Miracle-dz/UniFAR-FTUs huggingface dataset:
An Investigative Review of Human Authentication Based on Fingerprint

Owing to the improvements in pattern recognition systems biometrics are important. There are many prominent Biometrics applications like recognizing face and voice, retinal, and fingerprint. Currently, the most common use of biometrics and the most low-cost of them is fingerprint. The fingerprint is a long-lasting characteristic which does not alter over time. A vast range of usages benefit from fingerprint recognition, including: security, cyber-crime and boundary control prevention, social enterprise, public aid, and immigration systems, verifying identity for passport and healthcare services and also commercial corporations. The central aim of this research is reviewing various fingerprint recognition techniques implemented in the field of biometrics. In this paper, authors thoroughly discuss the advantages and disadvantages of diverse approaches used in recognizing fingerprints that can make new perspectives for future researchers of this field. The current paper would be a handy beginning point for conducting new research in biometrics in the future.
Network Traffic Prediction Using Recurrent Neural Networks

The network traffic prediction problem involves predicting characteristics of future network traffic from observations of past traffic. Network traffic prediction has a variety of applications including network monitoring, resource management, and threat detection. In this paper, we propose several Recurrent Neural Network (RNN) architectures (the standard RNN, Long Short Term Memory (LSTM) networks, and Gated Recurrent Units (GRU)) to solve the network traffic prediction problem. We analyze the performance of these models on three important problems in network traffic prediction: volume prediction, packet protocol prediction, and packet distribution prediction. We achieve state of the art results on the volume prediction problem on public datasets such as the GEANT and Abilene networks. We also believe this is the first work in the domain of protocol prediction and packet distribution prediction using RNN architectures. In this paper, we show that RNN architectures demonstrate promising results in all three of these domains in network traffic prediction, outperforming standard statistical forecasting models significantly.
Multi-domain Software Defined Network Provisioning

Multi-domain provisioning can be a time consuming and complex process, especially when the provisioning occurs between different administrative domains. The Software Defined Networking (SDN) paradigm provides a flexible programmatic solution that can be used to improve network management and control. SDN can be used to implement automated flexible provisioning in multi-domain networks, and provides an opportunity to implement improved processes for automated provisioning between different administrative domains. In this paper, a SDN-based framework is proposed for multi-domain provisioning. The BGP UPDATE message is used to pass provisioning related information between the SDN domains. An application prototype was developed to test the framework with a Bandwidth on Demand use case. The evaluation shows that the framework behaved as expected and the application prototype successfully carried out multi-domain provisioning between different administrative domains.
Adversarial Context Aggregation Network for Low-Light Image Enhancement

Image captured in the low-light environments usually suffers from the low dynamic ranges and noise which degrade the quality of the image. Recently, convolutional neural network (CNN) has been employed for low-light image enhancement to simultaneously perform the brightness enhancement and noise removal. Although conventional CNN based techniques exhibit superior performance compared to traditional non-CNN based methods, they often produce the image with visual artifacts due to the small receptive field in their network. In order to cope with this problem, we propose an adversarial context aggregation network (ACA-net) for low-light image enhancement, which effectively aggregates the global context via full-resolution intermediate layers. In the proposed method, we first increase the brightness of a low-light image using the two different gamma correction functions and then feed the brightened images to CNN to obtain the enhanced image. To this end, we train ACA network using L1 pixel-wise reconstruction loss and adversarial loss which encourages the network to generate a natural image. Experimental results show that the proposed method achieves state-of-the-art results in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). 1
Interconnection and Damping Assignment Passivity-Based Impedance Control of a Compliant Assistive Robot for Physical Human–Robot Interactions

Interest in series elastic actuators (SEAs) for assistive robots has recently increased due to the unique properties of SEAs compared to those of rigid actuators, such as high force control accuracy, low output impedance, and tolerance to shocks. In this letter, an SEA with a clutch for an assistive robot is designed for accurate torque delivery to provide effective assistance. The mechanical clutch controls the engagement and disengagement of the power transmission during the gait cycle to ensure transparency and user safety. Precise torque control is the main challenge in the control of SEAs. The addition of a clutch in the SEA will result in a new force control challenge with the frequent release and engagement of the clutch aggravating the mechanical vibration and bringing the risk of unsafety to the assistive robot wearer. A new torque control method based on interconnection and damping assignment passivity-based control (IDA-PBC) for SEAs is adopted for the physical human–robot interaction. Experiments are conducted to verify that the IDA-PBC-based SEA torque controller can effectively control actuator timing and allow precise assistive torque control. This letter serves as a stepping stone toward utilizing and designing assistive robots to assist with walking.
Analysis of injection molding parameters for graphene/polypropylene composite material with thermal conductivity as quality objective

Polypropylene (PP) is a common thermoplastic polymer material with high impact resistance and strong mechanical properties, and it is thus widely used in the industry. With high thermal conductivity and low resistivity, graphene is expected to be used to develop a new generation of electrical components. In this study, we mix blended graphene and PP into a composite material for injection molding specimens. Experiments were designed using an orthogonal array in the Taguchi method to obtain the optimal parameter combination for thermal conductivity, the process parameters including A. injection temperature, B. holding time, C. injection pressure, and D. the graphene ratio. The study results indicate that the optimal parameter combination in the Taguchi analysis was A3BlC2D3, the resulting thermal conductivity coefficient of which was 0.102 (W/m2x2122; K). The optimal parameter combination of the original design in the L9 orthogonal array was A2BlC2D3, the thermal conductivity coefficient of which was 0.081 (W/m2x2122; K). Thus, the improvement rate 25.9%.
Novel Dual Power-Mode CMOS Differential Power Amplifier Design Using Single Supply Voltage and a Compact Reconfigurable Output Combining Network

This paper presents a novel design of dual power-mode CMOS differential power amplifier (PA). Based on a single supply voltage and a newly developed reconfigurable output combining network, PA efficiencies are maximized in both high-power and low-power operation. For demonstration, the proposed PA with center frequency of 2 GHz is implemented using $\pmb{0.35}\ \pmb{\mu}\mathbf{m}$ CMOS process. Simulation results indicate that, a power-added efficiency (PAE) of 47.7% at 27.7 dBm (high power-mode), and 38.0% at 22.3 dBm (low power-mode) can be obtained.
Towards Real-Time-Aware Intrusion Tolerance

Technologies such as Industry 4.0 or assisted/autonomous driving are relying on highly customized cyber-physical realtime systems. Those systems are designed to match functional safety regulations and requirements such as EN ISO 13849, EN IEC 62061 or ISO 26262. However, as systems – especially vehicles – are becoming more connected and autonomous, they become more likely to suffer from new attack vectors. New features may meet the corresponding safety requirements but they do not consider adversaries intruding through security holes with the purpose of bringing vehicles into unsafe states. As research goal, we want to bridge the gap between security and safety in cyber-physical real-time systems by investigating real-time-aware intrusion-tolerant architectures for automotive use-cases.
On the Feasibility of Inter-Domain Routing via a Small Broker Set

The Internet is a gigantic distributed system where the end-to-end (E2E) quality-of-service (QoS) plays an important role. Yet the current inter-domain routing protocol, namely, the Border Gateway Protocol (BGP), cannot provide E2E QoS guarantees. The main reason is that an autonomous system (AS) can only receive guarantees from its first-hop ASes via service level agreements (SLAs). But beyond the first-hop, QoS along the path from a source AS to a destination AS is not within the source AS's control regime. This makes it difficult to provide high quality-of-experience services to many Internet users even when many content providers are willing to pay for such high quality E2E guarantees. In this paper, we investigate the feasibility of providing high QoS-guaranteed E2E transit services by utilizing a (small) set of ASes/IXPs to serve as “<italic>brokers</italic>” to provide supervision, control and resource negotiation. Finding an optimal set of ASes as brokers can be formulated as a Maximum Coverage with <inline-formula><tex-math notation="LaTeX">$B-$</tex-math><alternatives><inline-graphic xlink:href="lui-ieq1-2865572.gif"/></alternatives></inline-formula>dominating path Guarantee (MCBG) problem, and we show that it is in fact NP-hard. To address this problem, we design a <inline-formula><tex-math notation="LaTeX">$(\frac{1{-}e^{-\! 1}}{2}){-}$</tex-math><alternatives><inline-graphic xlink:href="lui-ieq2-2865572.gif"/></alternatives></inline-formula>approximation algorithm and also an efficient heuristic algorithm when additional constraints (e.g., the path length) are considered. We further analyze the APX-hardness of the MCBG problem to reveal the existence of the best approximation ratio. Based on the current Internet topology, we demonstrate that it is indeed feasible to provide high QoS guarantees for most E2E connections with only a small broker set: with only 0.19, 1.9 or 6.8 percent ASes/IXPs serving as brokers, 53.13, 85.41 or 99.29 percent of all global E2E connections can receive high QoS-guaranteed services. Finally, we provide an economic model to study the behaviours of ASes when cooperating our brokerage scheme with the BGP protocol, and show that there are incentives to form and maintain such a brokerage coalition.
A Cloud Computing Platform for the Holistic Approach to the Needs of Children and Their Families in Natural Disasters Incidents

The main objective of our proposed system is to employ cloud computing technology in order to develop a platform for comprehensive management of natural disasters. More specifically, the platform will pertain to the natural disasters of flood, extreme storms and earthquake, which occur most commonly in Europe. The platform will promote a holistic management approach, in the way of addressing issues prior to, during and right after a natural disaster. Specific consideration for the children's education was taken into account as being the most vulnerable part of the population. Moreover, the timely provision of the appropriate instructions and guidelines is incorporated, in the form of text and voice messages. An essential issue after a natural disaster concerns the response time to the urgent needs of families and especially children. Thus, the platform will notify the emergency services about the incidents needing immediate attention, inform about their location and it will be used as an enabler for the better coordination of resources. In conclusion, the application of the proposed platform is anticipated to become a considerable tool for children's disaster education and to support families and children during and after a natural disaster occurrence.
Corrección de la odometría visual basada en la detección de cierre de lazo

An essential requirement in the field of robotics and automation is to know the position of a mobile robot along the time, as well as the trajectory that it describes by using on-board sensors. Nowadays, several methods exist for accomplishing this goal. In this work, we propose a novel approach focused on the use of cameras as perception sensors of the environment, that allows to perform a robust visual odometry, where correction algorithms based on loop closure detection are applied for localization in long-term situations. In order to satisfy the previous conditions, we carry out a methodological improvement of some classic computer vision techniques. In addition, new algorithms are implemented with the aim of correcting the drift produced in the visual odometry estimation along the traversed path. The main objective is to obtain an accurate estimation of the position, orientation and trajectory followed by a vehicle. Sequences of images acquired by an on-board stereo camera system are analyzed without any previous knowledge about the real environment. Due to this, correction techniques are needed when a place is revisited by the vehicle.
The Degrees of Freedom of the Oversampled Non-Coherent Channel

The degrees of freedom of a class of discrete-time non-coherent channels with oversampling, termed the Oversampled Non-Coherent (ONC) channel, are shown. The ONC channel is obtained from the classic continuous-time AWGN channel by considering the scenario in which the channel output is also corrupted by phase noise (PN) and processed by a multi-sample receiver. The continuous-time PN process has high variability which results in receiver output samples affected by a discrete-time PN process iid uniformly distributed over the unit circle. The ONC channel models the non-coherent detection scenario in which oversampling is employed for phase recovery but the PN process has such a high variance that its samples appear independent and uniformly distributed despite the oversampling. As such, the assumption of independent and uniformly distributed discrete PN is a limiting assumption that, generally speaking, well approximates the scenario in which the PN coherence time is much smaller than the oversampling time. In this paper, we obtain the generalized degrees of freedom for the case in which the oversampling factor L grows with the transmit power P as $P^{\alpha}$. Perhaps surprisingly, we show that no degree of freedom for reliable information transfer is available for $L \gt P^{2}$. We conjecture that the same capacity asymptotic holds for other PN channels with oversampling, such as the oversampled Wiener PN channel, when the noise variance grows to infinity faster than the sampling rate.
A Theory of Aircraft Position Verification Using TDOA

Aeronautical surveillance systems, which provide aircraft position to air traffic controllers, are an important infrastructure for safe and efficient flights. One of the next generation systems is Automatic Dependent Surveillance - Broadcast (ADS-B) which requires aircraft to periodically broadcast their own position. Although ADS-B is more accurate than the conventional radars, it is susceptible to avionics failures or spoofing. A practical countermeasure is position verification by Time Difference Of Arrival (TDOA). This paper provides a theory of the TDOA-based position verification for ADS-B. The probability of detection, probability of false alarm, and detection threshold are derived by combining the binary hypothesis testing and the linearization known in the TDOA-based localization. The derived analytical expressions are convenient for analysis and system design.
Evaluation of Degradation Degree of Plated Base Metal Contacts by Impedance Measurement

Oxidation and wear affect the degradation of connectors with plated base metal contacts. Since these phenomena process gradually and they are accelerated by fretting motion caused by thermal cycle or mechanical vibration, it is important to evaluate such degradation degree of connectors especially of wire-harnesses for automobiles. The change in DC resistance of plated base metal contacts is well-known to behave characteristically as degradation process progresses. Since the change reflects both degradations of plating material and base material, in most cases, it is too complicated to evaluate the degradation degree by DC resistance measurement.Therefore, we introduce nondestructive impedance measurement as a degradation diagnosis method for contacts. It has been found that the changes in reactance at a certain frequency behave characteristically and independently of the DC resistance changes during fretting test. It is considered that the change reflects the micro-structural changes near contacts. The relationship between the degradation degree of contacts and the change in impedance has been observed.
A Frequency Domain Classifier of Steady-State Visual Evoked Potentials Using Deep Separable Convolutional Neural Networks

Steady state visual evoked potential (SSVEP)-based brain computer interface (BCI) systems has attracted paramount amount of attention due to their higher signal to noise ratio and high information transfer rate. In this paper a SSVEP-BCI-based on a convolutional neural network (CNN) classifier is presented. The visual stimulation is provided to the participants with with LED matrices blinking at 6, 7, 8 and 9 Hz respectively. A wireless EEG amplifier, the g.Nautilus was used to acquire the electroencephalogram (EEG) signals from eight parietal and occipital electrodes. The features were derived using Fast Fourier Transformation (FFT) of the 8 channels using a 2s moving window in the form of 8 × 8 grey scale images. The proposed CNN architecture has provided superior average accuracy of 94.7% for four subjects, compared to the average accuracy of 87.4% of the state of the art canonical correlation analysis (CCA) performance.
Kernel PLS Regression II: Kernel Partial Least Squares Regression by Projecting Both Independent and Dependent Variables into Reproducing Kernel Hilbert Space

we propose a regression method using partial least square (PLS) technique and kernel method, which we refer to as kernel partial least square regression II to distinguish the conventional kernel PLS regression. The motivation of the conventional kernel PLS regression is to establish a coordinator system where the independent and dependent variables have a stronger correlation, and which models a regression model using the coordinator system. However, it projects independent variables into a reproducing kernel Hilbert space (RKHS) alone. The proposal extends the basic framework of conventional kernel PLS regression. The proposed method does not only project independent variables into the RKHS, but also dependent variables, and establishes a coordinator system where the independent and dependent variables have a stronger correlation. We use two function regression cases to evaluate the proposed method compared with the conventional kernel PLS regression. The regression performance of the proposed method has almost the same regression accuracy arising from the evaluation result, and this depends on the regression tasks. We explain the correlation calculations of our proposed method, conventional kernel PLS regression, and PLS regression. The meaning of correlation depends on the application in question. We also analyse and discuss the algorithm implementation, correlation meaning, and other issues for further development of the proposal.
MaskAl: Privacy Preserving Masked Reads Alignment using Intel SGX

The recent introduction of new DNA sequencing techniques caused the amount of processed and stored biological data to skyrocket. In order to process these vast amounts of data, bio-centers have been tempted to use low-cost public clouds. However, genomes are privacy sensitive, since they store personal information about their donors, such as their identity, disease risks, heredity and ethnic origin. The first critical DNA processing step that can be executed in a cloud, i.e., read alignment, consists in finding the location of the DNA sequences produced by a sequencing machine in the human genome. While recent developments aim at increasing performance, only few approaches address the need for fast and privacy preserving read alignment methods. This paper introduces MaskAl, a novel approach for read alignment. MaskAl combines a fast preprocessing step on raw genomic data - filtering and masking - with established algorithms to align sanitized reads, from which sensitive parts have been masked out, and refines the alignment score using the masked out information with Intel's software guard extensions (SGX). MaskAl is a highly competitive privacy-preserving read alignment software that can be massively parallelized with public clouds and emerging enclave clouds. Finally, MaskAl is nearly as accurate as plain-text approaches (more than 96% of aligned reads with MaskAl compared to 98% with BWA) and can process alignment workloads 87% faster than current privacy-preserving approaches while using less memory and network bandwidth.
Energy Efficiency of Proactive Cooperative Eavesdropping Over Multiple Suspicious Communication Links

This paper studies the wireless surveillance of multiple suspicious communication links. We propose a cooperative eavesdropping scheme, where a primary legitimate monitor and an assistant legitimate monitor cooperatively jam the suspicious links for an effective proactive eavesdropping. The objective is to maximize the eavesdropping energy efficiency (EEE) of our proposed cooperative eavesdropping scheme, by optimizing jamming power allocation and the cooperative strategy of legitimate monitors subject to several constraints, i.e., energy transfer constraint and eavesdropping load balancing constraint. Furthermore, the formulated problem is a difficult mixed integer nonlinear programming problem and, therefore, is non-convex and combinational. To solve the problem efficiently, a threshold-based policy of optimal jamming power allocation is proposed. Therefore, the original optimization problem is decoupled into two sub-problems: the selection of optimal eavesdropper under the given jammer and the whole EEE maximization over the selection of optimal jammer. Numerical results show that our proposed cooperative eavesdropping achieves higher EEE and eavesdropping rate than non-cooperative proactive eavesdropping.