Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Stealthy Domain Generation Algorithms Using Heterogeneous Deep Neural Network Framework

Distinguishing malicious domain names generated by various domain generation algorithms (DGA) is critical for defending a network against sophisticated network attacks. In recent years, stealthy domain generation algorithms (SDGA) have been proposed and revealed significantly stronger stealthiness comparing to the traditional character-based DGA. Existing state-of-the-art detection schemes are not effective enough for detecting SDGA. In this paper, we exploit the character-level characteristics of the SDGA domain names and propose a heterogeneous deep neural network framework (HDNN) for detecting SDGA. HDNN employs a proposed improved parallel CNN (IPCNN) architecture with multi-sizes of convolution kernel for extracting multi-scale local features from a domain name. The framework also contains a proposed self-attention based bidirectional long short term memory (SA-Bi-LSTM) architecture which can extract the bidirectional global features with attention mechanism from a domain name. Besides that, the focal loss function is introduced to mitigate the imbalance of the sample quantity in the training phase. The benchmark experiments are carried out based on the database composed of the collected benign domain names, real-world DGA and SDGA ones. Compared to the 6 influential deep-learning-based DGA detection schemes, the proposed scheme has achieved state-of-the-art detection results on SDGAs, and also achieved state-of-the-art results on binary and multiclass classification for traditional DGAs.
Multi-Objective Optimization Scheduling Considering the Operation Performance of Islanded Microgrid

In recent years, with the increase of distributed generation(DG) penetration rate, the economic operation of microgrid (MG) has been fully developed, but the energy consumption system based on fossil energy has increased the contradiction of the economy, energy and environment. The process of accelerating the development and utilization of renewable energy are a new focus of social concern. This paper first analyzes the basic characteristics and related policies of the distributed power and demand response (DR) in MG, and introduces the evaluation index of the islanded microgrid. Then, established a multi-objective optimization scheduling model considering the operation performance of islanded microgrid. In model, considered the generation cost of microsources, environmental management cost and power supply reliability of MG, and selected different scene analysis and verification. Used the improved particle swarm optimization-exterior point method (IPSO-EPM) to solve the problem. When the constraint condition is included in the objective function, the problem of falling into the local extremum during planning is avoided as much as possible. Finally, an islanded microgrid is taken as an example to verify the rationality of the proposed model and the effectiveness of the algorithm.
On the Maximum Unambiguous Range of LFMCW Radar

The unambiguous range of a radar is the maximum range at which a target can be located so as to guarantee that the reflected signal/pulse from that target corresponds to the most recent transmitted pulse. It is an important factor for radar. Different modulation patterns of Linear Frequency Modulator Continuous Wave (LFMCW) have different maximum unambiguous range. For an easy way to calculate the factor, Single Period Ambiguity Function (SPAF) is proposed to analyze the unambiguous range of LFMCW radar. The receiver of a LFMCW radar can be analysed with SPAF. By analyzing cuts of SPAF along small Doppler shifts of symmetrical sawtooth LFMCW radar, it is demonstrated that its maximum unambiguous range will correspond to the shift equal to half of the modulation period. And the maximum unambiguous range of arbitrary LFMCW radar is αT (T is the period, and α is the symmetrical factor, 0<α≤1). With this result, it is easy and intuitional to calculate the maximum unambiguous range of LFMCW radar.
Virtualization in Programmable Data Plane: A Survey and Open Challenges

Programmable data plane (PDP) is an emerging technology for programming packet processing tasks by means of a domain-specific high-level language (e.g., programming protocol-independent packet processor (P4)) and programmable switch chips. Recently, several PDP virtualization schemes have been introduced to enable more flexible and elastic network management. In this article, we first give an overview PDP and P4. After that, existing PDP virtualization schemes are classified into hypervisor- and compiler-based approaches and their pros and cons are analyzed in detail. Finally, open challenges for PDP virtualization are identified and future research directions are presented.
Reliability of News

In modern times, because of the the advancement of social media platforms, fake news relating to different purposes has been increasing day by day. Fake News on the internet is defined as a fabricated article with the intention to mislead, usually for profiting. Fake news and hoaxes have been there since before the advent of the Internet. Hoaxes have existed for a long time, since the "Great moon hoax" published in 1835. Along with the increase in the use of social media platforms like Facebook, Twitter etc. news spreads rapidly among millions of users within a very short span of time. This paper’s purpose is to investigate the concepts, approaches and algorithms for identifying fake news articles and their creators from online social media platforms and assessing their performance. This paper introduces two models for detection of fake news. First by text classification where different classifier models were applied and it was found that RNN(LSTM) gave the best accuracy of 93 %. Second by crowd analysis where Parameter tuning method gave the best accuracy of 80 %.
Design of DC Reactors for DC Fault Ride-through on MMC-based Bipolar DC Grid

This paper provides the design criteria of the DC reactors for the most promising configuration option that is bipolar DC grid. The DC reactors on bipolar grid are divided into pole reactor and neural line reactor. The configurations of DC reactor are compared and shows the distributed configuration is suitable for neutral line reactor and pole reactor. Furthermore, the difference of these two reactors is investigated, showing that pole reactor is superior in limiting the fault injected by the non-fault stations and hold the dc voltage during the fault. Neutral line reactor would not affect the dynamic characteristics of the system and can protect the system from valve-side or dc bus fault that pole reactor can not. The design constraints of choosing DC reactor about hybrid DC circuit breaker (DCCB), protection requirement, additional fault current limiter (FCL) and stability are discussed. Finally, a three terminal meshed DC grid as a case study to verify the design method. For completeness, all fault analysis results are based on PSCAD/EMTDC.
An optimization method to improve the worst consistency level of hesitant fuzzy linguistic preference relations

Hesitant fuzzy linguistic preference relation (HFLPR) is an utilitarian tool to capture diverse information from decision makers under uncertain situations. How to improve the unacceptable consistency level for HFLPR is a key issue to guarantee the reliability of the decision information. This paper introduces the worst consistency index for HFLPRs to judge the logic of DMs in the worst case. Under the purpose of minimizing the amount of modification, an optimization model is constructed to achieve an acceptable worst consistency level for a HFLPR. A linear programming model is established to reduce the difficulty of solving the presented optimization model. It ensures the consistency indices related to all the possible linguistic preference relations (LPRs) from a given HFLPR meet the predefined consistency level requirement. Finally, the proposed model is applied to a numerical example to show it’s significance.
An Observation of Vision Based Underwater Object Detection and Tracking

The detection of an object or moving object is a comprehensive goal in the research domain in the era of computer vision. Numerous scientists are quick to think about the underwater world and the living articles in the deep ocean and they are incompletely effective to their objective. In this research work, , we observe the potentiality in underwater by a vision based object detection sphere by applying various previous work for highlighting the arising consequences in various underwater situation. We have collected the dataset from various source of water such as clean water, turbid water, bubbles water etc. We used OpenCV and Python programming language to detect the object. Finally the detection of object in underwater is studied for connecting with the basic color based HSV(Hue, Saturation, Value) segmentation.
Dynamic Channel Allocation Scheme for Handover Calls in Cellular Networks

Prioritizing handover calls is an important process in cellular networks. The blocking/dropping calls occurred when there is no free channel is assigned for the subscriber. So, we are going to devise a new scheme for handling the calls among the network. In this paper, the main idea is to maintain an acceptable blocking/dropping trade-off probability for the indoor cells. The proposed solution will show how to change dynamically the number of reserved channels and optimizing it based on the incoming offered traffic type (new or handover calls). The Guard Channel and Non-Prioritized schemes are considered. Finally, the results reveal that call blocking and dropping probabilities performs better while compared with the other existing scheme.
Propagation-Model-Free Base Station Deployment for Mobile Networks: Integrating Machine Learning and Heuristic Methods

As densification is the promising trend of future mobile networks, deployment of base stations (BSs) becomes increasingly difficult due to the laborious procedures in network planning; besides, unreasonable layout may lead to poor coverage performance. Hence, this paper firstly trains a propagation-model-free received signal strength (RSS) predictor based on machine learning (ML) models, and then optimizes coverage performance of BS deployment via multi-objective heuristic methods. Specifically, many practical features that affect signal propagation like geographical types and operating parameters of BS, are fed into ML models to predict RSS in a rasterized area; then based on the trained model, a well-designed multi-objective genetic algorithm (GA) is proposed to minimize the number of deployed BSs with coverage constraint. For the practical considerations of fast convergence and output-consistence, greedy algorithm with fixed initial solution and searching direction is also carried out. Moreover, the typical scenarios of incremental deployment (the mobile operator needs to deploy more BSs on the basis of the existing deployment) and BS outage compensation (one BS fails and other BSs need to adjust their configurations to fill the coverage gap), are also investigated for practical needs. Simulations show that multi-layer perceptron outperforms other ML algorithms in terms of RSS prediction with mean absolute error (MAE) yielded to 3.78 dB; and numerical results verify the convergence and availability of the proposed algorithms, which shows 18.5% gain than the real-world deployment in terms of coverage rate.
A Constant Time Complexity Spam Detection Algorithm for Boosting Throughput on Rule-Based Filtering Systems

Along with the barbarous growth of spams, anti-spam technologies including rule-based approaches and machine-learning thrive rapidly as well. In antispam industry, the rule-based systems (RBS) becomes the most prominent methods for fighting spam due to its capability to enrich and update rules remotely. However, the antispam filtering throughput is always a great challenge of RBS. Especially, the explosively spreading of obfuscated words leads to frequent rule update and extensive rule vocabulary expansion. These incremental obfuscated words make the filtering speed slow down and the throughput decrease. This paper addresses the challenging throughput issue and proposes a constant time complexity rule-based spam detection algorithm. The algorithm has a constant processing speed, which is independent of rule and its vocabulary size. A new special data structure, namely, Hash Forest, and a rule encoding method are developed to make constant time complexity possible. Instead of traversing each spam term in rules, the proposed algorithm manages to detect spam terms by checking a very small portion of all terms. The experiment results show effectiveness of proposed algorithm.
Design and Simulation of Look Ahead Carry Adder for Optical Computing

For High-speed processing and optical computing, look ahead carry adder is played a very important role. In this work look ahead carry adder based on electro-optic principle is proposed. Mach-Zehnder interferometer is used as an optical switching device. The performance parameters extinction ratio, contrast ratio, amplitude modulation, and insertion loss for the proposed device have been computed and obtained as 17.980dB, 16.450dB, 1.391dB, 0.022dB respectively.
New Cross-Switch Multilevel Inverter with Self-Voltage Balance and Boost Ability for Microgrid

The new cross-switch multilevel inverter (CSMLI) consisting of $n$ cross-switch cells (CSCs) and a three-level T-type converter is proposed in this paper. The capacitors in CSCs can get self-voltage balance without auxiliary methods. Moreover, the output level and boost factor of the proposed topology can be increased with additional CSCs, and the highest voltage stress among switches maintains at a low degree meanwhile. The operation principles and self-voltage balance are illustrated in detail. The comparative study among different multilevel inverters is also conducted in this paper, demonstrating its certain merits on reduced components and voltage stress and the suitability for microgrid. Finally, the nine-level prototype of the proposed topology with 1kVA rated output are implemented to verify the feasibility and performance.
Malware Detection Method based on Dynamic Variable Length API Sequence

Since previous researches leverage fixed length sequences of API calls to discriminate between benign and malicious programs, it is difficult to determine an optimal API subsequence length and takes full advantage of a lot of semantic information of different lengths. This paper proposed a malware detection method which used dynamic variablelength sequences of Native API as feature representation. The proposed method fully exploits complementarity of different length features and extracts more semantic information of Native API call sequence as much as possible. Experimental results shows that the proposed method achieves an accuracy of 99.2&, and the AUC value is 0.996, which is very close to optimal value 1. The proposed method outperforms fixedlength API sequence methods and other dynamic detection methods reported in related works.
Submap-Based Indoor Navigation System for the Fetch Robot

In this paper, we present a novel navigation framework for the Fetch robot in a large-scale environment based on submapping techniques. This indoor navigation system is divided into a submap mapping part and an on-line localization part. For the mapping part, in order to deal with large environments or multi-story buildings, a submap mapping framework fusing two-dimensional (2D) laser scan and 3D point cloud from RGBD sensor is proposed using Google Cartographer. Meanwhile, several image datasets with corresponding poses are created from the RGBD sensor. Thanks to the submap framework, the error is limited corresponding to the size of the map, thus localization accuracy will be improved. For the on-line localization, so as to switch the submaps, the on-line images from the RGBD sensor are used to match the database images using DeepLCD, a deep learning based library for loop closure. Based on the information from DeepLCD and odometry, adaptive Monte Carlo localization (AMCL) is reinitialized to finish the localization task. In order to validate the result accuracy, reflectors and a motion capture system are used to compute the absolute trajectory error (ATE) and the relative pose error (RPE) based on the Gaussian-Newton (GN) algorithm. Finally, the proposed framework is tested on the Fetch simulator and the real Fetch robot, including both submap mapping and on-line localization.
The Effect of Hydroxyapatite-Gelatin Composite with Alendronate as Injectable Bone Substitute on Various Hydroxyapatite-Based Substrate

Patients with osteoporosis in the world reached 9 million cases. The treatment for this case nowadays was using oral medication and therapy. The recent development to treat osteoporosis used injectable bone substitute. This study focused on synthesis and characterization of injectable bone substitute based on hydroxyapatite and gelatin with the addition of alendronate on various substrate. The synthesis was started by mixing hydroxyapatite, 2% (w/v) gelatin, 10% alendronate and 2% (w/v) hydroxypropyl methylcellulose to form a suspension. The substrates used in this study were HA, HA-SBF, HA-GEL, and HA-GEL-SBF. The setting time test showed that the addition of alendronate could increase the time of setting from 240-420 min in original substrate to 179-251 min in the other substrates. The density and porosity increased in all substrates which were 0.074-0.132 gr/cm3 and 28-40% for density and porosity, respectively. The pore size was 229.2-578.6 μm from morphology test. The compressive stress of the substrate increased to 1.56-3.24 MPa which was a good mechanical properties compared to compressive strength of human spine which was 0.5-50 MPa.
A Multistage Deep Transfer Learning Method for Machinery Fault Diagnostics Across Diverse Working Conditions and Devices

Deep learning methods have promoted the vibration-based machinery fault diagnostics from manual feature extraction to an end-to-end solution in the past few years and exhibited great success on various diagnostics tasks. However, this success is based on the assumptions that sufficient labeled data are available, and that the training and testing data are from the same distribution, which is normally difficult to satisfy in practice. To overcome this issue, we propose a multistage deep convolutional transfer learning method (MSDCTL) aimed at transferring vibration-based fault diagnostics capabilities to new working conditions, experimental protocols and instrumented devices while avoiding the requirement for new labeled fault data. MSDCTL is constructed as a one-dimensional convolutional neural network (CNN) with double-input structure that accepts raw data from different domains as input. The features from different domains are automatically learned and a customized layer is designed to compute the distribution discrepancy of the features. This discrepancy is further minimized such that the features learned from different domains are domain-invariant. A multistage training strategy including pre-train and fine-tuning is proposed to transfer the weight of a pre-trained model to new diagnostics tasks, which drastically reduces the requirement on the amount of data in the new task. The proposed model is validated on three bearing fault datasets from three institutes, including one from our own. We designed nine transfer tasks covering fault diagnostics transfer across diverse working conditions and devices to test the effectiveness and robustness of our model. The results show high diagnostics accuracies on all the designed transfer tasks with strong robustness. Especially for transfer to new devices the improvement over state of the art is very significant.
Optimized task scheduling on fog computing environment using meta heuristic algorithms

Fog Computing paradigm extends the cloud computing technology to the edge of the computer network. The basic concept is kind of similar to cloud computing and supports virtualizations as well. It is very useful in healthcare application, intelligent transportation systems, financial and smart cities. Optimal task scheduling is an important topic in fog computing virtualization. The task scheduling procedure is an NP-complete problem where the time needed to locate the solution varies by the size of the problem. There are various computation-based performance metrics use in scheduling procedure such as energy consumption and execution cost. Optimal task scheduling of tasks in fog computing can be classified as heuristic, meta-heuristics and hybrid task scheduling approaches. The heuristic task scheduling algorithms deliver ease to schedule the task and deliver the best possible solutions, but it doesn’t guarantee the optimal result. The meta-heuristics approaches can handle massive search space to discover better optimal solution for task scheduling problem within reasonable time. Smart healthcare application model is implemented and simulated in iFogSim simulator tool which is used to test and select the technique to introduce a Whale optimization algorithm. Whale optimization algorithm is compared with several heuristic algorithms (RR, SJF) and PSO meta-heuristic algorithm. The results show that proposed algorithm improved the average energy consumption of 4.47% and cost 62.07% relative to the RR, SJF algorithms and energy consumption of 4.50% and cost 60.91% relative to the PSO algorithm.
A New Step-Up Switched-Capacitor Voltage Balancing Converter for NPC Multilevel Inverter-Based Solar PV System

This paper proposed a grid connected solar Photovoltaic (PV) Systems with a new voltage balancing converter suitable for Neutral-Point-Clamped (NPC) Multilevel Inverter (MLI). The switched-capacitors used in the proposed converter is able to balance the DC link capacitor voltage effectively by using proper switching states. The proposed balancing converter can be extended to any higher levels and it can boost the DC input voltage to a higher voltage levels without using any magnetic components. This feature allows the converter to operate with the boosting capability of the input voltage to the desired output voltage while ensuring the self-balancing. In this paper the proposed converter is used for a grid connected solar PV system with NPC multilevel inverter, which is controlled using vector control scheme. The proposed grid connected solar PV system with associated controllers and maximum power point tracking (MPPT) is implemented in Matlab/SimPowerSystem and experimentally validated using dSPACE system and designed converters. The simulation and experimental results show that the proposed topology can effectively balance the DC link voltage, extract maximum power from PV module and inject power to the grid under varying solar irradiances with very good steady state and dynamic performances.