Reviewerly/exHarmony huggingface dataset:
Compositional Direct Sampling Simulation

In many instances it is desirable to capture more than the first two moments of the data when exploring their variability. In this chapter direct sampling simulation for compositional data is introduced, which explicitly incorporates multiple-point statistics in the simulation.
Path Planning for Construction Machinery Based on Improved Potential Field Ant Colony Algorithm

Abstract The path planning algorithm of unmanned construction machinery is studied, and the potential field ant colony algorithm is improved to be applied in the field of unmanned construction machinery. Firstly, the raster map modeling was optimized to eliminate the trap grid in the map. At the beginning of algorithm iteration, the heuristic information of artificial potential field method was added and the global pheromone updating model was improve the convergence speed of the algorithm. In addition, the weight coefficient of potential field force and local pheromone updating model were introduced to enhance the development of raster map in the later iteration of ant colony algorithm and reduce the influence of heuristic information of potential field force. Finally, the selection range of parameters such as optimal pheromone heuristic factor and ant colony number is determined by simulation, and it is verified that the algorithm is better than the basic ant colony algorithm.
The Application of Virtual Reality in Student Recruitment

In this paper we present details of a virtual tour and game for VR headset that are designed to investigate an interactive and engaging approach of applying VR to student recruitment for an undergraduate course. The VR tour employs a floating menu to navigate through a set of 360° panoramic photographs of the teaching environment and uses hotspot interaction to display further information about the course. The VR game is a fast-paced shooting game. The course information is embedded on cubes that the player needs to focus on and destroy. The game experience is expected to generate an engaging way to promote the course. This work in progress outlines the concept and development of the prototype, and discusses the next stages of testing in order to evaluate the effectiveness of applying VR to undergraduate student recruitment.
Color image retrieval based on improved quaternion convolutional neural network

Nowadays, when performing color image texture feature extraction, most of the color images are converted into grayscale images or the color images are processed by channels. The above-mentioned traditional processing methods will largely lose the effective information of the original image when performing color image retrieval. Aiming at the problem of effective feature description in content-based image retrieval, this paper proposes a color image retrieval method based on improved quaternion convolutional neural network. First, use the quaternion method to characterize the local texture feature LBP value and R, G, and B color information of the image, and use the quaternion discrete cosine transform (QDCT) as the theoretical basis to express the color image as a quaternion matrix form. Secondly, each component of the convolutional neural network is extended to the quaternion domain, and the quaternion matrix obtained above is directly input into the proposed neural network for training and classification. Finally, the result of image retrieval is obtained. Experiments show that this method significantly improves the performance of color image retrieval.
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNN

Anime is a storytelling medium similar to movies and books. Anime images are a kind of artworks, which are almost entirely drawn by hand. Hence, reproducing existing Anime with larger sizes and higher quality images is expensive. Therefore, we proposed a model based on convolutional neural networks to extract outstanding features of images, enlarge those images, and enhance the quality of Anime images. We trained the model with a training set of 160 images and a validation set of 20 images. We tested the trained model with a testing set of 20 images. The experimental results indicated that our model successfully enhanced the image quality with a larger image-size when compared with the common existing image enlargement and the original SRCNN method.
The Accuracy of Business English Curriculum to Students’ Communication Performance: The EFL Students’ Perception

Measuring the accuracy of a private university in Batam Business English course curriculum to the present needs of workplaces in Batam from students’ perception is essential. Because 85% of the university students are working, it is compulsory to maintain the gap low between what students learned in class and what is implemented in the working field. A questionnaire consists of three dimensions (curriculum, communication performance, and motivation) distributed randomly to 60 students from a population (N=328) of the third-semester students programmed Business English course at a private university in Batam. The result suggested that the curriculum needs to add more practices, peer work, and case studies to improve students’ communication performance. Students’ perspectives in the Business English course reflected a new technical vocabulary that needed to practice in a correlated context. The curriculum content’s relevance to the workplace’s contextual needs is moderately related because not all working students used the English language in their workplace. Additional focus on internal and external motivation through group work and peer work is needed to improve students’ communication performance and confidence. Finally, the existing Business English curriculum is reasonably accurate, supporting students’ communication performance, both written and oral, at their workplace.
Power Side-Channel Analysis for Different Adders on FPGA

Many IoT circuits are under threat from side-channel attacks such as power analysis attacks. Adders are one of the most fundamental components in electronic circuits. In this paper, we study the tolerance of adders against power analysis attacks on FPGA. The power side-channel leakage for three types of 32-bit adders is analyzed based on T-test. Our experimental results show the effectiveness of a ripple-carry adder with built-in carry chain in terms of not only power and delay but also the tolerance to power analysis attacks.
The analysis of commutation switching process mutation

In recent years, with the continuous advancement of my country's distribution network, the connected load has become more and more complex, resulting in an increase in the proportion of three-phase unbalanced load and three-phase unbalanced voltage, and the application of commutation switches has become more and more extensive. In order to study the performance and operating characteristics of the commutation switch, this paper analyzes the commutation switch at different power factor status of resistive-capacitive load, inductive load resistance mutations affect the amount of their causes and possible on the grid. It can be a good guide and application-related research work.
Lattention: Lattice-Attention in ASR Rescoring

Lattices form a compact representation of multiple hypotheses generated from an automatic speech recognition system and have been shown to improve performance of downstream tasks like spoken language understanding and speech translation, compared to using one-best hypothesis. In this work, we look into the effectiveness of lattice cues for rescoring n-best lists in second-pass. We encode lattices with a recurrent network and train an attention encoder-decoder model for n-best rescoring. The rescoring model with attention to lattices achieves 4-5% relative word error rate reduction over first-pass and 6-8% with attention to both lattices and acoustic features. We show that rescoring models with attention to lattices outperform models with attention to n-best hypotheses. We also study different ways to incorporate lattice weights in the lattice encoder and demonstrate their importance for n-best rescoring.
Developing An Automated Face Mask Detection Using Computer Vision and Artificial Intelligence

As the number of people affected by COVID-19 keeps on rising. Importance of wearing masks and washing hands has been the most important protocol right now to prevent the spread of COVID-19. As the pandemic has been going on for almost a year now, people have already started to go around to public places whether it is to eat out, work, or grocery shopping. Many people, however, have not been wearing masks properly by only putting them below their nose or putting it down until their chin. Hence, in this project a mask detection system is made to detect people live time who are wearing or not wearing a mask and can generate a business intelligence report for the shop owner to be aware of the number of people not wearing a mask per day. This system can detect the percentage of the mask is worn properly or not. The more proper it is worn (full up to nose), the higher the percentage will be. This system is useful in a pandemic like this as it is hard to keep track of the number of people who are not wearing masks, especially in a big crowd or in a large space as one person not wearing a mask can greatly affect others.
Optimal Trajectory Planning of a Mobile Manipulator for Disinfection Using Multi-objective Genetic Algorithm

Autonomous mobile robots are being used for disinfection tasks as an alternative to reduce the exposure of cleaning personnel. In this article a new mobile robot manipulator is introduced for disinfection, by means of a fog generator. The robot consists of a differential drive platform and a two degrees of freedom (DoF) arm which is used to handle a fogging nozzle. For this nozzle a coverage path and a trajectory in the configuration space of the robot needs to be planned. Thus, this work presents a solution to the trajectory planning problem for kinematic control of a predefined end effector path of our disinfection robot, based on the application of a Non Dominated Sorting Genetic Algorithm (NSGA-II), for optimization of energy and smoothness functions for the joint motions. It is assumed that in a previous stage the coverage path planning (CPP) problem was solved. However, a discussion is also presented on the different methods that could be used for CPP.
Analysis of rainfall classification over Tanah Laut disrict based on global climate indicators using support vector machine method

Abstract The Support Vector Machine (SVM) classification method can be applied in various fields, one of which is meteorology and climatology in rainfall forecasting. Thus, a study was conducted by classifying rainfall to recognize the relationship between global phenomena and rainfall and the results of applying the classification using the SVM method to rainfall in the Tanah Laut Regency. The analysis is carried out using the SVM Multiclass concept with 4 categories of rainfall classification: low, medium, high, and Extreme. The kernel used in SVM is the RBF kernel with optimization parameters used, namely Cost (C) 1,5,10,15 and Gamma (γ) 1,5,10,15. The dataset formed is based on the annual period, climatic conditions, and seasonality. The Spearman Rank correlation test describes the relationship between global phenomena and rainfall with a correlation range of (−0.1456 ) − (0.43144) for the entire dataset. The implementation of the SVM classification method shows that the Cost (C) 10 and Gamma (γ) ≥ 5 parameters obtained the highest accuracy of 100% on the training data. In contrast, in testing the data testing, the accuracy was good, namely the accuracy of 78.00% in La Nina and 81.38% in seasonal periods.
Effect of Sampling Method on the Regression Accuracy for a High-Speed Link Problem

We examine the effect of different sampling methods on the accuracy of support vector regression models for eye opening prediction of a high-speed link. Four different sampling methods are tested to generate training data for training the machine learning model for the equalizers on a high-speed link problem, and the accuracy of the models to predict eye opening is compared. Latin Hypercube shows superior performance in terms of mean square error and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex> compared to other methods tested.
English Teaching Ability Assessment Algorithm Based on Big Data Fuzzy k-Means Clustering

At present, our country is stepping up efforts to promote the development of big data technology. In this context, English teaching is also facing major reforms. When discussing the inaccuracy of data estimation of traditional English teaching ability, a targeted algorithm for estimation of English teaching ability based on big data fuzzy K-means clustering is proposed. Integrate and classify English teaching data, formulate appropriate courses for the allocation of teaching resources, and evaluate English teaching ability; after using the comparative analysis method, the following conclusions are reached: the period of formation of English teaching ability is after employment, especially the use of teaching methods, educational wit, and teaching and research abilities are all higher than 80%. In addition, the two abilities of teaching organization and management and teaching content processing are also more than 75% after the job.
A Cost Optimization Strategy for Workflow Scheduling in Cloud

Workflow scheduling in cloud is one of the most challenging issues of recent times. It focuses on executing workflow applications in a manner that maps interdependent tasks to virtual machines under specified quality of service requirements. Cloud service providers offer resources with different performance at different prices. The same workflow with different resources can result in different makespan and cost. One of the main problems of workflow scheduling in cloud is to find a cheaper scheduling method on the premise of meeting the deadline. In this paper, an improved L-ACO algorithm (imL-ACO) is adopted. It optimizes the parameters of L-ACO algorithm and can solve the above problem well for specific workflow. The experiment shows that imL-ACO algorithm can make the scheduling cost lower, allocate resources more effectively, and the effect is better than L-ACO algorithm.
Research on location of large logistics distribution network based on cloud VIKOR method

As the last link of logistics transportation system, distribution network directly interacts with customers, which directly determines customers' satisfaction with logistics. Accurate evaluation of large logistics distribution network can provide valuable decision-making reference for relevant departments, and improve the quality and efficiency of network work. This paper constructs the evaluation index system of large logistics distribution network from four aspects of scientificity, flexibility, statistics and integrity. By using cloud generator, the transformation of decision attribute from triangular fuzzy number to cloud model is realized. Finally, based on the VIKOR method, the large logistics distribution network is sorted. The example shows that the model is simple and effective, which provides a feasible method for the selection of large logistics distribution network.
Application of ID Assignment Features in Rosebrocks' Simple Object Tracker

Recent advancements in object detection methods give us the means to recognize what kind of object is present in the sensor field of view. Though indeed useful, this may not be enough for some specific application in which we need to distinguish an object from another object, that might belong to the same or different class object. This paper will discuss a good option for a high-speed/low computation cost method for ID assignment that can be used even for objects that belong to the same class. Utilizing the Centroid Tracker concept, Rosebrock applies an ID assignment based on distances between objects in the current and previous frame. This method has shown promising success in complement with object detection to help a computer vision system for tracking specific objects of interest.
Feature‐weighted <scp>AdaBoost</scp> classifier for punctuation prediction in Tamil and Hindi <scp>NLP</scp> systems

Abstract Punctuation marks play a vital role in text representation and interpretation, and are useful in enhancing the performance of modern Natural Language Processing (NLP) systems such as voice input typing aids, machine translation, and speech synthesis systems. Punctuation marks, except period, are inherently not available in Indian languages such as Tamil and Hindi. However, some modern forms of writing such as news articles, blogs, stories, and so forth, incorporate user‐defined punctuation marks in these languages. The current work proposes an automatic punctuation prediction system for texts in Tamil and Hindi using classification approach, where punctuation prediction is considered as a multi‐class classification problem. Word‐level text features are chosen and are analysed to validate their language‐dependency and significance towards punctuation prediction. A Feature‐weighted AdaBoost (FAda) classifier is proposed that defines a novel boosting factor to adjust the hypothesis weight of the weak classifiers, hence reducing the number of false classifications. It is observed that the proposed classifier outperforms the other classification techniques such as, AdaBoost, SVM, CART, CRF, and Bi‐LSTM by a maximum difference of 50% and 16% in the macro F1‐scores for Tamil and Hindi texts, respectively. The proposed classifier performs on par with the attention‐based classifier for both Tamil and Hindi texts. Further, as a proof of concept, the proposed punctuation prediction system is applied to voice keyboard, machine translation, and speech synthesis systems, to validate the effect of the punctuation marks on the performance of these Natural Language Processing (NLP) systems.
Multimedia Learning with Cognitive Tutors

Cognitive Tutors are effective AI-based learning environments. The following statement summarizes three core instructional features from past summaries of deliberate practice (shown in italics) along with three elaborations (shown in bold): Good learning-by-doing instruction requires repeated practice on well-tailored tasks in varied contexts with explanatory feedback and as-needed instruction. This chapter describes these six learning-by-doing principles and how they are achieved in Cognitive Tutors. We correct some key misconceptions about cognitive tutors, including that knowledge component (KC) decomposition does not exclude, but includes conceptual connections, teachers are not replaced but valued, and up-front-telling is not the instructional focus whereas learning-by-doing guidance is. We also point to a need for more experimentation on the benefits of as-needed versus up-front instruction, better integration of supports for enhancing student motivation, and better pathways for teachers to participate in co-design (during the inevitable need for Learning Engineering beyond Learning Science) and customization.
Enhanced Depth Map Estimation in Low Light Conditions for RGB Cameras

Most existing depth estimation methods predict depth for daytime images and do not perform well in low-light situations due to lack of clear environmental features, glare, overexposure, and noise. This is problematic for safe autonomous driving as pedestrian and guardrail detection at night is challenging and poses life-threatening situations. This paper addresses this problem by improving image quality of disparity maps obtained in low-light based on previous work. We introduce an algorithm that combines a defogging method, which enhances night images and improves luminance, with generative adversarial or fully-convolutional networks to accurately learn the correct disparity prediction. The experiments show that the proposed method outperforms state-of-the-art methods which do not use additional preprocessing.
Bogus User Profile Detection on Matrimonial Sites Using Machine Learning Approach

Marriage is an important part of human life but when it comes to India, it’s religious, as they knot for seven lifetimes as per says. Indians are more emotionally, religiously and socially attached to marriage. Quest for finding the bride and groom, is the part of each and every aspirant bride and groom throughout the world. Objective of development of a system to tackle the detecting bogus bride to be and groom to be from matrimonial platforms using machine learning approach. Labelled dataset is used for building the model based on Logistic Regression, Random Forest Classifier, XGB Classifier and Support vector machine classifier with good accuracy. This system will be helpful for keeping users intact to the platform rather than losing the users due to exploitation of bogus users. Random Forest Classifier based machine learning approach fits the data extremely well and detects the bogus or genuine profile with a high accuracy.
Text Mining for Standardized Quality Criteria of Natural-Language IT-Requirements

Without a precise specification, an IT project might not remain on time and on budget constraints, or it might lead to a different outcome than desired. A number of established standards define how requirements must be written to avoid such issues. This paper describes our ongoing work to derive a comprehensive set of standardized criteria that IT-requirements must meet in accordance with IEEE 1233-1996 and ISO/IEC/IEEE 29148-2011. We also use a text-mining approach to identify IT-requirements that violate these standards. Our preliminary results are promising: In our biased dataset, we can use text features that are easy to compute, to filter out requirements that do not comply with the standards. Our beneficiaries are auditors, developers, Scrum teams, customers and other stakeholders whose projects are highly dependent on extensive IT-requirements specification.
BERT Based Topic-Specific Crawler

Nowadays, retrieving certain information using search engines is very popular and one of the main applications of the Internet. To speed up the process of getting the required information(web pages), having a topic-specific crawler is essential to fetch and index only the relevant ones. This paper presents a multi-thread web crawler using a Sentence Bidirectional Encoder Representations from Transformers (S-BERT). The S- BERT is used to calculate the similarity between the predefined classes and the text of the downloaded web pages. This provides a lightweight model compared to using a word embedding with deep learning for text classification.
UIAE: Collaborative Filtering for User and Item based on Auto-Encoder

These years, deep learning achieves huge success on recommender systems. Collaborative filtering using past interaction information is the primary method for recommender system. Although some recent works employ deep learning for collaborative filtering, they only consider one dimension of interaction information. We present a new model called collaborative filtering for users and items based on Auto-Encoder (UIAE). It learns interaction information from two dimensions. UIAE is a union of User-based and Item-based collaborative filtering that gets better performance than each of them. Experiments on two data sets are made researching the impacts of different components of UIAE and comparison experiments with previous CF methods based on Auto-Encoder are made to prove our model has better performance.