Reviewerly/exHarmony huggingface dataset:
Density estimation of a mixture distribution with unknown point-mass and normal error

We consider the model Y=X+ξ where Y is observable, ξ is a noise random variable with density fξ, X has an unknown mixed density such that P(X=Xc)=1−p, P(X=a)=p with Xc being continuous and p∈(0,1), a∈R. Typically, in the last decade, the model has been widely considered in a number of papers for the case of fully known quantities a,fξ. In this paper, we relax the assumptions and consider the parametric error ξ∼σN(0,1) with an unknown σ>0. From i.i.d. copies Y1,…,Ym of Y we will estimate (σ,p,a,fXc) where fXc is the density of Xc. We also find the lower bound of convergence rate and verify the minimax property of established estimators.
Cost-Conscious Internal Information Maximization for Disentangling and Interpreting Multi-layered Neural Networks

This paper aims to introduce a new information-theoretic method called “cost-conscious internal information,” in which the internal information represents how weights are ordered or organized, paying due attention to the strength or cost of representation. The method can disentangle complicatedly interwoven connection weights to get individual and linear correlations between inputs and outputs. The method was applied to the online shoppers data set, and we used a multi-layered network with 20 hidden layers and 100 neurons to show that the present method is effective for excessively redundant networks. The results show that the method could detect linear and individual correlations between inputs and outputs, while the other conventional methods could not represent those linear relations. Thus, it can be said that neural networks even with a large number of excessive components can be used and interpreted more easily than by other conventional methods.
Application of Computer-Aided Translation Software in Business English Translation

With the wide availability of mobile phones and the Internet, translation software has also been widely used in English teaching. The use of translation software can also enable students to improve their learning efficiency. In the process of using translation software, the user’s command of Chinese plays an important role in the business English translation. Since the mastery and understanding of Chinese and English will have different results for the use of translation software, it is of great significance to guide students in the correct use of translation software in business English translation. In the face of this situation, computer-assisted translation software came into being. It not only greatly improved the accuracy and efficiency of translation, but also had a very positive impact on translation teaching. This article discusses the application and role of the computer-aided translation software in business English translation. First, it discusses the advantages and disadvantages of computer-assisted translation software, the internal requirements, current situation, and main problems of the reform of English teaching mode, and then analyze the reasons. The results show that it plays an important role in business English translation and provides empirical support for subsequent related research.
Optimal Control as a Tool for Solving Sonar Design, Resource Allocation, and Planning Problems in Search Applications

This paper employs a computational optimal control framework which provides trajectory planning for search applications based on specific vehicle and sensor features. With its capability to handle high-dimensional, nonlinear optimal control problems with uncertainty, this framework enables detailed modeling and leveraging of vehicle/sensor features to generate better performing search plans. This paper utilizes these trajectory planning tools for the inverse design problem of deciding on the vehicle and sensor characteristics themselves. Using Monte Carlo sampling, we generate multiple trajectories to examine the performance of different vehicle and sensor configurations. We use this method to study three examples: sonar mounting angle, vehicle asset allocation, and lane space planning for time-limited lawnmower search plans.
Advanced Superpixel-Based Features and Machine Learning Based Saliency Detection

The saliency map simulates human perception and is useful for several image-processing applications. Many advanced saliency detection algorithms applied superpixel-based features instead of pixel-based features for saliency map generation. With superpixels, the high-level features can be extracted and a better saliency detection performance can be achieved. Recently, the convolutional neural network (CNN) has been thrived in computer vision. However, it was difficult to integrate it directly with superpixel-based method since the CNN required grid-like input while a superpixel generally has an irregular size and shape. In this work, several strategies are adopted to well apply machine learning techniques for superpixel-based saliency detection. First, instead of applying the CNN directly, the support vector machine (SVM) is applied as the classifier to well process the feature information. Instead, we apply the CNN ass the pre-processing procedure of superpixel merging. Moreover, several advanced superpixel-based features, including boundary connectivity and manifold rank features, are proposed to improve the accuracy of saliency detection. The simulation results on four datasets show that the proposed algorithm outperform both conventional and learning-based saliency detection methods.
Implementation of Cryptographic Algorithm (One-Time-Pad) with a RISC-V Processor

This paper presents a hardware accelerator for RISC-V processor implemented on Rocket chip SoC. The design is oriented to cryptographic applications for security of data. A RISC-V core processor with RV32IMAC instruction set is the master device with One-Time-Pad Crypto engine in a Rocket chip SoC. The result shows the hardware accelerator has higher efficiency in computation when compared to software execution.
Human Capital Development: The Government’s Role In Improving Nigeria’s Informal Sector

From the era of legendary groundnut pyramids in the 1910s to FinTech development in the 2010s, the Nigerian people have continually proven that they are the nation’s greatest resources. Socially and economically, Nigerians have seen in the recent decades, less reasons to depend on the government and in consequence, have made MSMEs in the informal sector the engine of the Africa’s biggest economy. Admittedly, the government has realized this role and have recently put in place fiscal policies to aid the growth of this sector by reducing taxes, curbing multiplicity of taxes and the introducing monetary empowerment schemes. However, the focus of the government towards their obligation of providing the core of human capital development – education and health – has remained below average. This reality has caused decades-long strain on the most viable sector of our economy and has only seemed to get worse. This paper thus analyses, with both mathematical and doctrinal methods, the impact of education and health on the working capacity of a populace and finds that they are the direct human factors for economic growth and productivity of a nation. This paper thereafter recommends points for policy implementation suited for the Nigerian reality.
On The Utilization of Optical Hardware for Single Image Blind Deblurring

When it comes to natural images in real life, most often images get deformed and are required to be fixed/rebuilt. Most of the images get blurred and distorted at the time of image exposure. In the present study/research, researchers are dealing with natural images which are less blurred and will then deblur those images. Parameters for this research are Gyro values/coordinates used on/made for image exposure time. The opted parameters are then further utilized to construct a model which is deblur or PSF. This research will use quite basic functions and techniques/strategies for image processing. By deconvolution of these already blurred images by PSF, the original image will be deblurred. Various situations/scenarios are chosen for comparison to reach the utmost level of excellence.
Augmented Reality Application as a Food Purchasing Assistant

Possibility of application of apps with the function of augmented reality for daily use is considered by the example of development and subsequent use of food search app for pensioners in food stores. The experimental part of the work describes the process of selecting important functions of the application which must help pensioners to buy food in stores. Describes the architecture and process of creating a cross-platform application using Unity 3D, the interaction of the application with a web server through the processing of requests received from the mobile application. The results show examples of mobile application work, possibilities of this application, as well as the results of surveys of potential users about the usefulness of such applications. In summary, there are conclusions of the work, the advantages and disadvantages of the finished product.
Stain transfer for automatic annotation of malignant lymphoma regions in H&amp;E stained whole slide histopathology images

We propose a method to transfer a given pathology image stained by some immunostaining to a H&amp;E stained one. When one construct a classifier that estimates the subtype of malignant lymphoma from a given H&amp;E stained pathology image, one needs a set of training H&amp;E stained whole slide images in which the tumor regions are annotated. The annotation is not easy and requires large human resources. Here, it is known that some immunostaining stains only some specific tumor cells and the tumor region detection from the immunostained images is straightforward. It means once you transfer the immunostained images to H&amp;E stained ones, you can easily obtain a set of virtually H&amp;E stained images with annotation of tumor regions. In this manuscript, we report on the proposed method and experimental results of stain transfer from CD20 stained images to H&amp;E stained ones.
Discussion on Data Features and Construction Models of Translation Corpus in the Era of Big Data

With the rapid development of artificial intelligence in the current era of big data, the construction of translation corpus has become a key factor in effectively achieving a highly intelligent translation. In the era of big data, the data sources and data types of translation corpus are becoming more and more diversified, which will inevitably bring about a new revolution in the construction of translation corpus. The construction of the translation corpus in the era of big data can fully rely on third-party open source data, crowd-sourcing translation, machine closed-loop, human-machine collaboration and other multiple modes to comprehensively improve the quality of translation corpus construction to better serve translation practice.
Allocentric and Egocentric Controllers: Similarities and Differences

Abstract Gestural instruments can be divided into two categories based on controller type: that of the egocentric controller, centered on and following a point of the performer’s body, and the allocentric controller, using a stationary reference frame. This article discusses (1) the similarities and differences between egocentric and allocentric controllers for gestural instruments from the perspective of performer and instrument designer, and (2) the affordances and constraints of egocentric and allocentric controllers as they, to a large degree, define the characteristics of an instrument. The author presents the initial results of a subjective experiment to encourage future discussion and study of the subject.
A Multi-Purpose Testbench for Determining the Parameters of Moving Objects Based on the Processing of Heterogeneous Sensory Information in the On-Board System of an Autonomous Robot

This paper proposes a multi-purpose testbench for determining the parameters of moving objects in controlled laboratory conditions. The testbench include different types of the sensors, which are based on different physical principal and simultaneously observe multiple mobile robots. The paper discuses data acquisition and processing methods as well as experimental studies scenarios, that can be investigated using proposed testbench.
Imputation for Missing Items in a Stream Data Based on Gamma Distribution

During the collection of real-time data, data missing is a common phenomenon in some stream data, which leads to the difficulty of such stream analysis and knowledge mining. In this paper, we study an approach of data imputation based on gamma distribution, which determines the number of missing data items in each time interval and the values of missing data items in a stream. We also present some metrics, such as fitting degree, credibility, matching degree, to evaluate the effectiveness of data imputation. Experimental results show that our approach improves the credibility by 15.0% to 20.0% compared to EMI, a widely adopted approach of data imputation, and outperforms traditional techniques significantly.
Puber as Artificial Intelligence for Education Policy in Bandung City

Abstract The City Government of Bandung currently still lacks competent teachers. The distribution of teachers who have uneven competence in every junior high school (SMP). To accommodate the problem of the distribution of Competent Teachers, the Bandung City Education Office made one innovation, which is Puber (Learning Center). Puber can facilitate teacher rotation through technology. This study aims to dig deeper into the application of Puber in the city of Bandung. Researchers used a qualitative method to dig up information related to the application of Puber in every junior high school in the city of Bandung. The results of this study indicate that the Puber program makes Teachers at Favorit Schools provide learning to ordinary Schools. This research concludes that the Puber Program must continue to be improved and always get control from the Education Office and the School.
Study of Posture Estimation Using MIMO Sensor Based on CSI

In this paper, person postures are estimated by MIMO sensor and the machine learning using channel state information (CSI). MIMO sensors are superior to cameras used in conventional surveillance systems in that they do not invade privacy and can sense even outside the line of sight. CSI are calculated from the data obtained from the sensor measurements, and then the posture classification is performed by the machine learning model based on CSI. The model is trained from the actual measured data and its classification performance is evaluated.
Application of Solar Radiation Analysis Technology in the Optimization of Campus Planting Design

Abstract In recent years, the slogans and concepts of sustainable campus, eco-campus, and environmental protection campus have been continually put forward, but there is little ink about the maintenance and management of the most important plants on campus. However, landscape designers often neglect the requirements of plants for the growth conditions of sunlight, or the impact of surrounding buildings on the living environment of plants, resulting in an irreversible situation. To this end, this research hopes to use environmental analysis aided design software to simulate the solar radiation environment and provide references for campus planting design and suggestions for plant selection. The results show that sciophytes are suitable for planting in areas where solar radiation energy is less than 2.90 MJ/m 2 d; Heliophytes are suitable for planting in areas where solar radiation energy is greater than 5.40 MJ/m 2 d, and the area in between is suitable for planting mesophytes.
Preparation of a Database of Faces to Obtain Reliable Results of Human Identification System at the Entrance

Nowadays, biometric identification methods are widely used in the fields of security and access control systems, based on the unique physical and behavioral characteristics of a person. One of these methods is facial recognition system that allows you to automate access to protected objects by using the computer vision system. Creating a database of faces of people who have an access to the object is an important part of the facial recognition system. Therefore, the preparation of a database to improve the reliability of the results of human faces identification is an urgent task. In this paper, the principal component method is used to implement the face recognition process and the viola-Jones algorithm is used to detect faces in images. The Euclidean metric was used to calculate the distances between the test images and the database images. As a result, it is shown that preprocessing the database can improve recognition results.
Detection of Contextual Anomalies in Attributed Graphs

Graph anomaly detection have proved very useful in a wide range of domains. For instance, for detecting anomalous accounts (e.g. bots, terrorists, opinion spammers or social malwares) on online platforms, intrusions and failures on communication networks or suspicious and fraudulent behaviors on social networks. However, most existing methods often rely on pre-selected features built from the graph, do not necessarily use local information and do not consider context based anomalies. To overcome these limits, we present CoBaGAD, a Context-Based Graph Anomaly Detector which exploits local information to detect anomalous nodes of a graph in a semi-supervised way. We use Graph Attention Networks (GAT) with our custom attention mechanism to build local features, aggregate them and classify unlabeled nodes into normal or anomaly. Finally, we show that our algorithm is able to detect anomalies with high precision and recall and, outperforms state-of-the-art baselines.
NIP-GCN: An Augmented Graph Convolutional Network with Node Interaction Patterns

In this paper, we propose an augmented Graph Convolutional Network (GCN) mechanism wherein additional information of local interaction patterns between a node with its neighbors (specifically, in the form of distribution of cosine similarity values of a pre-trained node vector with its neighbors) is used to enrich a node's representation prior to training a GCN. This provides additional information about the structural properties of a node, which the standard convolution operation in a GCN can then leverage for obtaining potentially improved effectiveness in a down-stream task. Our experiments demonstrate that adding these node interaction patterns (NIPs) along with an additional noise-contrastive pairwise document similarity objective within a GCN improves the linked document classification task.
Dominant Color and Uniform Local Binary Pattern Based Image Retrieval

A Content-Based Image Retrieval (CBIR) system is expected to produce output images relevant to query image with reduced system complexity. It can be achieved either by feature integrated or cascaded approach. The feature integrated approach extracts multiple features for the whole database and combine these features using optimized feature combination techniques. On the other hand, the cascaded approach involves the extraction of a feature for the reduced set of database images at each cascaded level. As the former approach achieves effective retrieval at the cost of system complexity, the latter approach is preferred in the current work. However, the feature selection at each level is a challenging task in the cascaded approach. Hence the current work proposes an algorithm that exploits the advantages of dominant color and uniform local pattern i.e., texture feature in the cascaded approach based CBIR system. The proposed algorithm involves the extraction of a K-Means-based dominant color feature at the first level to reduce search space of the retrieval process and a uniform local binary pattern-based texture feature at the second level to retrieve the relevant images. Euclidean and Bray Curtis distance measures are used during the retrieval process to identify the similar images at each level. The performance of the proposed dominant color and uniform local binary pattern (texture) based cascaded image retrieval algorithm is evaluated using objective measures for the Wang's database and a retrieval accuracy of 75% is observed.
Auditors' Susceptibility to Affective Bias via Repeated Client Email Exchanges with Mixed Evidence

Client emails to auditors often contain affective statements that increase likeability. Prior research has documented the initial impact of these statements but has not examined their durability. We investigate whether repeated exposure to affective statements influences auditorsâ€™ responses to mixed audit evidence (i.e., client responses that align or do not align with evidence). Across two experiments we find auditors judge a client as more reliable and assess lower risk of material misstatement in response to positive affective cues, and this effect is most pronounced when auditors encounter responses inconsistent with evidence. We also show affect can be induced via short, subtle cues rather than long narratives used in prior research. Additionally, we find positive affect does not influence auditors unless the client initially establishes trust with the auditor. Our results inform academics and practitioners by investigating the formation, effects, and durability of affective bias across multiple email interactions.
Visualizing and Understanding Policy Networks of Computer Go

Deep learning for the game of Go achieved considerable success with the victory of AlphaGo against Ke Jie in May 2017. Thus far, there is no clear understanding of why deep learning performs so well in the game of Go. In this paper, we introduce visualization techniques used in image recognition that provide insights into the function of intermediate layers and the operation of the Go policy network. When used as a diagnostic tool, these visualizations enable us to understand what occurs during the training process of policy networks. Further, we introduce a visualization technique that performs a sensitivity analysis of the classifier output by occluding portions of the input Go board, and revealing parts that important for predicting the next move. Further, we attempt to identify important areas through Grad-CAM and combine it with the Go board to provide explanations for next move decisions.
Advanced Optimization of Data Analysis for Walking-in-Place Locomotion Using Inertial Sensors in VR

Locomotion in virtual reality (VR) can be implemented by different methods. In the simplest cases, the motion is triggered by using a joystick or by using head movement tracking. More advanced approaches enable natural mapping movements to transform it into VR locomotion. But providing users to walk through a virtual environment in a limited physical space is often a challenge. Walking-in-place (WIP) offers a natural and immersive virtual locomotion that can greatly reduce symptoms of simulation sickness. In this paper, we consider the optimization of data analysis for WIP locomotion using inertial sensors. The algorithms, which detect the user's steps and controls the user's motions in the virtual worlds are proposed. We provide the method of analysis of experimental data for the suppression of false steps and the general usability of the system.