Reviewerly/exHarmony huggingface dataset:
Merge-Exchange Sort Based Discrete Gaussian Sampler with Fixed Memory Access Pattern

Discrete Gaussian samplers are used to sample integers from a discrete Gaussian distribution. Since this functionality is used in operations such as key generation, signing, or key encapsulation of lattice-based schemes, it is a fundamental building block of these cryptographic algorithms. One required feature of modern discrete Gaussian samplers when used in cryptographic algorithms is to be constant-time, to ensure security against timing side-channel attacks. Further, it is often desired to minimize potential for power or EM side-channel attacks by limiting how much information an attacker can gain from measuring power traces. To address the need for having a Gaussian sampler with these features in hardware, this paper presents a novel hardware implementation of a constant-time discrete Gaussian sampler with fixed memory access pattern realized on FPGAs. The design uses an approach based on Cumulative Distribution Table (CDT). Further, the new sampler uses a merge-exchange sort algorithm that enables generating the samples in batches. In the hardware, due to the use of the merge-exchange sort algorithm, the memory access pattern is always fixed, regardless of the values of the secret samples. This increases the resistance of the sampler to potential power or EM side-channel attacks as memory usage and accesses are independent of the secret values. The presented sampler can be fully parameterized at compile-time with the following Gaussian parameters: standard deviation, precision, and tail cut, generating a hardware design that matches the exact parameters required by the cryptographic algorithm. In addition, it can be parameterized, at compile-time, with the batch size for the number of samples to generate at a time. The design evaluation is based on synthesis data for various Xilinx FPGAs.
Fast Distributional Smoothing for Regularization in CTC Applied to Text Recognition

Many recent text recognition studies achieved successful performance by applying a sequential-label prediction framework such as connectionist temporal classification. Meanwhile, regularization is known to be essential to avoid overfitting when training deep neural networks. Regularization techniques that allow for semi-supervised learning have a greater impact than those that do not. Among widely researched single-label regularization techniques, virtual adversarial training (VAT) performs successfully by smoothing posterior distributions around training data points. However, VAT is almost solely applied to single-label prediction tasks, not to sequential-label prediction tasks. This is because the number of possible candidates in the label sequence exponentially increases with the sequence length, making it impractical to calculate posterior distributions and the divergence between them. Investigating this problem, we have found that there is an easily computable upper bound for divergence. Here, we propose fast distributional smoothing (FDS) as a method for drastically reducing computational costs by minimizing this upper bound. FDS allows regularization at practical computational costs in both supervised and semi-supervised learning. An experiment under simple settings confirmed that upper-bound minimization decreases divergence. Experiments also show that FDS improves scene text recognition performance and enhances state-of-the-art regularization performance. Furthermore, experiments show that FDS enables efficient semi-supervised learning in sequential-label prediction tasks and that it outperforms a conventional semi-supervised method.
Mean-Field Analysis of Two-Layer Neural Networks: Non-Asymptotic Rates and Generalization Bounds.

A recent line of work in deep learning theory has utilized the mean-field analysis to demonstrate the global convergence of noisy (stochastic) gradient descent for training over-parameterized two-layer neural networks. However, existing results in the mean-field setting do not provide the convergence rate of neural network training, and the generalization error bound is largely missing. In this paper, we provide a mean-field analysis in a generalized neural tangent kernel regime, and show that noisy gradient descent with weight decay can still exhibit a kernel-like behavior. This implies that the training loss converges linearly up to a certain accuracy in such regime. We also establish a generalization error bound for two-layer neural networks trained by noisy gradient descent with weight decay. Our results shed light on the connection between mean field analysis and the neural tangent kernel based analysis.
Do Bitcoin Stylized Facts Depend on Geopolitical Risk?

We show that the dynamics of Bitcoin (BTC) price are strongly influenced by the level of global geopolitical risk. Indeed, a number of well established stylized facts about BTC cease to be true when we condition the evolution of BTC returns on the GPR index. In particular, we find that when geopolitical risk is high, BTC is no longer a perfect portfolio diversifier as it correlates strongly with Gold, US treasury yields and negatively with EUR/USD. We also find that BTC price bubbles are much more likely to occur when geopolitical risk is high, i.e. when investors flock to BTC as a digital safe haven or to a lesser extent when geopolitical risk is low and the BTC market behavior is speculative. Conversely, when geopolitical risk is moderate we find that BTC returns are approximately normally distributed and therefore do not seem to foster asset pricing bubbles. These results suggest that investors should adjust their portfolio holdings of BTC while adequately taking into account the amount of geopolitical risk present in the economy. Last, we find that the efficiency (in its weak form) of the BTC market increases with the level of geopolitical risk and that in fact the BTC market is rather efficient for moderate and high GPR.
Forecasting the probability of solar power output using logistic regression algorithm

Now days, Natural resources are depleting due to rapid urbanization and increasing demand of energy requirement. Hence, Renewable energy plays a vital role in sustainable development. The role and selection of solar variables and applying machine learning algorithm plays a vital role for forecasting the probability of solar power output for any selected day in the future. This article Includes datasets collected for over a period for around eleven months output of plant, incident solar radiations, and local temperature. These datasets are feed to one of supervised machine learning algorithms with logistic regression for short term forecasting of solar plant output on a particular day in future using previous year data. For efficient forecasting of solar plant output proper variable are to be selected and used to feed the logistic regression algorithm to obtain efficiency in training the data and predict the plant output. In this article the accuracy obtained for amount of data feed is studied and also the forecast the probability in order to predict the output from that plant.
Introduction to the Issue on Perception-Driven 360° Video Processing

The papers in this special section examine perception driven 360 degrees video processing applications. Covers such topics as perception models, VQA metrics, and compression/streaming techniques for 360° video/image.
Analisa Keamanan Jaringan Menggunakan Network Intrusion Detection and Prevention System

Security is an important aspect to be considered in computer networks. This security system can be a detection and prevention of attacks that are being done by the attacker (intruders). The problem of attacks that occur in computer networks is that intruders can do port scanning, enter the system using open ports such as telnet, ftp and others.. The purpose of this study is the implementation of IDPS, can be from. To do network security from various attack threats, a system that can detect and prevent it directly is needed. The method that can be used is Intrusion Detection and Prevention System (NIDPS). NIDPS can exchange and block the attacks. This security system is collaborated with IP Tables. IP Tables is used to filter incoming data packets and drop packets of data that are indicated by attack. With the Intrusion Detection and Prevention system, it can detect attacks and prevent them by blocking data packets sent by intruders through port scanning, FTP attacks, and telnets.
An Unsupervised Framework for Detecting Early Signs of Illness in Eldercare

Using non-wearable sensors in eldercare monitoring is a promising solution for improving care and reducing healthcare costs. Abnormal sensor patterns produced by certain resident behaviors can be linked to early signs of illness. We propose an unsupervised framework for detecting abnormal sensor patterns based on clustering activity sensor sequences. We use a 30-day normal window to build a baseline model of an elderly resident by clustering the activity sequences from these days. Each cluster represents different daily activities that are performed in most (normal) days and correspond to normal routines. If a new day contains fewer routine activities, we flag it as abnormal and label the day as one with a possible sign of early illness. A preliminary analysis of the method was conducted on data collected in TigerPlace, an eldercare facility that promotes aging-in-place, with information from our electronic health records (EHR). On a pilot sensor dataset from three residents, with a total of 1902 days, we achieved an average abnormal events prediction of 0.75.
Optimizing the Cloud Data Center Availability Empowered by Surrogate Models

Making data centers highly available remains a challenge that must be considered since the design phase.The problem is selecting the right strategies and components for achieving this goal given a limited investment.Furthermore, data center designers currently lack reliable specialized tools to accomplish this task.In this paper, we disclose a formal method that chooses the components and strategies that optimize the availability of a data center while considering a given budget as a constraint.For that, we make use of stochastic models to represent a cloud data center infrastructure based on the TIA-942 standard.In order to improve the computational cost incurred to solve this optimization problem, we employ surrogate models to handle the complexity of the stochastic models.In this work, we use a Gaussian process to produce a surrogate model for a cloud data center infrastructure and we use three derivative-free optimization algorithms to explore the search space and to find optimal solutions.From the results, we observe that the Differential Evolution (DE) algorithm outperforms the other tested algorithms, since it achieves higher availability with a fair usage of the budget.
An Algorithm for Auto-Correction in PaTik Bali Using Pasang Pageh Aksara Wianjana

Now Balinese script is included in the Unicode standard. The inclusion of Balinese script in Unicode makes it easy to display Balinese script in various media. In addition to entering into UNICODE, Balinese script was developed into the making of physical Balinese scripts. However, the use of scripts in information media has been wrong lately. It can be seen from some news in the newspapers stating that Balinese script writing is still not quite right. Therefore, we need a tool that can minimize errors in writing Balinese characters. PaTik Bali is a mobile-based keyboard application that has been installed more than 16,000 devices [1]. PaTik Bali has an auto-correction feature that can automatically make changes to words written by the user. This study discusses how the algorithm used by PaTik Bali in performing an auto-correction for Pasang Pageh Aksara Wianjana. The results of this study were tested using the BlackBox method by testing the functional PaTik Bali with several examples of words according to the Pasang Pageh Aksara Wianjana. The test results show that the application of the auto-correction algorithm to PaTik Bali is successful.
Automatic Chinese Handwriting Verification Algorithm Using Deep Neural Networks

Handwriting verification is to identify whether a script was written by a person himself or forged. Conventional handwriting verification algorithms are based on feature extraction. However, the features of scripts are highly affected by the writing instrument, the posture, and the force of writing, even if the scripts were written by the same person, the extracted features will be quite different. Moreover, since some writers might not write some strokes clearly or ignore some strokes, not all features can be well extracted in every script. Therefore, in this paper, we apply a deep neural network based algorithm for handwriting verification. With the proposed algorithm, the parts that are really powerful and robust for handwriting verification can be highlighted by the auto-encoder. Then, a very high accurate handwriting verification result can be achieved.
Paraphrase Identification Between Two Sentence Using Support Vector Machine

Paraphrasing sentence is to express a sentence using another form of language without changing the meaning of the previous sentence. In this study, a system of identification of meanings of sentences has been built using Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO) algorithm, the features used are Euclidean distance, cosine similarity, sentence length 1, sentence length 2, slices between sentences. The test results showed better results on SVM configuration using attribute filters achieving the best results 94.4% in training using the train set test and 61.9% in SVM configuration without using attribute filters.
Learning Based Noise Identification Techniques Using Time-Frequency Analysis and the U-Net

In wireless communication, it is inevitable that the signal is highly affected by the noise. For example, for the radar located in the sea shore, due to the effects of sea clutter and remote detection range, the signal to noise ratio (SNR) is only about 0~15 dB. In this manuscript, we develop an advanced noise determination and removal algorithm based on the deep learning method of the U-net. The U-net is a pixel-wise classification network and widely used in image segmentation. In this work, we find that it is also an effective way to determine whether a pixel in the time-frequency domain is the signal part or the noise part, even in the low SNR case. It is very helpful for reducing the noise effect and improving the accuracy of fundamental frequency analysis for radar signal processing.
Comparison Analysis: Large Data Classification Using PLS-DA and Decision Trees

Classification studies are widely applied in many areas of research.In our study, we are using classification analysis to explore approaches for tackling the classification problem for a large number of measures using partial least square discriminant analysis (PLS-DA) and decision trees (DT).The performance for both methods was compared using a sample data of breast tissues from the University of Wisconsin Hospital.A partial least square discriminant analysis (PLS-DA) and decision trees (DT) predict the diagnosis of breast tissues (M = malignant, B = benign).A total of 699 patients diagnose (458 benign and 241 malignant) are used in this study.The performance of PLS-DA and DT has been evaluated based on the misclassification error and accuracy rate.The results show PLS-DA can be considered as a good and reliable technique to be used when dealing with a large dataset for the classification task and have good prediction accuracy.
Facial micro-expression recognition based on local region of the key frame

Traditional studies on micro-expression feature extraction primarily focused on global face from all frames. To improve the efficiency of feature extraction, this paper proposes a new framework based on the local region and the key frame to represent facial micro-expressions. Firstly, the face feature point detection technique is used to acquire the coordinates of the 68 key points, and the region of interest is divided by those key point coordinates and the action unit. Secondly, in order to remove redundant information in the micro-expression video sequence, structural similarity index (SSIM) is used to select key frames for each local region of interest. Finally, the dual-cross patterns (DCP) are extracted for the local regions of interest and are concatenated into a feature vector for the final classification. The experimental results show that compared with the traditional micro-expression method, the proposed method has higher recognition rate and achieves better time computation performance.
Non contact Two Dimensional Strain Measurement Based on Improved Sub Image Search Algorithm using Correlation Technique

Strain is a proportion of the measure of stretch or pressure onward a component. Strain estimation is a fundamental factor of components analyzing. There are countless strain estimation systems: particularly electrical obstruction strain check, extrensometer, geometric Moire strategy also optical strain estimation technique among others. Encompassed by the various procedures for strain estimation, digital image correlation (DIC) has been demonstrated to be truly adaptable, powerful and simple to utilize. The target of this research is to build up an improved sub image algorithm based correlation method that figures the strain in a stacked structural part. This can be accomplished by utilizing digital image correlation techniques systems which matches the relocation of the irregular spots design in the reference with the twisted pictures. The new proposed calculation was coded into MATLAB program using the MATLAB's picture handling tool compartment. Tensile test tested worth and with conventional algorithm. And furthermore, our proposed strategy demonstrated that is given the better accuracy and execution time is diminished. All in all, the two dimentional proposed calculation had been effectively created
Obstacle Detection and Path Planning Based On Monocular Vision for Unmanned Aerial Vehicles

This article mainly introduces the algorithm design and simulation process of unmanned aerial vehicle obstacle detection and path planning based on monocular vision. In this article, we will first explain the basics of image processing, with focus on the design algorithms and the operations and principles that will be used in simulation experiments in the second half of the article. The subject is the obstacle detection and path planning of drones, so the article divides this topic into two sub-topics. Subtask 1 focuses on solving obstacle detection problems for drones. This paper will use the method of contour detection. Subtask 2 focuses on solving the problem of path planning after unmanned aerial vehicles detect obstacles. This paper uses A* path search algorithm. The simulation results show that the algorithm has a good degree of completion, and all the materials used can basically avoid obstacles accurately and plan the relatively safe and shortest optimal path.
Geometry-Guided View Synthesis with Local Nonuniform Plane-Sweep Volume

In this paper we develop a geometry-guided image generation technology for scene-independent novel view synthesis from a stereo image pair. We employ the successful plane-sweep strategy to tackle the problem of 3D scene structure approximation. But instead of putting on a general configuration, we use depth information to perform a local nonuniform plane spacing. More specifically, we first explicitly estimate a depth map in the reference view and use it to guide the planes spacing in plane-sweep volume, resulting in a geometry-guided manner for scene geometry approximation. Next we learn to predict a multiplane images (MPIs) representation, which can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline, to allow for efficient view synthesis. Our results on massive YouTube video frames dataset indicate that our approach makes it possible to synthesize higher quality images, while keeping the number of depth planes.
Single image super-resolution based on enhanced deep residual GAN

With the application of image more and more widely, People put forward higher requirements on the image quality of small objects and details in the image. In recent years, with the development of deep learning, it achieved good results in the research of image super-resolution. In this paper, we proposed EDSRGAN, a single image super-resolution(SISR) algorithm, based on enhanced residual network and the adversarial network. Compared with SRGAN, which is also based on the adversarial network, EDSRGAN can greatly reduce the high-frequency noise contained in the super-resolution(SR) image, and it also leads SRGAN in terms of peak signal to noise ratio and structural similarity evaluation indicators. Although EDSRGAN lagged behind EDSR in terms of peak signal to noise ratio and structural similarity, the SR images generated by EDSRGAN were sharper than EDSR in the object edges and targets details. EDSRGAN could achieve good results in image super-resolution on small targets.
Research on The Detection Method for Insulation Piercing Connectors and Bolts on The Transmission Line Based on SSD Algorithm

Aiming at the problem that the piercing clamp and bolt on transmission line is easily effected by illumination, occlusion, environmental background and shooting angle, a method for detecting the piercing clamp and bolt on transmission line based on SSD algorithm is proposed in this paper. In this method, the data set get enhanced by means of flipping, translation and angle rotation, The method of multi-scale feature fusion is used to fuse deeper features of shallow feature maps, increase the perception field of shallow feature maps and improve the detection accuracy for small objects, and the intrinsic VGGNet Network in SSD Architecture is replaced with the Deep Residual Networks(ResNet-50) with deeper network depth and less computational complexity to extract the image feature. The result turns out that the mAP value of SSD models with ResNet50 reaches 91.5%, which increases by 3.2% compared with that of the models using VGG16. By using the Deep Learning object detection model, the puncture clamp with different resolution and different position angles can be better detected, which has high practical value for engineering application.
Ranking of Odia Text Document Relevant to User Query Using Vector Space Model

In this digital world, there is no lack of digitize text, image, audio and video for any subject but extracting the conceptual information relevant material is very difficult and achieved by different information retrieval(IR) technologies. Boolean Matching Model, Probabilistic Model, Extended Boolean Model, Fuzzy Set Model and Vector Space Model are some of the widely used models for information retrieval. Among these, vector space model (VSM) is one of the classical and widely used model which works based on the mathematical concepts of linear algebra. In VSM, queries and documents are the vectors in the highly multidimensional space and terms are used as dimensions to construct the index key to represent the documents. In this paper, we consider different approaches of vector space model for information retrieval and compare the results to find a neat understanding of term count model, classical vector space model and normalized vector space model. All the three models works based on the concept of term frequency (tf) and inverse document frequency (idf). Here the vector space retrieval technology we have applied on odia text to get an overview how VSM works on regional language like Odia. Odia (officially changed the name from Oriya to Odia in November, 2011) is one of the language mainly spoken by the people live in Odisha, the south east state of India. This Model can also be used for text summarization and data mining purpose.