Reviewerly/exHarmony huggingface dataset:
Implementation of High Performance Hierarchy-Based Parallel Signed Multiplier for Cryptosystems

Digital Cryptosystems play an inevitable part in modern-day communication. Due to the complexity involved in the execution of crypto algorithms, it is realized as modular arithmetic modules. Generally, multipliers are the most time-consuming data path elements which influence the performance of modular arithmetic implementations. In this paper, the design of a hierarchy-based parallel signed multiplier without sign extension is presented. A mathematical model of the algorithm, two VLSI architectures, namely, Carry Save Adder (CSA)-based design and Parallel Prefix-based architecture are proposed. Mathematical equations of the multiplier are verified using MATLAB tool and the architectures are coded in Verilog HDL. The functionality of the same is tested using a Zynq Field Programmable Gate Array (FPGA) (XC7Z020CLG484-1), and the synthesized results are presented. Parameters, such as area, power, delay, Power Delay Product (PDP) and Area Delay Product (ADP), are compared by synthesizing the designs in Cadence RTL compiler with 180[Formula: see text]nm, 90[Formula: see text]nm and 45[Formula: see text]nm TSMC CMOS technologies. The results show that CSA-based multiplier architecture has achieved an improved PDP performance of 20% with an optimum area compared to recent work. It also shows that the parallel prefix architecture has made a 27% improvement in speed with a better PDP. By using the proposed signed multiplier, modulo [Formula: see text] and [Formula: see text] signed arithmetic modules are implemented.
A class of constacyclic codes over the ring Z4[u,v]/&lt;u2,v2,uv - vu&gt; and their Gray images

In this paper, we study (1 + 2u + 2v)-constacyclic and skew (1 + 2u + 2v)-constacyclic codes over the ring Z4 + uZ4 + vZ4 + uvZ4 where u2 = v2 = 0,uv = vu. We define some new Gray maps and show that the Gray images of (1 + 2u + 2v)-constacyclic and skew (1 + 2u + 2v)-constacyclic codes are cyclic, quasi-cyclic and permutation equivalent to quasi-cyclic codes over Z4. Further, we determine the structure of (1 + 2u + 2v)-constacyclic codes of odd length n.
Unsupervised Desmoking of Laparoscopy Images Using Multi-scale DesmokeNet

The presence of surgical smoke in laparoscopic surgery reduces the visibility of the operative field. In order to ensure better visualization, the present paper proposes an unsupervised deep learning approach for the task of desmoking of the laparoscopic images. This network builds upon generative adversarial networks (GANs) and converts laparoscopic images from smoke domain to smoke-free domain. The network comprises a new generator architecture that has an encoder-decoder structure composed of multi-scale feature extraction (MSFE) blocks at each encoder block. The MSFE blocks of the generator capture features at multiple scales to obtain a robust deep representation map and help to reduce the smoke component in the image. Further, a structure-consistency loss has been introduced to preserve the structure in the desmoked images. The proposed network is called Multi-scale DesmokeNet, which has been evaluated on the laparoscopic images obtain from Cholec80dataset. The quantitative and qualitative results shows the efficacy of the proposed Multi-scale DesmokeNet in comparison with other state-of-the-art desmoking methods.
Handwriting Recognition Using Active Contour

Handwriting recognition is the process of recognizing handwritten or printed alphabets in various documents and even old manuscript. This method also helps in preserving the texts and writings of ancient times. This paper aims to represent the work related to recognition of cursive handwriting of different languages. Cursive handwritings are connected to each other, hence segmentation is required. Segmentation is used for extraction: line segmentation to extract sentence, word segmentation to extract words and character segmentation to extract individual letters. The segmentation involves dividing based on contours by setting different kernel size. For classification, we have used classifiers—convolution neural networks. We carried our experiment using datasets collected from E-MNIST, UCI. The experimental accuracy for the E-MNIST dataset is 79.3% and for UCI Devanagari dataset is 93%.
Intelligent Web-History Based on a Hybrid Clustering Algorithm for Future-Internet Systems

Proposition systems can abuse semantic reasoning abilities to crush ordinary obstacles of current structures and improve the recommendations’ quality. In this paper, we present an altered proposition structure, a system that makes usage of depictions of things and customer profiles subject to ontologies in order to outfit semantic applications with redid organizations. The recommender uses zone ontologies to improve the personalization: from one point of view, customer’s interests are shown in an inexorably amazing and exact way by applying a space-based inducing system; on the other hand, the stemmer estimation used by our substance-based filtering approach, which gives an extent of the prejudice between a thing and a customer, is updated by applying a semantic likeness procedure Web Usage Mining accepting a basic occupation in recommender structures and web personalization. In this paper, we propose a feasible recommender structure subject to logic and Web Usage Mining. The underlying advance of the technique is isolating features from web files and building imperative thoughts. By then gather logic for the site use the thoughts and basic terms removed from reports and used for analysis. As demonstrated by the semantic closeness of web reports to amass them into different semantic points, the assorted subjects propose particular tendencies. The proposed technique consolidates semantic learning into Web Usage Mining and personalization shapes.
Adaptive Chirikov Map for Pseudo-random Number Generation in Chaos-based Stream Encryption

Information security plays an important role in modern technologies. Stream encryption is one of the common tools used for secure communications. The stream encryption algorithms require sequence with pseudo-random properties. The chaotic maps as a source of pseudo-random numbers with desired statistical properties is widely studied in the last decades. One of the known problems of the implementation of the chaos-based pseudo-random number generators implemented with low-precision arithmetic is a short period length. A possible solution of the short-cycle problem is a perturbation of the orbit or the nonlinearity parameter of the chaotic map for breaking out the cycle. In the present article, we propose a new approach for increasing the cycle length by changing the symmetry coefficient of the adaptive Chirikov map. We switch two values of the symmetry coefficient according to the output of the linear feedback shift register. We calculate the estimations of the period length for the perturbed and original Chirikov maps and confirm the efficiency of the proposed approach. Properties verification for the output sequences of generators based on the adaptive Cirikov map is carried out using correlation analysis methods and the NIST statistical test suite. The obtained results can be used in cryptography applications as well as in secure communication systems design.
Point-of-Interest Recommendation Based on User Contextual Behavior Semantics

By suggesting new visiting places, point-of-interest (POI) recommendation not only assists users to find their preferred places, but also helps businesses to attract potential customers. Recent studies have proposed many approaches to the POI recommendation. However, the data sparsity and complexity of user check-in behavior still pose big challenges to accurate personalized POI recommendation. To tackle these problems, in this paper, we propose a POI recommendation model named HeteGeoRankRec based on user contextual behavior semantics. First, we employ the meta-path of heterogeneous information network (HIN) to represent the complex semantic relationship among users and POIs. Second, we introduce different context constraints (such as time and weather) into the meta-path, to reveal the fine-grained user behavioral features. Afterwards, we propose a weighted matrix factorization model which considers the influence of geographical distance through the user–POI semantic correlativity matrices generated by multiple meta-paths. Finally, we present a fusion method based on learning to rank, which unifies the recommendation results of different meta-paths as the final user preference. The experiments on the real data collected from Foursquare demonstrate that HeteGeoRankRec has the better performance than the state-of-the-art baselines.
Anomalous amplification in almost-balanced weak measurement for measuring spin Hall effect of light

In this paper, a method to measure the tiny spin splitting of the spin Hall effect of light (SHEL) using the almost-balanced weak measurement (ABWM) is presented. The ABWM technique uses two orthogonal post-selected states to record all of the information, which is a precise measurement method being different from the standard weak measurement (SWM). The theory model to describe the SHEL measurement based on ABWM is established. As results, the ABWM scheme has a larger amplification factor, reaching ∼10 5 , which is nearly one order of magnitude higher than that of the SWM. When the post-selected angle is less than a certain value, the sensitivity and amplification factor of the ABWM scheme are higher than those of the SWM scheme, while the measurement precision and SNR of the ABWM technique are comparable to those of the SWM scheme. This research may have great potential for the precision metrology or sensing field based on the SHEL measurement.
The Rational Basis for the Use of Biomarkers as Ecotoxicological Tools

The key problem faced by ecotoxicological scientists is to recognize the damaging effects of chemical pollutants on natural biota. This chapter highlights the advantages of using nondestructive biomarkers approaches. Biomarkers are used extensively in all branches of medicine, but perhaps they have found most utility in the field of oncology where markers of tumor growth are routinely used to monitor the course of a disease and the effectiveness of treatment. Biomarkers have been used extensively in the evaluation of occupational health risks. The possibility of using biomarkers in the context of ecotoxicology has been recognized many times, but was not explicitly formulated into a coherent approach until attempts began to clearly define biomarkers. Biomarkers raise the possibility of determining where an organism lies on the health status curve; and thus potentially offer an early warning of when pollutant exposure induces early, reversible departures from homeostasis or from the compensatory zone.
Suppressing ripple distortions and spurious pistons in phase-shifting interferometry

In this paper, we present new phase-shifting algorithms (PSAs) that suppress the ripple distortions and spurious pistons in phase-shifting interferometry. These phase errors arise when non-uniform phase-shifting interferograms are processed with PSAs that assume uniform phase shifts. By modeling the non-uniform phase shifts as a polynomial of the unperturbed phase-shift value <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msub> <mml:mi>ω<!-- ω --></mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> , we show that the conditions for eliminating the ripple distortion and the spurious piston are associated with the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>m</mml:mi> </mml:math> th derivative of the PSA’s frequency transfer function (FTF). Thus, we propose an approach to design robust algorithms based on the FTF formalism and we present four ready-to-apply PSAs formulas. Finally, our conclusions are supported by computer simulations.
Reservoir computing and decision making using laser dynamics for photonic accelerator

The photonic accelerator is a new paradigm of photonic technologies for artificial intelligence, wherein the systems can accelerate information processing in electronic computing. Schemes for reservoir computing and decision making are promising examples of the photonic accelerator. In this work, we describe recent advances in the architectures of reservoir computing and decision making based on complex photonics, wherein the physics of light, including ultrafast dynamics in semiconductor lasers with optical feedback, plays a crucial role in the photonic accelerator.
Impact of Single Sign-on Adoption in an Assessment Triage Unit

BACKGROUND Clinician time is a valuable and costly resource. In an age where computer and clinical application usage is increasing, the need for providers to remember multiple usernames and passwords is increasingly inefficient. Further complexity is added to clinical workflows if clinicians must log into smart devices frequently without a simple solution for access and data security. OBJECTIVE The purpose of this study was to measure the average time required and time savings when taking vital sign measurements using the Single Sign-On (SSO) (badge scan and go) on the electronic medical record–connected vital sign monitors versus using 2-factor authentication (entry of username and password). In addition, user satisfaction, system usability, workflow preference, and efficacy of the deployment of Network Connectivity Engine, that is, the host middleware for SSO, were evaluated through surveys. METHODS A preimplementation and postimplementation study was performed in the unit piloting the initiative before hospital-wide deployment. Clinician usability and workflow preference surveys were conducted postimplementation. RESULTS Analysis showed significance in time savings by adopting SSO. CONCLUSION SSO had a positive impact on staff efficiency and was the preferred method versus the 2-factor log-in.
Optimizing laser triangulation displacement sensor of 3D positioning and posture using COA Based BPNN

Purpose The purpose of this paper is to propose cuckoo optimization algorithm (COA)-based back propagation neural network (BPNN) to reduce the effect of the nonlinearities presented in laser triangulation displacement sensors. The 3D positioning and posture sensor allows access to the third dimension through depth measurement; the performance of the sensor varies according to the level of nonlinearities presented in the system, which leads to inaccuracies in measurement. Design/methodology/approach While applying optimization approach, the mathematical model and the relationship between the key parameters in the laser triangulation ranging and the indexes of the measuring system were analyzed. Findings Based on the performance of the parametric optimization method, the measurement repeatability reached 0.5 µ m with an STD value within 0.17 µ m, an expanded uncertainty of measurement was within 5 µ m, the angle error variation of the object’s rotational plane was within 0.031 degrees and nonlinearity was recorded within 0.006 per cent in a full scale. The proposed approach reduced the effect of the nonlinearity presented in the sensor. Thus, the accuracy and speed of the sensor were greatly increased. The specifications of the optimized sensor meet the requirements for high-accuracy devices and allow wide range of industrial application. Originality/value In this paper, COA-based BPNN is proposed for laser triangulation displacement sensor optimization, on the basis of the mathematical model, clarifying the working space and working angle on the measurement system.
Sentiment Analysis of Bengali Reviews for Data and Knowledge Engineering: A Bengali Language Processing Approach

Opinion mining is very much attractive field inmachind learning system as it is very much needed for natural language processing. The opinion mining of Bengali written English word has been done successfully using four different classifiers—support vector machine, naive Bayes, logistic regression and random forest. For the work data set was extracted from local online shops using pursehub. The work was done with vital steps—data preparation, classifying reviews according to sentiment score and evaluate the system in all steps. The F1 score was obtained 85.25%, 88.12%, 88.12%, 82.43% for naïve Bayes, logistic regression, SVM, random forest, respectively. The accuracy score was obtained 85.31%, 88.05%, 88.11%, 81.82% for naïve Bayes, logistic regression, SVM, random forest, respectively. The precision score was obtained 85.56%, 88.54%, 87.59%, 79.14% for naïve Bayes, logistic regression, SVM, random forest, respectively. The recall score was obtained 84.95%, 88.72%, 88.80%, 85.30% for naïve Bayes, logistic regression, SVM, random forest, respectively.
Differentially Private Functional Mechanism for Generative Adversarial Networks

In recent years, generative adversarial network (GAN) has attracted great attention due to its impressive performance and potential numerous applications, such as data augmentation, real-like image synthesis, image compression improvement, etc. The generator in GAN learns the density of the distribution from real data in order to generate high fidelity fake samples from latent space and deceive the discriminator. Despite its advantages, GAN can easily memorize training samples because of the high model complexity of deep neural networks. Thus, training a GAN with sensitive or private data samples may compromise the privacy of training data. To address this privacy issue, we propose a novel \textit{Privacy Preserving Generative Adversarial Network} (PPGAN) that perturbs the objective function of discriminator by injecting Laplace noises based on functional mechanism to guarantee the differential privacy of training data. Since generator training is considered as a post-processing step while guaranteeing differential privacy of discriminator, the trained generator should be differentially private to effectively protect data samples. Through detailed privacy analysis, we theoretically prove that PPGAN can provide such strict differential privacy guarantee. With extensive simulation study on the benchmark dataset MNIST, we show the efficacy of the proposed PPGAN under practical privacy budgets.
Image Deconvolution with Deep Image and Kernel Priors

Image deconvolution is the process of recovering convolutional degraded images, which is always a hard inverse problem because of its mathematically ill-posed property. On the success of the recently proposed deep image prior (DIP), we build an image deconvolution model with deep image and kernel priors (DIKP). DIP is a learning-free representation which uses neural net structures to express image prior information, and it showed great success in many energy-based models, e.g. denoising, super-resolution, inpainting. Instead, our DIKP model uses such priors in image deconvolution to model not only images but also kernels, combining the ideas of traditional learning-free deconvolution methods with neural nets. In this paper, we show that DIKP improve the performance of learning-free image deconvolution, and we experimentally demonstrate this on the standard benchmark of six standard test images in terms of PSNR and visual effects.
Joint 3D Tracking and Forecasting with Graph Neural Network and Diversity Sampling

3D multi-object tracking (MOT) and trajectory forecasting are two critical components in modern 3D perception systems that require accurate modeling of multi-agent interaction. We hypothesize that it is beneficial to unify both tasks under one framework in order to learn a shared feature representation of agent interaction. To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units. First, we propose a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which multiple agents interact with one another. The GNN is able to model complex hierarchical interactions, improve the discriminative feature learning for MOT association, and provide socially-aware context for trajectory forecasting. Second, we use a diversity sampling function to improve the quality and diversity of our forecasted trajectories. The learned sampling function is trained to efficiently extract a variety of outcomes from a generative trajectory distribution and helps avoid the problem of generating many duplicate trajectory samples. We evaluate on the KITTI and nuScenes datasets, showing that our unified method with feature interaction and diversity sampling achieves new state-of-the-art performance on both 3D MOT and trajectory forecasting. Our code will be made available at this https URL.
Vehicle Type Recognition Based on Radon-CDT Hybrid Transfer Learning

Recognition of vehicle type is an important and challenging tasks. To improve the accuracy and reduce the computational complexity of model for vehicle type recognition, this paper proposes the vehicle type recognition method based on Radon-CDT hybrid transfer learning. Deep features of images are obtained by the pre-trained CNNs, then Radon-CDT are used to capture the non-linearly property of the features. This method makes full use of the excellent feature extraction ability of convolution layer and the property to make data linearly separable of Radon-CDT. Experimental results show that the proposed method is a very promising alternative to deal with the recognition of vehicle type, which obtains a better recognition accuracy than other popular models (AlexNet, VGG and ResNet).
Deep neural networks with a set of node-wise varying activation functions

In this study, we present deep neural networks with a set of node-wise varying activation functions. The feature-learning abilities of the nodes are affected by the selected activation functions, where the nodes with smaller indices become increasingly more sensitive during training. As a result, the features learned by the nodes are sorted by the node indices in order of their importance such that more sensitive nodes are related to more important features. The proposed networks learn input features but also the importance of the features. Nodes with lower importance in the proposed networks can be pruned to reduce the complexity of the networks, and the pruned networks can be retrained without incurring performance losses. We validated the feature-sorting property of the proposed method using both shallow and deep networks as well as deep networks transferred from existing networks.
FPGA Based Novel True Random Number Generator using LFSR with Dynamic Seed

True random number generators are widely used in cryptography and VLSI testing. However, the output of random number generator with LFSR becomes predictable after sufficient observations. We proposed LFSR based random number generator with novel on the fly seed change using System monitor of FPGA. Temperature variation of the chip and power supply voltage variations are used for random seed generation. Proposed scheme is implemented on Xilinx Virtex-5 ML505 board. Experimental results show that on-chip resources take less area and power consumption. Randomness results are tested using NIST Statistical test suite and passed all the tests.