Miracle-dz/UniFAR-FTUs huggingface dataset:
Democratic Supervision Makes Controls in Software-Defined Networks More Secure

The centralized control of Software-Defined Networking (SDN) brings innovation and convenience to the network, but many current SDN controllers also have some security bugs that are easily exploited by attackers. Once the master controller which has sufficient management rights is compromised, entire network can be damaged. For this, we propose a mechanism of democratic supervision in SDN, which adds a proxy between the control plane and the data plane to monitor whether the master controller is abnormal. The proxy sends OpenFlow requests from the switch to multiple diverse controllers and collects flow entries that they respond to. Then it compares these flow entries to judge whether the behavior of the master controller is different from that of other controllers. However, flow entries with the same function may be different in number or content, so we need to analyze their forwarding semantics to compare them, instead of simply comparing their contents. The advantage of this supervision mechanism is that it allows the controller to defend against many known or unknown attacks without debugging all its vulnerabilities. Experimental results show that it is effective in detecting malicious behavior, and it is also efficient under a certain scale of networks.
Multi-criteria Collaborative Filtering Based on Matrix Filling

In multi-criteria collaborative filtering, the data sparsity is a critical factor affecting the effectiveness of the algorithm. Existing solutions generally remove low-efficiency data through dimensionality reduction, decomposition, etc. While at lower data dimension, they also lose original data information, which impacts the accuracy of predicted ratings. This paper adopts a new processing strategy and proposes to use the matrix filling method to handle the missing data. Firstly, an improved similarity algorithm combined with the Jaccard Similarity Coefficient is used to calculate the predicted rating. Secondly, the Reliable Factor is introduced to prevent the error of filling data. Finally, multiple linear regression is employed for aggregating the ratings of the criteria to eventually yield preferred predicted ratings. A series of experiments show that the proposed method can precisely reflect the user's similarity and achieve better results in predicted ratings, in contrast to traditional methods.
Uplink Interference Analysis between LEO and GEO Systems in Ka Band

The frequency reuse of Geostationary Orbit (GEO) satellite systems and Low Earth Orbit (LEO) satellite systems within the same spectrum to enhance the spectrum efficiency in Ka band has attracted great interests in satellite communication research lately. However, coexistence interference will occur when different satellite systems share the same spectrum. Therefore, it is crucial to analyze the coexistence interference and study the interference mitigation techniques. In this paper, uplink interference between GEO satellite system and LEO satellite system has been analyzed, including the worst-case of uplink interference and the effects of an exclusion angle on mitigating coexistence interference. Adopting the OneWeb system as a use case, the interference analysis results are given by simulation.
RGB-D Image Enhancement by Prior Guided Weighted Nonlocal Laplacian

Compared with RGB image, there are more meaningful information provided by depth sensor for RGB-D image. Therefore, the traditional image processing methods cannot be directly extended to RGB-D images. In this paper, a novel color guided weighted nonlocal Laplacian algorithm is proposed for depth image enhancement. With the guidance of color channels, the performance is improved dramatically against the traditional nonlocal weighted Laplacian method. In addition, the proposed method can be easily extended to the task of image colorization applications. Numerous experiments show that the proposed method outperforms the traditional weighted nonlocal Laplacian algorithm.
The Application of Reliability in the Information Hypernetworks Optimization

Hypernetworks can be used to represent information networks, such as computer systems and wireless communication networks, which have higher requirements for reliability. In this paper, a concept of reliability to measure the possibility for the hypernetwork to connect when hyperedge may fail is proposed. A greedy algorithm for calculating reliability is given. The application of the reliability to the study of two real-world complex systems has shown that these systems can be optimized by simple methods.
Text Mining to Concept Mining: Leads Feature Location in Software System

In the agile application development environment, automatically identifying relevant components in a large complex software system for software maintenance is still remain a research problem with the proliferation of software applications. Earlier, concept mining with formal concept analysis was one of the commonly applied techniques for legacy software systems of small to medium size. Recently, text mining is being widely used for locating features or concerns in a large complex software system. Nevertheless, the literature study reveals that combining text mining with other techniques always yield better accuracy in locating features. Even though it is efficient, applying formal concept analysis on the large systems poses limitation due to its exponential time complexity in constructing concept lattices. In this research work, a model is devised to combine text mining and concept mining for large systems. The unsupervised machine learning technique, Latent Dirichlet Allocation modeling also called as Topic Modeling is used to reduce the feature space on which K-Means clustering is applied to cluster the related documents and formal concept analysis is carried out on individual clusters. Three open source software systems namely JEdit, ArgoUML and JabRef are considered for the experimental study. The empirical evaluation of feature location measure of the proposed model shows a significant improvement in terms of accuracy, scalability, flexibility and efficiency over the contemporary methods existing in the literature.
Investigation of a nanoscale grooved stepped gate MOSFET to explore the self-heating effect

This work reports a new grooved gate silicon-on-insulator (GG-SOI) MOSFET with multi-layered (SiO2/Si3N4/SiO2) buried insulator structure to reduce self-heating effect (SHE). The proposed model is simulated using the Sentaurus TCAD simulator. As the thermal conductivity of SiO2/Si3N4/SiO2 buried insulator is higher than SiO2 buried layer, this innovative grooved gate SOI model is able to reduce the self-heating effect of the conventional GG-SOI MOSFET. Thus appropriate for high temperature solicitations. Performance comparison has been done between the multi-layer-buried recessed channel SOI and conventional SiO2 based GG-SOI MOSFET. The presented structure has well controlled the device temperature against the low thermal conductivity of conventional GG-SOI MOSFET. Further step gate concept is used for the improvement of analog performance and short channel effects (SCEs). Simulation results reveal the enhanced performance manifested by the proposed structure in terms of increased drain current, reduced device temperature and increased electron mobility.
A Survey on Energy Harvesting Cognitive Radio Networks

Drastic development in the wireless communication systems is leading to the issue of spectrum scarcity and to resolve this issue, cognitive radio (CR) is emerging as one of the most capable solution. Energy harvesting phenomenon in cognitive radio networks (EH-CRNs) makes these communication systems an energy efficient one. This paper reviews the different energy harvesting techniques in CRNs, existing work on EH-CRNs and future research directions in EH-CRNs. Different energy harvesting sources for CRNs are discussed and depending upon the power storage potential of the EH-CRNs, two types of harvesting models that is harvest-use model and harvest-store-use model are explained. The existing work on EH-CRNs which includes resource allocation, throughput maximization and secure information transfer with both linear as well as non-linear energy harvesting models is discussed. Finally, future research directions in energy harvesting cognitive radios are discussed briefly.
Hydrogen Energy System Based on Silicon Nanopowders: Prospects and Problems

Hydrogen source and the model of hydrogen energy system based on chemical reaction of silicon nanopowders with water are described. The ways of hydrogen bonding and extraction are analyzed. The problems of general hydrogen output for porous powders, as well as the questions of hydrogen generation stability are discussed. The calculations determining intensity of the hydrogen generation and output electrical power are carried out. The prospects of application are investigated for the autonomous hydrogen energy system based on silicon nanopowders.
Recent Advances in Filter Design Using The AFSIW Technological Platform

In this paper, the air-filled substrate integrated waveguide (AFSIW) technology for high performance filtering applications is presented. This technology offers an alternative technological platform for future communication systems. The AFSIW technology inherits the high degree of freedom of the PCB process as well as the high electrical performances of conventional metallic waveguide technologies. Authors expect its implementation in high performance microwave and millimeter-wave components for future systems and circuits on substrate.
Improving Attack Trees Analysis using Petri Net modeling of Cyber-Attacks

Cyber security is one general concern to all network-based organizations. In recent years, by significant increasing cyber-attacks in critical infrastructures (CIs) the need of smart prediction, awareness and protection systems is not deniable. The first step for security assessment is on recognizing and analyzing attacks. In this paper, one of the graphical security assessments named Attack Tree (AT) is used to illustrate one kind of cyber-attacks scenario in Industry 4.0 and the system’s behavior is analyzed by Petri Nets.
Throughput Analysis in Collaborative Communication Based on Relay Energy Harvesting

Wireless powered relaying is a promising cooperative communication technique which has drawn much attention recently. In this paper, we consider the problem of throughput maximization based on relay energy collection in wireless cooperative communication system. When source node transmits radio-frequency (RF) signal with two antennas, relay without initial energy starts to collect energy from RF signal, when collected energy reaches the preset fixed transmission power, relay begin to amplify-and-forward information received from source to destination. We propose two models of energy harvesting (EH) model at the relay—continuous time EH and discrete time EH. For continuous time EH, the EH time can be any percentage of the total transmission block time. For discrete time EH, the whole transmission block is either used for EH or information transmission (IT). The simulation results are perfectly matched with the derived throughput expression, which show the correctness of the expression. The simulation results also show that throughput of the proposed discrete time EH model is better than that of the continuous time EH model.
Security Assessment of Dynamic Networks with an Approach of Integrating Semantic Reasoning and Attack Graphs

Because of the high-value data of an enterprise, sophisticated cyber-attacks targeted at enterprise networks have become prominent. Attack graphs are useful tools that facilitate a scalable security analysis of enterprise networks. However, the administrators face difficulties in effectively modelling security problems and making right decisions when constructing attack graphs as their risk assessment experience is often limited. In this paper, we propose an innovative method of security assessment through an ontology- and graph-based approach. An ontology is designed to represent security knowledge such as assets, vulnerabilities, attacks, countermeasures, and relationships between them in a common vocabulary. An efficient algorithm is proposed to generate an attack graph based on the inference ability of the security ontology. The proposed algorithm is evaluated with different sizes and topologies of test networks; the results show that our proposed algorithm facilitates a scalable security analysis of enterprise networks.
Efficient Processing of Area Skyline Query in MapReduce Framework

In many application domains, it is badly needed to select a good location on a map. Selecting a location which is close to desirable facilities like nearby university or college areas or train/bus station but far away from the undesirable facilities like competitor cafe, noise sources and so on can be considered as a crucial problem in data mining research. A vast amount of information needed to be processed to answer such a query. The skyline query, which is one of the most powerful queries for selecting the interesting dataset, can be used to find some suitable space. Perhaps few researchers have already proposed a variant of the skyline query known as Area skyline query. However, the computation of area skyline query is a time-consuming process. Even if the dominance check using any efficient skyline query algorithms are not so suitable for selecting a good location when the area size is large. In this research, we have proposed an alternative way to select some good locations using the conventional skyline query in MapReduce architecture. MapReduce, proposed by Google Inc, is a programming model and an associated implementation for processing and generating big data sets with a parallel, distributed algorithm on a cluster. Hadoop, the open source implementation of Google's MapReduce framework, is our implementation framework. A series of experiments and comparisons with conventional area skyline query to check the efficiency of our proposed mechanism have been done.
Hybrid SDN Control in Mobile Ad Hoc Networks

Software defined networking (SDN) can be beneficial in mobile ad hoc networks (MANETs) to increase flexibility, provide programmability and simplify management. The high dynamics in mobile networks, however, raise new reliability challenges to the conventional centralized control plane of SDN. To increase reliability, methods such as placing multiple controllers in the network have been considered that add redundancy in the control plane in a brute force manner. However, these methods cannot by themselves fundamentally solve the reliability problem. To address this issue, this paper complements the controller placement methods with a new architecture that has a hybrid structure splitting the routing decision logic between the controllers and the data plane nodes. Specifically, the controllers can break the routing path into segments, similar to the segment routing technique, and broadcast the list of segment labels to the data plane nodes. The latter are able to make the actual forwarding decisions for each segment in a distributed manner, e.g., by running an existing MANET protocol like OLSR. Experiments on a testbed built from commercial mobile devices with integrated SDN functionality highlight the feasibility and benefits of the proposed architecture.
A 1.6-to-3.0-GHz Fractional-N MDLL with a Digital-to-Time Converter Range-Reduction Technique Achieving 397fs Jitter at 2.5-mW Power

This paper presents an inductor-less fractional-N clock multiplier with low integrated jitter and power. The architecture is based on a multiplying delay locked loop with a sub-sampling bang-bang phase detector and a novel digital-to-time converter (DTC) range-reduction technique which limits the jitter added to the reference signal, at no additional power penalty. The prototype, implemented in 65nm CMOS, covers the 1.6-to-3.0-GHz range and achieves an absolute RMS jitter (integrated from 30kHz to 30MHz) of 397fs at 2.5mW power consumption, leading to a jitter-power Figure of merit of -244dB. In-band fractional spurs are as low as -51.5dB and the occupied core area is 0.0275mm2.
Multi-touch Technology in Education:Digital Simulation Improves Student Comprehension, Cognitive Concept, and Anxiety

Digital simulation technology is widely used in science education and has enormous potential for its development. Numerous studies have shown that the use of digital simulation in an education environment, especially in scientific education, is conducive to intuitive interaction and improves students' participation in their learning activities. This study investigated the effects of digital simulation with different interactive modes (based on gesture simulation and button simulation) on students' cognitive concepts, attention, and anxiety levels in an elementary physics course. To collect the students' cognitive concepts, attention, and anxiety levels, we developed a digital simulation with two different interactive interfaces and used a quasi-experimental design, in which a total of 40 sixth-grade students were randomly assigned to a gesture-or digital-based digital simulation. The experimental results showed that the digital simulation of gestures effectively improved students' cognitive concept levels. The EEG analysis showed that students in the gesturebased digital simulation had higher attention levels compared with those who used button-based digital simulation. Moreover, the operation of digital simulation is relatively difficult; students using gesture-based digital simulation had greater anxiety than students using button-based digital simulation. This study suggests that the use of multi-touch gestures in science education can improve students' comprehension of scientific concepts and improve their concentration in learning activities.
Automatic Reverse Engineering of Unknown Security Protocols from Network Traces

Previous network-based protocol reverse engineering methods have only considered plaintext format of payload, and have not been suitable for security protocols which include many ciphertext data. We propose a novel approach to reverse security protocols from network traces-named SPREA (security protocols reverse engineering approach). SPREA extracts protocol keywords sequences hierarchically using sequential pattern mining for the first time, which would provide a new idea for plaintext format parsing. On this basis, SPREA utilizes the randomness feature of ciphertext data to locate ciphertext fields based on entropy estimation. Then SPREA infers the state machine using sequential pattern mining and Prospex method. Finally we evaluate SPREA on four classical security protocols. The experimental results show that without using dynamic binary analysis, SPREA can parse true protocol format and infer state machine purely from network traces with high accuracy.
Incorporating Forward and Backward Instances in a Bi-LSTM-CNN Model for Relation Classification

Relation classification is a task that identifies entity's semantic relationships from unstructured text. Lately, the method based on convolution neural network(CNN) achieves competitive performance comparing with other complex-structured networks by only using a standard convolution layer, a pooling layer and a softmax layer. However, CNN difficultly extract effective features or even wrong features when processing samples of large-pitch entities, decreasing their classification accuracy. Besides the existing methods could obtain inconsistent relation classifications for the forward and backward instances of the same sample. In this paper, a Bi-LSTM encoding layer is employed to enhance the ability of CNN to captured the contextual information of entities. Then, combining with the forward and backward instances of samples, a classification framework is proposed for relation classification. We conduct experiments on the public dataset SemEval 2010 task 8 verify to the effectiveness of our method. The method we proposed has the significant performance, even without additional artificial features.
Double Weighted RPCA Denoising Algorithm for Color Images

Image denoising is the process of removing the noise and recovering clean images from noisy images. Lots of algorithms have been proposed in last decades in this field, while most of them are aimed at gray images and ignore the correlation and difference between the RGB channels. In this paper, a double weighted Robust Principal Component Analysis (RPCA) denoising algorithm for color images (C-W2RPCA) is proposed. Superior to previous algorithms, it takes into account the spectral correlation, different noise levels for different channels, as well as the importance degree of image component described by singular values. Experiment shows that the proposed C-W2RPCA outperforms the competing methods.
Detecting a predefined solar spot group with a pretrained convolutional neural network

Image processing and machine learning techniques have been applied in several fields of research. Astronomy is one of these areas where relevant methods have been developed to deal mainly with identifying and classification. This work introduces a new approach to characterize and classify sunspots which appear in the solar photosphere for expressing intense magnetic fields. Theses magnetic fields present significant effects on Earth. Solar images were captured in the Intensitygram Flat format, found in the Helioseismic and Magnetic Imager (HMI), of the Solar Dynamics Observatory (SDO), which facilitate processing for identification and sunspots count. This method consists of two steps: image pre-processing and training phase using convolutional neural network (CNN) to identify and characterize spots and groups. Results showed a high potential of this processing that become a competitive classifier for the sunspots groups.
IEEE 1451 Standard-based Universal Heart Monitoring Scheme Using Narrow-Band IoT-Queueing Model Analysis

In human daily life, stressful workload or unhealthy lifestyle is brought in many diseases, e.g. Heart Problem. According to the data published by World Health Organization (WHO), heart-related disease such as cardiovascular problem has been the No.1 killer of human death, but there is obviously lack of heart monitoring equipment to predict or prevent death. In order to solve these problems, researchers have been dedicating on finding appropriate sensors like Electrocardiogram (ECG) sensor to monitor heart. However, it is difficult for heart monitoring service provider to unify heart-monitor sensors, since different manufacturer may produce out different hardware design and software incompatibility. Therefore, in this paper, a universal heart monitoring scheme using NB-IoT belongs to Low-Power Wide-Area (LPWA) wireless transmission protocol will be proposed under IEEE 1451 standard to solve above problems.
Challenges of developing a digital twin model of renewable energy generators

Although the recently wide propagated trend mainly known as digital twin is an essential step for optimal design and reliable functioning of large generators in the future, but no serious strategy and comprehensive study have been yet proposed. There is not even a standard description of this concept. This paper discusses the necessity and challenges of digital twin models of large renewable energy generators and introduces a comprehensive modeling strategy for developing a multi-domain live simulation platform of large generators for wind and hydro power plants.