nils-herrmann/clean_scirepeval huggingface dataset:
Automatic hadoop cluster deployment and management tool

With the rapid increase in data and its complexity of configuration and deployment bring a new challenge to the research community. In this scenario, automatic Hadoop cluster deployment and management tool provide a new horizon which plays a vital role in resource and packet management in distributed environment. This paper describes a low-cost Automatic Hadoop Cluster Deployment and Management tool supported with a Web Application. The application is capable of detecting machines in the network as well as setting up the cluster with the results being provided to the user on a Web Interface from where the user could manage the entire cluster intuitively to gain a better user experience. The web application helps the user to control the management of the cluster along with access of the remote systems whenever necessary.
Reconfigurable dual mode IEEE 802.15.4 digital baseband receiver for diverse IoT applications

IEEE 802.15.4 takes a center stage in IoT as Low-Rate Wireless Personal Area Networks(LR-WPANs). The standard specifies Offset Quadrature Phase Shift Keying Physical Layer (O-QPSK PHY) with half-sine pulse shaping which can be either categorized under the class of M-ary PSK signals (QPSK signal with offset) or as Minimum Shift Keying (MSK) signal. M-ary PSK demodulation requires perfect carrier synchronization and has minimal error. MSK signals which falls under Continuous Phase Frequency Shift Keying can be demodulated non-coherently but error performance is not as good. In our paper, this dual nature of IEEE 802.15.4 PHY is exploited to propose a dual mode receiver comprising of QPSK demodulator chain and MSK demodulator chain as a single system on chip. The mode can be configured manually depending on the type of application or based on feedback from a Signal to Noise (SNR) indicator employed in the proposed receiver. M-ary PSK chain is selected for lower SNRs and MSK for higher SNRs. Each of these properties are analyzed in detail for both demodulator chains and we go on to prove that MSK detection can be used for low power, low complex and low latency while QPSK detection is employed for minimal error.
An efficient signcryption scheme based on ECC with forward secrecy and encrypted message authentication

In this paper, an efficient signcryption scheme based on elliptic curve cryptosystem is going to be proposed which can effectively combine the functionalities of digital signature and encryption and also takes a comparable amount of computational cost and communication overhead. The proposed scheme provides confidentiality, integrity, unforgeability and nonrepudiation, along with encrypted message authentication, forward secrecy of message confidentiality and public verification. By forward secrecy of message confidentiality function we mean, although the private key of the sender is divulged inattentively, it does not affect the confidentiality of the previously stored messages. By the public verification function we mean, any third party can verify directly the signature of the sender of the original message without the sender's private key when dispute occurs. It enhances the justice of judge. In addition, proposed scheme will save great amount of computational cost. The proposed scheme can be applied to the lower computational power devices, like smart card based applications, e-voting and many more, due to their lower computational cost. The Proposed Scheme is discussed in this paper and is compared with the existing schemes with respect to computational cost and the security functions it provides.
A fast and area efficient 2-D convolver for real time image processing

Two dimentional (2-D) convolver is a basic processing unit used in real time video and image processing algorithms. VLSI chip area and external memory bus bandwidth are two major concerns of efficient and fast 2-D convolver. Till now people have considered both of these parameters separately. Achieving low complexity and high packing density at the same time is a difficult task. This paper proposes a new FPGA oriented architecture which uses a multiplierless 2-D convolver with area efficient buffering scheme which achieves high speed and saves up to 80% of chip area at the same time.
Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features

This paper proposes an accurate and generalizable deep learning framework for iris recognition. The proposed framework is based on a fully convolutional network (FCN), which generates spatially corresponding iris feature descriptors. A specially designed Extended Triplet Loss (ETL) function is introduced to incorporate the bit-shifting and non-iris masking, which are found necessary for learning discriminative spatial iris features. We also developed a sub-network to provide appropriate information for identifying meaningful iris regions, which serves as essential input for the newly developed ETL. Thorough experiments on four publicly available databases suggest that the proposed framework consistently outperforms several classic and state-of-the-art iris recognition approaches. More importantly, our model exhibits superior generalization capability as, unlike popular methods in the literature, it does not essentially require database-specific parameter tuning, which is another key advantage over other approaches.
Twin gate rectangular recessed channel (TG-RRC) MOSFET for digital-logic applications

In this paper, twin gate rectangular recessed channel (TG-RRC) MOSFET with independent gate control is used to realize its application in digital electronics by using it as two input logic. The input logic is controlled by the independent gates which have different work functions (Φ1 for gate 1 and Φ2 gate 2) which are separated by oxide layer of 2 nm, thus controlling various electrical parameters such as current density, potential etc. across the channel in a swift manner and also increasing the switching characteristics of the device. It can give the full functionality of “AND” and “NAND” gate. We have shown the structure and functioning of the gates with a simulation on Silvaco TCAD. Since it is one of the fundamental gates has much applicability in digital logics starting from basic Flip-flops to sequential circuits in SRAM or DRAM cells.
Removal of EOG Artifacts From Single Channel EEG Signals Using Combined Singular Spectrum Analysis and Adaptive Noise Canceler

The electroencephalogram (EEG) signals represent the electrical activity of the brain. In applications, such as brain-computer interface (BCI), features of the EEG signals are used to control the devices. However, while recording, EEG signals often contaminated by electrooculogram (EOG) artifacts; such artifacts degrade the performance of the BCI. In this paper, we proposed a new technique using singular spectrum analysis (SSA) and adaptive noise canceler (ANC) to remove the EOG artifact from the contaminated EEG signal. In this technique, first, we proposed a novel grouping technique for SSA to construct the reference signal (EOG) for ANC. Later, using the extracted reference signal, the adaptive filter was employed to remove EOG artifact from the contaminated EEG signal. To quantify the performance of the proposed technique, we carried out simulations on synthetic and real-life EEG signals. In terms of relative root mean square error and mean absolute error, the proposed SSA-ANC method outperforms the existing techniques.
Surface-modified superparamagnetic nanoparticles for drug delivery: preparation, characterization, and cytotoxicity studies

Superparamagnetic iron oxide nanoparticles have been used for many years as magnetic resonance imaging (MRI) contrast agents or in drug delivery applications. In this study, a novel approach to prepare magnetic polymeric nanoparticles with magnetic core and polymeric shell using inverse microemulsion polymerization process is reported. Poly(ethyleneglycol) (PEG)-modified superparamagnetic iron oxide nanoparticles with specific shape and size have been prepared inside the aqueous cores of AOT/n-Hexane reverse micelles and characterized by various physicochemical means such as transmission electron microscopy (TEM), infrared spectroscopy, atomic force microscopy (AFM), vibrating sample magnetometry (VSM), and ultraviolet/visible spectroscopy. The inverse microemulsion polymerization of a polymerizable derivative of PEG and a cross-linking agent resulted in a stable hydrophilic polymeric shell of the nanoparticles. The results taken together from TEM and AFM studies showed that the particles are spherical in shape with core-shell structure. The average size of the PEG-modified nanoparticles was found to be around 40-50 nm with narrow size distribution. The magnetic measurement studies revealed the superparamagnetic behavior of the nanoparticles with saturation magnetization values between 45-50 electromagnetic units per gram. The cytotoxicity profile of the nanoparticles on human dermal fibroblasts as measured by standard 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay showed that the particles are nontoxic and may be useful for various in vivo and in vitro biomedical applications.
Impact of Channel Doping and Gate Length on Small Signal Behaviour of Gate Electrode Workfunction Engineered Silicon Nanowire MOSFET at THz Frequency

In this paper, we investigate the impact of gate length and channel doping of GEWE-SiNW MOSFET on the small signal behavior in terms of S-parameters such as reflection and transmission coefficients at 100Hz-4THz frequency range using device simulators: ATLAS and DEVEDIT-3D. The main aim of this work is to optimize the values of gate length and channel doping that will be projected for future reference in context of high performance radio frequency applications. The Simulation results show improvement in S-parameters as we scale down the channel length and increase the channel doping. Therefore, it opens the possibility for the implementation of Silicon-On-Chip for telecommunication including both base-band and RF circuits.
Non blind digital watermarking technique using DCT and Cross chaos map

Digital watermarking is the process of embedding secret information into a digital signal which may be used to verify its authenticity or the identity of its owners. In this paper, a non-blind digital image watermarking algorithm based on Discrete cosine transform, Cross chaotic map and Arnold transform has been described. Initially, Arnold's transformation is applied on the watermark signal. Then, Cross chaos sequence is applied on this encrypted watermark to encrypt further. Discrete cosine transformation is applied to the cover image to transform the signal from the spatial domain to the frequency domain, where the watermark signal is embedded. It is a kind of non-blind scheme as the extraction needs the original image. Peak signal to noise ratio (PSNR) is used to evaluate the algorithm. The quality of the extracted watermark is evaluated using term Normalized cross-correlation (NC). From the results, it may be concluded that our algorithm is resistant enough against the common attacks.
A CNN-Based Framework for Comparison of Contactless to Contact-Based Fingerprints

Accurate comparison of contactless 2-D fingerprint images with contact-based fingerprints is critical for the success of emerging contactless 2-D fingerprint technologies, which offer more hygienic and deformation-free acquisition of fingerprint features. Convolutional neural networks (CNNs) have shown remarkable capabilities in biometrics recognition. However, there has been almost nil attempt to match fingerprint images using CNN-based approaches. This paper develops a CNN-based framework to accurately match contactless and contact-based fingerprint images. Our framework first trains a multi-Siamese CNN using fingerprint minutiae, respective ridge map and specific region of ridge map. This network is used to generate deep fingerprint representation using a distance-aware loss function. Deep fingerprint representations generated in such multi-Siamese network are concatenated for more accurate cross comparison. The proposed approach for cross-fingerprint comparison is evaluated on two publicly available databases containing contactless 2-D fingerprints and respective contact-based fingerprints. Our experiments presented in this paper consistently achieve outperforming results over several popular deep learning architectures and over contactless to contact-based fingerprints comparison methods in the literature.
Effect of target substrate distance on the properties of RF sputtered PZT films

Lead zirconate titanate (PZT) films have been deposited by RF sputtering using an oxide target with excess lead oxide, on different substrates. The structural and optical properties have been studied as a function of target-substrate (T-S) distance and postdeposition annealing. The as-grown films were amorphous. On postdeposition annealing in an excess PbO+PZT atmosphere at 450 degrees C, the films grown at a 4-cm T-S distance showed crystallinity, whereas films deposited at a higher T-S distance remained amorphous. The surface roughness and the film thickness decreased with increasing T-S distance. The as-grown films were highly transparent. The optical bandgap was estimated by plotting alpha /sup 2/ versus h upsilon and the value of the bandgap depended on the T-S distance. The fundamental absorption edge shifted to a lower wavelength when subjected to postdeposition annealing.<<ETX>>
On the Company We Keep: Combined Scale-and-Scope Externalities in the Growth of IT Industry Co-agglomerations

Most information technology (IT) industry clusters exhibit the co-agglomeration of several industries. We propose that the synergies between industries do not only consist of separate scale effects and scope effects. They also exhibit combined scale-and-scope effects or externalities, so the scale of one industry affects others' growth. Our study analyzes the co-agglomeration of four IT industries in 3,142 counties in the United States. Using econometric methods, we find that the co- agglomerated computer manufacturing industry benefits the growth of the semiconductors manufacturing industry. Similarly, collocated firms in the semiconductors manufacturing and computer systems design industries appear to benefit each other. We find little evidence like this for the software publishing industry though. Our study offers policy implications for industrial development planners in the global economy.
Control of grid connected cascaded H-bridge multilevel converter during grid voltage unbalance for photovoltaic application

With a large penetration of distributed power, transmission system operators (TSO) have more stringent demands regarding the interconnection of the distributed power generation system (DPGS) to the utility grid in order to maintain a stable power system. Among the new demands, power generation systems are requested to ride through grid disturbances and to provide ancillary services in order to behave as a conventional power plant. So, they should have the capability to sustain the utility network in the situation of grid voltage unbalance. Therefore, the control of DPGS under the grid voltage unbalance is important. In this paper, control of cascaded H-bridge (CHB) multilevel converter for grid integration of large scale photovoltaic power plants during grid voltage unbalance has been analysed. Operation for feeding constant power to the grid has been investigated and it has been found that it is not possible as loading of cells of CHB will be different which will cause the dc link voltages to diverge. A control technique based on negative sequence current control has been used to equalize the loading of cells.
Alignment of 3D point cloud, CAD model, real-time camera view and partial point cloud for pipeline retrofitting application

Alignment of 3D laser scanner point cloud data within the video of real environment view from the camera is very new and efficient method for construction, monitoring and retrofitting of complex engineering models in heavy industrial plants. Connecting the laser scanner generated point cloud data with physical world helps the users to find, analyze and modify the specific element of the model corresponding to the physical model. One of the most difficult aspects in alignment process is that models generated from LiDAR sensors must be aligned appropriately with the models in the scene of the video. This paper presents a framework for alignment of LiDAR generated point cloud within the video scene using frame-by-frame registering method. Finally, the significance of proposed alignment method in pipeline retrofitting applications in plant engineering illustrated with inserting CAD models.
Palmprint identification using palmcodes

This paper investigates a new approach for the palmprint identification using real Gabor function (RGF) filtering. Inkless composite hand images have been used to automatically to extract the palmprints from peg-free imaging setup. These palmprints, after normalization, are subjected to selective feature sampling by a bank of RGF. Each of these filtered images has been used to extract significant features (PalmCode) from each of 6 concentric circular bands. Our preliminary experimental results using 400 low-resolution palmprint images achieve the recognition rate of 97.50% and also illustrate the shortcomings of results presented in earlier work. The results show the uniqueness of palmprint texture, even in the two hands of an individual and its possible use in biometrics based personal recognition.
A Cell-Array-Based Multibiometric Cryptosystem

This paper presents a new framework for a biometric cryptosystem in which a cryptographic key is concealed with biometric modalities. In this paper, the candidate biometric modality is secured using two functions: 1) BCH encoding, which delivers the parity-code stored for the alignment of the query biometric template and 2) the Hash function to compute hash-code in order to safeguard its integrity. The cryptosystem is formed by creating two different cell-arrays. The hash-code is scattered on one cell-array by a randomly chosen column position, and the secret key is distributed over the second cell-array on the same position. The other cell-array locations are filled with the randomly generated chaff vectors. The parity-code is then jumbled up using a regenerative XORCoding in order to hide it from unauthorized access. At the unlocking stage, the parity-code is regenerated using XORcode and used to align the query template to the original one. If the hashed-code computed from the aligned template can locate the correct locations of the original hashed-code from the feature-array, it can get back the secret-key from the key-array and the correspondingly secret message. The proposed algorithm is implemented and evaluated in two modes: 1) unimodal and 2) multimodal. The experimental results from publicly available databases confirm the superiority of the multimodal cryptosystems over the unimodal cryptosystems.
Palmprint recognition using rank level fusion

This paper investigates a new approach for the personal recognition using rank level combination of multiple palmprint representations. There has been very little effort to study rank level fusion approaches for multi-biometrics combination and in particular for the palmprint identification. In this paper, we propose a new nonlinear rank level fusion approach and present a comparative study of rank level fusion approaches which can be useful in combining multi-biometrics fusion. The comparative experimental results from the real hand biometrics data to evaluate/ascertain the rank level combination using (i) Borda count, (ii) Logistic regression/Weighted Borda count, (iii) highest rank method and (iv) Bucklin Method are presented. Our experimental results presented in this paper suggest that significant performance improvement in the recognition accuracy can be achieved as compared to those from individual palmprint representations. The rigorous experimental results presented in this paper also suggest that the proposed nonlinear rank-level approach outperforms the existing approaches presented in the literature.
Accurate Periocular Recognition Under Less Constrained Environment Using Semantics-Assisted Convolutional Neural Network

Accurate biometric identification under real environments is one of the most critical and challenging tasks to meet growing demand for higher security. This paper proposes a new framework to efficiently and accurately match periocular images that are automatically acquired under less-constrained environments. Our framework, referred to as semantics-assisted convolutional neural networks (SCNNs) in this paper, incorporates explicit semantic information to automatically recover comprehensive periocular features. This strategy enables superior matching accuracy with the usage of relatively smaller number of training samples, which is often an issue with several biometrics. Our reproducible experimental results on four different publicly available databases suggest that the SCNN-based periocular recognition approach can achieve outperforming results, both in achievable accuracy and matching time, for less-constrained periocular matching. Additional experimental results presented in this paper also indicate that the effectiveness of proposed SCNN architecture is not only limited to periocular recognition but it can also be useful for generalized image classification. Without increasing the volume of training data, the SCNN is able to automatically extract more discriminative features from the input data than a single CNN, therefore can consistently improve the recognition performance. The experimental results presented in this paper validate such an approach to enable faster and more accurate periocular recognition under less constrained environments.
Parallelogrammic neighborhood (PN) system for nonparametric MRF based texture synthesis

In this paper we introduce a new neighborhood system based Nonparametric Markov Random Field (NMRF) model for texture synthesis. The objective of this work is to generate minimal neighborhood system, in MRF based texture synthesis algorithms. Compared to the earlier neighborhood system (circular), the proposed one is not isometric and has fewer number of pixels within the neighborhood.This reduction in the number of neighborhood pixels does not degrade the synthesis results compared to the existing neighborhood systems.Two popular texture synthesis algorithms (based on nonparametric MRF) have been tested with the new neighborhood system for two standard texture databases.The results establish the efficacy of the proposed neighborhood system in terms of computational gain, with no visible degradation compared to the earlier systems.
Transfer in inverse reinforcement learning for multiple strategies

We consider the problem of incrementally learning different strategies of performing a complex sequential task from multiple demonstrations of an expert or a set of experts. While the task is the same, each expert differs in his/her way of performing it. We assume that this variety across experts' demonstration is due to the fact that each expert/strategy is driven by a different reward function, where reward function is expressed as a linear combination of a set of known features. Consequently, we can learn all the expert strategies by forming a convex set of optimal deterministic policies, from which one can match any unseen expert strategy drawn from this set. Instead of learning from scratch every optimal policy in this set, the learner transfers knowledge from the set of learned policies to bootstrap its search for new optimal policy. We demonstrate our approach on a simulated mini-golf task where the 7 degrees of freedom Barrett WAM robot arm learns to sequentially putt on different holes in accordance with the playing strategies of the expert.