Miracle-dz/UniFAR-FTUs huggingface dataset:
A role-based statistical mechanism for DDoS attack detection in SDN

There is a transformation of the traditional network into Software Defined Networking (SDN) which is an outstanding developing area recently. Among the most exciting features of SDN are the remarkable control over network infrastructure and decoupling of control and data plane. Although it helps more flexible network management, SDN should be considered current and upcoming security threats associated with its deployment. One of them is the DDoS attack which is a malicious attempt to bring down networks, applications, or services by overwhelming these resources with too much data or impairing them in some other ways. In SDN, we can offer or change the network functions or behavior program by monitoring controller to realize DDoS attacks. This paper presents an approach of DDoS attack detection in SDN environment by utilizing the entropy metric with consideration of differences in host role profile to suspect under-attack state, we also deal with time factor in information collecting activities. Then, a statistical method is used for investigating flow information sent from OpenFlow switches to confirm the previous suspicion.
Improving Traffic Information Retrieval in VANET with NDN

Traffic Information System (TIS) is a major application over vehicular networks. Decentralized TIS relies on vehicle-2-vehicle communications, and efficient traffic information dissemination is a major challenge. In this paper, we propose a preliminary design to support the traffic information retrieval in vehicular ad hoc networks (VANET). Our design adopts an information-centric communication model, and a probabilistic forwarding strategy based on names and geolocations. We implemented a prototype of our design and evaluated its performance in ndnSIM.
Precoding and Detection Techniques for Large Scale Multiuser MIMO TDD Systems

We consider multiuser wireless system with large scale multiple-input multi-ple output (MIMO) technology. Using of proposed precoding and detection techniques provide to increase noise immunity on base station (BS) user equipment (UE) side. Computer simulation results provide us to compare noise immunity on receiver side between different types precoding and detection techniques.
Sliding-Mode-Based Trajectory Tracking and Load Sway Suppression Control for Double-Pendulum Overhead Cranes

Overhead cranes with double-pendulum effect seem more practical than those with single-pendulum effect. However, in this case, the dynamic performance analysis and the controller design become more difficult. Moreover, achieving both high-precision tracking control and load sway suppression is a more significant issue for crane systems. In order to solve the aforementioned problems, the nonlinear dynamics of a 2-D overhead crane with double-pendulum effect is derived for controller design. Then, a novel sliding mode controller with nonlinear sliding surface is presented. Unlike a traditional linear sliding surface, the nonlinear one can change the damping ratio of the closed-loop system from its initial low value to final high value. The low value can provide a quick response, whereas the high value one can eliminate overshoot to make the cart track the given trajectory more precisely. The stability of the whole system is confirmed by the Lyapunov technique. The numerical simulation and experimental results validate that the proposed method has superior control performance and robustness with respect to parameter variations and external disturbance.
Scalable Detection of Rural Schools in Africa Using Convolutional Neural Networks and Satellite Imagery

Many countries typically lack sufficient civic data to assess where and what challenges communities face. High resolution satellite images can provide honest assessments of neighborhoods and communities to guide aid workers, policy makers, private sector, and philanthropists. Although humans are very good at detecting patterns, manually inspecting high resolution satellite imagery at scale can be costly and time consuming. Machine learning has the potential to scale this process significantly and automate the detection of regions of interest. Here we tackle the problem of identifying schools in northeastern rural Liberia as a case study for evaluating the value of high resolution satellite imagery and machine learning. In our case study we utilize unsupervised learning with pre-trained convolutional neural networks. Our results suggest that using machine learning with high resolution satellite images can reduce the search space, help find schools with high recall and aid appropriate and relevant resource allocations.
Enhancement of Cogging Torque Reduction on Inset Permanent Magnet Generator by Using Magnet Edge Shaping Method

This paper handled with the investigation of the cogging torque reduction method in Inset-Permanent Magnet Generators using fractional slot stator/rotor combination. Cogging torque of Inset-Permanent Magnet Generator is analyzed to be cut down by slot opening width reduction and permanent magnets shaping. Electromagnetic simulations of the Inset-PMG in this paper were implemented by applying finite element analysis. Finite element method magnetic (FEMM 4.2) tool is employed to determine and analyze the cogging torque. It can be proved that by mixing of slot opening reduction and permanent magnet shaping at a particular shoe height can diminish the cogging torque of Inset-Permanent Magnet Generator sharply.
Multi-Channel Based Sybil Attack Detection in Vehicular Ad Hoc Networks Using RSSI

Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance.
A Hybrid Approach to Paraphrase Detection

In this paper, we present a hybrid approach to the paraphrase detection task. The approach takes advantage of both feature-engineering and neural-based methods. First, we represent words and entities in a given sentence by using their pre-trained vectors. Then, those pre-trained vectors are encoded by a bidirectional long-short term memory network. The output matrix is fed into an attention network to obtain an attention vector. The final representation of the sentence is inner product of the matrix and the attention vector. We conduct experiments on the Microsoft Research Paraphrase corpus, a popular dataset used for benchmarking paraphrase detection methods. The experimental results show that our approach achieves competitive results.
Design Automation Methodology and Tools for Superconductive Electronics

Josephson junction-based superconducting logic families have been proposed to implement analog and digital signals, which can achieve low energy dissipation and ultra-fast switching speed. There are two representative technologies: DC-biased RSFQ (rapid single flux quantum) technology and its variants that achieve a verified speed of 370 Ghz, and AC-biased AQFP (adiabatic quantum-flux-parametron) that achieves an energy dissipation near quantum limits. Despite extraordinary characteristics of the superconducting logic families, many technical challenges remain, including the choice of circuit fabrics and architectures that utilize the SFQ technology and the development of effective design automation methodologies and tools. This paper presents our work on developing design flows and tools for DC- and AC-biased SFQ circuits, leveraging unique characteristics and design requirements of the SFQ logic families. More precisely, physical design algorithms, including placement, clock tree routing, and signal routing algorithms targeting RSFQ circuits are presented first. Next, a majority/minority gate-based automatic synthesis framework targeting AQFP logic circuits is described. Finally, experimental results to demonstrate the efficacy of the proposed framework and tools are presented.
Using Firework Algorithm for Multi-Objective Hardware/Software Partitioning

Hardware/software partitioning plays an important role in the co-design system of software and hardware. It can improve the performance of the embedded system to a great degree. Multi-objective hardware/software partitioning aims to optimize the system performance from multi-aspects simultaneously. In recent years, more and more heuristic algorithms are utilized to solve multi-objective problems. In this paper, we apply a firework algorithm (FWA) to solve the problem of multi-objective hardware/software partitioning. The sorting method for multi-objective solutions is described in detail. The calculation of explosion amplitude is modified according to the number of iterations. Due to binary coding, the method of generating new solutions is updated. Finally, a multi-objective FWA (MOFWA) for multi-objective hardware/software partitioning is proposed. To validate the performance of the MOFWA, experiments on six instances are conducted. The proposed MOFWA is compared with three famous multi-objective optimization algorithms, the nondominated sorting genetic algorithm II, the strength Pareto evolutionary algorithm 2, and the Pareto envelope-based selection algorithm in terms of S-metric. The experimental results show that the MOFWA significantly outperforms the three other algorithms.
Analysis of the Dual-Threshold-Based Shrinking Scheme for Efficient NB-LDPC Decoding

In this paper, the dual-threshold-based shrinking (DTBS) scheme for efficient non-binary low-density parity-check (NB-LDPC) decoding, which has been initially presented in our previous work [1], is further studied. Particularly, the TIT-MSA is an example to employ this scheme presented in [1]. Its computational complexity is theoretically analyzed in this paper. Numerical results show that more than 70% computational complexity is reduced compared with the SMSA, while the error performance loss is negligible. Besides, when considering memory consumption, we extend this scheme by setting a maximum number of kept values with an efficient searching method, and apply it to the TIT-MSA. Simulation results indicate that the new decoding algorithm achieves both comparable error performance and significant memory reduction. For example, the memory consumption is reduced by about 61% for a (256, 203) code over GF(28) and about 31% for an (837, 726) code over GF(25).
A Standard-cell Based A/D Converter with a Back-gate VCO and a Fat Tree Encoder

Analog-to-digital converters (ADCs) using voltage-controlled oscillators (VCOs) digitize analog signals in the phase domain, requiring no analog comparators and resistor ladders. This VCO-based ADC can be implemented with only digital standard cells provided by process vendors to maximize the advantages of technology scaling and design automation. A conventional VCO-based ADC consisting of the standard cells consumes additional power consumption and operates at the limited sampling frequencies due to buffers or sense amplifiers, keeping the output amplitude of the VCO constant, and a phase detector based on counters, respectively. We present an ADC with a back-gate VCO and a phase detector consisting of NAND gates and a fat tree encoder. The VCO produces rail-to-rail output signals not depending on the input signals, requiring no buffers and amplifiers. The phase detector can operate at higher sampling frequencies than the ones based on counters. The proposed VCO-based ADC, designed in a 65-nm digital CMOS process, achieves an SNR of 57.9 dB, excluding harmonics, in 20-MHz bandwidth with a sampling frequency of 800 MHz and a power consumption of 2.7 mW on simulation. This results in the best FoM (105 fJ/step) among the previously reported VCO-based ADCs consisting of standard cells.
An Effective Similarity Measure for Neighborhood-based Collaborative Filtering

Thanks to its successful application in recommendation systems, collaborative filtering (CF) technique has become one of the most popular research topic in data mining and information retrieval. The two more dominant approaches to CF are neighborhood-based models and latent factor models. Currently, the state-of-the-art recommendation systems are mainly based on the latter approach owing to its ability to explore hidden factors connecting items to users. In this work, we propose a novel similarity measure which helps improve the accuracy of the neighborhood-based models. Intensive experiments show that our proposed model not only outperforms the best latent factor model with respect to RMSE by 2.2% but also works at least 2 times faster than its counterpart.
Detecting System Anomalies in Multivariate Time Series with Information Transfer and Random Walk

Detecting major system anomalies with observed multivariate time series requires not only the characteristics of each time series but also the status of the entire time series dynamics. Therefore, we propose a method that can detect substantial anomalies by generating a transfer network and an influence network from a multivariate time series. To form a transfer network, each vertex represents a single time series. Each edge indicates the strength of the information flow between each pair of time series using transfer entropy. With the transfer network, we exploit the random walk approach to calculate the affinity score between two vertices and create an influence network that reflects both the direct and indirect influences. In our experiment, we show the efficacy of the proposed method using simple synthetic time series networks and the real data set such as world stock indices and key performance indicators of the SAP HANA in-memory database system.
Electric Vehicle Traction System Performance Enhancement Using FO-PI Controller

This paper investigates the effect of speed regulators on the battery bank voltage and current profiles for a prototype electric vehicle traction system. A 400 V, 6.6 Ah Li-Ion battery bank is established to power an indirect field oriented induction motor drive system. The performance of integer-order PI and fractional-order PI controllers is investigated while the electric vehicle traction system executes the urban and extra urban drive cycles. Also, the controllers' effect on the battery bank is examined under the sudden load disturbance. The experimental results reveal that the nonlinear fractional order PI controller provides better speed tracking with lesser control effort. The reduced control effort of FO-PI controller draws lesser current from the battery thus improving the battery voltage profile and efficiency.
An Ensemble of Shallow and Deep Learning Algorithms for Vietnamese Sentiment Analysis

Sentiment analysis also known as opinion mining refers to the use of natural language processing to systematically identify and categorize opinions expressed in a piece of text. Recently, deep learning with ensemble techniques has achieved state of the art results in sentiment analysis. However, this approach has not been studied for Vietnamese corpus. In this paper, we propose an ensemble method by combining traditional (shallow) and deep learning algorithms. We tested our method on three Vietnamese sentiment datasets. The Experimental results showed that these approaches improve the accuracy of sentiment classification when compared to both individual deep and shallow algorithms.
Joint Faces Scheduling and Bitrate Switching for Dynamic Adaptive Streaming over NDN Based on Stochastic Optimization

With the fast increase of video traffic transmitting over the Internet, dynamic adaptive streaming (DAS) has emerged as a conventional way for video streaming distribution. At the same time, Named Data Network (NDN) has been proposed to deal with various problems existed in the Internet. However, existing researches in DAS over NDN did not pay much attention to multihoming capability of NDN. Consumers who request data using multiple faces can aggregate bandwidth dof different links which results in choosing higher layers of the video, increasing the quality of video requested. Multiple faces streaming can also smoothen the throughput seen by the client, leading to less fluctuation of the layer switching. Moreover, applying multiple faces can decrease the risk of port failure, increase reliability of the system. In this paper, we propose a dynamic adaptive algorithm of joint faces scheduling and bitrate switching for video streaming request over NDN aiming at optimizing long-term quality of Quality of Experience (QoE) and the cost of consumers under a constraint of playback smoothness and fluctuation. The proposed algorithm is based on Lyapunov Optimization which does not need priori knowledge of the dynamic network state information for each port. Both our theoretical analysis as well as experiments show that this algorithm is effective.
Addressing the DAO Insider Attack in RPL’s Internet of Things Networks

In RPL routing protocol, the destination advertisement object (DAO) control messages are announced by the child nodes to their parents to build downward routes. A malicious insider node can exploit this feature to send fake DAOs to its parents periodically, triggering those parents, in turn, to forward the fake messages upward to the root node. In this letter, we show how this behavior can have a detrimental side effect on the performance of the network, increasing power consumption, latency, and reducing reliability. To address this problem, a new scheme is introduced to mitigate significantly the effect of the DAO attack on network performance.
Message-Passing Receiver Design for Joint Channel Estimation and Data Decoding in Uplink Grant-Free SCMA Systems

The conventional grant-based network relies on the handshaking between the base station and active devices to achieve dynamic multi-user scheduling, which may result in large signaling overheads as well as system latency. To address those problems, a grant-free receiver design is considered in this paper based on sparse code multiple access (SCMA), one of the promising air interface technologies for 5G wireless networks. With the presence of unknown multipath fading, the proposed receiver performs joint channel estimation and data decoding without knowing the user activity in the network. Formulating a factor graph representation for the problem, we devise a message-passing receiver for the uplink SCMA that performs joint estimation iteratively. Motivated by the idea of approximate inference, we use expectation propagation to project the intractable distributions into Gaussian families such that a linear complexity decoder is obtained. The simulation results show that the proposed receiver can detect active devices in the network with a high accuracy and can achieve an improved bit-error-rate performance compared with existing methods.
Cluster-based Selective Cooperative Caching Strategy in Vehicular Named Data Networking

Vehicular named data networking (VNDN) is a potential candidate to be deployed in information rich applications of vehicular communication. Due to vehicle mobility, it is difficult to construct stable and reliable connections between nodes. Consequently, VNDN is suffering low quality of experience and heavy network load in data transmission. In order to solve these problems and improve network performance, we propose a Cluster-based Selective Cooperative Caching (CSCoC) strategy in VNDN. The main idea of CSCoC is to cache popular data at a set of vehicles that may establish stable communication with others. In our strategy, cluster dividing method is used to divide vehicles and select cluster heads as caching vehicles based on their mobility patterns. To mitigate the impact from fast vehicle mobility, we design a cooperative caching strategy which allows cluster members to request and fetch data from their cluster heads. Additionally, we put forward a method of computing the local data popularity, and a cache replacement strategy based on the data popularity. Compared with existing strategies, simulation results demonstrate how CSCoC increases VNDN performance by around 70% in terms of average access delay, average hop count and cache hit ratio.
Sizing Of A Hybrid (Photovoltaic/Wind) Pumping Systembased On Metaheuristic Optimization Methods

The energy future must be focused on clean and renewable energies. Sizing is an indispensable step in the optimization of renewable energy systems because of their intermittent nature. In this context, the research work presented focuses on the development of a methodology for analysis and technical-economic evaluation carried out for a hybrid (PV/wind) system where the results are compared and discussed. Two models describe this methodology; the reliability model developed using the Loss of Power Supply Probability Concept and the Life Cycle Cost Model. As the proposed optimization problem is strongly nonlinear, to solve it we opt for the use of metaheuristic methods. In order to achieve our goal, we proposed four optimization algorithms namely: BAT Algorithm, Cuckoo Search Algorithm, FireFIy Algorithm, and Flower Pollination Algorithm. A case study is conducted to analyze a hybrid water pumping system to supply in water a habitation, located in the site of Ghardaia, south of Algeria. The results of the simulation relating to the different configurations of the system and their corresponding costs are promptly presented.
MTPA Trajectory Tracking for IPMSM Drives: A Comparative Study and Analysis

In this paper, a comparative study and analysis of two different maximum torque per ampere (MTPA) speed control approaches is laid out for interior permanent magnet synchronous machine (IPMSM) drives. The well-known classical field-oriented vector control (FOC) MTPA is compared with another modified, yet simpler MTPA approach. The presented comparison investigates the performance of the IPMSM under various operating ranges. Moreover, different load conditions are used to show the major differences between both control methods. A nonlinear friction model is also included to have a better representation for the system dynamics. Simulation results demonstrate at which conditions the simpler MTPA scheme is a good candidate to replace the classical MTPA control for low-cost implementation of the drive while maintaining the high efficiency performance of IPMSMs under MTPA control.