Reviewerly/exHarmony huggingface dataset:
Optimized Load Balancing approach in cloud workflow for parallel computing

CC (Cloud Computing) is said to be the particular paradigm which offers the resource on demand; also it provides flexibility and scalability; also due to its dynamic nature cloud computing has been popular SWM (Scientific Workflow management) gives several support such as task dependencies, provenance tracking, job execution and job scheduling to perform scientific computing. However, considering the dynamic environment, it is essential to design a mechanism that can balance the above-described operation. Hence, in this paper, we propose a load balancing mechanism named OLBP (Optimal load balancing in parallel computing), this is achieved through considering through server speed consideration and power balancing; OLBP achieves the load balancing through optimization process and achieves the power consumption. The OLBP is evaluated considering the cybershake scientific workflow considering various parameters such as execution time and power consumption.
Blockchain Decentralized IoT Trust Management

IoT adds more flexibility in many areas of applications to makes it easy to monitor and manage data instantaneously. However, IoT has many challenges regarding its security and storage issues. Moreover, the third-party trusting agents of IoT devices do not support sufficient security level between the network peers. This paper proposes improving the trust, processing power, and storage capability of IoT in distributed system topology by adopting the blockchain approach. An application, IoT Trust Management (ITM), is proposed to manage the trust of the shared content through the blockchain network, e.g., supply chain. The essential key in ITM is the trust management of IoT devices data are done using peer to peer (P2P), i.e., no third-party. ITM is running on individual python nodes and interact with frontend applications creating decentralized applications (DApps). The IoT data shared and stored in a ledger, which has the IoT device published details and data. ITM provides a higher security level to the IoT data shared on the network, such as unparalleled security, speed, transparency, cost reduction, check data, and Adaptability.
Visual Attention Based LSB Data Hiding in 360° Videos

In this paper, we propose a visual attention based least significant bit (LSB) data hiding method for 360° videos. In particular, information about the relative frequency of pixel access is used to control the amount of secret data to be hidden at the different latitudes of 360° videos. In this way, the typical behavior of users paying more attention to the equator region compared to the north and south poles when viewing 360° videos on a head-mounted display is taken into account. An analytical expression for capacity offered by this method to hide secret data in 360° cover videos is derived. A performance assessment of the visual attention based LSB data hiding method is conducted in terms of the peak signal-to-noise ratio (PSNR) and weighted-to-spherically-uniform PSNR (WS-PSNR) which both quantify the fidelity of a 360° stego-video with reference to the related 360° cover video.
Constructions and bounds on quaternary linear codes with Hermitian hull dimension one

Abstract Due to their practical applications, hulls of linear codes have been of interest and extensively studied. In this paper, we focus on constructions and bounds on quaternary linear codes with Hermitian hull dimension one. Optimal $$[n,2]_4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mn>4</mml:mn> </mml:msub> </mml:math> codes with Hermitian hull dimension one are constructed for all lengths $$n\ge 3$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≥</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:math> , such that $$n \equiv 1,2,4 \ (\mathrm{mod}\ 5)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≡</mml:mo> <mml:mn>1</mml:mn> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mn>4</mml:mn> <mml:mspace /> <mml:mo>(</mml:mo> <mml:mi>mod</mml:mi> <mml:mspace /> <mml:mn>5</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> . For positive integers $$n \equiv 0,3 \ (\mathrm{mod}\ 5)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>≡</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>3</mml:mn> <mml:mspace /> <mml:mo>(</mml:mo> <mml:mi>mod</mml:mi> <mml:mspace /> <mml:mn>5</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , good lower and upper bounds on the minimum weight of quaternary $$[n,2]_4$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mn>2</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mn>4</mml:mn> </mml:msub> </mml:math> codes with Hermitian hull dimension one are given.
Clustering Analysis to Improve Web Search Ranking Using PCA and RMSE

Classification of web pages is the first step of web page ranking (or we can call it indexing), one of the most common ways to achieve indexing process is clustering that pages into groups as per the similarity, whenever the misclassification is less, the result will be perfect. Moreover, clustering is a collection of algorithms that dive the data into groups related to each other. Thus, we chose Microsoft learn to rank dataset, to achieve the analysis and model building on it, this dataset is specially designed for researches in this field, and it has huge and different information about ranking process. Because of the quantity of the information, we chose randomly 16,015 observations only from MSLR-WEB30K_2 _ fold 1, in this study according to the ability of our hardware, and the algorithms of analysis, some of algorithms which were used in analysis (determine the optimal number of clusters) cannot handle the huge quantity of observations. Hence, in this paper, we are going to use clustering analysis to improve the web search ranking using principle component analysis (PCA) with root main square error as a feature reduction technique to compute the errors rate and the accuracy of the model result to get the best number of attributes; this process was achieved with cross-validation approach using extreme gradient boost algorithm as a training model to estimate the sum of errors during training operation.
Short Answer Grading Using Contextual Word Embedding and Linear Regression

Abstract—One of the obstacles in an efficient MOOC is the evaluation of student answers, including the short answer grading which requires large effort from instructors to conduct it manually. Thus, NLP research in short answer grading has been conducted in order to support the automation, using several techniques such as rule and machine learning based. Here, we’ve conducted experiments on deep learning based short answer grading to compare the answer representation and answer assessment method. In the answer representation, we compared word embedding and sentence embedding models such as BERT, and its modification. In the answer assessment method, we use linear regression. There are 2 datasets that we used, available English short answer grading dataset with 80 questions and 2442 to get the best configuration for model and Indonesian short answer grading dataset with 36 questions and 9165 short answers as testing data. Here, we’ve collected Indonesian short answers for Biology and Geography subjects from 534 respondents where the answer grading was done by 7 experts. The best root mean squared error for both dataset was achieved by using BERT pretrained, 0.880 for English dataset dan 1.893 for Indonesian dataset.
Study of Users Attitude and Classification of Comments and Likes from Facebook Using RapidMiner

In today’s world, more than 80% of people in the world are using social networking websites to connect with their friends, loved ones, and family to share their feeling, emotions, experiences, opinions, feedbacks, etc. Several platforms help people in socializing. These platforms generate big data daily which includes the emotions of the users. In the current paper, RapidMiner data mining tool will be used for classification of the big data and to study the attitude and sentiments of the users from the Facebook social networking website. Datasets are gathered, i.e., likes and comments from the website and apply optimized selection (brute force) technique. Post that three distinct data classifiers are implemented on the big data gathered. Then, the three outputs of each classifier are compared to find the one giving the best results. RapidMiner is a freeware data mining tool that will be used. It helps in fabricating the data classifier and to apply it to the dataset.
Studying the Evolution of Static Methods and their Effect on Class Testability

While developing complex software projects testing is performed to ensure that the system meets the requirements and functions correctly. However, this activity might be hindered by the lack of testability of the system's production classes. Up until now the studies that address software testability have focused on characteristics related to the project's size and complexity. We are more interested in specific design problems that make a production class difficult to test. Several articles mention the usage of static methods as one of the factors that affect class testability. We would argue that not all static methods have a negative impact on this quality aspect. In order to prove it we first categorize the methods based on 1) the classes of which they are part of and 2) whether or not they utilize mutable state. Then we use historical data to study how they have evolved over the lifespan of a project. Finally, we quantify the testability of production classes by combining code coverage information with test suite metrics for the classes addressing them. An empirical study was conducted using 6 open-source systems to investigate the aspects discussed above. The results highlight multiple categories of static methods that appear more frequently depending on the specific characteristics of a software project. They also confirm our initial expectation that not all static methods affect testability in a negative way.
Quantum teleportation of an arbitrary four-qubit state via three-uniform state of eight qubits

Recently, Zang et al. constructed a three-uniform state of eight qubits. In this work, we show that such an eight-qubit entangled state can be used to realize the deterministic quantum teleportation of an arbitrary four-qubit state based on the orthogonal basis measurement.
Object-Level Reasoning with Logics Encoded in HOL Light

We present a generic framework that facilitates object level reasoning with logics that are encoded within the Higher Order Logic theorem proving environment of HOL Light. This involves proving statements in any logic using intuitive forward and backward chaining in a sequent calculus style. It is made possible by automated machinery that take care of the necessary structural reasoning and term matching automatically. Our framework can also handle type theoretic correspondences of proofs, effectively allowing the type checking and construction of computational processes via proof. We demonstrate our implementation using a simple propositional logic and its Curry-Howard correspondence to the lambda-calculus, and argue its use with linear logic and its various correspondences to session types.
epBRM: Improving a Quality of 3D Object Detection using End Point Box Regression Module

We present an endpoint box regression module(epBRM), which is designed for predicting precise 3D bounding boxes using raw LiDAR 3D point clouds. The proposed epBRM is built with sequence of small networks and is computationally lightweight. Our approach can improve a 3D object detection performance by predicting more precise 3D bounding box coordinates. The proposed approach requires 40 minutes of training to improve the detection performance. Moreover, epBRM imposes less than 12ms to network inference time for up-to 20 objects. The proposed approach utilizes a spatial transformation mechanism to simplify the box regression task. Adopting spatial transformation mechanism into epBRM makes it possible to improve the quality of detection with a small sized network. We conduct in-depth analysis of the effect of various spatial transformation mechanisms applied on raw LiDAR 3D point clouds. We also evaluate the proposed epBRM by applying it to several state-of-the-art 3D object detection systems. We evaluate our approach on KITTI dataset[1], a standard 3D object detection benchmark for autonomous vehicles. The proposed epBRM enhances the overlaps between ground truth bounding boxes and detected bounding boxes, and improves 3D object detection. Our proposed method evaluated in KITTI test server outperforms current state-of-the-art approaches.
Dynamic Fuzzy Membership Intervals with Two-Stage Objective Function for Ball and Beam System based on GA Tuning

This paper proposed a two-stage objective function with genetic algorithm (GA) to refine an additional fuzziness layer on dynamic membership intervals of Type-I fuzzy logic control(FLC). The refined dynamic membership intervals Type-I FLC is applied on a traditional ball and beam system, serving as a prototype for a friction fruit conveyor system. The ball and beam system governed under Type-I FLC is excited with step input, the corresponding system performance factors are captured - rise time, settling time and overshoot. A probabilistic random search on optimum controller parameters is carried with GA method, multiple cost functions - ISE, IAE, ITSE and ITAE, with are evaluated to form a performance cost matrix, which is the first stage of the objective function. The optimum parameter search stops with two conditions; one is that the maximum number of chromosome generation is reached, and the other one is that performance cost stops improving consecutively for ten generations. The second stage of the objective function is proceeded to decide the found optimum controller parameter solution. The result is decided by taking account of the previously captured performance factors. These factors are normalized and combined with a heuristic weight set to determine a minimum decision cost. The minimum cost chromosome, with ITAE, is the optimum from global found solution space and sets fixed intervals on the dynamic membership range of Type-I FLC. With two-stage objective function, improved rise time and settling time performance are indicated on the GA tuned Type-I FLC dynamic membership intervals on the ball and beam system than the conventional Type-I FLC, and is satisfactory.
Reviewing On-Policy/Off-Policy Critic Learning in the Context of Temporal Differences and Residual Learning

Critic learning is a fundamental problem in Reinforcement Learning. This paper aims to review some of the basic contents, that are essential to understand critic learning. We review the most important objective functions in the context of critic learning, state some general error sources of policy evaluation methods and explain problems occurring for the off-policy case. Using this knowledge we then compare the fundamental approaches for critic learning, Temporal Differences and Residual Learning. In the end we give a short overview about some more recent critic-learning methods.
Weakly Supervised Fine-Grained Image Recognition Based on Multi-Channel Attention and Object Localization

The task of fine-grained image classification recognizes similar subcategories that belong to the same superclass. The subtle differences between inter classes and intra-class diversity make it a challenging task. The existing methods focus on how to locate the most discriminative parts, which usually ignored the whole feature of objects. In this paper, we propose a fine-grained image recognition method based on multi-channel attention and object localization, which mainly includes two parts: multi-channel attention (MCA) module is used to learn different discriminative regions, attention object location (AOL) module can locate the object from the input image. The method we proposed can be trained in an end-to-end manner without adding bounding box/part annotation. At the same time, we build a new application-oriented fine-grained dataset: Poyang Lake birds, which contain 370 species of birds in Poyang Lake. We conducted extensive experiments on our dataset and three commonly used fine-grained datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft). Extensive experiments show that our model shows compelling performance whether it is on our dataset or the other existing datasets.
NJUNLP’s Submission for CCMT20 Quality Estimation Task

Quality Estimation is a task to predict the quality of translations without relying on any references. QE systems are based on neural features but suffer from the limited size of QE data. The best models nowadays transfer bilingual knowledge from parallel data to QE tasks. However, the distribution between parallel data and QE data may lead to the value of parallel data not being used for best. More specifically, there are no errors in parallel translations while there may be more than one error in the translations of QE data. To alleviate this problem, we propose a model that will mask some tokens at the target side on parallel data but still need to predict every target token. And based on this model, we propose a variant model that uses a masked language model at the target side to obtain deep bi-directional information. Besides, we also try different ensemble methods to get better performance of the CCMT20 Quality Estimation Task. Our system finally won second place in the ZH-EN language pair and third place in the EN-ZH language pair.
Quantum Computation and Simulation A Distinguished Demonstration Using the BruteForce Algorithm

From the ashes of Moore's Law and from the womb of Neven's law, raised the motivation to develop a system, i.e, a Quantum Computer, that reshapes the future of the computing industry by providing an exponential speed up. From the past decade, the development of quantum computing technology has been picking up the pace by increasing the qubit count and improving fidelity with circuit optimization and error correction. This document starts by defining the properties of a quantum computer and then draws a fine line between quantum computers and simulators by demonstrating how a quantum simulator works and showing the code to an ideal quantum simulator which concludes by showing the discrepancies between the current classical computer, quantum computer, and a quantum simulator by finding the secret number in a box using a classical algorithm called Brute Force and executing it with the three above mentioned computing methods.
Construction mining

Abstract The German Constructicon Project ( www.german-constructicon.de ) aims at documenting grammatical constructions in contemporary standard German on the basis of annotated corpus examples, including relations between constructions and between constructions and evoked semantic frames. So far, the research focus has been mainly on the development and computational implementation of a constructicographic workflow (including a parsing pipeline) that allows for addressing any kind of constructions on varying levels of schematicity, idiomaticity, and abstractness. However, such an exemplar-driven procedure precludes us from systematically identifying constructional candidates. In this article, we scrutinize ways to operationalize and implement data-mining procedures to inductively identify construction candidates.
AsArcFace: Asymmetric Additive Angular Margin Loss for Fairface Recognition

Fairface recognition aims to the mitigate the bias between different attributes in face recognition task while maintaining the state-of-art accurancy. It is a challenging task due to high variances between different attributes and unbalancement of data. In this work, we provide an approach to make a fairface recognition by using asymmetric-arc-loss training and multi-step finetuning. First, we train a general model with an asymmetric-arc-loss, and then, we make a mutli-step finetuning to get higher auc and lower bias. Besides, we propose another viewpoint on reducing the bias and use bag of tricks such as reranking, boundary cut and hard-sample model ensembling to improve the performance. Our approach achieved the first place at ECCV 2020 ChaLearn Looking at People Fair Face Recognition Challenge.
A Lightweight Handwriting Recognition System Based on an Improved Convolutional Neural Network

For decades, how to recognize handwritten characters quickly and efficiently has been a difficult problem in research. Handwritten character recognition has been widely used in our life. The existing handwriting recognition system relies on the computer and can't recognize the text in real time. In this paper, a new improved handwritten character recognition method of convolutional neural network is proposed, which adds Batch Normalization Layer and Residual Network Structure to the general convolutional neural network, and adopts multi-scale prediction method. The model is proved to have higher recognition rate by test set. On the computer side, an improved convolutional neural network was built through Keras to train the EMNIST data set and obtain the model. After migrating the model to the mobile terminal, an off-line real-time handwritten character recognition system was developed.
Survey and Experimental Comparison of Machine Learning Models for Motorcycle Detection

Road accidents are largely related to motorcycles and many cases resulted in severe injuries and fatalities. Automatic detection of motorcycles is considered a useful measure for monitoring evaluating predicting and eventually planning to prevent further motorcycle-related accidents. Many researchers proposed techniques to detect and track vehicles but only few focused on detecting motorcycles. In this paper we reviewed and conducted experiments on 20 different machine learning models with different sizes and views (front rear top) of traffic images to evaluate motorcycle detection's accuracy and time. By setting 60% threshold of accuracy on images of 480×270 pixels the best detection model is rfcn_resnet101_coco which provided average accuracy detection time and efficiency at 65.85% 1.91 frames/second and 34.55 respectively.
Dynamic Multi-Swarm Fractional-best Particle Swarm Optimization for Dynamic Multi-modal Optimization

While many particle swarm optimization (PSO) algorithms have been developed to find multiple optima to multimodal optimization problems, very few PSO algorithms exist to both find and track multiple optima in dynamically changing search landscapes. This paper presents a novel multi-swarm PSO algorithm, where the number of sub-swarms change dynamically over time to more efficiently adapt to problems where the number of optima changes over time. In addition, a repelling mechanism is employed to prevent sub-swarms from converging to the same optimum. Instead of designating one particle as the global best position, the global best position is determined by combining the best components from different particles. The new algorithm, called the dynamic multi-swarm fractional-best PSO algorithm, is compared to the best available dynamic multi-modal PSO algorithms on a large set of dynamic optimization problems with varying dynamics. The results show that the dynamic multi-swarm fractional-best PSO performs the best with reference to offline error, and second best with reference to the average number of optima found. The new algorithm's offline error is also shown to be insensitive to change severity.