Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Analysis of Blackhole and Wormhole Attack in MANET Based IoT

In Mobile Ad-hoc Network based Internet of things (MANET-IoT), nodes are mobile, infrastructure less, managed and organized by themselves that have important role in many areas such as Mobile Computing, Military Sector, Sensor Networks Commercial Sector, medical etc. One major problem in MANET based IoT is security because nodes are mobile, having not any central administrator and are also not reliable. So, MANET-IoT is more defenseless to denial-of-service attacks for-example Blackhole, Wormhole, Gray-hole etc. To compare the performance of network under different attacks for checking which attack is more affecting the performance of network, we implemented Blackhole and Wormhole attack by modifying AODV routing protocol in NS-3. After preprocessing of data that is obtained by using Flow-monitor module, we calculated performance parameters such as Average Throughput, Average Packet Delivery Ratio, Average End to End Delay, Average Jitter-Sum and compared it with no. of nodes in MANET-IoT network. Throughput and goodput performance of each node in the network is also calculated by using Trace metric module and compared with each node in the network. This approach is also very helpful for further research in MANET-IoT Security.
A Side-Viewing Endoscopic Probe With Distal Micro Rotary Scanner for Multimodal Luminal Imaging and Analysis

This paper reports a novel micro electromagnetic actuator serving as the distal rotary scanner of an endoscopic probe developed for side-viewing luminal tissue screening and analysis enabled through different endoscopic modalities. The micro rotary optical scanner, for the first time, offers stepwise, low-speed, and high-speed rotational motions to be compatible with endoscopic optical coherence tomography (OCT) and Raman spectroscopy. In comparison with preceding designs, this rotary scanner is shown to provide up to $125\times $ higher revolution speeds per power via a ~50% narrower body while limiting its operation temperature within a biologically safe level for in-vivo scanning purposes. A preliminary experiment on human skin tissues is performed using the prototyped side-viewing OCT endoscopic probe equipped with the developed distal scanner. The results demonstrate the probe’s ability for real-time 360° imaging of live tissue with both high speed and high resolution. These results pave the path for further testing on human internal lumens. [2020-0376]
Implementation of the NucleoCortical pathways inside a Spiking Neural Network model of Cerebellar Nuclei

The cerebellum is involved in a large number of neural processes, especially in motor control and motor learning. From a functional point of view, the Deep Cerebellar Nuclei (DCN) constitute the core of the cerebellar processing, as they exploit the spatiotemporal filtering action provided by the cerebellar cortex to generate motor corrections. To investigate the contribution of the cerebellar nuclei in motor coordination, in-silico models of the DCN are often exploited in virtual simulations of complex sensorimotor tasks. Since the outcomes of this analysis may depend on the accuracy of the DCN model reconstruction, we here advanced existing spiking neural network models of the cerebellar nuclei. Specifically, the nucle-ocortical pathways, which provide feedback signals back to the cerebellar cortex, have been implemented. This reconstruction required the development of a new neural population in the DCN (Glycinergic-Inactive neurons). Using the neural simulator NEST, Glycinergic-Inactive neurons have been modeled as Extended-Generalized Leaky Integrate and Fire models, while nucleocortical pathways have been implemented considering their anatomical organization and functional behaviour. By exploiting these improvements, the DCN models may be used for more accurate simulations of complex cerebellum-driven tasks, investigating the signal integration process between sensorimotor inputs and cerebellar output internal feedback.
Harmonic Compensation by Limit Cycle Model Predictive Control

This work develops a novel limit cycle model predictive controller aimed at harmonic compensation. The proposed nonlinear controller embeds the dynamics of a circular limit cycle of a supercritical Neimark-Sacker normal form directly into its cost function. This enables the controller to steer the state dynamics into a fundamental harmonic shape in systems where a periodic solution is desired. The proposed cost function structure is analyzed for possible convex formulations that could lead to more effective methods for solving the optimization problem. A simulation study in the field of electric power systems integrated with renewable energy resources is given. The new controller is used to set the reference of the compensation current of an active power filter to address the harmonic distortion in the grid. The results show that the controller can mitigate the harmonic disturbance content in voltages and currents to the required levels while maintaining the correct amplitudes at the desired fundamental frequency.
Convolutional Neural Network Approach in Covid-19 Screening in Asymptomatic Individuals

The new coronavirus (COVID-19) spread rapidly around the globe and hits around six million of cases as reported by the World Health Organization (WHO) at the time of writing. This growth in number of cases made it hard for doctors and radiologists to correctly diagnose the COVID-19 disease. The use of automated methods can be helpful to efficiently and accurately detect the disease, and can also be used to assist doctors who lack the proper tools for diagnosis. In the present study, we provide a novel method using Convolutional Neural Networks (CNN) to accurately classify COVID-19 cases from raw chest X-ray images by 95%. The proposed model is lightweight and can be easily deployed to the Cloud or mobile devices with little compute power.
DAEMON: Dataset/Platform-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining

Numerous metamorphic and polymorphic malicious variants are generated automatically on a daily basis. In order to do that, malware vendors employ mutation engines that transform the code of a malicious program while retaining its functionality, aiming to evade signature-based detection. These automatic processes have greatly increased the number of malware variants, deeming their fully-manual analysis impossible. Malware classification is the task of determining to which family a new malicious variant belongs. Variants of the same malware family show similar behavioral patterns. Thus, classifying newly discovered malicious programs and applications helps assess the risks they pose. Moreover, malware classification facilitates determining which of the newly discovered variants should undergo manual analysis by a security expert, in order to determine whether they belong to a new family (e.g., one whose members exploit a zero-day vulnerability) or are simply the result of a concept drift within a known malicious family. This motivated intense research in recent years on devising high-accuracy automatic tools for malware classification. In this work, we present DAEMON—a novel dataset-agnostic malware classifier. A key property of DAEMON is that the type of features it uses and the manner in which they are mined facilitate understanding the distinctive behavior of malware families, making its classification decisions explainable. We’ve optimized DAEMON using a large-scale dataset of ×86 binaries, belonging to a mix of several malware families targeting computers running Windows. We then re-trained it and applied it, without any algorithmic change, feature re-engineering or parameter tuning, to two other large-scale datasets of malicious Android applications consisting of numerous malware families. DAEMON obtained highly accurate classification results on all datasets, establishing that it is not only dataset-agnostic but also platform-agnostic. We analyze DAEMON’s classification models and provide numerous examples demonstrating how the features it uses facilitate explainability.
Identity-Based Partially Blind Signature Scheme: Cryptanalysis and Construction

Blind signature is a special type of digital signature, the signer cannot see the specific content signed. However, blindness may cause users to abuse their rights. Partial blind signature allows the signer to embed pre-negotiated public information in the blind signature without losing blindness, which can prevent users from abusing their rights. Islam et al. presented an identity-based partial blind signature scheme and claimed that it is provable secure. However, in this paper we proved that the scheme is vulnerable against the tampering attacks with public information. We then proposed a new identity-based partial blind signature scheme, showed the security proofs under the assumption that the elliptic curve discrete logarithm problem (ECDLP) is difficult. The new scheme does not use pairing operations and enjoys less computation cost.
MADMAX: Browser-Based Malicious Domain Detection Through Extreme Learning Machine

Fast and accurate malicious domain detection is an essential research theme to prevent cybercrime, and machine learning is an attractive approach for detecting unseen malicious domains in the past decade. In this paper, we present MADMAX (MAchine learning-baseD MAlicious domain eXhauster), a browser-based application leveraging extreme learning machine (ELM) for malicious domain detection. In contrast to the existing work of ELM-based domain detection, MADMAX newly introduces two methods, i.e., selection of optimized features to provide higher accuracy and throughput based on permutation importance and real-time training to retrain a model with an updated malicious dataset for continuous malicious domain detection. We demonstrate that MADMAX fairly outperforms the existing work with respect to accuracy and throughput by virtue of the selection of optimized features. Moreover, we also confirm a model with real-time training stably detects even unseen malicious domains, whereas accuracy of a model without the real-time training decreases due to the unseen domains. The source codes of MADMAX is publicly available via GitHub.
Direction-Finding Technology of Aliased Sources Based on Pattern Division of Array Data

In this paper, an idea of pattern division based on array received data is proposed. First, using energy searches, pattern division, source number estimation, and data updates, the array received data is divided into three modes: independent narrowband data, wideband data, and aliasing narrowband data. Then, a judgment matrix is constructed using the orthogonality of the direction vector with the noise subspace in different modes. Finally, the direction-of-arrival (DOA) is estimated using spectral peak analysis. The proposed algorithm addresses the issues of the DOA estimating with wide/narrow band sources at high aliasing, and improves the range in practical applications. Results from simulations verified the feasibility of the proposed method.
The Energy Meter Translation by Optical Character Recognition Technique

This paper presented an automatic energy meter reading system using the OCR process. Results showed that the system can recognize the energy meter reading digits in only three processes with an accuracy of 91 %. The work presented in this paper is a valuably consequential idea to design an algorithm of the energy meter translation using Optical Character Recognition (OCR).
The simulation of CSI 300 Index Option Pricing Based on Black-Scholes Model

The paper goes on a simulation pricing study for Chinese CSI 300 Index based on the Black-Scholes option pricing model, according to pricing model we get that the price of a three-month CSI 300 Index call option, whose strike price is 4006.44, is approximately 33.46 at November 30, 2017, and the price of put option is zero. And the simulation results show that given historical data, an option price could reflect the future price trend of underlying asset in some degree, and CIS 300 Index have quite large probability of going up.
Performance Benchmark of Multi-Layer Neural Network Based Solar MPPT for PV Applications

A multi-layer feed-forward ANN-based approach is discussed in this paper to achieve maximum power point tracking (MPPT). 70% of the data-set is trained with ANN algorithm, 15% is used for validation and 15% is utilized in testing of neural network. The photo generated output power after MPPT is interfaced with photovoltaic (PV) converters and subsequently to the solar-driven lead-acid battery charge controllers. The research effort aims to have a comparative performance analysis of the ANN approach against the well-known Perturb and Observe algorithm (P&O) and incremental inductance considering MPPT effectiveness for the PV modules and the charging and discharging response of the lead-acid based battery. The simulation results reveal that the neural network algorithm-based approach is more effective than the conventional algorithms for solar MPPT to mitigate the mismatch losses due to partial shading condition as well as for achieving the optimal efficiency of PV battery charge controllers and the overall PV system.
Electromagnetic Performance Analysis of Modular Segmented Permanent Magnet Consequent Pole Flux Switching Machine Employing Flux Gap

Permanent Magnet (PM) Flux Switching machines (PMFSMs) are durable applicants for high speed brushless application due to high torque and power density. However, despite of excessive PM usage and leakage flux, conventional PMFSMs are not excellent fault tolerant. To overcome the abovesaid shortcomings, this paper introduces Modular Segmented Permanent Magnet Consequent Pole Flux Switching Machine (MSPMCPFSM) which reduces PM volume by 46.54% utilizing segmented PM configuration and added fault tolerant capability by introducing flux gap in stator. Moreover, influence of the Flux Gap (FG) is investigated on electromagnetic performance. Analysis concludes that FG offers physical partitioned between phases to limit fault propagation during fault condition between each phase. In additional, with the increase in FG, phase self-inductance increases that mitigates short circuit current at higher speed whereas mutual inductance is suppressed to avoid mutual coupling of faulty and healthy phases. Finally, developed MSPMCPFSM is compared with conventional PMFSMs topologies. Analysis concluded that at reduced PM, MSPCPFSM exhibits 2.6 times higher torque density, and 3.81 times more power density with excellent fault tolerant capability.
A Neurocomputational Model of Posttraumatic Stress Disorder

Despite the well-defined behavioral criteria for posttraumatic stress disorder (PTSD), clinical care is complicated by the heterogeneity of biological factors underlying impairment. Eye movement tasks provide an opportunity to assess the relationships between aberrant neurobiological function and non-volitional performance metrics that are not dependent on self-report. A recent study using an emotional variant of the antisaccade task demonstrated attentional control biases that interfered with task performance in Veterans with PTSD. Here we present a neuroanatomically-inspired computational model based on gated attractor networks that is designed to replicate oculomotor behavior on an affective anti-saccade task. The model includes the putative neural circuitry underlying fear response (amygdala) and top-down inhibitory control (prefrontal cortex), and is capable of generating testable predictions about the causal implications of changes in this circuitry on task performance and neural activation associated with PTSD. Calibrating the model with the results of behavioral and neuroimaging studies on patient populations yields a pattern of connectivity changes characterized by increased amygdala sensitivity and reduced top-down prefrontal control that is consistent with the fear conditioning model of PTSD. In addition, the model makes experimentally verifiable predictions about the consequences of increased prefrontal connectivity associated with cognitive reappraisal training.
Increased frontal synchronization likelihood in ADHD children

Synchronization Likelihood (SL) characterizes the likelihood of auto-recurrence of similar patterns generated from multiple dynamical systems. It has been used to unveil nonlinear dependencies between signals of EEG (electroen-cephalography) channel pairs. Its successful application has demonstrated neural deficits in a variety of disorders, but there are still some inconsistencies in the findings for Attention-Deficit/Hyperactivity Disorder (ADHD). In this study we aim to further investigate alterations of SL in children with ADHD compared to an age-matched control group in resting-state EEG. We found an increase in the SL (FDR-corrected p = 0.059) of the ADHD group both in the frontal interhemispheric theta and in the frontal interhemispheric beta activity that may be associated with an impaired segregation function in this population.
Android Malware Detection and Classification Based on Network Traffic Using Deep Learning

Users of smartphones in the world has grown significantly, and attacks against these devices have increased. Many protection techniques for android malware detection have been proposed; however, most of them lack the early detection of malware. Hence, there is an intense need before to expand a mechanism to identify malicious programs before utilizing the data. Moreover, achieving high accuracy in detecting Android malware traffic is another critical problem. This research proposes a deep learning framework using network traffic features to detect Android malware. Commonly, machine learning algorithms need data preprocessing, but these preprocessing phases are time- consuming. Deep learning techniques remove the need for data preprocessing, and they perform well on malware detection problems. We extract local features from network flows by using the one-dimensional CNN and employ LSTM to detect the sequential relationship between the considerable features. We utilize a real-world dataset CICAndMal2017 with network traffic features to identify Android malware. Our model achieves the accuracy of 99.79, 98.90%, and 97.29%, respectively, in binary, category, and family classifications scenarios.
Nonlinear Noise Modeling: Using Nonlinear Circuit Simulators to Simulate Noise in the Nonlinear Domain

Noise is a random signal, a perturbation of dc bias and RF signals. It is usually necessary that digital electronics provide a low bit error rate or error vector magnitude to guarantee the reliable digital transmission of data. Microwave electronics are commonly expected to amplify or mix these digital signals with high linearity and without adding significant noise, which translates to the requirement of a 10-dB signal-to-noise ratio (SNR) in a communication system. In the case of a nonlinear circuit, it might not always be clear how noise could be an issue at all. Firstly, the signal is several tens of decibels larger than in the smallsignal domain. Secondly, one might consider nonlinear distortions to be the first limiting factor.
Fast Real-Time Reinforcement Learning for Partially-Observable Large-Scale Systems

We propose a fast real-time reinforcement learning (RL) control algorithm for large-scale partially-observable linear dynamic systems. We first develop a one-shot RL method for designing model-free optimal controllers based on a finite-time history of the inputs and the outputs. However, when the system dimension is large, this method may suffer from a long learning time. To overcome this problem, in the second half of the paper we introduce a new notion of approximation to the design, where the original set of input-output history is replaced by a much shorter set. We show that this approximation can lead to a nearly optimal controller that is based on a lower-dimensional approximant of the original system in terms of reachability and observability. We provide a guideline for determining an appropriate length of the input-output history to reduce the suboptimality gap. The dimension of the resulting suboptimal controller is far less than that of the optimal controller, thereby speeding up learning time. The learned controller, however may cause instability when implemented in the original high-dimensional system by adversely exciting the approximation error. We theoretically establish the conditions for closed-loop stability using robust control theory, followed by numerical investigations of the trade-offs between learning time, length of input/output history, and closed-loop performance. The effectiveness of the method is illustrated using examples from electric power systems, modeled by partially-observable nonlinear differential-algebraic equations.
Speech Modality Classification with Cortical EEG Signals

The present work aimed to classify three speech modalities (i.e., spoken speech, intended speech and imagined speech) by using their corresponding cortical EEG signals. Eleven participants were recruited to take part in the experiments to produce 70 Mandarin-Chinese monosyllables in different speech modalities. The EEG signals were recorded during the experiments and processed by discrete wavelet transform (DWT), and the extracted features were selected by a minimum redundancy maximum relevance (MRMR) algorithm. Finally, 50 selected features were trained and tested by a multi-class Support Vector Machine for the task of speech modality classification. The average classification accuracy across participants for distinguishing three speech modalities was 66.0%, with pairwise classification accuracies ranging from 71.5% to 85.7%. The results of this work demonstrated the feasibility of identifying different speech modalities in speech-based BCIs by employing EEG signals.
State of the Art in Vision-Based Localization Techniques for Autonomous Navigation Systems

Vision-based localization systems, namely visual odometry (VO) and visual inertial odometry (VIO), have attracted great attention recently. They are regarded as critical modules for building fully autonomous systems. The simplicity of visual and inertial state estimators, along with their applicability in resource-constrained platforms motivated robotic community to research and develop novel approaches that maximize their robustness and reliability. In this paper, we surveyed state-of-the-art VO and VIO approaches. In addition, studies related to localization in visually degraded environments are also reviewed. The reviewed VO techniques and related studies have been analyzed in terms of key design aspects including appearance, feature, and learning based approaches. On the other hand, research studies related to VIO have been categorized based on the degree and type of fusion process into loosely-coupled, semi-tightly coupled, or tightly-coupled approaches and filtering or optimization-based paradigms. This paper provides an overview of the main components of visual localization, key design aspects highlighting the pros and cons of each approach, and compares the latest research works in this field. Finally, a detailed discussion of the challenges associated with the reviewed approaches and future research considerations are formulated.