS signals for three different mental tasks (mental arithmetic, mental counting and puzzle solving) are measured from prefrontal cortex of eight healthy subjects. After the acquisition of signals, filters are applied to remove different noises (cardiac, respiratory and Mayer). A dual threshold-based vector phase analysis is used for the detection of initial dip. Features used for classification of initial dips are signal mean, signal slope, skewness, kurtosis and peak in three different window sizes 0~1, 0~1.5 and 0~2 sec. The features are then passed from multi-class linear discriminant analysis for classification of three different initial dips. The average classification accuracy in offline analysis obtained using dual threshold circles in the vector phase analysis is 69.12 % in the case of window size 0~2 sec. These results are a significant improvement compared to the existing literature and are more reliable for use in BCI applications.
Density Based Algorithm for Spatiotemporal Data

Clustering is a method to discover inherent natural structure in a set of objects involved in any phenomenon. In this study, we extended DBSCAN algorithm for spatiotemporal data by defining attribute based mass function, density function and hence modifying definition of core objects for clustering. The proposed work generalizes the concept of using an attribute to define notion of relative importance of an object to define density in the dataset. We have used a real fire dataset to validate the proposed approach. We also compare our algorithm with DBSCAN based algorithm which is extended for spatiotemporal data. The experimental results reveal that our proposed algorithm is able to identify intrinsic information based hidden clusters, which DBSCAN based algorithm is unable to identify.
Buffer-Aware Dynamic Adaptive Streaming over Content Centric Networks

Recent developments in video streaming applications have contributed to the increase of traffic over already highly loaded networks. These advancements have brought to public attention the need to develop new methods which can help control the network traffic. As the increase in network traffic is expected to continue, it becomes obvious that the existent IP-based network infrastructure struggles to meet the traffic demand. To address this issue a novel internet architecture, Content Centric Network (CCN), has gained attention in the recent years. CCN aims to replace the host-to-host communication structure with a named based content delivery approach. In this context, there is a need to perform video delivery over CCN such as to satisfy users’ expectations and a good solution includes adaptive video streaming in CCN. This paper proposes an innovative Buffer-Aware Dynamic Adaptive Streaming solution over CCN (BADASC). The solution leverages the playback-buffer capacity to determine the rate at which the video is delivered to ensure high user satisfaction.
Agile Approach for Cyber-Physical Systems (CPS) Development using Cloud Computing

This paper aims to present an agile approach for developing CPS using cloud computing. This study demonstrates in three limitations including, 1) lack of tools for deploy, simulation and continues improvement(CI), 2) limited visibility of customer environment, and 3) lack of repository and version control management. This paper applied the Azure cloud platform as a tool to facilitate the developer to use an agile approach for developing four cases of CPS are Blink, Temperature, sound and I2C serial LCD. All cases produced with two methods are Agile approach with cloud platform and Agile with the non-cloud platform. From study founds that cloud computing can easily use an agile approach for CPS development than non-cloud platform development. Especially, Continues Integration, simulation, deployment, repository management, production and operation, and Usage and visualisation of data. However, some aspects that can study in the future. For example, HW/SW dependency may solve with containers such as micro-service, docker and IaaS Platform. Besides, an integration between Simulink and cloud platform is a challenge for modelling specification.
Analysis and Architecture for the deployment of Dynamic License Plate Recognition Using YOLO Darknet

Dynamic License Plate Recognition (DLPR) has been a successive subject of research because of numerous functional applications. Be that as it may, a significant number of the present arrangements are still not reliable in certifiable circumstances, usually relying upon innumerable limitations. This paper exhibits an active and productive DLPR framework and architecture, which can be implemented for dynamic license plate identification based on the best in class YOLO object identifier. The Convolutional Neural Networks (CNN) are prepared and calibrated so that their robustness is sustained under diverse setups (e.g., varieties in the camera, illumination, and foundation). Extraordinarily for character segregation and identification, we structure a methodology utilizing straightforward information enlargement instances, for example, reversed LPs and inverted characters. The subsequent DLPR modus operandi accomplished noteworthy outcomes in the data sets. Our test results show that the proposed strategy and deployment architecture, with no parameter adjustment, performs exceptionally well on the data collected dynamically from a video using a raspberry pi and has been successful in identifying multiple license plates and extracting the characters, the process, however, is time exhaustive.
Research on Situational Sensing Method of Grid Cyber-Physical System under Network Attack

Strong smart grid and energy Internet as a typical cyber-physical system (CPS), security and reliability issues of information systems may cause physical system crashes and operational risks, the impact of information links on physical systems will become more prominent, smart grid is the new grid that runs on IT technology. In order to study the information system failure, how does the risk spread from the information layer to the power layer and how it affects the system. This paper models the information physical of power systems. Obtaining important factors by establishing physical models to remove redundant factors, machine learning algorithm mining data association relationship, basing on this, combining artificial intelligence algorithms with physical models. A method that combines the characteristics of a physical device and the characteristics of a machine learning algorithm to mine data capabilities and perceive the type of attack the system is exposed to and the on-off status of the line. Compared with general iterative algorithms and simulation methods This method effectively improves the identification accuracy and the calculation rate. Finally, the active defense measures that the cyber physical system should take under network intrusion are prospected.
Effective Video Compression with two Quantization Parameters

In this paper, we propose the novel methodology to improve the rate-distortion characteristics by the difference of the quantized DCT coefficients using two different quantized parameters in HEVC. Under the special condition of the quantization property in HEVC, we evaluate the binary difference of the quantized DCT coefficients derived from two different quantized parameters and compressed it with simple CABAC algorithm based on the characteristics of the binary difference.The experimental result shows that the proposed algorithm improves the compression performance of the rate-distortion property in comparison to the conventional HEVC compression method.
Latency-Minimized and Energy-Efficient Online Task Offloading for Mobile Edge Computing with Stochastic Heterogeneous Tasks

Mobile edge computing (MEC) is a promising paradigm to meet the low latency requirement of applications like virtual/augmented reality, which moves computation workloads from remote cloud to network edge. Task offloading is a crucial problem in a MEC system, which significantly concerns with the achieved latency of tasks and the energy consumption on edge nodes. Although many efforts have been paid on the problem, most of them ignore the hierarchical architecture of MEC and the heterogeneity of tasks generated by different applications. In this paper, we consider a three-layer MEC system with tasks of various types stochastically arrive in real time, in which each task can be executed on mobile smart devices, offloaded to the edge server, or offloaded to the remote cloud. The task offloading problem in such a MEC system with the objective to minimize the averaged latency over time is NP-hard. To solve it, we employ Lyapunov optimization and duality theory to reformulate the problem and decompose it into a set of subproblems. Each subproblem can be distributedly solved by a mobile device or the edge server individually. Extensive simulations are also performed to verify the feasibility and superiority of our approach.
Multi-Path Routing Oriented Flow Statistics Collection in Software Defined Networks

In Software Defined Networks (SDNs), one of the key tasks in control plane is the monitoring and measurement of the whole network. A typical SDN consists of a set of switches and a logically centralized controller responsible for network state monitoring and flow scheduling, to which low cost and efficient flow statistics collection plays an important role. However, existing flow statistics collection methods mainly focus on single-path routing and cannot accurately capture the flow statistics in the case of multi-path routing (MPR), which is widely used in modern networks. In this paper, we are motivated to propose a Multi-Path oriented Flow Statistics Collection (MFSC) strategy to minimize the total communication cost for flow statistics collection. The problem is first formulated into an integer linear programming (ILP) form. After analyzing the complexity of this problem, we present a relaxation-based algorithm with an approximation factor p, where p is the maximum number of switches passed by each flow. The experiment results demonstrate that our proposed algorithm can reduce the total communication cost by over 14% compared with the traditional solutions.
OPC UA Information Model and a Wrapper for IEC 61499 Runtimes

OPC UA is considered as an important communication technology for Industry 4.0 components. On the other hand, IEC 61499 is one of the technologies available for developing applications to the same components. In this paper, a proposal for an OPC UA information model for IEC 61499 systems is developed in order to enhance communication with IEC 61499 applications through OPC UA. Already existing related information models, particularly OPC UA for Devices and IEC 61131-3, are utilized as models and parts of the design. An OPC UA wrapper for IEC 61499 runtimes is presented as one possible way to implement the information model. Both the information model and the wrapper are then tested through a case study.
Estimation of Feasiblity of Using Bus Topology in Ttethernet Networks

Currently, TTEthernet supports 2 topologies: Star and Ring. Network components that can be found in TTEthernet network are switches and end systems. In this paper, we discussed about possibilities for using Hub topology in TTEthernet networks. The concept of TTEthernet operation is described, as well as necessary adaptations to be done for Hub topology support. Alongside with that, advantages and lacks of this approach are discussed, whence we can conclude whether this topology is acceptable at all in domain of TTEthernet networks applications. It is shown that reliance of the complete TTEthernet network only to bus topology is not a good option, because of lower fault-tolerant abilities. Possible application could be a mixed-topology, where bus topology will be used for low-critical traffic, while star of ring topology will be used for high-critical traffic.
Clustering-Based Algorithm for Services Deployment in Mobile Edge Computing Environment

In the edge computing, the service is deployed to the edge server through virtualization technology. Most strategies of service deployment are singleness and ignore the diversity of users' requirements and the service deployment cost at the edge. This paper proposes a clustering-based algorithm for service deployment, which considers the delay at the user side and the edge-side services deployment cost, and establishes a service deployment model based on multi-objective integer linear programming. Firstly, the K-means clustering algorithm is optimized to solve the problem of hotspot migration in the process of service deployment and reduce the deployment cost at the edge server side, then alternative enhanced heuristic algorithm is proposed to find the approximate optimal solution of services deployment. Experiments show that the algorithm can reasonably deploy services. Compared with traditional heuristic algorithms, the algorithm proposed in this paper has better performance in terms of user side and edge server side.
Boost Integrated Half-Bridge Soft-Switching DC-DC Converter with Voltage Doubler Output Rectifier

This paper presents a systematic analysis, design, detailed simulation and experimental results for a boost integrated half-bridge step-up dc-to-dc converter with high-frequency transformer isolation and voltage doubler rectifier for use with photovoltaic (PV) module output and other energy applications. Based on the steady-state analysis and design equations, a systematic design procedure is illustrated with a design example of 400W, 200V output converter with an input voltage of 40V to 80V. PSIM simulation results are given to evaluate the performance of the designed converter and an experimental converter was built to verify this. These results demonstrate soft-switching characteristics of the converter for the complete range. This dc-to-dc converter can form the front-end of utility interfaced PV array converter system and other step-up dc-dc converter applications requiring high-frequency transformer isolation.