Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk Analysis in Electronic Payments and Settlement System Using Dimensionality Reduction Techniques

Credit risk analysis forms an important criterion for the endurance of the banking sector. Understanding credit liability of customer enhances the security stability of the banker. This research is aimed at comparing the predictive accuracy of data mining techniques with feature selection and extraction techniques in the default of credit card clients and suggesting the most efficient technique for analyzing credit card default. Customer's default payments in Taiwan were used as the dataset. Machine Learning classification algorithms like K-nearest neighbor(KNN), Naive Bayesian(NB), Decision Tree(DT) and Support Vector Machines(SVM) were applied to the dataset. Feature filter methods like information gain (IG), gain ratio (GR) and chi-square (CS) and Wrapper based methods like Best First search(BFS) and Hill Climbing search (HCS) were used to identify minimum features required. Feature extraction methods such as Principal Component Analysis(PCA) and Linear Discriminant Anal-ysis(LDA) were also applied. The minimum features of feature selection algorithms along with PCA and LDA were then applied to Base classifiers and their accuracies were tabulated and compared. SVM with feature selection and extraction techniques had the best predictive accuracy.
A Social Multi-agent Cooperation System based on Planning and Distributed Task Allocation: Real Case Study

Planning and distributed task allocation are considered challenging problems. To address them, autonomous agents called planning agents situated in a multi-agent system should cooperate to achieve planning and complete distributed tasks. We propose a solution for distributed task allocation where agents dynamically allocate the tasks while they are building the plans. We model and verify some properties using computation tree logic (CTL) with the model checker its-ctl. Lastly, simulations are performed to verify the effectiveness of our proposed solution. The result proves that it is very efficient as it requires little message exchange and computational time. A benchmark production system is used as a running example to explain our contribution.
SCANNET: A Software for Identifying Network Communities

The Network Similarity Analysis (NSA) method is a powerful tool for identifying communities in weighted networks whose links depend on the edge's weight. The method can be applied to different types of networks, with nodes and edges receiving different interpretations. For instance, the network can be constructed based on the similarity between proteins that play the same function in distinct species, or between DNA sequences that share the same evolutionary origins. The original implementation of the NSA method consisted of pipeline of algorithms implemented in different programming languages, making it difficult to use. SCANNET is a software developed to integrate all algorithms of the NSA method into a unique computational environment, to perform all the steps needed to identify communities in networks in a single workflow. As an example for this paper, we applied SCANNET to implement a single workflow that correctly identified the communities in protein networks containing different sequences of enzymes. SCANNET has correctly generated the protein networks, the dendrograms, and the color representation of the neighborhood matrix for each of the studied case. The experiments have shown that SCANNET is a simple, efficient, reliable, and intuitive implementation of the NSA method for identify communities in the case of protein networks. In addition, SCANNET generates charts with all the distances between pairs of neighboring networks, which facilitate the identification of critical networks for community identification. SCANNET also generates dendrograms, and color representations of neighborhood matrices that can be manipulated by different analytical software, such as Origin Lab® or Matlab®, offering a friendly interface for the users.
Activation of the integrated stress response in nociceptors drives methylglyoxal-induced pain

Abstract Methylglyoxal (MGO) is a reactive glycolytic metabolite associated with painful diabetic neuropathy at plasma concentrations between 500 nM and 5 &mgr;M. The mechanisms through which MGO causes neuropathic pain at these pathological concentrations are not known. Because MGO has been linked to diabetic neuropathic pain, which is prevalent and poorly treated, insight into this unsolved biomedical problem could lead to much needed therapeutics. Our experiments provide compelling evidence that ∼1-&mgr;M concentrations of MGO activate the integrated stress response (ISR) in IB4-positive nociceptors in the dorsal root ganglion (DRG) of mice in vivo and in vitro. Blocking the integrated stress response with a specific inhibitor (ISRIB) strongly attenuates and reverses MGO-evoked pain. Moreover, ISRIB reduces neuropathic pain induced by diabetes in both mice and rats. Our work elucidates the mechanism of action of MGO in the production of pain at pathophysiologically relevant concentrations and suggests a new pharmacological avenue for the treatment of diabetic and other types of MGO-driven neuropathic pain.
Robust Controller Design for a Tail-sitter UAV in Flight Mode Transitions

This paper proposes a nonlinear robust control design method for tail-sitters. The vehicle control system design is challenging due to the two different flight regime and the uncertainties and disturbances existed in the vehicle dynamics. Dynamic inversion technique is adopted to establish the tracking error system. A state feedback controller is designed to guarantee the stability of the designed control system. A robust compensator is synthesized based on the signal compensation theory to ensure the robustness properties of the tracking error system. Simulation results are presented to demonstrate the effectiveness of the proposed nonlinear controller.
Experimental Studies of DTC-SVM Control of Six-Phase Induction Motor with Application of the Super-Twisting Sliding Mode Controllers

In this paper the Direct Torque Control with Space Vector Modulation (DTC-SVM) of six-phase induction motor with application of the Super-Twisting Sliding Mode (STSM) controllers has been presented. The mathematical model of the six-phase squirrel-cage induction motor has been described. The descriptions of the Space Vector Modulation (SVM) for six-phase inverter has been presented. The DTC-SVM control method with application of the STSM controllers has been designed and explained. The experimental system has been developed and the experimental studies of the DTC-SVM control of six-phase induction motor with STSM controllers have been carried out. The obtained results of the experimental studies have been shown and discussed. The conducted experimental studies confirmed the good performance of the considered control system. The authors original contribution includes analysis and experimental studies of considered control method of six-phase induction motor with application of the STSM controllers.
Noise Variance Estimation in Digital Images using Finite Differences Filter

The paper presents a novel method for estimating the variance of noise in a digital image. This method is based on filtering corrupted image using finite differences filter. As a result, the original signal is removed while noise remains so its variance can be calculated. The proposed method was verified on test images corrupted by the additive Gaussian distributed noise. The method was compared with several previously published estimation methods.
H-infinity Control of an Electrohydraulic Power Steering System

The paper presents an optimal reference tracking algorithm for electrohydraulic steering systems which is based on multivariable system identification and H-infinity controller. A laboratory test-bench composed of electrohydraulic control module, hydraulic steering unit (HSU with steering wheel), steering servo-cylinder, 32-bit microcontroller and joystick supports experimental work. Conventional approach for reference tracking in steering is usually based on classical control algorithms. In contrast, in this paper reference tracking is performed by using advanced control techniques, which can take into account the multivariable nature of the process. In this way a higher closed-loop performance is achieved.
Superhighway: Bypass Data Sparsity in Cross-Domain CF

Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains. Many traditional methods focus on enriching compared neighborhood relations in CF directly to address the sparsity problem. In this paper, we propose superhighway construction, an alternative explicit relation-enrichment procedure, to improve recommendations by enhancing cross-domain connectivity. Specifically, assuming partially overlapped items (users), superhighway bypasses multi-hop inter-domain paths between cross-domain users (items, respectively) with direct paths to enrich the cross-domain connectivity. The experiments conducted on a real-world cross-region music dataset and a cross-platform movie dataset show that the proposed superhighway construction significantly improves recommendation performance in both target and source domains.
Gust Load Alleviation for a Regional Aircraft with Actuator Oscillatory Failure Case

This paper details the development and application of fault tolerant controller to minimize the gust loading and against actuator faults on a regional aircraft. The expected benefits of the controller mainly include mitigation of gust load responses even when the control surfaces are experiencing actuator faults, reduction in weight, and improvement in passenger comfort. Model reference adaptive control and minimum error excitation suboptimal control techniques are utilized to design the fault tolerant controller. The performance effectiveness of the designed controller is tested through studying the control of the flexible aircraft numerical model with the worst discrete gust form a gust family. The simulation results show that the designed controller can effectively alleviate the structural loads on the flexible wing and maintain rigid motions in the event of gust disturbances.
Design of Two LNA Topologies in 0.35 µm CMOS Technology for 2 GHz Frequency Range

This paper presents two LNA topologies for 2 GHz frequency range. The aim was to investigate the impact of inductors on the design flow of the amplifiers. The first topology contains two inductors and utilizes two feedback paths (positive and negative), while the other topology is inductorless and has two signal paths splitting up the input signal and adding them back together after amplification. The LNAs achieve gains of 15.8 dB and 14 dB respectively, and noise figure (NF) of 6.5 dB–7.2 dB. The IIP3 post-layout simulation results show −2.83 dBm and −1.82 dBm, the DC power consumption is 27.2 mW and 22.36 mW at 3.3 V supply voltage, respectively. Both topologies are stable over the frequency range of interest. The LNAs are designed with AMS 0.35-µm CMOS process. Designs occupy total area of 0.171 mm2 and 0.029 mm2 respectively.
Defense Against HTML5 XSS Attack Vectors: A Nested Context-Aware Sanitization Technique

The authors suggested an offline and online based model based on nested context aware sanitization method for detection and alleviation of malicious XSS attack vectors for OSN's. The offline mode extracts JS from webpage, calculates features and stores them in the depository for additional usage. The online approach embodies URI link extraction and feature estimation thus detecting anomaly on comparison with offline modes feature repository. The authors have developed their prototype in J avaScript and its infrastructure settings are implemented as an extension on infrastructure settings of browser. Our proposed design is implemented and tested on five OSN platforms vulnerable to XSS. The results estimated have the competency to identify the XSS worms with acceptable little false positives in comparison to recent state of art. The outcome of our design draws upon nested context of JS for efficacious sanitization.
A Survey of Round Trip Time Prediction Systems

The performance of many networked applications improves when Round Trip Time (RTT) is reduced. With lower RTTs, human-to-human interactions become more realistic, query-response activities (such as DNS lookups) are more responsive, and transport protocols (such as transmission control protocol) can better utilise a path’s available capacity for long-lived file transfers. Additionally, RTT prediction systems are beneficial in distributed networks in order to improve performance and resource allocation. RTT prediction systems predict RTT for all nodes in the network without additional exchange of packets. We discuss 13 RTT prediction systems under three classes: 1) localisation measurement systems; 2) network coordinate systems; and 3) matrix factorisation systems. RTT prediction systems have been applied with positive results to various applications and services, such as peer-to-peer networks (e.g., BitTorrent clients, content distribution networks, and Internet of Things) or client-server networks (e.g., game server discovery).
Soldering problems of large size SMD PET capacitors during vapour phase soldering process

The reflow soldering process of large size SMD components - having a dimension over 5 mm - were always challenging for the microelectronics industry because of the higher risk of component displacement type soldering failures like tombstone formation or skewing. In this paper, the possibility of such soldering failures was investigated during vapour phase soldering by numerical simulations on large size (4036) SMD capacitors positioned on FR4 printed circuit board substrates. A 3D computational fluid dynamic model was used for this purpose. The aim was to calculate the onset differences in the melting of the solder alloy at the opposite leads of the component. Then from the time difference the possibility of the component displacement during the reflow soldering could be predicted. It was found that considerable time differences can form in the melting of the solder paste which can cause the component displacement. The time difference is defined by the position of the component on the substrate. Not recommended zones on substrate were defined to avoid the component displacement failure during the vapour phase soldering process.
Fault Detection and Location of Photovoltaic Based DC Microgrid Using Differential Protection Strategy

A new differential current-based fast fault detection and location scheme for multiple Photovoltaic-based dc microgrid is proposed in this paper. A multiterminal dc (MTDC) distribution network is an effective solution for present grid scenario, where local distribution is incorporated primarily by power electronics based dc loads. PV systems with auxiliary power sources and local loads are used for MTDC connection, especially when ac utility grid is integrated with it by voltage source converters. Pole to pole and pole to ground faults are basically considered as dc distribution network hazards. As PV is connected through dc cable, high resistive dc arc fault is also studied in present literature. The proposed PV system is considered with arc-fault circuit interrupters as backup protection and is used to detect arcing series fault. Fast acting dc switching is considered for proposed differential current-based unit protection. A discrete frame differential current solution is considered to classify the fault type by modified cumulative sum average approach. By calculating unknown dc cable resistance accurately by non-iterative Moore-Penrose pseudo inverse technique, the fault distance is calculated. TMS320C6713 DSP based test-bench is used for verification of the scheme.
Scaling of sensorimotor delays in terrestrial mammals

Whether an animal is trying to escape from a predator, avoid a fall or perform a more mundane task, the effectiveness of its sensory feedback is constrained by sensorimotor delays. Here, we combine electrophysiological experiments, systematic reviews of the literature and biophysical models to determine how delays associated with the fastest locomotor reflex scale with size in terrestrial mammals. Nerve conduction delay is one contributor, and increases strongly with animal size. Sensing, synaptic and neuromuscular junction delays also contribute, and we approximate each as a constant value independent of animal size. Muscle's electromechanical and force generation delays increase more moderately with animal size than nerve conduction delay, but their total contribution exceeds that of the four neural delays. The sum of these six component delays, termed total delay, increases with animal size in proportion to M0.21—large mammals experience total delays 17 times longer than small mammals. The slower movement times of large animals mostly offset their long delays resulting in a more modest, but perhaps still significant, doubling of their total delay relative to movement duration when compared with their smaller counterparts. Irrespective of size, sensorimotor delay is likely a challenge for all mammals, particularly during fast running.
The Other Side of the Coin: Unsupervised Disambiguation of Potentially Idiomatic Expressions by Contrasting Senses

Disambiguation of potentially idiomatic expressions involves determining the sense of a potentially idiomatic expression in a given context, e.g. determining that make hay in ‘Investment banks made hay while takeovers shone. ’ is used in a figurative sense. This enables automatic interpretation of idiomatic expressions, which is important for applications like machine translation and sentiment analysis. In this work, we present an unsupervised approach for English that makes use of literalisations of idiom senses to improve disambiguation, which is based on the lexical cohesion graph-based method by Sporleder and Li (2009). Experimental results show that, while literalisation carries novel information, its performance falls short of that of state-of-the-art unsupervised methods.
Robotic Arm Control with Hand Movement Gestures

This paper presents an application on how to control a robotic arm with the hand movement of the operator. Hand movement controlled robotic arms can move more naturally by following the movement of the operator's hand. The implementation is done in two versions. The first one uses the accelerometer from a smart watch and based on the operator's hand movements the robotic arm is controlled. The second one uses the accelerometer from a smart phone and moves the robotic arm accordingly. With the smart phone the robotic arm can be controlled in two ways by finger gestures on the touch screen or by moving the phone in the air. The smart phone connects to a computer which controls the robotic arm by sending commands on the serial interface. The smart phone can connect to the computer via Wi-Fi or Bluetooth. The smart watch connects to the computer via a special wireless protocol using a USB wireless stick, shipped with the smart watch.
Speed Estimation for Control of an Unmanned Ground Vehicle using Extremely Low Resolution Sensors

In mobile robotics, the low-level control is a key component that translates the desires of the high-level system into actual voltages and currents to drive the motors. PID controllers have been extensively used for speed control, but their performance depend heavily on the quality of the process variable (PV) estimation. In fact, noise and outliers –if not properly filtered– might lead to system instability. In this work, we present a speed estimation strategy that enables us to develop an inexpensive, accurate and easy-to-install speed control solution. The proposed system relies on a Hall effect sensor and a Single-Dimensional Kalman Filter and its suitability is demonstrated through a number of real experiments controlling the speed of an Unmanned Ground Vehicle. We detail the design, implementation and validation processes and provide a GitHub repository with the developed software and CAD designs.
Approximate Exploration through State Abstraction

Although exploration in reinforcement learning is well understood from a theoretical point of view, provably correct methods remain impractical. In this paper we study the interplay between exploration and approximation, what we call approximate exploration. Our main goal is to further our theoretical understanding of pseudo-count based exploration bonuses (Bellemare et al., 2016), a practical exploration scheme based on density modelling. As a warm-up, we quantify the performance of an exploration algorithm, MBIE-EB (Strehl and Littman, 2008), when explicitly combined with state aggregation. This allows us to confirm that, as might be expected, approximation allows the agent to trade off between learning speed and quality of the learned policy. Next, we show how a given density model can be related to an abstraction and that the corresponding pseudo-count bonus can act as a substitute in MBIE-EB combined with this abstraction, but may lead to either under- or over-exploration. Then, we show that a given density model also defines an implicit abstraction, and find a surprising mismatch between pseudo-counts derived either implicitly or explicitly. Finally we derive a new pseudo-count bonus alleviating this issue.
Suboptimal nonlinear feedback control laws for collective dynamics

We present feedback control methodologies for the stabilization of nonlinear collective dynamics. The proposed controllers circumvent the curse of dimensionality associated to dynamic programming of large-scale nonlinear dynamics by means of different structural assumptions on the nonlinearity and the associated Hamilton-Jacobi-Bellman equation. We explore a power series expansion method, and a state-dependent Riccati equation approach. The proposed designs are studied for a relaxed optimal power flow model, and for binary interactions arising in opinion and consensus dynamics.
Nonlinear Adaptive Lift Control Of A Variable-Pitch Quadrotor With Optimum Power Consumption*

The problem of lift control law design for an electric-driven variable-pitch quadrotor enabling optimization of power consumption is addressed. The mathematical model of the electric variable-pitch propeller is first presented and the optimal blade pitch and rotational speed curve is investigated. Since the armature voltage is used to adjust the rotational speed and in turn generates the lift, the rotor speed control problem is then dealt with by means of nonlinear adaptive methods. The implementation is also considered. Based on the nonlinear controller, the overall adaptive lift control scheme is developed. The feasibility of the proposed controller is comparatively demonstrated by the test results.
Control of Grid Tied Smart PV-DSTATCOM System Using an Adaptive Technique

This paper presents a control of smart photovoltaic (PV)-distribution static compensator (DSTATCOM) grid tied system using an adaptive reweighted zero attracting control algorithm with perturb and observe maximum power point tracking technique for a three phase system to improve power quality and support the three phase ac grid by supplying power both to the grid and the connected loads. The proposed PV grid tied system is capable of working round the clock. During sunshine conditions, the proposed system performs dual functions of improving power quality by working as DSTATCOM and also transfers power to the load and the grid obtained from PV array. However, during night or cloudy conditions, the proposed system works as DSTATCOM improving power quality and the power is transferred from the grid to the load. The system is termed as a smart as it is able to perform both modes automatically sensing the PV power and is capable of multidirectional power flow. The experimental validation is carried out on a developed prototype in the laboratory under varying modes.