Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-Performance-Counters-based anomaly detection in massively deployed smart industrial devices

Energy providers are massively deploying devices to manage distributed resources or equipment. These devices are used for example to manage the energy of smart factories efficiently or to monitor the infrastructure of smart-grids. By design, they typically exhibit homogeneous behavior, with similar software and hardware architecture. Unfortunately, these devices are also of interest to attackers aiming to develop botnets or compromise companies' security. This paper presents a new protection approach based on Hardware Performance Counters (HPC) to detect anomalies in massively deployed devices. These HPC are processed using outlier detection algorithms. Compared to existing solutions, we propose a lightweight approach based on a comparative analysis of devices' HPC without relying on the modeling of the software applications running on the devices. To assess the relevance and the effectiveness of the approach, a thorough experimental analysis is carried out in a representative industrial-type environment, sampling the data from 100 Raspberry Pi to simulate about 10,000 devices deployed simultaneously. The results show high detection and performance efficiency under different software profiles and attack payloads. Moreover, the calibration of the approach depends primarily on the hardware rather than the application software running on the devices. It should ease its deployment in an operational environment.
The Relationship between Contact Resistance and Roughness (Sq) of a Bi-layered Surface using a Finite Element Model

The application of a structured and rough surface, and the effect the roughness has on contact resistance is considered in an electrical contact application. The surfaces are designed to enhance roughness, with an amplitude parameter (Sq, RMS) of 0.1-1 µm. An established finite element model of a bi-layered rough contact surface is extended to include the pre-processing and analysis of a model rough surface. The application is a gold-coated multi-walled carbon nanotube composite (Au/CNT), designed as a solution for electrical contacts in low current switching applications. To determine the contact resistance for these surfaces, a preliminary step is required to determine the effective resistivity.Model surfaces are compared to a measured rough surface, where the sample length of the surface and the sensor interaction with the measured surface are identified as key parameters. The finite element model provides an output of the interactions between a hemi-spherical ball and the rough Au/CNT surface, with the results shown as a map of the points of contact as a function of the applied force (mN). An automated method of post processing the image data is used to determine the number of contact points and the best fitting radius of the contact area. It is shown that the contact resistance increases with surface roughness (Sq).
Android Ransomware Detection using Machine Learning Techniques: A Comparative Analysis on GPU and CPU

Cyber-criminals perform ransomware attacks to make money from victims by harming their devices. The attacks are rapidly increasing on Android-based smartphones due to its vast usage world-wide. In this paper, a framework has been proposed in which we (1) utilize novel features of Android ransomware, (2) employ machine learning models to classify ransomware and benign apps, and (3) perform a comparative analysis to calculate the computational time required by machine learning models to detect Android ransomware. Our proposed framework can efficiently detect both locker and crypto ransomware. The experimental results show that the proposed framework detects Android ransomware by achieving an accuracy of 99.59% with Logistic Regression in 177 milliseconds and 235 milliseconds on the Graphics Processing Unit (GPU) and Central Processing Unit (CPU) respectively.
Investigating Degradation in Perovskite and Perovskite/Silicon Tandem Solar Cells Using Spatially and Spectrally-Resolved Absorptivity

Instability is the one of the main challenges preventing the commercialization of perovskite solar cells. We briefly summarize our recent development of a contactless, nondestructive approach to study degradation across perovskite and perovskite/silicon tandem solar cells. We employ spectrally and spatially-resolved photoluminescence maps of perovskite solar cells to extract their corresponding absorptivity maps at sub-bandgap wavelengths. The sub-bandgap absorptivity reflects real degradation in the perovskite film and is much more robust and sensitive than its optical bandgap and luminescence intensity. The absorptivity maps allow us to study various common factors which induce and accelerate degradation in perovskite solar cells including air and heat exposures and light soaking. Also, we employ the technique to extract the individual absorptivity component from the perovskite layer in a monolithic perovskite/silicon tandem structure.
Revisiting Determinants of Investor Sentiment in the FX Option Market by Machine Learning Approaches

While FX option transactions are known to reflect risk perceptions of market participants, the over-the-counter nature of the FX option market has limited data availability on counterparty information, posing challenges to analyzing what factors could affect investor sentiment and how they may differ across investor types. With novel, granular FX option data based on the reporting from trade repositories and financial institutions in Japan, this paper employs machine learning approaches (Random Forest and XGBoost) to exploring the determinants of investor sentiment for USD/JPY by a different investor category, measured by the positioning of “long call and short put (bullish for USD/JPY)” minus “long put and short call (bearish for USD/JPY)”. The analysis shows that the uncertainty over the US trade policy is one of the most important variables for the sentiment of non-financial corporates, while the US yield curve appears to affect the sentiment of both non-financial corporates and institutional investors. The results imply that the recent increase in hedging behavior for a sharp fall of the dollar against the yen could be attributable to the US-China trade tensions and an inversion of the US yield curve which suggests a slowdown of global economy.
Growing MIDI Music Files Using Convolutional Cellular Automata

This paper describes a novel generative system based on cellular automata (CA). The main contribution of this work is CARLA, a Cellular Automata Rule Learning Algorithm that learns CA update rules with lower computational overhead (i.e. a smaller neural architecture) than other current approaches. A formal model is developed, situating the algorithm in terms of machine learning. The algorithm is then validated on the 256 possible elementary cellular automata rules. Next, the CARLA algorithm is incorporated into a larger generative system that learns musical patterns by transforming MIDI-encoded music into cellular automata. A novel postprocessing algorithm called Tendril is then developed and integrated with CARLA. This system is then used to generate a novel MIDI composition based partially on update rules learned from Chopin’s Waterfall Étude, demonstrating the utility of this new algorithm for creative applications.
The Making of Indicator of Compromise using Malware Reverse Engineering Techniques

Malware threats often go undetected immediately, because attackers can camouflage well within the system. The users realize this after the devices stop working and cause harm for them. One way to deceive malicious content detection, malware authors use packers. Malware analysis is an activity to gain knowledge about malware. Reverse engineering is a technique used to identify and deal with new viruses or to understand malware behavior. Therefore, this technique can be the right choice for conducting malware analysis, especially for malware with packers. The results of the analysis are used as a source for making creating indicator of compromise in the YARA rule format. YARA rule is used as a component for detecting malware using the indicators obtained in the analysis process.
Multi-user MIMO with Jamming Suppression for Spectrum-Efficient Tactical Communications

Being spectrum-efficient and robust to adversarial interference caused by jammers are critical to tactical wireless systems. Leveraging multiple-input multiple-output (MIMO) techniques, this paper investigates the realization of spectrum-efficient multi-user MIMO communications in the presence of high-power jammers. Unlike most existing work that only exploits the MIMO degree of freedom to nullify the jamming signal, we also aim to improve the spectral efficiency of the system with the MIMO spatial multiplexing capability. To that end, we first design a combiner at the receiver spanning the null space of the jamming channels, which can completely remove the jamming signals and optimize the communication reception. We further propose two methods for the design of precoders at the transmitter to mitigate multi-user interference. Simulation results are presented to verify the effectiveness of the proposed schemes in radio-frequency contested environments.
Performance of Low-Rate NOMA-based Uplink Underlay Networks

We analyze the performance of underlay networks in which non-orthogonal multiple access (NOMA) signalling is used for uplink signalling by low-rate users. The primary network imposes an interference temperature limit (ITL). Peak interference constraints are imposed on the secondary nodes to ensure compliance to the ITL. We show how opportunistic user selection can be performed for NOMA signalling, and demonstrate how the ITL can be apportioned between the users transmitting concurrently. The suggested scheme along with the evolved power control mechanism can double the spectrum utilization efficiency in many scenarios. Expressions are derived for outage probability and throughput. Computer simulations demonstrate accuracy of the derived expressions.
Investigating and Understanding S-shape Curves in PEDOT:PSS Based Silicon Heterojunction Solar Cells

Heterojunction silicon solar cells with poly(3,4-ethylenedioxythiophene): polystyrene sulfonate (PEDOT:PSS) were fabricated and the formation of S-shape curves was investigated. The S-shape curves are synonymous to degradation observed in these cells. The cells degrade over the period of several weeks, and through a series of electrical and spectroscopic measurements, the source of the S-shape curves and degradation was narrowed to two possible sources: 1. ) the junction between the PEDOT:PSS and the Si substrate, or 2. ) the electrical conductivity of the PEDOT:PSS. The composition, defects of the SiO2 and any electrical present at the interface between the crystalline silicon substrate and the PEDOT:PSS was studied using laboratory Xray photoelectron spectroscopy (XPS), destructively, and synchrotron-based hard X-ray photoelectron spectroscopy (HAXPES), non-destructively.
Modeling the Mechanisms of Fracture Formation in Nanomodified Polymers

This work is devoted to the issues of numerical modeling of evolutionary processes occurring in nanomodified polymers during their deformation under static loading. The finite element method was used. A special eight-node finite element has been developed and numerically implemented. Solving the problems of nonlinear deformation of nanomodified polymer compositions is carried out on the basis of the Newton-Kantorovich algorithm, supplemented by a block that implements the method of variable stiffness, taking into account the modified Morse potential. Comparison of the results of numerical solutions of control problems with other numerical solutions known from the literature indicates their good agreement. As an example, the results of modeling the evolution of progressive fracture when pulling out a nanotube from a reactoplastic polymer matrix are presented. The developed methods make it possible to reliably determine the physical and mechanical characteristics of nanomodified polymers and the strength of structures based on them.
TRA: Effective Authentication Mechanism for Swarms Of Unmanned Aerial Vehicles

The swarm of the unmanned aerial vehicles is researched to meet the special mission requirements given both in the military and civilian sectors such as fire detection, sky reconnaissance, and other areas of human activities. These swarms can directly contact via ground control station or direct communication. However, when communicating remotely via wireless and fine maneuverability of the unmanned aerial vehicle, many mechanisms face several security gaps, such as the authentication mechanism in enemy attacks. In this study, we propose safe and effective authentication mechanism appropriate for the dynamic environment of the unmanned aerial vehicle. In the frames of this approach, a new operating mechanism in the proposed protocol is described. The protocol provides authentication security for swarms of the unmanned aerial vehicle. Our scheme allows good communication and computing performance as well as storage cost. Finally, simulation and results in the communication environment between drones are presented.
E-DBSCAN: An evidential version of the DBSCAN method

In later years, data have grown enormously and dealing with them to extract information has become a necessity. Data mining is a subfield of both computer science and statistics that aim to extract useful information in a comprehensive structure. The importance of clustering techniques in data mining has lead to the development of many methods in order to deal with data. Among these methods, we name density-based techniques, such as DBSCAN, that partitions data into heterogeneous shapes according to their local densities. DBSCAN can be suitable when handling big data that have noises and outliers. However, the classic DBSCAN method fails in identifying clusters with a variable density distribution and overlapping borders which is accurate in real-world data. In this paper, we propose an unsupervised learning technique in an uncertain context, that combines the DBSCAN method and the framework of the belief function theory, in order to generate clusters having overlapping borders. The proposed evidential clustering method, that we called E-DBSCAN, has the ability to handle cluster membership degree uncertainty of objects by using the belief function theory.
Sensitivity of different optimization solvers in LSTM algorithm for temperature forecast over Mars at Jezero Crater landing site

Exact forecast of surface temperature over MARS is important and critical. Surface temperature is fundamental to the environmental parameter that has a direct impact on designing and operating the land rovers that explore the MARS planet. In this paper, We used well known long Short-Term Memory (LSTM) algorithm to build a data-driven model to predict the surface temperature over the planned landing site Jezero Crater for Mars 2020 Rover. The data-driven model is built using a dataset based on the Mars Climate Database (MCD) which derived from the Global Climate Model (GCM) simulations for MARS. The temporal availability of this data from martian year 24 to 33. we evaluated the effect of the three different optimization solvers on surface temperature prediction over the landing site Jezero Crater for two different numbers of epochs. The solver that provides the lowest RMSE is used to predict the surface temperature over the landing site from martian year 34 to martian year 36.
Proxy Signcryption Scheme for Vehicle Infrastructure Immune to Randomness Leakage and Setup Attacks

We propose a proxy signcryption scheme for a multi-party setting, resistant to randomness leakage and setup attacks. Our scheme is an alternative to typical constructions, based on a double Schnorr signature approach, where the linear combination of long term secrets and ephemeral random values occurs both at the initiator and proxy nodes. Our scheme is provably secure in a new stronger model, where the adversary can control the randomness of both parties. Moreover, our proposition is well suited for networks of many independent and moving nodes; especially modern railway infrastructure and vehicle-to-vehicle/infrastructure (V2X) environments, where a broad range of devices with potentially weak computational power and inadequate randomness, is used. Early benchmarks and performance analysis from our proof of concept implementation, suggest that nodes, which use regular Schnorr based schemes, could be successfully upgraded to our more secure alternative construction. Collected timings are still at the acceptable level, proving the applicability of our scheme in modern railway and V2X environments.
Optimizing the Energy Consumption of Neural Networks

Embedded systems only have a limited amount of energy and in consequence embedded product design requires choosing low cost and low spec processors. However, such systems require fast response of software applications that implement algorithms of considerable complexity. Deep Learning models have a high energy consumption especially when performing complex calculations such as real time object recognition in images. Inference time together with energy consumption and accuracy are opposing optimization criteria and constitute a multi-objective optimization problem. We propose to use a methodology that can deal with the multiple objective optimization of Convolutional Neural Networks in regard to those aspects. The method uses the NSGA-III algorithm with customized operators to find an enhanced network architecture. Proof of concept is given by using the GTSRB dataset as benchmark. Results are promising and show that a practically relevant trade-off between accuracy and computing effort can be determined with the evolutionary approach presented here.
A ROS Based Automatic Control Implementation for Precision Landing on Slow Moving Platforms Using a Cooperative Fleet of Rotary-Wing UAVs

In this paper we present an industrial implementation of an efficient method to solve the problem of the automatic precision landing for rotary-wing UAVs, ready to be used inside a cooperative fleet of drones. The realized software module and tests are part of a large industrial R&D Vitrociset project called SWARM: an AI-Enabled Command and Control (C&C) system, able to execute and review ISR missions for mini/micro cooperative fleets of heterogeneous UAVs. Preparatory to the presented results, it was the identification of a non-linear mathematical model as well as the realization of a robust PID-based control system capable of controlling a single drone of the fleet. A discrete-time Kalman filter was integrated and tested to estimate the possible displacement of the landing points, in order to improve the control law through predictive connotations in case of slow moving tags. The presented approach is featured by the balance between computational efficiency and versatility, in particular in the discovering stage of multiple and different AprilTag during the landing phase. The still under test software module uses the Open Source Robotic Operating System (ROS) libraries for both the acquisition of the data necessary to the control laws, and for the execution of the computer vision algorithms implemented for the precision landing. After analyses and simulations campaigns in a synthetic environment and multiple hardware in the loop (HIL) stress tests, the final prototype algorithm was deployed on a commercial-off-the-shelf mini-class UAV, demonstrating landing capacity on a fixed target with an error of less than ten centimeters; moreover, with slow-moving tags, appreciable tracking abilities emerged on sufficiently smooth trajectories. A special interface with the HIL flight controller was then integrated, with the capability of using its telemetry data for distributing them to all the members of the cooperative fleet, making it possible to access the real-time estimate of the states of each single drone, and making each one of them aware of the selected landing areas of the others, by navigation sensors data fusion with a five meters GPS precision.
A Multi-Armed Bandit Strategy for Countermeasure Selection

Swarms of unmanned aerial systems may pose a threat to soldiers on the battlefield in the future. Land vehicles may be armed with countermeasures to counter these attacks, but in the presence of a large number of threats it will be difficult for human operators to select the countermeasures best suited for maximising the survivability of the vehicle. This paper presents a novel algorithm using multi-armed bandit strategies to recommend countermeasures to human operators. The performance of this algorithm is evaluated in simulation across randomly generated scenarios. The results are compared to the performance of a baseline greedy algorithm that selects the countermeasure with the highest probability of hitting a threat. Even with limited prior knowledge of the probabilities of hits, the multi-armed bandit approach performs competitively against the greedy algorithm. With access to equivalent information, the multi armed bandit approach outperforms the greedy method in over 65% of tests.
Heuristic Resource Allocation Algorithm for Controller Placement in Multi-Control 5G Based on SDN/NFV Architecture

The integration of Software Defined Networking (SDN) and Network Function Virtualization (NFV) is considered to be an efficient solution that enables the forecasting of highly scalable, optimal performance of 5G networks by providing an effective means of network functionality. The distributed multi-controller architecture approach is an emerging strategy that primarily aims to support network functions performed through the application of a control plane, to provide versatile network traffic management. However, the management of resource allocations across multiple data centers is an important issue that still affects 5G core networks. Using such a strategy in 5G core networks requires the controllers to be correctly located, in order to improve network reliability and cost-effectiveness. Thus, to address the controller placement problem (CPP) in a distributed 5G network, we proposed an efficient, heuristic multi-objective optimization approach, using dynamic capacitated controller placement problem (DCCPP). It is based on the K-center problem, to solve the capacitated controller placement problem (CCPP), which acts as a resource location problem, in which the location and number of controllers can be allocated to maximize resources. A Greedy Randomized Search (GRS) algorithm was used to solve the dynamic assignment of nodes to controllers to achieve load balancing. The design of the heuristic method provides proper load balancing, efficient cost management, and network resource management, as compared to the basic CCPP model. The results indicate that the allocation and the optimum number of controllers under an effective decentralized policy could achieve a higher degree of efficiency through resource assignment in such a densified network.
A Comprehensive Smart IoT Tracker for the Children, Elder, and Luggage With the Assistance of Mobile App

This paper represents an IoT based tracking system, through which it is very easy to track the child, elder, or any type of luggage. This system has separate features for tracking each of these things, which have been completed by the IoT device and the Android app. It is possible to track anything manually or automatically with this device. NodeMCU, GPS, and GSM have been used as hardware to build the system, and Firebase Server and Google API services have been used for the Android app. Parcel exchange has become very common nowadays. With this system, in addition, the location of the parcel can be easily tracked, and administrative help can be taken for any unforeseen situation.