Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Palmprint Authentication System Using Level 3 Pore Feature

Ability to authenticate the true identity of an individual for secure applications is the main advantage of a biometric system. Palmprint provides distinct and distinguishing feature such as principal lines, wrinkles, delta points, ridge features namely ridge density, ridge patterns, minutiae points, pores, incipient ridges etc for identifying a person. Ridge features can be extracted only from the high resolution palmprint images which are mainly employed in high security forensic applications. Images captured by forensic experts are almost partial. The use of pores for authenticating these images is a promising approach. It is desirable to improve the performance of the palmprint biometric system by introducing pore feature. Moreover, as the size of pores is very small, it is very difficult to replicate them. Robust algorithms need to be developed for an efficient partial palmprint authenticating system to improve its performance. This paper presents a high resolution palmprint authenticating system based on pore feature. In our proposed system, we use Gaussian filter to extract the pores and Hausdorff distance for palmprint matching. This method follows the steps used in fingerprint recognition but each step is designed specifically to process palmprint.
DA-Net: Learning the Fine-Grained Density Distribution With Deformation Aggregation Network

The major challenges for accurate crowd counting stem from the large variations in the scale, shape, and perspective. In fact, dealing with such difficulties depends on the geometric transformation capabilities of the network. Thus, we propose the deformation aggregation network (DA-Net) that can incrementally incorporate adaptive receptive fields to capture the fine-grained density distribution. Our model starts from a fundamental diamond network based on the successive steps of rising and falling on the channels, which utilizes small-fixed kernels accounting for preserving spatial information. For sake of accommodating to scale variations, we further apply deformable convolutions to deliver precise localization by augmenting the spatial sampling locations with additional offsets, capable of transformation-invariant speciality on diverse scenes. In addition, we assign adjustable weights to supervise network to learn the fusion proportion adaptively and aggregate multiple levels of abstractions to get the final density map. DA-Net is a fully convolutional network that is able to take input of arbitrary size. The proposed method delivers the state-of-the-art performance on four benchmarks (ShanghaiTech, WorldExpo’10, UCSD, and UCF_CC_50). To further verify the generality of our model, we conduct an extended experiment on vehicle data set TRANCOS and achieve significant results.
Implementation of Software-Defined Network Nodes Based on Ultra-Low Power Microcontrollers for VANET

The paper describes the implementation of software-defined network nodes on the basis of ultralow power consumption microcontroller MSP430G2553, by Texas Instruments. The MiniFlow protocol implementing the concept of networks on the selected element base including all its restrictions has been developed. There is a model that includes 5 boards, 1 controller and 4 clients working on the basis of the protocol. The architecture of the network allowing extension of the number of network nodes has been proposed taking into account the selected element base. The model in practice proves the feasibility of microcontrollers MSP430 in the implementation of software-defined network nodes for Vehicular Ad Hoc Network.
Data Center Control Strategy for Participation in Demand Response Programs

This paper presents a framework for the optimal operation of data centers, leveraging their heating, ventilation, and air conditioning unit, delay-tolerant information technology workload and battery storage system for participating in demand response programs. In this context, an model predictive control based control framework has been developed that guarantees the reliable operation of the data centers core activities. We derive a modeling approach to represent the dynamics of the data centers subsystems and validate it for a data center test bed via practical experiments. Hereby, the thermal subsystem leads to deviations of less than 0.60 K in the modeled outlet temperature. The validated model is used for incremental prototyping of the proposed control via simulations under uncertainties. The results demonstrate a mean absolute error of the relative deviations between the data center consumption and the target load profile of 2.71% for an incentive-based scenario and a cost reduction of 3.86% for a price-based scenario.
Android Smartphone Based Visual Object Recognition for Visually Impaired Using Deep Learning

In this paper, we propose a deep learning based visual object recognition model, to guide a visually impaired person in an outdoor environment using a smartphone equipped with internet connectivity. The deep learning based computing is performed by the high-end server and is to be hosted on a cloud. The boosted optimized convolutional neural network (CNN) based visual object recognition model is proposed by experimenting various CNN models, different optimizers, and varying learning rate. Visually impaired person captures the video and sends frame as input to the server for object segmentation and object recognition using proposed model. He then receives the information about surrounding objects and obstacles as voice input to the hearing device attached to the smartphone. The proposed system provides measurable high accuracy for recognition of 11 outdoor objects, to guide visually impaired person more effectively than traditional guiding cane.
Dynamic Characteristic Analysis of Planar Piezoelectric Six-Axis Force/Torque Sensor

To research the dynamic characteristics of a planar piezoelectric six-axis force/torque(F/T) sensor in an actual measurement, a mathematical model method, a simulation method, and a calibration method are presented. The structure model of the sensor, its working principle, and research method are explained. A mass-spring equivalent model with multi-degrees of vibration based on the sensor is established, and differential equations based on the vibration system are derived from this model. According to the experimental prototype parameters of the sensor, its numerical model is established, the numerical simulation method and dynamic characteristic are investigated, and the amplitude–frequency and phase–frequency characteristics are analysed using the harmonic response method. The natural frequency of this sensor is calibrated using the impact response method. The experimental results indicate that the natural frequency in the six directions of the planar piezoelectric six-axis F/T sensor is higher than 3.95 kHz, and that the results of the analytical mathematical method and numerical simulation method are consistent with the calibration results. The proposed analytical mathematical method, simulation method, and calibration method can be used in the design, prediction, and calibration of the dynamic characteristics of these six-axis F/T sensors.
Detection and Classification of High Impedance Faults in Transmission Line Using Alienation-based Analysis on Voltage Signals

Modern day protection systems require easy implementation of detection and classification algorithms. Analysis of three phase voltages can impart substantial information regarding the detection and type of the faults. These signals vary with several factors like fault location, inception angle and impedance. In this study, the detection and classification of high impedance faults in a transmission line is proposed using alienation coefficients which are obtained by juxtaposing half cycles of absolute voltage signals using the moving window technique. The alienation coefficients obtained are compared with the corresponding threshold values to detect and classify the faults. Case studies undertaken by the variation of fault locations and fault inception angles have successfully validated the proposed algorithm.
Design and Implementation of a Network Based on Multipath TCP of Local Production

In the developed research we present a local production network designed and implemented with Multipath TCP, to improve reliability through redundant links. In particular, the MultiPath protocol was evaluated, which improved the performance and increased the solidity against possible faults in one of the routes. MutiPath TCP conventional and MultiPath TCP were implemented with integration of name-based sockets. The capture procedure and the analysis fields were: data flow, bit rate, protocol type, source IP address, destination IP address, sent bytes, received bytes, start time, source port and destination port. The Argus software was used to monitor network traffic on the server and the client. The implementation of the MultiPath server was performed on the Ubuntu 15.04 Linux platform, and the Wireshark tool to capture protocol data units. For the implementation of conventional MultiPath TCP an average transfer rate of 54.86Mbps was obtained and when it was integrated into the MultiPath TCP communication with name-based socket integration, an average transfer rate of 60.89Mbps was obtained, so optimization for the second case is around 10%. It has been proven that the production network works correctly and with better performance.
SmaCoNat: Smart Contracts in Natural Language

Smart contracts enable autonomous decentralized organizations (DADs) in large, trustless and open trading networks by specifying conditions for automated transactions of cryptographically secured data. This data could represent cryptocurrencies but also sensor data or commands to Cyber-Physical Systems (CPS) connected to the Internet. To provide reliability, the contract code is enforced by consensus and the transactions it triggers are nonrevertible, even if they were not intended by the programmer, which could lead to dangerous system behavior. In this paper, we conduct a survey over existing smart contract platforms and languages to determine requirements for the design of a safer contract language. Subsequently we propose concepts that enhance the understanding of code by limiting confusing language constructs, such as nesting, arbitrary naming of operations, and unreadable hash identifiers. This enables human reasoning about the contract semantics on a much higher abstraction layer, because a common understanding can be derived from the language specification itself. We implement these concepts in a new domain specific language called SmaCoNat to illustrate the feasibility and show that our concepts are barely covered by existing languages but significantly enhance readability and safety without violating deterministic parsability.
Greening the Cloud Through Power-Aware Virtual Machine Allocation

Cloud computing has tremendous ability to deliver services on time, conditional to host redundancy to guard against failure and excess server capacity while handling spiky demand. This results in a requirement of more servers than actually required and their average utilization is about 10% or less. This leads to huge amounts of power consumption as servers draw nearly the same amount of power regardless of their current utilization. This huge amount of power consumption can be controlled by power-efficient allocation of resources. In this paper, we proposed algorithms for the power-aware allocation and migration of virtual machines. Power saving is achieved through power efficient consolidation of virtual machines on a smaller number of servers and by putting idle nodes in sleeping mode. The decision of virtual machines allocation and consolidation on a server is based on server efficiency, i.e. minimum energy consumption with the maximum utilization. The simulation results show that the proposed method performs better than the recent power efficient approach.
Next-generation sequencing reveals two populations of damage-induced small RNAs at endogenous DNA double-strand breaks

Abstract Recent studies suggest that transcription takes place at DNA double-strand breaks (DSBs), that transcripts at DSBs are processed by Drosha and Dicer into damage-induced small RNAs (diRNAs), and that diRNAs are required for DNA repair. However, diRNAs have been mostly detected in reporter constructs or repetitive sequences, and their existence at endogenous loci has been questioned by recent reports. Using the homing endonuclease I-PpoI, we have investigated diRNA production in genetically unperturbed human and mouse cells. I-PpoI is an ideal tool to clarify the requirements for diRNA production because it induces DSBs in different types of loci: the repetitive 28S locus, unique genes and intergenic loci. We show by extensive sequencing that the rDNA locus produces substantial levels of diRNAs, whereas unique genic and intergenic loci do not. Further characterization of diRNAs emerging from the 28S locus reveals the existence of two diRNA subtypes. Surprisingly, Drosha and its partner DGCR8 are dispensable for diRNA production and only one diRNAs subtype depends on Dicer processing. Furthermore, we provide evidence that diRNAs are incorporated into Argonaute. Our findings provide direct evidence for diRNA production at endogenous loci in mammalian cells and give insights into RNA processing at DSBs.
Separability of Human Voices by Clustering Statistical Pitch Parameters

Speech is embedded with multiple information which includes the message to be transferred, the speaker information and the prosodic information. The physical properties of sound waves, such as mel-frequency cepstral and linear predictive cepstral coefficients are used in the speaker recognition system. While the prosodic features, like pitch and intensity, plays a vital role in human speech perception. In this paper, we investigate the speaker separability problem in real-time by clustering the statistical pitch feature. The goal is to identify the number of speakers in a given voice sample. For this, we analyze the space distribution of the prosodic parameter (pitch) to form clusters using the statistical parameters: mean, standard deviation, skewness and kurtosis. We experimentally observe that the statistical parameter belonging to a voice sample lie in the range of similarity. We calculate the variability for multiple speaker clusters and found that the intra cluster distance is 13 units, whereas the inter cluster distance varies according to the number of users in the voice sample.
Characterization Study of Developers in Non-Reproducible Bugs

Software bugs are inevitable. Increasing size and complexity of software systems makes bug handling a cumbersome and time-consuming task. In such scenarios, Non-Reproducible (NR) bug reports are an additional overhead for software developers as these bugs are hard-to-reproduce. This difficulty in reproducing NR bugs is due to varied reasons such as the absence of information required to create the same test environment, resource and time constraints, inability of the assigned developer to fix the issue, etc. However, when NR marked bug reports are reconsidered, a few percentages of these bug reports get Fixed (NRF). This fixation occurs either due to the trial of new solutions to reproduce the bug or due to the assignment of a different developer for the bug report. To find whether a change in developer helps in resolving NR bugs, this paper investigates the developers associated with NR bug reports. We gauged developer similarity (in terms of developer marking bug report as NR and Fix), tossing trends, presence of isBack and expertise level of developers who marked NRF bug reports as NR and Fix. We studied the change history of 24, 995 NR bug reports of Mozilla Firefox project. Our results show that 87.34% NRF bug reports are fixed by a developer who had not marked the bug report initially as NR. Also, we found that average tossing path length in NRF bug reports is three times higher than tossing path length in NR bug reports. This higher rate of bug tossing results in higher fixation probability of NR bug reports. It has also been observed that developers who fix NR bugs possess higher expertise than developers who marks bug reports as NR.
Innovating at the Connected Industry: SDN and NFV Experiences and Lessons Learned

The aim of this poster is to present the SN4I (Smart Networks for Industry) infrastructure, which will be used to interconnect real machine tools among them and with research laboratories in order to allow experimentation in Industry 4.0 services based on NFV (Network Function Virtualization) and SDN (Software Defined Networking) technologies. In this poster, we present the insights of the SN4I infrastructure as well as the challenges faced during its deployment and the main outcomes achieved so far.
Cross-Task Dynamic Load Balancing Strategy

In a multi-computer request application scenario, a certain number of computer resources need to be used to process multiple computer tasks at the same time. However, currently there is no load balancing strategy that can be effectively applied in such scenarios. This paper proposes the cross task load balancing strategy CTLB (Cross Task Load Balancing), the strategy at the beginning of each task, re-estimate the resource overhead of each task, and then according to the amount of tasks to re-allocate the available computer re-sources. This strategy is based on the "time continuity" feature in the computer technology, so it is a dynamic load balancing strategy of the past-frame type. The strategy can handle a plurality of computer requests in a balanced manner with a certain amount of computer resources, and improve the performance of the parallel computer system in a multi-projection or multi-request scenario. This paper first briefly describes the CTLB dynamic load balancing strategy, and then gives the specific algorithm of the dynamic load balancing strategy. Then describes the dynamic load balancing strategy on the GGPRF integration, and finally the per-formance of the dynamic load balancing strategy.
A Novel Classification for EEG Based Four Class Motor Imagery Using Kullback-Leibler Regularized Riemannian Manifold

Recent advances in the Brain-Computer Interface (BCI) systems state that the accurate Motor Imagery (MI) classification using Electroencephalogram (EEG) plays a vital role. In this paper, we propose a novel real-time feature extraction and classification architecture for four class MI using a combination of Kullback-Leibler Regularized Riemannian Mean (KLRRM) and Linear SVM. By using the KL regularization, the robustness of the features extracted to the noise and outliers is improved. The performance of the proposed architecture is analyzed on the four class MI dataset 2a from the BCI Competition IV. The performance analysis shows that the proposed architecture achieves an average classification accuracy of 74.43% and 51.53% for both the good and noisy subjects respectively. Also, the emphasis is laid on understanding the performance of regularization, and the improvement of robustness to the noise and outliers is demonstrated using the noisy subjects.
A Markov-Modulated End-to-End Delay Analysis of Large-Scale RF Mesh Networks With Time-Slotted ALOHA and FHSS for Smart Grid Applications

A new mathematical model and methodology are proposed to evaluate the performance of large-scale RF-mesh networks that use time-slotted ALOHA with frequency hopping spread spectrum. This type of architecture is quite usual in advanced metering infrastructures. An analytic formulation for the delay, based on Markov-modulated modeling of the system, is derived. The formula can be extended to evaluate other important performance metrics. The proposed methodology is applied to a large scale network of several thousands of nodes, and numerical results are reported to show the wide variety of performance evaluations that are enabled. The usefulness of the assessment of the feasibility of different types of applications (e.g., smart-metering and sensor networks) is shown. An analysis of the scalability of this methodology and a comparison with simulation results are also presented.
Evaluate Implementation of Enterprise Resource Planning (ERP) System

This study aims to evaluate the successful implementation of ERP system that has been done in the Ministry of Finance Unit. The research methodology is that would be used is literature, collect data sample and data analysis. Method collected data sample with observation and questionnaire. Method data analysis using factor analysis. Method the results obtained from this study is the analysis of new factors and indicators that can be used as a basis for determining that the effect of a successful ERP implementation. Data obtained from questionnaires to 120 respondents in Ministry of Finance unit. Data can be processed from the 92 questionnaire respondents. Data were analyzed using reliability and factor analysis method after that proceed by using regression analysis. Results obtained in the form of a new model that can be used by the Finance Ministry is to evaluate the success of ERP Systems implementation. By using the new model of successful ERP System Implementation evaluate this, we can determine the minimum and maximum values that affect the successful of ERP system implementation to make a reference to improvement and development process of the system for the better system ahead.
Two-Stage Power Converter Design and Control for Renewable Energy Systems

In this study, a power converter was designed to use in electrical appliances the energy generated from renewable energy sources or supplied from the battery packs. This converter consists of a cascade connection of DC-DC boost converter and a single phase full bridge inverter. The DC-DC Boost Converter is used to increase the DC voltage of the battery or renewable energy system and is controlled with the PI, PID and Fuzzy Logic (FL) controllers to keep a constant level the DC Link voltage. The single phase full bridge inverter converts the DC-Link voltage to a two-level AC voltage by using the sinusoidal PWM control signal. The output voltage of the inverter controlled by the unipolar SPWM switching signal contains less harmonics than the bipolar switching signal.
Higher-Order Statistics applied to machine learning as an approach to identify broken rotor bars in induction motors

Induction motors are reported as the horse power in industries. Due to its importance, researchers have been studied how to predict its faults in order to improve reliability. Condition health monitoring plays an important role in this field, since it is possible to predict failures by analyzing its operational data. This paper proposes the usage of vibration signals, combined with Higher-Order Statistics (HOS) and machine learning methods to detect broken bars in a squirrel-cage three-phase induction motor. The Multi-Layer Perceptron and Optimum-Path Forest have presented as promising approaches for faults classifications in an induction motor.