Reviewerly/exHarmony huggingface dataset:
A Walkthrough of Blockchain Technology and Its Potential Applications

The Internet has become the hub of centralized applications. In order to give power to every member of a system, the decentralized methodology has been introduced. Blockchain is the leisure of blocks that are synchronously created on a peer to peer network. The characteristics of Blockchain such as decentralization, transparency, synchronous, and immutability provides it the power of many new applications. This paper aims to explore Blockchain and its process of working. Furthermore, it aims to elaborate on various applications keeping in mind the most recent developments.
Developing a Multilingual Annotated Corpus of Misogyny and Aggression

In this paper, we discuss the development of a multilingual annotated corpus of misogyny and aggression in Indian English, Hindi, and Indian Bangla as part of a project on studying and automatically identifying misogyny and communalism on social media (the ComMA Project). The dataset is collected from comments on YouTube videos and currently contains a total of over 20,000 comments. The comments are annotated at two levels - aggression (overtly aggressive, covertly aggressive, and non-aggressive) and misogyny (gendered and non-gendered). We describe the process of data collection, the tagset used for annotation, and issues and challenges faced during the process of annotation. Finally, we discuss the results of the baseline experiments conducted to develop a classifier for misogyny in the three languages.
Outlier Detection Based on Residual Histogram Preference for Geometric Multi-Model Fitting

Geometric model fitting is a fundamental issue in computer vision, and the fitting accuracy is affected by outliers. In order to eliminate the impact of the outliers, the inlier threshold or scale estimator is usually adopted. However, a single inlier threshold cannot satisfy multiple models in the data, and scale estimators with a certain noise distribution model work poorly in geometric model fitting. It can be observed that the residuals of outliers are big for all true models in the data, which makes the consensus of the outliers. Based on this observation, we propose a preference analysis method based on residual histograms to study the outlier consensus for outlier detection in this paper. We have found that the outlier consensus makes the outliers gather away from the inliers on the designed residual histogram preference space, which is quite convenient to separate outliers from inliers through linkage clustering. After the outliers are detected and removed, a linkage clustering with permutation preference is introduced to segment the inliers. In addition, in order to make the linkage clustering process stable and robust, an alternative sampling and clustering framework is proposed in both the outlier detection and inlier segmentation processes. The experimental results also show that the outlier detection scheme based on residual histogram preference can detect most of the outliers in the data sets, and the fitting results are better than most of the state-of-the-art methods in geometric multi-model fitting.
Adaptive image denoising for speckle noise images based on fuzzy logic

Abstract Speckle noise is a kind of ubiquitous noise in medical image, which will damage the texture structure of image and affect the analysis of image structure by doctors. Therefore, we propose an image denoising model based on fuzzy logic, which can eliminate speckle noise in the image well, improve the recognition of the image, and facilitate the acquisition of image information by doctors. The main work arrangement of the algorithm model is to design a membership function that can traverse the noise image and preprocess the noise image to make the image smooth. Then, a mask template of 5 × 5 is designed by the definition of g‐l calculus, and there is mainly an unknown parameter in this template. We design the functional relation between this parameter and the image gradient, which makes the model algorithm adaptive. Finally, the convolution operation is performed between the template and the smooth image. By comparison with the existing mainstream models, the overall denoising effect of this model is better than other models, and the relevant numerical indexes are better than other models. This model is an extension of the denoising model of fuzzy theory, which is beneficial to the future research and development.
Brain Image Enhancement Approach Based on Singular Value Decomposition in Nonsubsampled Shearlet Transform Domain

In this work, a novel image enhancement algorithm using NSST and SVD is proposed to improve the definition of the acquired brain images. The input brain image is computed by CLAHE, then the processed brain image and input brain image are decomposed into low- and high-frequency components by NSST, the singular value matrix of the low-frequency component is estimated. The final enhancement image is obtained by inverse NSST. Results of this experiment demonstrate that the proposed technique has good performance in terms of brain image enhancement when compared to other methods.
Posterior Calibrated Training on Sentence Classification Tasks

Most classification models work by first predicting a posterior probability distribution over all classes and then selecting that class with the largest estimated probability.In many settings however, the quality of posterior probability itself (e.g., 65% chance having diabetes), gives more reliable information than the final predicted class alone.When these methods are shown to be poorly calibrated, most fixes to date have relied on posterior calibration, which rescales the predicted probabilities but often has little impact on final classifications.Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities.We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives.Our PosCal achieves about 2.5% of task performance gain and 16.1% of calibration error reduction on GLUE (Wang et al., 2018) compared to the baseline.We achieved the comparable task performance with 13.2% calibration error reduction on xSLUE (Kang and Hovy, 2019), but not outperforming the two-stage calibration baseline.PosCal training can be easily extendable to any types of classification tasks as a form of regularization term.Also, PosCal has the advantage that it incrementally tracks needed statistics for the calibration objective during the training process, making efficient use of large training sets 1 .
Design and Implementation of Autonomous Quadcopter using SITL Simulator

In recent years Quadcopter has been used in many applications such as military, security &amp; surveillance, service delivery, disaster rescue and much more due to its flexibility of flying. In this paper, Quadcopter will be used for mail delivery between many locations that is received from the end user. The Quadcopter will execute an autonomous flight using the concept of companion PC. Raspberry PI 3 (RPI3) will control the Quadcopter by command the controller of the drone (Pixhawk) by using DroneKit-Python API to send MAVLink messages to the Ardupilot. This concept is useful to perform an additional task to the autopilot and provide such a smart capability like image processing and path planning which cannot be done by the flight controller alone. Basically, the idea has been stimulated and the code has been tested by using the SITL Simulator with MAVProxy under Ubuntu environment. The result of controlling the Quadcopter using Python script was excellent and give a motivation to implement the same script on a real Quadcopter. The implementation on real Quadcopter was perfect as it has given the same behavior as the SITL drone in the simulation.
Statistical Mechanics of Generalization in Kernel Regression.

Generalization beyond a training dataset is a main goal of machine learning. We investigate generalization error in kernel regression using statistical mechanics and derive an analytical expression for it applicable to any kernel. Focusing on the broad class of rotation invariant kernels, which is relevant to training deep neural networks in the infinite-width limit, we show several phenomena. When data is drawn from a spherically symmetric distribution and the number of input dimensions, $D$, is large, we find that multiple learning stages exist, one for each scaling of the number of training samples with $\mathcal{O}_D(D^K)$ with $K\in Z^+$. In each stage $\mathcal{O}_D(D^K)$ degenerate spectral modes corresponding to the $K$-th kernel eigenvalue are learned. The mathematical analysis of a learning stage reduces to that of a solvable model with the dimensionality of the feature space extensive in the number of samples and a white kernel spectrum, including linear regression as a special case. The behavior of the learning curve in each stage is governed by an effective regularizer and an effective target noise that are related to the tail of the kernel and the target function spectra. When effective regularization is zero, we identify a first order phase transition that corresponds to a divergence in the generalization error. Each learning stage can exhibit sample-wise \textit{double-descent}, where learning curves show non-monotonic sample size dependence. For each stage an optimal value of effective regularizer exists, equal to the effective noise variance, that gives minimum generalization error.
Invasive Weed Optimization Algorithm for Tuning Transitioning from Type-1 to Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters

Some studies tried to make transition from Type-1 to interval Type-2 membership functions, but they get the problems of choosing the footprint uncertainty size in the Interval Type-2 Membership Functions.In this paper, our objective is to employ two optimization methods: Invasive Weed Optimization (IWO) and Particle Swarm Optimization (PSO) for tuning the Transitioning from Type-1 To Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters and compare their performances.Also, we will discuss the effects of the PID values in the operation of transition from Type-1 to interval Type-2 fuzzy logic Controller for Boost DC-DC Converters.The simulation results show IWO optimization methods is helpful to Tuning the Transitioning from Type-1 To Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters.Moreover, when we tune both PID values and the FOU size in T2-MFs of Interval Type-2 Fuzzy Logic PID-controller, we will get the best performance for interval Type-2 Fuzzy Logic PID-controller for Boost DC-DC Converter.To sum up; the optimal footprint of uncertainty (FOU) size in interval Type-2 membership functions, they have an essential role and good effect in the performance of Interval Type-2 fuzzy logic Controller for Boost DC-DC Converters.
Unifying Person and Vehicle Re-Identification

Person and vehicle re-identification (re-ID) are important challenges for the analysis of the burgeoning collection of urban surveillance videos. To efficiently evaluate such videos, which are populated with both vehicles and pedestrians, it would be preferable to have one unified framework with effective performance across both domains. Unfortunately, due to the contrasting composition of humans and vehicles, no architecture has yet been established that can adequately perform both tasks. We release a Person and Vehicle Unified Data Set (PVUD) comprising of both pedestrians and vehicles from popular existing re-ID data sets, in order to better model the data that we would expect to find in the real world. We exploit the generalisation ability of metric learning to propose a re-ID framework that can learn to re-identify humans and vehicles simultaneously. We design our network, MidTriNet, to harness the power of mid-level features to develop better representations for the re-ID tasks. We help the system to handle mixed data by appending unification terms with additional hard negative and hard positive mining to MidTriNet. We attain comparable accuracy training on PVUD to training on the comprising data sets separately, supporting the system's generalisation power. To further demonstrate the effectiveness of our framework, we also obtain results better than, or competitive with, the state-of-the-art on each of the Market-1501, CUHK03, VehicleID and VeRi data sets.
Crowd Verifiable Zero-Knowledge and End-to-End Verifiable Multiparty Computation

Auditing a secure multiparty computation (MPC) protocol entails the validation of the protocol transcript by a third party that is otherwise untrusted. In this work, we introduce the concept of end-to-end verifiable MPC (VMPC), that requires the validation to provide a correctness guarantee even in the setting that all servers, trusted setup primitives and all the client systems utilized by the input-providing users of the MPC protocol are subverted by an adversary. To instantiate VMPC, we introduce a new concept in the setting of zero-knowlegde protocols that we term crowd verifiable zero-knowledge (CVZK). A CVZK protocol enables a prover to convince a set of verifiers about a certain statement, even though each one individually contributes a small amount of entropy for verification and some of them are adversarially controlled. Given CVZK, we present a VMPC protocol that is based on discrete-logarithm related assumptions. At the high level of adversity that VMPC is meant to withstand, it is infeasible to ensure perfect correctness, thus we investigate the classes of functions and verifiability relations that are feasible in our framework, and present a number of possible applications the underlying functions of which can be implemented via VMPC.
THE EFFECT OF ANAGRAM GAME ON STUDENTS’ VOCABULARY ACHIEVEMENT AT GRADE EIGHT OF SMP NEGERI 8 PEMATANGSIANTAR

This research is aimed to find out whether using Anagram Game give significant effect on students’ vocabulary achievement at grade eight of SMP Negeri 8 Pematangsiantar. Problem raised in this research is “What is the effect of Anagram Game on students’ vocabulary achievement at grade eight of SMP Negeri8 Pematangsiantar?”. To answer this problem the researcher uses some theories, namely: Bachtiar (2014), Harmer (2002), Hiebert and Kamil (2005), Hatch and Farhady (1982) and Sutherland (2009). This research is quantitative research design. Experimental was conducted as the method. The sample of this research are two classes which are VIII-4 as a control class because they are active with better achievement than other class and VIII-9 as an experimental class because they have lower achievement. The result shows that using Anagram Game on students’ vocabulary achievement at grade eight of SMP Negeri 8 Pematangsiantar got high level where as the score of t-test (4.65) was higher than t-table (1.679) at level of significant 5% for two tailed test. So Null Hypothesis was rejected and Alternative Hypothesis was accepted. After analyzing the data, the researcher concludes that using Anagram Game significantly affect the students’ vocabulary achievement and her suggestion is English teacher should have creative method in teaching English to their students especially to increase their vocabulary by using Anagram Game, so that they are not boring to learn English and make the enjoy on it.
CT-TEE Image Registration for Surgical Navigation of Congenital Heart Disease Based on a Cycle Adversarial Network

Transesophageal echocardiography (TEE) has become an essential tool in interventional cardiologist’s daily toolbox which allows a continuous visualization of the movement of the visceral organ without trauma and the observation of the heartbeat in real time, due to the sensor’s location at the esophagus directly behind the heart and it becomes useful for navigation during the surgery. However, TEE images provide very limited data on clear anatomically cardiac structures. Instead, computed tomography (CT) images can provide anatomical information of cardiac structures, which can be used as guidance to interpret TEE images. In this paper, we will focus on how to transfer the anatomical information from CT images to TEE images via registration, which is quite challenging but significant to physicians and clinicians due to the extreme morphological deformation and different appearance between CT and TEE images of the same person. In this paper, we proposed a learning-based method to register cardiac CT images to TEE images. In the proposed method, to reduce the deformation between two images, we introduce the Cycle Generative Adversarial Network (CycleGAN) into our method simulating TEE-like images from CT images to reduce their appearance gap. Then, we perform nongrid registration to align TEE-like images with TEE images. The experimental results on both children’ and adults’ CT and TEE images show that our proposed method outperforms other compared methods. It is quite noted that reducing the appearance gap between CT and TEE images can benefit physicians and clinicians to get the anatomical information of ROIs in TEE images during the cardiac surgical operation.
Process-Induced Distortions Characterization of MBWK Fabric Reinforced Composite Helmet Shell

In order to characterize the process-induced distortions of 3D thin shell composites with complex shape, the multilayered biaxial weft knitted (MBWK) fabric reinforced high-performance composite helmet was selected as the research object, and the 3D laser scanning machine was used to scan the helmet surface, then the 3D scanning data was compared with the CAD model to evaluate the deformation. The results and discussion indicated that the conventional method was workable, but the speed of convergence was slow and the calculation results were easy to drop into local optimization. According to detailed analysis, a measurement method focusing on the principle of “Feature Distance” was developed. The measurement results shown that this method can not only give accurate results, but also reduce working procedure and greatly save the computing resources, which is proved to be a feasible approach for the deformation measurement foundation of 3D thin shell textile composites.
Optimization Driven MapReduce Framework for Indexing and Retrieval of Big Data

With the technical advances, the amount of big data is increasing day-by-day such that the traditional software tools face a burden in handling them.Additionally, the presence of the imbalance data in big data is a massive concern to the research industry.In order to assure the effective management of big data and to deal with the imbalanced data, this paper proposes a new indexing algorithm for retrieving big data in the MapReduce framework.In mappers, the data clustering is done based on the Sparse Fuzzy-c-means (Sparse FCM) algorithm.The reducer combines the clusters generated by the mapper and again performs data clustering with the Sparse FCM algorithm.The two-level query matching is performed for determining the requested data.The first level query matching is performed for determining the cluster, and the second level query matching is done for accessing the requested data.The ranking of data is performed using the proposed Monarch chaotic whale optimization algorithm (M-CWOA), which is designed by combining Monarch butterfly optimization (MBO) [22] and chaotic whale optimization algorithm (CWOA) [21].Here, the Parametric Enabled-Similarity Measure (PESM) is adapted for matching the similarities between two datasets.The proposed M-CWOA outperformed other methods with maximal precision of 0.9237, recall of 0.9371, F1-score of 0.9223, respectively.
Analysis and Improvement of JPEG Compression Performance using Custom Quantization and Block Boundary Classifications

JPEG (Joint Photographic Experts Group) compression is the global standard for digital image compression introduced in 1992, and is still wide spread use.However, at low bitrates the JPEG process can introduce unwanted visual artifacts such as the blocking effects or edge ringing.This paper describes a method for modification and customizing of the JPEG compression.A nonlinear relationship between the quantization matrix, reflecting the compression ratio and the peak signal-to-noise ratio (PSNR), as an objective quality measure, was experimentally determined.The estimation of the quantization matrix and approximation of its mapping to a PSNR is accomplished relying on transformation of eleven test images using all quantization matrices.The linear approximation to this relation in the region of interest was proposed enabling fine tuning of the reconstructed image quality by either selection of the desired PSNR value or a decompressed image quality.In the decompression phase, post-processing is applied to reduce the blockish artifacts introduced by the compression process.The image block boundaries are first classified for an automatic identification of high blockiness, to constrain the application of a pre-processing algorithm and further loss of an image detail.Upon the reconstruction, the quality of the reconstructed image is measured using the PSNR and structural similarity index (SSIM).The effects of compression on the spectral properties are analyzed by comparison of the original and decompressed image spectra.
Probabilistic Method for Managing Common Risks in Software Project Scheduling Based on Program Evaluation Review Technique

Project management and project scheduling are crucial to help development teams keep track of timing as well as resource allocation. In order to manage software projects, project managers need to anticipate, analyze the risk factors that may occur as well as their impacts on the progress of the project, and assess and adapt the project resource allocation. This paper concentrates on a quantitative approach for risk analysis in software project scheduling by taking advantage of Bayesian networks capacity (including related mathematical calculations) in modeling and assessing uncertainty and incorporates them in software project scheduling with program evaluation and review technique (PERT). Common risk factors in project scheduling are also examined, and a Bayesian networks model of 19 common risk factors and their causal relationships is proposed and confirmed. The research also borrows and implements categories and levels of risk from construction projects into software projects. A tool was built to experiment and validate the proposed model.
Analyzing Cataloging Job Descriptions: Are Cataloging Jobs Disappearing, Changing, or Merging?

To determine the state of the cataloging job market, this study compares job advertisements from August 2016-August 2018 advertising for jobs with cataloging in the title, metadata in the title, and cataloging and metadata in the title. Given ongoing concerns about the impact of metadata jobs on the cataloging job market, this study asks the questions: Are cataloging jobs in fact “disappearing” and becoming metadata jobs? Are entry-level jobs disappearing, perhaps contributing to the idea that there is a lack of qualified catalogers? Is there a difference in position level between cataloging and metadata job advertisements that can account for a lack or perceived lack of available cataloging jobs? Are there more entry-level or mid/supervisory positions advertised? Are cataloging and metadata jobs really separate, or should they be considered one position called cataloging and metadata?